esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Calculus of the Bending Rigidity of the Plate
For this purpose, by comparing the bending rigidity variation curves depending on the number of layers with the analytical value, for different values of the power coefficient, the most suitable value of the number of layers  
Figure 8: Bending rigidity versus power coefficient k, for different Poisson’s ratio (**a**) and layers (**b**). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Multilayer Concept, \(D^{*}\) [Nm]**} \\ \cline{2-7}
**Power** & \multicolumn{6}{c}{**Number of Layers**} \\ \cline{2-7}
**Coeff.** & \multicolumn{6}{c}{**5**} & **10** & **20** & **30** & **40** & **80** \\ \hline \(k=0.05\) & 2129.23 & 2120.22 & 2115.80 & 2114.21 & 2113.70 & 2112.71 \\ \(k=0.50\) & 1489.11 & 1459.76 & 1449.26 & 1446.45 & 1445.62 & 1444.36 \\ \(k=1.00\) & 1126.43 & 1114.23 & 1111.12 & 1110.30 & 1110.34 & 1110.15 \\ \(k=2.00\) & 856.44 & 863.69 & 865.58 & 865.69 & 866.06 & 866.18 \\ \(k=4.00\) & 727.03 & 746.87 & 751.98 & 752.66 & 753.27 & 753.59 \\ \(k=6.00\) & 676.06 & 705.83 & 713.71 & 714.83 & 715.71 & 716.21 \\ \(k=8.00\) & 637.03 & 675.67 & 686.24 & 687.80 & 688.94 & 689.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Layer number influence on bending rigidity. Considering that the variation of the bending rigidity of the plate with the number of layers is represented on the basis of a small number of points (only 5), between which the variation is linear (a less credible case), we proceeded to find a mathematical relationship with non-linear variation, which will pass as close as possible, through the respective points. The respective relationship allows us to find out the stiffness of the respective plate for any number of layers in the abscissa values range of the graph. This methodology applied to those 5 points where the plate rigidity was known, led to very close results, for which the calculus errors for 10 or more layers is below 1%, as seen in Table 6.  
Looking at Figure 10, it can be seen that a number of 20 layers is the most suitable for using the multilayer plate concept, except for the case when for \(k=0.05\), the most suitable number is 30.
## 6 Results and Discussion
### Sentiment Analysis
#### 6.2.1 Word2 vec Models  
Generally, the Word2 vec models that were trained using the Watan corpus presented the best performance. Moreover, for the Watan models, as shown in Figure 10a, the skip-gram models performed better than the CBOW models.  
Figure 10: SA accuracy of the models based on window sizes of 5, 7, and 10 against the vector sizes 200, 300, and 400 for the CBOW and skip-gram approaches. (**a**) Word2 Vec models SA results for the Watan corpus. (**b**) FastText models SA results for the Watan corpus. (**c**) Word2 Vec models SA results for the Book corpus. (**d**) FastText models SA results for the Books corpus. (**e**) Word2 Vec models SA results for the Wiki corpus. (**f**) FastText models SA results for the Wiki corpus.  
Figure 9: Word2 Vec models sentiment analysis results.  
In fact, the best results for the Watan corpus were achieved by the skip-gram model with a vector size of 200 and a window size of 10. The CBOW models seemed to perform worse with higher vector sizes. There is no clear effect of the hyperparameter values, but the only noticeable trend is that the models' accuracy showed good scores with a vector size of 200. Perhaps the reason behind this effect is that Watan has a small vocabulary size; thus, using a smaller window size can provide a more meaningful vector representation for the words that exist in the vocabulary.  
For the Books models, the skip-gram models generally outperformed the CBOW models, as shown in Figure 10c. Indeed, the CBOW models' results did not show any evident effect regarding different hyperparameter values. On the other hand, the skip-gram models showed a visible pattern, with their accuracy increasing as the window size increased. The skip-gram models provided the best accuracy with a window size of 10 and a vector size of 200.  
In general, the Wiki models performed well in most cases. Although the CBOW models had lower accuracy than the skip-gram models, their performance was stable among different vector sizes. The CBOW models' performance tends to improve slightly with the increase in the window size value, but it decreases as the vector size values increase. Overall, similar to the Books models, the skip-gram models have the best performance with a vector size of 200.  
Overall, for the Word2 vec models, it appeared that the corpus size does not have a huge effect on model performance.
# Non-Probabilistic Reliability Analysis of Slopes Based on a Multidimensional Parallelepiped Model
###### Abstract  
Aiming at the problem that the non-probabilistic reliability analysis method of slope engineering, which is based on an interval model, cannot consider the cross-correlation of geotechnical parameters, a non-probabilistic reliability analysis method of slopes based on a multidimensional parallelepiped model is proposed. This method can effectively alleviate the problem of difficult data survey in the field of geotechnical engineering. Using the limited sample data of soil parameters, the multidimensional parallelepiped model is constructed. The performance function of the slope is constructed based on Latin hypercube sampling and the quadratic response surface method. Then, the limit state equation of the slope can be standardized using the multidimensional parallelepiped model. The non-probabilistic reliability indexes of the slope are calculated based on the global optimal solution to judge the stability state of the slope. The example analysis verifies the feasibility of the proposed method. The results show that the correlation of shear strength parameters of soil has a great influence on the non-probabilistic reliability indexes of the slope. When the correlation coefficients of the shear strength parameters are between \(-1.0\) and \(0\), the smaller the correlation coefficient is, the greater the non-probabilistic reliability index of the slope is; when the correlation coefficients of the shear strength parameters are between \(0\) and \(1.0\), the non-probabilistic reliability index of the slope does not change with the correlation coefficient. The non-probabilistic reliability indexes of the slope obtained using the multidimensional parallelepiped model are between the results obtained using an ellipsoid model and those obtained using an interval model, which are validated by Monte Carlo method and relatively more reasonable. In the absence of a large number of geotechnical sample data, this method provides a new way for slope stability analysis and expands the application field of calculation methods based on non-probabilistic theory.  
slope; stability analysis; non-probabilistic reliability; multidimensional parallelepiped model; correlation +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effect of GO and rGO on Membrane Flux Increase  
The results showed that graphene nanomaterials (GO and rGO) increased the pure water flux with both PSf and PVDF membranes with only specific graphenes contents (Figure 4) via their hydrophilicity and pore expansion effects. The highest flux enhancement was observed with the PSf/GO membrane due to its superior hydrophilicity; however, flux enhancement was very low with rGO, as it is less hydrophilic and has fewer surface functional groups.  
Figure 4: _Cont._  
Figure 3: FE-SEM images of cross-section morphologies for (**a**) GO-blended polysulfone (PSf) membrane and (**b**) GO-blended polyvinylidene fluoride (PVDF) membrane. Scale bars, 5 \(\upmu\)m.  
To better understand the effect of the graphene content on the water flux, the flux change trend was analyzed for the type of polymer and graphene used, and the results are shown in Figure 5. For the PSf/GO membrane, the water flux increased with an increase in the GO content up to 0.10 wt% but then decreased at a GO content of 0.15 wt% (Figure 5a). This trend was influenced by the GO content and was related to the water contact angle; the angle continued to decrease with increases in the GO content (PSf blank: 88.5\({}^{\circ}\), 0.01 wt%: 87.1\({}^{\circ}\), 0.05 wt%: 82.7\({}^{\circ}\) 0.10 wt%: 79.3\({}^{\circ}\), 0.15 wt%: 78.2\({}^{\circ}\)), which indicated that the membrane had considerable hydrophilicity. However, excess amounts of GO increased the viscosity of the polymer solution and consequently delayed de-mixing, which allowed for creep relaxation to relieve stress and smaller pores to form [12]. This fact suggests that the optimum GO fraction value for pure water flux is 0.10 wt% GO.
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 2 Introduction of Measurement Procedure and Method
### Data Processing Method
#### 2.2.1 Mean Wind Speed and Wind Direction  
There are two time series in the date: wind speed and wind direction. The two components of horizontal wind speed are \(u_{x}(t)\) and \(u_{y}(t)\), calculated by Formulas (1) and (2) [35; 36], respectively:  
\[u_{y}(t) =u(t)\sin\phi(t) \tag{1}\] \[u_{x}(t) =u(t)\cos\phi(t) \tag{2}\]  
With a standard time interval of 10 min, the mean wind speed \(U\) and the horizontal wind direction \(\varphi\) are determined using the vector decomposition method, that is, Formulas (3) and (4) [35], respectively:  
\[U=\sqrt{\overline{u_{x}(t)}^{2}+\overline{u_{y}(t)}^{2}} \tag{3}\]  
\[\cos\varphi=\overline{u_{x}(t)}/U \tag{4}\]  
where \(\overline{u_{x}(t)}\) and \(\overline{u_{y}(t)}\) are the 10 min average wind speed of \(u_{x}(t)\) and \(u_{y}(t)\), respectively.
## 5 Experiments
### Comparison of Assignment Models  
In order to verify the superiority of the weighted item assignment model, three types of assignment models were compared, as shown in Table 11. Each assignment was optimized by the proposed algorithm with only one maximum generation number. The first assignment, denoted as (A), was updated from the initial assignment when considering only lifts. The fitness values in column (A) were calculated according to Equation (2). The second assignment was denoted as (B) and was updated from the initial assignment when considering both lifts and relative profits. Denoted as (C), the third assignment was updated from the initial assignment when considering lifts, relative profits, and relative frequencies. The numbers in column (C) are the fitness values of Equation (3).  
Table 11 shows that the assignments denoted as (C) are better solutions when compared to the other assignments. For example, according to the experimental results of DB4, if retailers assign items to sites based on (A), which only considers lifts, they will lose around 94% of the chance of gaining profits compared to assignment (C). In addition, placing items at sites based on (C) increases the chance of sales by 27% more than assignment (B), which only accounts for lifts and relative profits. The experimental results in Table 11 demonstrated that the weighted item assignment model can mimic real situations in retail more accurately than the previous model that considered only lifts and even shows better performances as the problem size increases.
## 4 Performance Evaluation
In addition, we proved that the mARF technique can enhance VDE-TER performance: when the mARF technique is applied with an adaptive threshold. The mARF technique with an adaptive threshold can increase the throughput by 18% compared with the traditional ARF technique. Figure 6: Comparison of throughput in three-ray channel model. Figure 7: Comparison of normalized throughput as a weighted factor.
## 4 Simulation Model Description
### Patient Arrival Process  
Simulation models often use Poisson distribution to generate the entities (patients in the present case) since it is known to be the best distribution fitting to describe arrival events [28]. Such distribution only requires the input of one parameter, the arrival rate of the event, which in our case is the arrival of a patient. As mentioned previously, the average arrival was determined for every hour of the week, as shown in Figure 6. These values, as is easily perceived, represent the input parameter of the Poisson distribution, which is used by the simulation model to generate the arrival of a new patient. For the purpose of clarification, consider as an example the arrival pattern of the 10 th hour of Monday, shown in Figure 6a, i.e., 16 green patients. This means that the simulation model will take 16 as the input parameter of the Poisson distribution for the tenth hour of Monday.
# Fractal Dimension and Retinal Pathology: A Meta-Analysis
## 4 Discussion  
### Limitations/Stability of Fractal Dimension  
The outcomes of fractal analysis show decreased retinal FD in myopia, glaucoma, and hypertension but show inconsistent results for diabetic retinopathy. Other diseases that were considered in this review: AMD, MacTel2, NAION, and retinal occlusions were not investigated extensively enough in the literature for meta-analysis or to form a conclusive consensus. It is difficult to compare fractal analysis outcomes due to heterogeneity in methods. Often studies considered different regions of interest and vascular layers, as well as ignoring certain types of blood vessels instead of calculating over the entire tree. Variation in imaging methods, image processing, and fractal analysis tools is also concerning when comparing research between studies. Previously, the robustness of the FD parameter was explored with respect to different methodological setups and shown that retinal FD can be misleading in clinical applications due to its sensitivity to image quality and technique used [13]. Similarly, a significant dependence of FD on the vessel segmentation and dimensional calculation methods used has been found [51]. Even lesser factors such as image brightness, contrast, and focus can have significant impact on the final FD estimate [52]. A review has also been conducted on the association between retinal FD and neurodegenerative diseases: Alzheimer's, cognitive impairment, and stroke [53]. Although a general decrease in retinal FD for patients with neurodegenerative pathology was observed, difficulties were also expressed with study comparison and there was a call for uniformization and standardization of procedures related to calculating retinal FD before establishing clinical applications [53]. Another important limitation of this study is that certain diseases may have significant changes in one retinal layer, but not for another layer.
# Design and Performance Analysis of BDS-3 Integrity Concept
## 5 Results and Discussion
### Test Results for B1I Signal  
The SIS-URE of the B1I signal at the WUL is calculated based on the B1I broadcast message from 7 January 2019 to 31 July 2019, with a sampling interval of 30 s. The URE sequence of the 18 satellites of the basic BDS-3 constellation are shown in Figure 5, in which the red dots indicate the URE value. The x-axis is in units of days of the year, and the y-axis is in meters.  
Figure 5: _Cont._Figure 5: _Cont._  
According to Section 2.3.1, the URE of B1I should be compared with the NTE, which is 4.42 times the URA. The URA can be obtained through the user range accuracy index (URAI) parameters broadcast in the D1 NAV message of B1I, and 4.42 times the URA is currently a fix value equal to 17.68 m. It can be seen from Figure 5 that the URE of all satellites are far less than 4.42 times the URA (among them, the largest URE appears in the satellite with a PRN code of 32, which is about 6 m). Therefore, there is no satellite service failure condition of the B1I signal during the test.
## 2 Operation of the BB84 Protocol
### Sifting  
In the sifting phase, the parties announce the polarization bases employed for the signal preparation and the remaining signals. Notably, [PERSON] and [PERSON] discard the data in which the polarization preparation and their measurement outcomes differed. Thus, the remaining data are called the \"sifted data\".
## 3 Results and Discussion
### Stuck Fermentation Altered the Profile of Non-Volatile Metabolites in Summer Black Tea
#### 3.2.2 Phenolic Acids  
Phenolic acids are beneficial bioactive compounds and the primary building blocks of flavonoids and catechins. They are the determinants of the color and flavor of tea soup and the main source of bitterness and astringency [23]. Stuck fermentation led to a significant decrease in phenolic acids (Figure 3B), especially those connected to glycosides, such as 6-O-galloyl-\(\beta\)-D-glucose (CAS:13186-19-1) and protocatechuic acid-4-O-glucose. Microorganisms contribute to tea quality by secreting enzymes that break these glycosidic linkages and release sweet substances that directly relieve the bitterness of phenolic acids [8]. Chlorogenic acid (CAS:327-97-9) and neochlorogenic acid (CAS:906-33-2) impart a bitter taste  
Figure 3: Heatmap of significantly different non-volatile metabolites for each class. (**A**) Amino acids and derivatives. (**B**) Phenolic acids. (**C**) Flavonoids. (**D**) Organic acids. (**E**) Alkaloids. (**F**) Sacharides. (**G**) Lipids.  
to the tea soup, and were reduced by microbial stuck fermentation. Similar results were reported by [PERSON] et al. as well [21]. Several hydroxycinnamic acids also contribute to the flavor of tea soup. For instance, [PERSON] et al. identified trans-4-O-p-coumaroylquinic acid as a low-threshold astingent compound in tea [24]. We observed a reduction in 4-O-p-coumaroylquinic acid levels (CAS:32451-86-8) after stuck fermentation, which may partly explain the obvious reduction in the bitterness and astringency of the fermented summer black tea. Some phenolic acids showed an increase after stuck fermentation, including catfaric acid (CAS:67879-58-7), 3-(4-Hydroxyphenyl)-propionic acid (CAS:501-97-3), and syringe acid (CAS:530-57-4). [PERSON] et al. also observed an increase in these phenolic acids in the early stages of fermentation [23]. Overall, the improvement in bitterness and astringency of summer black tea may be related to the reduction in certain phenolic acids with this flavor profile.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 4 Privacy-Preserved Trustworthy Seamless Service Agility
### UAV/UE Mutual Authentication  
Once the UE has trouble reaching its home network, it starts pinging its assigned eNB/gNB (home network) by sending a Radio Resource Control (RRC) connection request message. The delegated UAV from the home network intercepts this message as a part of its delegated authority role. The UAV then starts sending its proxy signature message to self-authorize toward the UE and waits for the UE response with its valid concealed ID. The authentications process then follows the following steps,  
* The UAV \(D_{i}\) includes a fresh \(\sigma\) signature to the UE that contains the following signature tuple: \[(\sigma_{i}|_{T_{1}},r_{i},w_{i},y_{i})||T_{1}.\]
* The UE validates the signature (i.e., the encrypted service provider delegation warrant \((w_{i})\) using \((r_{i},y_{i})\) and compare it to the attached \(w_{i}\).
* The UE sends an encrypted version of the released delegation warrant \(w_{i}\) along with the token key provided by the home network core \((TTK_{1})\) and a concealed ID \(C1D\)as \(E_{y_{i}}(w_{ACK},CID)\), where \(E_{y_{i}}\) is an encryption function based on the UAV public key. Furthermore, \(w_{ACK}\) is an acknowledgment of the received delegation warrant.
* The UAV confirms the \(T_{1}\) and \(TTK_{1}\) in the UE authentication response, then registers the \(CID\) of the UE to be used for the agility service.
## 3 Results and Discussion
### Drone Detection
#### 3.1.2 Signal 2 (Test 2)
The maximum detection range depends on a few factors; in the FSR system, the factors that influence the maximum range are the drone FS RCS, the sensitivity of the FS receiver and satellite transmit power. Based on the theoretical calculations of the direct power received (P\({}_{\text{dir}}\)) earlier stated, the maximum practical detection range is 100 m, as depicted in Table 1.  
A target was detected based on the amplitude modulation of the perturbed direct signal from the satellite. However, the detected signal does not reveal the type of a target. Hence, the signal is further processed by EMD to extract the micro-Doppler scattered by the drone's blade. By analyzing the IMF, the micro-Doppler and Doppler pattern can be seen, which resemble the Doppler scattered by the drone's blade and body, respectively. The Doppler from the drone body follow a V-shape-like pattern, indicating a target approaching the FS baseline, and then fly away from the baseline; the latter action shows the increases in Doppler frequency. The Doppler frequency is at the lowest value when the target is on the baseline. The micro-Doppler pattern with higher frequency can be seen from IMF4 to IMF6 for both signals. Figure 14: Extracted Doppler Component IMF5 (**a**) time domain and (**b**) its corresponding STFT plot.
## 3 Results
### Rate of Force Development (RFD)
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**LOP**} & \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**PRE**} & \multirow{2}{*}{**POST**} & \multirow{2}{*}{**5 min**} & \multirow{2}{*}{**10 min**} & \multirow{2}{*}{**15 min**} & \multicolumn{2}{c}{**ANOVA**} & \multirow{2}{*}{**_p_**} & \multirow{2}{*}{**ES**} \\ \cline{3-4} \cline{6-9} \multicolumn{1}{c}{**Condition**} & & & & & & & & & \\ \hline \multirow{4}{*}{40\%} & PF & 100.85 & 101.29 & 103.57 & 102.82 & 105.39 & \multirow{4}{*}{0.90} & \multirow{4}{*}{0.432 a} & \multirow{4}{*}{0.04} \\  & (kg) & (23.02) & (24.04) & (25.95) & (28.76) & (29.57) & & & \\ \cline{2-9}  & PF & 989.30 & 993.68 & 1016.00 & 1008.70 & 1033 & \multirow{4}{*}{0.90} & \multirow{4}{*}{0.432 a} & \multirow{4}{*}{0.04} \\  & (Nw) & (225.86) & (235.85) & (254.53) & (282.17) & (290.02) & & & \\ \cline{2-9}  & IP & 4386.36 & 4202.66 & 4326.52 & 4348.86 & 4404.46 & \multirow{4}{*}{0.76} & \multirow{4}{*}{0.490 a} & \multirow{4}{*}{0.03} \\  & (Nw) & (1200.61) & (1002.80) & (977.46) & (1161.56) & (1121.59) & & & \\ \hline \multirow{4}{*}{80\%} & PF & 99.26 & 104.13 & 104.43 & 103.33 & 105.02 & \multirow{4}{*}{1.70} & \multirow{4}{*}{0.159} & \multirow{4}{*}{0.08} \\  & (kg) & (15.43) & (19.35) & (21.04) & (22.87) & (21.10) & & & \\ \cline{2-9}  & PF & 973.77 & 1021.49 & 1024.42 & 1013.70 & 1030.24 & \multirow{4}{*}{1.70} & \multirow{4}{*}{0.159} & \multirow{4}{*}{0.08} \\  & (Nw) & (151.34) & (189.79) & (206.39) & (224.34) & (207.01) & & & \\ \cline{2-9}  & IP & 4316.26 & 4308.13 & 4314.78 & 4488.44 & 4433.14 & \multirow{4}{*}{0.71} & \multirow{4}{*}{0.583} & \multirow{4}{*}{0.04} \\  & (Nw) & (833.05) & (764.27) & (876.33) & (1043.68) & (853.72) & & & \\ \hline \multirow{4}{*}{0\%} & PF & 103.98 & 98.01 & 102.29 & 104.41 & 99.82 & \multirow{4}{*}{3.07} & \multirow{4}{*}{0.020 a} & \multirow{4}{*}{0.11} \\  & (kg) & (22.07) & (22.43) & (24.69) & (26.54) & (26.65) & & \\ \cline{2-9}  & PF & 1020.05 & 961.52 & 1003.45 & 1024.27 & 979.19 & \multirow{4}{*}{3.07} & \multirow{4}{*}{0.020 a} & \multirow{4}{*}{0.11} \\  & (Nw) & (216.54) & (220.07) & (242.23) & (260.33) & (261.46) & & \\ \cline{2-9}  & IP & 4477.08 & 4172.79 & 4344.06 & 4414.64 & 4325.49 & \multirow{4}{*}{0.09} \\ \cline{2-9}  & (Nw) & (1001.31) & (1084.39) & (1056.19) & (1167.51) & (1152.71) & & \\ \hline \hline \end{tabular} Note. Values are mean (SD). PF: peak force; IP: impulse; LOP: limb occlusion pressure; IMTP: isometric mid-high pull; Nw: Newtons. \(p\) values with asterisk (?) indicate statistical significance (\(p\) < 0.05). ES: size effect. \({}^{\text{a}}\): Greenhouse–Geisser correction applied.  
\end{table} 
Table 2: Summary statistics and repeated-measures ANOVA for IMTP performance across time points and LOP conditions.