esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Vibration on Adjacent Pipelines under Blasting Excavation
## 3 Blast Vibration Attenuation Law
\(\rho\) and \(c\) can be assumed as constants, and we can analytically conclude the functional relationship between \(v\) and \(\left(\frac{1}{Q^{1/3}r^{-1}}\right)^{\beta 1}\left(\frac{d}{v}\right)^{ \beta 2}\) by Equation (4), which can be written as:  
\[\ln v=\left[\alpha_{1}+\beta_{1}\ln\left(\frac{\sqrt[3]{Q}}{r}\right)\right]+ \left[\alpha_{2}+\beta_{2}\ln\left(\frac{d}{r}\right)\right] \tag{6}\]  
Let \(\ln v_{0}=\alpha_{1}+\beta_{1}\ln\left(\frac{\sqrt[3]{Q}}{r}\right)\); then, the following equation is derived:  
\[\ln v_{0}=\alpha_{1}+(\beta_{1}\ln Q)/3-\beta_{1}\ln r \tag{7}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are environmental coefficients related to the geological conditions, and \(\beta_{1}\) is the attenuation index related closely to the medium. \(-\beta_{1}\ln r\) represents the _PPV_ attenuation with \(r\) rising, and \(\alpha_{1}+(\beta_{1}\ln Q)/3\) reflects the impact of the medium and explosive charges. In setting \(\ln k_{1}=\ln\alpha_{1}\), then  
\[v_{0}=k_{1}\left(\frac{\sqrt[3]{Q}}{r}\right)^{\beta 1} \tag{8}\]  
where \(k\) and \(\beta\) are the site coefficients related to the geological conditions, \(Q\) is the maximum charge per hole, and \(r\) is the length of the blast source. The above equation is transformed into logarithmic form:  
\[ \begin{cases}\ln v=\ln k+\beta\ln\rho\\ \rho=\frac{\sqrt[3]{Q}}{r}\end{cases}  \tag{9}\]  
The above equation is expressed in the form of a binary primary function:  
\[y=ax+b \tag{10}\]  
where \(y=\ln v\), \(x=\ln\rho\), \(a=\beta\), and \(b=\ln k\). Table 4 shows the _PPV_ prediction model for each direction of the ground soil and the correlation coefficient. A good fit result was obtained in which all had high-fitting accuracy and where the composite vector, _PPV_, had the highest correlation coefficient of 0.89. Therefore, it is workable for Equation (11) to reflect the vibration propagation law by using the following model. \[PPV=70.17\left(\frac{Q^{1/3}}{r}\right)^{1.31} \tag{11}\]
## Materials and Methods  
### Material Characterization  
The ZSM-5 zeolite material was acquired from Shanghai Fuxu Molecular Sieve Limited Company in China. The particle size of the raw material is around 3.5 um and the Na\({}_{2}\)O concentration is 0.046%. To improve the purity of the sample material, the raw material is pretreated in a tube furnace under 873 K for six h. After decontamination pretreating, the properties of the zeolite are characterized by using Quantachrome Autosorb-iQ-MP gas sorption analyzer (Quantachrome Instruments, Boynton Beach, FL, USA). Its specific surface area and pore volume are determined to be 638.01 m\({}^{2}\)/g and 510 mm\({}^{3}\)/g, respectively. Through a Non-Local Density Functional Theory (DFT) analysis, both micro and meso pores are discovered in the sample. The micropore size is 0.524 nm and the mesopore size is around 2.114 nm. It is believed that the glycerol molecules only intrude into the mesopores (2.114 nm) under the infiltration pressure of 15-20 MPa in the present work, because the infiltration pressure of water molecules into MFI zeolite's micro pores around 0.5 nm are above 100 MPa, according to previous studies [5; 32], and the size of glycerol molecule (about 0.62 nm) is larger than the size of water molecule. Although the glycerol molecules cannot intrude in the micropores (0.524 nm), the existence of micropores is of great importance for both infiltration and defiltration processes, especially for gaseous phase that is trapped in micropores, which cannot be neglected. So, the most probable pore size of mesopore (2.114 nm), the total surface area (638.01 m\({}^{2}\)/g), and pore volume (510 mm\({}^{3}\)/g) should be used for the calculation of relative parameters.
## 1 Introduction
The methods were compared on their level of energy consumption, the quality of data transmissions, the amount of excessive network resources used or the network overhead, the extent to which the method can be applied to different scales without affecting overall quality of the model, and the extent to which the model can adapt to different scenarios. The model's performance in each of these categories was broadly subdivided into a series of subcategories of Low, Minimal, Moderate, and High (in order of least to greatest values), while the data transmission quality was ranked as being Moderate, Efficient, Adaptive, or Optimal, in order of least to greatest. Of the various methods listed in Table 1, Ant Colony Optimization Routing demonstrates the most optimal properties, with low energy consumption, optimal data transmission, minimal network overhead, and high scalability and adaptability. Meanwhile, Swarm Intelligence Optimization has the least optimal properties, with moderate energy consumption, moderate data transmission, low network overhead, high scalability, and moderate adaptability. The strategies listed in Table 1 will aid in the energy efficiency of WSNs used for online shopping. The complexity of the algorithms is measured using the values of their energy consumption, network overhead, scalability, adaptability (Low, Minimal, Moderate, High), and data transmission (Moderate, Efficient, Adaptive, or Optimal). The study aims to evaluate network resilience, energy efficiency, scalability, flexibility, and durability. When applied to networks' primary nodes, all techniques achieve respectable standards for energy consumption. Setting up energy-efficient wireless sensor networks for online shopping requires parameter input, technique selection, implementation, and performance evaluation, as shown in Figure 1. Figure 1 showcases the loop by which an optimal method is selected. The optimal method is selected based upon a variety of operating characteristics, including the energy efficiency, network lifetime, and data transmission rates. Optimization is achieved by comparing the results of the current method against the optimum method. If the current method contains similar or greater degrees of efficacy as the previous method, then it is selected as the optimum method and compared against the next method. In this manner, an optimal energy-efficient wireless sensor network is formulated. The goal of this research study is to improve the usage of wireless sensor networks for consumer electronics in e-commerce through the introduction of AI-based decision
# Hop-by-Hop Multipath Overlay Routing for Optimizing Network Resource Allocation in WANs
## 3 System Design
### Resilience in Overlay Routing  
Although this study primarily focuses on addressing the congestion issues caused by large-scale data transmission, as it focuses on overlay multipath routing, it also provides resilience against link and node failures. We discuss this from the perspective of overlay node placement and multipath routing.  
For Overlay node placement, research on resilience typically aims to find multiple paths that do not share overlapped links/nodes. In this study, the definition of path diversity in node placement is closer to the link overlapped scenario. As the number of independent paths increases, the intermediate node can switch paths to avoid disruptions in the event of a link failure. The definition of path diversity can easily be extended to link overlapped scenarios. Therefore, we believe that the overlay node placement algorithm MOP can also improve the resilience of WAN.  
As the overlay routing in this study is based on existing multipath algorithms, it can also support multipath algorithms like red-black trees and SPDP, which can obtain completely non-overlapping paths for resilience. Compared with the limited non-overlapping paths maintained at the edge, this study utilizes intermediate nodes to maintain a greater number of non-overlapping paths and to immediately respond to link/node failures by switching the next-hop node. For overlay routing, we believe that failures are a subset of congestion issues. When the failure of the link/node is detected, it can also be bypassed by changing the next hops.
# Biflavonoid Profiling of _Juniperus_ Species: The Influence of Plant Part and Growing Location
## 2 Materials and Methods
### Biflavonoid Profiling  
A total of seven biflavonoids were included in the analysis. Amentoflavone, bilobetin, ginkgetin, isoginkgetin, and sciadopitysin were obtained from PhytoLab (Vestenbergsgretuth, Germany), cupressuflavone from Extrasynthese (Genay, France), and hinokiflavone from Sigma-Aldrich (Darmstadt, Germany). All reference standards were of HPLC grade purity. Standard stock solutions of seven biflavonoids were prepared individually in DMSO at a concentration of 1000 \(\upmu\)g/mL. Four working solutions were prepared for each standard (concentrations 1.0, 10, 50, and 100 \(\upmu\)g/mL) and were injected into an Agilent 1260 Infinity II high-performance liquid chromatography system (Agilent, Santa Clara, CA, USA) with a diode array detector (DAD). Separation of all seven biflavonoids was performed on a Zorbax 300 Extend-C18 column (Agilent, Santa Clara, CA, USA) maintained at 40 \({}^{\circ}\)C, and was successfully achieved under the following conditions: mobile phase A-0.1% formic acid solution and acetonitrile as mobile phase B, with a flow rate of 1 mL/min. The gradient elution profile was set as follows: 0 min-98% A, 10 min-79% A, 15 min-77% A, 20 min-75% A, 25 min-64% A, 30 min-62% A, 35 min-51% A, 40 min-25% A, 43 min-8% A, and 45 min-98% A. The injection volume for both samples and standards was 10 \(\upmu\)L. Data acquisition and subsequent processing were performed using Agilent OpenLAB CDS software (version 2.6, Agilent, Santa Clara, CA, USA). Biflavone identification was performed by comparing the sample spectra with those of the reference standards. Chromatograms were recorded at 330 nm. The concentrations of 3\({}^{\prime}\)-8\({}^{\prime\prime}\) biflavones in the samples were determined using standard calibration curves (Table S2) and expressed in \(\upmu\)g/mg of dry weight (dw). The chromatogram of all seven biflavonoids at 330 nm is presented in Figure 4. Representative chromatohograms for analyzed samples are presented in Supplementary Material Figures S1 and S2.  
Figure 4: Biflavonoid standard chromatogram at 330 nm.
## 7 Conclusion and Future Works  
In this paper, the principles of an innovative camera surveillance network placement approach named \"HybVOR\" are presented. This approach aims to provide the best placement of a monitoring surveillance network, while optimizing the number of cameras to be deployed. The idea behind HybVOR is to generate a Voronoi Diagram from buildings' footprint and to use the Voronoi edges to place cameras of surveillance. Then, in order to assess the quality of the network coverage, we have combined; (1) a raster based viewshed calculation that uses a raster Digital Surface Model of the study region with (2) a 3D vector based analysis of visibility that relies on a 3D model containing the main entrances of buildings to be monitored. This combination of raster and vector visibility analysis increases the effectiveness of assessing the level of coverage expected from deploying the surveillance camera network.  
The obtained results are very interesting in terms of providing an almost complete surface coverage, while choosing the locations of cameras that offer a full monitoring of the main entrances of buildings in addition to optimizing the number of deployed cameras. Our further investigations will focus on solving the automatic displacement of cameras resulting from our proposed method to resolve any lack of coverage. To achieve this goal, an Agent Based Model (ABM) will be developed; it aims to automate the displacement process without the need for manual intervention especially when performing 3D vector based analysis of visibility. This ABM will be based on the rules and constraints that come from placement principles of the HybVOR approach.  
Finally, it is important to mention that the HybVOR approach is also useful for monitoring other elements of an urban landscape such as traffic movement and people surveillance. HybVOR is an appropriate method for placing cameras to monitor areas with high security interest. In addition, our proposed approach may be very effective for the deployment of mobile cameras that may be mounted on UAVs (Unmanned Aerial Vehicles). The configuration of such a network can be performed on the fly depending on the dynamic of the environment, the number of available UAVs and the importance of specific targets.  
The authors are grateful to the graduated students [PERSON] and [PERSON] for carrying out the field works related to topographical surveys. Also, the authors would like to thank the editors and reviewers for their interesting and constructive comments.
## 3 Results
### Effects of Different Treatments on Decomposition Characteristics of Straw
#### 3.1.2 Effects of Different Treatments on Carbon Content in Residual Straw  
The change in total organic carbon content of straw over time under different treatments during strip rotting is illustrated in Figure 2. The figure demonstrates that as the duration of the experiment increased, the total organic carbon content of straw in each treatment exhibited varying degrees of decline. Initially, there was a rapid decrease in carbon content from 0 to 60 days, followed by a gradual slowing of this rate over time. By the end of 150 days, the carbon content of straw treated with T2 and T3 was notably lower compared to the other two treatments. Specifically, the carbon content of stalks treated with T2, T3, and T4 decreased by 49.60%, 49.04%, and 47.78%, respectively, whereas the decrease in stalks treated with T1 was only 45.89%.
# Building Survivable Software Systems by Automatically Adapting to Sensor Changes
## 5 Extensions  
### Estimating Adaptation Quality  
To build survivable software, estimating the quality of adaptation is also important since it enables higher-layer software components to determine whether or not to accept a proposed adaptation. Towards this end, we developed a method to estimate an error interval for the gap between the reconstructed sensor value and the ground truth.  
We would like to obtain such an error interval for each reconstructed sensor value and for each sample in the target domain. Given a reconstructed sample in the target domain \([\mathbf{z}_{t,1:K^{\prime}};\mathbf{f_{0}}(\mathbf{z}_{t})]\) and a specific reconstructed sensor value, we estimated its error interval from similar samples in the source domain:  
1. Find its \(\kappa\) nearest neighbors in the source domain according to distances defined in Equation (3).
2. Compute the standard deviation \(\sigma\) on the given reconstructed sensor value among the \(\kappa\) neighbors found in Step 1.
3. Set the estimated error interval to be \([-\alpha\sigma,\alpha\sigma]\), where \(\alpha>0\) is a scaling factor. An ideal \(\alpha\) makes the error interval as tight as possible. \(\alpha\) can be tuned on source-domain samples by optimizing the \"excess error\" notion defined below.  
Excess Error of the Error Interval: To quantify the tightness of the estimated error interval, we used the notion of excess error. It is defined as the gap between the ground-truth value and the closest endpoint of the error interval, when the interval contains the ground-truth value. Figure 8 illustrates this notion. If the interval did not contain the ground-truth value, we considered the interval invalid. In practice, we could tolerate a small failure rate of the estimated error interval by setting a recall parameter (e.g., 90%). We could then find the smallest \(\alpha\) to achieve the given recall and compute the corresponding excess error. Clearly, we favored a smaller excess error as it resulted in a tighter error interval. We present the results on excess errors in the next section.  
Figure 7: Visualization of the reconstructed wind gust by ASC. The blue curve represents observed sensor values and the red curve represents reconstructed sensor values.
## 3 Exploring Speaker-Independent Dysarthric Speech Recognition
### Results
#### 3.4.2 Training Involving Dysarthric Speech  
The PER results for models trained with dysarthric speech are presented in Table 3, rows **E8-E15**. The significant overall improvement compared to models trained solely on in-domain canonical data underscores the critical role of dysarthric-specific knowledge transfer in enhancing DSR performance. While using only in-domain dysarthric data (**E9**) achieves reasonable accuracy, incorporating other source data in the initial training phase and subsequently adapting the model to in-domain dysarthric data (**E10-E15**) yields even better performance.  
The highest recognition accuracy (**E13-E15**) reflects the optimal training strategy. This approach builds on the best-performing models (**E6-E7**) from the previous scenario.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**EXP** & \multicolumn{3}{c}{**Training Strategy**} & **PER** \\ \hline
**E10** & & & ✓ & ✓ & 0.194 \\ \hline
**E11** & ✓ & & & ✓ & 0.185 \\ \hline
**E12** & & ✓ & & ✓ & 0.182 \\ \hline
**E13** & & & ✓ & ✓ & **0.181** \\ \hline
**E14** & ✓ & & ✓ & ✓ & **0.181** \\ \hline
**E15** & & ✓ & ✓ & ✓ & **0.180** \\ \hline \end{tabular} \(\mathcal{V}\): The data is used for model training in the corresponding experiment; **Bold numbers**: Best PER(s) within the statistical significance level of the middle or bottom table; *: train only the output layer.  
\end{table}
Table 3: _Cont._This optimal strategy uses rich-resource and in-domain canonical data to provide a broad and relevant vocabulary base while employing in-domain dysarthric speech to enhance vocabulary knowledge and integrate crucial dysarthric-specific knowledge.  
Notably, similar recognition results are observed between models trained with C_rich and C_rich_tgt, such as in E6-F7, E11-E12, and E13-E15. This similarity arises because the base model itself is pretrained on a rich-resource multilingual dataset, diminishing the unique impact of C_rich as a fundamental factor in achieving high recognition accuracy.
## 3 Overview of the Study Area and Data Sources
### Characteristics of Landslides in the Study Area  
The survey results indicated 220 disaster points along the pipeline, including 135 unstable slopes, 44 collapses, and 41 landslides. Landslides and unstable slopes are the main geological disasters along the pipeline, accounting for more than 80% of the geological disasters, with a total area of 380,000 km\({}^{2}\). The largest and smallest landslide areas are 4.5 km\({}^{2}\) and 19 m\({}^{2}\) (Figure 4).
## 3 Results
### Characteristics of the Water Chemical Indicators for Four Typical Water Bodies  
The water chemical indicators of the four urban water body types are shown in Table 2. Compared with the Surface Water Environmental Quality Standard (GB 3838-2002) [29] in China, the average TN contents in the four urban water bodies slightly exceeded the surface water Class-V water quality standard (2.0 mg/L). The average TN contents in Canal, Lake, Pond, and River were 1.29, 1.22, 1.23, and 1.23 times the Class-V water quality standards, respectively. In particular, the order of the average TD content in the four urban water bodies was Canal (2.58) > Pond (2.55) > River (2.46) > Lake (2.44). The variation trends of the TDN and TN contents in the four water bodies were considerably different. The order of the average TDN content in the four urban water bodies was Pond (2.46) > Canal (2.42) > Lake (2.31) > River (2.28). Further analysis of the NH\({}_{4}\)\({}^{+}\)-N and NO\({}_{3}\)\({}^{-}\)-N contents in the water bodies showed that the NH\({}_{4}\)\({}^{+}\)-N content in the four water bodies was basically the same, with an average content of 0.07-0.10 mg/L. The NO\({}_{3}\)\({}^{-}\)-N contents in Canal, Pond, and River exhibited small differences, with an average content of 1.76-1.81 mg/L. Moreover, the NO\({}_{3}\)\({}^{-}\)-N content (1.64 mg/L) in Lake was slightly lower than that in the other three water bodies. The order of the average Chl-a content in the four water bodies was Canal (5.42) > Lake (4.35) > Pond (3.68) > River (3.37). Moreover, the order of average COD\({}_{\text{Mn}}\) values in the four urban water bodies was Lake (3.47) > Canal (3.41) > Pond (3.29) > River (3.09). Thus, Pond and River exhibited a relatively low Chl-a content and COD\({}_{\text{Mn}}\) value compared with the other two water bodies. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Water Quality Parameters**} & \multirow{2}{*}{**pH**} & \multicolumn{2}{c}{**DRP**} & \multicolumn{2}{c}{**DO**} & \multirow{2}{*}{**EC (uS/cm)**} & \multirow{2}{*}{**TDS (mg/L)**} \\  & & & **(REDOX)** & & & & \\ \hline \multirow{3}{*}{Canal (n = 8)} & Range & 7.45–9.96 & 121.52–184.66 & 4.06–11.25 & 123.00–209.67 & 79.60–135.67 \\  & Mean & 8.84 & 159.77 & 8.83 & 174.05 & 112.68 \\  & SD & 0.8 & 24.56 & 2.84 & 26.59 & 17.23 \\ \hline \multirow{3}{*}{Lake (n = 8)} & Range & 7.95–9.96 & 94.52–201.34 & 4.36–9.21 & 117.86–462.20 & 76.14–299.80 \\  & Mean & 8.87 & 167.78 & 7.41 & 224.59 & 145.56 \\  & SD & 0.9 & 32.89 & 1.67 & 104.15 & 67.62 \\ \hline \multirow{3}{*}{Pond (n = 8)} & Range & 7.00–9.37 & 73.66–214.26 & 4.78–10.17 & 147.00–231.40 & 94.88–150.00 \\  & Mean & 8.65 & 182.96 & 6.94 & 194.18 & 125.75 \\  & SD & 0.72 & 46.55 & 2.32 & 30.96 & 20.18 \\ \hline \multirow{3}{*}{River (n = 8)} & Range & 7.82–9.72 & 160.70–212.75 & 2.84–13.29 & 97.60–230.00 & 62.80–149.14 \\  & Mean & 8.47 & 185.94 & 8.04 & 148.13 & 95.83 \\ \cline{1-1}  & SD & 0.59 & 16.88 & 3.67 & 48.07 & 31.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the basic water quality parameters of canals, lakes, ponds, and rivers.