esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 4 Description of Experimental _I-V_ Curves Database
### Generation of Training Set and Validation Set for a Healthy PV Panel  
The reference values of the SDM parameters, the named target values, must be calculated for each experimental _I-V_ curve that is used during the ANN training and validation processes. Since the ANN training phase is performed offline, in this paper, the target dataset can be generated by using the nonlinear least-square solver of Matlab to assure high-quality fitting among the experimental _I-V_ curves and the ones generated by the single diode model.  
Such a Matlab toolbox needs boundaries, a representative function, and the initial values for the parameters. The boundaries represent the upper and lower limits that every parameter could have. Table 2 shows the boundaries and the parameters initial values configured for this work. They are based on the indication given in [33].  
Concerning the initial conditions summarized in Table 2, three special values are defined there. First, the short-circuit current \(I_{sc}\) is a characteristic value of every PV device. Second, the slopes of the tangent lines \(R_{so}\) and \(R_{slo}\). They are defined as follows:  
\[R_{slo}=-\frac{dV}{dI}\biggm{|}_{SC},\ \ R_{so}=-\frac{dV}{dI}\biggm{|}_{OC} \tag{5}\]  
Finally, the representative function that describes the PV generator is based on Equation (1):  
\[f=I_{ph}-I_{sat}\cdot[e^{\frac{V_{pv}+I_{pv}\cdot R_{s}}{\eta V_{t}}}-1]-[ \frac{V_{pv}+I_{pv}\cdot R_{s}}{R_{slh}}]-I_{pv} \tag{6}\]  
For every selected experimental _I-V_ curve, and thus, for the known \((G,T)\), the fitting procedure calculates the set of five parameters \(\mathbf{p}=[I_{ph},I_{sat},\eta,R_{s},R_{sl}]\) that minimize the mean square error between the experimental data and the _I-V_ curve generated by using the single-diode model. In this way, the \(\mathbf{p}\) vector associated to the healthy _I-V_ curve is the target used to train the neural network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Boundaries**} & \multirow{2}{*}{**Initial Conditions**} \\ \cline{2-2} \cline{4-4}  & **Lower** & & **Upper** \\ \hline \(I_{ph}\) [A] & 0 & 12 & \(I_{sc}\) \\ \(I_{sat}\) [A] & \(1\times 10^{-12}\) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) \\ \(\eta\) & 1 & 2 & 1.5 \\ \(R_{s}\) [\(\Omega\)] & 0 & 5 & \(R_{s0}\) \\ \(R_{slh}\) [\(\Omega\)] & 50 & \(10\times 10^{3}\) & \(R_{slh0}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial values and boundaries for the fitting.  
It is worth highlighting that the single diode model allows to describe the electrical constraint between \(I\)-\(V\) measurements (\(V_{pv},I_{pv}\)) and SDM parameters in the following form:  
\[f(V_{pv},I_{pv},\mathbf{p})=0 \tag{7}\]  
but the ANN allows to detect the following unknown relations:  
\[\mathbf{p_{H}}(G,T)=[I_{ph}(G,T),I_{sat}(G,T),\eta(G,T),R_{s}(G,T),R_{sh}(G,T)] \tag{8}\]  
where the vector \(\mathbf{p_{H}}(G,T)\) is the estimation of the healthy SDM parameters for the environmental conditions \(G,T\).
## 4 Results and Discussion
### Simulation Result Analysis
#### 4.1.3 Anti-Interference Performance  
There are a lot of interferences in the MCCS system that cause pressure changes in the main pipe, such as pressure changes caused by the centrifugal pump itself, valve jitter, water belt contraction and so on. In view of the accumulation characteristics of the MCCS system, the main pipe pressure fluctuation caused by the water source switching is also a factor that cannot be ignored. Set the load simulation ball valve opening to 100%; channel 1, channel 2 and channel 4 run in parallel, channel 3 is on standby. Switch between channel 2 and channel 3. The switching time for channel 2's valve delays the start by 4 s and 10 s, respectively. The pressure value of the main pipe is set as 0.0392 MPa. According to the pressure of channel 1 before switching, the set pressure of channel 1 is 0.096 MPa. Then, at the same time, the system using different control methods is switched, and the real-time value of the main pipe's pressure is recorded. The main pipe pressure change curves under the control of traditional PID, adaptive fuzzy PID, variable universe fuzzy PID and master and auxiliary composite control are obtained, as shown in Figure 15.  
It can be seen from Figure 15 that, under the same switching conditions, compared with other controls, the main pipe's pressure fluctuation value is smaller, and the recovery time to the set value is shorter. Therefore, the master and auxiliary composite control method based on the variable universe fuzzy PID has the stronger anti-interference ability, and is a more suitable smooth switching control strategy.
## 5 Literature Review by Domain Area
### Education and Training  
AR has emerged as a powerful tool in education and training, revolutionizing traditional teaching methods and providing immersive learning experiences [68]. By seamlessly blending the real world with virtual elements, AR enhances the educational process, fostering engagement, interactivity, and knowledge retention. In the field of education, AR offers students a dynamic and interactive learning environment. Textbooks can be transformed into multimedia experiences, with 3D models, animations, and interactive elements overlaying printed content. Complex concepts, such as anatomical structures or historical landmarks, can be visualized in three dimensions, allowing students to explore and interact with virtual objects, bringing abstract ideas to life [69].  
Furthermore, AR enables collaborative learning experiences, where students can interact with virtual content together, fostering teamwork, communication, and problem-solving skills. For instance, group projects can involve creating and manipulating virtual objects or solving puzzles in a shared AR space, promoting active learning and collaboration [69]. AR also has significant potential in training scenarios, particularly in industries that require hands-on experience and practical skills [70]. AR can simulate real-world situations, allowing trainees to practice and refine their abilities in a safe and controlled environment. For example, in medical training, AR can overlay virtual organs onto a mannequin, enabling aspiring surgeons to practice complex procedures with precision and accuracy [71]. Similarly, in industrial settings, AR can guide workers through complex tasks, providing real-time instructions and visual cues. By superimposing digital information onto the physical workspace, AR can assist technicians in equipment maintenance or assembly processes, reducing errors and improving efficiency [71]. These are some of the most recent developments in the \"education and training\" domain:  
* **ZooAR:** The ZooAR project, developed by researchers at the University of Melbourne, Australia, leverages augmented reality to enhance zoology education. It allows students to interact with virtual 3D models of animals, providing a unique and immersive learning experience. Students can explore the anatomy, behavior, and habitats of various animals through their smartphones or tablets [72].
* **ARChem:** ARChem is an AR application developed by a team at the University of Bristol, UK. It focuses on teaching chemistry concepts through interactive AR experiences. Students can visualize and manipulate molecular structures in real-time, fostering a deeper understanding of chemical compounds and reactions [73].
* **Google Expeditions:** Google Expeditions is an AR application designed for educational purposes. It enables teachers to take students on virtual field trips to various locations around the world using AR technology. Students can explore historical sites, natural wonders, and cultural landmarks, enhancing their knowledge and global awareness [74].
###### Abstract
Therefore, it is necessary to study these algorithms in depth. We delve into the packet detection algorithms and the main work is as follows:  
1. We describe four packet detection algorithms in detail and verify the principle of the algorithms through simulation experiments. 2. We identify two important factors that affect the accuracy of packet detection and propose methods for algorithm improvement. 3. We compare the performance of the four algorithms from three dimensions, analyze the characteristics of different algorithms, and point out the applicable scenarios. 4. We summarize the open challenges and future direction of data packet detection research and introduce several new algorithms that are being studied but have not been widely used yet.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 6 Analysis and Validation of Statistical Model for AMQP IoT Attack Detection
### Detecting the MiTM Attack
#### 6.3.1 Detection Results for Normal and Malicious Datasets in MiTM Attacks  
Figure 14 presents the detection results of the statistical model applied to differentiate between normal, malicious, and duplicate IP packets associated with AMQP Man-in-the-Middle (MiTM) attacks. The script aggregates packets based on their IP addresses and categorizes them as malicious if duplicate IP addresses are detected. All other packets are classified as normal.  
The results indicate the script's ability to accurately identify and classify MiTM attack signatures:  
* A total of 99.95% of packets were classified as normal.
* A total of 0.02% of packets were identified as malicious.
* A total of 0.04% of packets were recognized as duplicate IP address MiTM packets.  
These findings demonstrate the effectiveness of the script in identifying MiTM attack characteristics while maintaining a high accuracy rate.
# Analysis of Distributed Measurement Method for Array Antenna Position
## 2 Scheme and Principle
### Fiber Bragg Grating (FBG) Sensing Technology
Then, the adjacent continuous arc length is used to simulate the synthetic curve, and finally the multiple curves are used to simulate the synthetic surface, so as to accurately obtain the spatial position coordinates of each sub node on the distribution POS. It can be seen from the definition of curvature that curvature is the quadratic differential value of the curve, and the coordinate value of each point on the curve can be recursively derived from the slope of each point. Suppose that the coordinates of point \(n\) and point \(n+1\) are \((x_{n},y_{n})\) and \((x_{n+1},y_{n+1})\) respectively, the slopes are \(k_{n}\) and \(k_{n+1}\) respectively, and the arc length between the two points is \(\Delta s_{n}\):  
\[\left\{ \begin{array}{l}x_{n+1}=x_{n}+\frac{\Delta s_{n}}{\sqrt{1+k_{n}{}^{2}} }\\ y_{n+1}=y_{n}+\frac{k_{n}\cdot\Delta s_{n}}{\sqrt{1+k_{n}{}^{2}}}\end{array} \right. \tag{6}\]  
The formula above is the recurrence formula of the coordinate value of each point, connecting each point to realize the reconstruction of the curve.
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 4 Experimental Results  
### Experimental Setting  
To testify the proposed PWTA method for the SCTV prediction, it is compared with the following surrogate models: (1) Polynomial Response Surface (PRS), (2) Ordinary Kriging (OK), (3) Radial Basis Function (RBF), (4) Weighted aggregation method (WTA) and (5) BestGMSE surrogate, which are introduced in Section 2. DACE toolbox of MATLAB is used to construct the OK model [30]. The parameter settings are presented in Table 1. The following metrics are used to evaluate the predictive capabilities of the surrogate models in the sub-domains.  
For the high-quality surrogate model, the R\({}^{2}\) correlation coefficient should be close to 1, while NRMSE and NMAE should be low.
# Energy-Efficient Subcarrier Pairing and Power Allocation for DF Relay Networks with an Eavesdropper
## 5 Complexity Analysis  
In this section, the complexity analysis is presented to demonstrate the complexity reduced by the near-optimal and the suboptimal algorithms when the penalty factor \(\Psi\) converges in \(L\) iterations.  
We begin with the complexity analysis of the near-optimal EEM algorithm as follows. Due to \(K\) subcarriers in each hop, \(K^{2}\) subproblems are required to be solved in order to find the optimal solutions of (P.3). Since the near-optimal solution \(\left(P_{S,i}^{\star},P_{R,j}^{\star},\Omega_{i}^{\star}\right)\) is found with the total power constraint, the complexity is \(\mathcal{O}\left(V^{3}+1\right)\), where the maximum power level for the source and the relay nodes on each subcarrier are denoted by \(V\). Moreover, finding the optimal subcarrier pairing using the Hungarian produces a complexity of \(\mathcal{O}\left(K^{3}\right)\). The complexity due to updating a dual variable is \(\mathcal{O}\left(2^{e}\right)\) (for example, \(\varrho=2\) for the ellipsoid method [21]). Hence, the overall complexity for updating dual variables is \(\mathcal{O}\left(2^{2e}\right)\). Supposing the dual objective function converges in \(W\) iterations, the entire complexity for the near-optimal EEM algorithm is \(\mathcal{O}\left(2^{2e}K^{2}WL\left(V^{3}+K+1\right)\right)\).  
However, the implementation of the subcarrier pairing in step 1 of suboptimal algorithm requires a complexity of \(\mathcal{O}\left(2K\right)\), while the power allocation has the complexity of \(\mathcal{O}\left(K\left(V^{3}+1\right)\right)\) and the total complexity for updating dual variables is \(\mathcal{O}\left(2^{2e}\right)\). If the dual objective function converges in \(W^{\prime}\) iterations, the overall complexity for the suboptimal algorithm becomes \(\mathcal{O}\left(2^{2e}KWL\left(V^{3}+3\right)\right)\) with \(W^{\prime}=W\). Finally, the complexity of the optimal exhaustive search (ES) algorithm [22] is given by \(\mathcal{O}\left(2^{2e}WLK!\left(V^{3}+1\right)\right)\). Table 1 presents the summary of the complexity comparison of the three algorithms.
# Model Predictive Control of Quadruped Robot Based on Reinforcement Learning
## 3 Reinforcement Learning for PDMPC
### Parameters to Be Larned  
#### 3.1.1 Swing Planner  
The swing planner is used to choose the gait pattern, and determines the phase relation and lift-off schedule. The duty cycle is 0.5, and the result schedule information will be transmitted to the PDMPC controller. The foot point position vector \({}^{w}\mathbf{h}\) in the world coordinate system is as follows.  
\[\left\{\begin{array}{c}{}^{\omega}\mathbf{h}_{xy}={}^{\omega}\mathbf{h}_{0 xy}+\big{(} \alpha_{x}V_{x}+\alpha_{y}V_{y}\big{)}t_{s}\\ {}^{\omega}\mathbf{h}_{z}={}^{\omega}\mathbf{h}_{0z}+\mathbf{1}(\varphi)l_{z}\end{array}\right. \tag{11}\]  
where \(\mathbf{h}_{0}\) is the initial foot point position vector at the lift-off event. The foot trajectory is determined by the heuristic of single inverted pendulum model, \(t_{s}\) is the duration of the support period, \(\alpha_{x}\) and \(\alpha_{y}\) is the heuristic coefficient in different motion directions (normally set to 0.5). \(l_{z}\) is the maximum height of foot in vertical direction during swing  
Figure 4: Framework of reinforcement learning based on PDMPC.  
phase. The gait phase variable \(\varphi\in[0,\ 2\pi]\), \(\mathbf{1}(\varphi)\) is an indicator function, 1 stands for swing period, 0 stands for support period.  
\[\mathbf{1}(\varphi)=\left\{\begin{array}{ll}0&\varphi\in(0,\ \pi]\\ 1&\varphi\in(\pi,\ 2\pi]\end{array}\right. \tag{12}\]  
So, we have 7 parameters to be learned for swing planner, including the four lift heights of each leg \(l_{21}\), \(l_{22}\), \(l_{23}\) and \(l_{24}\), the two heuristic coefficients \(\alpha_{x}\) and \(\alpha_{y}\), the half support duration \(t_{s}/2\).
# Greening the NetFPGA Reference Router
## 5 Experiments
### Transition Time
The frequency transition time on the software implementation of the control policy consists of the delay in software reading buffer usage, frequency selection according to the control protocol and setting the appropriate frequency register, which involves communications between hardware and software through reading and writing registers. For example, in theory, the transition time in the ALR is significantly reduced to 1 ms through a newly-defined handshake mechanism. However, the control policy in the ALR is implemented by software-based utilisation monitoring and buffer thresholds. The actual transition time ranges from 10 ms to 100 ms due to software restraints [1]. The transition time in the work [20] built on the NetFPGA reference router is reported a little bit longer (3.4 ms) than that in the reference router (2 ms) [20; 32]. In the work [21], the transition time of adapting the link rates of Ethernet port is reported to be approximately 2 s. However, the transition time of frequency scaling is not measured in this work. The power savings from most green approaches are mostly achieved at the cost of transition time overhead. The transition time is the time between the request for a new state and the actual state transition. Transition from one state to another can lead to potential performance degradation, especially in the case of transition from a lower performance state to higher ones. Longer transition times can result in higher network delay and even severe packet loss during the transition. The transition time in a hardware implementation eliminates the delay in communications between hardware and software. The control policy of the FSR is directly implemented in hardware. This involves building hardware modules and adding corresponding software instructions to incorporate with the hardware modules. Results reveal that the transition time in the FSR is only \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multicolumn{5}{c}{**Transition Time (ms)**} \\ \cline{2-6}  & **125 MHz** & **62.5 MHz** & **31.3 MHz** & **15.6 MHz** & **7.8 MHz** \\ \hline
125 MHz & 0 & 0.348 & 0.329 & 0.297 & 0.352 \\
62.5 MHz & 0.306 & 0 & 0.273 & 0.316 & 0.405 \\
31.3 MHz & 0.302 & 0.344 & 0 & 0.332 & 0.362 \\
15.6 MHz & 0.358 & 0.263 & 0.323 & 0 & 0.299 \\
7.8 MHz & 0.291 & 0.337 & 0.488 & 0.293 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Transition time in the FSR. around 0.3-0.4 ms, which is the hardware frequency transition time without the delay caused by communications between hardware and software.
# Removal of Fluoride and Arsenate from Aqueous Solutions by Aluminum-Modified Guava Seeds
## 3 Results and Discussion
### Effect of pH on Fluoride Removal  
Since changing pH of the solution in sorption processes can modify the surface charge of an adsorbent and this change directly affects ionization and chemical speciation of the adsorbate, then pH is an important parameter in these types of processes. Figure 9 shows the variation in fluoride sorption capacity at various pH values using Al-GSs as an adsorbent. The removal of F\({}^{-}\) ions was the highest in acidic conditions (pH 2 and 3), but this increase could be explained by HF formation at these pH values since pKa value of HF is 3.17. At pH 4 and 5, where the F\({}^{-}\) species begins to predominate on aqueous solutions, a significant decrease in fluoride sorption is observed and this could be attributed to aluminum modification of the biosorbent, since the point of zero charge (pH\({}_{\text{PZC}}\)) for this type of modified adsorbents has been reported approximately at a pH value of 4 to 5. This is also in agreement with the pH\({}_{\text{PZC}}\) = 5 reported for Al(OH)\({}_{3}\)[28], which is the proposed form of the aluminum present on the surface of this biosorbent. The highest removal of F\({}^{-}\) ions can be observed at pH 6 where ligand exchange can be the main sorption mechanism. At higher pH values the removal decreases significantly. This can be explained by different reasons: less availability of sites for fluoride electrostatic adsorption,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Kinetic Parameters** & **Correlation Coefficient (R)** \\ \hline Lagergren & K\({}_{\text{L}}\) (min\({}^{-1}\)) = 0.0235 & 0.9218 \\  & q\({}_{\text{e}}\) (mg/g) = 834.26 & 0.8850 \\ \hline pseudo-second-order & K\({}_{\text{2}}\) (g/mg min) = 5\(\times\) 10\({}^{-5}\) & 0.8850 \\  & q\({}_{\text{e}}\) (mg/g) = 712.5 & \\ \hline  & a (mg/g min) = 5.3 \(\times\) 10\({}^{-9}\) & \\  & b (g/mg) = 0.0420 & 0.7003 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetic parameters for arsenate biosorption by Al-GSs.  
Figure 8: Adsorbent dose effect on the adsorption of fluoride by Al-GSs.  
or competition between fluoride ions with a high concentration of hydroxyl ions for the positively charged sites on the Al-GS surface [43]. Fluoride removal increased at pH 8 compared to pH 7. This behavior could be explained since aluminate ion, Al(OH)\({}_{4}\)\({}^{-}\), is formed at pH above 8 due to the dissolution of the aluminum present on the surface of the adsorbent [44]. This aluminate ion may compete with F\({}^{-}\) at pH values around 7, but at pH values near 8 the Al(OH)\({}_{2}\)F\({}_{2}\)\({}^{-}\) complex could be formed in solution [45; 46]; consequently, a decrease in the concentration of free fluoride ions in solution occurred with a corresponding increase in the percentage of removal.
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### PPPM Analysis and the Derivation of Answers to Questions
#### 4.5.10. PPPM Analysis of SaCoFa (Q2.2.2)  
In this section, we present the quantitative results of applying the _SaCoFa_ PPPM model in _PRIPEL_ on our real-life event logs under two different aspects as result and time utility qualities. Here, the parameter \(\epsilon\) regulates the privacy risk. All the other hyperparameters are drawn from the authors' original publication. In particular, for the BPIC13 and Sepsis event logs, the values were set due to their similar high trace variant count nature. For each \(\epsilon\) value, the experiments were run 10 times and averaged to reduce the nondeterministic behavior of DP.  
Result Utility Quality  
The Figure 20 heatmap shows the applied _SaCoFa_ model for the PM quality metrics with respect to five different \(k\) and \(t\) parameter values behavior on our real-life event logs. For all the results, the baseline metric values without any anonymization are included in Table 3 and employed for the comparative interpretations. Since the runtime performance is very low (>30 min timeout limit) within the _PRIPEL_ framework, this model does not apply to the Road Traffic Fines event log for all configurations despite reducing the size of the event data by 10% with random sampling.  
All the _SaCoFa_ anonymized event logs showed alignment with their baseline fitness and generalization values. However, for all the event logs, there was an increase in precision for a weak privacy budget (when \(\epsilon\) goes high) in comparison with their baselines. The  
Figure 19: Discovered LaplacianDP anonymization runtime (s) metric results.  
CoSeLog event log showed a significant increase in precision besides generalization for the strongest privacy protection. Specifically, the precision and generalization applied to the Sepsis event log exhibited deviations for different \(\epsilon\) values. On the other hand, for the BPIC13 event log, the metric results showed well-observed trends for each parameter configuration.  
Time Utility Quality  
Figure 21 includes the runtime (s) performance metric results of the applied _SaCoFa_ PPPM model in _PRIPEL_ on our real-life event logs. During the experimentation with the _SaCoFa_ algorithm, a steady trend in the model runtime was observed with increasing privacy levels as a consequence of specific \(\epsilon\) privacy parameter modifications. On the BPIC13 event log, the runtime for the anonymization values fluctuated with the \(\epsilon\) parameter value explicitly. However, for the Sepsis and CoSeLog event logs, the runtime values were steady between the \(\epsilon\) values. The BPIC13 event log also had detrimental runtime performance for all strong privacy levels so that it had the highest count number of instances.