esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 2 Current Landslide Monitoring Methods
### Geotechnical Methods

Table 1: Summary of geodetic methods. compression [61]. While both inclinometers and settlement gauges are useful for detecting vertical movements, they are unable to monitor internal shifts occurring beneath the ground surface without causing visible displacements. The complementarity of these techniques is vital, as no single instrument is adequate for comprehensive monitoring [62]. For example, an integrated network combining GNSS, extensometers, drone photogrammetry, and hydro-meteorological sensors was employed to synergistically investigate the Urbas landslide in northwestern Slovenia [63]. Joint interpretation of FOS and TDR data also enabled differentiation between soil suction and consolidation effects [64]. A significant limitation of geotechnical methods is their inability to capture pre-failure behaviors, as these instruments typically detect displacements only after a triggering event has occurred [65]. Furthermore, the requirement for direct installation in the subsurface may not always be feasible in hazardous zones where failures are incipient or developing. High costs associated with geotechnical networks also render large-scale implementation impractical, particularly when continuous monitoring is essential, such as during heavy rainfall events. Table 2 summarizes the advantages and disadvantages of various geotechnical methods for landslide monitoring.
## 3 Results and Discussion
### Physico-Chemical Characterization of the Hydrolysates
#### 3.1.5 Functional Properties  
The data in Table 2 show the functional properties of the hydrolysates; BKH, RKH and WKH, after 30 min Alcalase-hydrolysis. The BKH has the highest water holding capacity (WHC), recording about a 30% solubility increase over the original intact proteins (BKPI),  
Figure 2: Hydrolysis degree (**A**) and pH-solubility curves (**B**) of black, red and white kidney seed protein hydrolyses (BKH, RKH and WKH) prepared by 30 min hydrolysis with Alcalase at 37 \({}^{\circ}\)C and pH 6. Means with different lowercase letters indicate significant differences at \(p\leq 0.05\) by LSD.  
The other two hydrolysates, RKH and WKH, recoded lower relative solubility increases over their intact protein (RKI and WKI), amounting to 15%, and 20%, respectively. The oil absorbance capacity (OAC) of BKH recorded the highest significant (\(p<0.05\)) increase of 35% over the intact protein (BKI), against only 20% and 25% relative increases in the case of RKH, and WKH over their intact mother forms. On the other hand, the three hydrolysates showed higher foaming stability at pH 5, i.e., 88, 75, and 50 min for BKH, RKH, and WKH, respectively, with relative increases about 25-40% over the respective intact protein forms, i.e., BKI, RKI and WKI. Similar results were reported by [PERSON], et al. [47], who found higher forming stability of black gram protein hydrolysate at pH 5 than pH 3 and 7. The foaming stability index (FSI) of protein hydrolysates was in the range of 15.7-89.0 min. Generally, protein hydrolysates have higher ESI at pH 3 and 5, against lower pH 7. In addition, [PERSON], et al. [76] stated that foaming capacity (FC) of fava bean protein pepsin-hydrolysate increased by 74% at pH 5 and 50% at pH 7 over the intact protein. In addition, the oil holding capacity (OHC) increased from 6.12 to 8.21% g/g by pepsin hydrolysis. Generally, the enzymatic hydrolysis significantly increased the functional properties of protein isolates. [PERSON], et al. [27] stated that the inclusion of high solubility proteins in foods enhances the technological properties in supplemented foods, and it was required in many functional food applications.
# Privacy Policy Analysis of Banks and Mobile Money Services in the Middle East
## 4 Methodology
### Dataset  
To build our dataset, we first compiled a list of mobile money services that were developed in the Middle East [29] (Figure 4), by searching the Apple and Google Play stores, websites of mobile money providers, and the GSMA Mobile Money Tracker [28]. This resulted in a comprehensive list of 30 mobile money services. We then leveraged the Banker database [30] to retrieve a similar number of top Middle Eastern banks which were rated globally on attributes, such as digital and financial services. This resulted in a list of 25 banks. We then retrieved the privacy policy URL (if available) from the websites of banks and mobile money services in our dataset. Table 1 shows the Middle Eastern banks and mobile money services used in our dataset. The complete Middle Eastern banks and mobile money services dataset with links to privacy policies is available at ([[https://bit.ly/2](https://bit.ly/2) CyZKoI]([https://bit.ly/2](https://bit.ly/2) CyZKoI)).  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline
**Country** & **Mobile Money Services** & **Banks** \\ \hline Bahrain & BenefitPay, bwallet & Ahli United Bank \\ \hline Cyprus & MTN Mobile Money & - \\ \hline Egypt & NBE-PhoneCash, Vodafone Cash, Flous, Orange Money & - \\ \hline Iraq & ZainCash, AsiaHawala & - \\ \hline Jordan & Dinarak, Mahfazti by Umniah, Orange Money, AYA PAY & Arab Bank \\ \hline Lebanon & PinPay & - \\ \hline Oman & Pay+, Ooredoo Oman, efloos & BankMuscat \\ \hline Qatar & Thawani Pay, Ooredoo Qatar & Qatar National Bank, Qatar Islamic Bank (QIB), Commercial Bank \\ \hline Saudi Arabia & STC Pay, BayanPay, Halalah, Career Pay, AlinmaPay & National Commercial Bank (AlAhli Bank), Al Rajhi Bank, Samba Financial Group, Riyadh Bank, Saudi British Bank (SABB), Banque Saudi Fransi, Arab National Bank, Allnma Bank \\ \hline Turkey & BKM Express, fastPay, Paycell & - \\ \hline UAE & eWalletAE, mePay & First, Abu Dhabi Bank, Emirates NBD, Abu Dhabi Commercial Bank, Dubai Islamic Bank, Mashregbank, Union National Bank \\ \hline Yemen & Floosak & \\ \hline Israel & - & Bank Hapoalim, Bank Leumi, Israel Discount Bank \\ \hline Kuwait & - & National Bank of Kuwait, Kuwait Finance House \\ \hline \end{tabular}
\end{table}
Table 1: Middle Eastern Banks and Mobile Money Services Dataset.
# Patent Keyword Analysis Using Time Series and Copula Models
## 2 Research Background  
In this paper, we analyze patent keywords for technology analysis. A patent document contains rich and valuable information about researched and developed technology, because the patent system protects the inventor's exclusive right to use the technology for a period of time [1; 14]. In most of the studies related to patent analysis, the authors first constructed a patent-keyword matrix by extracting keywords from patent documents for patent analysis [6; 8; 12; 15; 16; 17; 18]. The matrix is composed of a patent row and keyword column, and it represents the frequency value of each keyword in a patent. In the previous research, this matrix was used for patent analysis by using statistics and machine learning algorithms. The results from the patent analysis are useful for technology management processes such as research and development (R&D) planning, new product developments, technology forecasting, etc. Therefore, technology management based on patent analysis has been carried out in diverse fields. The general process of patent analysis is as follows [6; 14; 19]:  
1. Select and understand the target technology
2. Make a keyword equation for patent searching and collect patents related to the target technology
3. Transform the collected patent documents into structured data
4. Analyze structured data (a patent-keyword matrix) using statistics or machine learning
5. Apply patent analysis results to technology management.  
We first determined the target technology for which we want to perform patent analysis. Next, we made a keyword search expression to search for patent documents related to the target technology. If the target technology is limited to the technology of a specific company such as Apple, instead of the keyword search equation, we then searched for all the patents filed and registered by the company. In this paper, we analyze the technology registered by Apple, so we searched the patent documents applied for and registered by Apple. Patent analysis cannot be done directly on the searched patent documents. This is because patent document data is not a table structure which consists of a row of observations (tuple) and a column of variables (attribute) for statistical analysis. To solve this problem, we have to transform the patent documents into patent-keyword matrix (structured data) which is composed of patent rows and keyword columns, and includes the frequency value of each keyword in a patent. We used various text mining techniques to build the structured patent data. Then, we analyze this matrix using statistical methods and machine learning algorithms. Lastly, we applied the results of patent analysis to technology management processes such as R&D planning or technology forecasting.
## 3 Results and Discussion
### Mathematical Modeling of Dehydration and Rehydration Kinetics
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Model Name** & **SSE** & **R\({}^{2}\)** & **RMSE** & **X\({}^{2}\)** & **Model Constants** \\ \hline \multirow{8}{*}{MW350} & [PERSON] and [PERSON] & 0.10 & 0.99 & 0.04 & 0.00 & a = \(-0.015746\) b = \(2.388\times 10^{-05}\) \\  & Approximation of Diffusion & 0.05 & 0.99 & 0.03 & 0.00 & a = \(2.188\) b = \(-2.6626\) k = \(0.0094144\) \\  & Page & 0.01 & 0.99 & 0.01 & 0.00 & k = \(0.0017301\) y = \(1.7232\) \\  & Logarithmic & 0.05 & 0.99 & 0.03 & 0.00 & a = \(2.1816\) b = \(-1.0988\) k = \(0.0094554\) \\  & Midilli & 0.01 & 0.99 & 0.01 & 0.00 & k = \(0.09722\) b = \(-0.053816\) \\  & Two-term exponential & 0.05 & 0.99 & 0.03 & 0.00 & a = \(-0.091877\) m = \(0.6355\) \\ \hline \multirow{8}{*}{MW500} & [PERSON] and Singh & 0.03 & 0.99 & 0.03 & 0.00 & a = \(-0.01716\) b = \(-9.6673\times 10^{-05}\) \\  & Approximation of Diffusion & 0.02 & 0.99 & 0.02 & 0.00 & a = \(8.1429\) b = \(-1.824\) k = \(0.0029664\) \\ \cline{1-1}  & Page & 0.02 & 0.99 & 0.02 & 0.00 & k = \(0.0016625\) y = \(1.8959\) \\ \cline{1-1}  & Logarithmic & 0.01 & 0.99 & 0.02 & 0.00 & a = \(73.5186\) b = \(-72.4614\) \\ \cline{1-1}  & Midilli & 0.01 & 0.99 & 0.01 & 0.00 & a = \(0.99761\) b = \(-0.023706\) \\ \cline{1-1}  & Two-term exponential & 0.08 & 0.98 & 0.04 & 0.00 & k = \(-0.052838\) n = \(0.17678\) \\ \hline \multirow{8}{*}{MW650} & [PERSON] and Singh & 0.02 & 0.99 & 0.03 & 0.00 & a = \(-0.020247\) b = \(-0.00025716\) \\ \cline{1-1}  & Approximation of Diffusion & 0.02 & 0.99 & 0.03 & 0.00 & a = \(9.9362\) b = \(-17107\) k = \(0.0032205\) \\ \cline{1-1}  & Page & 0.02 & 0.99 & 0.02 & 0.00 & k = \(0.001962\) y = \(2.0056\) \\ \cline{1-1}  & Logarithmic & 0.02 & 0.99 & 0.02 & 0.00 & a = \(94.5642\) b = \(-93.4931\) \\ \cline{1-1}  & Midilli & 0.01 & 0.99 & 0.01 & 0.00 & k = \(0.00031942\) \\ \cline{1-1}  & Two-term exponential & 0.08 & 0.98 & 0.05 & 0.00 & a = \(2.1813\) k = \(0.076869\) \\ \hline \hline \end{tabular} \(\alpha\), b, c, k, n, and y are the constants of models.  
\end{table} 
Table 4: Dehydration kinetics models. Figure 3: Drying curves of the Microwave drying process of tree tomato fruit.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 2 Virtual Inertia and Current Control Techniques
### Proportional-Resonant Controller  
The PR controller uses a stationary frame-based current control approach while achieving similar transient and steady-state performance as a PI controller [24]. The implementation of the controller in the synchronous frame means that complex transformations, as in the case of PI controllers, are not required, which is especially beneficial for single-phase systems where PI controllers are difficult to implement [19]. The PR controller shows improved performance in tracking sinusoidal signals compared to PI controllers without the need for the decoupling terms and/or voltage feed-forward terms [25]. A PI controller typically has a high gain at lower frequencies, whereas the PR controller can achieve a high gain around a narrow band near the designed resonant frequency. Because of this high gain, the PR controller is able to track sinusoidal signals around this resonant frequency. The transfer function of a typical PR controller is given by:  
Figure 2: Schematic diagram of the grid-connected inverter benchmark and PI (proportional-integral) Type-2 based current controller; PLL: Phase Locked Loop; PWM: Pulse Width Modulation; “\(\ast\)” represents the reference values of the signals.  
\[C_{PR}(s)=K_{P}+K_{I}\frac{s}{s^{2}+\omega^{2}} \tag{4}\]  
where \(K_{P}\) is the proportional gain and \(K_{I}\) is the resonant gain. \(K_{I}\) defines the width of the frequency band (a high \(K_{I}\) resulting in a wider band). This is of special importance while designing PR controllers for virtual inertia systems in weak grids. The frequency of the grid may change significantly, which means a relatively wide frequency band is required for a proper current control scheme. For large frequency deviations, the PR controller may show poor performance [25]. There is another slight variation of the transfer function of the PR controller in [26], but it is known to have poor transient performance, as described in [24].  
Figure 3 shows the control structure for the PR controller. Again, the PLL is used to extract the phase information of the grid. The reference \(dq\) currents are transformed into \(abc\) signals. It is important to note that the transformation is just used for providing the reference currents in the \(dq\) domain, keeping consistency with the other controllers analyzed in this paper. The reference currents could easily be provided in the \(abc\) domain, omitting the need for this transformation. The reference current \(i_{abc}^{*}\) is then compared against the feedback current \(i_{abc}\), and the error is passed onto the PR controller. The output of the PR controller is finally used as the modulating signal for the PWM modulator.
## 4 Example and Comparison Results
### The MSVi Calculation Phase  
Step 1. The second earliest \(ECT\) value of task \(i\) can be selected as the \(MSV\) value while task \(i\) has the maximum \(SV_{i}\) value among all \(SV\) values. As shown in Table 6, the task with the largest \(SV_{i}\) value is task \(g\); thus, the second earliest \(ECT\) (here, it is \(21486\)) of \(ECT_{aB}\) is selected as the \(MSV\) value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Node A** & **Node B** & \(SV\) & \(MSV\) \\ \hline Task \(a\) & 22345 & 23526 & 1181 & - \\ Task \(b\) & 16667 & 17930 & 1263 & - \\ Task \(c\) & 31083 & 31897 & 814 & - \\ Task \(d\) & 24712 & 25156 & 444 & - \\ Task \(e\) & 17018 & 18069 & 1051 & - \\ Task \(f\) & 12050 & 13289 & 1239 & - \\ Task \(g\) & 19035 & 21486 & 2451 & 21486 \\ Task \(h\) & 13911 & 14602 & 691 & - \\ Task \(i\) & 8016 & 8536 & 520 & - \\ Task \(j\) & 13618 & 14596 & 978 & - \\ Task \(k\) & 27861 & 28156 & 295 & - \\ Task \(l\) & 43336 & 44731 & 1395 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Calculation of the MSV values of tasks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Node A** & **Node B** & **Node C** & **Node D** \\ \hline Task \(a\) & 22345 & 23526 & 24323 & 25328 \\ Task \(b\) & 16667 & 17930 & 18696 & 20187 \\ Task \(c\) & 31083 & 31897 & 32034 & 34678 \\ Task \(d\) & 24712 & 25156 & 25774 & 26330 \\ Task \(e\) & 17018 & 18069 & 18664 & 20348 \\ Task \(f\) & 12050 & 13289 & 14911 & 15918 \\ Task \(g\) & 19035 & 21486 & 22371 & 24031 \\ Task \(h\) & 13911 & 14602 & 15655 & 17678 \\ Task \(i\) & 8016 & 8536 & 8948 & 9685 \\ Task \(j\) & 13618 & 14596 & 15331 & 16558 \\ Task \(k\) & 27861 & 28156 & 28655 & 29791 \\ Task \(l\) & 43336 & 44731 & 46777 & 49659 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Illustration of the expected execution times of tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Node A** & **Node B** & \(SV\) \\ \hline Task \(a\) & 22345 & 23526 & 1181 \\ Task \(b\) & 16667 & 17930 & 1263 \\ Task \(c\) & 31083 & 31897 & 814 \\ Task \(d\) & 24712 & 25156 & 444 \\ Task \(e\) & 17018 & 18069 & 1051 \\ Task \(f\) & 12050 & 13289 & 1239 \\ Task \(g\) & 19035 & 21486 & 2451 \\
# An Experimental Study on Repairing of Reinforced Concrete Beams Having Damaged Longitudinal Bars
## 2 Materials and Methods
### Repairing Details and Procedures  
The repair techniques employed in this study were designed in accordance with the ACI 318-19 [44] and ACI 440.2R-17 [40] standards. For the SNSM/NSM strengthening approach, the required area of reinforcement was calculated based on the flexural design provisions outlined in ACI 318-19 [44]. Similarly, the necessary FRP area for EBR method using CFRP sheets was determined according to the guidelines provided in ACI 440.2R-17 [40]. To ensure effective adhesion, bond length calculations were performed, taking into account  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & **Adhesive Mortar** & **Epoxy Primer** & **Epoxy Resin** \\  & **(MB ADH 1406)** & **(MB P 3500)** & **(MB SAT 4500)** \\ \hline Compressive Strength & 75 MPa (7 days) & - & \(\geq\)60 MPa \\ Flexural Strength & 25 MPa (7 days) & \(\geq\)20 MPa (7 days) & \(\geq\)50 MPa \\ Bond Strength to Concrete & 3 MPa (28 days) & - & \(\geq\)3 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mechanical properties of adhesive mortar, epoxy primer and resin.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bar** & **Yield Strength** & **Ultimate Strength** & **Type** \\
**Diameter (mm)** & **f\({}_{\text{gy}}\) (MPa)** & **f\({}_{\text{su}}\) (MPa)** & **Type** \\ \hline
6 & 330 & 420 & Deformed \\
12 & 480 & 584 & Deformed \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of reinforcements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & **CFRP Sheet** & **GFRP Rebar** \\  & **(SPN U 300)** & **(MAPEROD G)** \\ \hline Elastic modulus (GPa) & 240 & 40.8 \\ Tensile strength (MPa) & 4200 & 760 \\ Diameter (mm) & - & 10 \\ Thickness per (mm) & 0.18 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical properties of CFRP and GFRP materials.  
factors such as concrete strength, the type of FRP used, and relevant bond stress models. A similar methodology was applied to EBR systems utilizing steel and GFRP rebars.
# Physics-Informed Neural Networks for Modal Wave Field Predictions in 3D Room Acoustics
## 2 Room Acoustic Application
### Case Study Setup  
In particular, a simplified room-like 3D domain \(\Omega=[0,1]^{3}\) m\({}^{3}\) is considered. This room setup presents the lowest geometrical complexity while retaining the wall reflections interacting with the distributed and confined source regions. The inhomogeneous Helmholtz equation as the governing equation with homogeneous Neumann boundary conditions on \(\partial\Omega=\Gamma_{N}\) reads as  
\[(\Delta+k^{2})p(\omega,\mathbf{x}) =g(\omega,\mathbf{x})\quad\mathbf{x}\in\Omega \tag{1}\] \[\
abla p(\mathbf{x})\cdot\mathbf{n} =0,\quad\mathbf{x}\in\Gamma_{N}\,. \tag{2}\]  
In this equation, \(p\in\mathbb{C}\) is the time-harmonic acoustic pressure, \(\mathbf{x}=(x,y,z)\) is the space coordinate, \(k=\omega/c=2\pi/\lambda\) is the wave number, \(\lambda\) the wavelength, \(\omega\) the angular frequency, \(c=1\) the speed of sound, \(g\) the forcing term, \(\Delta=\
abla\cdot\
abla\) is the Laplacian operator, and \(\
abla\) the gradient. Homogeneous Neumann boundary conditions model the room walls. The forcing consists of  
\[g(x,y,z)=-2k^{2}\cos(kx)\cos(ky)\cos(kz)\mathrm{e}^{-\frac{||\mathbf{x}-\mathbf{x}_{0}|| ^{2}}{2\rho^{2}}}\,, \tag{3}\]  
with a source sharpness parameter \(s\), the Euclidean distance \(||\mathbf{x}-\mathbf{x}_{0}||_{2}\), and a source location \(\mathbf{x}_{0}=(0.5,0.5,0.5)\) m. In the case of \(s\to 0\), the source leads to a point source confined in the source location \(\mathbf{x}_{0}\). As a follow-up to the previous 3D room acoustic examples [9; 10; 11; 12; 13; 14; 15], the source is modeled more realistically by a point source in the center of the room modeled for sufficiently small \(s\).
# Numerical and Experimental Characterization of a Coanda-Type Industrial Air Amplifier
## 5 Results
### Experimental Characterization  
#### 5.1.1 Blowing Mode: Analysis of the Flow Entrained from an Upstream Location  
In this configuration (see Figure 7), a Venturi tube-mounted piping system is installed downstream from the air amplifier. The flow is absorbed from the atmosphere and discharged into the straight section of the Venturi tube.  
Varying the inlet total pressure coming from the compressed air tank from 0.25 MPa to 0.70 MPa, clearance gap values of \(\delta=0.08\) mm, 0.15 mm, and 0.23 mm were experimentally analyzed, with a total of 20 operating points collected. The gaps were manually adjusted by employing feeler gauges.  
Figure 16(left and middle) depicts, respectively, the high-pressure line and the entrained volumetric mass flow rates, given in SLPM units. According to the results, both mass flows follow a linear trend with respect to the inlet total pressure, together with a direct relation between the mass flow value and the gap width \(\delta\).  
Finally, Figure 16(right) reflects the effect of the gap width on the device amplification factor, which tends to increase as the gap decreases. However, the effect of the inlet total pressure is the opposite: the amplification factor decreases with a certain linearity as the inlet total pressure increases until it loses this linearity and remains almost constant. This effect is more noticeable as the size of the gap decreases.
## 1 Introduction
* highlight the advantages and shortcomings of current methods in the hopes of improving the quality of future research. * to provide a discussion of existing challenges in the field and ways to overcome them. The remainder of the paper is organized as follows. Section 2 gives a brief clinical background and explains differences in used terminology regarding EAT and PAT. Section 3 gives a theoretical overview of commonly used algorithms and methods for segmentation and quantification of EAT and PAT. Section 4 presents methods for EAT segmentation via pericardium delineation. In Section 5 we present direct EAT segmentation methods. Methods used for PAT segmentation are described in Section 6. Discussion about significant findings, remaining challenges, and future developments in the field are given in Section 7. Finally, the conclusion is given in Section 8.