esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 3 Results
### Wave Separation for the Hard-to-Hard Single-Interface Model  
For the hard-to-hard single-interface model, the full wave and extracted reflected wave for the first receiver of the cross-dipole tool in the COG domain are shown in Figure 7. Figure 7,b show the full wave and reflected SH wave, respectively. Both are theoretically modeled. The amplitude of the reflected wave is much smaller than that of the flexural wave. In Figure 7, the reflected wave extracted by MC-ConvTasNet is relatively complete. However, when the arrival time of the direct wave and the reflected wave on trace 12-trace 16 are close, the extracted reflected waves have smaller amplitudes than the theoretical reflected waves. This is because the arrival time of the reflected waves is closer than that of direct waves. MC-ConvTasNet can handle situations in which the arrival times of the reflected wave and the direct wave are close to each other; however, it is still more suitable for situations where the reflector is far away from the borehole. The reflected waves extracted by parameter estimation, the median filter and the F-K filter are shown in Figure 7, Figure 7 and Figure 7, respectively. The parameter estimation only reduces the amplitude of the direct wave. The median filter effectively preserves the arrival time information of the reflected wave, but the amplitude of the reflected wave is jagged, which will affect the image of the reflector in later processing. The F-K filter extracts the reflected wave well, while there are obvious interference waveforms on trace 1-trace 4 and trace 13-trace 16. As the direct and reflected waves approach each other, the amplitude of the reflected wave extracted by MC-ConvTasNet decreases, leading to a larger R\({}_{\text{std}}\) (0.044) than that obtained via the F-K filter (0.006). Nevertheless, regarding the extraction of reflected waves, phase consistency is more crucial than amplitude scaling. MC-ConvTasNet preserves the reflected wave's amplitude less effectively than the F-K filter. In the hard-to-hard single-interface model, the R\({}_{\text{std}}\) of MC-ConvTasNet is 34.7 dB, which is the largest. This indicates that the extracted reflected wave aligns more closely with the theoretical reflected wave in terms of phase.  
Figure 7: Cont.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**R\({}_{\text{rmse}}\) (dB)**} \\ \cline{2-5}  & \begin{tabular}{c} **MC-** \\ **ConvTasNet** \\ \end{tabular} & \begin{tabular}{c} **Median Filter** \\ **(1)** \\ \end{tabular} & \
\begin{tabular}{c} **Parameter** \\ **Estimation** \\ \end{tabular} &
\begin{tabular}{c} **F-K Filter** \\ \end{tabular} \\ \hline
1 & 0.044 & 0.077 & 2.080 & 0.006 \\
2 & 0.010 & 0.030 & 1.893 & 0.003 \\
3 & 0.028 & 0.133 & 2.146 & 0.133 \\ \hline \end{tabular}
\end{table}
Table 5: Average R\({}_{\text{rmse}}\) of four methods over three models.
## 2 Overcoming Sagging and Altitude Loss Problems: Effective Solutions
### Effective Thrust Control Configuration for BWB Aircraft  
The research subject selected for this study is a self-constructed small-scale BWB demonstrator, a schematic model of which is illustrated in Figure 2. This model integrates a BWB design with distributed propulsion, featuring six small BLI fans, each with a diameter of 90 mm, strategically positioned along the wingspan at the upper trailing edge of the fuselage.  
In the following design of thrust control configuration, the fundamental approach to addressing the tightly coupled relationship between thrust and lift involves controlling the thrust by altering the direction of the airflow while maintaining a constant airflow intake, without reducing the aircraft's lift. Furthermore, ensuring minimal changes in the overall pitch moment of the aircraft during continuous thrust control is given priority consideration. Four potential thrust control configurations were designed to solve the sagging and altitude loss problems of BWB aircraft, which are described in detail in Sections 2.2.1-2.2.4.
## 6 Conclusions  
In this paper, we presented a comprehensive analysis of MIMO-VLC systems using NRZ, RZ, and QPSK modulation techniques, evaluating their performance in both ambient light noise-free and noise-affected scenarios. The results demonstrated that MIMO-based VLC systems offer significant improvements in transmission range, BER, and received power compared to SISO systems, particularly when utilizing NRZ and QPSK modulation techniques. In the absence of ambient light interference, MIMO achieved longer transmission distances, with QPSK reaching up to 1450 m. However, ambient light noise caused performance degradation, with MIMO systems experiencing a 13.6% reduction in distance, while SISO systems saw a 6.2% reduction. Despite this, the advantages of MIMO diversity outweigh the performance losses, with MIMO consistently outperforming SISO across various metrics.  
Future work could explore advanced techniques to mitigate ambient light interference further, such as adaptive filtering and advanced modulation schemes, to enhance the reliability and efficiency of MIMO-VLC systems under real-world conditions. Additionally, extending the study to include higher-order MIMO configurations and hybrid modulation techniques could provide deeper insights into optimizing VLC for high-performance applications in both indoor and outdoor environments. Plans are also underway to extend the system's evaluation under more complex and dynamic scenarios, including experimental validations and further optimization of noise-cancellation techniques. Moreover, future simulations will incorporate the spatial dependency of ambient noise sources, potentially through radiometric modeling approaches, to more accurately reflect real-world lighting geometries and their effect on system performance.  
Figure 19: Comparison of data transmission rates for NRZ, RZ, and QPSK modulation schemes under SISO and MIMO configurations.  
**Author Contributions:** Conceptualization, E.S.H.; Methodology, E.S.H.; Software, E.S.H.; Validation, A.J. and A.A.A.; Formal analysis, E.S.H.; Data curation, E.S.H.; Writing--original draft, E.S.H.; Writing--review & editing, A.J. and A.A.A.; Visualization, A.J. and A.A.A.; Supervision, E.S.H.; Funding acquisition, E.S.H., A.J. and A.A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors gratefully acknowledge the funding of the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia, through Project Number JU-2050252-DGSSR-RP-2025.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 The Proposed Method  
Under the motivation of the above analysis, a method combined the ACFHNR index based on the SVD and Teager energy operator (TEO) demodulation technology is proposed for the early fault diagnosis of rolling bearings. The framework of the proposed method is shown in Figure 3, and its implementation can be described as follows:  
Firstly, select the appropriate delay step to construct Hankel matrix. When the computational cost and calculation accuracy are taken into account, there is a trade-off between computational efficiency and decomposition performance when deciding an appropriate choice of \(m\)[15]. In this paper, the m is set as \(15\leq m\leq 30\), and the singular value decomposition is performed for the constructed Hankel matrix.  
Secondly, for the original vibration signal, the theoretical fault characteristic frequency can be calculated according to the geometry size and defect frequencies multiple of running speeds. Therefore, the prior period of impulses \(T\) can be computed through Equation \(T\ =\ f\mathrm{s}/f\), where \(f\) is the fault characteristic frequency of the rolling element bearings, while fs is the sampling frequency. With the prior period of impulses \(T\), the ACFHNR index for each singular component can be calculated.  
Thirdly, the singular component with the largest ACFHNR index is employed to construct the new Hankel matrix. The average of the inverse diagonal elements of the reconstructed Hankel matrix is calculated, which is the denoising signal of the raw vibration signal.  
Finally, we implemented the Teager energy operator envelope analysis to the reconstructed signal for early fault diagnosis. The fault type can be decided on the basis of the extracted characteristic frequency information.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 4 Cryptanalysis of [PERSON] et al.'s Protocol
### Replay Attack  
[PERSON] et al. claimed that their protocol can withstand replay attacks because a \(MU_{a}\) cannot calculate the correct \(SK=h(g^{a}\) mod \(p)\)\(\oplus R_{MU}\oplus R_{FA}\) without the random number \(R_{FA}\) and \(R_{MU}\). However, according to Section 4.1, \(MU_{a}\) computes \(R_{MU}=U\oplus R\) and obtains \(R_{FA}\) in an open channel. Furthermore, \(MU_{a}\) can extract the secret parameter \(\{C_{MU},R\}\) stored in the mobile device. \(MU_{a}\) computes \(SK=C_{MU}\oplus h(R)\oplus R_{MU}\oplus R_{FA}\). In addition, according to Section 2.1, \(MU_{a}\) can obtain the counter value \(K\) in the mobile device. Thus, [PERSON] et al.'s protocol [16] is insecure against replay attacks.
## 2 Materials and Methods
### Feature Extraction
#### 2.5.2 B-Scan Features  
To achieve a damage classification independent of the underlying floor construction, we calculate the following scalar statistical values for each \(1\times 100\) A-scan feature vector \(\vec{F}_{1}\) to \(\vec{F}_{5}\), each including the respective feature elements \(F_{1}\) to \(F_{5}\) for all 100 A-Scans within one B-scan.  
* Feature \(F_{A}\): Standard deviation of \(\vec{F}_{1}\)
* Feature \(F_{B}\): Standard deviation of \(\vec{F}_{2}\)
* Feature \(F_{C}\): Span of \(\vec{F}_{3}\)
* Feature \(F_{D}\): Standard deviation of \(\vec{F}_{4}\)
* Feature \(F_{E}\): Span of \(\vec{F}_{5}\)  
These measures for statistical distributions along a recorded survey line are motivated by the assumption that water damage often shows inhomogeneous deviations inside the respective B-scan. Such deviations can also be suitable to evaluate the spatial continuity of present damage. Both findings were generally recognized during our studies and will be discussed in the results Section 3. For the lower resolved frequency features, the span is expected to achieve better variance compared to the standard deviation, which works well on amplitude features with a higher resolution and range of values.  
Figure 8 summarizes the discussed processing steps including the extraction of A-scan feature vectors out of B-scans and the following reduction to scalar B-scan features. The val  
Figure 7: Exemplary A-scan with three prominent amplitudes influenced by present material interfaces.  
ues shown for \(F_{A}\) to \(F_{E}\) are derived from the depicted A-scan feature plots (normalized by their means). They do not represent the actual values that were used for classification, since those were standardized with the _StandardScaler_ function from the Scikit-Learn library. It does a mean removal for all features and scales them to unit variance, which is usually required by the classifiers discussed in Section 2.6. Regarding the magnitudes of amplitude, time, and frequency values, which are widely apart from each other, this step is necessary to avoid a baseless and unwanted dominance of certain features during the training process.
## 3 Results
### Soil Storage and Retention Capacity of perennial Sweet Pea  
The soil's water storage capacity and retention ability are primarily demonstrated through its physical properties, encompassing soil bulk density, particle density, total porosity, capillary porosity, and non-capillary porosity. This experiment was conducted in Dongying, Shijiazhuang City. As evidenced by Table 5, the soil bulk density of the perennial sweet pea plot measured 1.38 g/cm\({}^{3}\), noticeably lower than that of the bare ground (CK). The total porosity of the soil in the perennial sweet pea plot was recorded at 46.88%, indicating an increase of 2.55% compared to the bare ground (CK) and 3.64% compared to the natural weed-infested plot. Furthermore, the non-capillary porosity of the soil in the perennial sweet pea plot was calculated at 15.64%, demonstrating a 4.92% rise over the bare ground (CK) and a 5.78% increment over the natural weed-infested plot.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Rainfall** \\ **Intensity** \\ \end{tabular} } & \multicolumn{3}{c}{**Runoff (m\({}^{3}\))**} & \multicolumn{3}{c}{**Sediment Content (kg/m\({}^{3}\))**} \\ \cline{2-7}  & **Bare Ground** & **Natural** & **Perennial** & **Bare Ground** & **Natural** & **Perennial** \\  & **(CK)** & **Vegetation Plot** & **Sweet Pea Plot** & **(CK)** & **Vegetation Plot** & **Sweet Pea Plot** \\ \hline Severe & & & & & & \\ Rainstorm & & & & & & \\ Heavy Rainfall & 0.09192 & 0.06192 & 0.06192 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Runoff reduction capability of perennial sweet pea (four-year growth).  
Figure 4: Water Absorption Process of perennial Sweet Pea Litter.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Soil Layer**} & \multirow{2}{*}{**Soil Gravity**} & **Dry Soil Bulk** & **Total Porosity** & **Capillary Porosity** & **Non-Capillary Porosity** \\  & & **Density (g/cm\({}^{3}\))** & & & & **Porosity** \\ \hline \hline \begin{tabular}{c} perennial Sweet \\ \end{tabular} & 2.6041 & 1.38 & 46.88\% & 31.23\% & 15.64\% \\ \
\begin{tabular}{c} Bare Ground (CK) \\ \end{tabular} & 2.6728 & 1.49 & 44.33\% & 33.60\% & 10.73\% \\
\begin{tabular}{c} Natural Vegetation \\ \end{tabular} & 2.6035 & 1.48 & 43.24\% & 33.37\% & 9.87\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of perennial sweet pea on soil physical properties.  
In summary, the soil physical properties within the perennial sweet pea trial area conspicuously surpass those of both the bare ground and the naturally weed-infested area. This observation underscores the perennial sweet pea's capability to engender a favorable soil granular structure, enhance soil physical attributes, elevate soil water stability indices, and amplify soil infiltration and water retention capabilities.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Carotenoids
#### 4.2.1 \(\beta\)-Carotene  
\(\beta\)-carotene (Figure 3), known for imparting vibrant color to fruits and vegetables, primarily contributes to anti-aging and disease prevention. It also exhibits prebiotic-like properties, providing protection to the intestinal mucosa [55].  
Moreover, \(\beta\)-carotene possesses high antiradical activity and the ability to neutralize singlet oxygen. Due to these properties, it can slow down skin aging processes and prevent sun damage. Additionally, it stimulates melanogenesis while simultaneously reducing the risk of sun-induced irritation, further enhancing its anti-aging effects. In vitro studies have demonstrated that \(\beta\)-carotene protects liquid crystal lipid structures from UV radiation, lowers lipid oxidation levels, and inhibits UV-induced proline oxidation in collagen [55; 57].  
Ex vivo studies have revealed that \(\beta\)-carotene in the human epidermis is converted into retinal, which is subsequently reduced to produce retinol. Retinol is then esterified  
Figure 3: Chemical structures of antioxidant carotenoids: (**5**) beta-carotene, (**6**) lutein, (**7**) lycopene, (**8**) astaxanthin, and (**9**) ubiquinone Q10.  
by fatty acids, resulting in the formation of retinyl esters, which are the final products of \(\beta\)-carotene bioconversion into retinoids by human skin [58]. As a natural colorant, \(\beta\)-carotene has wide applications in color cosmetics [57].  
Currently, \(\beta\)-carotene used in cosmetics is primarily obtained through synthetic chemical processes or biotechnological methods. Synthetic production involves the chemical synthesis of \(\beta\)-carotene from simple organic compounds such as acetylene and farnesol. Natural sources are mainly utilized in organic cosmetics but face limitations related to availability and stability. Microbial fermentation, a biotechnological approach, is gaining popularity as an eco-friendly alternative to industrial synthesis. \(\beta\)-carotene can be produced by microorganisms such as _Blakeslea trispora_ (a filamentous fungus), _Dunaliella salina_ (a microalga), and genetically modified _Saccharomyces cerevisiae_ (yeast) [59].
## 3 Optimal Tracking and Disturbance-Rejection  
Besides using the reference-weighting parameter \(b\) (note that \(c=b\)), the closed-loop tracking response can be improved by applying a reference filter. The main advantage of using a reference filter is that such a filter can substantially improve the tracking response without deterioration of the disturbance-rejection response (the optimal one, as calculated with \(b=0\)). In this way, the best overall response can be achieved. A similar approach was proposed in Reference [125] for non-integrating processes. The filter (\(G_{F}\)) in the closed-loop configuration with the PID controller (\(G_{C}\)) and the process (\(G_{P}\)) is shown in Figure 6. For the proposed tuning method with a reference filter, the controller parameters are calculated for the weighting factor \(b=0\) to retain the good disturbance rejection properties, while the controller structure is 1-DOF PID (\(b=1\)).  
Here, two controller structures with reference filters are proposed according to the following filter order: The PID controller with the second-order filter (PID\({}_{\text{f2}}\)) and the higher-order filter (PID\({}_{\text{fh}}\)).
## 3 The Consecutive Iteration Based on Signal Cancellation (SCCI) Algorithm
### Process Optimization  
The core objective of optimizing the SCCI algorithm workflow was to reduce the impact of jamming power on the model estimate \(Y_{e}(f)\). This reduction minimizes the difference between the final estimated value of \(Y_{e}(f)\) and the true signal power spectrum \(P(f)\). This optimization accurately sets the false alarm probability for the next step. Therefore, the steps after obtaining the estimated value \(Y_{e}(f)\) in the improved algorithm are essentially consistent with the SCCME algorithm. The main difference lies in how the estimated value \(Y_{e}(f)\) is obtained. The following explanation will focus on this aspect.  
The SCCI algorithm obtains the final estimate of \(Y_{e}(f)\) through multiple iterations. For example, one iteration process consists of the following steps: (1) obtain the initial estimate of the signal power spectrum by applying the approximate model in Equation (1) to the input signal power spectrum; (2) identify the frequency points where the power values exceed the estimated power spectrum and set these values to the corresponding frequency point's estimated power value, denoted as \(Y_{e}(k)=aG_{s}(k)+b\), while keeping the power values at other frequency points unchanged, which forms a new signal power spectrum; and (3) apply the new signal power spectrum to the approximate model to obtain a new estimate of the signal power spectrum. Repeat the second and third steps iteratively until the iteration meets the threshold condition threshold (T). Due to the continuous elimination of jamming signal power, the estimated signal power spectrum approaches its true value. When the iteration stops, the final estimate of \(Y_{e}(f)\) is output. The iterative process is illustrated in Figure 2.  
The above process demonstrates that each iteration continuously reduces the jamming signal by eliminating the portion of jamming signal power greater than the power spectrum estimate obtained in the previous iteration. Through multiple iterations, the jamming signal component exceeding the true signal power spectrum is nearly eliminated, making the estimated signal power spectrum converge closer to the true values. Additionally, the  
Figure 2: Iterative process flow chart.  
iteration process in each round employs the gradient descent method, with the minimum variance between the input signal power spectrum and the estimated values from the approximation model as the criteria. This process iteratively determines the values of parameters a and b in each round.
## 1 Introduction
limonene, limonene/\(\beta\)-pinene, and limonene/\(\beta\)-pinenee, and limonene/\(\beta\)-pinenee/\(\gamma\)-terpinene. In this study, the peel oil of C. _australasia_ presented a unique profile among the citrus fruit, with sabinene (19.6%) as the second most abundant compound after limonene (51.1%) [7]. [PERSON] and [PERSON] (2009) and [PERSON] et al. (2015) investigated the VOCs profile of peel samples from three Australian-grown finger lime varieties by GC-MS and GC-FID analysis, identifying three chemotypes never observed in any other _Citrus_ species, including limonene/sabinene, limonene/citronellal/isomenthone, and limonene/citronellal/citronellol [8; 9]. The comparative analysis performed by [PERSON] and [PERSON] (2009) and [PERSON] et al. (2015) also identified some volatile constituents which were detected for the first time in a _Citrus_ extract and some VOCs which tended to be specific to one finger lime CV [8; 9]. Very recently, [PERSON] et al. (2022) determined the total phenolic compounds, the antioxidant capacity the total anthocyanin content, the ascorbic acid, and the amino acids citrulline and arginine content of five Australian-grown finger lime CVs. The VOCs profile was performed by GC-MS, with a total of 113 volatile compounds identified in the peel extracts. The predominant volatile compounds identified in the finger lime CVs included limonene, \(\gamma\)-terpinene, \(\beta\)-citronellol, and citronellal. As a result of its high ascorbic acid content, moderate phenolic and flavonoid content, and a unique VOCs profile responsible for its characteristic organoleptic properties, the Australian finger lime was thought to have the potential for commercial development as a functional food [1]. Generally, the compounds present at lower concentrations are more important in determining the organoleptic profile compared to those in high amounts. Moreover, the contents of these VOCs, called odor-active volatiles, are sometimes so low as to be hard to detect [10]. In the last decades, the introduction of the headspace solid phase micro-extraction (HS-SPME), coupled with gas chromatography-mass spectrometry (GC-MS), has allowed for a more accurate investigation of the aromatic profile in food. This solvent-free extraction technique is quite simple and does not require any kind of sample pre-treatment, thus avoiding any loss of VOCs reflecting the true native volatile pattern of the citrus fruit without any modification [10]. The aim of the present study was to characterize and compare, both from a qualitative and semi-quantitative point of view, the VOCs profile of peel and, for the first time, of the pulp of three finger lime CVs, namely Pink Pearl, Sanguinea, and Faustrime, grown in the Sicily (southern Italy), by HS-SPME/GC-MS methodology, as these metabolites play a crucial role in the organoleptic sensation of the fruit and consumers' acceptability.