esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Field Tests and Results
### On-Site Monitoring and Result  
Field measurements of the roof and two sides of AR 4106 were made during tunneling and mining of the adjacent working faces with the new support to determine the synergistic roof control mechanism of the long flexible bolt support and deep-hole blasting pressure relief scheme, as shown in Figure 9 and Table 1.  
Figure 9a shows that surface displacement initially increased rapidly after roadway tunneling and then gradually slowed and stabilized. The main convergent deformation occurred 20 m from the excavation face. The maximum roadway roof subsidence under the new scheme was 35 mm and the maximum convergence of the two sides was 58 mm. Deformation of the roof and two sides was reduced by 69.57% and 81.93%, respectively, compared with the original scheme (Table 1). Upon mining of WF 4105, the roof strata of the mined-out side of the roadway sank to form a voussoir beam structure. Roadway deformation gradually increased owing to the stress disturbance until stabilizing. As shown in Figure 9b, the maximum subsidence of the roadway roof was 57 mm and the maximum convergence of the two sides was 114 mm; a reduction of 87.19% and 97.09%,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**State**} & **Rof** & **Deformation** & **Shrinkage of** & **Deformation** \\  & **Convergence/mm** & **Reduction Rate/\%** & **Ribs/mm** & **Reduction Rate/\%** \\ \hline Original support—tunneling & 115 & & 321 & \\ New support—tunneling & 35 & & 58 & \\ Original support—mining & 445 & & 883 & \\ New support—mining & 57 & 87.19 & 114 & 87.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of roadway deformation under original support and new support.  
Figure 9: Monitoring data of roadway deformation under new support: (**a**) roadway deformation during tunneling and (**b**) deformation of roadway during mining of adjacent working face.  
respectively, compared with the original support scheme. The deformation and stability distance of the roadway was approximately 150-160 m, which is essentially the same as the original scheme. The stability of the roadway was fundamentally altered during mining of the adjacent working face, as shown in Figure 10.
# On the Optimal Calculation of the Rice Coding Parameter
## 5 Multi-Parameter Rice Coding
### Two Simple Heuristics for Constrained IoT Devices
```
1:functionRiceEncodeMultiHeurS(\(\mathbf{n}\), \(N\), \(\Delta\))
2:\(i^{\prime}\gets 1\)
3:\(L^{\prime}_{\text{max}}\gets 0\)
4:\(L^{\prime}_{\text{min}}\leftarrow\infty\)
5:\(\hat{s}\gets 0\)
6:for\(1\leq i\leq N\)do
7:\(l\gets L(n(i))\)
8:if\((l>L_{\text{max}}-\Delta)\) and \((l<L^{\prime}_{\text{min}}+\Delta)\)then
9:\(L^{\prime}_{\text{max}}\leftarrow\max(l,L^{\prime}_{\text{max}})\)
10:\(L^{\prime}_{\text{min}}\leftarrow\min(l,L^{\prime}_{\text{min}})\)
11:\(\hat{s}\gets 8+|n|\)
12:else
13:\(s\leftarrow\left\lfloor\log_{2}\left\{\frac{\hat{s}\cdot\ln 2}{l-l^{\prime}} \right\}\right\rfloor\)
14:RiceEncode(\(\mathbf{n}\),\(i-1\), \(s\))
15:\(i^{\prime}\gets i\)
16:\(L^{\prime}_{\text{max}}\gets l\)
17:\(L^{\prime}_{\text{min}}\gets l\)
18:\(\hat{s}\leftarrow|n|\)
19:endif
20:endfor
21:if\(i^{\prime}\
eq N\)then
22:\(s\leftarrow\left\lfloor\log_{2}\left\{\frac{\hat{s}\cdot\ln 2}{N-l^{\prime}+l} \right\}\right\rfloor\)
23:RiceEncode(\(\mathbf{n}\),\(i^{\prime}\), \(N\), \(s\))
24:endif
25:endfunction
```  
**Algorithm 4** Heuristic for Rice encoding with partitioning with complexity \(O(N)\).
# EC-GAN: Emotion-Controllable GAN for Face Image Completion
## 2 Related Work
### Image Completion with Single Result  
Typically, the image completion methods with only one result generally use encoder-decoder architectures to visualize meaningful semantic content with properly designed reconstruction losses. For the photorealistic rendering of the generated image, an adversarial discriminator is usually added to the decoder during training. Context-Encoder(CE) [14] is the first GAN-based image completion network in which the encoder takes in the corrupted image and the decoder creates the missing image content. Based on CE, GLCIC [17] employs dilated convolutions and the global and local discriminators to inpaint random holes. This idea is followed by DeepFill [16]. DeepFill V1 [16] introduces a contextual attention layer in a feed-forward generative network, and DeepFill V2 [28] presents gated convolution to dynamically select features for each channel and improves color consistency for free masks. In PConv [15], a partial convolution is used to ensure that only valid pixels are covered to obtain a more accurate prediction. [PERSON] et al. [12] proposed a Pyramid-Context Encoder Network (PEN-Net). PEN-Net adds an attention transfer network, combining high-level feature maps between the visible and invisible areas to ensure the consistency at visual and semantic levels. A more recent network, RFR-Net [29] exploits the correlation between pixels and develops a Knowledge Consistent Attention module in order to produce detailed results. CR-Fill [19] adds a contextual reconstruction branch to train an attention-free generator. EdgeConnect [18] and CTSDG [30] use the edge information to guide the reconstruction of image structures. MISF [31] proposes a kernel prediction model to learn the multi-level features of images. AOT-GAN [32] adopts information from distant contexts and rich patterns of interest for context reasoning. Although these methods are capable of producing one determined result for a given image, they cannot generate other possible results that also match the style and semantics of the given image.
# A Simple Technique for Studying Chaos Using Jerk Equation with Discrete Time Sine Map
## 5 Numerical Simulation & Results
### Bifurcation Diagram  
The bifurcation diagram of the proposed system was found by varying control parameter '\(\beta\)' from 5 to 12 with a step size of 0.01. The initial conditions were set to \(\mathbf{x_{0}}=[0.5\ 0.5\ 0]^{\prime}\), and the period-doubling route to chaos was plotted in Figure 5 for all three states of the system.  
Figure 4: Maximum Lyapunov Exponent spectrum for the proposed discrete–time map based chaotic jerk system.  
Figure 5: Bifurcation Diagram: (**a**) local maxima of \(x_{1}\) vs. Control Parameter \(\beta\), (**b**) local maxima of \(x_{2}\) vs. Control Parameter \(\beta\), (**c**) local maxima of \(x_{3}\) vs. Control Parameter \(\beta\).  
It is to be noted that for \(\beta=18\), the system has no oscillation, and this value is termed as the critical value. As \(\beta\) starts to decrease from 12 onward, the jerk based system starts to bifurcate from stable period 1 to period-doubling route to chaos shown in Figure 5.
## 4 Finite Element Modeling
### Boundary Conditions  
For the simulation of early-age cracking in CRCP under external varying temperature conditions, 3D staggered structural-flow analysis was performed in two parts. In the first part of the analysis, the transient heat flow analysis calculates the temperature development, and the structural elements are transformed into the flow elements. Then, in the second part of the analysis, the output obtained from the first part is used as input for the structural analysis. The self-weight of the concrete slab is taken as a gravity load in the FE analysis. Smaller increments of loading are applied to avoid convergence problems.  
The structural and thermal aspects of the boundary conditions are defined with respect to the staggered structural-flow analysis. For the structural aspect, the bottom face of the concrete slab is restrained in an upward direction (Uz = 0), assuming a stiffer ground base layer beneath the concrete slab. By taking advantage of the symmetric conditions, the concrete slab is restrained in both the Y-Z planes and the symmetrical inner plane X-Z plane; however, the plane on the saw-cut side is free to expand and contract. It translates that the concrete slab on the symmetric planes can contract, but it cannot expand because of the infinite surrounding concrete. The contact interface between the bottom face of the concrete slab and ground base has been modeled using a nonlinear elastic friction model [55].  
For the thermal boundary conditions, the slab top face and the outer edge face on the saw-cut side are directly exposed to external varying temperature field conditions, as demonstrated in Figure 8. Considering the potential heat flow analysis, the convective interface boundary elements are used to simulate the heat transfer through the concrete slab and the outside environment. The value of the heat transfer coefficient is assumed as 7.5 W/m\({}^{2}\) \({}^{\circ}\)C based on previous studies [43; 51; 56; 57]. The proposed boundary conditions of the FE model are shown in Figure 9.  
Figure 8: External varying temperature field.
# Toward the Utilization of 3D Blading in the Cantilevered Stator from Highly Loaded Compressors
## 4 Application of 3D Blading in a Cantilevered Stator
### Discussion of the Experimental Results
#### 4.2.2 Effect of 3D Blading on the Flow Field Distribution
As for P3 and P4 conditions, the flow separation in the corner region is reduced, owing to the weakening of the secondary flow. Figure 35: Oil-flow results on the blade suction surface and hub wall for the datum stator. Figure 36: The distribution of the normalized axial speed at the stator outlet. To sum up, the interaction between the endwall leakage flow and the transverse secondary flow determines the effect of the 3D blading. For the present cantilevered stator, at large mass flow rates, the experimental results demonstrate that the 3D blading pushes the leakage flow too close to the pressure side of the adjacent blade, thereby inducing secondary leakage. However, at small mass flow rates, the inhibition of the leakage flow to the endwall secondary flow is not strong enough; hence, the corner separation requires further elimination. The above results also imply that the design of 3D blading needs to optimize the evolution of corner flow structures at different operating conditions.
## 2 Conventional SHM Techniques
### Static Monitoring Systems
The continuous monitoring of stress variations is another essential aspect for the safety of historic masonry structures, especially considering that the creep phenomenon to which masonry structures can be subject has occasionally led to the collapse of historic buildings [52]. Pressure and load cells can be used to monitor pressure and load variations. [PERSON] et al. [53] described using several pressure cells to monitor the dome-drum system of the Basilica of Vicoforte. Some were placed horizontally to control stress variations in the eight pillars and the dome's buttesses. A pressure cell was placed vertically near the top of the dome to determine the circumferential compression stress. Moreover, the load condition of a system of 56 active tie-bars, applied at the base of the dome as a reinforcement, is constantly monitored by load cells and may be regulated by using jacks. [PERSON] et al. [54] developed a new device for the long-term monitoring of stress based on the use of flat jacks and displacement sensors. The new SHM system was applied to monitor, in real-time, the variation in the stress levels of several masonry buttresses of the Church of the Major Seminary of Comillas (Spain) during the actuation of strengthening interventions [54,55]. The authors concluded that although the system still needed further studies to control the influence of thermal variations, it is of great interest to increase the safety of historic buildings and the workers involved in the retrofitting interventions. [PERSON] et al. [56] presented a preliminary assessment of the structural health of Modena Cathedral. The building had experienced various transformations and interventions over the centuries, mainly due to earthquakes and soil settlement effects. This led to a complex and remarkable damage pattern. A static SHM system was installed in 2003 to provide data that can be integrated with structural analysis results. Specifically, the monitoring system installed in the cathedral consisted of biaxial and triaxial joint meters, inclinometers, deformometers, and thermometers which were used to monitor the main cracks across the walls and vaults, the tilting of the external longitudinal walls, the relative displacements between the cathedral and the adjacent tower (the Ghirladina Tower [57]), and the internal temperature. The analysis and interpretation of data revealed the structure's principal vulnerabilities and failure mechanisms. They also provided essential information for developing and calibrating simple and FE models used to investigate the long-term performance of the overall structure.
## 3 Results and Discussion
### Characterization of nZVISLP
#### 3.1.3 Zero-Point Charge  
The zero-point charge (pHZPC) is an essential characteristic of any adsorbent, and it represents the particular pH value at which the surface charge of that adsorbent is neutral. The determination of pH\({}_{\text{ZPC}}\) of the synthesized adsorbent was done using a 0.01 M solution of NaCl, the pH of which was adjusted in the range of 2 to 10 by adding 0.1 M HCl and 0.1 M NaOH. For this, 0.20 g of the prepared  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Element** & **Weight (\%)** \\ \hline C (K) & 54.31 \\ O (K) & 15.03 \\ Si (K) & 1.71 \\ Ca (K) & 3.10 \\ Fe (K) & 11.98 \\ Zr (L) & 13.86 \\ Total & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elemental composition of zero-valent iron nanoparticles synthesized from sweet lime pulp waste (nZVISLP).  
Figure 3: (a,b) SEM images of nZVISLP; (c) SEM energy-dispersive X-ray spectroscopy (EDX) analysis.  
nZVISLP was added to 50 mL of NaCl (0.01 M) in a conical flask, and then the solution pH was adjusted in the range of 2 to 10, stirring for 48 h [31]. Then, the final pH of the solution was determined. The point at which both pH curves crossed was specified as the pH\({}_{\text{ZPC}}\) of the adsorbent. The pH\({}_{\text{ZPC}}\) of the synthesized nZVISLP was determined at different pH (2-10) (see Figure S1, Supplementary Materials). The pH\({}_{\text{ZPC}}\) of nZVISLP was found to be 4.5, representing that the surface charge of the material is neutral at this pH, whereas it is positive below this pH.
# Prediction of Cotton Yarn Quality Based on Attention-GRU
## 3 Case Analysis
### Dataset Preparation  
Considering the variations in spinning methods, process workflows, and spinning equipment among different yarn varieties, we have selected yarn type C27.8 as the subject of experimentation for this section. This particular yarn variety follows a processing route of pure cotton carding and encompasses the utilization of key spinning equipment, including the TCS-1 type cotton-carding machine, the FA306 type drawing frame, the RSB-D45c type drawing frame, the FA468E type roving frame, and the JWF1516 JM type ring-spinning frame.  
The chosen output parameter for the model is yarn tensile strength, which serves as a crucial indicator of yarn quality. Yarn tensile strength directly determines the processing performance and final application of the yarn. In the field of cotton spinning, factors influencing yarn strength primarily encompass fiber properties and yarn structure. Higher fiber strength, finer fineness, and longer length result in higher yarn strength. Conversely, yarn strength decreases when the yarn experiences folding, bending, or kinking. Furthermore, both fiber properties and yarn structure are significantly influenced by the process parameters of each spinning stage. Therefore, employing the regular carding process as a case study, we proceed to conduct an in-depth analysis of the influence exerted by each individual processing stage on yarn tensile strength:  
* The speed of the carding roller and the tin roller in the carding machine is one of the key factors influencing the carding quality [27].
* Increasing the speed effectively enhances the carding rate and area, thereby reducing cotton knots and impurities. However, higher speeds intensify the increase in short fiber content where for every 1% increase in short fiber content below 16 mm in cotton yarn, there is a corresponding decrease in yarn strength by 1-2% [28].
* The blending process in the drawing frame elongates and evens out cotton fibers. The spinning speed of the drawing frame can impact the uniformity of fiber blending, thus influencing yarn strength [29].
* There exists a parabolic relationship between the coefficients of the coarse and fine yarn processes and cotton yarn tensile strength [30; 31]. As the twist coefficient increases, the intermolecular cohesion of cotton fibers strengthens. However, the introduction of additional twist reduces axial forces, leading to uneven fiber breakage. Furthermore, spindle speed in both processes is a critical factor affecting yarn strength.  
Therefore, in the selection of input indicators, we consider six parameters, such as the micronaire value as indicators of raw cotton performance. Meanwhile, we utilize the collected crucial spinning equipment information as indicators for the process parameters of each stage. Subsequently, based on these indicators, 50 sets of training samples and 10 sets of testing samples for the neural network model are selected. (All yarns are spun under the same production environment). Table 1 presents a subset of the sample data used in this section.
# Interference of Spread-Spectrum EMI and Digital Data Links under Narrowband Resonant Coupling
## 2 Communication Channel Modeling under Resonant EMI Coupling
### Channel Modelling  
A band-limited communication channel corrupted by additive white Gaussian noise (AWGN) with unilateral power spectral density \(N_{0}\) is considered in what follows. The transmitted signal and the background noise are both described by two wide-sense stationary (WSS), statistically independent gaussian random processes. In particular, the signal process is assumed to have a total power \(P_{\mathrm{S}}\) and a power spectral density (p.s.d.) \(P_{\mathrm{S}}/B\) constant over the channel bandwidth \(B=\left(f_{B_{\mathrm{min}}},f_{B_{\mathrm{max}}}\right)\), as depicted in Figure 3, while the noise is assumed to have unilateral power spectral density \(N_{0}\).  
Figure 1: Effects of resonant coupling mechanisms on non-modulated and SS-modulated EMI.  
In the framework of Shannon information theory, the upper bound of the information than can be reliably transmitted over the AWGN channel considered above in the unit time, is given by the Shannon-Hartley equation [43]:  
\[C_{0}=B\log_{2}\left(1+\frac{P_{\mathrm{S}}}{BN_{0}}\right)=B\log_{2}\left(1+\alpha\right) \tag{1}\]  
where  
\[\alpha=\frac{P_{\mathrm{S}}}{BN_{0}}\]  
Figure 3: EMI resonant coupling in the frequency domain: power spectral densities of nominal signals, EMI, background noise and resonant coupling transfer function.  
Figure 2: Digital communication channel with EMI resonant coupling: Block Diagram.  
is the signal-to-noise ratio (SNR) of the AWGN channel.
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
## 3 Energy of Acceleration Signals  
As suggested by [PERSON] et al. [23], the energy of vertical acceleration signals measured on a passing train contains useful and important information regarding the railway track condition. For example, if the train passes over a bump or a defect on the track, the acceleration signal measured on the train might contain high amplitudes that represent high levels of energy at that location. [PERSON] et al. [23] propose using the average amplitudes of the signal using a moving window along the track. The signal energy in each window is then obtained by summing the amplitudes of the  
Figure 3: Frequency content of the accelerations of Pass 3, (**a**) the Fast Fourier Transform (FFT) spectrum and (**b**) Short Time FFT (STFFT) spectrum.  
Figure 2: The acceleration signals of the first five passes.  
acceleration signal. Here, the signal amplitudes are obtained while using the Hilbert transform. Hence, the energy is represented by the true local peaks of the signal instead of averaging.
## Appendix B  
This appendix gives a formal derivation of Equation (11) from Section 2.4, which relates the THz pulse delay in admixture gasoline solution with respect to the pulse delay after propagation in pure gasoline based on the Gladstone-Dale mixing rule.  
The equation describing the difference of THz pulse TOFs in isopropanol-gasoline solution and pure gasoline, \(\Delta t\), the Gladstone-Dale mixing rule equation for the refractive index, \(n_{D}\), of the isopropanol gasoline solution, and the relationship between the mass fractions of isopropanol, \(\phi_{1}\), and gasoline, \(\phi_{2}\), are given, respectively, by Equations (A10)-(A12) [32; 33]:  
\[\Delta t=t_{n_{D}}-t_{n_{D2}}=\frac{l}{c}(n_{D}-1)-\frac{l}{c}(n_{D2}-1)=\frac {l}{c}(n_{D}-n_{D2}),\] (A10)  
\[n_{D}-1=\phi_{1}(n_{D1}-1)+\phi_{2}(n_{D2}-1),\] (A11)  
\[\phi_{1}+\phi_{2}=1.\] (A12)  
Here, \(l\) denotes the length of the cuvette, \(c\) denotes the speed of light in vacuum, \(n_{D1}\) represents the refractive index of pure isopropanol, and \(n_{D2}\) indicates the refractive index of pure gasoline. Equation (A11) can be rewritten in the form:  
\[n_{D}-1=\phi_{1}n_{D1}+\phi_{2}n_{D2}-(\phi_{1}+\phi_{2}),\] (A13)  
which together with Equation (A12) yields:  
\[n_{D}=\phi_{1}n_{D1}+\phi_{2}n_{D2}.\] (A14)  
The above equation was quoted in the main text as Equation (10). By inserting Equation (A14) into Equation (A10), one obtains:  
\[\begin{array}{l}\Delta t=\frac{l}{c}(n_{D}-n_{D2})=\frac{l}{c}(\phi_{1}n_{D 1}+\phi_{2}n_{D2}-n_{D2})=\ldots\\ \ldots=\frac{l}{c}\left(\phi_{1}n_{D1}+\phi_{2}\frac{\phi_{1}}{\phi_{1}}n_{D2 }-\frac{\phi_{1}}{\phi_{1}}n_{D2}\right)=\frac{l}{c}\left(\phi_{1}n_{D1}+ \frac{\phi_{2}\phi_{1}-\phi_{1}}{\phi_{1}}n_{D2}\right).\end{array}\] (A15)  
The fraction of the expression in the brackets can be expanded and, after the substitution of \(\phi_{2}\) determined based on the Equation (A12), takes the form:  
\[\frac{\phi_{2}\phi_{1}-\phi_{1}}{\phi_{1}}=\frac{(1-\phi_{1})\phi_{1}-\phi_{1 }}{\phi_{1}}=\frac{\phi_{1}-\phi_{1}\phi_{1}-\phi_{1}}{\phi_{1}}=-\phi_{1},\] (A16)  
such that finally Equation (A15) can be rewritten to obtain the final formula for \(\Delta t\):  
\[\Delta t=\frac{l}{c}\left(\phi_{1}n_{D1}+\frac{\phi_{2}\phi_{1}-\phi_{1}}{ \phi_{1}}n_{D2}\right)=\frac{l}{c}(\phi_{1}n_{D1}-\phi_{1}n_{D2})=\frac{l}{c} (n_{D1}-n_{D2})\phi_{1}.\] (A17)  
The difference of the THz pulse TOFs in gasoline with isopropanol and pure gasoline is proportional to the difference of the refractive indices of the dissolved substance and pure gasoline, the isopropanol concentration and the ratio of the cuvette length divided by the speed of light. Equation (A17) has the same form as Equation (11) from the main text.
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 5 Performance Evaluation
### Performance Matrices  
The performances of the proposed clustering protocols (DLC) and routing (DLRS) are evaluated in terms of the following metrics [8]:  
* PDR refers to the ratio of the number of the received data packet at a destination node over the number of the transmitted packet at a source node.
* The routing delay refers to the average time to establish the route between a source node to a destination node per session.
* The control overhead refers to the average number of control packets to establish a route per session per node.
* The average number of cluster head changes refers to the average number of cluster head changes per cluster per session.
* Packet loss ratio refers to the ratio of the number of packet losses to the total number of sent packets.  
These specified metrics collectively provide a comprehensive evaluation of the proposed DLC and DLSR performance. They address essential aspects of VANETs operation, including communication quality, efficiency, and network stability. By analyzing the protocol's performance across these metrics, researchers can assess its suitability for real-world VANETs scenarios and make informed decisions about its deployment and optimization.