esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Silane in Concrete Bridge Decks Using Transmission X-ray Microscopy
## 4 Conclusions  
Samples from 14 concrete bridge decks treated with silane that were in service for 1 year were obtained. The depth of silane penetration and the ability of the silane to resist outside chemical penetration were investigated with optical staining and X-ray microscopy techniques. These results were compared to samples taken from the same concrete that was not treated with silane.  
The following observations were made:  
* The average thickness of the silane coating for the 14 projects was 2.5 mm, and the silane reduced the penetration of outside chemicals on average 9\(\times\) more than samples that were not treated with silane.
* For the samples not coated with silane, there was large variability among the samples. At 2 mm in depth, some samples had iodide concentrations as low as 1% by weight and some had concentrations greater than 4%. Despite this high variability, the same concrete that was treated with silane showed low iodide concentration at 2 mm, varying between 0.03% and 0.66%.
* Following the application of silane, a high reduction in chemical penetration occurred regardless of the ability of the nontreated concrete to resist chemical penetration. This shows the effectiveness of silane to resist chemical penetration for concretes with a wide range of qualities.
* Outside chemicals were observed to penetrate past silane coatings when surface cracking was observed. This shows that cracks can compromise silane coatings. This will be an area of future study.  
This work shows the usefulness of silane coatings for improving the durability of in-service concrete to outside chemical penetration only before surface cracking. This would benefit from a systematic laboratory study to control the concrete quality and then evaluate the depth of penetration and the effectiveness of the silane coating.  
Methodology, A.B., S.M.A., M.T.L. and N.J.D.; formal analysis, A.B.; investigation, A.B., S.M.A. and A.R.; data curation, A.B.; writing--original draft preparation, A.B. and S.M.A.; writing--review and editing, M.T.L. and N.J.D.; supervision, M.T.L. and N.J.D.; project administration, M.T.L. and N.J.D.; funding acquisition, M.T.L. and N.J.D. All authors read and agreed to the published version of the manuscript.  
This research was funded by Oklahoma Department of Transportation (ODOT SP&R ITEM NUMBER 2287).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank [PERSON] for her help with XCT imaging.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Optimization of the Laser-Plasma-Based Hard X-ray Pulse Source
As shown in Figure 2a, the maximum fluctuation of generated Cu-K\({}_{\alpha}\) X-ray flux in 100 min is less than 10 percent, which is equivalent to 1% (RMS). It is worth mentioning that these designs also ensure a high total flux of Cu-K\({}_{\alpha}\) X-ray photons above 10\({}^{11}\) photons/s by optimizing the laser focal size and the moving speed of the copper tape. By assuming an isotropic production of Cu-K\({}_{\alpha}\) radiation at a solid angle of 4\(\pi\), we estimate the laser intensity-dependent flux of Cu-K\({}_{\alpha}\) photons (see Figure 2b). The nearly proportional relationship between laser intensity and X-ray photon flux suggests that vacuum heating is dominant in the range of intensities accessible in our experiments [22]. Figure 1: **(a)** Schematic diagram of the table-top laser-plasma-based X-ray source. **(b)** Top view of the X-ray generation vacuum chamber. **(c)** Image of the laser focal spot. The white lines show that the Full-With Half-Maximum (FWHM) values in the vertical and horizontal are 7.8 and 8.1 \(\upmu\)m, respectively. **(d)** The X-ray pulse spectrum. The black and red lines represent the spectrum without and with an 8 \(\upmu\)m nickel foil filter. To test the generated X-ray pulses, we have performed the static X-ray diffraction (XRD) experiments on both single-crystal Au and polycrystalline Cu samples. As shown in Figure 2c, XRD of a single crystal is taken with a reflection geometry. In this geometry, we shaped the emitted Cu-K\({}_{\alpha}\) radiation by a 300 \(\upmu\)m wide tantalum slit mounted 35 mm away from the X-ray source and then diffracted off the Au (111) film mounted 156 mm away from the slit. The X-ray diffraction signal is captured by the X-ray CCD camera with 2048 \(\times\) 2048 pixels (with a pixel size of 13.5 \(\upmu\)m) located 191 mm away from the sample. With the exposure time of 20 s, an ideal signal-to-noise Bragg diffraction pattern has been obtained, as shown in Figure 2c. While for polycrystalline sample, a transmission geometry is taken, as shown in Figure 2d. A polycapillary X-ray optics was used to focus Cu-K\({}_{\alpha}\) radiation on a 20 \(\upmu\)m polycrystalline Cu foil with a 600 \(\upmu\)m spot in diameter. Thanks to the polycapillary X-ray optics with the magnification of 300 gain, about 10\({}^{7}\) X-ray photons/s on the sample are available. In this geometry, the X-ray CCD camera is mounted about 10 mm behind the sample, and the diffraction pattern shown in Figure 2d was acquired with the exposure time of 300 s. These results indicate the feasibility of the generated X-ray pulses to perform UXRD measurements on different types of samples ranging from single crystal to polycrystalline.
# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
## 3 QoS Analysis
### Communication Range QoS Parameter  
Considering the communication range QoS parameter, as explained in Section 2, we use 100 mW transmission power (which corresponds to less than 500 m transmission range). According to Table 1, it is obvious that mainly scenarios 1, 3 (communication range short to medium) can be implemented and probably scenarios 2, 4 (communication range short). We must note here that all Table 1 scenarios correspond to urban cases. In addition, the geometry-based channel model used, considers the fast\(-\)fading effects from reflections of mobile scatterers. As future work, we intend to extend our proposed scenarios in highways  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Table 2 Scenario** & **t\({}_{\text{d1}}\) (SOVA)** & **t\({}_{\text{d2}}\) (log-MAP)** & **t\({}_{\text{d3}}\) (max-log-MAP)** & **Table 1 Scenario** \\ \hline
1 & 95.76 & 100.368 & 106.128 & 3 (SOVA), 1 and 4 (log-MAP, max-log-MAP), 2 (all algorithms) \\ \hline
2 & 92.88 & 95.184 & 98.064 & 2, 3 (all algorithms) \\ \hline
3 & 95.76 & 100.368 & 106.128 & 3 (SOVA), 1 and 4 (log-MAP, max-log-MAP), 2 (all algorithms) \\ \hline
4 & 92.88 & 95.184 & 98.064 & 2, 3 (all algorithms) \\ \hline
5 & 101.52 & 110.736 & 122.256 & 1, 2, 4 (all algorithms) \\ \hline
6 & 95.76 & 100.368 & 106.128 & 3 (SOVA), 1 and 4 (log-MAP, max-log-MAP), 2 (all algorithms) \\ \hline
7 & 101.52 & 110.736 & 122.256 & 1, 2, 4 (all algorithms) \\ \hline
8 & 95.76 & 100.368 & 106.128 & 3 (SOVA), 1 and 4 (log-MAP, max-log-MAP), 2 (all algorithms) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Worst case end-to-end latency (ms) and corresponding Table 1 implementation scenario for each algorithm and scenario of Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vehicle Density** & **t\({}_{\text{d1}}\) (SOVA)** & **t\({}_{\text{d2}}\) (log-MAP)** & **t\({}_{\text{d3}}\) (max-log-MAP)** & **Table 1 Scenario** \\ \hline V\({}_{\text{D}}\) = 55 & 117.04 & 122.672 & 129.712 & 1, 2, 4 (all algorithms) \\ \hline V\({}_{\text{D}}\) = 65 & 138.32 & 144.976 & 153.296 & 1, 2, 4 (all algorithms) \\ \hline V\({}_{\text{D}}\) = 75 & 159.6 & 167.28 & 176.88 & 1, 2, 4 (all algorithms) \\ \hline V\({}_{\text{D}}\) = 85 & 180.88 & 189.584 & 200.464 & 1, 2, 4 (all algorithms) \\ \hline V\({}_{\text{D}}\) = 95 & 202.16 & 211.888 & 224.048 & 1, 2, 4 (all algorithms) \\ \hline V\({}_{\text{D}}\) = 105 & 223.44 & 234.192 & 247.632 & 1, 2, 4 (all algorithms) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Worst case end-to-end latency (ms) and corresponding Table 1 implementation scenario for each algorithm for different vehicle density values, frame length 512 bits, 12 Mbps data rate, 60 km/h speed.  
with very high vehicle speeds (about 250 km/h), where the effect is bigger due to Doppler shift. On the other hand, in this type of scenario a less complicated statistical channel can be used which will be based on a log-distance model for capturing the large scale fading of the transmitted signal.
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 2 Reference Assumption PLL Features
### The Parameter Design of SSV RRAM
#### 2.2.1 The Determination of CIT  
The length of the CIT is restricted to two factors, the length of navigation data bit and integration loss caused by frequency error. In weak-signal and high-dynamic environments, which of the two factors is dominant should be identified with quantitative calculation as follows. In order to keep the integration loss caused by frequency error \(f_{e}\) below 3 dB [13], we have:  
\[\left|\text{sinc}(f_{e}T_{coh})\right|\leq\frac{1}{\sqrt{2}} \tag{4}\]  
Under high-dynamic circumstances, \(f_{e}\) mainly comes from the relative movements between the navigation satellite and the user vehicle. Take \(v_{LOS}\) and \(a_{LOS}\) as projections of relative velocity and acceleration from the satellite to the vehicle in the \(LOS\) direction, respectively; the Doppler frequency is:  
\[f_{d}=-\frac{f_{L1}\cdot v_{LOS}}{c} \tag{5}\]The rate of Doppler frequency \(\dot{f}_{d}\) equals the time derivative of \(f_{d}\):  
\[\dot{f}_{d}=\frac{df_{d}}{dt}=-\frac{f_{L1}\cdot a_{LOS}}{c} \tag{6}\]  
Furthermore, the rate of \(\dot{f}_{d}\) can be expressed with \(J_{LOS}\):  
\[\ddot{f}_{d}=\frac{d^{2}f_{d}}{dt^{2}}=-\frac{f_{L1}\cdot J_{LOS}}{c} \tag{7}\]  
Then the frequency error during the integration processing can be computed as:  
\[f_{e}=f_{d}+\dot{f}_{d}\cdot T_{coh}+\frac{1}{2}\ddot{f}_{d}T_{coh}^{2}=-\frac {f_{L1}\cdot v_{LOS}}{c}-\frac{f_{L1}\cdot a_{LOS}}{c}T_{coh}-\frac{f_{L1}\cdot J _{LOS}}{2c}T_{coh}^{2} \tag{8}\]  
Assume that the first two terms of the right side of Equation (8), related to \(v_{LOS}\) and \(a_{LOS}\), respectively, is completely compensated by the third-order loop; then the equation is simplified as:  
\[f_{e}=-\frac{f_{L1}\cdot J_{LOS}}{2c}T_{coh}^{2} \tag{9}\]  
Substituting Equation (9) into Equation (4), we have:  
\[\left|\text{sinc}(-\frac{\pi f_{L1}J_{LOS}T_{coh}^{3}}{2c})\right|\leq\frac{1} {\sqrt{2}} \tag{10}\]  
Thus, the upper limitation of \(T_{coh}\) is determined:  
\[T_{coh}=1000\left[\sqrt[3]{\frac{0.886c}{\pi f_{L1}J_{LOS}}}\right](ms) \tag{11}\]  
where the operator \([x]\) represents the maximum rounding operation below the value \(x\), for the reason that \(T_{coh}\) equals the integer multiples of C/A code period (1 ms). Figure 1 presents the bound of \(T_{coh}\) over the variation of \(J_{LOS}\), and proves that CIT restricted to the length of navigation data bit is dominant when \(J_{LOS}\) does not exceed 100 m/s\({}^{3}\). With regard to the mission in Section 5, whose peak jerk is 40 m/s\({}^{3}\), a CIT of 20 ms would not lead to an unacceptable power loss.  
Figure 1: The selection of CIT over different LOS jerks.
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 3 Anycast Routing
### Anycast CSG Routing
#### 3.2.1 Anycast group semantics  
To fulfill the former requirement, a CSG gateway needs to determine whether it is part of \(A\), which can be accomplished by defining the destination address \(d\) either as a compounded identifier or as a reserved identifier for the anycast group. The task is facilitated by the use of string identifiers by the CSG to name network nodes. In the first case, \(d=A\), which offers the advantage that the source can dynamically modify the group composition without needing to communicate the details to other nodes, but requires extending the header size of the bundles with an overhead that is proportional to \(|A|\). In the second case, \(d=a\), where \(a\) is a string that identifies the group \(A\). The size of \(a\) can be much shorter than \(|A|\) but the method requires informing \(A\) to each \(k\in A\), and so any group membership modifications are more involved than with the first method. In either case, the anycast group members need not confirm their membership to the source node. For greater flexibility, the CSG anycast method supports both methods so that the decision of which method will be used would depend on the features and the logistic constraints of specific missions.
## 4 The Structure Fidelity Data Collection Framework
### Energy Consumption  
Unlike other models using the radio distance in calculating the energy cost, we analyze the energy consumption from the perspective of the number of working/sleeping sensor nodes in unit of cluster on the learning and data collection phases, since our approach aims to energy saving by reducing the number of nodes that have no need to be active. First, all member nodes send the readings to the cluster head node during the learning phase, thus there is not any sleeping node (or energy conservation). The energy cost in a cluster with \(N\) nodes during the learning phase is  
\[E_{l}=T_{l}Ne \tag{15}\]  
where \(e\) is the energy cost on transmitting a unit data between two nodes.  
During the data collection phase, the energy cost in the cluster is  
\[E_{d}=T_{d}N_{a}e \tag{16}\]  
where \(N_{a}\) is the number of active nodes.  
Compared the energy consumption without any nodes scheduling, the percentage on energy saving within a cluster during a cycle (_i.e._, learning and data collection) is  
\[P_{E}=1-\frac{T_{l}Ne+T_{d}N_{a}e}{(T_{l}+T_{d})Ne}=\frac{T_{d}(N-N_{a})}{(T_ {l}+T_{d})N}=\frac{T_{d}}{T_{l}+T_{d}}\frac{N-N_{a}}{N} \tag{17}\]  
Equation (17) indicates that a good energy conservation can be achieved by extending the data collection period \(T_{d}\) and reducing the number of the working nodes \(N_{a}\). \(T_{d}\) and \(N_{a}\) depend on the sensed environment and the pre-defined fidelity threshold respectively. We will evaluate them in the following simulations.
# Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques
## 3 Research Methodology
### System Model
#### 3.1.2 Multi-Phased Feature Selection  
Among the important possible code metrics, a few can have vital significance in refactoring prediction. This research applies a multi-phased feature selection method using three statistical significance predictors.  
1. _Wilcoxon Signed Rank Test (WRS);_
2. _Significant Test;_
3. _Cross-Correlation Test._  
A brief of these algorithms is given as follows:  
Wilcoxon Signed-Rank Test  
Significant Test  
Univariate Logistic regression (ULR) is a standard method for estimating the degree of correlation between independent and dependent variables, similarly to the rank test. Refactoring proneness can be predicted by analyzing the code metrics of each class. In the previous selection phase, we applied ULR to the chosen code metrics or features (i.e., rank-sum selected features). ULR evaluated the significance of the selected metrics to identify or characterize class-level refactoring. The independent variable (refactoring proneness) was used to estimate the dependent variable's variance (change %). (i.e., code-metrics).  
\[\text{logit}[\pi(\text{x})]=\alpha_{0}+\alpha_{1}\text{X} \tag{1}\]  
In Equation (1), logit \(\pi(\text{x})\) and X state the dependent (i.e., refactoring proneness) and the independent (code-metrics) variables, respectively. Here, it signifies the probability factor of the significance of each category in Equation (2).  
\[\pi(\text{x})=\frac{\text{e}^{\alpha_{0}+\alpha_{1}\text{X}}}{1+\text{e}^{ \alpha_{0}+\alpha_{1}\text{X}}} \tag{2}\]  
In our proposed model, the \(p\)-value of the regression coefficient is used to figure out how important each code-metric is. Any metric with a \(p\)-value of more than 0.05 was thought to be important for predicting refactoring (proneness). Metrics with a \(p\)-value of less than 0.05 were taken out of the chosen final set of chosen features.  
In this method, we ran a cross-correlation test using the Pearson correlation estimation algorithm after extracting ULR-filtered code-structural metrics. Code-metrics with a \(p>0.5\) correlation coefficient were used to classify refactoring proneness. After receiving codemetrics, we normalized and augmented data to improve computation. The next section describes the pre-processing procedures.
## 2 Literature Review
### Demand Forecasting Methodology
[PERSON] and [PERSON] [19] combined the logistic regression algorithm with an improved neural network algorithm to create a model for forecasting logistics demand. They further optimized the ladder network structure to better suit the unique features of the data mining assignment, combining its decision-making component with the shallow network to boost the model's effectiveness in logistics demand prediction. [PERSON] et al. [20] applied the grey prediction model and BP neural network model in combination and designed the GM-BPNN algorithm to solve the regional logistics demand forecasting problem with the characteristics of mutability, instability, non-linearity, etc. The results proved that the designed combined model integrated the advantages of the two algorithms and compensated for the shortcomings that were prominent in the forecasting data. For the shortcomings of traditional neural networks with slow convergence and low accuracy, [PERSON] and [PERSON] [21] optimized the iterative initial weights and thresholds of the BP neural network model using the improved particle swarm algorithm and applied the model to forecast the logistics demand of fresh agricultural products in Shanghai, and the experimental results proved that the model converged faster and with higher accuracy. [PERSON] et al. (2020) used a combination of Lasso regression forecasting and polynomials in solving the problem of future natural gas demand forecasting in China, built a combined forecasting model, and optimized the weights of the combined model. The experimental results showed that the model increased the dimensionality of the data and had higher forecasting accuracy than the benchmark model. [PERSON] et al. (2020) In the study of carbon emission forecasting in Hubei Province, in order to avoid the interference of multicollinearity and retain the key information in the data to the greatest extent, a ridge regression model was introduced to regress the existing model. According to the existing research results, we found that the logistics demand impact indicator is different from some research indicators with large data volume; generally, the length of the data series is only about 20 items (the unit being one year, i.e., 20 years), and fewer researchers pay attention to the characteristics of the data of the logistics demand impact indicator and look for the suitable neural network activation function and training algorithm according to the characteristics of their data. The goal of this study is to discuss how to select the activation function and training algorithm suitable for the characteristics of its indicator data and optimize the improved model according to its shortcomings to improve the prediction accuracy of the neural network and its stability.
# Numerical Simulation of Polymer Solutions in a Single-Screw Extruder
## 2 Simulation Methodology
### Rheological Properties of Polymer Solutions  
This study examined aqueous solutions of polyacrylamide and sodium carboxymethyl cellulose. PAA is used very widely because of its gelling property. For example, it is used in the process of wastewater treatment, in paper production, extraction of mineral resources, oil production, agriculture, medicine, construction, and other industries. Polyacrylamide is one of the best polymers used as a drag reduction agent and vortex inhibitor because of its flexibility and long chain [32].  
CMC is currently the most widely used biopolymer in the world made of cellulose. Owing to its availability, it is also in great demand in various industries, for example, in oil and gas production, mining-and-chemical industry, production of detergents, as a stabilizer, thickener, filler in the food industry, pharmaceutical industry, etc. Besides, it is used as a binder in the production of coal dust biquettes, pencils, and chalks, as well as in granulation of seeds [33].  
Polymer solutions have a nonlinear viscoelastic flow pattern. The paper does not consider viscoelastic effects because of the complexity of the problem being solved. This assumption is quite common [34].  
Cross model was used to describe the rheological behavior of these fluids [35; 36]:  
\[\eta=\eta_{\infty}+\frac{\eta_{0}-\eta_{\infty}}{1+\left(\lambda\dot{\gamma} \right)^{n}} \tag{5}\]  
where \(\eta_{0}\), \(\eta_{\infty}\) Newtonian plateaus (asymptotic values of dynamic viscosity at \(\dot{\gamma}\to 0\) and \(\dot{\gamma}\to\infty\) respectively); \(\lambda_{c}\)--specific time; \(n\)--coefficient determining the slope of the viscosity curve; \(\dot{\gamma}\)--shear rate, 1/s. It should be mentioned that the Cross model was also used in the following studies [17; 32; 37; 38].  
The parameters of the Cross model for 1.5% CMC and 0.5% PAA are tabulated in Table 1. The experimental data obtained from [39] served as the basis for obtaining the parameters of the Cross model for 0.5% PAA.  
Figure 3 shows the curves of viscosity versus shear rate, where the experimental data were taken from [39].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Concentration** & \(\eta_{0}\) **(Pa-s)** & \(\eta_{\infty}\) **(Pa-s)** & \(A_{\text{c}}\) **(s)** & **n (-)** & \(\rho\) **(kg/m3)** \\ \hline
1.5\% CMC [35] & 1500 ppm & 1.97 & 0.02 & 0.172 & 0.64 & 1025 \\
0.5\% PAA & 500 ppm & 1.08 & 0.0023 & 5 & 0.8 & 999 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cross model—characteristic material parameters at 294 K.
## 4 Outline and Methodology  
### Arrangement of Sections  
To evaluate the consistency of MCD12Q1, the conceptual diagram and designed method are shown in Figure 2. When the data sources and preprocessing are conducted in Section 3, harmonization of land cover classification is performed based on the knowledge rule to reclassify the land cover types between MCD12Q1 and GlobeLand30 in Section 4.2. Meanwhile, the C4.5 decision tree classification algorithm is also introduced to finish the classification in Section 4.3. Finally, the evaluation of consistency is performed in Section 4.4 through four indicators of area consistency (Section 4.4.1), spatial consistency (Section 4.4.2), classification accuracy (Section 4.4.3) and landscape diversity (Section 4.4.4).
## 4 Targeted Delivery of Antibiotics to Sites of Infection
### Passive Targeting: Enhanced Permeability and Retention Effect
developed a charge-adaptive nanocarrier, based on triblock polymers PEG, PCL, and poly(\(\beta\)-amino ester) (PAE), which can quickly realize acidity-sensitive charge alterations and extend antibiotic delivery (Figure 8) [62]. Basically, given the pH-dependent charge conversion characteristic of PAE (pKa 6.7), a nanocarrier holds a negative charge and a hydrophilic PEG shell at physiologic pH 7.4, which are helpful for prolonged circulation. However, under acidic conditions at the infectious site, the nanocarrier gradually became positively chargeddue to the protonation of PAE, facilitating interactions with the negatively charged elements of the bacterial cell wall. In addition, the hydrophilic conversion of PAE also allowed a higher antibiotic release at the infectious site (endogenous-stimuli release), contributing to effective antibacterial therapy, as demonstrated in an infected subcutaneous model.