esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions
### Effect of Temperature  
The variation of corrosion rate (\(W_{corr}\)) and inhibition efficiency \(E_{u}\) (%) in the temperature range 25-50 \({}^{\circ}\)C for different concentrations of St63 Gly37 bio-copolymer, obtained by weight loss studies, are displayed in Figures 5 and 6.  
Results showed that the inhibition efficiency increases with increasing temperature. As expected, corrosion process and inhibition efficiency are significantly dependent on the temperature [31; 32; 33]. Optimum temperature was found equal to 323 K, with a maximum efficiency of 98.07% with inhibitor concentration of 300 mg L\({}^{-1}\).  
In the absence of corrosion inhibitor (blank solution), corrosion rate increases with increasing temperature, but when St63 Gly37 bio-copolymeris added, the dissolution of C-Mn steel is widely  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wavenumber: \(\upsilon\) (cm\({}^{-1}\))** & **Attribution** & **Nature** \\ \hline below 600 & Vibration of the polysaccharide backbone & \\
998.71–1000.10 & Antisymmetric C–O–C bond & Elongation \\
1078.59 & Antisymmetric C–O–C bond & Elongation \\
1149.84 & Antisymmetric C–O–C bond & Elongation \\
1362.59–1359.95 & –OH in the plan & Deformation \\
1416.76–1415.06 & C–H bond & Deformation \\
1641.98–1644.62 & H–O–H vibration of adsorbed water & Deformation \\
2929.18–2931.80 & C–H and CH2 of a polysaccharide & Elongation \\
3274.32–3284.70 & OH associated & Elongation \\ \hline \hline \end{tabular}
\end{table}
Table 7: Attribution of the different vibration bands of St63 Gly37.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Inhibitor** &
\begin{tabular}{c} **Inhibitor Concentration** \\ **(mg L\({}^{-1}\))** \\ \end{tabular} & \(E_{uv,\max}\) (\%)** & **Ref.** \\ \hline Glycerin-grafted starch & 300 & 94 & This work \\ _Ochrosiaoppositiglia_ extract & 25 & 94 & [24] \\ Olive leavesextract & 900 & 91 & [25] \\
2-amino-4-methylpentanoicacid & 7200 & 87.46 & [26] \\ L-trytproblem & 2000 & 90.8 & [27] \\ Gallicacid & 1000 & 59.64 & [28] \\
3,5-bis(n-aminophenyl)-4-amino-1,2,4-triazole & 300 & 99 & [29] \\ Vanillin & 1520 & 86.1 & [30] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Data from the literature correlated to the type, concentration, and maximum inhibition efficiency (\(E_{uv,\max}\)) obtained at 25 \({}^{\circ}\)C in HCl for Carbon-steel.  
retarded. These results indicate that the corrosion inhibition mechanism might be more complex than a simple physisorption process on the steel surface. The values of inhibition efficiency, obtained using the weight loss method in the experimented temperature range, show that higher temperatures might favor the inhibitor sorption onto the steel surface. This might be explained in terms of chemisorption of polymer on the steel surface. In fact, in case of chemisorption, the extent of adsorption increases with rise in temperature, as reported in a previous work [34].
# A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms
## 7 Results
Next, the performance of the OLMS-BF algorithm is evaluated along with the JO-NLMS algorithm [29], which is applied for the identification of the global impulse response of length \(ML=512\). The results are presented in Figures 15 and 16, using WGNs and AR(1) input signals, respectively. As specified in Section 6, the JO-NLMS algorithm is the regular counterpart of the OLMS-BF in a classical (one-dimensional) system identification scenario. We can see that the proposed solution (tailored for bilinear forms, i.e., exploiting the two-dimensional decomposition) offers both faster convergence and tracking, as well as a lower misalignment, as compared to the JO-NLMS algorithm. The performance improvement is even more important in case of AR(1) input signals. Figure 14: Normalized misalignment of the OLMS-BF and NLMS-BF (using different step-size parameters). The input signals are AR(1) processes and \(ML=512\). Figure 13: Normalized projection misalignment of the OLMS-BF and NLMS-BF (using different step-size parameters): (**Top**) identification of the temporal impulse response \(\mathbf{h}(n)\), (**Bottom**) identification of the spatial impulse response \(\mathbf{g}(n)\). The input signals are AR(1) processes, \(L=64\), and \(M=8\). Finally, to validate our approach, we assess the performance of the OLMS-BF algorithm when applying it in a context which is closer to a real scenario. The temporal impulse response \(\mathbf{h}(n)\) is a real-world echo path of length \(L=512\). The spatial impulse response \(\mathbf{g}(n)\), of length \(M=4\), is generated using an exponential decay with the elements \(g_{m}=0.5^{m}\), \(m=1,\dots,M\). Both impulse responses are then normalized such that \(\|\mathbf{h}(n)\|=\|\mathbf{g}(n)\|=1\). The input signal is an AR(1) process and we compare the behaviors of the OLMS-BF and NLMS-BF algorithms. The performance is illustrated in Figures 17 and 18. We can notice that the proposed solution slightly outperforms the fastest convergence rate of NLMS-BF, given by \(\alpha_{\widehat{\mathbf{R}}}=\alpha_{\widehat{\mathbf{g}}}=0.5\), but at the same time offering a much lower value of the misalignment. If, however, we use the NLMS-BF algorithm with the smaller  
Figure 16: Normalized misalignment of the OLMS-BF and regular JO-NLMS algorithms. The input signals are AR(1) processes and \(ML=512\). Figure 15: Normalized misalignment of the OLMS-BF and regular JO-NLMS algorithms. The input signals are WGNs and \(ML=512\). step-sizes (in order to obtain a better misalignment), the resulting convergence rate is much lower than the one of the OLMS-BF algorithm.
## 2 Materials and Methods
### K-Fold Cross Validation  
Cross-Validation (CV) is a well-known resampling technique used to evaluate ML models in the case of a limited data sample. This method splits the data sample into several groups determined by the \(k\) parameter. The model validation approach of CV assesses the generalizability of statistical analysis results to a different dataset. It has a diverse range of methods, for instance, leave-one-out CV, K \(\times\) 2, K-fold, and repeated random sub-sampling validation. This research used K-fold cross-validation for comparisons and assessments of the model's performance [41; 42; 43]. This technique randomly divides the original samples from the dataset into K-sub samples. Following that, a validation set is held using one sub-sample for model testing, and the model is trained using the remaining \(k\)-1 sub-samples. In this method, each of the \(k\) sub-samples serves as the validation data once during the Kth iteration. After that, it is possible to construct a single estimation by averaging (or combining) the \(k\) results from the folds. The fundamental process of a K-fold CV is as follows:  
1. Random shuffling of the dataset.
2. Splitting the dataset into \(k\) number of groups.
3. For every distinct \(k\)-group: 1. Use one group as a validation set; 2. Use the remaining groups as a train set.; 3. Use the train set for model training and the test set for model validation; 4. Preserve the results from each fold and discard the predictive model.
4. Using a sample of model assessment scores to summarize the skills of the model.  
It is common practice to summarize the results of a K-fold cross-validation run using the mean of the model skill scores. It is also a good idea to use a variance metric, such as the standard error or standard deviation, to evaluate the model's performance.  
The value of \(k\) is typically set to 5 or 10, but this is not a strict guideline. As \(k\) increases, the size difference between the resampled subsets and the training set decreases, leading to a reduction in bias [44]. In this study, \(k\) was set to 5 for the MPR model. In addition to the MPR model, two other ML algorithms were developed for evaluation: the Random Forest (RF), which is a black-box model, and the Linear Regression (LR), which is a white-box model. These models were constructed using the WEKA software version 3.9.6 and subjected to the same 5-fold cross-validation as the MPR model.
# Influence of Hydrothermal Carbonization on Catalytic Fast Pyrolysis of Agricultural Biomass
## 2 Materials and Methods
### Cyclonic TGA  
The cyclonic thermogravimeter (TGA) is a bespoke device developed by the University of Twente (Thermal Engineering group). The key element of this rig (Figure 2) is a heated cyclonic chamber, where devolatilization takes place. N\({}_{2}\) is pre-heated before being injected into the chamber in a tangential direction. The whole reactor is located on a very sensitive  
Figure 1: Hydrothermal carbonization test rig: 1—autoclave; 2—fittings able to withstand the pressure; 3—thermocouple; 4—pressure sensor; 5—safety valve; 6—thermocouple measuring the temperature inside of the hot fluidized bed; 7—fluidized bed used for heating the autoclave; 8—band heaters; 9—flow meter; 10—needle valve; 11—balance; 12—hydraulic press for dewatering; 13—oven for drying.  
balance, which measures mass loss over time. The balance itself consists of a high-speed damped load cell (Tedea-Huntleigh, type 9010) in combination with a Penko QMA indicator. Flexible hoses were used for connections between the reactor, and other devices (N\({}_{2}\) supply and controls) were flexible to minimize their impact on the recorded weight. The temperature inside the reactor chamber was controlled by a programmable logic controller (PLC) and measured using K type thermocouple.  
The Cyclonic TGA was developed in order to determine pyrolysis kinetics at heating rates exceeding the possibilities of state-of-the-art analytical thermogravimetry since the heating rate has a significant influence on the kinetics of pyrolysis [68; 69; 70; 71]. The setup was developed in order to ensure heating rates comparable to the fast pyrolysis process [72]. Its operating principle is similar to a fast pyrolysis reactor developed at the University of Twente (PyRos). In the reactor, solids slide down the walls while the processed biomass is in direct contact with hot inert sand ensuring heat transfer of 500-700 W/m\({}^{2}\)K [73]. This operating principle also ensures easy application of any catalyst in situ. Produced char is used in a fluidized bed combustor to obtain the heat necessary for the process [73; 74]. Fast pyrolysis experiments were performed using Cyclonic TGA at temperatures between 450 \({}^{\circ}\)C and 500 \({}^{\circ}\)C, using sieved feedstock samples (miscanthus and hydrocarbons) with particle size ranging between 63 and 425 \(\upmu\)m. Pyrolysis was performed in an inert atmosphere, with a majority of N\({}_{2}\) being pre-heated in the heated spiral pipe, inside of the thermal insulation of the reactor, and subsequently injected tangentially. A small volume of N\({}_{2}\) (approx. 100 mL at 3 bar pressure) was used for rapid injection of the sample into the cyclonic reactor. Blank trials with no sample have been performed in order to eliminate the effect of the inertia of injected gas, and short-time fluctuation of the scale has been deducted.
## 2 Materials and Methods
### Volatile Organic Compounds Analysis
#### 2.8.2 Gas Chromatography-Quadrupole Mass Spectrometry Analysis (GC-qMS)  
VOCs analysis was carried out by inserting the HS-SPME fiber into the injector port of a gas chromatographer Agilent, model GC 7890A, coupled with a mass spectrometer 5975C (system from Agilent Technologies, Santa Clara, CA, USA). VOCs were thermally desorbed for 5 min at 250 \({}^{\circ}\)C and directly transferred to a capillary column HP-Innowax for the analysis. The GC oven temperature program was initially set at 60 \({}^{\circ}\)C for 5 min, then increased to 200 \({}^{\circ}\)C at 6 \({}^{\circ}\)C min\({}^{-1}\), and finally maintained until 240 \({}^{\circ}\)C at 15 \({}^{\circ}\)C min\({}^{-1}\) and held at this temperature for 5 min. Mass spectra were acquired at an ionization energy of 70 eV, and VOCs were detected via mass selective detector. The detector operated in a mass range between 30 and 300 u with a scanning speed of 2.7 scans/s. The identification of VOCs was based on mass spectra matching with the standard NIST05/Wiley07 libraries (National Institute of Standards and Technology, Gaithersburg, MD, USA), comparing the retention indices (RI) (as Kovats indices) with literature data and from authentic standard compounds when available. The area of individual VOC was determined by the total ion chromatogram (TIC) and semi-quantified via relative comparison with the peak area of the IS (relative peak area, RPA %). Each sample was analyzed in triplicate with a randomized sequence in which blanks were also run.
## 4 Numerical Examples and Discussion  
### Parameter Selection  
In order to verify the validity and effectiveness of the proposed model, some numerical examples are presented by the specific parameters. These examples cover standard ellipse, double eccentric arc, free deformation and forced deformation. The structure of the flexspline is shown in Figure 5. Tables 1 and 2 show the detailed parameters of the flexspline and the wave generator. The model in this paper is simplified on the basis of the correct calculation: (1) The tooth wall thickness of the flexspline is simplified equivalently with \(\sqrt[3]{1.67}\) times of the original [28]. (2) The flexible bearing and the wave generator as a whole are assembled in the inner ring of the flexspline [29]. Hexahedron cells are used for the wave generator and tetrahedron cells are used for the flexspline. The shaft support constraint is applied to the inner hole of the flexspline, fixed constraints are applied to the bottom surface of the cladding plate and the rigid outer surface of wave generator is defined as the target surface. The friction coefficient between the flexspline and the wave generator is set as 0.1 and the stiffness coefficient is set as 0.1. In addition, the material parameters of the flexspline and wave generator in FEM are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbols** & **Values** \\ \hline Diameter of neutral line (mm) & D\({}_{\text{m}}\) & 41.98 \\ Wall thickness (mm) & h & 0.26 \\ Width of tooth ring (mm) & b\({}_{\text{r}}\) & 8 \\ Width of cup (mm) & b & 23.5 \\ Thickness of cup bottom (mm) & h\({}_{\text{w}}\) & 3 \\ Outside diameter of cup bottom (mm) & D\({}_{0}\) & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the flexspline.
# Polar Code BP Decoding Optimization for Green 6G Satellite Communication: A Geometry Perspective
## 2 BP Decoding Algorithm for Polar Codes
In fact, since \(\hat{a}^{t}=\text{sgn}(\eta_{L}^{t})\), when the difference between \(\eta_{L}^{t}\) and \(\eta_{L}^{t+1}\) is sufficiently small, a correct decoding result can still be obtained. Therefore, the core of the algorithm lies in utilizing differential geometry theory to design a convergence threshold. This threshold, based on the non-Euclidean properties of soft information in geometric space, calculates the Riemannian distance or divergence to obtain a geometric distance, which is used to measure the minute differences in information updates during the iteration process. When the geometric distance of the soft information for the same information bit in two consecutive iterations is less than this threshold, the algorithm determines that the iteration has converged and prematurely terminates the decoding. For Algorithm 3, during the implementation of the algorithm, the maximum number of iterations \(I_{\text{max}}\) is first set, and a convergence threshold eps is also established based on differential geometry principles. In the initialization stage, the Log-Likelihood Ratio (LLR) values for left-propagated soft information of the information nodes on the leftmost side of the factor graph required for decoding are prepared, and a geometric model is constructed to calculate the geometric distance between soft information. During the iteration process, the algorithm updates the LLR values for each information bit one by one and calculates the geometric distance of the soft information between the current and previous iterations. If the geometric distance is less than the threshold eps, the iteration is deemed to have converged, and the iteration is terminated; if the number of iterations reaches \(l_{\max}\) without convergence, the iteration is forcibly terminated. Ultimately, the algorithm decides and outputs the decoding result based on the results of the final iteration. Since the threshold design is based on differential geometry principles, the algorithm achieves an improvement in decoding efficiency while ensuring decoding error correction performance. ```
0: Maximum number of iterations \(l_{\max}\), Threshold eps
0: Decoding result
1: Initialize
2:for\(t=1\) to \(l_{\max}\)do
3:for\(j=1\) to \(N\)do
4: Update LLR
5:endfor
6:\(\mathrm{covt}=\max_{i}D[(\eta_{L}^{t})_{i},(\eta_{L}^{t+1})_{i}]\)
7:if\(\mathrm{covt}<\mathrm{eps}\)then
8:break
9:endif
10:endfor
11:Decide and output decoding result
```

**Algorithm 3** Early Convergence Algorithm Based on Information Geometry

### Calculation of Geometric Distance

#### 4.2.1 Riemann Distance

In this context, the Fisher Information Matrix degrades into the Fisher Information. According to reference [17], the Fisher Information is expressed as follows:

\[I(\lambda)=-E[\frac{\partial^{2}}{\partial\lambda^{2}}\ln p(x;\lambda)]. \tag{17}\]

\(E\) represents the expectation with respect to \(x\).
## 3 An On-Line Monitor System for OASLM Modulation
Since the [PERSON] wave-front sensor uses the wave-front slope and the wave-front variation to calculate the actual wave-front indirectly, here the modulation result of the binary image exceeds its measurement dynamic range, so the wave-front directly measured has a large recovery error and serious discrepancies compared to the theoretical distribution. Figure 8b is a comparison of the 1D transmittance between the result measured by the CCD and the result deduced from the transmittance gray-level relationship. The two results demonstrate good consistency. This implies that the monitoring system has a good response to the modulation of binary grayscale images. Furthermore, we loaded a binary image with varying periods on our SLM to determine the spatial resolution of the system; the modulation result is shown in Figure 8c, from which we can see that the spatial resolution is at least 200 \(\upmu\)m. In Figure 8d, the normalized MTF can be calculated from the relation \(MTF=(T_{max}-T_{min})/(T_{max}+T_{min})\). From this we can also obtain the spatial resolution of 5 lp/mm (period of 200 \(\upmu\)m, the value of the MTF is 0). At the same time, we can see that the 1 lp/mm (period of 1 mm, the value of the MTF is 1) is a division. When the value of the X-axis is larger than this, the MTF curve drops sharply to 0, so the minimum distinguishable contrast is 1 mm. Figure 9b is a chessboard grayscale image with 70-pixel period in the horizontal direction and 128-pixel (the pixel-pitch of the write-light system is 20 \(\upmu\)m) period in the vertical direction. The modulation results are shown in Figure 10. The black lines in (a) and (b) correspond to the transmittance measured by the CCD and are basically consistent with the red lines, the theoretical results of the transmittance gray-level relationship. The black lines in (c) and (d) are the 1D distributions of the wave-front measured by the WFS, which have significant differences compared to the theoretical values deduced from the phase-change function and represented by the red lines. Therefore, the monitoring system plays a very important role in the measurement of the modulation results on-line. Figure 7: Comparison of the designed wave-front and the actual one. (**a**) Wave-front designed using Equation (4). (**b**)Actual wave-front measured by the WFS. (**c**) Comparison of the central row one-dimensional (1D) distribution. Figure 8: Modulation results of grayscale map with value mutation. (**a**) 1D wave-front. (**b**) 1D transmittance. (**c**) The modulated result obtained by CCD when the spatial-light modulator (SLM) is loaded with a binary image of varying variable periods. 1, 760 \(\upmu\)m; 2, 520 \(\upmu\)m; 3, 440 \(\upmu\)m; 4, 320 \(\upmu\)m; 5, 240 \(\upmu\)m; 6, 200 \(\upmu\)m; 7, 120 \(\upmu\)m. (**d**) Modulation transfer function (MTF) of the OASLM as a function of spatial resolution. Figure 9: Binary images with gray values of 0.25 and 0.45. (**a**) 1D value change binary image. (**b**) Two-dimensional (2D) value change binary image.
## 4 Experimental Results and Security Analysis
### Statistical Random Number Tests
#### 4.3.3 Random Test Result under SP800-22  
For the random test of SP800-22, we select different random keys and different random initial values to produce 100 output keystreams. The length of each keystream output for test is 10,000,000 bits. Table 4 shows the test results of SP800-22 for the improved CML, and compound CML, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**(a) Improved Four Stage CML Based on Logistic Map**} \\ \hline
**Statistical Tests** & \(p\) **Value** &
\begin{tabular}{c} **Pass Rate under 10\({}^{7}\)** \\ **Bits/Sample** \\ \end{tabular} \\ \hline Frequency & 0.015521 & 95\% \\ Block Frequency & 0.182061 & 95\% \\ Runs & 0.604356 & 99\% \\ Longest Runs of Ones & 0.123875 & 98\% \\ Rank & 0.621752 & 97\% \\ Discrete Fourier Transform & 0.654347 & 96\% \\ Non-overlapping Templates Matching & 0.410517 & 95\% \\ Overlapping Templates Matching & 0.570552 & 97\% \\ Universal Statistical & 0.383693 & 98\% \\ Linear Complexity & 0.394963 & 97\% \\ Serial & 0.751610 & 97\% \\ Approximate Entropy & 0.428403 & 96\% \\ Cumulative Sums & 0.021606 & 95\% \\ Random Excursions & 0.458795 & 95\% \\ Random Excursions Variant & 0.302402 & 95\% \\
**(b) Improved Four Stage CML Based on Tent Map** \\ \hline
**Statistical Tests** & \(p\) **Value** &
\begin{tabular}{c} **Pass Rate under 10\({}^{7}\)** \\ **Bits/Sample** \\ \end{tabular} \\ \hline Frequency & 0.026589 & 95\% \\ Block Frequency & 0.457855 & 96\% \\ Runs & 0.564585 & 97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical test results of the proposed improved CML under NIST SP800-22. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{3}{c}{**Pass Rate under 20,000 Bits/Sample**} \\ \hline
**Scheme** & **Monobit Test** & **Poker Test** & **Runs Test** & **Long Run Test** \\ \hline (a) & 100\% & 100\% & 100\% & 100\% \\ (b) & 100\% & 100\% & 100\% & 100\% \\ (c) & 100\% & 100\% & 100\% & 100\% \\ (d) & 100\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical test results of the proposed improved CML under FIPS PUB 140-1. (a) The improved CML based on logistic map; (b) the improved CML based on tent map; (c) the improved four stage compound CML, combining both logistic map and tent map in a single stage; (d) the improved four stage compound CML, combining logistic map and tent map in an alternate stage. From Table 4, we can find that the pass rate of each random test for the proposed improved CML-based chaotic stream cipher is at least about 95%. Table 4a,b show the pass rate of each random test for the proposed improved CML chaotic stream cipher. Table 4c shows the pass rate of the random test for the proposed stream cipher based on the improved compound CML, combining both the logistic map and tent map in a single stage. Its pass rate is at least about 97%. Table 4d presents the random test result for the proposed proposed method.