esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Bioconversion of Platycoside E to Deapiose-Xylosylated Platycodin D under AP and HHP  
The catalytic bioconversion of platycoside E into deapiose-xylosylated platycodin D was performed with 0.5 mg/mL cyclase PCL5 and 1 mM platycoside E as a substrate, under AP and HHP. The enzyme completely converted platycoside E into deapiose-xylosylated platycodin D within 15 and 4 h with productivities of 66.7 and 250 \(\upmu\)M/h under AP and HHP, respectively (Figure 5). The productivity was approximately 3.75-fold higher under HHP than AP, indicating that HHP is much more effective for the production of deapiose-xylosylated platycodin D. Under the HHP condition, platycodin D3 was not detected throughout the reaction due to high hydrolytic activity. During the time-course for the reaction, the highest concentration of deapiosylated platycodin D was lower under HHP (467 \(\upmu\)M at 1 h) than AP (609 \(\upmu\)M at 6 h). Based on substrate specificity, this result  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Substrate**} & \multicolumn{2}{c}{**Specific Activity (nmol/min/mg)**} \\ \cline{2-3}  & **AP** & **HHP** \\ \hline PE & 15,601.2 \(\pm\) 50.2 & 48,738.2 \(\pm\) 101.2 \\ PD\({}_{3}\) & 281.2 \(\pm\) 18.0 & 1056.4 \(\pm\) 31.5 \\ PD & 35.1 \(\pm\) 1.8 & 141.5 \(\pm\) 2.1 \\ Deapi-PD & 15.3 \(\pm\) 1.3 & 71.9 \(\pm\) 2.5 \\ Deapi-xyl-PD & ND & ND \\ \hline \hline \end{tabular} PE, platycoside E; PD\({}_{3}\), platycodin D3; PD, platycodin D; Deapi-, deapiosylated; Deapi-xyl-, deapiose-xylosylated; ND, not detected.  
\end{table}
Table 1: Specific activities of cytolase PCL5 for different platycoside substrates under AP and HHP.  
Figure 4: Thermal inactivation of cytolase PCL5 under (**a**) atmospheric pressure and (**b**) high hydrostatic pressure. The enzyme was incubated at 45 (closed triangle), 50 (open triangle), 55 (closed square), 60 (open circle), and 65 \({}^{\circ}\)C (closed circle). Data represent the means of three experiments \(\pm\) standard deviation.  
may be attributable to the higher fold increase in the activity of cyclase PCL5 towards deapiose-xylosylated platycodin D than that toward other platycoside intermediates.
# Numerical Investigation of Slurry Fracturing during Shield Tunneling under a Reservoir
## 2 Numerical Method
### Embedding of Tension-Shear Fracture Criterion  
There are six fracture criteria in the ABAQUS extended finite element: maximum principal stress, maximum principal strain, maximum nominal stress, maximum nominal strain, secondary nominal stress, and secondary nominal strain [38]. These six fracture criteria are tensile failure; that is, failure will not occur under compression. However, the failure of soft soil layers is mostly shear failure. In order to use the extended finite element to simulate the soft soil layers failure more realistically, it is necessary to embed the shear fracture criterion into the extended finite element through secondary development. The fracture criterion of the ABAQUS extended finite element allows users to customize the fracture criterion through subprograms, which provides the possibility to realize the embedding of shear fracture. Since both shear and tensile failure of soil may occur under different stress states, the newly defined fracture criterion should include both tensile and shear failure. As a relatively mature and perfect shear failure criterion, the Mohr-Coulomb failure criterion has been widely used in the shear failure of rock and soil mass. Based on the basic principle of the Mohr-Coulomb failure criterion, this paper writes its failure principle into the newly defined fracture criterion through a subroutine, and the tension failure criterion adopts the maximum principal stress criterion. Figure 4 illustrates the flow chart of the unit from an initial state to failure under the newly defined failure criterion.
## 3 Results and Discussion
### Analysis of Prediction Results of BP Neural Network
Moreover, in order to achieve the repeatability of the model, the weights of the established IANN model are listed in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Epochs**} & \multicolumn{8}{c}{**MSE Values for Neuron Number (\(\times 10^{-4}\))**} \\ \cline{2-10}  & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline
300 & 3.7578 & 4.5403 & 2.4953 & 3.5101 & 2.3489 & 4.7521 & 2.7562 & 1.7478 & 2.1324 \\
400 & 2.9890 & 2.6727 & 2.5036 & 3.7639 & 1.7403 & 2.4159 & 2.2206 & 0.97334 & 1.3887 \\
500 & 5.8078 & 1.6159 & 3.2015 & 2.5594 & 2.2823 & 3.0795 & 2.1183 & 0.89931 & 2.1599 \\
600 & 4.5154 & 2.6319 & 2.0265 & 1.2636 & 1.8356 & 1.3300 & 2.1651 & 1.2676 & 1.4908 \\
700 & 4.1400 & 2.6848 & 3.0314 & 1.6838 & 1.2706 & 2.0658 & 2.0319 & 1.3243 & 1.4640 \\
800 & 3.9628 & 2.6995 & 2.4877 & 1.7637 & 1.8480 & 1.2178 & 2.5307 & 1.3522 & 1.0449 \\
900 & 3.1647 & 3.5374 & 2.5379 & 1.9526 & 1.2983 & 1.3416 & 2.1309 & 1.8244 & 0.79806 \\
1000 & 3.7801 & 2.387 & 2.8735 & 3.3274 & 2.3157 & 1.6443 & 1.7539 & 1.1109 & 1.2262 \\
1100 & 3.3921 & 2.6832 & 2.2898 & 1.7675 & 2.8269 & 1.7395 & 1.7649 & 1.2327 & 1.3550 \\
1200 & 3.6763 & 2.7611 & 2.2023 & 2.1266 & 2.0861 & 2.7173 Figure 8: The training results of the 9-2-12-3 IANN model (**a**) and the 7-2-10-3 OANN model (**b**).
## 5 Conclusions and Perspectives
### Perspectives  
Fire is one of the most harmful situations for various buildings, and the strength of concrete can be seriously compromised when exposed to fire. Researchers often rely on residual mechanical property analyses to assess fire damages to concrete structures; therefore, the topic thereof has been studied extensively. This review focused on the residual mechanical strength of SFRC after elevated temperature exposure, summarized the latest knowledge on this topic, and analyzed the effect of steel fibers on the residual properties of concrete and the action mechanism. However, the behavior of SFRC after elevated temperature exposure depends on many factors, such as the heating rate, duration of exposure, and cooling regime. The lack of standard procedures for heating and cooling concrete samples in various studies and the selection of different heating rates and duration of exposure in various studies renders it difficult to accurately compare test data. Meanwhile, a constant heating rate is not a good representation of structural fire, because the heating rate is variable during fire [102]. Although natural cooling has been used in most studies, rapid cooling has been used in limited studies to simulate firefighting processes. Rapid cooling can cause thermal shock to the heatedconcrete, resulting in a greater damage to the microstructure [54; 102; 103]. This suggests that the residual mechanical properties measured by natural cooling rather than rapid cooling may have been overestimated in most previous studies. Therefore, more experimental data under the condition of rapid cooling must be obtained for the comprehensive evaluation of the residual mechanical properties of SFRC. To fully understand the properties of structures made of SFRC during fire, research on fire resistance of SFRC not only requires residual tests, but also hot tests are essential. The thermal property of concrete is an important component of fire safety design, but research results regarding hot tests on SFRC are still limited hitherto. Therefore, further research is necessitated. In addition, modeling is an effective and useful tool to quantitatively understand the key processes of SFRC changes at high temperatures, which can be used to represent and simulate the field behavior of SFRC more accurately. Therefore, there should be focus on modeling as well [104]. In summary, although the performance of SFRC exposed to high temperature has been investigated extensively, fires are complex and volatile; therefore, more tests and research are warranted to realize the full potential of SFRC as a fire-resistant building material.  
Conceptualization, P.Z., L.K. and J.W.; formal analysis, P.Z., L.K., J.W., J.G.; resources, J.G.; writing--original draft preparation, P.Z., L.K., and J.W.; writing--review and editing, L.K. J.G. S.H. and Y.L.; supervision, S.H.; project administration, J.W.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflict of interest.
# Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications
## 4 Experimental Results
### Evaluation of Performance for the Hyperspectral Image Application
#### 4.2.1 Automatic Extraction of the Spatial Region of Interest for the Transmittance Mode  
As mentioned in the previous section, the light is limited to a finite region within the camera FOV for the transmittance illumination mode. For that reason, it is necessary to perform a selection of that region of interest (ROI) within the spatial dimension for the subsequent analysis. To perform this automatically, we made use of image processing techniques. In this case, in order to automatically locate the light in the ROI, we calculated the derivative of the spatial profile of the light intensity (Figure 10e). The derivative is shown in Figure 12a. The maximum and minimum values of such derivative indicate the maximum positive and negative slopes of the spatial profile, and hence, such information can be used to identify the spatial location of the backlight illuminator (Figure 12b). Finally, this information was used to extract the pixels where there is light intensity (Figure 12c). After applying this method, we were able to accurately identify the position of the light within the camera's field of view and focus the analysis on that particular area.
# Sloping Farmlands Conversion to Mixed Forest Improves Soil Carbon Pool on the Loess Plateau
## 4 Discussion
### Effects of Vegetation Restoration Type on Soil Microbial Activities  
Soil enzymatic activity plays an important role in C cycling and nutrient dynamics [36]. As sensitive indicators of the influence of land use changes or vegetation restoration on soil [6], changes in plant cover, SOC, and soil environmental conditions (e.g., pH and BD) after farmland abandonment would change the soil microbial composition and enzyme activity [6,36]. Plant residues in aforested ecosystems contain more roots and substrates than farmlands that stimulate the synthesis of soil enzymes [37,38], such as urease, phosphatase, saccharase, cellulase, and catalase activities (\(p<0.05\)) (Table 2). In addition, farmland has suffered serious soil erosion in the study area of the Loess Plateau, which has caused severe nutrient loss and has ultimately resulted in lower soil enzyme activities [5,24].  
Long-term natural grassland had a lower urease activity (\(p<0.05\)) than sloping farmland. Soil microorganisms do not need to secrete more enzymes to obtain additional nutrients because the efficiency of the enzymes is negatively correlated with nutrient availability [39]. To obtain access to more N, soil microorganisms secrete urease when soil N availability is low [6]. The N content in the soil increased significantly through long-term grassland restoration due to the continuous inputs of plants, which provide sufficient N for the growth and metabolism of microorganisms [6]. Moreover, the amylase and polyphenol oxidase activities were also reduced by vegetation restoration compared to sloping farmland (\(p<0.05\)) (Table 2), indicating that there was a lower SOM decomposition rate at the late stage of vegetation restoration (\(\sim\)30 years). This can be concluded from the microbial respiratory quotient (qCO\({}_{2}\)) being reduced after farmland was converted to grasslands, shrubs, forests and mixed forests (Figure 4).  
Compared with sloping farmland (\(p<0.05\)), vegetation restoration increased microbial biomass, which resulted in higher BR in the grasslands, shrubs, forests and mixed forests (Figure 4). This was possibly due to the greater plant diversity, biomass and residues after vegetation restoration providing more nutrient pools and niches for soil microorganisms. The mixed forest had the highest MBC, MBN and MBP (Figure 4), which also indicated that mixed forests are a good measure to improve the soil quality on the Loess Plateau. In addition, the total C, N, and P contents in microorganisms and soil synchronously increased after farmland abandonment, demonstrating that there was a potentially strong interaction between soil and microorganisms following vegetation restoration.
## 3 SA-PSO Method for FKP
### SA-PSO Algorithm  
The SA-PSO algorithm is introduced by the following Algorithm 1. The difference between the standard PSO and SA-PSO is the selection of the individual optimum.  
```
0: Length of outriggers \(I\), Accuracy \(\varepsilon\), PSO parameters: (Population size \(N\), Dimension \(D\), Learning factors \(c_{1}\)\(c_{2}\), Maximum number of iterations \(K_{max}\), Weight limit \(w_{max}\), \(w_{min}\), Position limit \(x_{max}\), \(x_{min}\), Velocity limit \(v_{max}\), \(v_{min}\)), Boltzmann constant \(\kappa_{B}\).
0: Predicted posture \(q=(x_{trans},y_{trans},z_{trans},x_{rot},y_{rot},z_{rot})\).
1: For each particle \(i\)
2: Initialize the position \(x_{i}^{0}\) the velocity \(v_{i}^{0}\) with the permissible range
3: End for
4: For each particle i
5: Calculate the fitness value \(fit_{i}^{0}=IK(x_{i}^{0})\)
6: Initialize the individual optimum and fitness \(P_{i,best}^{0}=x_{i}^{0}\), \(P\_fit_{i}^{0}=fit_{i}^{0}\)
7: End for
8: Initialize the global optimum \(G_{best}^{0}=x_{j}^{0}\) (\(j=index(\min\left(fit^{0}\right))\)) \(G\_fit^{0}=fit^{0}\)
9: Iteration \(k=1\)
10: Do
11: Calculate the weight by \(w^{k}=w_{max}-k(w_{max}-w_{min})/K_{max}\)
12: For each particle \(i\)
13: Calculate velocity \(v_{i}^{k}\)
14: Update particle position
15: Calculate the fitness \(fit_{i}^{k}=IK(x_{i}^{k})\)
16: End for
17: For each particle \(i\)
18: Find the individual best optimum \(P_{i,best}^{k}\) and update the \(P\_fit_{i}^{k}\)
19: End for
20: Update the global optimum of iteration \(k\):
21: \(G_{best}^{k}=x_{j}^{k}\) (\(j=index(\min\left(P\_fit^{k}\right)\)) \(G\_fit^{k}=P\_fit_{j}^{k}\)
22: Annealing: \(T^{k}=\kappa_{B}T^{k-1}\)
23: \(k=k+1\)
24: While the \(G\_fit^{k}<\varepsilon\) or \(k=K_{max}\)
25: Output: the predicted posture \(q\) is \(G_{best}\)
```  
**Algorithm 1**Simulated Annealing Particle Swarm Optimization (SA-PSO) for FKP.
## 4 Seepage Control System in the Xigou Tailings Dam with an Increased Height
### Numerical Results
#### 4.4.2 Comparison of the Seepage Results with Different Drainage Conditions  
To further evaluate the effectiveness of the new drainage system, the performance assessments of the seepage without drainage measures, with only horizontal drainage measures and with the proposed drainage measures were compared and analyzed. The process of increasing the height of the dam was divided into five stages to simulate the sequential raising of the dam. In each filling stage, the influence of the seepage control system and the variation in the seepage gradient were researched under the three different drainage conditions. Figure 13 shows the change in the maximum seepage gradient values and the minimum depth of the phreatic surface when the tailings dam was raised sequentially, indicating that if there was no drainage system, then the behavior of the phreatic surface was inconsistent and changed sharply in the location position, leading to a large seepage gradient and a shallow burial depth. After the horizontal drainage pipe was arranged in the heightened slope, the overall and local seepage gradient was improved, causing the maximum seepage gradient to be reduced by approximately 15% and the minimum burial depth to be increased by approximately 5.6 m; however, the minimum burial depth was slightly higher than the national standard during the last two stages. Due to the effect of the water guide of the upward bending slotted pipe, the proposed drainage system acted as a drainage channel, which could accelerate the dissipation of pore water stresses and the process of consolidating the tailings slurry to further reduce the free surface. A stable seepage field could be observed during various raisings. The maximum seepage gradient was reduced by approximately 40%, and the minimum burial depth was increased by approximately 9.3 m. Compared with only horizontal drainage pipes, all the minimum burial depths with new drainage pipes met the national standard. All three of these cases reflected the characteristics of the increase in the seepage gradient and the decrease in the depth with the rise of the tailings pond. However, the performance of the new seepage control system was better than the other two cases.
## 3 Results and Discussion
### PdM Assessment of Case Study
#### 3.1.4 Step 4: Sensor and Coverage Index  
In this step, the sensors and their coverage index must be identified. The sensor coverage index highlights the connection between sensor parameters and the failure modes and mechanisms. It expresses the relative value a specific parameter can detect in terms of failure mechanisms connected to a certain failure mode. It is estimated by dividing the sum of failure mechanisms detected by the sensor under study by the total number of failure mechanisms associated with the specific failure mode.  
The centrifugal compressor under study has sensors monitoring health indicators (i.e., vibration and bearing temperature), process indicators (i.e., pressure, temperature, gas composition), and performance indicators (i.e., speed and polytrophic efficiency).
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 5 Simulation Results
### Decoding Information Bits  
All communication receivers are assigned the same weight vectors. The lookup table is shared with all communication receivers in advance. The shape of the composite signal at the transmitted and received side is shown in Figure 10.  
The FFT analysis reveals that both the transmitted and received signals have the same frequency content. Similarly, in Figure 11, each bit is correctly decoded after filtering.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 4 Mechanism of Biofilm Formation
### Biofilm Maturation
#### 4.3.2 Outer Membrane Vesicle as Extracellular Substance  
Gram-negative bacteria, such as _H. pylori_, have been found to constitutively secrete outer membrane vesicles (OMVs) into the extracellular environment, which serve as essential mediators for intercellular communication, survival, and pathogenesis [52]. In the context of biofilm formation, a study on the TK1402 strain of _H. pylori_ highlightedthe significance of OMVs in this process [53]. These OMVs have been observed both on glass surfaces and bacterial surfaces, with transmission electron microscopy revealing their presence at the substratum-bacterium interface, suggesting a potential role as an extracellular polymeric substance (EPS) matrix for biofilm development [54]. The proteomic content of OMVs from Gram-negative bacteria has been a subject of interest, as these vesicles contain biologically active proteins that contribute to various biological processes [55]. Furthermore, OMVs have been associated with diverse functions, including promoting inflammatory responses in hosts, facilitating adhesion to host cells, and delivering virulence factors [56]. The ubiquity of OMV production among Gram-negative species underscores the importance of these vesicles in facilitating essential functions for organisms living in biofilm communities [57].  
Moreover, OMVs have been implicated in cell-to-cell communication, genetic exchange, cargo delivery, and pathogenesis, highlighting their versatile roles in bacterial physiology and interactions [58]. The presence of OMVs within biofilm matrices, as demonstrated in various studies, suggests that these vesicles play a crucial role in biofilm structure and function [59]. Additionally, the association of OMVs with biofilm disper-sion and their impact on biofilm composition further emphasize the significance of these vesicles in the dynamics of bacterial biofilms [60]. The secretion of OMVs by Gram-negative bacteria, including _H. pylori_, represents a sophisticated mechanism for intercellular communication and biofilm formation. Understanding the roles and functions of OMVs in bacterial communities is essential for unraveling the complexities of biofilm development and bacterial pathogenicity. An alternative approach to decrease OMV release could involve the utilization of peptidyl arginine deaminase inhibitors [61]. This could make bacteria more susceptible to antibiotic treatment and potentially decrease the development of antibiotic resistance linked to the creation of biofilms. An alternative strategy could entail the utilization of quorum sensing inhibitors (QSIs) to control bacterial pathogenicity [62]. QSIs can disrupt biofilm development by targeting the signaling systems that are involved in the creation of OMVs.
# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 2 Background
### Nr-U
Sub-7 GHz bands include the 2.4, 3.5, 5, and 6 GHz bands; meanwhile, mmWave bands encompass the 37 and 60 GHz bands [10]. As confirmed by 3 GPP, the 5 and 60 GHz bands are attractive candidates for NR-U, since they are currently not very crowded as 2.4 GHz and can offer a large amount of contiguous bandwidth. They both have common global availability and for which most major geographical areas worldwide have authorized the use of wide unlicensed spectrum bandwidth. Table 5 describes the frequency characteristics. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Mode** & **Asynchronous** & **Synchronous** \\ \hline
**Name** & Evolutionary path of 5G & Revolutionary path of 5G \\ \hline
**Spectrum under consideration** & 5 GHz & 6 GHz \\ \hline
**Technology adoption** & Global & Under consideration (U.S. and Europe) \\ \hline
**Spectrum efficient** & No & Yes \\ \hline
**Spatial and** & No & Yes \\
**predictable spectrum sharing** & No & Yes \\ \hline
**Synchronization of contention windows** & No & Yes \\ \hline
**Predictable latency** & No & Yes \\ \hline
**URLLC** & Not supported & Supported \\ \hline
**Time synchronization** & Not Needed & Needed \\ \hline
**Channel occupancy time (“COT”)** & COT \(\leq\) 1 msec & COT \(\leq\) 6 ms \\  & & COT \(\leq\) 12 ms \\ \hline
**Coordinated multi-point (CoMP) mode gain** & Difficult to realize & High probability \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the asynchronous and synchronous modes of spectrum sharing. URLLC, ultra-reliable low-latency communications. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Frequency** & **Adoption** & **Total Bandwidth** & **Incumbent Technology** \\ \hline
**2.4 GHz [6]** & Global & 100 MHz & Bluetooth, ZigBee, WiFi \\ \hline
**3.5 GHz [33]** & U.S. & 150 MHz & Satellite, military radar \\ \hline
**5 GHz [37]** & Global & 600 MHz & WiFi, WiGig, radar \\ \hline
**6 GHz [38]** & Europe & 1200 MHz & Broadcast, fixed P2P and satellite service, cable TV relays \\ \hline
**60 GHz [10]** & Global & 7 GHz & P2P fixed wireless bridging/backhaul \\ \hline \hline \end{tabular}
\end{table} 
Table 5: NR-U unlicensed/shared frequency.