esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, meta-learning and denoising approaches were proposed for diabetes prediction and glucose level prediction for smart health systems using wearable sensors of ECG and glucose sensors with HRV parameters. Wearable sensor technology often needs to operate accurately across a wide range of individual users who each have unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. In these cases, meta-learning can be particularly useful. These models can then quickly adapt to the nuances of new users or specific applications with minimal data. This approach helps overcome the hurdle of limited target data, enhancing the performance and personalization of wearable sensor technologies.  
Moreover, it is time-consuming and complex to collect sufficient training data with real wearable sensor experiments; meta-learning promises to reduce the amount of biomedical wearable sensor training data needed to train the predictive models in the target domain by learning from previous knowledge or experience. Therefore, the proposed method is suitable for scenarios where a sufficient amount of wearable sensor training data collection is challenging, expensive, or limited. Moreover, the proposed meta-learning approach addresses certain challenges, such as the need for sufficient training data, expensive operational costs, and other problems associated with traditional approaches in smart health systems. The experimental results demonstrate that the proposed meta-learning method boosted the prediction results in smart health systems. Furthermore, based on the results of the numerical analysis, the proposed fine-tuned meta-learning method achieved higher prediction results with an accuracy of 96.2% and AUC of 99%, and reduced detection error of diabetes with a lower RMSE of 0.010798 than without pre-train method with only a small amount of target data and less iteration using the same environment. Therefore, the proposed system is cost-effective, flexible, adaptable, and faster for the Internet of Medical Things (IoMT) or smart healthcare system applications using wearable sensors or devices.  
Conceptualization, M.A.T., A.M.D., Y.C.M. and P.-C.P.; methodology, M.A.T., A.M.D., Y.C.M. and P.-C.P.; software, M.A.T., Y.C.M. and P.-C.P.; model validation, M.A.T.; formal analysis, M.A.T., A.M.D., Y.C.M., C.-K.Y., S.D.B. and P.-C.P.; investigation, M.A.T., A.M.D., Y.C.M. and P.-C.P.; writing--original draft preparation, M.A.T.; writing--review and editing, M.A.T., A.M.D., Y.C.M. and P.-C.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science and Technology Council, Taiwan, under Grant NSTC 112-2221-E-027-076-MY2. Data Availability Statement: Data available in a publicly accessible repository at [[https://doi.org/10.1016/j.imu.2018.09.003](https://doi.org/10.1016/j.imu.2018.09.003)]([https://doi.org/10.1016/j.imu.2018.09.003](https://doi.org/10.1016/j.imu.2018.09.003)) (accessed on 25 July 2024).  
The authors declare no conflicts of interest.
# Smart Grid Testing Management Platform (SGTMP) +
## 2 Background
### Related Works  
To our knowledge, there are no existing platforms that can be directly compared to SGTMP. The one that is nearest in terms of implementation is the one in Annor-Asante and Pranggono [17], where a smart grid testbed with low-cost hardware and software is proposed based on the Arduino microcontroller, XBee radio modules (wireless data acquisition modules), a botnet simulator, and intrusion detection systems. The aim of the platform is to aid in research and education, similar to our previous work [22]. However, the focus of the platform in Annor-Asante and Pranggono [17] is more on cybersecurity and not on the provision of a general management platform for testing and simulations, as is SGTMP.  
Other comparable platforms are SmartGridLab [14], GridSim [15], Smart-Grid Common Open Research Emulator (SCORE) [16], and GridLAB-D [18], although these platforms are more focused on the simulation of communication networks within SGs, compared to SGTMP [45]. SmartGridLab [14] proposes a wireless network to emulate a network composed of smart meters, power switches, suppliers, and appliances. Such a testbed can be used for different scenarios, such as demand-response energy pricing changes. GridSim [15] allows simulating power grid operation, control, and communications at the scale required for meaningful SG scenarios. It comprehends power systems simulations, communication, and control center applications. SCORE [16] is an emulator platform for both SG power and communication networks. The communication module supports several wireless communication models and protocols, while the power module can emulate power flows and energy models. SCORE can be used to evaluate several SG scenarios (e.g., real-time demand-response pricing changes). GridLAB-D [18] presents a power system modeling and simulation environment, allowing integration with third-party data management and analysis tools. Compared to SGTMP, GridLAB-D is more focused on the algorithmic part of simulations, allowing one to model and simulate various aspects of SGs (e.g., peak load management).  
Figure 1: Proposed testing process for SGTMP based on ISO/IEC/IEEE 29119.
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 3 Strategies
### Effect of Morphology
The above description of different piezoelectric materials provides a brief overview of how the morphologies of the materials affect the output performance of the fabricated device, and some examples are mentioned in Table 1. As the active surface area increases, the stress distribution improves, thus enhancing the piezoelectric response. The performance of the same material varies with different morphologies; for example, ZnO nanoflakes generate a better response compared with other morphologies (nanowires, nanorods, and nanodiscs). Several unique morphologies have been reported to improve the performance of PENGs. Figure 5: **(A) BaTiO3-based flexible and transparent nanogenerator. (a) Schematics of the as-developed BaTiO3-based PENG. (b) SEM image of BaTiO3 nanotube/PDMS composite. (c) TEM image of the synthesized BaTiO3 nanotubes. (d) High-resolution TEM image of synthesized BaTiO3 nanotube [70]. Adapted with permission from [PERSON] et al. (2012). Copyright © 2012 American Chemical Society. (B) (a) Fabrication of PVDF/KNN NR film via the electrospinninging method. (b) Fabrication of PVDF/KNN NR nanocomposite-based PENG [75]. Reprinted with permission from Bairagi and Ali. (2019). © 2019 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim. ** \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sr. No**} & \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Morphology**} & \multirow{2}{*}{**Synthesis Method**} & \multirow{2}{*}{**Operation Temperature**} & \multirow{2}{*}{**Output Voltage (V)**} & \multirow{2}{*}{**Output Current**} & \multirow{2}{*}{**Power Density**} & \multirow{2}{*}{**Ref.**} \\ \cline{6-9}  & & & & & & & & \\ \hline \multirow{4}{*}{1.} & \multirow{2}{*}{ZnO} & Nanowire & Physical vapor deposition & 150 \({}^{\circ}\)C & 2.03 & 107 nA & 11 mW/cm\({}^{3}\) & [14] \\ \cline{2-9}  & & \multirow{2}{*}{Nanodisc} & Seed-assisted solution route & 150 \({}^{\circ}\)C & 17 & 150 nA/cm\({}^{2}\) & - & [19] \\ \cline{2-9}  & & \multirow{2}{*}{Nanored} & Chemical vapor deposition & 95 \({}^{\circ}\)C & 9-9.5 & - & - & [62] \\ \cline{6-9}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some examples of piezoelectric materials with their different morphologies.
# User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA
## 6 Conclusions  
In this paper, we presented a two-stage hybrid model to study the influencing factors and effects of user acceptance of information feed advertising. The results of the SEM analysis in the first stage show: (1) consistency has a significant positive impact on user acceptance; (2) informativeness has a significant positive impact on user acceptance; (3) sociality has a significant positive impact on user acceptance; (4) advertising reward has a significant positive impact on user acceptance; (5) perceived advertising clustering has a significant negative impact on user acceptance, and (6) accuracy has no significant positive effect on user acceptance. The fsQCA analyzing results in the second stage shows that combining different factors can effectively enhance users' willingness to accept information feed advertising.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 7 Conclusions  
Based on the graphs and results obtained, the following conclusions are drawn:  
* A 3-DoF Scorbot model robot was designed using the Toolbox, Simscape, and Robotics System, applying design information according to technical specifications. Simulation tests were performed to observe the movement of the robot's joints with geometric trajectories, considering four- and five-point coordinated paths.
* Controllers (PD Computed Torque, PID Computed Torque, PD-PI + fuzzy, and MPC) were designed and simulated using the dynamic model of the robot. Graphs of the desired and actual trajectories were generated, verifying the sequences of the proposed trajectories.
* Classical PID control was included to observe the robot's behavior under the same trajectories, as well as a PID control with Gain Scheduling to enhance its performance. Trajectories for object evasion were also designed.
* Performance indices were calculated, highlighting that the IA indicator approximated a value close to one.
## 3 Distance-Based Opportunistic Push/Subscribe Content Dissemination Model
### Introduction of DOPS  
DOPS content dissemination model we proposed in this paper is composed of three layers: Application layer, Decision-making layer and Network layer. The structure of DOPS is shown in Figure 1.  
The application layer copes with the things about the subscribing content and subscription management. The decision-making layer decides whether to publish the content requested by others. The network layer deals with relay nodes selection. Different opportunistic routing protocols can be selected to work in this layer.  
Figure 1: The structure of DOPS.
## 5 Implementation
### Circuit Diagram  
We now present a circuit diagram detailing the steps taken by the traffic signal control system and the relationship between its various components:  
Figure 2: Schematic diagram of the LDR and laser.  
1. Arduino Mega is the main microcontroller of the system; it controls traffic signal operations and processes sensor data.
2. Traffic lights: Four traffic lights are connected to Arduino Mega. Each traffic light has a set of pins connected to specific digital instruments on Arduino, allowing the color of the traffic lights (red, yellow, or green) to be controlled.
3. Each side street is equipped with two LDR sensors and a laser. Lasers are used in LDRs to detect the presence of a vehicle.
4. Each laser is connected to a power supply and the ground, with the output signal connected to an analog input port on the Arduino.
5. Each LDR is connected to a voltage divider circuit, which outputs a signal proportional to the received light. This signal is connected to an analog input pin on the Arduino to detect the motor.
6. The ESP8266 Wi-Fi Module is used to send sensor data to the Thing$Peak IoT platform.
7. The ESP8266 is connected to a power supply of 3.3 V or less.
8. The TX (transmit) and RX (receive) ports of the ESP8266 are connected to the corresponding RX and TX ports on Arduino Mega for the purpose of serial communication.
9. An attached power supply provides power for Arduino Mega, traffic lights, sensors, and the ESP8266 module. The power supply is maintained at a level sufficient for all components, and they are properly connected to prevent electric shock.
10. Additional resistors for LDR circuits, capacitors for fixed voltage, and connectors for secure wiring are included in the system.
## 4 Research on Platform Performance Testing
### Platform Motion Accuracy Testing
#### 4.4.2 Repeated Positioning Accuracy Testing  
The repeated positioning accuracy was measured using a fixed step size and multi-point cyclic measurements. The platform adjustment motion mainly involved a focusing motion along the Z-axis and pitch and yaw motions around the X- and Y-axes. Therefore, the accuracies of the Z-direction translation motion and the rotation motions in the Rx and Ry directions were the focus of this research. The testing of each step size was repeated three times.  
Figure 23a,b show the three-cycle translational test curves along the Z direction in the ranges of 0 \(\upmu\)m \(\rightarrow\) 10 \(\upmu\)m \(\rightarrow\) 0 \(\upmu\)m \(\rightarrow\) -10 \(\upmu\)m \(\rightarrow\) 0 \(\upmu\)m with a 1 \(\upmu\)m step size and 0 mm \(\rightarrow\) 5 mm \(\rightarrow\) 0 mm \(\rightarrow\) -5 mm \(\rightarrow\) 0 mm with a 0.5 mm step size, respectively. After processing all the test data, the repeated positioning accuracies were 0.168 \(\upmu\)m and 0.225 \(\upmu\)m, respectively.  
Figure 24a,b show the three-cycle rotating test curves in the Rx direction in the range of 0\({}^{\circ}\)\(\rightarrow\)0.01\({}^{\circ}\)\(\rightarrow\)0\({}^{\circ}\)\(\rightarrow\)-0.01\({}^{\circ}\)\(\rightarrow\)0\({}^{\circ}\) with a 0.001\({}^{\circ}\) step size and 0\({}^{\circ}\)\(\rightarrow\)5\({}^{\circ}\)\(\rightarrow\)0\({}^{\circ}\)\(\rightarrow\)-5\({}^{\circ}\)\(\rightarrow\)0\({}^{\circ}\) with a 0.5\({}^{\circ}\) step size, and the repeated positioning accuracies were 0.181\"and 0.440\", respectively. The repeated positioning accuracy in the Ry direction could be obtained by testing in a similar manner, with results as shown in Table 3.  
Figure 22: Definition of the coordinate system for the parallel platform and layout of the grating length gauges.
## 5 Conclusions  
In order to study the failure of a typical Pb-free circuit board, a series of methods was conducted to make failure analysis. Nondestructive examinations were used, and the failure position was located at the BGA solder joint. Then, destructive examinations were conducted, and the cause of failure was confirmed as the change of material characteristic under extremely low-temperature. A contrast test supported this result. Finally, verification experiments were conducted to verify the failure mechanism and find the transition temperature range of SAC305, which could make a guide for Pb-free circuits' deep space operation.  
The results of failure analysis indicated the cause of failure. The Pb-free circuit board went through the shock tests at \(-100\)\({}^{\circ}\)C. At this temperature, the fracture characteristic of SAC305 was brittleness, which was different from ductileness under room temperature. When the circuit underwent stress caused by shock test, it was easy for the solder joint to have cracks. When the crack grew to a specific length, the failure occurred. In the meanwhile, the transition temperature range of material property was also confirmed as \(-70\)----\(80\)\({}^{\circ}\)C.  
Figure 16: SEM graph of fracture surface at \(-100\)\({}^{\circ}\)C (brittle).  
Through this failure analysis case, it alerts us to focus attention on the reliability of Pb-free material applications for deep space exploration. Because of the low-temperature characteristics of SAC305, there may be a higher failure risk of actual operation. According to our study, the environment temperature needs to be kept higher than \(-\)70 \({}^{\circ}\)C. More studies need to be conducted to improve the reliability of Pb-free soldering used for aerospace components. Meanwhile, associated preventive methods, such as thermal preservation, are necessary.  
Conceptualization, B.W.; Methodology, Y.L.; Validation, Y.L.; Formal Analysis, G.F.; Investigation, Y.L.; Writing--Original Draft Preparation, Y.L.; Writing--Review and Editing, M.J.; Visualization, Y.L.; Supervision, W.Z. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors are thankful to School of Reliability and Systems Engineering, Beihang University for providing the test equipment.  
The authors declare no conflict of interest.
## 2 Transmitarray Antenna Design
### Optimization Result
The method to enhance the aperture efficiency is also a research direction, which is related to the _f_/D, spillover radiation, and edge scattering. Generally, the aperture efficiency is based on (11) and (12). \[e_{ap}=\frac{10^{G/10}}{D_{\text{max}}} \tag{11}\]  
Figure 11: A 3D radiation pattern of the optimized TA antenna at 28 GHz. Figure 12: Photograph of experimental setup of the proposed antenna. \[D_{\text{max}}=\frac{4\pi\cdot A}{\lambda_{0}^{2}} \tag{12}\]  
where \(e_{ap}\) is the aperture efficiency, \(D_{\text{max}}\) is the directivity in the main beam (unit: dimensionless), \(G\) is the maximum gain (unit: dB), the physical area of TA is marked as \(A\) (unit: m\({}^{2}\)), \(\lambda_{0}\) is the wavelength at the desired central frequency point (unit: m\({}^{2}\)), which is 28 GHz in this paper. Then (12) will be substituted into (11), so we can get the aperture efficiency of the optimized TA antenna at \(Ka\) band, which shows 23% in simulation. Finally, it is obvious that the TA antenna can achieve a performance of high gain, low SLL and high aperture efficiency with the optimized feed horn and the optimized phase distribution using PSO. As shown in Table 1, the TA antenna has higher gain, wider bandwidth and higher aperture efficiency at the central frequency of 28 GHz. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Freq.** & **TA Thickness** & **Array** & **SLL (dB)** & **Number of** & \multirow{2}{*}{**Bandwidth**} & **Gain\({}_{\text{max}}\)** & **Aperture** \\  & **(GHz)** & **(\(\lambda_{0}\))** & **Size (\(\lambda_{0}^{2}\))** & & & **Elements** & & **Efficiency (\%)** \\ \hline
[25] & 28 & 0.18 & 89.5 & \(-\)18 & 484 & 11.\% & 24.2 & 24.5 \\
[28] & 20 & 10.5 & 368.5 & \(-\)22.5 & 2032 & 10.2\% & 33.0 & 40 \\
[38] & 13.5 & 0.76 & 164.7 & \(-\)22 & 621 & 9.8\% & 29.95 & 50 \\ This paper & 28 & 0.14 & 173.3 & \(-\)30 & 660 & 11.2\% & 27 & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the optimized TA antenna with some published array antenna. Figure 14: Comparison of the radiation pattern in two cut planes at 28 GHz: (**a**) E-plane comparison and (**b**) H-plane comparison. Figure 13: Simulated and measured S11 of the proposed antenna.
# A State-of-the-Art Review on the Study of the Diffusion Mechanism of Fissure Grouting
## 4 Research Status of the Numerical Simulation of Fissure Grouting in Rock Masses  
### Numerical Simulation Methods  
The key to successful grouting is to select appropriate grouting parameters, including grouting pressure, grout volume, grouting rate, and grouting duration, to achieve the best grouting effect. With the development of computer technology, numerical simulation hasbecome one of the most commonly used approaches for studying the grouting process in fissured rock masses. Numerical simulation technology provides a very intuitive and detailed display of the grouting process and is relatively easy and low-cost. This technology can be used not only to investigate the slurry diffusion law in fissures but also to determine the actual amount of grout utilized in practical grouting projects. In recent years, extensive study has been undertaken on numerical simulation methods and the application of grouting in fissured rock masses. The finite element method, the boundary element method, and the discrete element method are currently the most widely used numerical simulation methods.
## 4 Analysis and Suppression Method of Short Circuit Current in MMC-Based MTDC Power Grid
### DC Short Circuit Current Suppression Method in MMC-Based MTDC Power Grid  
The existing short-circuit current suppression methods dealing with the DC short-circuit current can be divided into three types [13]:  
* Using the MMC topology with DC fault ride through capability such as FB-SMs, CD-SMs, and 5 LCC-SMs, etc.
* Installing current limiting device and cooperating with DC circuit breakers to isolate the DC fault line.
* Design MMC control strategies in order to suppress the short circuit current.
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
## 1 Introduction
### Background
The correlation coefficient between the Time-Related VPN-encrypted traffic features is calculated, and the importance ranking is outputted. Then, redundant features are eliminated, and to obtain the optimal set of Time-Related VPN-encrypted traffic features. (2) For the imbalance of VPN-encrypted traffic data classes, this paper proposes Xgboost-encrypted traffic identification model based on Focal Loss. The Focal Loss function is designed on based on the cross-entropy loss function to improve the Xgboost model. This can change the calculation weight of VPN traffic samples, and realize the processing of data imbalance between VPN-encrypted traffic data classes. (3) For the problem of the low identification rate of the Xgboost model for encrypted traffic identification, a Bayesian-based parameter optimization method for VPN-encrypted traffic identification model is proposed. The method enables to solve the problem of the low identification rate of VPN-encrypted traffic identification model and achieve the accuracy optimization of this model. The rest of this article is structured as follows. Section 2 introduces the theoretical basis of the realization of the VPN-encrypted traffic identification model; Section 3 performs mathematical modeling of the proposed research method and analyzes the experimental performance of the proposed method; Section 4 gives the conclusion of this article.