esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
monocytogenes_ growth, which were caused by the curing method used--direct addition of nitrite or addition of plant extracts rich in nitrates. Generally, _L. monocytogenes_ growth on the surface of meat products is affected by the initial nitrite concentration regardless of its origin (sodium nitrate or plant extract [26]). Therefore, it might be concluded that in AC sausages used in the present study plant extracts delivered a lower concentration of nitrites (in the form of nitrates) than was added to control (nitrite cured sausages). However, when developing new formulations for food products e.g., alternatively cured sausages, their sensory quality is a key issue. Both products used in the present study were produced on a large, industrial scale, and were sold to consumers, which indicated that both had a satisfactory sensory quality. However, obtaining alternatively cured meat products containing nitrate-rich plant extracts with a good sensory quality might be challenging [30]. [PERSON] et al. [31] reported that overall acceptability of cooked sausages was affected by the origin of nitrates incorporated during the production. Sausages with either fruit extract powder (0.6%, containing pomeranate, lemon, red beet, rosmear), or purple sweet potato powder (0.45%), or fruits and vegetable extract powder (0.5%, containing radish 42.9%, beet 40.6%, blackcurrant 5.49%, apple), or gardenia red (0.04%) extract were scored lower than control version (produced with nitrites) in terms of overall acceptability. However, sausages produced with paprika and blueberry powder were scored higher than the control, whereas sausages with celery powder (0.8%) gained similar scores to the control. Not only the plant type but also the concentration of plant additives (powder, extracts) affects the sensory quality of alternatively cured meat products [32; 33]. Therefore, increasing the amount of plant extracts to obtain a higher efficiency (e.g., similar to that of nitrite used in the highest acceptable dose) in inhibiting bacterial growth might not be possible without sacrificing sensory acceptability, which in turn might negatively affect consumer decisions and willingness to purchase such products.
## 2 Materials and Methods
### Experimental Interpretation Through Flexure Beam Theory  
In this section, the analytical solution of the bending beam is briefly recalled, and it is used to relate the measured strain with the expected deflection of the structure. The theoretical model of the beam is its axis, i.e., the straight line of the centroids of the cross-sections. The \(z\) axis of the reference frame coincides with the beam axis. The beam deforms into a line belonging to the vertical \(yz\) plane. The \(y\) coordinate of the curved line is the bending displacement \(v(z)\).  
Starting from the measured strains \(\varepsilon\) and knowing the aluminum tube radius, \(R\), it was possible to calculate the beam curvature \(\chi\)  
\[\chi=\frac{d\varphi}{ds} \tag{1}\]  
Recalling that under the [PERSON]'s hypothesis of plane sections that remain perpendicular to the beam's deformed axis, and the assumption that the displacements are infinitesimal, \(ds\to dz,dv\rightarrow-d\varphi\cdot dz\), one obtains the curvature-displacement linear law  
\[\chi=\frac{d\varphi}{dz}=-\frac{d^{2}v}{dz^{2}}=\frac{\varepsilon}{y} \tag{2}\]  
By a first numerical integration of the \(\chi\) function, one gets the rotation function of the beam (\(v_{i}^{\prime}\) (\(z\))),  
\[v_{i}^{\prime}(z)=\left(-\frac{\mu\varepsilon_{i}+\mu\varepsilon_{i-1}}{2R \cdot 10^{6}}\right)\Delta z_{i}+v_{i-1}^{\prime}(z) \tag{3}\]  
where the factor \(\frac{1}{10^{6}}\) is the conversion factor from \(\mu\varepsilon\) to \(\varepsilon\);  
\(R=0.041\) m is the radius of the tube-sensor system, given by the sum of the radius of the aluminum tube \(R_{1}=0.04\) m and an estimate of half the sensor radius set equal to \(R_{2}=0.001\) m  
\[\Delta z_{i}=z_{i}-z_{i-1} \tag{4}\]  
Moreover, by a second numerical integration, one obtains the displacement function  
\[v_{i}(z)=\left(\frac{v_{i}^{\prime}(z)+v_{i-1}^{\prime}(z)}{2}\right)\Delta z _{i}+v_{i-1}(z) \tag{5}\]  
The integration requires the knowledge of the initial value of displacement and its derivative, which must be evaluated by imposing the following boundary condition:  
\[\left\{\begin{array}{l}v(0)=0\\ v(l)=0\end{array}\right. \tag{6}\]
## 4 Layered SDN-Based Optimization Framework
### Model Definitions  
We consider a network with \(O\) distinct operators, indexed by \(o=1,2,\ldots,O\). (The main model definitions are summarized in Table 1.) Each operator manages a set \(\mathcal{G}_{o}\) of GWs indexed by \(g\in\mathcal{G}_{o}\). In turn, each GW \(g\) manages a set of eNBs, indexed by \(n\in\mathcal{N}_{g}\). Let us also define the set \(\mathcal{N}\triangleq\bigcup_{o=1}^{O}\bigcup_{g\in\mathcal{G}_{o}}\mathcal{ N}_{g}\) of all the eNBs and the set \(\mathcal{G}\triangleq\bigcup_{o=1}^{O}\mathcal{G}_{o}\) of all the GWs. The queue at a given eNB \(n\in\mathcal{N}\) is denoted by \(Q_{n}\) and its dynamics are  
\[Q_{n}[t+1]=\left[Q_{n}[t]-z_{n}[t]\right]^{+}+a_{n}[t+1], \tag{1}\]  
where \(a_{n}[t]\) and \(z_{n}[t]\) represent, respectively, the exogenous packet arrival process and the backhaul service rate that is granted to eNB \(n\) during time slot \(t\). Also, \([\cdot]^{+}\) denotes the projection onto the nonnegative orthant (\([\gamma]^{+}=\max(\gamma,0)\)). The service rate \(z_{n}[t]\) represents the backhaul (bitrate) resources allocated for the upstream (eNB to GW) transmission between \(t\) and \(t+1\) to the specific eNB \(n\).  
The multi-layer multi-timescale optimization framework developed in this section is applicable for the wide range of optimal resource allocations to distributed entities. In particular, the developed optimization framework is well suited for scenarios with substantial signaling delays between the distributed entities and a central controller so that purely centralized decisions are impractical. Aside from large-scale wireless access networks, such resource allocation problems arise for instance in supply and demand management [83] and in transactive energy markets [84; 85; 86; 87].
## 3 Results and Discussion
### Lipid Production and Fatty Acids Profiles
In this study, the percentage of C16-C18 fatty acids reached 97.23%, 96.11%, and 96.72% in 10%, 20%, and 50% whey wastewater, respectively, compared to 97.20% in BBM medium. Furthermore, the concentration of linolenic acid (C18:3), which is restricted to a maximum of 12% in biodiesel according to the European standard EN 14214 [16, 66], was about 6.80% in 10% whey wastewater, meeting the biodiesel quality requirements. These results suggest that _Chromochloris zofingiensis_ cultivated in 10% whey wastewater diluted in BBM medium has the potential to produce high-quality biofuel. The fatty acid profile aligned well with the standards for biodiesel production, making this cultivation method an attractive option for sustainable biofuel development. This study focused on a low-cost approach using autoclaved, non-filtered whey wastewater. Future research may explore the integration of sustainable enzymatic pre-treatment methods to enhance nutrient bioavailability. As demonstrated by [PERSON] et al. [67], enzymatic hydrolysis of whey can significantly increase the release of bioavailable compounds, potentially improving microalgal growth, nutrient uptake, and the overall production of valuable metabolites within an integrated biorefinery framework [67].
# A Method of Ultrasonic Finger Gesture Recognition Based on the Micro-Doppler Effect
## 4 Conclusions  
In this paper, a new method of ultrasonic finger gesture recognition is proposed. Firstly, a hardware structure of ultrasonic active sensing, which uses only two ultrasonic transducers, is constructed. Second, based on the micor-Doppler effect of a moving finger, the received echos from finger gestures are processed in preprocessing stage, including sampling, bandpass filtering, IQ demodulation and lowpass filtering. In micro-Doppler processing, coherent processing and FFT are used to obtain high resolution micro-Doppler feature maps of finger gestures resulting from finger motions. Third, a recognition model based on convolution neural networks is built. Some experiments are designed to optimize some parameters of the model. The results of 5-fold cross validation show that our proposed method can recognize five finger gestures and achieve an average accuracy of 96.51%; the accuracy of each finger gesture is more than 96%.  
[PERSON] wrote the paper. [PERSON] and [PERSON] investigated the method and designed the experiment. [PERSON] and [PERSON] performed the experiment and analyzed the data. [PERSON] revised the paper.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors are grateful to [PERSON] for providing powerful data acquisition equipment, to [PERSON] for his suggestions to this paper. The authors would like to thank the reviewers for the careful review and valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l|c c c c} \hline The Number of Epochs & 300 & 500 & 800 & 1000 \\ \hline Recognition accuracies & 94.55\% & 96.00\% & 96.78\% & 97.44\% \\ \hline \end{tabular}
\end{table}
Table 7: Recognition accuracies vs. Number of epochs.  
\begin{table}
\begin{tabular}{|c|c c c c|c|} \hline Es & \multicolumn{3}{c|}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ Ac & a & b & c & d & e \\ \hline a & 96.28\% & 0.32\% & 1.55\% & 1.85\% & 0.00\% \\ b & 0.77\% & 97.57\% & 0.13\% & 0.53\% & 1.00\% \\ c & 0.44\% & 0.22\% & 98.24\% & 0.88\% & 0.22\% \\ d & 1.34\% & 0.37\% & 0.78\% & 97.41\% & 0.11\% \\ e & 0.00\% & 0.66\% & 1.35\% & 1.87\% & 96.12\% \\ \hline \end{tabular}
\end{table}
Table 8: The result of 5-fold cross validation.  
Figure 10: The confusion matrix of accuracy.
# Bridge Displacements Monitoring Method Based on Pixel Sequence
## 4 Experimental Results and Analysis
### Edge Detection Effectiveness  
#### 4.2.1 Image Preprocessing  
The images output by the camera are RGB images, consisting of a three-dimensional array of size MN3. Processing RGB images generally takes more time due to their complexity. Therefore, grayscale conversion is essential in the proposed algorithm to reduce the time and hardware consumption needed for extracting structural deformation.  
Common methods for image grayscale conversion include averaging, weighted averaging, and min-max normalization. In this study, the weighted averaging method is used  
Figure 11: Structure state at each dynamic load level.  
Figure 12: Deformation curves of the structure under dynamic load condition.  
for grayscale conversion of the collected image information, with the results as follows (Figure 13):  
In the experiment, although the initial output image size is 8688 \(\times\)5792, the portion of the image containing the bridge structure is only 8688 \(\times\) 350, approximately 6% of the total area. Once the Region of Interest (ROI) is extracted, the processing speed is anticipated to increase significantly, from several to over ten times faster. Therefore, manual selection of the ROI is adopted. The height of the rectangular ROI is determined to be 1.5 times the height of the experimental girder. Initially, the lower edge of the structure is positioned on the centerline of the ROI, as illustrated in Figure 14.
## 3 Results
### Behavior of Bioactive Compounds during In Vitro Digestion
#### 3.2.2 Total Flavonoids  
The propolis extracts from TU (Teotitlan de Flores Magon), presented the highest content of flavonoids with a range of 18,107.7 to 19,523.2 mg QE/100 g, followed by the extracts from SP (San Pedro Ocopetillo) with 3361.0 to 4152.3 mg QE/100 g and the extracts with the lowest content were those from SJ (San Jeronimo Tecoalt) with 2859.8 to 3237.9 mg QE/100 g (Table 3). All the extracts obtained by ultrasound have the highest content of flavonoids. In general, during in vitro digestion, the flavonoid content was modified in each of the phases. At the end of the digestion, the treatments SJ U and SJ M were the most affected with a reduction greater than 98%, and TU and TM presented a reduction of 91.6%. Therefore, we can deduce that the digestion process negatively affects the bioavailability of bioactive compounds (flavonoids) of propolis extracts, therefore its functional properties could be affected.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Phase**} & \multicolumn{4}{c}{**Total Phenolic Content (mg GAE/100 g)**} \\ \cline{2-6}  & **T U** & **T M** & **SP U** & **SP M** & **SJ U** & **SJ M** \\ \hline
**Ext** & 33,433.3 \(\pm\) 293.9 \({}^{\mathrm{a,A}}\) & 31,642.4 \(\pm\) 1110.0 \({}^{\mathrm{a,B}}\) & 33,316.5 \(\pm\) 178.4 \({}^{\mathrm{a,A}}\) & 34,406.6 \(\pm\) 116.7 \({}^{\mathrm{a,A}}\) & 31,253.1 \(\pm\) 233.5 \({}^{\mathrm{a,B}}\) & 30,824.9 \(\pm\) 526.6 \({}^{\mathrm{a,B}}\) \\
**GP** & 7262.6 \(\pm\) 189.9 \({}^{\mathrm{a,B,C}}\) & 6746.7 \(\pm\) 321.1 \({}^{\mathrm{b,A}}\) & 6921.9 \(\pm\) 87.5 \({}^{\mathrm{b,A,B}}\) & 7476.7 \(\pm\) 58.3 \({}^{\mathrm{b,C}}\) & 7291.8 \(\pm\) 73.4 \({}^{\mathrm{b,C}}\) \\
**IP** & 7572.8 \(\pm\) 245.2 \({}^{\mathrm{b,A,B}}\) & 7245.8 \(\pm\) 88.1 \({}^{\mathrm{b,A}}\) & 7187.4 \(\pm\) 245.2 \({}^{\mathrm{b,A}}\) & 7315.9 \(\pm\) 88.1 \({}^{\mathrm{b,A}}\) & 7374.3 \(\pm\) 123.0 \({}^{\mathrm{b,A}}\) & 8098.4 \(\pm\) 455.5 \({}^{\mathrm{b,B}}\) \\
**IPB** & 7273.8 \(\pm\) 157.9 \({}^{\mathrm{b,A}}\) & 7495.8 \(\pm\) 420.4 \({}^{\mathrm{b,A}}\) & 6958.5 \(\pm\) 20.2 \({}^{\mathrm{b,A}}\) & 7180.4 \(\pm\) 0.0 \({}^{\mathrm{c,A}}\) & 7472.4 \(\pm\) 438.1 \({}^{\mathrm{b,A}}\) & 8208.2 \(\pm\) 72.9 \({}^{\mathrm{b,B}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Behavior of total phenols during the in vitro digestion of different propolis extracts.  
Figure 1: Yield in propolis extracts, from three places and two extraction methods. (T U = Teotitlan de Flores Magón, Ultrasound; T M = Teotitlan de Flores Magón, Maceration; SP U = San Pedro Ocopetitillo, Ultrasound; SP M = San Pedro Ocopetitillo, Maceration; SJ U = San Jeronimo Tecoalt, Ultrasound; SJ U = San Jeronimo Tecoalt, Maceration).
## 2 Operation and Control
### Operation Support Tools
#### 2.5.1 Agents and MAS  
In recent years, numerous projects based on MAS have been developed. Despite this, its benefit is not clear and sometimes it is misleading to decide between MAS or a classic alternative.  
The aforementioned systems are based on control entities (CEs), which are responsible for the energy trade in local electricity markets (LEMs). The main objective is to maximize a cost function associated with the CE, to subsequently send offers to the LEMs. Subsequently, LEM will send the results of the auction. In other words, CE acts as a seller or buyer of the managed energy. MAS are also used in the control of the network, mainly to guarantee the voltage, frequency and power of the same.  
SG presents characteristics of distributed and heterogeneous elements, and this is the main reason that makes using MAS more attractive. The agents that makeup MAS are elements with particular interests, which also fulfill a global function.  
The literature presents works based on agents and MAS. [PERSON] and [PERSON] [101], [PERSON] et al. [102] and [PERSON] and [PERSON] [103] propose a MAS to locate and isolate faults, to efficiently predict the system stability and decentralized control architecture for microgrids respectively.  
It is possible to use MAS to do distributed control in a microgrid. [PERSON] et al. [104] design a system with these characteristics, and the system is validated in a real scenario with positive results. Numerous projects have been developed under the perspective of the MAS. However, it is necessary to understand that not all scenarios are likely to be addressed with MAS.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 3 Design of Vehicle State Estimator Based on IMM-MCCKF
### Derivation of the IMM-MCCKF Algorithm
#### 3.5.2 Model Filtering  
Based on the MCCKF iteration process, \(\hat{x}_{(0),k-1|k-1)}\) and covariance \(P_{(0),k-1|k-1)}\) from the interaction is substituted into the \(j\)th model, resulting in the estimation value \(\hat{x}_{(j,k|k)}\), covariance matrix \(P_{(j,k|k)}\), measurement output \(\hat{z}_{(j,k-1|k-1)}\), and corresponding covariance matrix \(P_{(j|k-1)}^{zz}\) after model filtering.  
Figure 2: IMM-MCCKF algorithm’s iterative process.  
1. Update of the probability model. The probability distribution function of the \(j\)th model: \[\Lambda_{k}=\frac{1}{(2\pi)^{n/2}}\Big{|}S_{(j,k)}\Big{|}^{1/2}\exp\biggl{\{}- \frac{1}{2}v_{j}^{\mathsf{T}}(S_{(j,k)})^{-1}v_{j}\biggr{\}}\] (57) \[v_{(j,k)}=z_{k}-\hat{z}_{(j,k-1|k-1)}\] (58) \[S_{(j,k)}=P_{(j,k|k-1)}^{z\Xi}\] (59) Model \(j\)'s probability: \[\mu_{(j,k)}=\Lambda_{(j,k)}\bar{c}_{j}/c\] (60) \[c=\sum_{j=1}^{r}\Lambda_{(j,k)}\bar{c}_{j}\] (61)
2. Output interaction: \[\hat{x}_{(k|k)}=\sum_{j=1}^{r}\hat{x}_{(j,k|k)}\mu_{(j,k)}\] (62) \[P_{(k|k)}=\sum_{j=1}^{r}\mu_{(j,k)}\biggl{\{}P_{(j,k|k)}+\left[\hat{x}_{(j,k| k)}-\hat{x}_{(k|k)}\right]\cdot\left[\hat{x}_{(j,k|k)}-\hat{x}_{(k|k)}\right]^{ \mathsf{T}}\biggr{\}}\] (63)  
The derivation of the IMM-MCCKF is completed as outlined above, with the pseudocode for the IMM-MCCKF presented in Algorithm 1.  
```
For \(i=1\): N 1 Input: \(\hat{x}_{(i,k-1|k-1)}\), \(P_{(i,k-1|k-1)}\), \(Q_{(k-1)}\), \(R_{k}\), \(z_{k}\), \(p_{ij}\), \(\tau\), 2 Interaction  Using Equation (54) to calculate \(\mu_{(ij,k-1|k-1)}\)  Using Equations (55) and (56) to initialize \(\hat{x}_{(0j,k-1|k-1)}\) and \(P_{(0|j,k-1|k-1)}\) 3 Model Filtering  Using Equations (25) and (26) to calculate \(\hat{x}_{(k-1|k-1)}\)and \(P_{(k|k-1)}\)  Using Equations (29) and (31) to calculate \(\hat{z}_{(k|k-1)}\)and \(P_{(k|k-1)}^{z\Xi}\)  Using Cholesky decomposition to calculate \(V_{(p,k|k-1)}\) and \(V_{(R,k)}\)  Let \(t=1\), \(\hat{x}_{(k|k)}^{0}=\hat{x}_{(k|k-1)}\) and repeat  Using Equations (49) and (50) to calculate \(W_{(x,k)}^{t-1}\) and \(W_{(z,k)}^{t-1}\)  Using Equations (45)-(48) to calculate \(\hat{x}_{(k|k)}^{t}\)  if \(\left\|\hat{x}_{(k|k)}^{t}-\hat{x}_{(k|k)}^{t-1}\right\|/\left\|\hat{x}_{(k-1 )}^{t-1}\right\|\geq\varepsilon\), Update \(t,t=t+1\)  Using Equation (52) to calculate \(P_{(k|k)}^{t}\) 4 Model Probability Update  Using Equations (57)-(59) to calculate \(\Lambda_{k}\)  Using Equations (60) and (61) to calculate \(\mu_{(j,k)}\)  Using Equations (62) and (63) to calculate \(\hat{x}_{(k|k)}\) and \(P_{k|k}\) 5 Output: \(\hat{x}_{(k|k)}\) and \(P_{k|k}\)
```  
**Algorithm 1**IMM-MCCKF Pseudocode.
## 4 Materials and Methods
### Subcellular Localization of TPSs  
Full-length ORFs of _CsTPS3 GT_, _CsTPS4 WS_, and _CsTPS5 TH_ were cloned into pENTR vectors using the pENTR(tm)/D-TOPO(r) Cloning Kit (Thermo Fisher Scientific, Singapore) and transformed into XL1-blue competent cells. Plasmids from the positive clones were isolated and cloned into the destination vector pBA-DC-YFP [71], which contains the YFP in frame at the C-terminal and cauliflower mosaic virus (CaMV) 35S promoter, to generate CsTPS3 GT-YFP, CsTPS4 WS-YFP, and CsTPS5 TT-YFP, respectively. The constructs were transformed into the _Agrobacteriumium tumefaciens_ EHA105 strain by a heat shock method [72]. The transformed EHA105 cells were cultured at 28 \({}^{\circ}\)C and resuspended in a solution containing 100 \(\mu\)M acetosyringone, 10 mM MES (pH 5.6), and 10 mM MgCl\({}_{2}\). The above mixture was incubated at room temperature for 3 h and later infiltrated into _N. benthamiana_leaves using a 1 mL syringe. After 2 d, the infiltrated leaves were excised, and the fluorescence signals were observed under a confocal scanning laser microscope (LSM 5 Exciter, ZEISS, Jena, Germany). All constructs were verified by DNA sequencing.
# Joint Multi-Scenario-Based Earthquake and Tsunami Hazard Assessment for Alexandria, Egypt
## 4 Multi-Scenario Physics-Based Tsunami Hazard Analysis: Data and Methods
### Selected Tsunami Scenarios for Alexandria  
The uncertainty in the locations and magnitudes of the causative earthquakes represent the main obstruction for assessing tsunami hazards in the EM. Consequently, the region has a wide range of potential earthquake-induced tsunami scenarios (e.g., [60; 68; 69]). [PERSON] et al. [70] have indicated that the two most devastating historical tsunamis recorded in the region, those of 365 and 1303, were probably generated by strong earthquakes at the Hellenic plate boundary, and there is strong evidence for them, as discussed in  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Scenario** & **Lat\({}^{\circ}\)** & **lon\({}^{\circ}\)** & **Magnitude** & **Depth [km]** & **Strike\({}^{\circ}\)** & **Dip\({}^{\circ}\)** & **Rake\({}^{\circ}\)** & **Legenth** & **Width** [km]** & **Slip (m)** \\ \hline S1 & 34.79 & 23.90 & 8.5 & 45 & 326 & 30 & 90 & 100 & 75 & 20 \\ \hline S2 & 34.51 & 24.01 & 8.5 & 45 & 326 & 35 & 90 & 170 & 75 & 20 \\ \hline S3 & 36.50 & 21.66 & 8.5 & 45 & 326 & 30 & 90 & 175 & 75 & 20 \\ \hline S4 & 35.57 & 22.77 & 8.5 & 45 & 326 & 30 & 90 & 150 & 75 & 20 \\ \hline S5 & 34.09 & 24.78 & 8 & 45 & 250 & 30 & 90 & 90 & 47 & 20 \\ \hline S6 & 34.79 & 26.85 & 8 & 45 & 250 & 30 & 90 & 122 & 47 & 20 \\ \hline S7 & 35.01 & 28.23 & 8 & 45 & 250 & 30 & 90 & 100 & 47 & 20 \\ \hline S8 & 36.61 & 28.71 & 8 & 40 & 250 & 35 & 90 & 100 & 47 & 90 \\ \hline S9 & 34.23 & 33.51 & 8 & 5 & 280 & 20 & 90 & 100 & 46.7 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Seismological parameters of earthquake scenarios adopted in this work.  
Figure 10: Nine seismogenic/tsunamigenic source models in the Eastern Mediterranean region compiled from the DISS-3.3 [67] database and individual studies adopted for tsunami hazard assessment extracted from [PERSON] et al. [3] and adopted in this work, as shown in Table 3 [68] (last access: January 2023). The black square polygon defines the area of interest, while the yellow polygon forms the tsunamigenic source models/scenarios.  
the Introduction section, with references available. However, there is no clear evidence from seismology alone for the existence of the faults that slipped in the tsunamigenic earthquakes of 365 or 1303. Therefore, these reasons motivated [PERSON] et al. [70] to develop a seismogenic source model that took advantage of recent understanding of the tsunami hazard at this plate boundary, which was developed by combining geological, geophysical, geodetic, geomorphological, geochronological, and historical data. To achieve the aim of this study, we selected eight source models (i.e., scenarios S2 to S9) adopted in the work of [PERSON] et al. [3], while SI was taken from the DISS-3.3 [68] database. These selected scenarios represent the most significant ones responsible for the maximum tsunami wave amplitude in the Alexandria coastline. These sources are reproduced, and their details are presented in Table 3 and Figure 11.  
Figure 11: _Cont._