esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Existing Redundancy Protocols
It also allows for true shortest path forwarding in a mesh Ethernet network context utilizing multiple equal cost paths. SPB further provides fast connectivity restoration after failure. TRILL was standardized by the Internet Engineering Task Force (IETF) and is a layer 2 forwarding protocol that operates within one IEEE 802.1-compliant Ethernet broadcast domain. It replaces the spanning tree protocols by using IS-IS routing to distribute link state information and calculate the shortest paths through the network. TRILL is implemented by devicescalled routing bridges (RBridges). RBridges are compatible with and can incrementally replace previous IEEE 802.1 customer bridges. MMRP [33] is a layer 2 protocol for registering group MAC addresses (_i.e._, multicast) on multiple switches. It is a multiple registration protocol (MRP) application [33]. The purpose of MMRP is to allow multicast traffic in bridged LANs to be confined to the areas of the network where it is required. Several redundancy protocols have been developed and standardized in the International Electrotechnical Commission (IEC) 62439 standard series to provide protocol-independent redundancy in substation automation networks, such as MRP [34], CRP [35], BRP [36], PRP [1], and HSR [1]; however, among these protocols, only two--PRP and HSR--are suitable for seamless communications. Both HSR and PRP are based upon the principle of providing duplicated frames for separate physical paths with a zero recovery time [1]. Unlike PRP, which requires dual redundant independent networks, HSR can be applied to a single network, in particular single rings and connected rings while simultaneously retaining its zero recovery time property.
## 3 Results
### Mathematical Modeling Results  
#### 3.2.1 Statistical Analysis of Data  
Statistical analysis of data {\(X\)(\(t\)), \(P\)(\(t\)), \(Z\)(\(t\))}, \(t\) = 1,2,..., \(n\) was performed in the Statistica 10 software [43]. The research carried out was further used in the development of mathematical models. Table 1 shows a fragment of the initial data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(t\) & \(P\) & \(X\) & \(Z\) \\ \hline
1 & 4 & 2 & 3 \\
2 & 3 & 0 & 6 \\
3 & 1 & 0 & 4 \\
4 & 1 & 1 & 6 \\
5 & 0 & 2 & 14 \\
6 & 1 & 1 & 16 \\
7 & 1 & 0 & 53 \\
8 & 2 & 1 & 57 \\
9 & 3 & 0 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fragment of the initial data.  
Figure 8: Sentiment of the actors’ digital footprints.  
Figure 9: Dynamics of messages with various sentiment types.  
The calculated sample estimates of the correlation coefficients (Table 2) indicate that the indicators under consideration have a linear relationship.  
We investigated the behavior of the dependence of indicators \(X\), \(Z\) (non-normalized indicators are considered). Figure 10 shows a graph of polynomial smoothing of the dynamics of indicators \(X\), \(Z\) in the OXZt plane.  
When constructing a mathematical models, the initial data are transformed according (2.3) to the rule: \(x(t)=X(t)/S(t)\); \(p(t)=P(t)/S(t)\); \(z(t)=Z(t)/S(t)\).  
According to the calculated estimates of the correlation coefficients (Table 3) between variables x and z, the correlation coefficient is high.  
A negative sign in front of the correlation coefficient may indicate that the number of people with a negative attitude is increasing due to the transition of actors from the group of those with a neutral attitude towards the group of those with a negative attitude.
# Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption
## 4 Trajectory Optimization Based on GWO  
Although the trajectory shape and motion law of the Par4 parallel robot have been planned in the previous sections, the problem of parameter selection for the Lame curve remains unresolved. Previously, the parameters of the Lame curve have mainly been determined in the planning process through prior knowledge, and certainly there are some optimization designs done already, such as the method for a Delta parallel robot in [37], which is a brute force optimization method where the parameters of the Lame curve were traversed at a step length of 1 mm, thus causing not only a heavy workload but also a large degree of optimization error. Therefore, in this paper, we adopt the recently proposed Grey Wolf Optimizer (GWO) to achieve the trajectory optimization with the target of minimal energy consumption for the Par4 parallel robot. In this section, the GWO algorithm is first introduced, and then the trajectory optimization method is elaborated.
## 5 Conclusions  
This study introduces a high-precision dual-prism compensation method based on aberration theory, aiming to solve the problem of image plane deformation in doubly telecentric projection optical systems. Through the design and simulation verification of the dual-prism compensation device, the proposed method has demonstrated significant improvements in image quality, with image plane deformation reduced to 3.011 \(\upmu\)m, max RMS WFE less than 0.11 \(\uplambda\), and average RMS WFE less than 0.07 \(\uplambda\). These results fall within the acceptable range of high-precision systems and exhibit good telecentricity. This work not only improves the accuracy of optical compensation but also provides a systematic, analysis-driven deformation compensation framework that is more predictable and accurate than existing methods.  
When adjusting the defocusing tilt compensation device to compensate for image plane deformation, different adjustment methods can produce different aberrations and even lead to an increase in induced aberrations and higher-order aberrations. Since the aberrations generated by each surface have been quantified, it is possible to eliminate aberrations during the adjustment process and reduce residual aberrations by adding free-form surfaces to appropriate surfaces according to the needs of the application scenario, thus achieving better compensation.  
In the actual experiment, manufacturing, assembly, and material errors, among other factors, will affect the imaging quality of the doubly telecentric projection optical system. Further experimental verification is planned in subsequent studies, with the method being supplemented and refined based on the experimental results.  
Conceptualization, Y.X. and H.W.; Methodology, Y.X., G.S. and H.Y.; Software, J.Z. and Y.H.; Validation, H.Y., J.Z. and Y.H.; Writing--original draft, Y.X.; Writing--review & editing, Y.X., H.W., G.S., H.Y., J.Z. and Y.H.; Supervision, H.W. and G.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) (62475122).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 13: Schematic diagram of actual deformation compensation residuals.
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
###### Abstract  
The rapid deployment of the Internet of Drones (IoD) across different fields has brought forth enormous security threats in real-time data communication. To overcome authentication vulnerabilities, this paper introduces a secure lightweight framework integrating deep learning-based user behavior analysis and cryptographic protocols. The proposed framework is verified through AVISPA security verification against replay, man-in-the-middle, and impersonation attacks. Performance analysis via NS2 simulations based on changing network parameters (5-50 drones, 1-20 users, 2-8 ground stations) validates enhancements in computation overhead, authentication delay, memory usage, power consumption, and communication effectiveness in comparison with recent models such as LDAP, TAUROT, IoD-Auth, and LEMAP, thereby establishing our system as an optimal choice for safe IoD operation.  
internet-connected drones (IoD); cryptography protocols; AVISPA verification; machine learning security; real-time authentication; lightweight authentication; drone data security  
accademic Editor: [PERSON]
# MCIDN: Deblurring Network for Metal Corrosion Images
## 1 Introduction
To leverage frequency features effectively, we propose a frequency channel attention module (FCAM) designed to facilitate interactions among feature channels, enhancing frequency representation learning for image deblurring. FCAM selectively extracts relevant frequency components, aiding image deblurring through channel-wise attention. During reconstruction, FCAM enriches the high-frequency signal spectrum, significantly improving deblurring outcomes. By attenuating the lowest frequency components within features, FCAM accentuates high-frequency signals, refining the deblurring process. The principal contributions of this paper are as follows:  
* We propose a novel metal corrosion image deblurring network (MCIDN) based on deep learning principles. The network employs an encoder-decoder architecture designed to enhance image deblurring by enriching representation learning in both spatial and frequency domains. * We introduce a SCAM, integrating the strengths of convolution and attention mechanisms to effectively capture local dependencies crucial for precise deblurring. * We develop an FCAM to aggregate essential frequency components, highlighting our novel approach in utilizing frequency information for image deblurring. * We create a comprehensive dataset of blurry metal corrosion images, capturing a wide range of corrosion ratings to ensure the robustness and practical applicability of our deblurring model in real-world scenarios. Extensive experimental results demonstrate that our method achieves superior performance on this dataset, effectively handling various ratings of metal corrosion blur.
## 5 Result and Discussions
### Evaluation of Trained Neural Network Model
#### 5.2.2 Analyzing obtained Experimental Results  
We used trained NN model to predict Environment Type of three different Data Set obtained by reading Real Time Environment values by EEMS in three different environment setup i.e., In-door(home), Out-Door(park), Highly Polluted (Industrial area). Test Data Set Design shown in Table 10 and Results obtained from Trained NN given below in Table 11. We use statistical measure Precision, Recall and Accuracy to evaluate performance of trained NN model. We used formulas for Precision, Recall and Accuracy given below in Equations (10-12) respectively.  
* True positive TF: NN Predicted Real Favorable class as Favorable;
* False Favorable FF: NN predicted real Unfavorable class as Favorable;
* True Unfavorable TUF: NN predicted Real Unfavorable class as Unfavorable;
* False Unfavorable FUF: NN predicted real Favorable class as Unfavorable;  
\[\text{Precision}=\text{TF}/\left((\text{TF}+\text{FF})\right)*100 \tag{10}\] \[\text{Recall}=\text{TF}/\left(\text{TF}+\text{FUF}\right)*100\] (11) \[\text{Accuracy}=\left(\text{TF}+\text{TUF}\right)/\text{Total}*100 \tag{12}\]  
Neural Network Model prediction results for three different data set taken from in-door, park and industrial area are shown in Figure 21, showing that Indoor environment having high precision and accuracy rate compare to other two environment NN predicted more classes for continuous wound healing for home data set with 99% accuracy rate as indoor environment have suitable atmosphere for skin wound healing and promotes continuous wound healing, while outdoor environment like park have negative impact on wound healing and may cause impaired healing, NN showed accuracy rate is 96% for outdoor data, third data set of industrial area have more severe exterior environment, hence showed more chances of impaired wound healing, this dataset showed less precision 92% meaning that it have less positive classes with accuracy rate 96%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Data Set** & **Total Readings** & **Favorable (Continuous)** & **Unfavorable (Impaired)** \\ \hline Home & 800 & 749 & 51 \\ Park & 1000 & 678 & 322 \\ Industrial Area & 1200 & 546 & 654 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Three Test Data Design.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Test Data** & **Total** & **True** & **FF** & **TUF** & **FUF** & **Precision** & **Recall** & **Accuracy** \\ \hline Home & 800 & \begin{tabular}{c} 1 = 749 \\ 0 = 51 \\ 1 = 678 \\ 0 = 322 \\ \end{tabular} & 749 & 0 & 44 & 7 & 100\% & 99\% & 99\% \\ Park & 1000 & \
\begin{tabular}{c} 1 = 678 \\ 0 = 322 \\ 1 = 546 \\ 0 = 654 \\ \end{tabular} & 678 & 34 & 288 & 0 & 95\% & 100\% & 96\% \\ Industrial Area & 1200 &
\begin{tabular}{c} 1 = 546 \\ 0 = 654 \\ \end{tabular} & 546 & 43 & 611 & 0 & 92\% & 100\% & 96\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance Measure Results of Trained NN implementation.
## 1 Introduction  
One of the most significant scientific challenges presently is to generate power in the most efficient and economical way possible. To fulfill this objective, several different techniques for using fuels in solid, liquid, and gaseous forms have been developed [1]. The first oil crisis in the 1970s attracted widespread interest in coal conversion technologies. The aim of these studies was to replace oil with coal. Subsequently, several in-depth research studies were conducted, with emphasis on coal gasification, liquefaction, and combustion. On the other hand, together with these studies, recently, many studies were also conducted on coal-liquid mixtures, which are also called coal-liquid slurries [2]. Many of these production technologies are already commercially available. Coal-liquid mixtures are widely presented as an alternative to oil consumption, and for this reason, their market position is entirely linked to oil prices [3]. A typical coal-water slurry or mixture (CWS or CWM) is formed from the following [4]:  
* 50-75% of coal, with a maximum particles size of approximately 300 \(\upmu\)m;
* 25-50% of water;
* 1% of additives, such as dispersants or surfactants.  
However, when a finely ground coal is mixed with water, the behavior of the resulting slurry flow can vary, changing the solid particle concentration and the interfacial properties of the water. An ideal CWS with a higher coal load must present static and dynamic stability, as well as low viscosity [5].  
The atomization will also affect the rheological behavior of a CWS in a power plant furnace. The efficient use of a CWS is only possible when the slurry is prepared to allow a maximum solid load with higher viscosity and maintains a uniform concentration. Therefore, to study whether a particular CWS is suitable to be used as a fuel, rheological studies of the slurry must be conducted beforehand [6].  
Nowadays, fast-growing energy consumption worldwide makes the efficient and cleaner use of coal a necessity. With the actual increment of mining mechanization and washing processes, large amounts of fines are available and can provoke serious environmental problems if not properly managed [7]. Several different uses of these coal fines can be identified as the following:  
* Direct combustion;
* Briquette production;
* Fuel slurry production.  
CWS is an option to be used as a fuel alternative for power production, and the interest in its use is related to economic, logistic, and technological factors. A CWS has several advantages over the common use of micronized coal, one of which is that it can be transported through pipelines over long distances. Obviously, it is necessary to meet several safety requirements during the process; however, its use is justified even from an environmental perspective, since this fuel emits less sulfur and nitrogen oxides. No special conditions are required for storage or transport, so loading and unloading costs are virtually nonexistent. Thus, this option presents itself as an excellent alternative to heavy oil, which can be replaced by CWM [8].
## 3 Theory
### Estimation of the Inherent Parameters \(T_{ng}\), \(T_{g}\) and \(C\)
#### 3.2.4 Estimation of \(T_{g}\) and \(T_{ng}\)  
The non-grazing reverberation time \(T_{ng}\) is given by  
\[T_{ng}=\frac{0.161V}{A_{ng,ceiling}+A_{furniture}+A_{surface}+4 mV} \tag{33}\]  
where \(A_{ng,ceiling}=\alpha_{ng}S_{ceiling}\) and \(\alpha_{ng}\) is the absorption coefficient corresponding to the angle given by the maximum in the weighted normalized absorption coefficient, as described  
Figure 6: The number of modes in the non-grazing sector estimated by the empirical (solid) and theoretical (dashed) approaches.  
in Section 3.2.3. \(A_{furniture}\) is the Sabine equivalent absorption area for the furniture. An estimation of \(A_{furniture}\) is given in [49] as \(A_{furniture}=V_{furniture}^{2/3}\). \(A_{surface}\) is the equivalent absorption area for the walls and floor. Normally, the absorption coefficients for those surfaces are rather small and can be found in tables, e.g., in [49]. The air absorption is taken into account by the term \(4 mV\) where \(m\) is the energy attenuation constant in air and \(V\) is the room volume.  
Equation (33) is similar to the Sabine formula, but the skewness in the energy distribution is taken into account by using \(\alpha_{ng}\), as described in the paragraph above.  
The grazing reverberation time \(T_{g}\) is given by a 2-dim version of Sabine formula [55]  
\[T_{g}=\frac{0.127V}{A_{g,ceiling}+A_{sc}+A_{surface}+\pi mV} \tag{34}\]  
where \(A_{g,ceiling}=\alpha_{g}S_{ceiling}\) and \(\alpha_{g}\) represents the absorption coefficient for the grazing sector, as derived in Section 3.2.2. In this formula, we also introduce the parameter equivalent scattering absorption area \(A_{sc}\). This parameter quantifies the absorption and scattering effects of furniture and other objects in rooms with absorbent ceiling treatment. Thus, it also accounts for the directional scattering effects that can appear in these types of rooms, depending on the objects' orientation relative to the absorbent ceiling. The estimation of \(A_{sc}\) will be further discussed in the next paragraph. \(A_{surface}\) is similar, as in Equation (33). It could be stated that a 2-dimensional statistical absorption coefficient should be used instead of a 3-dimensional one, but as the difference is small [55], \(A_{surface}\) is calculated in the same way, as in Equation (33). It is assumed that the contribution of the floor is small and that it can be represented by the statistical absorption coefficient. However, for the air absorption, the distinction between the 2- and 3-dimensional sound fields is accounted for by using \(\pi mV\) instead of \(4 mV\).
## 2 Experimental Programs
### Cyclic Behaviors of Specimens  
Figure 5 illustrates the load-displacement history of both specimens. In Case-1, the horizontal load-bearing capacity stabilizes after 1% Drift, and the maximum load capacity of 119.8 kN is reached at 3% Drift. Subsequently, it maintains a nearly stable horizontal load-bearing capacity of up to 3.5% Drift. In Case-2, which features a smaller diameter and denser arrangement of longitudinal reinforcements, the maximum horizontal load of approximately 82 kN is reached at 1.5% Drift.  
Figure 4: Strain Gauge.  
Figure 3: Loading Protocol.  
As the effective height \(h_{ef}\) of both specimens differs, the bending moment at the base of the column (P-\(h_{ef}\)) is calculated for each specimen, the Moment-Drift history normalized to the maximum bending moment is shown in Figure 6a, and the envelope curves is shown in Figure 6b. The Case-1 specimen maintains a stable load-bearing capacity from 1.0% Drift and withstands up to 3.5% Drift on both positive and negative sides. However, in Case-2, after reaching the maximum horizontal load at 1.5% Drift, the load-bearing capacity begins to degrade, and ultimately only about 40% of the maximum value remains.  
Furthermore, the load-displacement history of both specimens exhibits a significant pinching phenomenon. The pinching phenomenon refers to a state in which the stiffness of the RC column decreases during unloading due to the complex material nonlinearity of reinforced concrete when the column is subjected to repeated loading. The degree of pinching of the hysteresis curve is a critical evaluation criterion to evaluate the RC structure for seismic energy absorption, so it is also essential to quantify the pinching phenomenon.  
To quantify the pinching phenomenon of both specimens, the stiffness degradation of the specimens was investigated in this research. Figure 7 presents the stiffness degradation history of both specimens at each Drift. To evaluate the normalized stiffness, the stiffness \(K_{i}\) and the yield stiffness \(K_{e}\) at each Drift were employed. \(K_{i}\) is obtained by the ratio of the moment at the column base position to the displacement at the loading position at 1% Drift, and \(K_{e}\) is the stiffness when the longitudinal reinforcement yields (1.0% Drift). In both specimens, stiffness decreases as loading progresses, and the stiffness degradation rate of Case-2 with slender reinforcement is faster than that of Case-1. The final stiffness of Case-2 is only half that of Case-1, suggesting that the pinching phenomenon in the load-displacement history of Case-2 is more severe than that of Case-1.  
Figure 5: Lateral Force versus Lateral Drift Relationships.  
Figure 6: Normalized Hysteresis Curves. (**a**) Normalized Bending Moment History. (**b**) Envelope Lines.  
Although various causes contribute to the pinching phenomenon in RC columns, in the case of the slender RC columns used in this study, previous research has indicated that the primary cause is a bond failure in the reinforced concrete [1; 22; 23].
## 2 Methodology
### The GRU Component  
The GRU component helps the M2S-MFCRGRU capture the global time-related information efficiently. Specifically, GRU mainly employs the update gate and the reset gate to selectively update and forget information from the previous hidden state, while also incorporating new input information into the current hidden state. The operation process of GRU is as follows:  
\[\begin{cases}z_{t}=sigmoid(W_{x}x_{t}+U_{z}h_{t-1}+b_{z})\\ r_{t}=sigmoid(W_{t}x_{t}+U_{t}h_{t-1}+b_{r})\\ \tilde{h}_{t}=\tanh(W_{h}x_{t}+U_{h}(r_{t}\odot h_{t-1})+b_{h})\\ h_{t}=z_{t}\odot h_{t-1}+(1-z_{t})\odot\tilde{h}_{t}\end{cases} \tag{1}\]  
where \(z_{t}\), \(r_{t}\), \(\tilde{h}_{t}\), and \(h_{t}\) represent the update gate, the reset gate, the candidate state, and the current hidden state, respectively. \(x_{t}\) represents the \(t\)-th element of the input sequence and \(h_{t-1}\) is the previous hidden state. \(W_{z}\), \(U_{z}\), \(W_{r}\), \(U_{r}\), \(W_{h}\), and \(U_{h}\) are the weights; \(b_{z}\), \(b_{r}\), and \(b_{h}\) are the corresponding biases. The operator \(\odot\) denotes element-wise multiplication.  
Figure 1: The framework of the M2S multi-task FCRN and GRU hybrid network (M2S-MFCRGRU). (**a**) The network of M2S-MFCRGRU. (**b**) The details of the residual block.  
To be more specific, \(z_{t}\) and \(r_{t}\) take values between 0 and 1. Higher values of \(z_{t}\) indicate a higher probability of retaining \(h_{t-1}\). As \(r_{t}\) approaches 1, \(\tilde{h}_{t}\) is more dependent on \(h_{t-1}\). This selective mechanism of the information flow enables GRU to capture long-term dependencies in sequence data by selectively ignoring information from the previous time step and updating the essential information for the current time step.  
Figure 2 shows the structure of the GRU component. In the figure, \(1-\) represents using a matrix with all 1 elements for subtraction operations; \(+\) and \(\times\) represent addition and element-wise multiplication, respectively.