esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermomechanically Induced Precipitation in High-Performance Ferritic (HiperFer) Stainless Steels
## 3 Results and Discussion
### Impact of Deformation Parameter Variation
#### 3.2.3. Holding Time  
The influence of prolonged holding time after deformation at 800 \({}^{\circ}\)C is depicted in Figure 9. Shifts in particle density, size distribution, and morphology were observed during particle evolution. Immediate cooling after deformation (t\({}_{\rm H}\) = 0 s) prevented the typical linear arrangement of precipitates (Figure 9a) and resulted in a comparably low density and area fraction of Laves-phase particles, because of the limited time for precipitation. After immediate quenching, the size of the Laves-phase particles presented a wide distribution up to about 400 nm, with a main peak at around 120 nm, but a low particle density (Figure 9b). The highest density of Laves-phase precipitates appeared at 60 s holding time (Figure 9a). Particle area fraction increased only slightly, to about 4%, while the size distribution simultaneously changed dramatically.  
After a prolonged holding time of 80 s, the particle area fraction remained almost constant at -4 vol. % (within the range of scatter, cf. Figure 9a). Within 20 s difference (Figure 9b: 60 s to 80 s), coarsening of precipitates changed the size distribution significantly. While at 60 s the size distribution presented a plateau from 20 to 85 nm, the plateau disappeared and a peak was encountered at 125 nm after 80 s of holding. Further prolonging the holding time to 300 s did lead to an increase in  
Figure 9: Impact of holding time on (**a**) particle density and area fraction, (**b**) particle size distribution, and (**c**) tensile properties (T = 800 \({}^{\circ}\)C, \(\varphi\) = 0.5, t\({}_{\rm H}\) = 0, 60, 80, 300 s).  
amount density and area fraction (Figure 9a) along with a backshift of the peak in size distribution to about 90 nm (Figure 9b) ECD, and accompanied by a change in particle geometry from preferentially disc-shaped to spherical (cf. Figure 9a, micrographs 80 s vs. 300 s). This might indicate a complex equilibrium of metastable particle species, that were kinetically favored for a short period. Further investigation into this issue is necessary. The differences encountered in microstructure consequently led to differing tensile performance (Figure 9c). The highest tensile strength (710 MPa) was reached with 60 s of holding time, while the other conditions produced tensile strength values ranging around appr. 600 MPa but differed in ductility.
## 3 Materials and Methods
### Formative Assessment Algorithms
#### 3.2.1 Elo-Bandit Algorithm
Where they were chosen for human readability. The new student's Elo rating on a given topic \(sElo_{t}\) after a student gives either correct, \(X_{ans}\) = 1, or incorrect, \(X_{ans}\) = 0, answer to an item is calculated based on the expected probability of correct answer:  
\[sElo_{t}^{\prime}=sElo_{t}+k(\ X_{ans}-P_{c}) \tag{3}\]  
Constant \(k\) in Equation (3) specifies the sensitivity of Elo rating to change, or the maximum change Elo rating can make given the most unlikely outcome. If \(k\) is small, the Elo estimation converges slowly, if \(k\) is large then Elo score oscillates with large amplitude around the true score without ever reaching it. A common solution is to use a decaying constant, which converges fast on true value when there are few data points to estimate Elo rating, but ultimately settles on a more accurate value than a large \(k\) value. There are more mathematically rigorous methods of modeling uncertainty inherent in the Elorating estimation and using it as a basis for the constant, but they do not produce superior results [55]. Convergence of item Elo rating estimation _iElo_ and student's topic Elo rating _sElo_ towards a value estimated using Maximum Likelihood Estimation (MLE) method can be seen in Figure 2.  
Seed Elo used to generate synthetic data is also plotted in the figure. A more detailed description of seed Elo and synthetic student generation is presented in the following sections. To summarize, the algorithm probes each student's topic knowledge and simultaneously estimates item difficulties. Then this knowledge can be used to tailor feedback for each student. In this paper, we chose to select the easiest items from the weakest topic for a student, as this would allow a student to progress from easier to hard content and possibly motivate them. However, the differently inclined instructor may choose another approach, for example selecting items with Elo score best matching student's topic knowledge. Thus, post assessment each student can receive not just a mark and a set of items which they got wrong, but a number of areas where their knowledge is weakest and a way to correct these deficiencies starting from the simplest items.
# Defect Tailoring in HfO2/Si Films upon Post-Deposition Annealing and Ultraviolet Irradiation
## 3 Results
### Structural Characterization  
#### 3.1.1 X-Ray Diffraction  
Figure 2 presents the grazing incidence X-ray diffractograms of the p-Si substrate and the S1-S4 samples, compared with the powder diffraction file PDF 36-0104 corresponding to the monoclinic phase of HfO\({}_{2}\). The S1 and S2 films present diffraction peaks at 2 theta 29.24\({}^{\circ}\) as well as 2 theta 29.24\({}^{\circ}\) and 32.62\({}^{\circ}\), respectively, related with the (\(-\)111) and (111) reflections of monoclinic HfO\({}_{2}\)[4, 5]. The presence of the second peak of the (111) plane in sample S2, annealed at 350 \({}^{\circ}\)C, indicates higher crystallinity. The observed 2 theta shift of 1\({}^{\circ}\) of both peaks with respect to the reported PDF is related to the low X-ray incidence angle. S3 and S4 films did not present diffraction peaks apart from the Si substrate, indicating  
Figure 1: Process flow diagram used for the production of HfO\({}_{2}\) samples.  
that the UV process led to amorphous films. Future work will explore the effect of film crystallinity when varying both the temperature and annealing time around the monoclinic phase dominion [4; 5] and its effect on the film's electronic properties.
## 6 Comparison of PSO, BA, and DE Regarding Data Classification
### NN Classifications with PSO-Based Initialization Approaches
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**S. No**} & \multirow{2}{*}{**Data Sets**} & \multirow{2}{*}{**Type**} & **BPA** & **PSONN** & **SO-PSONN** & **H-PSONN** & **TO-PSONN** & **WE-PSONN** & **KN-PSONN** \\ \cline{3-9}  & & & **Ts. Acc** & **Ts. Acc** & **Ts. Acc** & **Ts. Acc** & **Ts. Acc** & **Ts. Acc** & **Ts. Acc** \\ \hline
1 & Diabetes & 2-Class & 65.3\% & 69.1\% & 69.1\% & 71.6\% & 73.3\% & 74.1\% & **78.5\%** \\
2 & Heart & 2-Class & 68.3\% & 72.5\% & 67.5\% & 72.5\% & 77.5 & 77.5\% & **79\%** \\
3 & Wine & 3-Class & 62.17\% & 61.11\% & 66.66\% & 67.44\% & 69.44\% & 69.6\% & **72\%** \\
4 & Seed & 3-Class & 70.56\% & 77.77\% & 84.44\% & 77.77\% & 88.88\% & 91.11\% & **93\%** \\
5 & Vertebral & 2-Class & 84.95\% & 92.85\% & 92.85\% & 92.85\% & 94.64\% & 94.64\% & **96\%** \\
6 & Blood Tissue & 2-Class & 73.47\% & 78.6\% & 78.66\% & 70\% & 82.66\% & 84\% & **87\%** \\
7 & Memo Graphy & 2-Class & 71.26\% & 76.66\% & 63\% & 85\% & 88.88\% & 96.66\% & **98\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of 10-fold classification rates of ANN-training methods in 7 datasets for accuracy. The classification testing accuracy were imported from Microsoft Excel Spreadsheet to the software RStudio version 1.2.5001 to get assurance of the winner approach statistically among all the other approaches. The testing accuracy of all seven variants of PSONN were analyzed by the one-way ANOVA test and post-hoc [PERSON]'s multi-comparison test [60] having a 0.05 significance level. Table 12 depicts the results of one-way ANOVA of the testing accuracy of classification data. The significance value in Table 11 is 0.04639 which is less than 0.05, giving evidence that there is a significant difference among all variants of PSONN with a 95% confidence level. According to this, the variants of PSONN are significantly distinct from each other. Figure 56 represents the graph of one-way ANOVA results, which conclude that KN-PSONN significantly outperforms than all other variants of PSONN. Figure 57 represents the results of multi-comparisons of PSONN variants through the post-hoc [PERSON]'s test. The resultant graph depicts that the KN-PSONN variant is significantly different from all other variants. According to the results in Figure 55, KN-PSONN is proved statistically different from all other approaches of PSONN with a 95% confidence level.
# An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec
## 5 Conclusions  
A two-stage search algorithm is presented herein for the improvement of search performance in the ISF vector quantization of an AMR-WB speech codec. At stage 1, an input vector was assigned to a search subspace in an efficient manner using the BSS-VQ algorithm, and a codebook search was performed over the subspace using the ITIE approach at stage 2. Through the use of the codeword rejection mechanisms equipped in both stages, the search load was reduced remarkably as intended. Experimental results show that the upgraded version of BBS-VQ provided an overall LR of up to 94.20% at \(TQA\) = 0.90, and was validated to outperform its counterparts. As compared with the original version of the BSS-VQ algorithm, the upgraded version provided a computational load reduction of up to 51%, a significant outperformance over the benchmark. This work would be beneficial for reaching the energy saving requirement when implemented on an AMR-WB codec of mobile devices. On the other hand, the contribution of this work is also applicable to the speech coding in EVS codec, which is a new standard of audio codec and provides interoperation with AMR-WB. In future work, the authors will pay their efforts to make further improvements on the computational complexity of EVS codec.  
All authors have worked on this manuscript together and all authors have read and approved the final manuscript.  
This research was sponsored by the Ministry of Science and Technology, Taiwan, under grant number MOST 107-2221-E-167-021.  
The authors declare no conflict of interest.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 2 Problem Modeling  
This section proposes a framework for an interference avoidance scheme for the LEO satellite to the GSO satellite system. First, it must ensure that the LEO satellite system does not generate unacceptable interference to the system. Then it is necessary to ensure seamless coverage of the Earth by the LEO satellite constellation and finally to design the interference avoidance scheme by maximizing the average communication capacity of the system. The detailed notations used in this paoper are sumarized in Table 1.  
We need to avoid inline interference of a single satellite according to the EPFD simulation of satellites in the same orbital plane in Appendix A because the EPFD maximum is determined by the satellite that contributes the most to the EPFD.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 5 Conclusions  
With the miniaturization of electronic packages, the resolution limits of traditional time domain AMI and frequency domain AMI have been reached. SR is a time-frequency domain AMI method that can improve longitudinal resolution. The SR method with traditional Gabor dictionary distinguishes overlapping echoes based on the differences in the time-frequency parameters of the echoes, and is often used to separate two echoes with certain differences. For the overlap problem of multiple similar echoes in electronic packages detection, the SR method has limited separation effect.  
In this paper, the CD-SR method is proposed to achieve super-resolution and high robustness. The innovation lies in: (1) Quantitative discussion on the overlap of multiple echoes. The degree of overlap and the performance of the method are quantitatively analyzed. (2) Constraint dictionaries are proposed. The constraint dictionary differs from the conventional dictionary in that its time-frequency parameters are determined according to the AMI signal to be processed and used to achieve higher matching accuracy. (3) The sparse decomposition and reconstruction algorithm of AMI signals is improved to achieve more accurate decomposition and reconstruction.  
Simulation and experimental results demonstrated that the reconstruction errors of CD-SR remains within 13% when the echo overlap degree is as high as 60%, with superior longitudinal resolution and robustness. Although the Gabor dictionary is used in this paper for constraint, the proposed constraint idea is universal and can be used for different types of dictionary to solve the problems in ultrasonic detection, such as resolution improvement, noise suppression, pulse detection, etc.  
Conceptualization, K.W., T.L. and J.M.; methodology, K.W. and T.L.; validation, J.M. and G.L.; simulation, K.W. and T.L.; experiment, K.W. and T.L.; data curation, K.W.; writing--original draft preparation, K.W.; writing--review and editing, T.L., J.M., G.L. and C.Z.; visualization, T.L., J.M., G.L. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 2 Material and Methods
### 4-CP Adsorption and Degradation Tests  
The degradation tests of 4-CP were performed in 100 mL flasks containing 50 mL of the reaction solution formed by mixing PS and MPHAC. The solution was agitated at 180 rpm and 20 \(\pm\) 3 \({}^{\circ}\)C for up to 90 min. Subsequently, the suspension was filtered and the residual concentration 4-CP was determined using an UV spectrophotometer (DR 2000, Hach, Germany) at the wavelength 510 nm [2]. The effects of the solution pH (3-9), the MPHAC dose (250-1250 mg/L), the PS dose (50-350 mg/L), and the 4-CP initial concentration (100-250 mg/L) were investigated together with the MPHAC reusability. In order to perform the 4-CP extraction from MPHAC, 5 mL of acetonitrile/methanol (1/1, _v/v_) was mixed with the MPHAC sample for 2 h under 180 rpm, and finally dissolved with 5 mL of methanol for 4-CP detection.  
To determine the 4-CP distribution in the aqueous phase (\(A_{1}\)) and solid phase (\(A_{2}\)), Equations (3) and (4) were employed.  
\[A_{1}=C_{aqueous}\times V/C_{0}\times V \tag{3}\]  
\[A_{2}=C_{MPHAC}\times M/C_{0}\times V \tag{4}\]  
where \(C_{aqueous}\) is the 4-CP concentration in the aqueous phase (mg/L), \(C_{MPHAC}\) is the concentration of 4-CP adsorbed on the MPHAC (mg/g), \(C_{0}\) is the initial 4-CP concentration in the aqueous solution (mg/L), \(V\) is the volume of the aqueous phase (L), and \(M\) is the MPHAC mass in the aqueous solution (g).  
The following equations were used to calculate the degradation of 4-CP by PS, its adsorption by MPHAC, and its removal factor:  
\[Degradation(\%)=[1-(A_{1}+A_{2})]\times 100 \tag{5}\]  
\[Removal\quad factor\ \ (\%)=(1-A_{1})\times 100 \tag{6}\]  
\[Adsorption(\%)=Removal(\%)-Degradation(\%) \tag{7}\]
## 4 Soil Moisture Retrieval from Reflection Measurements: A Background on the Discipline  
In this section, we briefly review the basic principles of the soil moisture estimate using GNSS reflectometry. A review of the theoretical background of this discipline is necessary in order to clearly motivate the implementation choices made in the prototype design.  
In order to quantitatively estimate the soil moisture, the soil dielectric constant has to be evaluated, applying models that take into account the soil characteristics, including in particular the soil composition [18, 30, 31]. Different methods exist, which either exploit the LHCP reflected signal only or both the LHCP and the RHCP reflections. The soil dielectric constant depends in particular on two parameters related to the GNSS signal: the soil reflection index and the incidence angle (which directly depends on the satellite elevation angle).  
In the following paragraphs, the main equations are provided, explaining the physical principles at the basis of the soil moisture retrieval through the measurement of the reflected GNSS signal. The principle is that the soil dielectric constant changes depending on the soil water content, which has an impact on  
Figure 7: Comparison of the estimated power spectral densities of the signals at the two channels of both FEs, generated through a hardware signal generator and supplied to the FEs input ports through a wired RF connection. (**A**) Channel IF; (**B**) Channel BB.  
the soil reflectivity properties; it is highly dependent on the soil composition, as well. In particular, the more the soil is moist, the less an incising electromagnetic wave penetrates it in depth, which translates into a higher reflected power [32]. However, a role is played also by the surface roughness, which decreases the specular reflected power.  
The two basic principles summarized hereafter exploit, on the one hand, the LHCP measurements only, on the other one, the joint processing of the LHCP and RHCP measurements.
## 4 Methodology
### Obtaining a Configuration Based on the Exhaustive Strategy  
The first strategy in the proposed framework is the exhaustive strategy, whose relevance is that it ensures obtaining the best configuration among all possible ones. In general, an exhaustive strategy is a brute-force method to a problem involving the search for a solution among all possible ones, e.g., those obtained from combinatorial objects, such as permutations or combinations. In this case, for a configurable process tree \(Q^{\alpha}\), we analyze all possible configurations that belong to \(C(Q^{\alpha})\). Algorithm 1 presents the exhaustive strategy, which can be described as follows:  
* Generate the set of all possible configurations, \(C(Q^{\alpha})\), in a systematic manner, using the Cartesian product of the configuration sets for all configurable nodes.
* Loop over all configurations. At each iteration, the function _derive_(\(Q^{\alpha}\),\(c\)) is used to obtain  
```
1:procedureExhaustive(Log \(L\), CPT \(Q^{a}\), Weights \(W\))
2:\(best\gets null\)
3:\(C(Q^{a})\gets CartesianProduct(\alpha(n_{1}),\alpha(n_{2}),\ldots,\alpha(n_{|N^{ a}(Q^{a})|}))\)
4:for all\(c\in C(Q^{a})\)do
5:\(m\leftarrow\text{\it derive}(Q^{a},c)\)
6:if\(\text{\it conformance}(m,L,W)>\text{\it conformance}(\text{\it derive}(Q^{a}, best),L,W)\)then
7:\(best\gets c\)
8:endif
9:endfor
10:return best
11:endprocedure
```  
**Algorithm 1** Obtain a configuration based on the exhaustive strategy.
## 3 Results and Discussion
### Effect of Temperature
#### 3.4.2 Comparing with Scaling Theory  
It can be compared first with that of linear PSS macro-ions, which can be calculated from the scaling theory [24]. Indeed, a strongly charged PE can be roughly treated as a weakly charged one with an effective charge fraction \(f_{\rm eff}=a/l_{\rm B}\), for monovalent counterions. Here, \(a\) is the contour length of the monomer unit, which is equal to 2.5 A for PSS; \(l_{\rm B}\), the Bjerrum length that is equal to 7.12 A in water at room temperature (hence \(f_{\rm eff}=0.35\)). The end-to-end distance for linear macro-ions in theta solvent is then [24]:  
\[R\approx N\cdot a\cdot[{f_{\rm eff}}^{2}\,(l_{\rm B}/a)]^{1/3}\;=\;N\cdot a \cdot(a/l_{\rm B})^{1/3} \tag{5}\]  
With an electrostatic blob size:  
\[\xi_{\rm el}\approx a\cdot[{f_{\rm eff}}^{2}\,(l_{\rm B}/a)]^{-1/3}\;=\;a\cdot (l_{\rm B}/a)^{1/3} \tag{6}\]  
Figure 5: Mean radii of NaPSS stars (\(R\)) according to their arm degrees of polymerization (\(N_{\rm a}\)). For each star polyelectrolyte, \(R\) is obtained from the \(q_{1}\)* value at \(c\)* (\(R=\pi/q_{1}\)*). A comparison with some precursor PS stars is also offered.  
Equations (5) and (6) lead to a scaling law \(R(\AA)\approx 1.76\) N and an electrostatic blob size \(\zeta_{\rm ed}\approx 3.54\) A for linear PSS macro-ions. Hence our measurement is close (1.41 instead of 1.76) to the prediction of the scaling theory for linear chains in the dilute regime, and therefore, in a very extended state [24]. This prediction is very close to the prediction from the scaling approach of [PERSON] and [PERSON] for the dilute osmotic regime of star polyelectrolytes [14]. In this approach, the radius of a star is determined by the balance of (i) the counterion entropy favoring the increase in the star volume and (ii) the entropic elasticity of arms that opposes this expansion. Balance of these two contributions then leads to a star radius independent of the number of arms and close to the end-to-end distance of a linear chain associated with one arm. In other words, the arms of the star are not stretched with respect to the size of a corresponding individual PE chain with counterions condensed on it. In practice we summarize that our experimental result for \(R\) agrees with both equal scaling predictions, the one for a linear chain [24] and the one for the dilute osmotic regime of stars [14].
## 3 Proposed Architecture  
The proposed overall architecture of this paper is as follows. This paper focuses on accelerating the position estimation of a sound source based on a PF using an acoustic signal sensor array in a GPU. The TDOA measurement is required for the sound source localization based on the proposed GPU, and the location of the current target can be determined using three TDOA measurements in the three-dimensional space. In order to obtain three TDOA measurements, four acoustic sensors are required including one reference acoustic sensor. In this study, we used GCC-PHAT to obtain TDOA measurements. To obtain TDOA measurements at high speed, the GCC-PHAT process of the acoustic signal input by multiple sensors is performed in parallel using GPU.  
The proposed method uses a PF to estimate the current state of a target using TDOA measurements, and we accelerate it. The PF process used in the study is as follows. Using the computed TDOA measurements and the state equations of the system, we predict the current state from the previous state of the system and obtain the observed value from the state of the updated system. Then, the weight of each particle is obtained by comparing with the actual measurement. In this process, we propose accelerating the state update and weight calculation process of the particle mentioned above by parallel operation of each particle in the kernel using GPU. Also, PF operation is customized using multiple system state equations based on GPU for tracking sudden movements of moving targets which also can be used for tracking multiple target. The proposed architecture is shown in Figure 1. Moreover, this proposed architecture can be used in IoT applications as shown in Figure 2, that is consisted with many sensors. Due to the amount of signal data from many sensors, it is reasonable using GPU in terms of high performance computing to accelerate signal processing for detecting target more accurately by increasing the number of sensors.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Reflector Antenna  
The reflector antenna simply modifies the radiation pattern of an antenna by redirecting the received electromagnetic energy in the range of the electromagnetic spectrum wavelength, thereby increasing the gain in a particular direction.