esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimisation of Synchronous Grouting Mix Ratio for Shield Tunnels
## 2 Materials and Methods
### Mix Proportion Optimisation and Verification  
Based on the theoretical model, the rheological test results of the slurry were nonlinearly fit to obtain the rheological index. Using IBM SPSS Statistics 25, a multiple linear regression analysis was performed with the yield shear stress and viscosity coefficient as dependent variables to analyse the influence of the five experimental factors on the yield shear stress and viscosity coefficient of the slurry. IBM SPSS Statistics 25 was used to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**No. Test**} & \multirow{3}{*}{\begin{tabular}{c} **Ratio of Water to** \\ **Cementitious** \\ **Material Content** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Ratio of Bentonite** \\ **to Water Content** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Ratio of Water** \\ **Cementitious** \\ **Sand Content** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Ratio of Water** \\ **Reducer to** \\ **Cementitious** \\ **Material Content** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Ratio of Late** \\ **Powder to** \\ **Cementitious** \\ **Material Content** \\ \end{tabular} } \\  & & [\%] & [\%] & [\%] & [\%] \\ \hline
1 & 88 & 20 & 64 & 0.30 & 0.100 \\
2 & 73 & 20 & 69 & 0.20 & 0.075 \\
3 & 73 & 25 & 64 & 0.10 & 0.125 \\
4 & 73 & 30 & 74 & 0.30 & 0.050 \\
5 & 88 & 35 & 74 & 0.10 & 0.075 \\
6 & 78 & 35 & 64 & 0.20 & 0.050 \\
7 & 73 & 35 & 69 & 0.40 & 0.100 \\
8 & 88 & 25 & 79 & 0.40 & 0.050 \\
9 & 83 & 25 & 74 & 0.20 & 0.100 \\
10 & 83 & 30 & 64 & 0.40 & 0.075 \\
11 & 78 & 25 & 69 & 0.30 & 0.075 \\
12 & 78 & 30 & 79 & 0.10 & 0.100 \\
13 & 78 & 20 & 74 & 0.40 & 0.125 \\
14 & 83 & 20 & 69 & 0.10 & 0.050 \\
15 & 83 & 35 & 79 & 0.30 & 0.125 \\
16 & 88 & 30 & 69 & 0.20 & 0.125 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Uniform experimental design mix proportion for synchronous grouting slurry in shield tunnels.  
analyse the test results and obtain the regression equation. The regression equation under specific constraints was optimised using the MATLAB R2018a optimisation toolbox, and the fmincon function was adopted from the nonlinear constraint optimisation methods to obtain the optimal mix proportion [33; 34] and conduct experimental verification. This function, which is based on the principles of the Sequential Quadratic Programming (SQP) algorithm, is a highly efficient computational method for solving nonlinearly constrained optimisation problems [35].
# Phtyromediation of Toxic Metals: A Sustainable Green Solution for Clean Environment
## 9 Enhancement Technique of Phyto-Extraction Efficiency
### Common Approaches to Increase Toxic Metal Bioavailability
#### 9.1.2 Biological Sulfur Oxidation to Reduce pH and Enhance Metal Bioavailability in Soil  
Elemental sulfur, which has a slow-release acidifying property and is easily available, has a beneficial influence on soil pH and helps to improve metal solubility [256; 257; 258; 259; 54]. Moreover, sulfur is one of the most common and cost-effective natural acidifying components, among most other factors. The most active bacterium is Thiobacillus [260]. Thiobacillus acidifies the soil by oxidizing one mole of elemental sulfur and returning two moles of hydrogen ions [261]. For example, [262] found that soil fed with elemental sulfur at rates of 0.5, 1, and 2 g/kg soil resulted in pH decreases from 7.51 to 6.66, 5.45, and 4.8, respectively, after a 40-day experiment. Although soil temperature and humidity are two important parameters that influence the amount of microbial sulfur oxidation acid produced by Thiobacillus bacteria in soil, the ratio of sulfur to total soil solids is also important [263].
## 3 Algorithm Improvement and Scheme Evaluation
### Improvement Strategy for the NSGA-III Algorithm
#### 3.1.3 Optimization of Selection Strategy  
In the traditional NSGA-III algorithm, the tournament selection strategy randomly selects candidate individuals for comparison. A fixed number of individuals are chosen in each round and, through comparing the dominance relationships among individuals, the superior ones are selected to enter the mating pool. Although this method considers the non-dominated rank of individuals during selection, it does not dynamically control the number of candidates and the diversity of individuals. This may lead to insufficient early convergence of the population or local convergence due to poor individual quality. To address these issues, this paper proposes an adaptive tournament selection strategy with the following key improvements.  
(1) Dynamic Adjustment of Candidate Number:  
In each round of tournament selection, the number of candidate individuals \(K\) is dynamically adjusted based on the number of individuals in the first non-dominated front (i.e., the Pareto front) of the population, in order to accelerate the selection of high-quality individuals. The specific calculation method is as follows:  
\[K=\left\lceil\frac{FN}{3}\right\rceil \tag{11}\]  
Here, \(FN\) represents the number of individuals currently in the first non-dominated front of the population, and \(\lceil*\rceil\) denotes the ceiling operation. By dynamically adjusting the value of \(K\), the strategy can prioritize the retention of superior individuals in the early stages of evolution, aiding the population in exploring better solutions early on and enhancing the overall quality of the population.  
(2) Priority Selection Based on Non-Dominated Sorting:  
Among the randomly selected \(K\) candidate individuals, sorting is performed based on their non-dominated ranks. The individual with the lowest non-dominated rank is prioritized as the winner, enabling the population to quickly converge towards the Pareto front.
# A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT
## 2 The RPL Protocol
### Clone ID Attack on the RPL Protocol
Additionally, the geographical information of each node is not deemed to track its position, according to its node parent hierarchy, such that a single cloned node can be distributed in multiple leaves on the DODAG graph. Crafting and deploying a Clone ID Attack is directly linked to the absence of security methods contained in the implementation of RPL on conventional devices. With this in mind, sensors are susceptible to physical capture attacks, such that adversaries can easily program an unlimited number of replicated nodes by spoofing the WSN. In agreement with the taxonomies presented in [31], node replication is an application-independent attack over IoT networks that can be reproduced in static or mobile ways, depending on the WSN architecture. In a static manner, each node is randomly created with a fixed geographical position inside a DODAG graph; in counterpart, in the mobile form, nodes can move through dynamic routing. The steps to reproduce a Clone ID Attack, either static or mobile, are listed as follows: 1. A malicious actor inside a vulnerable WSN captures sensor nodes physically; 2. A selected legal node is isolated in a span of time from the network; 3. The node is analyzed and sensible information is collected (node id, secret keys, cryptographic data, and so on); 4. The attacker replicates the selected node by counterfeiting its identity and mounts it in key geographical positions. Additionally, an intruder can decide if is worth replicating multiple nodes in the network and place them in other positions [15]. The latter makes it a challenging duty to accurately detect a malicious node request; however, beyond this, the impact of a Clone ID attack can lead to serious consequences. As with any impersonation threat, the losses can involve private data sniffing, data modification and alteration, unwanted data re-transmission and, in its worst case, when the DODAG  
Figure 4: (**a**) Clone ID attack launching and (**b**) outcome. root node is compromised, the ecosystem can have repercussions from lateral movement attacks, data ex-filtration, and privilege escalation access [32].
## 2 Results and Discussion  
To grow the crystal fiber, high pure powders of scandium and yttrium oxides have been mixed in an agate mortar up to a homogeneous state. The mixture of Y\({}_{2}\)O\({}_{3}\), Sc\({}_{2}\)O\({}_{3}\) and \(RE_{2}\)O\({}_{3}\) (Tm\({}_{2}\)O\({}_{3}\) or Nd\({}_{2}\)O\({}_{3}\)) was used as precursor to grow \(RE\)-doped crystal fibers. Then, the mixture has been transferred to a platinum crucible and annealed at a temperature of 1100 \({}^{\circ}\)C for 48 h. The powder rod of Y\({}_{2}\)O\({}_{3}\)-Sc\({}_{2}\)O\({}_{3}\) composition has been obtained at the pressure of 20 MPa. The powder rod had length of 50 mm and diameter of 1 mm. The rod was fixed vertically, and its tip was heated and melted at CO\({}_{2}\) laser irradiation. An yttria-stabilized zirconia (YSZ) crystal was used as a seed. It was dipped into the melt and raised slowly, pulling a crystal fiber with an approximately round cross-section from the melt. At a growth rate of about 80 mm/h, a YScO\({}_{3}\) crystal fiber with a diameter of 0.85 mm and length of 50 mm has been obtained. The image in back-scattered electrons, the brightness of which depends on the average atomic number and the density of the substance, demonstrates the homogeneity of the crystal composition (Figure 1a). According to X-ray spectral analysis (SEM-EDS) the amounts of Sc and Y are almost equal and unchanging across the fiber (Figure 1b). Therefore, the crystal composition may be presented as Y\({}_{0.95}\)Sc\({}_{1.05}\)O\({}_{3}\), while taking into account three oxygen atoms per formula unit. Structural characteristics of the crystal fiber, as well as powders of Y\({}_{2}\)O\({}_{3}\) and Sc\({}_{2}\)O\({}_{3}\) precursors, have been obtained with the X-ray phase analysis. The X-ray pattern of YScO\({}_{3}\) includes good narrow peaks that point to a high degree of crystallinity (Figure 1c). According to X-ray data, the YScO\({}_{3}\) crystal fiber is homogeneous and belongs to the bixbyite structural type (space group \(l\bar{a3}\)), similar to yttrium and scandium sesquioxides (Figure 1c, insert). The lattice parameters of YScO\({}_{3}\), Y\({}_{2}\)O\({}_{3}\) and Sc\({}_{2}\)O\({}_{3}\) were obtained as \(a\) (YScO\({}_{3}\)) = 10.228(1) A, \(a\) (Y\({}_{2}\)O\({}_{3}\)) = 10.604(1) A and \(a\) (Sc\({}_{2}\)O\({}_{3}\)) = 9.841(4) A, respectively. Due to the low concentration of Nd\({}^{3+}\) and Tm\({}^{3+}\) ions in the YScO\({}_{3}\) host (about 0.1 at. %), \(RE\) impurity does not influence bixbyite-type crystal structure, phonon structure and crystal quality. XRD (Supplementary Materials Figure S1, Table S1) and Raman spectra (Figure S2) of the pure and rare-earth doped YScO\({}_{3}\) crystal fibers have been added as Supporting Information. Table 1 shows indexation of the most intensive peaks.
## 4 Discussion
### Similarity Metrics and Problem Repositories  
We considered three factors in WOJR performance: the two aggregation operators, the two similarity metrics, and the two sets of problem repositories.  
In the second experiment, the maximum operator is better overall than the average operator since the average operator is not robust to noisy reference code. For example, if a similar problem contains several submissions of very similar reference code and many submissions of not similar reference code, the average operator concludes that the problem is not similar. In JPlag, this tendency is remarkable. The number of helpful similar problems of JPlag with average and maximum operators are four and six, respectively.  
We also find that the set of AOJ and AtCoder is overall better than only AOJ with respect to the number of valid similar problems since the size of the set is larger than the size of only AOJ when we employ the maximum operator. In our dataset, the numbers of problems in AOJ and AtCoder are 1222 and 2656, respectively. Since the average operator is not robust to noise, the differences in the repository sets are small when we employ the average operator. However, we find no clear difference in the repository sets with respect to the number of helpful similar problems. Thus, we conclude that AOJ alone is enough to find helpful similar problems.  
Although the number of valid similar problems found by TD-IDF is higher than JPlag, there is one assignment problem for which only JPlag can find a helpful similar problem. The goal of the assignment problems in the lecture is to help students learn representative data structures and algorithms in computer science. These data structures and algorithms have well-known names such as stack, heap, queue, and sort. TF-IDF tends to outperform JPlag when finding similar problems for such data structures and algorithms. However, some algorithms such as dynamic programming (e.g., General 8) do not have a specific keyword in source code. It is difficult for TF-IDF to find similar problems for such algorithms. Therefore, we conclude that we should employ multiple similar metrics and merge multiple sets of found similar problems to deal with various algorithms.
## 1 Introduction
Several notable applications of ILs include the use as a solvent or media in biomass conversion, as a catalyst for reaction, as corrosion inhibitors, and as a supporting material in membrane separation [3; 14; 25; 26; 27; 30]. IL can act as a green solvent, with a wide range of solubility, and as a catalyst to increase the HMF yield [3; 25; 26; 27; 34]. [BMIm]Cl-based IL and hydroxyl apatite-supported chromium chloride were used for glucose conversion and produced HMF with a yield of 40.0% [15; 35]. In 2007, [EMIm]Cl (1-ethyl-3-methylimidazolium chloride) and CrCl\({}_{2}\) were applied for glucose dehydration, producing a nearly 70.0% HMF yield [16]. In previous studies, [PERSON] et al. [36] used imidazolium-based IL, namely, [EMIm]Cl and [Epyr]Cl, in glucose dehydration. Both ILs demonstrated better recoverability, recyclability, and ability to perform as double agents (solvent and catalyst) than conventional organic solvents [33; 36; 37; 38]. [PERSON] et al. [38] investigated IL's usage with lanthanide as catalysts in glucose conversion, obtaining an approximately 24.0% HMF yield [39]. The literature review results showed that IL acted as a more powerful solvent and Lewis acid catalyst in biomass conversion [3; 6; 14; 23; 25; 27; 28; 40; 41]. However, several ILs still have drawbacks in the hydrolysis process of biomass, glucose, and fructose due to the incompatible chemical structures of the ILs, resulting in a low HMF yield. The chemical structures of ILs play an essential role in glucose conversion [3; 39; 18; 23; 3]. An adequate and suitable chemical structure of ILs (more hydrophobic as main priority) is required as a medium and catalyst to assist the dehydration reaction, accelerate the glucose conversion process to HMF, and increase the yield of HMF. Therefore, it is important to find more hydrophobic ILs and study their potential as media or solvents in glucose conversion. We selected a 1,3-dibutyl-2-(2-butoxyphenyl)-4,5-diphenylimidazolium iodide ([DBDim]I)-based IL as a medium in glucose conversion due to its highly hydrophobic structure, good thermal resistance, and ability to dissolve a wide range of solutes [30]. The main objective of this study was to evaluate the performance of [DBDImJ]I as a hydrophobic solvent for glucose conversion to HMF, LA, and FA under various temperatures and reaction times. The results showed that [DBDIm]I is a potential medium for glucose conversion to several biofuel precursors.
## 2 3-Phase AC/DC PWM Converter Used in DC Distribution System
### Positive Sequence Voltage and Phase Detector [15; 16]  
The phase angle information of the grid is needed in a grid-connected DC distribution system. In the case of power factor control, active/reactive power control, and harmonic current compensation, the current or voltage reference must be synchronized with the phase angle of the input voltage. A PLL is used for this phase synchronization. If the 3-phase power source is balanced, then the phase angle can be obtained by calculating the period between the zero-crossing points where the voltage of one phase or any line-to-line voltage changes from negative to positive. However, in the case of unbalanced 3-phase source, a miscalculated phase angle can result in harmonic components of voltage and current, which can lead to unforeseen problems. To avoid this problem that is caused by the unbalanced 3-phase source, the phase angle is detected by calculating the positive sequence voltage.  
The equation for calculating the positive sequence voltage is given by the following Equation (4):  
\[\left[\begin{array}{c}v_{pa}\\ v_{pb}\\ v_{pc}\end{array}\right]=\frac{1}{3}\cdot\left[\begin{array}{ccc}1&\alpha& \alpha^{2}\\ \alpha^{2}&1&\alpha\\ \alpha&\alpha^{2}&1\end{array}\right]\cdot\left[\begin{array}{c}v_{as}\\ v_{bs}\\ v_{cs}\end{array}\right] \tag{4}\]  
where \(\alpha=e^{j\frac{2\pi}{3}}=-\frac{1}{2}+j\frac{\sqrt{3}}{2}\) and \(\alpha^{2}=e^{j\frac{4\pi}{3}}=-\frac{1}{2}-j\frac{\sqrt{3}}{2}\).  
\(\alpha\) and \(\alpha^{2}\) can be substituted into Equation (4), and Equation (5) is then defined, as follows:  
\[\left[\begin{array}{c}v_{pa}\\ v_{pb}\\ v_{pc}\end{array}\right]=\left[\begin{array}{c}\frac{1}{2}v_{as}-\frac{1}{2 \sqrt{3}}(v_{bs}-v_{cs})\\ -(v_{pa}+v_{pc})\\ \frac{1}{2}v_{cs}-\frac{1}{2\sqrt{3}}(v_{as}-v_{bs})\end{array}\right] \tag{5}\]  
Figure 5 shows the control block diagram of the positive sequence voltage and PLL while using Equations (4) and (5). As shown in the figure, the \(d\)-\(q\) transformation is performed after extracting the positive sequence voltage while using the 3-phase voltage of the input power source. In the \(d\)-\(q\) transformation, the information on the \(q\) axis refers to the magnitude of the voltage output of the converter, and the information on the \(d\) axis is related to the phase error between the converter output voltage and the grid voltage. Therefore, the phase output voltage of the converter must control the \(d\)-axis voltage to zero for phase synchronization with the grid voltage. The \(d\)-axis voltage in the synchronous reference frame, as calculated by the PI controller, then performs a PLL that controls the phase angle to be zero so that the phase angle matches the actual phase angle of the grid side. When the input power source includes harmonics due to noise, the harmonics also appear in the \(d\)-axis  
Figure 4: Phasor diagram of a 3-phase AC/DC PWM converter.  
voltage used as an input of the PLL. Therefore, as shown in Figure 6, the first order low pass filter is used to prevent pulsation.
# Graphene Quantum Dots Doped PVDF(TBT)/PVP(TBT) Fiber Film with Enhanced Photocatalytic Performance
## 2 Experimental Section
### Photocatalytic Activity  
A PVDF(TBT)/PVP(TBT)-GQDs photocatalyst was placed on a photochemical reactor for the Rhodamine B (RhB) degradation experiments. In specific, RhB was selected as a model sample in the photocatalytic experiment. The various catalysts prepared were placed in an 80 mL quartz tube containing 40 mL of a 15 mg/L RhB solution. The quartz tubes were numbered and put into the photochemical reactor (XPA-7(C8) multi-tube stirring photochemical reactor, Xujiang electromechanical company, Nanjing, China). To improve the accuracy of the test, a blank test without a photocatalyst was used as a reference. The photochemical reactor was equipped with an 800 W xenon lamp as a light source. In the experiment, the photocatalyst and RhB solution in the quartz cuvette were first stirred in the dark for 30 min to achieve the adsorption-desorption equilibrium. Then, 4 mL of the reaction solution was taken out of the quartz cuvette every 30 min. The reaction solution was centrifuged to remove the photocatalyst. The absorbance of the reaction solution was measured with a UV-Vis spectrophotometer. The measured solution in the quartz cuvette was put back into the photochemical reactor. After 30 min, the photochemical reactor was closed and the reaction solution was taken out for measurement. The above operation was repeated six times.
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 3 Subjective Listening Tests Using the MUSHRA Methodology
### Experiment 1-Single Point Audio Sources
#### 3.4.1 Content  
Five audio samples were used with durations of 7 to 15 s. Various musical sounds were selected from CDs and the EBU music database [27]. The content was chosen as for the compression challenges they pose to codecs and the samples have previously been evaluated for stereo quality [28]. Additionally, one audio sample (pinkReverb) was synthetically generated. For reverb samples, a simple mono to mono impulse response convolution was initially applied to the original mono reference signal. All clips had a sampling frequency of 48 kHz (one example was re-sampled from 44.1 kHz) and were recorded in stereo format. Details of the audio samples used in experiment 1 can be found in Table 1.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 3 Implementation of MBSS Technique Using SDR Technology
### Impulsive Noise Reduction
#### 3.3.1 Multiresolution Analysis: PSD Decomposition  
First, to the \(R_{i}(k)\) signal (PSD in a linear scale, the result of applying the Welch estimator), the MRA is sequentially applied to \(i\)-th device, resulting in the approximation  
Figure 4: PSD estimation via the Welch method.  
Figure 5: Operation of the impulse noise reduction module.  
coefficients \(a_{Li}\) at decomposition level \(L\) and the detail coefficients \(d_{ji}\) at levels going from \(j=1,2,\ldots,L\), as given by Equation (4) [9].  
\[R_{i}(k)=a_{Li}+\sum_{j=1}^{L}d_{ji}. \tag{4}\]  
The detail coefficients correspond to the discrete wavelet transform of \(R_{i}(k)\) in a dyadic scale. It is well known that the detail coefficients of a signal decomposition, via the MRA, keep the information about singularities (abrupt changes in the signal), which is the case concerning impulsive noise. Because of that, the proposal is based on modifying these detail coefficients to alleviate this problem generated by the impulsive noise. For this, the proposal is only considered at level \(L=1\), i.e., the approximation coefficients \(a_{Li}\) and the detail coefficients \(d_{ji}\). The constructed wavelet space from the MRA is the wavelet 'Haar'. This is shown in Figure 6.