esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Research Hot Regions and Sensitive Regions
For the Dupangling Area in southern Hunan, although the terrain conditions and accessibility are slightly better than those in the typical rocky desertification areas, due to the higher vegetation coverage and lower population density in this area, the amount of research in this area is not high. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Mode** & **Area** & \begin{tabular}{c} **Research** \\ **Hoeness Index** \\ \end{tabular} & \begin{tabular}{c} **Sensitivity** \\ **Index** \\ \end{tabular} & \begin{tabular}{c} **Slope (*)** \\ **Cloverage (*)** \\ \end{tabular} & \begin{tabular}{c} **Vegetation** \\ **(People/km)** \\ \end{tabular} & \begin{tabular}{c} **Population Density** \\ **Density (km/km)** \\ \end{tabular} \\ \hline Highly Matched Area & \begin{tabular}{c} Typical Rocky \\ (High Sensitivity \\ High Research Hotness) \\ \end{tabular} & 4.7 & 5.04 & 3.13 & 0.82 & 573.21 & 0.56 \\ \hline \multirow{4}{*}{Space Decoupling Area (High Sensitivity )} & \begin{tabular}{c} Quinontial \\ Area, Sichuan \\ \end{tabular} & 1 & 4.11 & 10.91 & 0.86 & 139.66 & 0.44 \\ \cline{2-7}  & \begin{tabular}{c} Wushan-Shemongia \\ Area, Hubei \\ \end{tabular} & 1 & 4.17 & 6.7 & 0.93 & 144.82 & 0.50 \\ \cline{2-7}  & \
\begin{tabular}{c} Henglau Mountain Area, \\ Western Yunnan \\ \end{tabular} & 1 & 3.90 & 8.66 & 0.84 & 94.76 & 0.46 \\ \cline{2-7}  &
\begin{tabular}{c} Dupangling Area, \\ Southern Human \\ \end{tabular} & 1 & 3.30 & 2.92 & 0.85 & 435.46 & 0.62 \\ \hline \hline \end{tabular}  
* The major countries of all regions: (1) typical rocky desertification area includes Bije, Lipuranshui and Guiyang in central and western Guizhou; (2) Goughian mountain-langhan area includes Deong, Ratang, Baiyu and other counties in Sichuan; (3) Wushan-shemongia area includes Yi’an and other counties in Hubei; (4) Henglau mountain area includes Zhenkang, Shidian, Ximeng and other counties in western Yunnan; (5) Dupangling area includes Youju and other counties in southern Human.  
\end{table} 
Table 1: Hotness Index, Sensitivity Index, Environmental and Social Backgrounds Index in Key Regions.
## 4 Results and Discussion
### Workability  
Workability refers to the degree of ease with which a cement mortar mixture flows and consolidates. It has a significant impact on the durability of the mixture once it hardens. Inadequate fluidity can result in the consolidation of newly mixed cement mortar, leading to the creation of voids, hence diminishing the mechanical characteristics of the mortar.  
Cement hydration is a chemical reaction that occurs when cement comes into contact with water, leading to the formation of cement paste that binds the aggregates together. The calcium salt composition and pH level of cement also significantly affect the workability and flocculation of the mixture, as researched [9; 10]. A high-pH environment (around 12-13) is necessary for the proper hydration of cement. If the pH is too low, the hydration process can be slowed down or disrupted, affecting the workability of the mortar. A proper pH ensures that the cement particles are adequately dispersed in the mix, leading to a more uniform and workable mortar. If the pH is not optimal, it can result in poor dispersion of the cement particles, making the mortar either too stiff or too fluid, both of which are undesirable for workability.  
Regarding the workability of the mortar mixture, the results from Table 6 and Figure 5 clearly showed that the addition of fly ash has slightly increased the fluidity. These results were quite close to the recent research [21], which pointed out that the workability of mortar mixtures when changing GO content was not significantly different, namely using GO from 0.01 wt% to 0.03 wt%. This change was only significant when GO increased above 0.04 wt%. It also partly reveals the limitations of GO [11; 12] and also shows that adding fly ash is appropriate [22]. Due to its smooth and spherical structure, fly ash increases the filling and lubricating effect, which partly overcomes the limited workability of the mixture in the presence of GO.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No**} & **Mix** & **GO** & **FA** & **Workability** \\  & **Designation** & **(\%)** & **(\%)** & **(mm)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 6: Workability of mixture samples.
###### Abstract  
Extracellular electron transfer (EET) is key to the success of microbial fuel cells (MFCs). _Clostridium_ sp. often occurs in MFC anode communities, but its ability to perform EET remains controversial. We have employed _Clostridium pasterianum_ DSM 525 as a biocatalyst in a glycerol-fed MFC, designated \(\mathrm{MFC_{DSM}}\). We have also followed the EET of this biocatalyst in the presence of a mediator, namely soluble neutral red (NR), soluble methyl viologen (MV), neutral red film (FNR), or methyl viologen film (FMV). \(\mathrm{MFC_{DSM}}\) provided power and current densities (_j_) of 0.39 \(\mathrm{\mu W}\cdot\mathrm{cm}^{-2}\) and 2.47 \(\mathrm{\mu A}\cdot\mathrm{cm}^{-2}\), respectively, which evidenced that the biocatalyst performs direct electron transfer (DET). Introducing 150.0 \(\mathrm{\mu M}\) NR or MV into the \(\mathrm{MFC_{DSM}}\) improved the current density by 7.0- and 3.7-fold (17.05 and 8.45 \(\mathrm{\mu A}\cdot\mathrm{cm}^{-2}\)), respectively. After 20 cyclic voltammetry (CV) cycles, the presence of FNR in the \(\mathrm{MFC_{DSM}}\) anodic chamber provided an almost twofold higher current density (30.76 \(\mathrm{\mu A}\cdot\mathrm{cm}^{-2}\)) compared to the presence of NR in the \(\mathrm{MFC_{DSM}}\). Introducing MV or \(\mathrm{FMV}\) into the \(\mathrm{MFC_{DSM}}\) anodic chamber gave practically the same current density after 10 CV cycles. The \(\mathrm{MFC_{DSM}}\) anodic electrode might interact with FMV weakly than with FNR, so FNR is more promising to enhance _C. pasterianum_ DSM 525 EET within \(\mathrm{MFC_{DSM}}\).  
Clostridium pasterianum_ DSM 525; glycerol; mediated electron transfer (MET); neutral red (NR); methyl viologen (MV)
## 3 Results
### Test Observation and Failure Modes
Specim SJ-7 exhibited this failure mode, as shown in Figure 11. Because a plastic hinge at the root of the beam led to out-of-plane instability in the beam, the loading stopped at a larger drift ratio, demonstrating good deformability. As shown in Figure 12a,b, for the failure modes described above, the anchored part of blind bolts in the concrete were basically intact, including the bolt sleeve, threaded cone, bolt shank, and anchor nut. There was no damage, such as breaking or bending, for the anchored part. However, notable scratches were found on the anchor nut, apparently caused by the interaction between the mechanical anchorage and the concrete. As shown in Figure 12c,d, for specimen SJ-5, there was a significant gap between the steel tube wall and the C-channel; in contrast, the steel tube and the C-channel fitted closely together for specimen SJ-3. This result implies that the steel tube wall and the C-channel can work well together when a plug weld was adopted between them. Figure 11: Failure mode III. (**a**) SJ-7; (**b**) Column wall in specimen SJ-7. Figure 12: Failure pattern of the components. (**a**) Embedded extension of blind bolt; (**b**) Anchor nut; (**c**) Bolt holes in specimen SJ-3; (**d**) Bolt holes in specimen SJ-5. The failure mode was affected by the relative strength of the main components, which included the steel tube, blind bolt, end-plate and steel beam, and the failure mode depends on the weakest component. In the test, the steel tube thickness and the beam section size directly affected the change in the three types of failure modes for such joints. Owing to the large spacing between the blind bolts, the anchorage method did not alter the failure mode. In the failure mode II, the deformation pattern depended on the relative strength-weakness relationship of the bolt and the end-plate. There were three possible modes: the root of the end-plate flange yield, the root of the end-plate flange yield and blind bolt failure, and blind bolt failure. In addition, local strengthening connection method and stiffeners had an effect on the deformation pattern. When modified blind bolts (Hollo-Bolts) and a locally strengthened steel tube in the panel zone were used for the joints to the CFST column, the strength of the blind bolts can be fully utilized, thereby providing the joint favorable stiffness and strength. Using thinner end-plates and weaker steel beams for the proposed connection can avoid premature brittle failure to the greatest extent and result in favorable deformation capacity. The use of a plug weld to connect the C-channel to the steel tube enables these components to exhibit good synergistic deformation capability.
## 3 Blast Design and Monitoring of Vibration  
### Blast Design  
Considering that the surrounding environment of the cofferdam was complex, it was completely demolished within five steps. Firstly, region II was demolished by mechanical crushing. The rest was demolished by blasting in turn as shown in Figure 4. Of special note was that the economic section was conformed at 272 m elevation due to land blasting above the economic section. In order to prevent the impact of water pressure, underwater blasting was adopted under the economic section except for regionI. In addition, explosives charge weight per delay had been accurately designed to control blast-induced vibration.  
All regions contained deep hole blasting, pre-splitting blasting, and cushion blasting. Besides this, region I contained shallow holes below 5 m. The depth of drilling holes varied from 1.5 m to 19.5 m. To control the influence of blast-induced vibration, the diameter of the shallow holes below 5 m depth was 42 mm, while the deep hole over 5 m depth was 76 mm. According to the results of the blasting  
Figure 4: Blasting sequence and blasting hole arrangement.  
Figure 3: A-A profile of the transverse cofferdam.  
demolition of the longitudinal cofferdam of Xiangjiaba hydropower station [17], the recommended explosive unit consumption (EUC) in land blasting was 0.50-0.60 kg/m\({}^{3}\).  
To ensure the safety of adjacent structures, the total explosives charge weight of region I, region III, and region IV were 2337.6 kg, 2578.9 kg, and 4724.8 kg, respectively. The shallow holes were charged with \(\Phi\)32 mm rock emulsion explosive, while the deep holes were charged with \(\Phi\)50 mm rock emulsion explosive. The density, detonation velocity, and minimum distance of sympathetic explosion of rock emulsion explosive was 1140 kg/m\({}^{3}\), 4600 m/s, and 6 cm respectively. Accordingly, the maximum explosives charge weight per hole was controlled at 12 kg, 16 kg, and 18.4 kg by air-decked charging. In particular, the explosives were fixed on bamboo slices to ensure calculated spacing between explosive decks in the blasthole. The surface connectors with three delay times (9 ms, 17 ms, and 42 ms) were used in initiation network, and detonating cord was additionally connected to all explosive decks to ensure detonation transfer between all explosive decks. The typical blast network structure diagram and charge structure diagram are shown in Figure 5.  
Figure 5: _Cont._
## 3 Simulations  
Equations from Section 3.2 were implemented through the use of the MATLAB/Simulink software. Several simulations were conducted. Some model parameters were constant. Their values are presented in Table 1.  
Damping and friction coefficients were changed in subsequent simulations. Distance \(x_{h}\) was also modified. Its value directly determined the required length of the launcher.
## 3 Inherent Optical Properties (IOPs)
### Atmosphere IOPs  
The stratified vertical structure of the bulk properties of an atmosphere is a consequence of hydrostatic balance. By equating pressure forces and gravitational forces and invoking the ideal gas law, one may derive the barometric law for the pressure \(p(z)\) as function of altitude \(z\) above the surface \(z_{0}\)[14]:  
\[p(z)=p(z_{0})\exp\Big{[}-\int_{z_{0}}^{z}dz^{\prime}/H(z^{\prime})\Big{]} \tag{64}\]  
where \(H(z)=kT(z)/\bar{M}g\) is the atmospheric scale height, \(\bar{M}\) is the mean molecular weight, \(k\) is is [PERSON]'s constant, \(g\) is the acceleration due to gravity, and \(T(z)\) is the temperature. The ideal gas law allows one to write similar expressions for the bulk density \(\rho(z)\) and the bulk concentration \(n(z)\). Clearly, from a knowledge of the surface pressure \(p(z_{0})\) and the variation of the scale height \(H(z)\) with height \(z\), Equation (64) allows us to determine the bulk gas properties at any height. Equation (64) applies to well-mixed gases, but not to short-lived species such as ozone, which is chemically created and destroyed, or water, which undergoes phase changes on short time scales.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
###### Abstract  
The emergence of the Internet of Things and the proliferation of mobile wireless devices has brought the area of mobile cognitive radio sensor networks (MCRSN) to the research spot light. Notwithstanding the potentials of CRSNs in terms of opportunistic channel usage for bursty traffic, the effect of the mobility of resource-constrained nodes to route stability, mobility-induced spatio-temporal spectral opportunities and primary user (PU) protection still remain open issues that need to be jointly addressed. To this effect, this paper proposes a mobile reliable geographical forwarding routing (MROR) protocol. MROR provides a robust mobile framework for geographical forwarding that is based on a mobility-induced channel availability model. It presents a comprehensive routing strategy that considers PU activity (to take care of routes that have to be built through PU coverage), PU signal protection (by the introduction of a mobility-induced guard (mguard) distance) and the random mobility-induced spatio-temporal spectrum opportunities (for enhancement of throughput). It also addresses the issue of frequent route maintenance that arises when speeds of the mobile nodes are considered as a routing metric. As a result, simulation has shown the ability of MROR to reduce the route failure rate by about 65% as against other schemes. In addition, further results show that MROR can improve both the throughput and goodput at the sink in an energy-efficient manner that is required in CRSNs as against compared works.  
mobility; cognitive radio _ad hoc_ wireless networks; cognitive radio sensor network; geographical forwarding; speed; reliable routing; opportunistic routing; routing protocol; virtual clustering +
Footnote †: journal: Sensors
# Issues with Modeling a Tunnel Communication Channel through a Plasma Sheath
## 5 Conclusions  
In conclusion, we would like to note that our research shows the efficiency of the proposed resonator. The efficiency varies across the required bandwidth (the loss due to resonator imperfection is 0.7 dB in the 14.8 MHz band and 0.44 dB in the 7.4 MHz band). We have achieved this due to solutions of two key issues of a real tunnel communication channel through a plasma sheath. (1) We have analyzed the incidence of electromagnetic waves at an arbitrary angle. (2) We have taken into account the presence of an antenna in a dielectric resonator. Thus, the proposed resonator provides an extensive communication range, making telemetry and control transmissions possible during the entire flight of spacecrafts. We have prepared a plan for a set of laboratory experiments to validate the proposed method.  
Conceptualization, N.V.K. and A.M.P.; software, investigation, visualization, A.V.B., A.E.S., M.V.T. and E.M.L.; formal analysis, A.M.P. and N.V.K.; writing--original draft preparation, A.V.B., A.E.S. and M.V.T.; writing--review and editing, A.M.P. and N.V.K. All authors have read and agreed to the published version of the manuscript.  
The wave interaction with thin metal layers was calculated with the support of the RSF project 20-69-47013. [PERSON] and N.K. are grateful to the Interdisciplinary Scientific-Educational School of the Moscow State University \"Photonic and Quantum Technologies. Digital Medicine\" for access to the necessary scientific and technical information. N.K. is the winner of the Master's program faculty grant competition 2020/2021 of the [PERSON] fellowship program.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Communication range through the tunnel communication channel at operating frequency 2.632 GHz and bandwidth 14.8 MHz.
## 3 Proposed Method
### Parallelized Sum Pooling  
In the previous subsection, we discussed the limitations of parallelizing CNNs due to the loop dependency of Sum-pooling. In this section, the process of parallelizing the operation by releasing this loop dependency is introduced. In this paper, a method is proposed to reconstruct the computational structure of the sum-pooling method of CNN to parallelize it. First, the sum-pooling operation of a typical CNN consists of moving a kernel of a predefined size over the input data by the height and width of the input, iterating over the number of channels, accumulating the obtained values, and determining a single feature of the output. As a result, the number of iterations of a single 2D Convolution operation _iter_ is calculated as follows:  
\[iter=out_{channel}\times out_{width}\times out_{height}\times in_{channel} \times kernel_{size}^{2} \tag{1}\]  
Figure 2 shows a schematic representation of the process and direction of the operation described above. The color change of the block means the direction of the operation. The iterative structure described above calculates one feature of the output and moves on to the next feature when the previous calculation is finished. In this paper, we propose a method to avoid loop dependency by calculating the value of all features belonging to a channel simultaneously based on one channel, rather than calculating one feature with a dependency on the output. Figure 3 shows a schematic representation of the sum-pooling method proposed in this paper.  
In Figure 3, the direction of the operation is changed from the conventional Sum-pooling in Figure 2. Hear also the color change of the block means the direction of the operation. In the proposed method in Figure 3, the computation proceeds in the Channel direction so that the dependency can be ignored. This does not eliminate the data dependency, but it moves the loops with the data dependency away from each other, making it possible to ignore the dependency under specific conditions. The specific conditions are described inSection 3.4. As a result, this computational structure is a hardware-friendly computational processing structure that cannot be beneficial to CPUs with sequential processing but can be greatly beneficial to hardware FPGAs with parallel processing characteristics.
# Robust Sampling Frequency Offset Estimation for OFDM over Frequency Selective Fading Channels
## 5 Simulation Results and Discussions
Figure 6 depicts the uncoded bit error rate (BER) obtained with the adoption of the conventional and proposed estimators followed by a one-tap frequency domain equalizer with perfect knowledge of CSI when \(\Delta_{f}=10^{-2}\), \(\Delta_{s}=200\) ppm, and CM5 is used. As a benchmark, the performance is compared with that of the perfectly synchronized DRM system. Here, the received OFDM symbols are directly compensated soon after SFO is estimated every \(D_{t}+1\) symbol periods. One can observe a substantial performance gap between the BER with perfect synchronization and the BER with estimated SFO for all estimation schemes especially at low SNR values. As reported in Figure 3, a slight performance degradation of the proposed scheme at low SNR is also inherent in the case of BER. On the other hand, the fact that no irreducible floor is visible indicates that the proposed estimation scheme is capable of effectively mitigating the ICI induced by frequency errors with a reduced computational complexity. Figure 4: MSE of the conventional and proposed frequency estimators versus sampling frequency offset (SFO) for DRM modes A and B when signal-to-noise ratio (SNR) \(=25\) dB. Figure 5: MSE of the conventional and proposed frequency estimators versus SFO for DRM modes A and B when SNR \(=40\) dB.
# Advancements in Flame-Retardant Systems for Rigid Polyurethane Foam
## 3 Additive-Type Flame Retardants
### Addition of Expandable Graphite and Derivatives  
Expandable graphite (EG) and derivatives have emerged as a promising flame retardant for rigid polyurethane foam (RPUF) due to their exceptional fire-resistant properties [(1997); (1998); (2000)].  
Figure 5: The synthesis of reactive flame-retardant HPHPCP.  
Expandable graphite (EG) is produced by introducing sulfuric acid, acetic acid, or nitric acid into the crystalline structure of graphite. This process results in a unique material with exceptional thermal expansion properties when exposed to heat (Figure 6) [85]. As a result, EG effectively decreases the flammability and heat release of RPUF. [PERSON] et al. [86] employed a heterocoagulating method to encapsulate EG using magnesium hydroxide (MH). The incorporation of 11.5 wt.% core-shell EG@MH increased the limiting oxygen index (LOI) of the rigid polyurethane foam (RPUF) to 32.6% and improved the storage modulus by approximately 55.0%.  
When considering the utilization of RPUF in refrigerators, it is essential that the material exhibits a thermal conductivity falling within the range of 19 to 22 mW/(m K) and a compressive strength exceeding 110 kPa. [PERSON] et al. [87] developed a flame-retardant RPUF using 15 wt.% EG and 5 wt.% ammonium pentaborate (APB). The outcomes revealed that this RPUF exhibited a remarkable 42.8% reduction in the THR and a 77.0% decrease in the TSR compared to the pristine foam. Furthermore, it demonstrated an LOI of 27.9% coupled with a low thermal conductivity of 20.41 mW/(m K) and a high compressive strength of approximately 125 kPa, thus displaying promising characteristics for its potential utilization in refrigeration applications.
# Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network
## 2 Basic Theories and Methods
### Convolutional Neural Network  
In this paper, the CNN in MATLAB deep learning toolbox is used to realize data pre-training and testing. The typical CNN contains at least a convolutional layer, a pooling layer, a fully connected layer, and a softmax output layer.  
Figure 2 shows the network architecture of 1D-CNN structure. The 1D-CNN in this paper is composed of an input layer, a convolutional layer (kernel size is 3, number is 5, stride is 1), a max pooling layer (kernel size is 3 in S1 and S4, 2 in S2 and S3, stride  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wavelet Order** & \(\mathbf{S_{L}(E_{j})}\) **(_p = 1.5_)** & **Wavelet Order** & \(\mathbf{S_{L}(E_{j})}\) **(_p = 1.5_)** \\ \hline
11 & 532.7851 & 16 & 493.4817 \\
12 & 512.3428 & 17 & 494.3682 \\
13 & 480.7129 & 18 & 498.6675 \\
14 & 554.1102 & 19 & 496.5085 \\
15 & 479.8986 & 20 & 518.0319 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cost function values for decomposing Daubechies with level 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Decomposition Level** & \(\mathbf{S_{L}(E_{j})}\) **(_p = 1.5_)** & **Computational Time** \\ \hline
1 & 951.6591 & 1.496 s \\
2 & 891.3953 & 1.417 s \\
3 & 623.9734 & 1.447 s \\
4 & 479.8986 & 1.479 s \\
5 & 355.8308 & 1.531 s \\
6 & 252.9848 & 1.475 s \\
7 &  
is 2), a batch normalization layer, a rectified linear unit (ReLU) layer, a flatten layer, a fully connected (FC) layer, and softmax output layer. Due to the data type, the prediction category is targeted to specific structural types, so the size of the input and output layer is fixed, and the accuracy of the final classification of CNN is directly related to the internal parameter setting. The CNN network architecture also can be used in the fields of bearing damage in mechanical structures and loosening of bolt structures.