esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 5 Simulation Verification
### Test Results and Analysis  
In order to test the performance of the algorithm with different parameter settings, 200 tasks are randomly generated, and the generated 200 tasks are scheduled with the scheduling algorithm of this paper and three comparative scheduling algorithms, respectively.  
Figure 4 compares the number of tasks completed by the four algorithms with different numbers of concurrent containers supported by the edge devices. The number of concurrent containers supported by the set edge devices is 2, 3, 4, 5, and 6, respectively.  
In Figure 4, the number of tasks completed by both CECA and OEDA increases as the number of containers supported by the edge device for concurrency increases. OCA has the same number of task completions because it is not affected by the edge devices. FCFS schedules microservices for one task at a time, which will only occupy one container and does not involve the concurrent operation of containers. In the same case, CECA can handle more tasks simultaneously.  
Figure 5 compares the task completion times of the four algorithms with different numbers of concurrent containers supported by the edge devices. The number of concurrent containers supported by the edge devices is set to 2, 3, 4, 5, and 6, respectively.  
In Figure 5, both CECA and OEDA task completion times decrease as the number of concurrent containers supported by the edge device increases. OCA and FCFS are not affected. In the same case, CECA task completion time is the least. Combining the results in Figure 4, CECA can complete more tasks in less time and has higher task processing efficiency.  
Figure 6 compares the number of tasks completed by the four algorithms at different cloud edge communication times. The cloud edge communication time is set to the default value of \(1\times\), \(20\times\), \(40\times\), and \(60\times\).  
Figure 4: The impact of the number of concurrent containers of the edge device on task completion.  
In Figure 6, the number of tasks completed by both CECA and OCA decreases as the cloud edge communication time increases, but the number of tasks completed by CECA decreases relatively slowly and OCA decreases significantly, indicating that CECA is less affected by changes in cloud edge communication resources. Even in an environment with poor cloud-edge communication, CECA is able to make more tasks processed in a timely manner by means of inter-device collaboration.  
Figure 7 compares the task completion times of the four algorithms for different cloud edge communication times. The cloud edge communication time is set to 1\(\times\), 20\(\times\), 40\(\times\), and 60\(\times\) of the default value.  
In Figure 7, both CECA and OCA task completion times increase as the cloud-edge communication time increases, and the CECA task completion time is the shortest with the other same settings. Combining the results in Figure 6, CECA still has high task execution efficiency when the communication environment is disturbed, which in turn improves the security of task scheduling.
## 4 Simulation and Experimental Analysis
### Neural Network Structure Determine  
Prediction accuracy has a significant relationship with the depth of the bidirectional LSTM neural network. The dynamic characteristics of the load data will be extracted based on the interaction of the different layers of the neural network. The internal relevance information of the load profiles will be deep learned with the different stacked layers, and the nonlinearity of the load sequence can be described in different dimensions. The parameters of the input units, forget units, and output units of the proposed model are shown in Table 1.  
The prediction accuracy of the different layers of the LSTM neural work is shown in Figure 6. It can be seen that the proposed multi-layer bidirectional LSTM neural network is an effective method and is accurate enough for the load forecast problem. Furthermore, with the increasing numbers of layers, the prediction result will be more accurate. However, when there are four layers, the prediction accuracy will increase, on the contrary. It is said that three layers is suitable for the prediction of the load sequence data in this paper. Table 2 shows the prediction errors of MAPE between the different layers of the different neural network model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Input Gate**} & 0.023 & 0.020 & 0.120 & 0.127 & 0.033 & 0.975 & 0.044 & 0.037 & 0.579 & 0.035 \\ \cline{2-11}  & 0.044 & 0.049 & 0.017 & 0.012 & 0.034 & 0.025 & 0.041 & 0.001 & 0.043 & 0.037 \\ \cline{2-11}  & 0.027 & 0.025 & 0.135 & 0.128 & 0.070 & 0.975 & 0.049 & 0.043 & 0.540 & 0.007 \\ \cline{2-11}  & 0.025 & 0.047 & 0.042 & 0.029 & 0.034 & 0.025 & 0.048 & 0.043 & 0.005 & 0.040 \\ \hline \multirow{3}{*}{**Forget Gate**} & 0.024 & 0.006 & 0.030 & 0.008 & 0.032 & 0.015 & 0.050 & 0.016 & 0.024 & 0.005 \\ \cline{2-11}  & 0.012 & 0.015 & 0.013 & 0.046 & 0.013 & 0.045 & 0.041 & 0.048 & 0.050 & 0.019 \\ \cline{2-11}  & 0.049 & 0.044 & 0.046 & 0.005 & 0.000 & 0.023 & 0.015 & 0.046 & 0.019 & 0.037 \\ \cline{2-11}  & 0.007 & 0.014 & 0.030 & 0.007 & 0.043 & 0.016 & 0.044 & 0.025 & 0.047 & 0.037 \\ \hline \multirow{3}{*}{**Output Gate**} & 0.030 & 0.002 & 0.043 & 0.038 & 0.005 & \(-0.033\) & 0.049 & 0.001 & 0.012 & 0.014 \\ \cline{2-11}  & 0.006 & 0.024 & 0.012 & 0.001 & 0.046 & 0.043 & 0.049 & 0.036 & 0.039 & 0.014 \\ \cline{2-11}  & 0.008 & 0.048 & 0.029 & 0.037 & 0.038 & \(-0.044\) & 0.023 & 0.012 & 0.002 & 0.035 \\ \cline{2-11}  & 0.021 & 0.045 & 0.002 & 0.017 & 0.006 & 0.048 & 0.019 & 0.020 & 0.023 & 0.012 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the equivalent model.  
Figure 6: The load forecasting of different layers of LSTM.
## 3 Improved Active Disturbance Rejection Control Strategy Design
### ADRC Regulation Design after the RBF Neural Network
\right.\] (30) The index function of the RBF for ESO controller tuning is \[F(k)=\frac{1}{2}e_{rr}^{2}(k)\] (31) The final control output target is \[\left\{ \begin{array}{l}i_{q}(k)=i_{q}(k-1)+\Delta i_{q}(k)\\ \Delta i_{q}(k)=\beta_{01}ee(1)+\beta_{02}ee(2)+\beta_{03}ee(3)\end{array} \right. \tag{32}\]
4. Adaptive parameter adjustment By using the gradient descent method, the change in \(\beta_{01}\), \(\beta_{02}\), and \(\beta_{03}\) is adjusted as follows:  
\[\left\{ \begin{array}{l}\Delta\beta_{01}=-\eta_{01}\frac{\partial F}{\partial \beta_{01}}=-\eta_{01}\frac{\partial F}{\partial\beta}\frac{\partial\varphi}{ \partial\Delta t_{0}}\frac{\partial\Delta t_{0}}{\partial\beta_{01}}=\eta_{01} e_{rr}(k)\frac{\partial\varphi}{\partial\Delta t_{0}}ee(1)\\ \beta_{01}(k)=\Delta\beta_{01}+\beta_{01}(k-1)\\ \Delta\beta_{02}=-\eta_{02}\frac{\partial F}{\partial\beta_{02}}=-\eta_{02} \frac{\partial F}{\partial\beta}\frac{\partial\varphi}{\partial\Delta t_{0}} \frac{\partial\Delta t_{0}}{\partial\beta_{02}}=\eta_{02}e_{rr}(k)\frac{ \partial\varphi}{\partial\Delta t_{0}}ee(2)\\ \beta_{02}(k)=\Delta\beta_{02}+\beta_{02}(k-1)\\ \Delta\beta_{03}=-\eta_{03}\frac{\partial F}{\partial\beta_{03}}=-\eta_{03} \frac{\partial F}{\partial\varphi}\frac{\partial\varphi}{\partial\Delta t_{0 }}\frac{\partial\Delta t_{0}}{\partial\beta_{03}}=\eta_{03}e_{rr}(k)\frac{ \partial\varphi}{\partial\Delta t_{0}}ee(3)\\ \beta_{03}(k)=\Delta\beta_{03}+\beta_{03}(k-1)\\ \end{array} \right. \tag{33}\]
## 5 Numerical Results and Discussion
### CPO Robustness  
In this part, we study the detection performance of the LLR schemes given in (8) and (16) in fading channel with changing carrier phase, where \(\rho=1\). The phase \(\theta\) is modeled as a Wiener process, wherein its initial value is uniformly chosen from \((-\pi,\pi)\). As shown in Figures 12 and 13, the performance of the proposed receiver does not significantly degrade if we increase the standard deviation of jitter from 0 degrees to 7 degrees. In addition, an irreducible error floor is observed for the LLR given in (8) and (16) with the increase in SNR. As shown in Figure 13, our proposed LLR scheme in (16) is clearly robust to dynamic phase jitter.  
Figure 11: Cont.  
Figure 10: Detection performance for LDPC coded IEEE 802.15.4c MPSK signals under phase noncoherent channel with \(\Delta\sigma^{2}=\alpha\sigma^{2}\), where \(\rho=1\). (**a**) BER performance; (**b**) SER performance; and (**c**) PER performance.  
Figure 11: Detection performance for LDPC coded IEEE 802.15.4c MPSK signals under Rayleigh fading channel with \(\Delta\sigma^{2}=\alpha\sigma^{2}\), where \(\rho=1\). (**a**) BER performance; (**b**) SER performance; and (**c**) PER performance.  
Figure 12: Cont.  
Figure 12: Detection performance for LDPC coded IEEE 802.15.4c MPSK signals under dynamic CPO channel, wherein LLR given in (8) is considered. BP algorithm is adopted for decoding, where \(\rho=1\). (a) BER performance; (b) SER performance; and (c) PER performance.  
Figure 13: Cont.
# High Frequency Transformers for Solid-State Transformer Applications
## 2 Modulation of the Converter System
### Solid-State Transformer
The main challenge in the design of conductors for high frequency operation is the effect of eddy currents in the windings and their contributions to system losses, namely, the losses due to skin effect and proximity effect, outlined as follows:  
* Skin effect: the passage of an alternating current through a conductor causes a time-varying magnetic field, which, according to [PERSON]'s law, causes an electric field. The electric field induces eddy currents in the conductor, which oppose the variation in current density. The combination of the magnetic field and the induced electric field, both time-varying, creates electromagnetic induction in the conductor. This forces most of the electric current to flow at the surface of the conductor, instead of flowing uniformly. The skin effect is aggravated with increased operating frequency [20; 21];
* Proximity effect: when two conductors close to each other are crossed by an alternating current, the current distribution in each conductor is affected by the changing magnetic field of the other conductor. Due to the induced eddy currents, the current will flow in undesired patterns, and this effect is also aggravated with increasing frequency [20; 21]. Figure 1: Solid-state transformer topologies. Figure 2: Topologies of solenoidal transformers. Ultimately, these effects can also be interpreted as increases in the resistivity of the conductor as the frequency increases. Of the mechanisms developed so far, the most efficient for reduction is the Litz wire. This consists of a set of single conductors isolated from each other, which can also be braided together in a uniform pattern, as illustrated in Figure 3[22]. For a given application, there is not just one ideal conductor topology. The best solution will depend on the objectives, e.g., minimization of volume, minimization of losses, or minimization of costs. Determining the best conductor topology for a given application requires calculation methods, as in [20].
## 3 Results
### Pca  
From the analysis of the values which measure the amount of variance due to each main component, 17 PCs were identified in MCS and 19 in HC (Figure 4). In order to simplify the interpretation of the PCs we used the varimax method and Kaiser/Guttman criterion. This indicated as significant the components with a coefficient higher than 1 [39], thus leaving only seven PCs for analysis in both groups. The optimal factorial solution is that with two extracted factors because they explained a maximal amount of variance. Proportion of variance expressed by the first factor in MCS and HC was 31.47% and 25.44%, respectively, and the second factor also expressed 20.01% in MCS and 18.5% HC.  
Figure 4: Scree plot depicting HC (on the left) and MCS (on the right) eigenvalues, percentage of total variance, cumulative eigenvalues and cumulative percentage of total variance. Vertical black rod indicates numbers of principal components resulting from Kaiser/Guttman criterion and vertical red rod indicates number of principal components needed to overcome 50% of total variance.  
Figure 3: An example of a single MCS and HC subject respectively represented in (**A**) and (**D**) for video Head Impulse Test trace and results; in (**B**) and (**E**) for fast Fourier transform results; in (**C**) and (**F**) for X-Y plane plot. CE, closed eyes; OE, open eyes; X, spectral values of body oscillations on X plane; Y, spectral values of body oscillations on Y plane; L, low frequency interval; M, middle frequency interval; H, high frequency interval; FC, foam carpet; MCS, multiple chemical sensitivity; HC, healthy subject.  
FFT and classical posturography parameters were found respectively to positively and negatively correlate with the same component in HC (component coefficients shown in scree plots in Figure 5).
# Marine Internet for Internetworking in Oceans: A Tutorial
## 1 Introduction
To allow more researchers to understand marine Internet for internetworking in oceans, this tutorial systematically introduces its principle, architecture and applications with the following contributions. * The main applications of marine Internet are discussed in Section 2, along with an introduction to its best-effort networking principle. * The environment for communication in oceans is described in Section 3, where possible objects and facilities that might be used for communication in oceans are introduced, such as those located along coastlines, on islands, and on/under water surface as well as in air and space. Communication media and power sources probably available in oceans are also discussed. * The well-developed and under developing communication systems in oceans are introduced in Section 4, with a comparison between them in terms of their suitability in oceans. They typically include maritime radio, mobile communication systems, satellites, high-altitude platform (HAP), wireless ad hoc networks (WANETs), cabled underwater observatory systems and UANs. * The internetworking strategies of marine Internet is discussed in Section 5, which introduces a large scale cooperative heterogeneous wireless network structure that uses an integrated coastline network plus vessel-based WANETs as a backbone network. A network selection policy is also discussed. * Some challenging issues necessary for further studies to foster the development of marine Internet are discussed in Section 6, including how to interconnect networks with no explicit borders, how to enable various WANETs in marine Internet to make smart networking decisions, and how to maximize the end-to-end reliability and security of marine Internet, as well as mobility management with mobile base stations and WANETs. The paper is concluded in Section 7.
## 2 Methodology Overview  
This work is based on the techno-economic approach proposed in [36] and is revised here. The algorithm optimizes the cable's design on the basis of the selected operating parameters, such as the rated cable power, the voltage level, or the line length. From a mathematical standpoint, the problem involves minimizing a multi-variable function, representing the cable system's cost, subject to the electric, geometric, and fluid dynamic constraints imposed by the user. The objective function is described in Section 4. The problem variables correspond to the selected individual design elements, affecting all other design parameters which are not directly involved in the optimization process. The solver then identifies the set of variables producing a design which corresponds to the lowest overall system cost. The use of a numerical solver is necessary due to the complex interconnections between the selected variables related to the cable system's design. These variables are linked in both explicit and implicit ways, making it almost impossible to manually predict the trends of their optimized combinations as a function of the set line parameters. To achieve this, the native MATLAB algorithm _ga_ was adopted, based on genetic algorithms. A key advantage of _ga_ over other solvers is that it can easily handle integer and non-integer variables simultaneously. It is worth noting that genetic algorithms are stochastic methods. Therefore, in practice, the entire solver runs several times, starting from the same initial solution and storing a combination of optimized variables at each iteration. Finally, the solution leading to the lowest cost while complying with all of the imposed constraints is selected.  
In the following paragraphs, the structures of the cable conductor and the whole cable system adopted in this study are presented, followed by descriptions of the system variables selected for the optimization process.
## 7 Conclusions  
The simulation studies show that the presented method of the adaptive operation of the f/N converter turns out to be effective in improving the operating parameters of the converter. It has been demonstrated that it is possible to select the \(N_{xi}\) threshold values to force a change in the frequency \(f_{0}\) of the clock signal of the f/N converter, allowing to improve the accuracy of processing information about the test signal and to extend the processing range.  
Possible problems that may occur with the wrong selection of the \(N_{xi}\) threshold values were indicated. It turned out that a considerable increase in the \(f_{0}\) reduces the quantization error, unfortunately, at the same time, increasing the demand for the capacity of the counter.  
It is possible to select the clock period of the f/N converter so that the maximum averaging error does not increase. The increase of period \(T_{0}\) reduces the \(N_{xi}\) values obtained from the f/N converter counter, which allows longer \(T_{xi}\) periods to be processed. Generally, increasing \(T_{0}\) allows to increase the lower range of measurable frequencies. The simulation results confirmed the correctness of the theoretical considerations.  
The LabVIEW development environment turned out to be useful for the development of an effective and graphically readable simulator application.  
This research was supported by Lublin University of Technology, Lublin, Science fund No. FD-20/EE-2/000.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data is stored in a custom format implemented in software. Some of the simulation data was stored only temporarily in computer memory while LabVIEW was processing it. If necessary, it is possible to repeat the simulations presented and generate a summary containing the necessary information.
## 3 Materials and Methods
### Evaluation Metrics  
For the effectiveness assessment of the method, Recall (R), Precision (P), F1-score, Intersection over Union (IoU), and Accuracy are evaluated according to Equations (1)-(5):  
\[R=\frac{TP}{TP+FN} \tag{1}\]  
\[P=\frac{TP}{TP+FP} \tag{2}\]  
\[Acc=\frac{TP+TN}{TP+FP+TN+FN} \tag{3}\]  
\[F1_{score}=2\cdot\frac{P\cdot R}{P+R} \tag{4}\]  
\[IoU=\frac{A\cap B}{A\cup B} \tag{5}\]  
where TP (True Positives) is the number of correct identifications of instances inside the image under test, FN (False Negatives) is the number of instances present in the image that the algorithm does not recognize, FP (False Positives) is the number of predictions mis  
\begin{table}
\begin{tabular}{c c c} \hline
**YOLOv8 Parameters** & **Classification Model** &
\begin{tabular}{c} **Detection and** \\ **Classification Model** \\ \end{tabular} \\ \hline Hsv\_h & 0.0 & 0.015 \\ Hsv\_s & 0.0 & 0.7 \\ Hsv\_v & 0.0 & 0.4 \\ Degrees & 0.0 & - \\ Scale & 0.0 & - \\ Shear & 0.0 & - \\ Perspective & 0.0 & - \\ Mixup & False & - \\ Erasing & 0.0 & - \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 8: Composition of the augmented training dataset with respect to the adopted augmentation techniques.  
classified as instances in the image, and TN (True Negatives) is the number of predictions correctly classified as negative. A is the predicted bounding box and B is the ground truth bounding box.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 4 CFRP Conductivity Measurements and Techniques for Non-Destructive Testing Applications
### Delamination Measurements Based on Electrical Conductivity
#### 4.2.1 Eddy Current Testing  
Eddy current testing (ECT) can be used to detect CFRPs. ECT has significant advantages. It can detect subsurface and surface damages as well as non-magnetic metallic items [130]. Based on electromagnetic principles, ECT is more suitable for electrical conductive materials such as CFRPs [39; 131].  
In ECT, a current-carrying coil is positioned just above the component surface to induce an electrical field in the composite. Flaws in the material can interrupt the current field and change the impedance in the coil. The defect severity can be explored based on the changes in amplitude and phase angle of electrical signals [132; 133].  
Theoretically, eddy current testing is not sensitive to delamination because eddy currents are flowing in the direction parallel to laminates and changed slightly by the delamination [1]. It is concluded that eddy current methods are much more sensitive to broken fibers than delamination [134]. The comparison between ECT and ultrasonic inspection confirmed that the ultrasonic method could precisely detect delamination defects, but the detection results were not consistent with ECT results. However, some satisfactory results have been obtained. ECT with the high reproducibility and good signal-to-noise ratio is a low-cost technique for detecting delamination in CFRPs [135].  
Three techniques mentioned above can be used to analyze the delamination in CFRPs [136]. However, the extent of delamination as viewed in the C-scan image and scanning acoustic microscopy (SAM) image differed from the results achieved by the eddy current inspection. Though it was not possible to distinguish the two types of defects due to the low resolution, ECT detected both delamination and interlaminar cracks. Experimental results in Reference [135] clearly showed that eddy currents could detect delamination growth. Therefore, ECT is a potential method for monitoring defects in CFRPs.  
However, if the sensor used is not optimized according to the frequency requirement for detecting deeper defects, when delamination occurs in deeper zones, chaotic conductivity arrangements increase the detection difficulty of delamination [137]. Therefore, these layers or the real volume extension of the delamination is not complete. Larger sensors with the lower resolution but deeper penetration depths at higher frequencies should be adopted in ECT. In summary, because of the low electrical conductivity of carbon fibers, the excitation frequency range of the inductors used in ECT for composites is ~100-100 MHz [138].