esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulations and Measurements
### Signal Processing Details  
The corpus and noises are downsampled to 16 kHz. The length of the analysis and synthesis frame is 30 ms with 50% overlap. A square-root Hann window is used in the signal framing in both the analysis and synthesis to avoid audible artifacts because of the cyclic convolution. To synthesis the signal, the overlap-add (OLA) method is used. The frequency resolution used in separation of an extended frame in Equation (3) to the one-third octave bands is limited by the length of \(P\) frames according to Table 1. According to length of \(P\) frames and the sampling rate of 16 kHz, it could be at least 4096 for the shortest room impulse response (\(T_{60}\) = 0.6 s) and 16,384 for the longest room impulse response (\(T_{60}\) = 5 s). The bins are grouped into 17 one-third octave band. Similar to the STOI [19], the lowest center frequency is set to 150 Hz and the highest one-third octave band has a center-frequency approximately equal to 6 kHz. A frequency resolution used in Equation (8) for analysis of weighted sub-frames in Equation (5) is determined by the window length and sampling rate equal to 512 bins. The RMS values of the processed signal are adjusted to that of the unprocessed signal to keep levels equal between the output of the NELE algorithms and the unprocessed signal.
## 2 Experimental Procedure  
### Materials  
Table 1 shows the chemical composition rate of the binders used. OPC showed the highest CaO content at 60.20%, whereas FA showed the highest SiO\({}_{2}\) content at 57.90%. This result was derived from X-ray fluorescence, using Axios PW 4400 equipment (Malvern Panalytical, Seongnam-si, Korea). In addition, the second highest constituents of OPC and FA were SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\), respectively, indicating that the chemical composition of the two materials was significantly different. Table 2 summarizes the physical characteristics of OPC, FA, aggregate, and chemical admixture used in this study. The OPC type 1 was used according to KS L 5201 [27], and in the case of FA, type 2 was used according to KS L 5405 [28]. As fine aggregates, crushed sand and washed sea sand were blended at a weight rate of 60:40, respectively. In addition, crushed granite aggregate was applied for coarse aggregate, and polycarboxylate superplasticizer (widely used in the field) was applied for the liquid admixture. The density and fineness of OPC and FA were calculated according to ASTM C188 [29] and ASTM C204 [30], respectively. The density and absorption of aggregates were calculated based on ASTM C127 [31], whereas the fineness modulus (FM) was calculated based on ASTM C136 [32].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Binder**} & \multicolumn{8}{c}{**Chemical Composition (\%)**} \\ \cline{2-10}  & **SiO\({}_{2}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** & **SO\({}_{3}\)** & **Others** \\ \hline OPC (\({}^{1}\)) & 21.60 & 3.30 & 5.15 & 60.20 & 2.30 & 0.53 & 0.99 & 1.50 & 4.43 \\ \hline FA (\({}^{2}\)) & 57.90 & 6.80 & 20.50 & 4.00 & 1.38 & 0.89 & 1.18 & - & 7.35 \\ \hline \hline \end{tabular} \({}^{()}\) OPC: Ordinary Portland cement; (\({}^{2}\)) FA: Fly ash.  
\end{table}
Table 1: Chemical compositions of the binders used.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Type**} & **Density** & **Fineness** & **Fineness** & **Absorption** \\  & & **(s/cm\({}^{3}\))** & **(cm\({}^{2}\)/g)** & **Modulus** & **(\%)** \\ \hline OPC & \begin{tabular}{c} Type 1 \\ (KS L 5201) \\ \end{tabular} & 3.15 & 3322 & - & - \\ \hline FA & \begin{tabular}{c} Type 2 \\ (KS L 5405) \\ \end{tabular} & 2.14 & 3964 & - & - \\ \hline \multirow{2}{*}{Fine aggregate} & S1 & \begin{tabular}{c} Washed sea \\ sand \\ \end{tabular} & 2.60 & - & 2.01 & 0.79 \\ \cline{2-6}  & S2 & \begin{tabular}{c} Crushed \\ sand \\ \end{tabular} & 2.57 & - & 3.29 & 0.87 \\ \hline Coarse aggregate & \
\begin{tabular}{c} Crushed \\ granite \\ aggregate \\ \end{tabular} & 2.60 & - & - & 0.76 \\ \hline Chemical admixture &
\begin{tabular}{c} Polycarboxylic \\ acid group \\ \end{tabular} & 1.05 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of the materials used.
## 4 Development of Functional Food Products  
Functional foods constitute conventional foods, and include compounds that confer health benefits on the body, as well as foods enriched with such ingredients. The growing consumers' consideration prompted the development of functional foods eliciting the ability to treat or prevent several diseases. Likewise, the shift towards a healthier nutrition regime emerged as an issue of paramount importance worldwide, placing food industry in the forefront of functional food product development. Therefore, the development of food products with functional properties has been extensively reviewed in the literature, elaborating the issues that concern the opportunities but also the challenges of manufacturing foods with possible health claims, along with the use of renewable resources and emerging technologies to produce them [75; 76; 77]. As described in the previous sections, despite the particular attention that has been assigned to the functional properties of raisins, their utilization in novel food formulations was overlooked the last decades (Table 4). Thus, recently raisins have been employed in the formulation of several products within the context of conveying health benefits deriving from their bioactive compounds. Notably, the inclusion of raisins in newly formulated foods will potentially entail modifications in the structural and textural properties of the final product, thereby the latter constitute a significant parameter to be evaluated together with sensory characteristics. Bread and bakery products comprise a wide range of products with universal consumption worldwide alongside nutritional value and sensory properties. Utilization of raisins in bread, cereal products and snacks has been long applied, as they can substitute for synthetic preservatives, due to the propionic acid and phenolic compounds they contain, thereby increasing the shelf life of bakery products [78]. Likewise, bread has been implemented in several investigations in the context of new formulations, to assess the implementation of raisin products. For instance, [PERSON] et al. [78] used raisin juice concentrate and raisin paste in bread formulations to evaluate the antimicrobial effect, indicating the strong mold retarding properties of raisins, entailing a subsequent extension in the shelf life of the final product. Raisin juice was also applied as sucrose substitute in combination with gluten-free flour to evaluate changes in baking properties sensory characteristics [79]. Enhanced loaf volume and color in the final product were observed with raisin juice in both concentrated and dried form, whereas sensory analysis suggested the acceptance of
## 2 Methods
### Data Processing Method
#### 2.3.1 SAR Data Processing Method
Temporal coherence (TC) can be used to select the CTs with high quality, and by using WLS estimation of the small baseline network, high-quality and high-precision timeseries deformation results of CTs in the study area can be obtained. TC can be expressed by the following:  
\[\gamma_{\text{tmp}}=\frac{1}{M}\left|\sum_{i=1}^{M}exp[j(\Delta\varphi_{i}-A \hat{\varphi_{i}})]\right| \tag{4}\]  
In Equation (4), \(j\) is the imaginary unit. In addition, due to the heavy regional atmosphere present in the images of Nanyang city, Henan Province, atmospheric correction was required before using the SBAS-InSAR method.
# Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm
## 3 Related Work
### Techniques for Numerical Association Rule Mining
#### 3.1.3 Range Optimization  
Many heuristic algorithms have been applied to solve association rule problems by using optimization [39; 40]. However, new trends in evolutionary computing are of interest. Thus, the methods defined by [41; 42] showed good results for certain types of optimization.  
Evolutionary computation has been successfully used to optimize both qualitative and quantitative association rules. The various existing algorithms in this segment have been adapted and adjusted to the problem in order to scale in terms of quality and variety. The authors of [43] presented a technique for finding frequent patterns in a database with numerical attributes without having to resort to discretization. An evolutionary algorithm was used to find the intervals of each attribute that made up a \"frequent pattern\", and the evaluation function calculated the widths of these intervals. Finally, this work was evaluated with real and synthetic data, which showed satisfactory results in frequent item sets that did not have overlaps.  
QUANTMINER [17] includes the optimization of the intervals in the numerical variables with a genetic algorithm. The definition of a rule template is done with the intervention of the user, which could be a disadvantage. However, it avoids the processing of unnecessary rules. The method dynamically searches for the best ranges for the numeric attributes present in the template. The quality of the rules is evaluated with a function developed based on the measures of support, confidence, and amplitude.  
The use of evolutionary algorithms for this problem spread in the scientific community, so the construction of evaluation functions with multiple objectives was implemented in order to scale in quality and performance. Reference [44] adapted the PSO (Particle Swarm Optimization) algorithm with a multi-objective approach. The inclusion of three important measures in the evaluation function made it possible to obtain understandable and interesting rules.  
The MOPNAR algorithm [45] has multi-objective characteristics and allows mining in search of both positive and negative numerical association rules with a low computational cost. This recent algorithm relies on decomposition to perform evolutionary learning of attribute ranges and a selection of conditions for each rule while introducing an external population and a reset process in order to store all unexplored rules from the set of rules obtained. In addition, this proposal maximizes three objectives--understandability, interest, and performance--to obtain rules that are interesting, are easy to understand, and provide good coverage of the dataset. The effectiveness of the proposed approach was validated on various real-world datasets.
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## Acronyms  
* RFRadio Frequency
* EEEnergy Efficiency
* SESpectral Efficiency
* RBResource block
* GAGradient decent algorithm
* RLReinforcement Learning
* QoSquality-of-service
* ZFZero-Forcing
* RISReconfigurable Intelligent Surface
* OFDMOrthogonal Frequency Division Multiplexing
* OFDMAOrthogonal Frequency Division Multiple Access
* SISOSingle Input Single Output
* MISOMultiple Input Single Output
* MIMOMultiple Input Multiple Output
* APsaccess points
* MUMultiple User
* SNRSignal to Noise Ratio
* SINRSignal to Interference Plus Noise Ratio
* AWGNadditive White Gaussian Noise
* CSIChannel State Information
* SCAsine cosine algorithm
## 3 FCI-VMD Method Based on BAS  
From the above descriptions, there are four parameters that require determination before signal decomposition: mode number \(k\), penalty parameter \(\alpha\), noise tolerance \(\xi\), and tolerance of convergence criterion \(\varepsilon\). Among these parameters, \(\zeta\) and \(\varepsilon\) have minimal impact on decomposition. \(\xi\) is typically set to zero to ensure effective convergence of the algorithm, while \(\varepsilon\) is set to a small positive value to ensure reconstruction accuracy. Conversely, mode number \(k\) significantly influences the decomposition outcome. If \(k\) is set too high, the signal is over-decomposed, resulting in different modes for signals at the same frequency and causing mode mixing. On the other hand, setting \(k\) too low leads to under-decomposition, where the same mode exhibits varying frequency characteristics. Thus, selecting an appropriate value for \(k\) holds paramount importance in signal decomposition. To address this issue, this paper proposes the FCI-VMD algorithm to optimize the selection of \(k\).
# Acceleration of OCT Signal Processing with Lookup Table Method for Logarithmic Transformation
## 4 Conclusions  
In this paper, a lookup table method was used to improve the processing speed of logarithmic calculation in FDOCT imaging. By replacing the logarithmic transformation with a lookup table, data processing can be accelerated without increasing the cost and complexity of the system.  
The performance of lookup table method was tested in SSOCT imaging. Compared with the logarithmic function calculation method, the optimized lookup table method is able to improve the processing rate by 24% with an error of less than 0.0043.  
By adjusting the relationship between the values of table components and signal intensity, the contrast of OCT image could also be optimized. Based on the lookup table method, the structural information can be presented in some alternative ways, such as false color imaging, therefore a visual improvement of OCT image can be realized. In future work, we plan to use the lookup table method for improvement of the contrast of OCT image and acceleration of signal processing, such as Fourier transformation, to achieve a high-quality real-time OCT imaging.  
Conceptualization, J.Z. and S.L.; methodology, X.L. and S.L.; software, X.L.; validation, X.L.; Data analysis, X.L. and S.L.; writing, X.L, J.Z. and S.L.  
This research was funded by the National Key R&D Program of China (No. 2017 YFC0112401), National Natural Science Foundation of China (No. 61505267) and the Science and Technology Program of Guangzhou (No. 201607010167). The authors declare no conflict of interest.
# FSI Application in the Parametric Analysis of Blade-Disk Connection of Turbojet Engine Compressor
## 4 Results
### The Influence of the Blade Root Thickness Angle (P2) on the Maximum Stresses  
The next stage of the parametric analysis included the assessment of the impact of changes in the P2 parameter (thickness angle) on the von Mises stresses in the structure, assuming constant values of the P1 parameter. The value of the P1 parameter varied from 80 to 106\({}^{\circ}\), and the initial value of this parameter was 92.475\({}^{\circ}\). The results (Figure 11) include two rotational speeds (65% N2 and 100% N2), three selected values of the parameter P1--92\({}^{\circ}\), 94\({}^{\circ}\), and 98\({}^{\circ}\)--and two flow loads (150 kPa and 200 kPa).  
In all considered cases, the values of stresses increased with the increase in P2. In the first of the three cases considered, where P1 = 92\({}^{\circ}\), the stresses increased from about 500 MPa for P2 = 1.26\({}^{\circ}\) to over 800 MPa for P2 = 1.6\({}^{\circ}\) at 65% N2. In the case of maximum rotor speed and P2 = 1.6\({}^{\circ}\), the stresses increased to almost 2000 MPa. The greatest growth occurred after the thickness angle of 1.4\({}^{\circ}\) was exceeded. The pressure value had an additional effect, with the greatest differences visible in the range of large values of the P2 parameter (above 1.5\({}^{\circ}\)).  
With the value of P1 = 94\({}^{\circ}\), the smallest stresses occurred for parameter P2 in the range from 1.26\({}^{\circ}\) to about 1.4\({}^{\circ}\). After exceeding 1.4\({}^{\circ}\), the stress value increased significantly. The trend of parameter changes is similar for both rotor speeds. In this case, increasing the angle of the blade root width caused an increase in stress by about 10% compared to the first case.  
The value of the stresses at P1 = 98\({}^{\circ}\) and rotational speed of 65% N2 varied from 500 MPa to about 800 MPa. The stresses in the angle range of 1.26\({}^{\circ}\)-1.33\({}^{\circ}\) remained constant and reached 1170-1200 MPa at maximum speed (100% N2). Only after exceeding the value of P2 = 1.33\({}^{\circ}\) did their values reach 1800 MPa.  
Summarizing the analyses carried out, it can be noted that for selected ranges of values of the P1 and P2 parameters, it is possible to find such combinations that decrease the stress values in the compressor rotor stage. In this case, small changes in the geometric dimensions affect the values of the maximum stresses. This may be important during the manufacturing and subsequent operation of the element.
###### Abstract  
The quantitative assessment of the impact of vegetation restoration on evapotranspiration and its components is of great significance in developing sustainable ecological restoration strategies for water resources in a given region. In this study, we used the Priestley-Taylor Jet Pro-pulsion Laboratory (PT-JPL) to simulate the ET components in the Helong section (HLS) of the Yellow River basin. The effects of vegetation restoration on ET and its components, vegetation transpiration (Et), soil evaporation (Es), and canopy interception evaporation (Et) were separated by manipulating model variables. Our findings are as follows: (1) The simulation results are compared with the ET calculated by water balance and the annual average ET of MODIS products. The R\({}^{2}\) of the validation results are 0.61 and 0.78, respectively. The results show that the FT-JPL model tracks the change in ET in the HLS well. During 2000-2018, the ET, Ei, and Es increased at a rate of 1.33, 0.87, and 2.99 mm/a, respectively, while the Et decreased at a rate of 2.52 mm/a. (2) Vegetation restoration increased the annual ET in the region from 331.26 mm (vegetation-unchanged scenario) to 338.85 mm (vegetation change scenario) during the study period, an increase of 2.3%. (3) TMP (temperature) and VPD (vapor pressure deficit) were the dominant factors affecting ET changes in most areas of the HLS. In more than 37.2% of the HLS, TMP dominated the change affecting ET, and vapor pressure difference (VPD) dominated the area affecting ET in 30.5% of the HLS. Overall, the precipitation (PRE) and VPD were the main factors affecting ET changes. Compared with previous studies that directly explore the relationship between many influencing factors and ET results through correlation research methods, our study uses control variables to obtain results under two different scenarios and then performs difference analysis. This method can reduce the excessive interference of influencing factors other than vegetation changes on the research results. Our findings can provide strategic support for future water resource management and sustainable vegetation restoration in the HLS region.  
evapotranspiration; PT-JPL model; climate change; vegetation restoration; structural equation model +
Footnote †: journal: _agronomy_  
0202414606. [[https://doi.org/10.3390/agronomy14112606](https://doi.org/10.3390/agronomy14112606)]([https://doi.org/10.3390/agronomy14112606](https://doi.org/10.3390/agronomy14112606))  
[[https://www.mdpi.com/journal/agronomy](https://www.mdpi.com/journal/agronomy)]([https://www.mdpi.com/journal/agronomy](https://www.mdpi.com/journal/agronomy))
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 1 Introduction  
In today's digital era, individuals and organizations increasingly rely on technology to carry out everyday tasks. However, this dependence exposes users to a wide spectrum of cyber threats, with malicious actors exploiting digital platforms to deceive victims and extract valuable information [1]. Email phishing remains particularly concerning among the various forms of cyberattacks, as email continues to be a primary mode of communication. These attacks often serve as an entry point for more severe security breaches, including theft of sensitive data, system compromise, and significant financial or reputational damage [2].  
Phishing schemes exploit human error, manipulate users by revealing login credentials, financial details, or unknowingly downloading malware that can disrupt systems. As attackers continually refine their methods, distinguishing legitimate from deceptive content has become increasingly difficult. Therefore, developing robust defenses against such threats is essential [1].  
An increasingly sophisticated tactic is zero-face phishing, an emerging email-based technique that manipulates text visibility by embedding characters styled with a font size of zero. These invisible characters bypass human detection while remaining visible to email filters, allowing attackers to evade conventional spam and phishing detection systems. This approach is often combined with other tactics, such as spear phishing or clone phishing, to increase the probability of deceiving the recipient [3].  
This research aims to investigate the Zero-Font phishing technique within the broader landscape of phishing attacks. It compares the effectiveness of Zero-Font phishing with traditional methods and evaluates the shortcomings of current detection tools. To address these challenges, the study proposes a phishing detection tool capable of identifying both conventional and Zero-Font-based attacks. The framework integrates multiple analytical layers to improve detection accuracy and reduce risk. By addressing both established and emerging phishing strategies, this work contributes a practical solution to strengthening email security and advancing the field of cybersecurity.  
The remainder of this paper is organized as follows: Section 2 outlines the motivation for this work, explores various phishing techniques and attack vectors, and reviews relevant research on phishing detection. Section 4 compares existing detection approaches. Section 5 describes the proposed methodology. Section 6 presents the experimental results, and Section 7 concludes the paper.