esa-sceva/satcom-chunk-collection huggingface dataset:
# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 6 Conclusions  
### Summary of the Performance  
To address the issue of the service QoS degradation or interruptions caused by dynamic changes in resource demand within edge computing environments, we conducted an in-depth study on the dynamic migration problem of SFCs where resources are scarce and constantly changing. The focus is on optimizing network performance through load balancing. We used the CNN-AT-LSTM machine learning model to predict the resource demands of VNFs. This approach allows us to anticipate changes in network node resource allocation and proactively develop migration strategies based on these predictions. By determining which VNFs to migrate from overloaded nodes, we ensure efficient resource management. We integrated the Proximal Policy Optimization deep reinforcement learning algorithm into our prediction model. This combination enables the system to detect changes in complex environments and select appropriate target nodes for migration. It also achieves network load balance, reduces the number of subsequent migrations, and minimizes the impact of unnecessary migrations.  
The experimental results demonstrate that the RP-LBM-PPO algorithm outperforms the RP-LBM-DQN algorithm in both the number of migrations and the service interruption  
Figure 8: Comparison of total number of SFC migrations between RP-LBM-PPO and RP-LBM-DQN under different resource conditions.  
Figure 9: Comparison of service interruption rate between RP-LBM-PPO and RP-LBM-DQN under different resource conditions.  
rate. When resources are sufficient or moderate, the PPO algorithm maintains a low service interruption rate with minimal migration overhead. However, in resource-scarce conditions, the DQN algorithm significantly increases the number of migrations and the service interruption rate due to frequent adjustments. In contrast, the PPO algorithm effectively reduces these negative impacts through stable policy optimization. These findings indicate that the PPO algorithm offers better efficiency and stability, making it more robust and adaptable in complex network environments.
# Field Tests on Eco-Friendly Railway Precast Concrete Slab
## 4 Field Test Results
### Dynamic Wheel Loads  
Figure 8 shows the comparisons of the specific time history of the dynamic vertical wheel load measured from the eco-friendly B2S panel and the conventional B2S panel, respectively. As shown in the graphs in Figure 8, the change of the vertical load due to the use of the eco-friendly B2S was minimal. Throughout the 11 runs of the field test, the maximum recorded dynamic vertical loads were 76.68 kN and 76.92 kN for the eco-friendly B2S and conventional B2S, respectively. In addition, the average vertical loads were found to be 69.4 (eco-friendly B2S) and 71.2 kN (conventional B2S). Figure 9a shows the comparison between the maximum vertical load measured from the eco-friendly and conventional slabs under 11 runs. \(t\)-test results (\(p=0.260\)) also show that there was no statistical difference between the dynamic vertical loads measured from each slab. In addition, the dynamic vertical wheel loads were presented with respect to the speed. Figure 9b indicates that the change of the dynamic vertical load was minimal as the speed of the trains increases for both B2S panels. The eco-friendly B2S showed slightly less dynamic vertical wheel loads than those measured from the conventional B2S. Therefore, the eco-friendly B2S panels would not increase the vertical load demand on rail and track structure.  
Lateral loads were also measured. The maximum measured lateral loads were 7.55 kN and 6.98 kN for the eco-friendly B2S and the conventional B2S, respectively. The average lateral loads were 5.83 kN from the eco-friendly B2S and 4.24 kN from the conventional B2S. The Korean railway track design guideline recommends that the lateral load to vertical load (L/V) ratio should be less than 0.8 to prevent any derailment due to any excessive lateral loads [21]. Figure 10 shows the computed average L/V ratio. The average L/V ratios for the eco-friendly B2S and conventional B2S panels were 0.08 and 0.04, respectively. Since the measurement was taken on the tangent line, both measured L/V ratios were insignificant and satisfied the standard.  
Figure 10: Average vertical load (L/V) ratio.  
Figure 9: The maximum vertical loads under each run (**a**) and vs. speed (**b**).
## 2 Theoretical Models
### Multi-Field Coupling Theoretical Model
#### 2.1.3 Multi-Field Coupling Analysis  
The wire-plate ESP in this work contains both a fluid field and a particle dynamic field inside, which are controlled by the external magnetic field and temperature field. It is necessary to explain that these fields are not independent but rather exhibit a certain coupling relationship internally. The electric field affects the fluid field by altering the wind speed of the ion wind. The modified fluid field, in turn, reacts to the electric field through ion convection, exerting forces such as aerodynamic drag and ion wind on the particle dynamic field. In addition, the particle dynamic field is also influenced by Lorentz force generated through magnetic field and electric field force, respectively. Conversely, the particle dynamic field also influences the electric field through space charge, thereby altering the distribution of the electric field. The fluid field, in a reverse manner, is influenced by the gas-solid two-phase coupling. A multi-field coupling model, considering the influence of the temperature field on the flow field, particle dynamic field, and electromagnetic field, is established [31], as depicted in Figure 2.
## 4 Data Analysis
### Statistical Analysis  
#### 4.1.1 B500-J Evaluation  
In this section, the values of the RMSD metric index for all the PZTs of \"B500-J\" are presented. Thus, in Figure 19, the RMSD values of the epoxy-bonded on the interfacial zone are depicted. As shown, the values of \"J1\" and \"J4\" align with the formed cracking in the left shear span. Further, a significant observation is that both PZTs show an early warning sign about the imminent damage in their area, indicating also the loss of cohesion in the interfacial surface, as at the Dam2 state, the cracking development was limited to a small distinctive shear one with no significant width and range (Figure 11). On the other hand, the PZTs located in the right span show negligible changes, especially if these are compared with the relative of the left span, which is in alignment with the span's structural integrity in the failure state. Further, the PZTs located in the middle of the beam demonstrate a similar performance and exhibit low values of RMSD compared to those of the left shear span, which is proven by the integrity of the lower mid-part of the beam.  
Figure 19: (**a**) Cracking at failure and \"J\" PZTs’ notation and position for “B500-J”, and (**b**) RMSD index values for “J” of “B500-J”.  
Moreover, the \"SA\" RMSD values (Figure 20b) follow the formation of the developed cracking pattern (Figure 10). Thus, SA2 demonstrates significantly higher RMSD values than all the relative embedded PZTs. The latter comes in agreement with the beam's structural integrity with respect to the PZTs' locations. Further, even though the externally epoxy-bonded PZTs are considered not as sensitive as the embedded ones, \"X2\" shows higher RMSD values than the relative of \"X3\", as depicted in Figure 21b.  
Figure 21: (**a**) Cracking at failure and “X” PZTs’ notation and position for “B500-J”, and (**b**) RMSD index values for “X” of “B500-J”.  
Figure 20: (**a**) Cracking at failure and “SA” PZTs’ notation and position for “B500-J”, and (**b**) RMSD index values for “SA” of “B500-J”.  
From all the above observations and the cracking pattern formation presented for each set of PZTs, it could be extracted that all the different PZT patches' configurations achieved successfully in identifying the critical effect that was assigned to investigate. Hence, the formation and the development of the damage, which finally led to a fatal shear critical crack in the left span of the beam for both the jacket and retrofitted beam, was successfully detected.
## 2 Results  
Demographic, Anthropometric, Body Composition, Physical Activity, Dietary, and Clinical Characteristics of the Study Population  
Thirty-one patients with advanced melanoma who were candidates for first-line anti-PD-1 immunotherapy or targeted therapy were enrolled. Patients were stratified into the following groups according to their therapeutic response: (i) responders, i.e., those with a complete or partial response to therapy or with stable disease (\(\geq\)6 months); and (ii) non-responders, i.e., those with progressive disease. Baseline characteristics for the entire cohort and for comparison between responders and non-responders are shown in Table 1 (demographic and clinical) and Table 2 (anthropometry, body composition, physical activity, and diet). Interestingly, all patients who were subsequently classified as responders were characterized by higher weight, body mass index (BMI), and total (TAT), visceral (VAT), subcutaneous (SAT), and intramuscular (IMAT) adipose tissue (\(p\leq 0.05\)). They also had a lower neutrophil-to-lymphocyte ratio (NLR) than non-responders and were predominantly non-sarcopenia (\(p\leq 0.02\)). Regarding the occurrence of adverse events during therapy, none of the patients experienced mucositis or other grade 3-4 adverse events, suggesting that there was a lack of correlation with therapeutic response. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic** & **Overall (n = 31)** & **Responders (n = 18)** & **Non-Responders (n = 13)** & _p Value_ \\ \hline Age (years), mean (SD) & 62 (11) & 62 (11) & 61 (12) & 0.5748 \\ Sex, n (\%) & & & & 0.4120 \\ Male & 23 (74) & 12 (52) & 11(48) & \\ Female & 8 (26) & 6 (75) & 2 (25) & \\ \hline Stage, n (\%) & & & & \\ IIIC & 4 (13) & 3 (75) & 1 (25) & 0.6207 \\ IV & 27 (87) & 15(56) & 12 (44) & \\ \hline ECOG performance status, n (\%) & & & & \\
0 & 26 (84) & 17 (65) & 9 (35) & 0.1337 \\
1-2 & 5 (16) & 1 (20) & 4 (80) & \\ \hline Planned anti-PD-1 treatment, n (\%) & & & & \\ Nivolumab & 19 (61) & 12 (63) & 7 (37) & 0.7777 \\ Pembrolizumabap & 5 (16) & 3 (60) & 2 (40) & \\ \hline Targeted therapy, n (\%) & & & & \\ Dabrafenib and tramatinib & 7 (23) & 3 (43) & 4 (57) & 0.4130 \\ \hline NLR \({}^{\text{a}}\), mean (SD) & 5.5 (7.5) & 2.8 (1.6) & 9.0 (10.4) & **0.0034** \\ NLR \({}^{\text{a}}\) & & & & **0.0196** \\ \(<\)d, n (\%) & 21 (70) & 15 (71) & 6 (29) & \\ \(\geq\)4, n (\%) & 9 (30) & 2 (22) & 7 (78) & \\ \hline Medication use \({}^{\text{b}}\), n (\%) & & & & \\ Antibiotics \({}^{\text{a}}\) & 10 (33) & 5 (50) & 5 (50) & 0.4611 \\ \hline Probiotics \({}^{\text{a}}\) & 6 (20) & 3 (50) & 3 (50) & 0.6599 \\ Proton-pump inhibitors & 7 (23) & 2 (29) & 5 (71) & 0.0994 \\ Corticosteroids & 6 (19) & 2 (33) & 4 (66) & 0.2076 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic and clinical characteristics of patients with advanced melanoma. Data are presented for the entire cohort and for responders and non-responders. Differences between groups were evaluated via [PERSON]’s test, [PERSON] test, and Student’s _t_-test, as appropriate. \(p\) values \(\leq\) 0.05 are shown in bold.
# A Proposal for a Zero-Trust-Based Multi-Level Security Model and Its Security Controls
## 3 Introduction of MLS Classification Criteria and Proposed Zero-Trust-Based MLS Model  
### Multi-Level Security Classification Criteria in the Republic of Korea  
In the Republic of Korea, the network segregation systems adopted by the government and public institutions have posed challenges to the adoption of cloud computing technologies and the application of AI technologies. The Republic of Korea is exploring transitioning to a Zero-Trust-based Multi-Level Security framework to overcome these limitations. The core of the Republic of Korea's Multi-Level Security framework lies in categorising data based on operational data sensitivity into Classified data (C), Sensitive data (S), and Open data (O), and applying differentiated security controls accordingly. Figure 2 shows the classification criteria based on data sensitivity [32].  
However, most information systems store and process data of varying classifications. In such cases, it may be challenging to apply differentiated security controls based solely on data sensitivity. To address this, information systems storing or processing data are also classified into three levels of equivalent data, and security controls are applied accordingly. The classification of an information system is determined based on the highest level of data stored or processed within that system. For instance, if both Classified data (C) and Sensitive data (S) are present within a single information system, the system is classified as C. Based on this principle, when data of different classifications are stored, generated, or transferred within the same system, the security principles illustrated in Figure 3 are applied [32].  
Figure 2: Classification criteria based on MLS data sensitivity [32].
# PVDF-TrFE Film on PDMS Substrate for Energy Harvesting Applications
## 2 Experimental Details
### Simulations: Strain, Stress, and Potential  
The strain and stress distribution on the P(VDF-TrFE) film was simulated using the COMSOL finite-element simulation software. A solid mechanics module was used to simulate the stress and deformation of the device. The Young's Modulus used in the simulation was 2.3 \(\times\) 10\({}^{6}\) Pa for P(VDF-TrFE) and 2.6 \(\times\) 10\({}^{6}\) Pa for PDMS. The simulation used the same dimensions as in the fabricated device (3.5 \(\times\) 3.5 cm for PDMS, and 2 \(\times\) 2 cm for P(VDF-TrFE)), as shown in Figure 4b. An acceleration of 1.75 g was applied onto half of the bottom surface of this device. Considering PDMS comprises more than 99.9% of the thickness of the entire structure, the strain distribution is mainly dependent on PDMS layer. Hence the deformation of active material P(VDF-TrFE) can be assumed to be same as the deformation of the upper surface of the PDMS layer where it is attached. Since the edge of the shaker will first hit the center-line of the device, this point will experience the most strain during the process. When the shaker is in contact with the device, the active material experiences a uniform acceleration for a short period of time. Then strain is mainly formed within the stretched (overhanging) half of the device, as shown in Figure 4c. The potential simulation of COMSOL was performed using the information provided by the P(VDF-TrFE) powder manufacturer (Piezotech) with regard to the piezoelectric voltage constants g\({}_{31}\), g\({}_{32}\) and g\({}_{33}\), which were 216, 19 and \(-339\times 10^{-3}\) Vm/Nm\({}^{2}\), respectively. These results are shown in Figure 4d.  
The resonance modes were investigated both analytically and through simulations. The fundamental resonant frequency (\(f_{0}\)) of the device was found to be \(\sim\)10.1 Hz from the equation:  
\[f_{0}=\frac{1}{2\pi}\sqrt{\frac{E}{\rho}}\frac{t}{wl} \tag{1}\]  
where \(E\) is Young's Modulus, \(\rho\) is density, \(t\) is thickness, \(w\) is width, and \(l\) is length. The resonant frequencies were also simulated using COMSOL, and \(f_{0}\),\(f_{1}\), and \(f_{2}\), turned out to be 11.77 Hz, 25.55 Hz, and 68.93 Hz, respectively. We excited the device at frequencies of 5, 10, 20 and 30 Hz, overlapping the range of frequency of fundamental and first order modes.
# An Analysis of Differential Evolution Population Size
## 5 Experimental Setup
### Evaluating Population Size Impact and Credibility of Existing Guidelines  
For the purposes of this experiment, a range of population sizes consisting of 10, 20, 30, 40, 50, 70, 100, 200, 300, 400, 500, 700, and 1000 individuals were utilized. The scale factor \(F\) and the crossover rate \(CR\) were set at 0.5 and 0.9, respectively, as suggested in [9]. For each benchmark problem, and for each dimension, the overall average fitness of the best solutions found over 51 independent runs was reported, along with the corresponding standard deviation. Notably, a lower average fitness value indicates a better solution. The performance assessment relied on the quality of the solutions obtained at the termination of the algorithm. Based on solution quality, the population sizes were ranked from best to worst. Thus, the population size that achieved the highest quality was assigned the topmost rank of 1, whereas the population size associated with the lowest quality was assigned a rank of 13. The average rank of every population size for each problem per dimension was reported. Furthermore, in order to evaluate the performance of each population size across all problems within a specific problem dimension, the mean average rank was calculated. The mean average rank represents the performance of a particular population size across all 30 problems per a specific dimension. The lower the mean average rank, the better the performance.  
Statistical tests were conducted to validate the hypothesis of whether the average ranks achieved with various population sizes were statistically significant. Accordingly,the statistical significance of the multiple comparisons among all population sizes for every problem per dimension was examined by means of the Friedman test followed by the post hoc Shaffer static procedure as suggested in [PERSON] et al. (2013). The tests were carried out at an \(\alpha=0.05\) significance level. A graphical illustration of the mean average ranks with respect to different population sizes and various dimensions for all problems is given in Figure 2.  
Figure 2: _Cont._  
Figure 2 illustrates that for all dimensions, the mean average ranks were relatively high for small population sizes (i.e., \(N_{P}=10\)). This high rank indicates a worsened performance. However, as the population size increased to a moderate value, the performance exhibited the best levels. Subsequently, the performance continued to improve only up to a specific limit, beyond which the performance gradually deteriorated again to reach its worst levels with extremely large population sizes (as can be observed for \(D=10\), \(D=30\), and \(D=50\)). This observation is in accordance with the research conducted by [PERSON] and [PERSON] [15]. An exception was for \(D=100\), where population sizes beyond \(N_{P}=30\) did not significantly affect performance. Generally, a population size within the range of 20 to 30 was found to be the most appropriate across all dimensions.
## 4 Discussion
The results of the experiments indicated that it is possible to set lower thresholds that perform accurate classification by fusing information from the two signal sources. The proposed first-stage classifier can provide timely and accurate fall detection. The second stage SVM-RBF-KNN algorithm performed favorably in terms of activity recognition, and achieved higher accuracy and fewer false warnings than did the other classifiers. The results also revealed that separate classification problems could be solved by providing a different set of features from multisensor data. We also found that the value obtained from an accelerometer on a single axis has large amounts of randomness, and needs to be transformed into a suitable feature space by SVM algorithms. The multisensor system incorporates multiple thresholds to avoid the effects of noise in a single sensor, and it integrates multiple single weak classifiers into a robust classifier. This multisensor fusion method also achieves a high detection rate while reducing the false alarm rate. The proposed multisensor activity recognition system can effectively meet a physician's needs for rehabilitation training. The sampling rate, number, and locations of sensors, and the employed algorithm and its corresponding ranges for such variables affected the detection results. The tension sensor data are more sensitive than the accelerometer data in the second stage of classification. The experiments demonstrate that 205 milliseconds can be saved compared to models that use a single sensor. The accuracy of the fusion method is almost 99.37%, and no false warnings occurred. These results verify the feasibility and efficiency of multisensor fuzzy data fusion algorithms and the calculation process. We also found that the tension signal is most reliable for identification, and the tension signal peak sequence reflects the fall direction (the largest peak value of the tension sensor in one direction indicates that the fall is occurring in the opposite direction). The proposed recognition system coupled with a new type of mobile chaperone lower limb rehabilitation training robot exhibits excellent control performance, good interaction performance, and accurate monitoring, which promotes the enthusiasm and confidence of patients during rehabilitation training. However, this new type of mobile chaperone lower limb rehabilitation training robot cannot improve recovery efficiency, because that efficiency is related to the pathological physical condition and medication of the patients; instead, our rehabilitation robots improve the efficiency of physicians by providing them with reliable data and allowing them to adjust the training strategies and training difficulty levels. Currently, neural network classifiers require more data for further training. Additionally, the experiments were performed by young students who were not hemiplegic patients. In future work, we plan to conduct experiments using hemiplegic patients from the China Rehabilitation Research Center (CRRC).
# Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System
## 4 Methods and Evaluation  
We note that PV power generation and weather data are related to time series. Thus, the deep learning algorithms chosen for one-day-ahead PV power forecasting were LSTM and GRU neural network models, and the traditional ANN model was added to compare the accuracy of the three models in this study. Hyperparameter tuning of the deep learning network will directly affect the model's performance and then influence the prediction results. This section introduces the method of hyperparameter tuning for each model. The accuracy evaluation methods used in this study are the most widely used metrics, namely, MAPE, RMSE, and MAE. They are described below.
# Principles and Application of Polyimide Fiber Bragg Gratings for Surface Strain Measurement
## 3 Strain Transfer Ratio Experiment  
### Bonding Method for Polyimide FBGs  
The most common mounting method for bare polyimide FBGs is bonding as shown in Figure 13. According to previous investigation, the adhesive is very important for bonding. Loctite 401 instant adhesive and E-120 HP epoxy adhesive are perfect choices. Bare FBGs can be bonded to substrate by Loctite 401 instant adhesive first. The instant adhesive can fix the fiber very quickly. Then, the fiber can be covered by the E-120 HP epoxy adhesive. This type of epoxy adhesive has a 120-minute work life. The epoxy adhesive not only provides enough shear stress, but also provides enough protection for the bare fiber after solidifying. In fact, an instant adhesive can provide some protection after solidifying. In some experiments, even epoxy is not necessary.  
Figure 12: The average strain transfer rate changed with adhesive above the fiber.  
Figure 13: Bonding method. (**a**) Loctite 401 and E-120 HP; (**b**) Bare polyimide FBG was bonded to the CFRP (Carbon Fiber Reinforced Polymer) plate by Loctite 401; (**c**) Bare polyimide FBGs were bonded to the CFRP textile by Loctite 401 first and then covered by E-120 HP; (**d**) Bare polyimide FBGs were bonded to the concrete specimen by Loctite 401 first and were then covered by E-120 HP.