esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Dual Authenticated Encryption Scheme Suitable for Social Networking Services
## 6 Conclusions  
For facilitating the three-party applications of many social networking services, in this paper, the author introduced a novel DAE scheme based on bilinear pairings. In the proposed scheme, a sender engaged in a three-party communication environment is able to generate a single authenticated ciphertext that could be solely decrypted and verified by the other two participants without compromising the confidentiality of their private keys. Unlike attribute-based encryption mechanisms in which a user's attribute is usually associated with the ciphertext or the decryption key, our scheme is implemented in the conventional public key system without managing any attribute. As for the security, we showed that our approach is computationally secure in the notion of IND-CCA2 and that of EF-CMA by utilizing the random oracle proof model. In addition, we compared our scheme with two previous straightforward protocols in terms of computational efforts. The experimental results clearly reveal that the proposed DAE scheme is really a better alternative for three-party communication environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Computational Costs of Sender**} & \multicolumn{2}{c}{**Computational Costs of**} & \multirow{2}{*}{**Total Computational Costs**} \\  & & & **Each Recipient** & \\ \hline \multirow{2}{*}{**LCL**} & 4\(T_{B}+4T_{M}\) & 2\(T_{B}+T_{M}\) & 8\(T_{B}+6T_{M}\) \\  & (\(\approx\)211.12 ms) & (\(\approx\)46.4 ms) & (\(\approx\)198.36 ms) \\ \multirow{2}{*}{**HL**} & 2\(T_{B}+8T_{M}\) & 3\(T_{B}+3T_{M}\) & 8\(T_{B}+14T_{M}\) \\  & (\(\approx\)91.06 ms) & (\(\approx\)79.17 ms) & (\(\approx\)249.4 ms) \\ \multirow{2}{*}{**IB**} & 6\(T_{B}+6T_{M}+2T_{E}\) & \(T_{B}+T_{M}+T_{E}\) & 8\(T_{B}+8T_{M}+4T_{E}\) \\  & (\(\approx\)198.74 ms) & (\(\approx\)46.59 ms) & (\(\approx\)291.92 ms) \\ \multirow{2}{*}{**CZXY**} & 2\(T_{B}+6T_{M}\) & \(T_{B}+3T_{M}\) & 4\(T_{B}+12T_{M}\) \\  & (\(\approx\)78.3 ms) & (\(\approx\)39.15 ms) & (\(\approx\)156.6 ms) \\ \multirow{2}{*}{**Ours**} & \(T_{B}+3T_{M}\) & \(T_{B}+3T_{M}\) & 3\(T_{B}+9T_{M}\) \\  & (\(\approx\)39.15 ms) & (\(\approx\)39.15 ms) & (\(\approx\)117.45 ms) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of computational costs in three-party communication environments.  
Figure 1: Comparison of computational costs among different quantities of ciphertext in the three-party communication environment.  
**Funding:** This work was supported in part by the Ministry of Science and Technology of Republic of China under the contract number MOST 107-2221-E-019-017.  
**Conflicts of Interest:** The author declares that he has no conflict of interest.
## 5 Results and Discussion
### Sentiment Classification
#### 5.1.2 Results Based on Yelp 2013 Dataset  
Table 2 represents a comparative analysis of the Sentiment Classification model for several performance metrics through altering training data. The analysis of TD-Spider TaylorChoA-GRU for the precision, recall and F-measure is listed below. In 80% of training data, the precision of the developed TD-Spider TaylorChoA-GRU is 0.914. The recall of TD-Spider TaylorChoA-GRU is 0.925 for 80% of training data and, considering 80% of training data, the F-measure of the developed model is 0.920.  
Figure 5 represents the accuracy and loss for the Sentiment Classification model. Here, when the iteration increases, the accuracy also increases. When the iteration is 300, the accuracy is 0.970. Similarly, when we increase the iterations, the loss is decreased. When the iteration is 300, the loss is 0.04. Hence, the proposed method prov  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & **Data (\%)** & **HSACN** & **MCNN** & **DCBVN** & **[3]** & **[34]** & **[17]** & **Proposed** \\ \hline \multirow{4}{*}{Precision} & 60\% & 0.754 & 0.775 & 0.814 & 0.825 & 0.842 & 0.848 & 0.857 \\  & 70\% & 0.765 & 0.785 & 0.825 & 0.848 & 0.865 & 0.868 & 0.875 \\  & 80\% & 0.795 & 0.814 & 0.847 & 0.865 & 0.904 & 0.908 & 0.914 \\  & 90\% & 0.825 & 0.841 & 0.885 & 0.907 & 0.933 & 0.938 & 0.947 \\ \hline \multirow{4}{*}{Recall} & 60\% & 0.775 & 0.795 & 0.833 & 0.858 & 0.875 & 0.879 & 0.887 \\  & 70\% & 0.785 & 0.796 & 0.854 & 0.875 & 0.896 & 0.898 & 0.901 \\  & 80\% & 0.801 & 0.825 & 0.875 & 0.898 & 0.914 & 0.918 & 0.925 \\  & 90\% & 0.833 & 0.865 & 0.895 & 0.916 & 0.941 & 0.948 & 0.950 \\ \hline \multirow{4}{*}{F-measure} & 60\% & 0.765 & 0.785 & 0.823 & 0.841 & 0.858 & 0.863 & 0.872 \\  & 70\% & 0.775 & 0.791 & 0.840 & 0.861 & 0.880 & 0.883 & 0.888 \\ \cline{1-1}  & 80\% & 0.798 & 0.820 & 0.860 & 0.881 & 0.909 & 0.913 & 0.920 \\ \cline{1-1}  & 90\% & 0.829 & 0.853 & 0.890 & 0.911 & 0.937 & 0.943 & 0.949 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of Sentiment Classification for Yelp 2013 dataset in terms of precision, recall and F-measure.  
Figure 4: Performance analysis based on (**a**) accuracy and (**b**) loss for Sentiment Classification using IMDB dataset.
# Image Quality for Near-Eye Display Based on Holographic Waveguides
###### Abstract  
The paper analyzes the image quality in augmented reality display based on holographic waveguides. Brightness, brightness non-uniformity, image noise, etc., depend on the parameters of the waveguide substrate, the configuration, and the relief shape of diffraction optical elements. The optimal structure of holographic waveguides obtained by analog holography has been studied. The presented recommendations to achieve the best image quality are based on experimental results for different configurations of holographic waveguides.  
holographic waveguide; image quality; brightness; analog diffraction gratings; holography; near-eye display; diffraction optical element +
Footnote †: journal: Medical Imaging  
1
Footnote 1: email: _Applied_  
also solutions based on the Bragg diffraction mode [21; 23]. In the case of an analog quasi-sinusoidal relief profile, the gratings have a diffraction efficiency of up to 34%; in the case of a binary (rectangular) relief profile, they have a diffraction efficiency of up to 41% [24]; in case of a slanted relief profile, they have a diffraction efficiency over 80% [20], acquiring the properties of Bragg gratings. The manufacturing of slanted diffraction gratings requires a complex technological process based on reactive ion etching and nanoimprint lithography [25]. This paper investigates holographic waveguides based on analog photoresist diffraction gratings with a quasi-sinusoidal relief profile.  
Image quality formed by holographic waveguides essentially differs from the image quality formed by standard projectors. This paper analyzes the influence of the DOEs configuration on the image quality, expressed by the presence of chromatic aberrations and different brightness uniformity artifacts. The research aims to ensure the quality of image projection through a holographic waveguide obtained using analog technology (holography). The paper presents various configurations of holographic waveguides and provides recommendations for those that meet the quality requirements of the output image.
## 4 Experiments
### Ablation Study
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multicolumn{2}{c}{**Modules**} & \multicolumn{2}{c}{**Decoders**} & \multirow{2}{*}{**Params (M)**} & \multirow{2}{*}{**FLOPs (G)**} & \multirow{2}{*}{**mIoU**} \\
**MSCA** & **SEM** & & & & & \\ \hline MSCA & ✓ & & & ✓ & 4.2 & 4.9 & 30.9 \\ MSCA + SEM & ✓ & ✓ & & ✓ & 4.8 & 41.7 & 38.9 \\ MSCA + SEM + IAM & ✓ & ✓ & ✓ & & 11.8 & 37.1 & 44.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation studies on different parts of the network. Simple represents the simple segmentation head which is a per-pixel segmentation head. IAM represents the instance activation map decoder head. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Output** & **Params (M)** & **FLOPs (G)** & **mIoU** \\ \hline \(\left\{\frac{1}{4},\frac{1}{8},\frac{1}{16},\frac{1}{32}\right\}\) & 11.8 & 37.0 & 44.4 \\ \(\left\{\frac{1}{8},\frac{1}{16},\frac{1}{32}\right\}\) & 11.8 & 36.5 & 42.9 \\ \(\left\{\frac{1}{16},\frac{1}{32}\right\}\) & 11.7 & 35.8 & 42.3 \\ \(\left\{\frac{1}{32}\right\}\) & 11.6 & 35.1 & 39.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation studies on the outputs at different scales of the backbone. We split the features into two, three, and four parts along the channel dimension. Each part has the same channel number. Each method uses the same 3 \(\times\) 3 convolution with different dilation rates. From Table 7, dividing the input features into three parts achieves the best accuracy, while the Params remains roughly equivalent to the other two methods. From the first row of Table 8, setting the dilation rate to 1, 2, and 3 achieves the best accuracy of 44.4 mIoU. At the same time, we conduct the experiment only using traditional 3 \(\times\) 3 convolution and the result demonstrates that the performance of 3 \(\times\) 3 convolution is 1.8 mIoU lower than using multi-scale dilated convolutions.
## 4 Mathematical Calculation of PSD
### Bipolar RZ (RZ-AMI) Signals PSD  
Return to Zero (RZ) is based on positive and negative voltage levels. The \"1\" is illustrated with \"A\", positive amplitude, while a \"0\" is mapped with \"-A\", negative amplitude. In some cases, bipolar RZ and AMI are considered with the same amplitudes. The amplitudes (\(\mathbf{a_{k+nk}}\) in both instances use different levels of voltage (+A, 0, -A). The \"1\" is modulated amplitude, \"A\" and \"-A\", alternately, while the \"0\" corresponds to zero voltage as shown in Equation (4) [26].  
\[P(f)=\frac{A^{2}T_{b}}{4}\Bigg{(}\frac{sinrfT_{\frac{b}{2}}}{\pi fT_{\frac{b}{2}}} \Bigg{)}^{2}sin^{2}(\pi fT) \tag{4}\]
For instance, in [32], the case is analyzed but only for time-constant delays. Furthermore, the implementation requires the exchange not only of positions but also of velocities. In [33], the use of PID controllers is studied, but once again, constant time delays are considered, and instead of the highly nonlinear dynamics of robot manipulators, simple linear transfer functions are considered. More recently, in [34], the even more complex case of consensus for networked Euler-Lagrange agents using a PID-like controller was solved. However, achieving a robust global stability result requires the use of strictly increasing linear saturation functions and some auxiliary variables. In [35], a simpler PID-like control law with observer design to solve the consensus and force reflection problems was proposed. However, the integral terms utilize a second-order sliding mode variable, and the stability result is only local. In a recent work [36], a standard PID controller has been used for the control of a 2-DOF teleoperated manipulator; however, no stability proof is provided, and the PID gains are found by using the well-known Ziegler-Nichols approach. This result is not surprising since, despite the lack of theoretical results, it has recently been shown that standard PID controllers exhibit strong robustness properties for nonlinear systems [37]. The main contribution of the present work is the following:  
1. This work presents a novel technical proof demonstrating the closed-loop stability of PID laws in the control of bilateral systems with time-varying delays, addressing an aspect not previously established in the literature. The proposed theoretical framework was validated using these experimental procedures: 1. The theory is tested by setting gains according to the main theorem of this contribution, and letting a human operator move both local and remote end-effectors to constant positions. 2. The well-known robustness properties of PID controllers are shown by carrying out experiments involving constrained motion of the remote manipulator, both with and without force feedback. The rest of the paper is organized as follows. The system's model and some properties are given in Section 2. The main theorem about closed-loop stability of bilateral robot manipulators in free motion using standard PID control laws is stated in Section 3. Section 4 presents experimental results to validate the developed theory. The paper concludes in Section 5.
# Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots
## 2 Methods  
In swarm foraging, a robotic system is homogeneous, and all robots follow the same behavior rules when foraging. In the whole foraging task, the robot switches between the three states, as shown in Figure 1. The task allocation refers to the process of transitioning from the waiting state to the searching state. Details about swarm robot foraging and state transitions will be given in Section 2.2. The states for swarm foraging are as follows:  
Waiting (\(S_{W}\)): At the outset of foraging, all robots are waiting in the nest. An individual robot initializes a timer at \(t_{1}=0\) to denote its waiting time in the nest, with the minimum waiting time \(T_{1}\) being given in advance. When the waiting time expires (\(t_{1}\geq T_{1}\)), the individual robot uses the DRTM to calculate its foraging probability \(Ps\). Based on \(Ps\), the robot determines whether to switch from the \(S_{W}\) state to the \(S_{S}\) state.  
Searching (\(S_{S}\)): When a robot begins foraging, the ASM controls its foraging behavior. When the robot captures a food token, it switches to the \(S_{R}\) state.  
Returning (\(S_{R}\)): In the \(S_{R}\) state, the ASM controls the returning behavior. When the robot arrives at its nest, it deposits the food token and then switches to the \(S_{W}\) state.  
To maintain the number of food tokens at the desired level, the swarm should use effective self-organized TA to respond to its changing environment. We employ a control strategy based on the ASM to control the movement of each robot. Herein, we propose the DRTM to generate self-organized TA to adjust the number of foraging robots dynamically.
## 5 Fuzzy Logic Approaches in Trust Management
However, these characteristics can vary significantly when fuzzy logic is combined with other methods:  
* **Advantages of Fuzzy Logic:** Fuzzy logic methodology offers several advantages for assessing trust in 6G networks. It enables trust evaluation based on experience, plausibility, and location accuracy, crucial components in trust assessment. Moreover, fuzzy logic systems have found extensive applications in various fields like web services, cloud computing, and social networks, showcasing their adaptability and utility. Additionally, fuzzy logic facilitates the development of classification criteria for assessing trust, resulting in a structured approach to trust evaluation and enhanced detection capabilities. * **Disadvantages of Fuzzy Logic:** Despite its utility in other network applications, employing fuzzy logic for trust assessment in 6G networks presents certain drawbacks. Challenges arise in precisely characterizing and interpreting language variables and fuzzy rules, potentially leading to uncertainty and imprecision in trust assessment. Furthermore, fuzzy logic may introduce computing overhead and complexity, impacting the real-time decision-making capabilities crucial for 6G networks. Careful consideration is necessary to address these challenges and ensure reliable and robust trust management in 6G networks, particularly when incorporating fuzzy algorithms into IoT trust management techniques. Figure 3: Fuzzy logic system (Mamdani model). Exploring the Application of Fuzzy Logic Algorithms in Trust Assessment across Diverse Network Environments
## Appendix B Proof of Proposition 1
### Exponential Stability  
To show exponential stability, let \(\epsilon>0\) (arbitarily small) and consider the Lyapunov function (A2) augmented with a cross-term:  
\[V_{\epsilon}=V+\epsilon e_{\eta}^{\top}Je_{\omega}.\]  
which is positive definite for small \(\epsilon\). The time-derivative of \(V_{\epsilon}\) along the closed-loop trajectories satisfies  
\[\dot{V}_{\epsilon}=\dot{V}+\epsilon e_{\eta}^{\top}J\epsilon_{\omega}+ \epsilon e_{\omega}^{\top}J\epsilon_{\eta}.\]  
We calculate the last two terms separately. From (A1) and (17) we get  
\[\epsilon e_{\eta}^{\top}J\epsilon_{\omega} =-\epsilon k_{p}e_{\eta}^{\top}e_{\eta}+\epsilon e_{\eta}^{\top}S (Je_{\omega})e_{\omega}-ee_{\eta}^{\top}[K_{d}-V_{a}D+S(\omega_{d})J-S(J\omega _{d})]e_{\omega}\] \[\leq-\epsilon k_{p}\|e_{\eta}\|^{2}+\epsilon\lambda_{\max}^{I}\| e_{\omega}\|^{2}+\epsilon(\lambda_{\max}^{K_{d}}+V_{\max}e_{\max}^{D}+2 \lambda_{\max}^{I}c_{\omega_{d}})\|e_{\eta}\|\|e_{\omega}\|,\]since \(\|e_{\eta}\|\leq 1\). The other terms becomes  
\[\epsilon e_{\omega}^{\top}\|e_{\eta}=-\epsilon e_{\omega}^{\top}\Big{[}S(\omega_{ d}^{\perp})e_{\eta}+S(\eta_{d})S(\eta)e_{\omega}\Big{]}\leq\epsilon\lambda_{\max}^{I}c_{ \omega_{d}^{\perp}}\|e_{\eta}\|\|e_{\omega}\|+\epsilon\lambda_{\max}^{I}\|e_{ \omega}\|^{2}.\]  
Combining this with (A3) gives  
\[\dot{V}_{\epsilon}\leq-\epsilon k_{p}\|e_{\eta}\|^{2}-(\gamma-2\epsilon \lambda_{\max}^{I})\|e_{\omega}\|^{2}+\epsilon(\lambda_{\max}^{K_{d}}+V_{\max} \sigma_{\max}^{D}+\lambda_{\max}^{I}(2\epsilon_{\omega_{d}}+c_{\omega_{d}^{ \perp}})\|e_{\eta}\|\|e_{\omega}\|.\]  
Let \(x\triangleq[\|e_{\eta}\|\ \ \|e_{\omega}\|]^{\top}\). Then, we can write \(\dot{V}_{\epsilon}\leq-x^{\top}Mx\), where the matrix \(M\) is given by  
\[M=\begin{bmatrix}\epsilon k_{p}&-\frac{\epsilon\zeta}{2}\\ -\frac{\epsilon\zeta}{2}&\gamma-2\epsilon\lambda_{\max}^{I}\end{bmatrix},\]  
where \(\zeta=\lambda_{\max}^{K_{d}}+V_{\max}\sigma_{\max}^{D}+\lambda_{\max}^{I}(2 \epsilon_{\omega_{d}}+c_{\omega_{d}^{\perp}})\). The matrix \(M\) is positive definite if the following inequality is satisfied:  
\[\epsilon<\frac{4k_{p}\gamma}{8k_{p}\lambda_{\max}^{I}+\xi^{2}}.\]  
Since \(\epsilon\) can be chosen arbitrarily small, this inequality can always be satisfied. With \(\dot{V}_{\epsilon}\) negative definite, and with quadratic bounds on \(V_{\epsilon}\), we conclude that the desired equilibrium is exponentially stable. For estimation of the region of exponential convergence, see [72].
## 3 Results and Discussion
### Evaluation of Sensory Properties
A significant difference was observed between the 250 MPa-60 \({}^{\circ}\)C treated sample and the control; the control was stored for two weeks, the 60 \({}^{\circ}\)C-250 MPa treated sample was stored for two weeks, and the 250 MPa-60 \({}^{\circ}\)C treated sample was stored for two weeks. The sample that exhibited the least browwing was the sample that had undergone heat treatment subsequent to fresh pressure treatment. Although the fresh control sample was browser, it is not possible to conclude that the treatment resulted in lightening. Rather, it may be assumed that the combined treatments slowed down the browning process. By the time the test samples were presented to the judges, the fresh control sample had browned slightly. This difference between the samples that had completely disappeared by the end of 14 days of storage and were uniformly browned by the judges is noteworthy. In terms of preference, the control was ranked first, while its two-week stored counterpart was ranked last. Once more, there was a significant difference between the fresh and stored samples. As might be expected, the judges gave preference to samples that were freshly prepared, according to their ranking numbers. The combined treatments were found to have an equally adverse effect on the fresh smoothie products. The rate of deterioration observed in the fresh samples was much more modest than that observed at the end of the storage period. Of the stored samples, the control sample was the least preferred. In a study conducted by [PERSON] et al. (2016), the sensory properties of apple, strawberry, banana and orange smoothies treated with HHP and gentle heat were investigated. They found that the freshness characteristics of the products, including fresh colour, fresh smell, fresh taste, and pink colour, gradually decreased during storage. This deterioration in flavour can be attributed to the degradation or oxidation of ester and aldehyde compounds, which is thought to be caused by residual enzyme activity.
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### Which Studies Provide Feedback for Participants Derived from Obtained Results? (RQ5)
#### 3.5.1 Who Receives the Feedback?  
Most studies delivering feedback target teachers as the primary recipients. The goal is typically to help them refine pedagogical strategies using insights derived from their own classroom discourse, for example, their use of specific talk moves, questioning patterns, or instructional vocabulary. In one case, teachers received personalized statistics on their use of mathematics terminology and the distribution of teacher-student talk-time, with comparisons against their own past data and the behavior of other platform users [100]. Other studies similarly provide post-session feedback summarizing discourse patterns or question types, encouraging gradual, data-informed pedagogical refinement [41; 71; 101; 102].  
While teacher-focused feedback dominates, a smaller group of studies extends feedback to students, either directly or through mediated teacher actions. Some systems benefit both teachers and students simultaneously by visualizing classroom participation in real time, for example, dashboards that display talk-time proportions or overlapping speech, prompting more balanced turn-taking [58]. Other systems offer individualized feedback to students, such as metrics on pronunciation accuracy or fluency scores. In these cases, teachers may also receive alerts when students fall below performance thresholds, allowing for timely instructional interventions [44; 45]. This dual-feedback approach can support not only student reflection but also responsive teaching.
## 4 Results and Discussions
### Comparison  
To further confirm the effectiveness of the proposed method, we apply the proposed ACA module to a two-stage detector GA-Faster R-CNN [25] and compare our methods with most of the SOTA one-stage and two-stage detectors. We could see that two-stage detectors commonly achieve better detection accuracy than one-stage detectors [6], but have lower inference speeds. And Table 3 shows that ACA-Net achieves an AP of 37.3%. Meanwhile, we could also see that ACA-Faster R-CNN achieves an AP of 40.3%, 1.2 points higher than the baseline GA-Faster R-CNN. These results indicate that both our one-stage and two-stage methods are comparable to most of the SOTA methods in accuracy. The results better confirm the effectiveness of the proposed ACA module. However, ACA-Net achieves an FPS of 7.6, 1.0 FPS lower than the baseline GA-RetinaNet. And compared with GA-Faster R-CNN, the FPS of ACA-Faster R-CNN decreases from 7.5 to 6.0. Nonetheless, it is worth noting that our aim is to verify the effectiveness of the proposed ACA module, instead of achieving SOTA performance in both accuracy and speed with many tricks. Besides, both ACA-Net and ACA-Faster R-CNN are obviously far faster than artificial visual inspection.  
For a more intuitive comparison, some detection examples on NEU-DET dataset are shown in Figure 7. We could find that both ACA-Net and ACA-Faster R-CNN detect all surface defects with high accuracies, which further confirms the effectiveness of our methods.