esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Influences of Parameter Setting on Recognition Performance
Nonlinear SVM is often appropriate for spectral data, which involves selection of the kernel function and corresponding parameters. If we choose the more common RBF kernel, we must consider the two parameters \(c\) and \(g\), where \(c\) represents the penalty coefficient that characterizes the algorithm's nonlinear fitting ability, and \(g\) denotes the radius of the kernel function which affects its shape. The effect of SVM in Table 1 is not as good as that of other algorithms, and the problem can be found through a careful analysis of its parameter optimization process. In parameter optimization, we searched for the optimal parameters in range \(\{2^{-5},\,2^{-4},\,\ldots\,\,2^{0},\,\ldots\,\,2^{4},\,2^{5}\}\) using the grid method, but the parameters did not reflect well the characteristics of the spectral data with high dimensionality. The optimal parameters obtained in the optimization process show that \(c\) reached the preset upper limit of the grid method, and therefore the optimal parameters were not good. However, the prior information of model parameters for data with unknown spectra is not complete, especially for highly dimensional data, so there will be more uncertainty. If the search range is expanded or the search step length is refined to optimize the parameters, the algorithm's workload will increase. When the search range of the parameters is expanded and the optimal parameters of the infrared spectral data are worked out, namely \(c\) = 2048 and \(g\) = 0.0313 in this case, the correct recognition rate of SVM can reach more than 90%. Therefore, SVM is another algorithm that relies heavily on the parameters. ANN and RF do not work well. RF allows the user to select large parameters (e.g., the number of trees) without concern about over-fitting. However, spectral data have relatively large dimensions. Especially for infrared spectral data, there are strong correlations between dimensions. Therefore, a large amount of sample data is required. The data dimensions must be reduced to achieve better performance at classifying the spectral data [30]. A major drawback of ANN is its inability to interpret its inference process. It converts the inference process to numerical computation, and it can give no prior information for uncertain samples. For example, the fault-tolerant terminal conditions of iteration [31] can lead to over-fitting of the prediction samples. In addition, for highly dimensional spectral data, the regression into a class label in the form of a simple integer can cause larger errors. In conclusion, LRSC has a weaker dependence than other algorithms on the parameters. LRSC can be fairly ideal for highly dimensional data such as spectral data.
# Structural Stress Characteristics and Joint Deformation of Shield Tunnels Crossing Active Faults
## 4 Parametric Study and -Discussions
### Vertical Distance between Fault Zone and Tunnel  
Figure 20 shows the relation between the maximum damage value of thesegments and the distance h subjected to the normal faulting of 10 cm and the reverse faulting of 15 cm. The tensile and compressive damage values of the segmental lining decreased significantly with the increase of the distance \(h\). With the increase of the distance \(h\), the decreasing trend of the segmental lining damage was gradually weakened. When the distance was selected as \(h=5\) m, the tensile damage value of the structure was 0.9, and the concrete at the circumferential joints was partially damaged. When the distance h reached 7.5 m, the maximum tensile damage value was 0.41, for which the maximum tensile damage value of segments was significantly reduced. When the distance \(h\) reached 20 m, the tensile damage value of segments was 0.1, and the concrete at the annular joints was in a safe state.  
Figure 19: Effect of the elastic modulus of stratum on the segmental joint deformation: (**a**) normal faulting condition and (**b**) reverse faulting condition.  
Figure 18: Effect of the elastic modulus of stratum on the maximum stress of joint bolts: (**a**) normal faulting condition and (**b**) reverse faulting condition.  
Under 10 cm normal fault displacement and 15 cm reverse fault displacement, the relation between the maximum stress of the longitudinal bolts and the distance is illustrated in Figure 21. The maximum stress of the ring and longitudinal bolts decreased with the increase of distance \(h\). The vertical distance \(h\) increased from 5 m to 20 m, the maximum stress of the annular bolts reduced from 410 MPa to 260 MPa, and the stress reduction rate of the annular bolts was 15.7%.  
Figure 22 illustrates the relationship between the maximum deformation of the segmental joints and the distance \(h\). Under the fault movement, the maximum opening of the annular and longitudinal joints decreased with the increase of the vertical distance. In other words, under the same fault displacement, the smaller the distance, the larger the deformation of the segmental joints and the weaker waterproof performance of the segmental tunnel.  
Figure 21: Effect of the vertical distance \(h\) on the maximum stress of joint bolts: (**a**) normal faulting condition and (**b**) reverse faulting condition.  
Figure 20: Effect of the vertical distance \(h\) on the structural damage of segmental lining: (**a**) normal faulting condition and (**b**) reverse faulting condition.  
The above research findings can be explained as follows. In the buried faulting cases, the soil between the shield tunnel and the top of the fault plane weakened the forced displacement caused by fault displacement, which had a certain protective effect on the existing tunnel. Therefore, it can be preliminarily concluded that the vertical distance \(h\) between the top of the fault plane and the existing tunnel is a key factor in evaluating tunnel safety.
# Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
## 3 Modern Design
### Analog Domain Processing at Transceiver
#### 3.3.1 Power Amplifier  
Piezoelectric transducer excitation has been previously carried out mainly using three power amplifier types: Class-B; class-D; and class-AB. When the class-B power amplifier becomes functional, positive and negative channel transistors usually remain in a closed state unless there is a signal input, which can be elaborated as when the positive phase signal is received, only the positive phase channel starts working while the negative phase channel remains closed. Furthermore, the two channels will not work simultaneously to avoid any power loss in the part where there is no signal. The use of a commercial class-AB amplifier can avoid the drawbacks of the class-B amplifier. Exploring the actual need and selecting the best available power amplifier, we decided to use the class-AB power amplifier in this design. The TDA2030A is a monolithic IC, intended for use as a low-frequency class-AB amplifier. With VS max = 44 V, it is particularly suitable for more reliable applications without regulated supply and for 35 W driver circuits using low-cost complementary pairs. The TDA2030A provides high output current and has very low harmonic and cross-over distortion. Furthermore,  
Figure 4: Functional diagram of the transmitter.  
Figure 5: Functional diagram of the receiver.  
the device incorporates a short circuit protection system comprising an arrangement for automatically limiting the dissipated power so as to keep the working point of the output transistors within their safe operating area. A conventional thermal shut-down system is also included.  
Normally, a class-D power amplifier is more efficient than a class-AB power amplifier. However, the class-AB amplifier peripheral circuit is relatively simple, which is more conducive to the miniaturization of the modem. In addition, the output audio quality of the class-AB power amplifier is better and less noisy than that of the class-D amplifier. With the requirement of modem miniaturization, it is difficult for class-D power amplifiers to achieve both low interference and circuit scale miniaturization at the same time and it may produce high-frequency harmonics. In general, the duration of underwater diving activity is several hours. Even though class-AB power amplifiers are less efficient than class-D power amplifiers, they can still meet this demand.
## 4 NFV and SFC Frameworks Taxonomy
### Performance Tuning Frameworks
#### 4.2.11 MVMP Framework  
The NFV platform proposed by [PERSON] et al. [84], i.e., the Multiple Virtual Middlebox Platform (MVMP) is a high performance framework that has been built using the Intel DPDK platform and Docker containers. The three major components of the proposed MVMP framework are (i) abstracted virtual devices, (ii) a control plane, and (iii) a shared memory space. Packet processing by NFs is achieved by an abstraction layer that supports the deployment of multiple NFs on a single hypervisor. Network functions are run in user space as processes, which makes them lightweight, thus requiring fewer resources for packet processing. Packets are polled directly from the NIC using the DPDK poll mode driver and sent to several network functions, which also adds to the fast packet processing speed of the proposed framework.  
In terms of implementation and evaluation, network functions were implemented and chained together, and the service chain performance was evaluated and compared with the OpenNetVm [13] framework, which yields 3x better throughput as the number of network functions is increased in the chain, with an overhead of approximately 4% with regard to network function isolation.
## 3 Results and Discussion
### Interaction between Uunami Dipeptides and Rattus Norvegicus mGluR1 Open-Open Conformation
E-containing dipeptide EE (Ki = 27.93 uM) was predicted to link to Tyr74, Arg78, Ser186, Glu292, and Lys409; whereas ES (Ki = 875.75 uM), with Tyr74, Ser186, Asp318, and Lys409; and EV (Ki = 29.93 uM) with Tyr74, Glu292 and Lys409. Finally, the dipeptide VG (Ki = 694.05 uM) was predicted to interact with key residues Glu292 and Tyr236. As in the previous case, more non-key residues seemed to be implicated in the stabilization of the dockings, such as Arg71 in DA, EE, EV, and VG; or Trp110 in AH, EE, ES, EV, and VG. Receptor residues Tyr74 and Glu292 would be able to make H-bonds and hydrophobic forces, while Lys409 could form H-bonds and salt bridges in the same complex. Still, main interactions are due to H-bonds, highlighting the role of Tyr74, Ser186, and Glu292. Otherwise, the dipeptide AH, followed by ES, would make the greatest number of H-bonds, while in the case of the closed-open conformation, the peptides EE and ES reached a greater number of H-bonds. These differences may be due to the conformational changes of the receptor. Still, polar C-terminal amino acids may contribute to the establishment of H-bonds. The number of salt bridges was greater than that of hydrophobic interactions in few cases, such as those from E and EE, while DA and DG lacked hydrophobic bonds. The Ki values increased from AH, to EE, EV, VG, DA, DG, E, and finally, ES, which revealed that the Ki would not be benefited from the C-terminal non-polar residues, as it occurred in the closed-open conformation. As mentioned before, these differences may be due to the conformational changes, which would expose the key residues in a non-identical manner, translating it into different interactions and Ki values.
# The Construction of a Digital Agricultural GIS Application Suite
## 2 Design Considerations and Methods
### Design Considerations
#### 2.1.3 Principles of Oriented Digital Agricultural Applications  
The principles of application orientation emphasize that based on specific application objectives, the design of a digital agricultural GIS application suite should fully consider core application scenarios such as agricultural information management, agricultural comprehensive query, agricultural knowledge browsing, agricultural dynamic monitoring, agricultural statistical analysis, and agricultural decision support.  
Agricultural information management: Integrate and manage agricultural area profile data, agricultural crop data, and agricultural infrastructure data. The corresponding infrastructure information covers the land area, agricultural output value, sown area, crop yield, and road facilities.  
Agricultural integrated query: the suite supports multi-dimensional queries based on keywords, time, and region, including crop acreage and yield data, historical meteorological data, soil fertility, disease and pest occurrence, and related farmland management strategies in a specific region.  
Agricultural knowledge browsing: the suite supports access to diverse agricultural knowledge resources, including the knowledge browsing of different crop types, best practices in agricultural production steps, the application of precision agricultural technologies, and ecological agricultural practices such as pest control strategies.  
Agricultural dynamic monitoring: The suite supports dynamic monitoring and the storage of crop indicators and agricultural environments, including field water and fertilizer, crop growth, and natural disasters. The monitoring of farmland water and fertilizer mainly focuses on soil water content and nitrogen, phosphorus, and potassium content. Crop growth monitoring includes crop height, crop chlorophyll content, and other indicators. Natural disaster monitoring covers disasters such as wind, heavy rain, pests, and diseases.  
Agricultural statistical analysis: the suite presents the status quo, trends, existing problems, and potential risks of agricultural production-related information through charts or thematic maps, including statistical indicators such as agricultural land structure, soil fertility, crop planting area and output, and the composition of agricultural output value.  
Agricultural decision support: The suite supports model simulations and algorithm optimization for a variety of critical decision analyses. It uses data-driven models to predict pests and diseases, assesses environmental changes, and provides weather warnings. Through the yield prediction model, the planting plan and resource allocation are optimized.
## 2 Materials and Methods
### Simple Black-Box Algorithm for UAPs
When generating a nontargeted UAP \(\delta_{\text{nt}}\) the attack success rate corresponds to the fooling rate \(R_{f}\); that is, the fraction of input images for which their predicted labels are altered due to adversarial attacks to all input images in set \(\mathbf{X}\), \(R_{f}=|\mathbf{X}|^{-1}\sum_{\mathbf{x}\in\mathbf{X}}\mathbb{I}(C(\mathbf{x})\
eq C(\mathbf{x}+ \delta_{\text{nt}}))\), where the function \(\mathbb{I}(A)=1\) if condition \(A\) is true, and \(\mathbb{I}(A)=0\) otherwise. When generating a targeted UAP \(\delta_{\text{t}}\), the attack success rate corresponds to the targeted attack success rate \(R_{s}=|\mathbf{X}|^{-1}\sum_{\mathbf{x}\in\mathbf{X}}\mathbb{I}(C(\mathbf{x}+\delta_{\text{t} })=y)\); that is, the fraction of input images predicted as target class \(y\) due to adversarial attacks to all input images in set \(\mathbf{X}\). Our algorithm (please see Algorithm 1) was implemented using the adversarial robustness toolbox (ART, version 1.7.0; github.com/Trusted-AI/adversarial-robustness-toolbox, accessed on 1 February 2021). ```
Input: Set \(\mathbf{X}\) of input images, classifier \(\mathbf{C}\), set \(\mathbf{Q}\) of search directions, attack strength \(\mathbf{c}\), cap \(\mathbf{\zeta}\) on \(L_{p}\) norm of the perturbation, norm type \(p\) (1, 2, or \(\infty\)), maximum number \(i_{\text{max}}\) of iterations. Output: nontargeted UAP vector \(\delta\)
```  
**Algorithm 1** Computation of a nontargeted UAP  
```
1:\(\delta\)\(\leftarrow\)0, \(r\)\(\leftarrow\) 0, \(i\)\(\leftarrow\) 0
2:while\(r\)\(<\) 1 and \(i\)\(<\)\(i_{\text{max}}\)do
3: Pick a direction randomly: \(q\)\(\in\)Q
4:for\(\mathbf{x}\in\{-\epsilon,\,\epsilon\}\)do
5:\(\delta^{\prime}\)\(\leftarrow\) project(\(\delta+\alpha q,\,p,\,\xi\))
6:if\(\sum_{\mathbf{x}\in\mathbf{X}}p_{C}(C(\mathbf{x})|\mathbf{x}+\delta^{\prime})<\sum_{\mathbf{x}\in \mathbf{X}}p_{C}(C(\mathbf{x})|\mathbf{x}+\delta)\)then
7:\(\delta\)\(\leftarrow\)\(\delta^{\prime}\)
8:break
9:endif
10:endfor
11:\(r\)\(\leftarrow\)\(|\mathbf{X}|^{-1}\sum_{\mathbf{x}\in\mathbf{X}}\mathbb{I}(C(\mathbf{x})\
eq C(\mathbf{x}+\delta))\)
12:\(i\)\(\leftarrow\)\(i+1\)
13:endwhile
```  
**Algorithm 2** Computation of a nontargeted UAP
# The Role of Elasticity in the Vortex Formation in Polymeric Flow around a Sharp Bend
## 3 Results
### Polymer Flow  
As previously discussed, there are four dimensionless numbers--Wi, Re, El, and \(\beta\)--typically used to characterize polymer fluid dynamics. Nevertheless, the elasticity number (EI) is defined as the ratio of the Weissenberg and Reynolds numbers. Consequently, only three dimensionless parameters are effectively needed to characterize polymer flow. Evaluating and predicting the effect on these parameters on polymer flow and vortex formation is the main goal of this research.  
It has already been shown, for the Newtonian case, that a difference in Reynolds number means a difference in vortex size. For polymer fluids, even though elasticity is present, this should be no different. Holding the Weissenberg number constant comparing two simulations with different elasticity numbers should give insight into how the Reynolds number affects the vortices.
## 3 Results
### Bulk-Flower Extraction and LC-DAD-QTOF Analysis  
#### 3.2.1 LC-DAD-QTOF Analysis and Identification of the Compounds  
The procedure for LC-DAD-QTOF analysis was developed using bulk-flower extraction of flowers at stage 4. A total of 18 compounds were identified based on their exact mass, molecular ion fragmentation, and UV/Vis absorption spectrum, as presented in Table 2. Out of them, 16 were detected in the UV/Vis spectrum, allowing for their use in a comparative analysis applying only a UV/Vis detector (Table 2 and Figure 2). These compounds were well separated except for the collective peak of querciturone + isoquercitrin (compounds **9** and **10**, Figure 2). Accordingly, the two compounds were reported collectively in the comparative LC/UV-Vis analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Flower Stage**} & \multicolumn{2}{c}{**Plant 3\_29**} & \multicolumn{2}{c}{**Plant 90\_44**} & \multicolumn{2}{c}{**Plant 139\_28**} & \multicolumn{2}{c}{**Across Genotypes**} \\ \cline{2-9}  & **Mean FW** & **RSD** & **Mean FW** & **RSD** & **Mean FW** & **RSD** & **Mean FW** & **RSD** \\ \hline
1 st & 15.14 & 4.83 & 23.24 & 12.05 & 19.04 & 3.86 & 19.14 \(\pm\) 4.05 a & 21.16 \\ \hline
2 nd & 21.79 & 4.66 & 41.92 & 14.58 & 34.16 & 7.93 & 32.62 \(\pm\) 10.15 a & 31.13 \\ \hline
3 rd & 47.36 & 2.36 & 53.63 & 1.24 & 52.58 & 6.02 & 51.19 \(\pm\) 3.36 b & 6.57 \\ \hline
4 th & 62.27 & 5.96 & 66.91 & 13.90 & 65.23 & 4.30 & 64.80 \(\pm\) 2.35 b & 3.63 \\ \hline
5 th & 43.48 & 4.32 & 48.92 & 2.43 & 60.08 & 4.31 & 50.83 \(\pm\) 8.46 b & 16.65 \\ \hline \end{tabular}
\end{table}
Table 1: Mean fresh weight (FW, mg) and relative standard deviation (RSD, %) of single flowers at 5 stages of flower development based on data from 3 plants representing different genotypes. Means that share a letter do not differ significantly at \(p<0.05\).  
Figure 2: LC/DAD/QTOF chromatogram of an extract from stage 4 flowers. (**a**) ESI-TIC in negative mode, (**b**) UV/Vis trace at 270 nm, and (**c**) UV/Vis trace at 590 nm. Compound numbering corresponds to Table 2.
# Reasoning about Confidence in Goal Satisfaction
## 2 Background
* _OR-Decomposition_: using the same notation conventions, here the maximum satisfaction value among the decomposing elements is propagated, as shown in Equation (2) and Figure 4. GRL's XOR-decomposition is computed in the same way, with a warning if more than one decomposing element has a satisfaction value different from 0. \[v(S)=Max(v(D_{1}),v(D_{2}),\ldots,v(D_{n})),\] (2)
* _Contribution_: in GRL, contributions can have a type/weight that indicates whether it is negative, neural (0), or positive, with a weight between 0 and 100. Hence, the contribution scale goes from \(-100\) to \(+100\). Here, the product of the weighted contribution values (\(C_{x}\) and the satisfaction values (\(v(D_{x})\)) of the \(n\) contributing elements are propagated to the targeted GRL element, as illustrated in Figure 5 and Equation (3). Note that the final satisfaction is also truncated to a value between 0 and 100, inclusively: \[v(S)=Max(0,Min(100,\frac{\sum_{x=1}^{n}(v(D_{x})\times C_{x})}{100}))\] (3)  
Figure 4: Propagation of element satisfactions in the case of an OR-decomposition. Figure 3: Propagation of element satisfactions in the case of an AND-decomposition. * _Dependency_: the satisfaction of the dependent element (\(v(S)\)) is truncated to the minimum satisfaction value of its depending elements (see Figure 6, where \(v(S)\geq 25\) initially, and Equation (4)). This means that the GRL element that is the source of dependencies cannot be better satisfied than the other elements it depends on. \[v(S)=Min(v(S),v(D_{1}),v(D_{2}),\ldots,v(D_{n}))\] (4)
# Physical and Mechanical Properties and Damage Mechanism of Sandstone at High Temperatures
## 3 Test Result
### Uniaxial Compression Test
#### 3.2.2 Variation Law of Peak Stress  
According to the data in Tables 1 and 2, the variation patterns of peak stress \(\sigma\), modulus of elasticity \(E\), and peak strain \(\varepsilon\) of sandstone were investigated at different temperatures and heating rates. From Figure 5(a), it can be seen that the peak stress of sandstone decreases with increasing temperature, and the peak stress reaches its maximum value at 500 \({}^{\circ}\)C. This trend is the same as that concluded by [PERSON] [27] et al. but with different threshold temperatures. According to [PERSON] [28], by conducting uniaxial compression tests on sandstone at high temperatures, it was found that the compressive strength decreases with increasing temperature at temperatures from 25 to 200 \({}^{\circ}\)C and increases with increasing temperatures from 200 to 600 \({}^{\circ}\)C and then starts to decrease after 600 \({}^{\circ}\)C, and this variability is due to the difference in the mineral composition of the rock. According to the results of this paper, from room temperature to 500 \({}^{\circ}\)C, the peak stress of sandstone increases with temperature, from 55.91 MPa at room temperature (25 \({}^{\circ}\)C) to 94.76 MPa at 500 \({}^{\circ}\)C, an increase of 38.85 MPa or 69.48%. As the temperature continues to rise, the peak stress of sandstone decreases, and the peak stress at 1200 \({}^{\circ}\)C is 40.66 MPa, which is 54.1 MPa lower than that at 500 \({}^{\circ}\)C, a decrease of 57.09%. It is worth noting that temperatures between 100 \({}^{\circ}\)C and 1100 \({}^{\circ}\)C promote the compressive strength of sandstone relative to room temperature, and 1200 \({}^{\circ}\)C has a deteriorating effect on the compressive strength of sandstone.  
Figure 5(b) shows the effect of heating rate on the peak stress of sandstone at 1200 \({}^{\circ}\)C. As the heating rate increases, the peak stress decreases, indicating that thermal shock decreases the compressive strength of sandstone. When the heating rate is 5 \({}^{\circ}\)C/min, the peak stress  
Figure 5: Total stress–strain curves at different heating rates at 1200 \({}^{\circ}\)C.  
is 51.14 MPa, and the peak stress is 32.54 MPa until the heating rate is 30 \({}^{\circ}\)C/min, which is a decrease of 17.6 MPa or 34.41%, and the average compressive strength of sandstone decreases by 0.704 MPa for every 1 \({}^{\circ}\)C/min increase in the heating rate.