esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid MCDM Model for Live-Streamer Selection via the Fuzzy Delphi Method, AHP, and TOPSIS
## 3 Research Method  
### Fuzzy Delphi Method  
The Delphi method, known as the expert survey method, primarily uses questionnaires to seek opinions from various experts on the problem, and then compiles and summarizes the comprehensive opinions. Next, the comprehensive opinions and questions are fed back to the experts before their opinions are sought once more. Thus, the expert consensus is gradually obtained after numerous iterations [42]. As the Delphi method is based on expert knowledge, complex issues are systematized after multiple iterations of questionnaire feedback to reach expert consensus. However, there are criticisms and limitations to its operation. For instance, this method involves many iterations of questionnaire feedback to achieve convergence, which is time-consuming, increases cost, and reduces response rate. Therefore, scholars continue to modify the operation of the Delphi method, ensuring its efficiency and reliability. After 1985, the fuzzy Delphi method was developed using the Delphi method mixed with the fuzzy theory to address the difficulties faced by the traditional Delphi method. Its advantages include the ability to deal with semantic ambiguity and retain more expert information [43].  
The fuzzy Delphi method is an element selection method. Compared to the traditional Delphi method, it possesses the following advantages [44]:  
1. It helps reduce the number of surveys.
2. It helps express expert opinions more completely.
3. It enables expert knowledge to be more rational and consistent with the need through the fuzzy theory.
4. It brings more economic benefits in terms of time and cost.  
The fuzzy Delphi method enables elements to integrate opinions from experts in related fields and can truly reflect fuzziness and uncertainty in social sciences [45]. The traditional Delphi method is time-consuming, and it produces incorrect results when there are semantically unclear items in the questionnaire. However, these shortcomings in the Delphi method were subsequently overcome by scholars by incorporating the fuzzy theory. Furthermore, as the properties of the geometric mean can reduce the effects of extreme values and highlight the correlations of elements, it is used as a reference for the final selection of elements [21].  
The fuzzy Delphi method has been successfully used in many areas [27]. As to the studies about personnel selection, it was utilized to screen the selection criteria for public relations personnel [28], tour guide [29], celebrity endorser [30], gamer for professional esports team [31], and variety show host [32]. As a result, opinions from the managers of shopping websites were extracted using the geometric mean in this study. Based on a previous study [41], a criterion with a geometric mean of over 7.2 is regarded as important and should be retained for selecting live-streamers.
# Analytical Investigation of Viscoelastic Stagnation-Point Flows with Regard to Their Singularity
###### Abstract  
Singularities in the stress field of the stagnation-point flow of a viscoelastic fluid have been studied for various viscoelastic constitutive models. Analyzing the analytical solutions of these models is the most effective way to study this problem. In this paper, exact analytical solutions of two-dimensional steady wall-free stagnation-point flows for the generic Oldroyd 8-constant model are obtained for the stress field using different material parameter relations. For all solutions, compatibility with the conservation of momentum is considered in our analysis. The resulting solutions usually contain arbitrary functions, whose choice has a crucial effect on the stress distribution. The corresponding singularities are discussed in detail according to the choices of the arbitrary functions. The results can be used to analyze the stress distribution and singularity behavior of a wide spectrum of viscoelastic models derived from the Oldroyd 8-constant model. Many previous results obtained for simple viscoelastic models are reproduced as special cases. Some previous conclusions are amended and new conclusions are drawn. In particular, we find that all models have singularities near the stagnation point and most of them can be avoided by appropriately choosing the model parameters and free functions. In addition, the analytical solution for the stress tensor of a near-wall stagnation-point flow for the Oldroyd-B model is also obtained. Its compatibility with the momentum conservation is discussed and the parameters are identified, which allow for a non-singular solution.  
viscoelastic models; stagnation-point flow; stress singularities; Weissenberg numbers +
Footnote †: journal: Applied Sciences  
0021
## 4 Experiments
### Evaluation Metrics
#### 4.2.1 Mean Absolute Error (MAE):  
MAE is most widely used evaluation metric in CF research literature ([PERSON] et al., 2016). MAE computes the average of absolute difference between the prediction ratings and the true ratings.  
\[MAE=\frac{\sum_{i=1}^{N}|p_{i}-r_{i}|}{n} \tag{14}\]  
where \(n\) represents the total number of ratings over all users, \(p_{i}\) is the predicted rating of item \(i\), and \(r_{i}\) is the actual rating of item \(i\).
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 4 Protocol
### ICRP (Internet-friendly Cryptographic Relay-detection Protocol)  
#### 4.2.1 Description  
Our protocol ICRP, described in Figure 11, runs between a sender \(\mathcal{S}\) and a receiver \(\mathcal{R}\). A full run of a protocol consists of \(n\) rounds and allows \(\mathcal{S}\) to gather one sample of \(n\) RTTs concurrently to the sending of \(n\) packets \(\{p_{i}\}_{i=1\ldots n}\) containing data or messages (unlike in Distance-Bounding protocols where the exchange only serves an authentication purpose). If the amount of data to be sent exceeds \(n\) packets, multiple runs of the protocol will be performed, we call each run a session of the protocol. Both parties must know the number of rounds \(n\) and, if defined, the number of sessions \(k\) prior to an execution, which makes them public parameters. When a session is initiated, \(\mathcal{S}\) marks \(n\) upcoming packets \(p_{i}\) by a random bit \(s_{i}\), creates a timestamp \(t_{i}\) and sends \(p_{i}||s_{i}\) to \(\mathcal{R}\). In each marked round, \(\mathcal{R}\) responds with a random bit \(r_{i}\). Upon reception of \(r_{i}\), \(\mathcal{S}\) creates a new timestamp \(t^{\prime}_{i}\). The RTT of the current round is actually the time difference \(t^{\prime}_{i}-t_{i}\).  
Once the \(n\) rounds have been performed, \(\mathcal{R}\) signs the hash of the \(p_{i}\)'s along with the \(s_{i}\)'s and the \(r_{i}\)'s; this hash is denoted \(H_{\mathcal{R}}\) and the signature \(\sigma_{\mathcal{R}}\). Finally, \(\mathcal{S}\) verifies that \(\sigma_{\mathcal{R}}\) is a valid signature on \(H_{\mathcal{S}}\) and let the collected sample of RTTs be analyzed by a decision function called Verify_Time (see Section 3).  
Note that the scheme displayed in Figure 11 describes the case where the party performing the verification is also the party willing to send the messages. In some cases, though, it is \(\mathcal{R}\) who needs to make sure that no relay was ongoing. This is achievable by swapping around the time measurements and by making \(\mathcal{S}\) send to \(\mathcal{R}\) some init message indicating to \(\mathcal{R}\) that the protocol may start, see Figure 12.  
Figure 10: [PERSON]’s distance-bounding protocol based on the Fiat-Shamir zero-knowledge authentication.
## 5 Performance Comparison
### Comparison to Benchmark DL Models
#### 5.2.1 Performance of VGG  
VGG was developed for Large-Scale Image Recognition [77]. VGG uses a large number of small convolution filters. The size of filters usually is \(3\times 3\) and \(1\times 1\) with the stride of one. The number of filters in VGG depends on the depth of the VGG model. VGG has been used in modulation classification in [35] and shows good performance when combined with 1D CNN.  
Table 3 summarizes the architectures of the two VGG classifiers which were trained and assessed. The input shape for the VGG3 and VGG16 classifiers was \((128,128,1)\), i.e., a \(128\times 128\) Boolean image of depth \(1\). VGG16 was built using the Keras library, according to the architecture explained in [77].  
Table 3 details the average precision, recall, F1-score, and accuracy for all the classifiers based on the VGG algorithm. Despite its depth, VGG16 cannot classify the protocols even  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Architecture Name** & **Architectures** & **Output Pooling Layer** & **\# Parameters** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline VGG3 & 3 VGG blocks & Max pooling layer & 3,269,605 & 4\% & 20\% & 7\% & 20\% \\ VGG16 & 16 VGG blocks & Max pooling layer & 14,716,101 & 4\% & 20\% & 7\% & 20\% \\ \hline Naive Inception & 1 Inception block & Max pooling layer & 7,947,461 & 4\% & 20\% & 7\% & 20\% \\ Inception2 & 2 Inception blocks & Max pooling layer & 10,572,293 & 4\% & 20\% & 7\% & 20\% \\ Inception-V3-MAX & Inception blocks [66] & Max pooling layer & 25,997,285 & 4\% & 20\% & 7\% & 20\% \\ Inception-V3-GAP & Inception blocks in [66] & Global average pooling layer & 21,812,453 & 40\% & 60\% & 47\% & 59.8\% \\ \hline ResNet-V2-MAX & Residual Networks & Max pooling layer & 57,482,661 & 4\% & 20\% & 7\% & 20\% \\ ResNet-V2-GAP & Residual Networks & Global average pooling layer & 54,343,845 & 11\% & 21\% & 14\% & 21\% \\ \hline
**The proposed DL** & \multirow{2}{*}{CNN+ CDAE weights} & \multirow{2}{*}{Max pooling layer} & \multirow{2}{*}{**51,401**} & \multirow{2}{*}{**100\%**} & \multirow{2}{*}{**100\%**} & \multirow{2}{*}{**100\%**} & \multirow{2}{*}{**100\%**} \\
**Model: CNN3 + AE** & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison to Benchmark DL Models using dataset of SNR 20 dB with AWGN (scenario 5). The highest performance number is highlighted. The lowest number of the model parameters is highlighted.  
in a very light noise scenario (SNR 20 dB with AWGN). The accuracy of VGG3 is 20%, equivalent to a random classifier for the five classes.
## 4 Discussion
### Deficiency and Prospects  
In this study, model selection, data correction, and local scenario settings were used to ensure the accuracy of the experimental results, but there are still some uncertainties. Although the CD data were revised with the help of meteorological factor correction models to bring them closer to estimates of regional CD, this correction was completely eliminated uncertainties, particularly in terms of inter-annual fluctuations and regional variations [59]. Specifically, the InVEST model may have overlooked several key factors, such as microbial activity and photosynthetic rate, in the process of CS estimation [60]. In addition, the model does not adequately account for the influence of the internal structure of LUCC and dynamic changes in vegetation age on CS potential [61]. Therefore, future studies should further refine the model and incorporate more environ-mental variables, human activities, and biochemical factors to improve the accuracy of CS estimates.  
Figure 12: The natural terrain gradient effect was used for regional zoning.
# An Efficient Steganographic Protocol for WebP Files
## 2 Materials and Methods
### Data-Based Methods
#### 2.4.2 Prediction Modes  
This method uses block prediction modes in the WebP format (derived from VP8) to store secret data. During encoding, the frame is divided into smaller segments called macroblocks. Then, based on previously processed blocks, the encoder is able to predict unknown pixel values in the macroblock. Redundant data may be subtracted from the block, which gives smaller residues that are efficiently compressed. It is called predictive coding.  
Different prediction modes used in WebP compression are presented in Figure 3. Each of them uses fairly simple arithmetic to extrapolate from already-calculated values. For example, horizontal prediction fills unknown parts of the block with a copy of the left-hand-side column. The mode with the best match is used for encoding the current block.  
In reference implementation, each of the prediction modes is represented as a number. To use them in steganography, we divided available modes into two groups, depending on their parity. In other words, even modes are in a separate group from odd modes. To hide a message, the encoder needs to select modes according to message bits. Normally, the chosen prediction mode is the one with the best match. This time we impose a limit to choose the best match only from the corresponding group. In this way, the stego file may be slightly bigger than the optimal image produced by the encoder, but the difference would be very small and inconspicuous. Also, there may occur little discrepancies between the optimal image and the stego image introduced by compression, which should not be visible. The capacity is one bit per block.  
Figure 3: Prediction modes used in WebP lossy compression ([[https://developers.google.com/speed/webp/docs/compression?hl=en](https://developers.google.com/speed/webp/docs/compression?hl=en)]([https://developers.google.com/speed/webp/docs/compression?hl=en](https://developers.google.com/speed/webp/docs/compression?hl=en)) (accessed on 2 August 2023); @).
# Accurate Modeling of Conductor Rough Surfaces in Waveguide Devices
## 3 Examples
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Modes**} & \multicolumn{3}{c}{**Resonance Frequency (GHz)**} & \multicolumn{3}{c}{**Quality Factor**} \\  & \(f_{\text{smooth,naly.}}\) & \(f_{\text{smooth,HFSS}}\) & \(f_{\text{rough,HFSS}}\) & \(Q_{\text{smooth,naly.}}\) & \(Q_{\text{smooth,HFSS}}\) & \(Q_{\text{rough,HFSS}}\) \\ \hline TM\({}_{010}\) & 6.562 & 6.647 & 6.646 & 12758 & 12624 & 10041 \\ TE\({}_{111}\) & 8.080 & 8.114 & 8.114 & 14373 & 14280 & 11129 \\ TE\({}_{011}\) & 12.222 & 12.330 & 12.329 & 26836 & 26621 & 20757 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Resonant frequencies and quality factors of an empty cylindrical resonator. Figure 5: Structure of a four-pole inductive rectangular filter operating in the Ka band.
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
## 5 Theoretical BER Performance
We assume:  
\[\mathbb{E}\left[\mathbf{Y}^{*}\mathbf{Y}^{T}\right]=\mathbf{R}_{ \mathbf{Y}}; \tag{24}\] \[\mathbb{E}\left[\overline{\mathbf{X}}^{*}\overline{\mathbf{X}}^{ T}\right]=\mathbf{R}_{\overline{\mathbf{X}},\overline{\mathbf{X}}};\] (25) \[\mathbb{E}\left[\mathbf{Y}^{*}X_{p}\right]=\mathbf{R}_{\mathbf{Y},X_{p}};\] (26) \[\mathbb{E}\left[\overline{\mathbf{X}}^{*}X_{p}\right]=\mathbf{R}_{ \overline{\mathbf{X}},X_{p}};\] (27) \[\mathbb{E}\left[\overline{\mathbf{X}}^{*}\mathbf{Y}\right]=\mathbf{R }_{\overline{\mathbf{X}},\mathbf{Y}} \tag{28}\]  
and  
\[\mathbb{E}\left[X_{p}X_{p}^{*}\right]=\mathbf{R}_{\mathbf{X}}. \tag{29}\]  
Equation (23), after some manipulation, can be written as:  
\[\Theta=\mathbf{F}^{H}\mathbf{R}_{\mathbf{Y}}\mathbf{F}+\mathbf{B}^{H}\mathbf{ R}_{\overline{\mathbf{X}},\overline{\mathbf{X}}}\mathbf{B}-2 Re\Big{\{} \mathbf{F}^{H}\mathbf{R}_{\mathbf{Y},X_{p}}\Big{\}}+2 Re\Big{\{}\mathbf{B}^{H} \mathbf{R}_{\overline{\mathbf{X}},X_{p}}\Big{\}}-2 Re\Big{\{}\mathbf{B}^{H} \mathbf{R}_{\overline{\mathbf{X}},\mathbf{Y}}\mathbf{F}\Big{\}}+\mathbf{R}_{ \mathbf{X}}. \tag{30}\]  
With Equation (30), it is possible to obtain the BER performance through Equation (15) for any of the receivers previously presented.
# A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network
## 4 Experiment and Result Analysis
### Gaussian Distribution
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Default Values** \\ \hline The number of beacon nodes & \(M\) & 6 \\ \hline NLOS error probability & \(P_{NLOS}\) & 0.5 \\ \hline The measurements’ noise & \(N\big{(}0,\sigma_{L}^{2}\big{)}\) & \(N\big{(}0,1^{2}\big{)}\) \\ \hline The NLOS errors & \(N\big{(}\mu_{NLOS},\sigma_{NLOS}^{2}\big{)}\) & \(N\big{(}5,6^{2}\big{)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Gaussian distribution parameter. Figure 4: **(a)** Performance comparison between the EKF, interacting multiple model (IMM)-EKF, modified probabilistic data association (MPDA), and the proposed algorithm (PIMM) under a different number of beacon nodes \(M\), where \(P_{NLOS}=0.5\), \(N\big{(}0,1^{2}\big{)}\), and \(N\big{(}5,6^{2}\big{)}\). **(b)** Performance comparison between the EKF, IMM-EKF, MPDA, and the proposed algorithm under different NLOS error probabilities \(P_{NLOS}\), where \(M=6\), \(N\big{(}0,1^{2}\big{)}\), and \(N\big{(}5,6^{2}\big{)}\). **(c)** Performance comparison between the EKF, IMM-EKF, MPDA, and the proposed algorithm under different mean values \(\mu_{NLOS}\) of the Gaussian distribution, where \(M=6\), \(\sigma_{NLOS}=6\),\(P_{NLOS}=0.5\), and \(N\big{(}0,1^{2}\big{)}\). **(d)** Performance comparison between the EKF, IMM-EKF, MPDA, and the proposed algorithm under different standard deviations \(\sigma_{NLOS}\) of the Gaussian distribution, where \(M=6\), \(P_{NLOS}=0.5\), \(\mu_{NLOS}=5\), and \(N\big{(}0,1^{2}\big{)}\). **(e)** The cumulative distribution function (CDF) of the localization error.
# Personalized Bone Reconstruction and Regeneration in the Treatment of Craniosomiasis
## 2 Biomaterials and Tissue Engineering Approaches
### Tissue Engineering Strategies
Shear thinning hydrogels consist in ex vivo pre-formed hydrogels that are delivered in vivo applying shear stress during injection (commonly through a syringe) and quickly self-heal after shear removal [130]. Conversely, in situ hydrogels either require a cross-linking agent (often toxic), or exploit physical properties (e.g., temperature, and pH), to transit from sol to gel upon being injected into the defect site. Therefore, the in vivo environment can affect the crosslinking behavior of in situ gelling agents, while it has a negligible effect in the case of shear thinning hydrogels [131]. PLGA-based colloidal gels, produced using poly(ethylene-co-maleic acid) (PEMA) and polyvinylamine (PVAm) as surfactants, loaded with dexamethasone (DEX), have been exploited in rat cranial bone defects treatment. The tested hydrogels supported osteoporonduction, promoting bone formation, whereas the untreated cranial defects showed negligible bone formation and collapsed [132]. Furthermore, a very low and delayed DEX release was achieved over two months from the loaded the PLGA nanoparticles and for one month when DEX was blended with the particles [132]. Another group developed a calcium sulfate (CaSO4) and FGF-18 (Fibroblast Growth Factor-18) loaded chitin-poly(lactide-co-glycolide) (PLGA) composite hydrogel for rat craniofacial bone defect regeneration. They revealed a sevenfold increase in the elastic modulus compared with the neat chitin-PLGA hydrogel, a sustained release of FGF-18, an enhanced alkaline phosphatase (ALP), increased endothelial cell migration, early and almost complete bone healing in comparison with chitin-PLGA/CaSO4, chitin-PLGA/FGF-18, chitin-PLGA, and sham control system, respectively [133].
## 3 Crafting the Future: Unveiling 6G's Pinnacle Features, and  
Delicate Trade-Offs  
Achieving the key features of 6G requires the application of multiple cutting-edge communication technologies. Let us begin by qualitatively comparing 5G and 6G communications.  
Figure 3 provides a qualitative comparison, considering that 5G's spectral efficiency is close to its limits. While 6G may not significantly improve spectral efficiency, it emphasizes enhancing privacy, security, and secrecy. In 6G, improvements are anticipated in intelligence, affordability, energy efficiency, and customization. The advancements will be driven by energy harvesting, artificial intelligence (AI), and novel networking architectures [1; 15]. In Figure 3, a hypothetical comparison is conducted across several dimensions: security, confidentiality, and privacy; spectral utilization; smart capabilities; energy conservation; and cost-effectiveness and personalization. The concentric lines and shaded areas do not correspond to fixed numerical increments or specific units of measurement. Rather, they provide a visual, high-level comparison of performance enhancements (e.g., spectral efficiency, privacy/security, affordability/customization, energy efficiency, and intelligence) that 6G may offer over 5G.  
It is important to emphasize that Figure 3 serves as a conceptual overview rather than a precise quantitative benchmark. Each axis in the figure repres  
Figure 3: Comparative analysis of 5G versus 6G communications in terms of quality. Adopted from [1], it is intended as a conceptual, high-level, qualitative illustration of the relative improvements anticipated with 6G as compared to 5G across multiple dimensions rather than actual numeral quantities.  
dimension, and the radial 'levels' reflect relative improvements rather than strict numerical values. While the underlying technical literature and references provide detailed frequency bands, energy targets, and other measurable parameters, the figure is simply a qualitative tool to illustrate the general trajectory of enhancements that 6G technologies are expected to achieve compared to 5G.
## 1 Introduction
NP number density and diffusion of particles in suspension may change depending on the particle size, which can change rock wettability. Thus, this study focused on the diffusion of NPs and NP number density in suspension for surface wettability. In addition, there is a serious lack of information in terms of how nanoparticle size influences IFT at the oil-water interface. According to previous studies, [PERSON] and [PERSON] [31] and [PERSON] et al. [32], showed that large NPs exhibit higher surface tension. They concluded that this trend is likely due to the strengthened van der Waals force between particles as particle size increases. However, [PERSON] [33] found that IFT decreased with increasing particle size. They concluded that the rate of adsorption of the nanoparticles at the oil-water interface decreases with decreasing nanoparticle size. [PERSON] et al. [34] also found that the effects of very small NPs (<20 nm) on IFT are less clear. They concluded that in cases of small nanoparticle adsorption, the resulting behavior is difficult to predict. Thus, previous research showed that the influence of particle adsorption on the oil-water interfacial tension is not well understood. For research on the variation of IFT and wettability, the NP spatial density at the oil-water interface is important in terms of interfacial stability. Nonetheless, the relationship of IFT reduction as a function of the interfacial area covered by differently sized NP and the desorption energy required to remove one NP has not been clearly elucidated. Thus, unlike the previous study, we tried to analyze how the size of NPs affects the interfacial area at the oil-water interface covering per NP, and, thereby, whether interfacial tension is affected. Here, we evaluated colloidal silica NPs (CSNPs) of various sizes (7, 12, and 22 nm), and a fumed silica NP (FSNP) to which the zwitterionic surfactant PSS-\(co\)-MA had been adsorbed (attracted by the negative charges of silica). Specifically, we focused on how NP size and type affected the IFT of the oil/nanofluid interface and rock surface wettability. To explore how NP density affected IFT, we calculated the interface areas covered by NPs, assuming that all NPs were located at the interface. We studied the effects of particle size on wettability by observing changes in the contact angles according to NP size in three-phase rock-oil-water systems. We also investigated the flow within porous rocks according to particle size and type in terms of oil recovery; we performed core flooding experiments using Berea sandstone.