esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Programs
### Specim Preparation  
The compression test was performed on cylindrical specimens with a diameter of 100 mm and a height of 200 mm. The pull-out specimen was a cube with a side of 150 mm, and a #6 rebar was embedded vertically along its central axis, as shown in Figure 2. The embedded length (\(l_{e}\)) was three times the rebar diameter (\(d_{b}\)) to create a local bonding. To achieve the effect of local bonding, when casting the pull-out specimen, the unbounded area of the rebar was sealed with PVC sleeves. In addition, to prevent the rebar of the specimens from being corroded during the curing process, the joints between the PVC sleeves and the rebar were filled with water-based sealant, and high-temperature anti-rust paint was sprayed on the surface of the protruding rebar. Moreover, the specimen contained three transverse stirrups made of #3 rebar to prevent its splitting failure when the longitudinal rebar was under tension.  
After the mixing operation of each group of concrete mixtures was completed, a slump test was performed, and the slump value was recorded. Then, twenty-four cylindrical specimens and twenty-one pull-out specimens were cast for each group of concrete mixtures. The specimens were demolded after 24 h. All the specimens were then immersed in laboratory-saturated limewater tanks. On the 14 th day, the specimens were taken out and placed in a programmable incubator at 40 \({}^{\circ}\)C for 14 days. After casting and curing for 28 days, compressive strength, high-temperature, and pull-out tests were conducted for  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Water-Binder** \\ **Ratio** \\ \end{tabular} & \begin{tabular}{c} **Water** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Cement** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Lightweight** \\ **Aggregate** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Fine Aggregate** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Steel Fiber** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Polypropylene** \\ **Fiber** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Superplasticizer** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} \\ \hline
0.45 & 220 & 489 & 345 & 734 & \(58.5\) & 1.17 & 0.978 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix proportions of the concrete.  
Figure 1: Appearance of expanded shale LWAs.  
each group of specimens. Then, different self-healing methods were used for each group of specimens damaged in the high-temperature and pull-out tests.
# Different Approaches to the Regeneration of Dental Tissues in Regenerative Endodontics
## 2 Materials and Methods  
What are the differences in regenerative endodontic treatment methods, based on selected clinical cases and the available literature?  
In accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines [54], an ample search was conducted through electronic databases of scientific and medical literature including \"PubMed\" and the \"Cochrane Library\". The search strategy included the following keywords: \"regenerative endodontic protocol\", \"regenerative endodontic treatment\", and \"regenerative endodontics\" combined with \"pulp revascularization\". Furthermore, a manual method was carried out for possible additional studies. The inclusion criteria were in vivo studies in which regenerative procedures were performed in immature and mature permanent teeth, with or without apical periodontitis. The exclusion criteria were: reviews and studies not in the English language, studies in animals, and studies published before 2009. Obtained studies were analyzed, and articles not meeting the applied criteria were excluded. The initial identification included the titles and abstracts of articles on regenerative endodontics: studies identified by searching databases (n = 346); PubMed (n = 250), Cochrane Library (n = 96), and studies identified through manual searching (n = 40). The reduction was made by excluding nonrelevant titles from the literature review and papers published before 2009. Subsequently, the number of articles was reduced by rejecting nonrelevant abstracts, duplicates, review articles, research on animals, and papers not in English. That resulted in 58 full-text articles being assessed for eligibility. Further selection required meticulous reading of full-text publications and excluding papers with observations shorter than 5 months, and with incomplete information about irrigants, root canal fillings, and scaffolds. Full-text publications were read in order to extract the following data: patient age, kind of tooth, root canal disinfection protocol and size of apical foramen, inter-visit medicaments, scaffold and capping material, recall time, and procedure outcome (Figure 1).
# Phoneme Recognition in Korean Singing Voices Using Self-Supervised English Speech Representations
## 4 Experiments
### Experimental Results
#### 4.3.2 Speech-Singing Adaptation  
After fine-tuning the model on the NIKLSEOUL dataset of 180 h, we used the CSD dataset, which included only 2.5 h of singing voice data, to adapt the model from the speech domain to the singing domain by joint pitch detection supervision. This adapted model is referred to as Model C in our results shown in Table 3. For comparison, we also have two other models: Model A and Model B in Table 3. Model B was trained only under phoneme supervision from the English-Korean adapted model, while Model A was trained only under phoneme supervision from a non-adapted model, without Korean speech prior. We evaluated the performance of phoneme recognition for these models on multi-level error rates using test datasets from both the CSD corpus and NIKLSEOUL corpus. Additionally, we assessed the performance of pitch detection. We used the linear probe method to evaluate the pitch detection performance of Model B, which was trained without pitch detection supervision. Table 3 shows the results.  
**Was the melodic supervision helpful?** Considering the results, in Model C, pitch detection supervision improved the F1 score of COn and the performance of phoneme recognition at every level. In more detail, it slightly reduced the Precision score, but achieved a better Recall score, meaning it tended to catch more correct onset times of phonemes. On the other hand, for Model B, it already had a fair degree of pitch detection performance, even without pitch detection supervision. This proved once again that the phoneme recognition task is highly related to the pitch detection task, although little benefit can be obtained by combining the tasks of phoneme recognition and melodic supervision.  
We observe that the performance in the singing domain is better in Model A, which lacks the English-Korean adaptation, compared to Model B, which includes it. In the speech domain, however, Model A performs much worse than Model B. A desirable model is expected to perform well not only with singing voices, but also with speech. Considering the significant performance gap in the speech domain, the superiority of Model A should be regarded as a type of overfitting.
## 3 Results and Discussion
### Electrical Conductivity of Alumina Nanofluids  
The electrical conductivity value is a unique property in the field of microelectronics and electromechanical instruments. To confirm the dispersion characteristics, electrical conductivity was also measured for different alumina concentrations by a conductivity meter. An electrical conductivity meter (Model CM-25R) is a contacting-type conductivity sensor usually consisting of electrodes. The titanium-palladium alloy electrode was specifically sized and spaced to provide a known cell constant. The photograph of the conductivity meter (Model CM-25R, DKK-TOA Co., Ltd., Tokyo, Japan) is shown in Figure 4A. Before the measurement, the calibration was performed with a potassium  
Figure 3: (**A**) Thermal conductivity measuring system; (**B**) thermal conductivity of DW and alumina nanofluids.  
Figure 2: UV-vis absorbance of the base liquid (distilled water (DW)) and nanofluids with three different alumina concentrations.  
chloride standard solution. Figure 4B shows the electrical conductivity of each concentration of nanofluids. In case of 0.5 wt%, the increased electrical conductivity was 73.2% compared to the base fluid (DW). As shown in Figure 4, the electrical conductivity also increased with alumina concentration.
## 3 Numerical Modeling
### Effectiveness of the Proposed Numerical Model
[6] with a diameter equal to 52 mm (R = 26 mm) simulated by our model, a tension in 0.8 H (0.8 \(\times\) 52 = 41.6 mm) is obtained along the loading diameter with a compression in 0.1 H on both sides (0.1 \(\times\) 52 = 5.2 mm) (Figure 2), which confirms our simulation procedure. Figure 4 shows the variation of the load in the center of the disk through the loading diameter as a function of step calculation obtained numerically in the case of Brisbane tuff tested by [PERSON] et al. [6] with an arch loading angle 2\(\alpha\) equal to 15\({}^{\circ}\). After a few steps of the elastic phase, the plastic phase is initiated (after the first crack initiation). The failure is brutal and qualified a brittle failure. As indicated by [PERSON] et al. [6], a crack initiation from the center of the disk for the different cases of arch loading angles 15\({}^{\circ}\), 20\({}^{\circ}\), and 30\({}^{\circ}\), respectively (Figure 5), has been observed. The obtained numerical result is in good agreement with the observation made by [PERSON] et al. [6], proving that the suggested numerical method, herein, effectively simulates the indirect test in its different configurations. Figure 4: Evolution of the horizontal stress in the center of the disk tested by [6] versus the number of iterations. Figure 3: Brazilian Test [30]. Figure 5: Cont. The effect of the specimen size on the tensile strength has been studied by [PERSON] et al. [8] for concrete and mortar disks with a diameter of 70 mm (R = 35 mm), a thickness t = (30-50 mm), and a loading arch of 20\({}^{\circ}\). Table 2 gives the predicted indirect tensile strength compared to those obtained by [7; 8]. The results shown in Table 2 are in good agreement compared with those obtained by [7; 8]. The maximum absolute relative difference between the present numerical results and results obtained by [8] is 0.56%. As indicated previously, the results of the analytically indirect tensile strength proposed by [25] is overestimated with an absolute relative difference equal to 4.19% compared with the experimental results of [8].
## 3 Theory Analysis  
### FSWT Algorithm  
FSWT is a new method of time-frequency analysis proposed by [PERSON] et al. [23]. This method has higher time-frequency resolution and can describe the non-stationary process of the signal delicately, which is suitable for the noise reduction research of PD signals.  
The FSWT of a noisy PD signal in the frequency domain can be expressed as  
\[W_{F}(t,\omega,\delta)=\frac{1}{2\pi}\int_{-\infty}^{\infty}\hat{F}(u)\hat{P} ^{*}\left(\frac{u-\omega}{\delta}\right)du \tag{10}\]  
In (10), t is the signal time, \(\omega\) is the frequency value, \(\delta\) is a constant, \(u\) is the estimated frequency, \({}^{*}\) represents a conjugate, \(\hat{P}\) is the FFT transform result of the frequency slice function and \(\hat{F}\) is the FFT transform result of the noisy PD signal. It can be seen from paper [24] that for the PD signal, \(\hat{P}(w)=e^{-0.5}\) has the better time-frequency concentration, so this paper has been selected for its frequency slice function.  
Compared with some popular time-frequency analysis methods, STFT limits its time-frequency resolution due to the fixed-window function. The scale of the wavelet basis function of continuous wavelet transform cannot be adjusted according to the frequency distribution of the signal. The center of the time frequency window is not the frequency of the signal, and there is a frequency measurement error [25]. The time-frequency analysis of noisy PD signals is carried out, and the transformation results of several popular time-frequency analysis methods are shown in Figure 2.  
It can be seen from Figure 2 that the time-frequency resolution of the STFT result is poor, and it is difficult to judge the amount of narrowband interference. The time-frequency resolution of the CWT result is slightly better, but the PD signal and the narrowband interference signal are seriously overlapped. The results of FSWT can see clearly the time and frequency characteristics of each signal. The narrowband interference signal has the characteristics of long duration and high intensity, while the frequency distribution of the PD signal is wide and has a time range. These features can be applied to extract the amount of narrowband interference skillfully.
# Four-Qubit Cluster States Generation through Multi-Coin Quantum Walk
## 2 Generating Cluster States through Multi-Coin Quantum Walk  
Before introducing the generation process, we briefly describe the process of multi-coin quantum walk in an arbitrary graph with \(m(m\geq 2)\) nodes. The whole evolutionary Hilbert space is \(\mathcal{H}=\mathcal{H}^{P}\otimes\mathcal{H}^{C_{1}}\cdots\otimes\mathcal{H} ^{C_{k}}\), where \(\mathcal{H}^{P}\) and \(\mathcal{H}^{C_{j}}\) represent the position space and the \(j\)-th coin space, respectively. At \(j\)-th step of quantum walks, the corresponding unitary operator is described as  
\[U_{j}=S_{j}\cdot(C_{j}\otimes I), \tag{1}\]  
where \(j\) implies the \(j\)-th step of quantum walk while \(S\) and \(C\) are conditional shift operator and coin operator, respectively. Here, \(C_{j}\) acts on the \(j\)-th coin space \(\mathcal{H}^{C_{j}}\) and \(S_{j}\) acts on the combination space of position \(\mathcal{H}^{P}\) and \(j\)-th coin space \(\mathcal{H}^{C_{j}}\). Assume that the initial state is \(|\psi(0)\rangle\), then the system state after \(k\) steps becomes  
\[|\psi(k)\rangle=(U_{k}U_{k-1}\cdots U_{1})|\psi(0)\rangle=(\prod_{j=1}^{k}U_{ j})|\psi(0)\rangle. \tag{2}\]  
where \(U_{j}=(S_{j}\otimes I^{\mathcal{H}^{C_{1}}}\cdots\otimes I^{\mathcal{H}^{C_{ j-1}}}\otimes I^{\mathcal{H}^{C_{j+1}}}\cdots I^{\mathcal{H}^{C_{k}}})\cdot(I^{ \mathcal{H}^{P}}\otimes I^{\mathcal{H}^{C_{1}}}\cdots\otimes I^{\mathcal{H}^ {C_{j-1}}}\otimes C_{j}\otimes I^{\mathcal{H}^{C_{j+1}}}\cdots I^{\mathcal{H} ^{C_{k}}})\)[14]. Conditional shift operator \(S^{n-com}\) in an n-complete graph [16] with \(m\) nodes can be described as  
\[S^{n-com}=\sum_{x,i=0}^{n-1}|(x+i)\,mod\,n\rangle\langle x|\otimes|i\rangle \langle i|, \tag{3}\]  
and when \(m=2\), corresponding \(S^{2-com}\) can be simplified to  
\[S^{2-com}=(|0\rangle\langle 0|\otimes|1\rangle\langle 1|)\otimes|0\rangle \langle 0|+(|1\rangle\langle 0|\otimes|0\rangle\langle 1|)\otimes|1\rangle \langle 1|. \tag{4}\]
# Self-Learning Power Control in Wireless Sensor Networks
## 5 Simulations
### Results
#### 5.2.2 Multi-Agent WSN
Increasing the  
Figure 10: Q-value of TX1 in the multi-agent system, while residing in the Q-state equal to 4 (**a**). The probability of residing in all Q-states is shown in (**b**). distance \(d\) to 4 m, QL-TPC achieves transmission powers shifted towards higher levels, preventing low SINR. In Figure 12a, when \(\mu=25\) ms, PRR is around 96.5%, higher than the requirement of 95%, between power levels \(-25.9\) and \(-23.9\) dBm. Such PRR value is similar to the value of HG at the same power level and lower than the maximum 97.17% at 7 dBm. In Figure 12b, the average latency among the transmitters is 13.13 ms compared to the minimum latency in HG that is equal to 11.83 ms at 7 dBm. The energy per bit is 2.03 uJoule/bit against the minimum 1.93 at \(-33\) dBm and the maximum 4.28 at 10 dBm. In general, the results show that using QL-TPC, the PRR is always within the constraint range 95-100%. Comparing our protocol with the optimal PRR (maximum) and latency (minimum) in HG, at the same inter-arrival time, the PRR values are 0.1-0.7% lower and the latency is higher, up to the 14%. However, the optimal network performance is obtained at higher transmission power levels than the ones selected by QL-TPC, the energy per bit is reduced in QL-TPC by 19.22-52.57% of the maximum value in HG at high power values. At the same time, the energy per bit in QL-TPC is higher than the minimum value in the homogeneous case by 0.35-5.18%, but achieved at lower transmission power levels, where PRR and latency deteriorate. Our protocol achieves a trade-off between network performance and energy consumption. The transmission power selected by QL-TPC is calculated as per Equation (13), an average that considers each contribution per Q-state and simulation re-run. The standard deviation of the transmission power is shown in Figure 13 for cases of \(d\) equal to 2 m and 4 m. It is averaged among the values of the 4 transmitters and its standard deviation is delimited by interval bars. The highest values are in the intervals \(2.4\pm 0.1\) (\(d=2\) m) and \(2.1\pm 0.4\) (\(d=4\) m), in the harshest conditions, when the interference is maximum. Otherwise, in the other conditions, the standard deviation is less than 1.4 dBm. Figure 11: Performance in WSN with eight nodes during the testing period (2 m node distance): average PRR (**a**); average latency (**b**); and energy consumption per bit (**c**).
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 2 Scheme of Verification System
### Verification System Demands Analysis  
SIL testing, HIL testing, and real vehicle tests are common methods for the development and verification of AEB control algorithms. The characteristics of these methods are compared, as shown in Table 1.  
The passenger car AEB control algorithm verification system shall be able to quickly complete the algorithm verification with reliable results. Considering the diversity of the verified control algorithms, the verification process should be as close to the real operating environment as possible, and the verification system should have both SIL testing and HIL testing functions.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **SIL** & **HIL** & **Real Vehicle** \\ \hline Test speed & Fast & Medium & Slow \\ Cost & Low & Medium & High \\ Confidence & Low & Medium & High \\ Real-time & Optional & Real-time & Real-time \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of verification methods.  
Figure 1: Schematic diagram of passenger car AEB system.  
The controlled parameters of different AEB algorithms are different, and the perception and matching information required for decision-making are also different. A series of studies on the existing AEB control algorithms were summarized, and the parameters required by the verification system were summarized, as shown in Table 2. Note that the actual parameters in Table 2 were used for HIL testing. Through the interaction of actual parameters, the relationship between the simulation model and physical hardware was established, and the whole process development and verification of the passenger car AEB control algorithm were realized.
# Coexistence in Wireless Networks: Challenges and Opportunities
## 4 Spectrum Sharing and Coexistence
### Coexistence in Satellite Communication
With ongoing research and development, these strategies can be refined to ensure that multiple satellite communication systems can operate efficiently and reliably within the same orbital space.
###### Abstract  
Aim: To incorporate different concentrations of zirconia/silver phosphate (ZrO2/Ag3 PO4) nanoparticles (NPs) in self-etch (SE) adhesive. Surface characterization, elemental analysis, survival rate assessment of _S. mutans_, micro-tensile bond strength (uTBS), and the degree of conversion (DC) of composite bonded to caries-affected dentin (CAD) were determined. Material and Methods: This study employed a comprehensive methodological approach, incorporating a variety of analytical techniques, including scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared (FTIR) spectroscopy, and uTBS testing. Eighty human third molars that had caries advancement up to the middle third of the dentin were included. Samples were distributed into four groups based on concentration of ZrO2/Ag3 PO4 NPs in the primers of the two-step SE adhesive: Group 1 (Control): unmodified SE adhesive; Group 2: 0.15 wt% ZrO2/Ag3 PO4 + SE adhesive; Group 3: 0.25 wt% ZrO2/Ag3 PO4 + SE adhesive; Group 4: 0.5 wt% ZrO2/Ag3 PO4 + SE adhesive. SEM was employed to investigate the morphological characteristics of ZrO2/Ag3 PO4 NPs. For elemental distribution EDX spectroscopy and to assess the of cured and uncured adhesive with changed concentrations of NPs, FTIR spectroscopy were performed. Antibacterial efficacy was calculated in adhesives with different concentrations of ZrO2/Ag3 PO4 using the pour plate method. For uTBS assessment, a compressive force was applied at the material-dentin interface at a crosshed speed of 0.5 mm/min. The debonding process of each specimen was measured in MegaPascals (MPa). One-way analysis of variance (ANOVA) and [PERSON]'s post hoc test were used to compare the means and standard deviation (SD) between groups. Results: The samples from Group 4, which were applied with 0.5 wt% ZrO2/Ag3 PO4 + SE, displayed the lowest survival rate (0.12 \(\pm\) 0.01 CFU/mL) of _S. mutans_. The strongest bond of composite to the CAD surface was observed in Group 4 (0.5 wt% ZrO2/Ag3 PO4 + SE) (20.12 \(\pm\) 0.79 MPa). The highest DC was observed in the control group (unmodified SE (69.85 \(\pm\) 8.37)). Conclusion: The self-etch adhesive modified with ZrO2/Ag3 PO4 nanoparticles showed a favorable effect on micro-tensile bond strength (uTBS) and demonstrated enhanced antibacterial efficacy against _S. mutans_.  
nanoparticles; adhesive; scanning electron microscope; FTIR; antimicrobial; micro-tensile bond strength; dentin; dentistry
## 4 Proposed DETD Algorithm and Simulation Results
### Theft Detection Using Other Factors Such as Weather Data  
The authors of the paper [18] state that electricity supply and demand are becoming increasingly weather-dependent. For a simple demonstration, we assume a hypothetical situation during 3 months of summertime, where the total energy consumption in the grid is high during periods from 12 noon to 4 p.m. (assuming increased AC usage). During this window of high energy demand, the malicious user could report corrupted meter readings with high probability. Thus, as a theft detector trained on weather-dependent theft cases, DETD could be the next logical choice for the utility company. As shown in Table 4, a  
Figure 5: (**Left**) DR performances and (**Right**) FPR performances at different training set ratios for an average of 50 random users.  
quick simulation of this simple experiment results in 97.3% DR with 8.1% FPR (training and testing on 3-month summer data). On a side note, this theft window resembles the f-ToU case discussed earlier in Section 2.3. Similarly, additional weather conditions can be combined to determine better theft windows and detectors could be trained accordingly using DETD.
# Two-Dimensional Metal-Organic Frameworks and Their Derivative Electrocatalysts for Water Splitting
## 2 Fabrication Strategy of 2D MOF and Their Derivatives
### Fabrication Strategy of 2D MOF
#### 2.1.2 Bottom-Up Fabrication
Certain restrictive conditions can also impact its applicability, like the limited synthesis space in interfacial synthesis. Nonetheless, among the various synthesis methods, the bottom-up approach has been gaining increasing interest owing to its ability to control the morphology and thickness and its higher yield efficiency.