esa-sceva/satcom-chunk-collection huggingface dataset:
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## 1 Introduction
### Objectives  
The main target of this article is to address the barrier highlighted by [PERSON] [2], regarding the integration of IoT solutions with existing technologies like cloud computing and platform multi-tenancy, but also with other newer technologies like edge computing or network slicing, as defined for 5G networks. This important integration part is related to the deployment, configuration and orchestration of the diverse components available in the IoT ecosystem into isolated end-to-end IoT slices.  
The article is particularly focused on the automation and orchestration aspects, introducing the architectural design of an IoT slice orchestrator. This design is compatible with the network slicing concept and intended to cover a wide range of IoT use cases. Furthermore, as a first step to prove the practical feasibility of our solution, the article presents a prototype implementation of this orchestrator which is able to deploy IoT functions (IoT gateways and IoT servers) in different locations of the network (edge and cloud) for a given slice. In order to have a realistic environment, several types of infrastructures are used for the functional validation of the prototype, including constrained computing nodes like Single Board Computers (SBCs) that can be found at edge domains and commercial cloud computing platforms like Google Compute Engine (GCE) [7].  
With the development and validation of this prototype the following objectives are achieved:  
* Prove that it is possible to coordinate, in a real scenario, the deployment of functions composing a multi-tenant (sliced) IoT solution, despite its heterogeneity.
* Verify that the orchestration can be done through different locations and is hence compatible with edge and cloud computing, taking advantage of their benefits, i.e., the low latency provided by edge computing whenever quick response times are required, and the flexible and automated allocation of resources supported by cloud computing technologies.
* Demonstrate that it is possible to perform this orchestration even with constrained devices where IoT functions can be executed.
* Confirm that lightweight virtualization techniques are propitious to achieve these purposes.  
The rest of this article is organized as follows: Section 2 reviews the existing literature and ongoing research on the usage of edge computing, microservice architectures and orchestration in IoT solutions. The architectural design of the proposed solution is included in Section 3. Section 4 includes details about the provided prototype and the results obtained from its validation. Finally, Section 5 concludes this article and provides some future research lines.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 3 Material and Methods
### Prototype Design
#### 3.2.1 Hardware  
For cost reasons, the RTK receiver model selected is the double-band RTK receiver simpleRTK2B and simpleRTK2 Blite by Ardusimple (see Figure 3), based on the module ZED-F9P of U-Blox. The double-frequency antenna selected is the AS-ANT2B-SUR-L1L2-25 SMA-00 of Ardusimple. The microcontroller selected is the STM32F746 ZG of ST, mounted in the development board NUCLEO-F746 ZG (see Figure 3).  
The GNSS receivers and antennas, as well as the microcontroller, meet all the requirements, and both devices are compatible with standard Arduino pins, reducing the number of welding points. The development board is oversized for the requirements of this work, but it was chosen due to its low cost and because it will allow the use of other peripherals in the future without changing the components. This board also includes a debugger, making it particularly appropriate. The board manufacturer also provides free software for the peripheral configuration (STM32 CubeMX) and a programming/debugging environment (STM32 CubeIDE).  
The system design is quite simple, since each receiver has two UART ports and the development board supports several UART ports. Figure 4 shows the system schematic and the level of components, power supply, data, and protocols.  
Figure 4: Attitude and positioning system scheme.  
Figure 3: RTK receivers (**left**) and development board (**right**).  
Given the system shown in Figure 4, the relative positions between the GNSS receivers can be obtained with maximum accuracy, allowing the computation of the system attitude with the best possible accuracy. However, this configuration does not allow one to know the global position of the receivers with an accuracy of centimeters, since they are not receiving corrections from any reference station.  
It is important to remark that the main objective of this work is to compute the system attitude with the maximum possible accuracy. However, if the global position must be computed with high precision, too, it can be obtained by simply providing RTCM corrections at the UART2 of the first GNSS receiver.  
To ease the system validation, a small screen was installed in the prototype to show the attitude information (see Figure 5). This screen is a commercial model that includes a microprocessor and directly prints the characters that are sent from the microcontroller by the UART port. The connections are not shown, since they are not a necessary element for the prototype; they are included just for validation purposes.
# Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
## 3 TC-CNN Model Framework
### Cnn
#### 3.1.2 Activation Function  
The nonlinear activation function is an indispensable key module in CNN. In order to prevent the gradient explosion or gradient dispersion, the commonly used ReLU function is used in this paper:  
\[f(x)=\text{ReLU}(x)=\text{max}(0,x) \tag{14}\]
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 1 Introduction
Therefore, the general public carrying smartphones is, in fact, a mobile sensing node that can use the sensors on the phones to record and transmit crack image information and corresponding spatial locations at high frequencies [40]. The traditional inspection process uses a camera to take pictures of cracks, record the location and damage description on papers, and return from the field for post-processing. Meanwhile, crowd sensing with smartphones can directly record and upload crack images and location information, which is more advantageous for digitizing and automating the recording of crack information and simplifying data processing [41; 42; 43]. Based on the idea of crowd sensing and the characteristics of bridge crack collection, this paper established a crowd-sensing-based bridge structural crack detection system. A crowd sensing crack information collection system was developed, which contains a cloud management platform as the center and the user mobile application as the sensing node. A deep neural network was adopted for the detection of cracks in the uploaded images. The investigation can provide reference for the establishment of crowd-sensing- and deep-learning-based system for crack inspection of bridge structures.
## 6 Conclusions and Future Work  
To eliminate noise from a noisy image without reference image and obtain good performance, this paper proposed a non-reference image denoising method based on enhanced dual-tree complex wavelet optimized by fruit fly algorithm and bilateral filter. In the absence of reference image, a hypothesis was proposed that the \(MSE\) of the denoising image and noisy image was supposed to increase as the noise is denoised. The IFOA searched the optimal threshold to gain best denoising performance. The bilateral filter was applied to process images in the late work, which can significantly improve the performance of images and enhance the edge information. To verify the performance of the proposed IFOA, it was compared with original FOA. Moreover, different inertia weights were tested to get the best inertia weight. As comparisons, six representative methods, DTCWT, BM3D, Median filter, Wiener filter, DWT and BF, were implemented. The results showed that the proposed denoising method outperformed other methods. Finally, the proposed IFOA-DTDWT-BF was applied to infrared thermal image of roller in running to test the actual effect.  
This work is valuable for image processing, which provides a non-reference denoising method. However, there are also some shortcomings. On the one hand, the calculation duration is still a problem, although it is faster than other adaptive methods. On the other hand, the proposed algorithm adopts the \(MSE\) as the denoising evaluation index, which may have imperfections. To surmount these  
Figure 23: Smoothness of images using different methods.  
Figure 22: Information entropy of images using different methods.  
shortcomings, the authors intend to improve the method with more effective intelligence algorithms and form an evaluation function with several evaluation indexes to assess the quality of the denoised image. Besides, the authors plan to combine other efficient denoising methods with the proposed methods to create a better method and apply the method in the aspect of industrial vision in the future.  
The authors would like to thank all of the reviewers for their constructive comments. The supports of National Natural Science Foundation of China (No. U1510117 and 51605477), National Key Basic Research Program of China (No. 2014 CB046301), and the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions in carrying out this research are gratefully acknowledged.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 2 Related Works
### Power Prediction  
Several research works have proposed using statistical modeling techniques, such as autoregressive integrated moving average (ARIMA) [17], and ML algorithms, such as SVM [18], decision trees (DTs) [19], convolutional neural networks (CNNs) [20], and recurrent neural networks (RNNs) [21; 22; 23], for power prediction. The experimental results demonstrate that ML provides promising outcomes. For ML algorithms to provide accurate prediction, they require being trained on large and diverse datasets. Therefore, the authors of [24; 25] proposed general power consumption predictors trained on the data of multiple customers. Unlike in previous works that focused on power consumption prediction, the authors of [26; 27; 28] investigated net power prediction in net metering systems. In these systems, the power load is the subtraction of the power used by customers from the electric utility and the power produced by the customers' distributed power resources, e.g., solar panels. Unlike [26], [PERSON] et al. [28] proposed incorporating solar irradiance readings with net power readings as inputs for the predictor to minimize prediction errors. However, none of the existing works have investigated accurate power consumption prediction while using the CAT approach for efficient data transmission.
## 2 Proposed PLL Architecture
### s-Domain Analysis  
A second order loop filter is implemented to avoid the unwanted ripple occurrence because of the exponential charging of C. The system will oscillate as there is just two integrators, hence the need to add a zero in the system to make it stable by placing an R in series with C [17; 18; 19; 20; 21; 22; 23; 24]. There is another C\({}_{\rm x}\) placed to suppress the jumpy response of the series RC network during charging and discharging. A s-domain analysis block diagram is shown in Figure 10.  
Open loop gain:  
\[\rm A=\frac{I_{0}}{2\pi}\frac{k}{s^{2}(C+C_{X})}.\frac{1+sCR}{1+sC_{1}R} \tag{2}\]  
Closed loop gain:  
\[\rm\frac{\rm\varnothing_{ref}}{\rm\varnothing_{out}}=\frac{1+sCR}{1+sCR+s^{2}(C+ C_{X})\frac{2\pi}{I_{0}K}+s^{3}\frac{(C+C_{X})C_{1}R2\pi}{I_{0}K}} \tag{3}\]  
The proposed architecture obtains a phase margin of 60\({}^{\circ}\), with C = 600 pF, C\({}_{\rm x}\) = 42 pF and R = 24.3 k\(\rm\Omega\). Hence, it is a stable system. For a clock synthesizer with a VCO of the frequency range 330 MHz, the feedback divider M in the loop equations is 66 times.
## 3 Results and Discussion
In addition, some of these models have employed  
Figure 8: Blood flow measurements from a line that projected from the Aov towards the LV cavity. (**a–d**) show the simulations at angle 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\) and 30\({}^{\circ}\), respectively, before and after AML splitting. (**e**) shows the line projected perpendicularly from the AoV into the LV cavity. Figure 9: Flow profile at the AoV. (**a–d**) show the simulations at angle 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\) and 30\({}^{\circ}\), respectively, before and after AML laceration. (**e**) shows the line where the flow velocities were measured. idealized 2D or 3D geometries, and most have concentrated on certain cardiac cycle stages like closure or opening rather than the full cycle. FSI simulations are cost-effective for studying intraventricular fluid dynamics. The presented model is an extension of our previously published study [26]. In addition, our model has been used and validated for different applications, such as modeling MV prolapse and replicating the motion of the MV leaflets in response to different pressures in the atria and ventricles, resulting in their opening and closing. The 3D-0D model of LV developed in this study accurately incorporates native MV anterior leaflet, MV prosthetic valve, and ventricular wall in the presence of HCM. In addition, MV annular plane (inlet) and Ao outlet boundary are linked to 0D closed-loop circulatory circuit, which incorporates implicit conservation of blood volume. This 0D model presents a more accurate representation of the cardiovascular system, and improves the accuracy in simulating cardiovascular dynamics.
# Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
## 4 Real Experimental Results and Discussion
An analysis of the measurement data, filtered tracking data, and recorded video indicate that the system is capable of reliably tracking the experimental vehicles. Moreover, after post-filtering, the vehicle's tracking trajectory becomes more consistent, with minimal deviation, even in the presence of measurement outliers. The filtered trajectory of the target vehicle matches its actual motion state during lane changes, with the velocity filter values converging without divergence. The MAST-UKF algorithm tracks target vehicles A and B. The actual vehicle experimental results provide the root mean square error (RMSE) of the absolute position of the vehicles in the Cartesian coordinate system, as shown in Table 4, and the root mean square error of the absolute velocit (Table 5). Compared to the AST-UKF algorithm, the distance error is reduced by 18.29%, and the velocity error is reduced by 15.25%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tracking Algorithm** & **A X-Axis Position (m)** & **A Y-Axis Position (m)** & **B X-Axis Position (m)** & **B Y-Axis Position (m)** \\ \hline AST-UKF & 0.1705 & 0.7218 & 0.1039 & 0.7052 \\
**MAST-UKF** & **0.1568** & **0.4892** & **0.0971** & **0.5193** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Table of average distance errors for different tracking algorithms. Figure 7: Tracking results: (**a**) objective A\({}_{i}\) (**b**) objective B. In addition, we also tested the operating speed of AST-UKF and MAST-UKF to evaluate their performance in actual system operations. This experiment was conducted in five rounds, comparing the two algorithms by continuously running them 1000 times on the computer. The time required for the continuous running of AST-UKF and MAST-UKF algorithms was recorded. The experimental results are shown in Table 6.  
It can be seen that the execution speed of MAST-UKF is slower than AST-UKF, with the time for 1000 iterations being 47.876 s, while AST-UKF took 48.444 s. The results show that the algorithm requires slightly more time for calculation. However, it is noteworthy that the average running time of MAST-UKF meets the practical processing requirements, reaching the lowest demand of 50 milliseconds per iteration. The table also shows that MAST-UKF, while slower than AST-UKF, has more practical efficiency and supports real-world applications, especially in dynamic environments that require high-efficiency target tracking and data processing. In summary, the MAST-UKF algorithm proposed in this paper can adaptively adjust the noise covariance using the improved attenuation factor. It achieves good tracking of large-range maneuvering targets when their motion state undergoes significant changes while ensuring real-time performance.
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 3 The Proposed EPT-VMD-TPA-TCN Model and the Restructured EPT-VMD-FE-TPA-TCN Model  
In this paper, we propose a short-term power load forecasting model (EPT-VMD-TCN-TPA) based on the mixed decomposition of load data and TCN-TPA network forecasting. The Tr(t) component and Re(t) component of the load series are first extracted using EPT. Then, the Re(t) component is decomposed into several IMFs with different center frequencies using VMD, and the Tr(t) component and each IMF component are fused with weather and other influencing factors separately. Finally, the TCN-TPA network model is used to train and predict the data. The reorganization model EPT-VMD-FE-TPA-TCN discussed in this paper is based on the EPT-VMD-TPA-TCN model and uses the concept of fuzzy entropy (FE) to calculate the complexity of each IMF component. The IMF components of similar complexity are combined into a new component (FE-IMF), and finally, the TCN-TPA network is used to train and predict each fused weather factor FE-IMF component and Tr(t) component. The structural framework of the EPT-VMD-TPA-TCN prediction model proposed in this paper is shown in Figure 7.  
The EPT-VMD-TCN-TPA prediction model proposed in this paper can be divided into three key parts: the EPT-VMD decomposition layer, the TCN-TPA network training and prediction layer, and the fully connected prediction result output layer. The EPT-VMD-FE-TCN-TPA recombination prediction model is added with the FE subsequence recombination layer after the EPT-VMD decomposition layer. This section details the key components of the experimental dataset and model.  
Figure 7: The structural framework of the EPT-VMD-TCN-TPA model.
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 5 Use Cases and Applications
### Healthcare  
Envisioned as a catalyst for progress in healthcare, 6G technology is poised to revolutionize robotic-assisted surgery, telemedicine, and integrate interconnected devices for deep-body implants. These medical applications necessitate the seamless collectionand transmission of continuous health data, predominantly supported by low-power, battery-operated wireless devices, and sensors.  
Wireless data transfer in medical devices, facilitated by active radios, can substantially deplete battery life. However, these devices have the potential to harness the signals of pre-existing wireless systems, such as WiFi APs found in hospitals or residential environments, to ensure reliable communication without draining power. Additionally, medical devices often transmit data to centralized servers, necessitating a localized network gateway. In the context of using symbiotic radio solutions, medical devices can effectively transmit their information online via WiFi, further streamlining the communication process [44; 96].  
Holographic teleportation emerges as the inherent evolution from AR and VR-based solutions. It stands apart by functioning within a genuine three-dimensional domain and harnessing all five senses--sight, hearing, touch, smell, and taste--to deliver an unparalleled immersive experience. This advancement finds diverse applications, particularly in advanced healthcare, encompassing remote diagnosis, telemedicine and surgery, high-resolution sensing for distant exploration, and lifelike video conferencing. However, 6G must evolve in a way that guarantees very high throughput (Tbps) and ultra-low latencies (\(<\)1 ms), which can be obtained in the new design of eMBB and URLLC [66].  
Current transport network architecture is ill-equipped to handle the required \(\leq\)1 ms latency, which requires a profound re-evaluation of network design. The solution will involve virtualizing the existing fiber infrastructure, facilitated by modern SDN and virtualization techniques and methodologies. Simultaneously, the main functions of the network will be structured in a microservices architecture capable of dynamic activation.
# Mental Map-Preserving Visualization through a Genetic Algorithm
## 3 Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & \multicolumn{2}{c}{**Year 2000**} & \multicolumn{2}{c}{**Year 2004**} & \multicolumn{2}{c}{**Year 2013**} \\  & \multicolumn{2}{c}{\(N=57\), \(E=175\)} & \multicolumn{2}{c}{\(N=70\), \(E=274\)} & \multicolumn{2}{c}{\(N=100\), \(E=514\)} \\ \cline{2-7}  & **Fitness Value** & **Execution Time (ms)** & **Fitness Value** & **Execution Time(ms)** & **Fitness Value** & **Execution Time (ms)** \\ \hline No PR & 0.13925 & 193575 & **0.09749** & 72666 & 0.13797 & 461389 \\ PR (only forward) & 0.14252 & 271469 & 0.10371 & 107872 & 0.13186 & 864191 \\ PR (only backward) & 0.14252 & 270114 & 0.09823 & 108254 & 0.13133 & 879561 \\ PR (both ways) & **0.13221** & 346010 & 0.09832 & 133309 & **0.13015** & 1051168 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experiment results of various PR implementations. worse solutions, and so, PR kept on calculating the exact solutions all over again. We did add a random movement so that the same solutions were unlikely to occur. Figure 9 shows the best fitness value in each generation for datasets 2004 and 2013, respectively. In every experiment, those using PR always converged much earlier than those without PR, as we can see from the curves on both graphs. However, once they reached convergence, using PR gave no better results than the ones without PR, and all of the experiments reached roughly the same results. We concluded that using PR gives a considerably faster convergence rate but not better results from these experiments.