esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 6 Conclusions and Future Work  
In this paper, we investigate the dynamic migration and resource allocation issues in MECC systems. We emphasize the importance of service migration in networks characterized by dynamic features. Our investigation covers both the migration process and its performance, and we conduct a modeling analysis of computational performance post-migration. To tackle the challenges posed by dynamic computational demands and user mobility, we propose a method based on the Advantage Actor-Critic framework. This method determines migration and resource allocation operations for each time slot, based on observed states, aiming to minimize the average task response delay. The simulation results demonstrate that our A2C-based approach consistently reduces the average task response delay across various scenarios and ensures the lowest task failure rate compared to benchmark methods.  
However, several aspects require further research. While our primary focus has been on the impact of migration on average response delay, the migration process also leads to additional network effects, including migration costs. Future research should explore strategies that simultaneously reduce migration costs and average response times. Additionally, our study mainly examines the impact of migration and computing resource allocation decisions. However, in real-world scenarios, the task offloading strategy significantly influences these migration and resource allocation strategies due to varying communication conditions associated with user mobility. This complex interplay between the offloading strategy and system performance in realistic settings demands more detailed investigation to optimize both cost and efficiency effectively. Another important issue to consider is that the centralized decision-making process may pose a risk of user data leakage. Therefore, finding ways to ensure data security while minimizing the impact of privacy protection mechanisms on system decisions has become a critical challenge.  
Conceptualization, Z.H.; Methodology, L.L. and Z.L.; Software, L.L.; Validation, Z.H. and L.L.; Formal analysis, J.Q.; Investigation, Z.H., L.L. and K.L.; Resources, K.L.; Data curation, Y.D.; Writing--original draft preparation, Z.H. and L.L.; Writing--review and editing, Z.H. and J.Q.; Visualization, L.L. and Y.D.; Supervision, K.L.; Project administration, K.L.; Funding acquisition, Z.H. and K.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under under Grant No. 62362068, in part by the Applied Basic Research Foundation of Yunnan Province under Grant Nos. 202201 AT070156 and 202301 AT070194, in part by the Open Foundation of Yunnan Key Laboratory of Software Engineering under Grant No. 2023 SE208.  
Data Availability Statement: Data are available on request from the authors.  
The authors declare no conflict of interest.
# Indoor Localization Using 6G Time-Domain Feature and Deep Learning
## 3 ResNet with SLAB and CLAB
### Channel Local Attention Block (CLAB)  
CLAB is designed to refine channel-wise feature representation by leveraging information. As shown in Figure 3, CLAB first applies average pooling and max pooling along the spatial dimensions of the input feature map (\(C\times H\times W\)), producing two-channel descriptors of size \(C\times 1\times 1\). These pooled features encode different aspects of the channel information. The pooled features are processed through a convolutional layer with a ReLU activation function, followed by another convolutional layer to generate a refined channel representation. The output is then passed through a sigmoid activation function to produce a channel attention map of size \(C\times 1\times 1\). This attention map is used to emphasize informative channels, improving the network's ability to capture meaningful features [18; 19]. Note that only using the average strategy could weaken the salient features; thus, we use max-pooling to gather other important information about distinctive object features to infer attention maps. The sigmoid activation layer is used to obtain the probability of which channel to focus on. In other words, CLAB is designed to focus on which channel is important for the given input feature map.
## 4 ITFWO for Various OPF Problems
### OPF with PV and WT Units
#### 4.2.2 Type 8: Considering the Total Cost with Carbon Tax  
Carbon tax (\(C_{tax}\)) has been imposed on any unit amount of liberated greenhouse gases for modelling investment in greener kinds of power such as solar and wind. The evolutionary function of emissions has been modeled in [27]:  
\[C_{E}=C_{tax}E \tag{48}\]  
\[J_{8}=J_{7}+C_{tax}E \tag{49}\]  
\(C_{tax}\) had been considered to be $20 per tonne in [27].  
According to the optimal results shown in Table 9, it is clear that the ITFWO achieves highly stable and quality optimal results in comparison with TFWO.  
\begin{table}
\begin{tabular}{c c c} \hline
**Variables** & **TFWO** & **ITFWO** \\ \hline \(P_{G1}\) (MW) & 134.90791 & 134.90791 \\ \(P_{G2}\) & 28.6365 & 27.873 \\ \(P_{us1}\) & 43.8208 & 43.3921 \\ \(P_{G3}\) & 10 & 10 \\ \(P_{us2}\) & 36.991 & 36.6362 \\ \(P_{ss}\) & 34.9256 & 36.3708 \\ \(V_{G1}\) (p.u.) & 1.0722 & 1.0722 \\ \(V_{G2}\) & 0.954 & 1.0572 \\ \(V_{G5}\) & 1.0996 & 1.0351 \\ \(V_{G8}\) & 1.04 & 1.0397 \\ \(V_{G11}\) & 1.1 & 1.0999 \\ \(V_{G13}\) & 1.0815 & 1.055 \\ \(Q_{G1}\) (MVAR) & 13.2357 & \(-\)1.94508 \\ \(Q_{G2}\) & \(-\)20 & 13.2188 \\ \(Q_{us1}\) & 35 & 23.1987 \\ \(Q_{G3}\) & 34.7168 & 35.0261 \\ \(Q_{us2}\) & 29.5148 & 30 \\ \(Q_{bs}\) & 25 & 17.5088 \\ Fuelvlvcost ($/h) & 441.0225 & 438.4895 \\ Wind cost ($/h) & 246.6480 & 243.9527 \\ Solar cost ($/h) & 94.6478 & 99.5521 \\ Total Cost ($/h) & 782.3182 & 781.9943 \\ Emission (t/h) & 1.76205 & 1.76224 \\ Power losses (MW) & 5.8819 & 5.7801 \\ V.D. (p.u.) & 0.53921 & 0.46395 \\ \hline \end{tabular}
\end{table}
Table 8: The optimization results for Type 7.  
Figure 9: Optimization process for Type 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **TFWO** & **ITFWO** \\ \hline \(P_{G1}\) (MW) & 123.73211 & 123.23758 \\ \(P_{G2}\) & 33.6227 & 32.2873 \\ \(P_{\mathrm{us1}}\) & 46.3179 & 45.6242 \\ \(P_{G3}\) & 10 & 10 \\ \(P_{\mathrm{us2}}\) & 38.9983 & 38.4356 \\ \(P_{\mathrm{ss}}\) & 36.01 & 39.0957 \\ \(V_{G1}\) (p.u.) & 1.0701 & 1.0697 \\ \(V_{G2}\) & 1.0567 & 1.0562 \\ \(V_{G5}\) & 1.0356 & 1.0352 \\ \(V_{G8}\) & 1.0615 & 1.0997 \\ \(V_{G11}\) & 1.0982 & 1.0983 \\ \(V_{G13}\) & 1.0503 & 1.0511 \\ \(Q_{G1}\) (MVAR) & \(-3.04844\) & \(-3.18412\) \\ \(Q_{G2}\) & 10.9594 & 10.7783 \\ \(Q_{\mathrm{us1}}\) & 22.2316 & 22.2315 \\ \(Q_{\mathrm{G3}}\) & 40 & 40 \\ \(Q_{\mathrm{us2}}\) & 30 & 30 \\ \(Q_{\mathrm{ss}}\) & 15.5342 & 15.8684 \\ Fuelvcost (\$/h) & 431.9829 & 426.2436 \\ Wind cost (\$/h) & 262.4784 & 258.0072 \\ Solar cost (\$/h) & 98.3979 & 108.3925 \\ Total Cost (\$/h) & 792.8592 & 792.6434 \\ Emission (t/h) & 0.90197 & 0.87689 \\ \(J_{8}\) & 810.8986 & 810.1812 \\ Power losses (MW) & 5.2811 & 5.2804 \\ V.D. (p.u.) & 0.45991 & 0.46169 \\ Carbon tax (\$/h) & 18.0394 & 17.5378 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The optimization results for Type 8.  
Furthermore, the convergence trends of the problem via the studied methods are shown in Figure 10.
# Face Mask Detection on Photo and Real-Time Video Images Using Caffe-MobileNetV2 Transfer Learning
## 3 MobileNetV2
### Depthwise Separable Convolution
Therefore, the output will be obtained as \(D_{P}\times D_{P}\times M\) as shown in Figure 3. \(D_{K}\times D_{K}\) multiplications are required for a single convolution operation, since the filter is multiplied by \(D_{P}\times D_{P}\) across each of the \(M\) channels. Equation (2) gives the depthwise convolution operation cost. \[DWC_{cost}=(D_{K}\times D_{K}\times 1)(D_{P}\times D_{P}\times M)=D_{P}{}^{2} \times D_{K}{}^{2}\times M. \tag{2}\]  
In the pointwise operation, \(1\times 1\) convolution is performed on the M channels. This procedure will employ a \(1\times 1\times M\) filter. The output size changes to \(D_{P}\times D_{P}\times N\) when \(N\) such filters are obtained as shown in Figure 4. Each convolution operation necessitates \(1\times M\) multiplications. Since \(D_{P}\times D_{P}\) times travel through the filter. The pointwise convolution operation cost is given in Equation (3). \[PWC_{cost}=(1\times 1\times M)(D_{P}\times D_{P}\times N)=D_{P}{}^{2}\times M \times N. \tag{3}\]  
Figure 3: Depthwise Convolution. Figure 2: Standard Convolution. The overall depthwise separable convolution (DWSC) cost is computed by sum of the cost of depthwise convolution and the cost of pointwise convolution. The computational cost of convolution operations is given in Equation (4). \[DWSC_{cost}=({D_{P}}^{2}\times{D_{K}}^{2}\times M)+({D_{P}}^{2}\times M\times N)= ({D_{P}}^{2}\times M)({D_{K}}^{2}+N). \tag{4}\]  
The proposed methodology MobileNetV2 utilized the depthwise separable convolution, which is well known for its fast computational time, and lower computational cost compared to other models. The reduced computation cost of DWSC can be computed by taking the ratio of the DWSC cost (\(DWSC_{cost}\)) and the SC cost (\(SC_{cost}\)), which is given below in Equation (5). \[Ratio(R)=\frac{DWSC_{cost}}{SC_{cost}}=\frac{({D_{P}}^{2}\times M)\times({D_{K} }^{2}+N)}{{D_{P}}^{2}\times{D_{K}}^{2}\times M\times N}=\frac{1}{N}+\frac{1}{ {D_{K}}^{2}}. \tag{5}\]  
For \(N=224\) and \(D_{K}=3\), the DWSC cost is reduced by nine times as compared to the SC cost.
## 4 Performance Evaluation and Discussion
### Model Establishment
#### 4.1.2 Instantaneous QoE Prediction by LSTM-QoE  
The instantaneous QoE values were estimated by the LSTM-QoE model [15]. The model was trained on the training set with 28 distorted videos driven by four features \(STSQ\), \(PI\), \(NR\) and \(TR\). The performance of this model was then quantified on the eight test videos using the Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Coefficient (SROCC). Consequently, the model achieved high accuracy with PCC of **0.9946** and SROCC of **0.8870**. The performance of the trained model is illustrated in Figure 7, demonstrating high accurate prediction.
## 3 Results and Discussion
### Spectroscopic Characterization of Copolymers  
The first product was obtained from the reaction of EGDMA with St. This system was chosen as the reference. The ATR-FTIR results are shown in Figure 4. The exact values of the wave numbers are also given in Table 2. In Figure S2, the ATR-FTIR spectra of monomers and copolymer (EGDMA-St) is visible. In the EGDMA and St spectra, the signal corresponding to C=C stretching vibration of vinyl group (1640 cm\({}^{-1}\)) is clearly noticed. The lack of this signal in the EGDMA-St spectrum indicates the correct course of the copolymerization reaction. Changes in the absorbance of some signals were observed in the spectrum as the amount of lignin increased. This effect was observed for the adsorption bands around 3400-3500 cm\({}^{-1}\), which causes the appearance of a hydroxyl group from the lignin molecule to be visible. Additionally, in Figure S3, the ATR-FTIR spectra in the range 2000-4000 cm\({}^{-1}\) of -OH groups in copolymers and original lignin are shown. With the addition of lignin amount, the changes in the intensity of signals are visible. Due to the increase in the color intensity of the microspheres, this growth in signal intensity is not directly proportional to the amount of lignin (impact of the ATR-FTIR method). Nevertheless, the general trend (increase of intensity of -OH groups) is maintained. Ring vibrations are visible around 1600 cm\({}^{-1}\) and come from the aromatic lignin part (Figure 2). The more lignin in the polymeric material, the lower are the signals from the aliphatic part. Both the spectra of EGDMA + St + L copolymers and pure lignin contain the band from C=C and C-O. Moreover, the spectra of EGDMA + St + 2,3,4L look very similar. The original lignin exhibited a wide absorption band at 3400-3200 cm\({}^{-1}\) indicating the presence of OH stretching vibrations in the aliphatic and aromatic hydroxyl groups.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **C–H aliph.** & **C–H arom.** & **C=C arom.** & **C–O** & **C=O** & **–OH** \\ \hline EGDMA + St & 2943 & 941 & 1453 & 1259 & 1723 & — \\  & 1386 & 862 & 1127 & & \\ EGDMA + St + 1L & 2939 & 941 & 1597 & 1265 & 1720 & 3402 \\  & 860 & 940 & 1128 & & \\ EGDMA + St + 2L & 2943 & 940 & 1599 & 1263 & 1722 & 3434 \\  & 861 & 1599 & 1262 & & \\ EGDMA + St + 3L & 2939 & 861 & 1599 & 1262 & 1722 & 3420 \\  & 861 & 1178 & & & \\ EGDMA + St + 4L & — & 939 & 1262 & & \\  & 860 & 1598 & 1128 & & \\  & 937 & 1598 & 1128 & & \\  & 860 & 1598 & 1128 & & \\ Lignin & 2950 & 930 & 1620 & 1300 & 1680 & 3510 \\  & 2951 & — & — & 1293 & 1717 & — \\  & 86 & — & 990 & 1600 & — & — & — \\ \hline \end{tabular}
\end{table}
Table 2: Wavenumbers [1/cm] of characteristic bands visible on Fourier Transform Infrared - Attenuated Total Reflectance spectra.
# Thin-Walled CFST Columns for Enhancing Seismic Collapse Performance of High-Rise Steel Frames
###### Abstract  
This paper numerically studied the collapse capacity of high-rise steel moment-resisting frames (SMRFs) using various width-to-thickness members subjected to successive earthquakes. It was found that the long-period component of earthquakes obviously correlates with the first-mode period of high-rises controlled by the total number of stories. A higher building tends to produce more significant component deterioration to enlarge the maximum story drift angle at lower stories. The width-to-thickness ratio of beam and column components overtly affects the collapse capacity when the plastic deformation extensively develops. The ratio of residual to maximum story drift angle is significantly sensitive to the collapse capacity of various building models. A thin-walled concrete filled steel tubular (CFST) column is proposed as one efficient alternative to enhance the overall stiffness and deformation capacity of the high-rise SMRFs with fragile collapse performance. With the equivalent flexural stiffness, CFST-MRF buildings with thin-walled members demonstrate higher capacity to avoid collapse, and the greater collapse margin indicates that CFST-MRFs are a reasonable system for high-rises in seismic prone regions.  
thin-walled structures; concrete filled steel tubes; high-rise buildings; seismic performance; dynamic collapse; local buckling Article
# A Novel Method of Calibrating Micro-Scale Parameters of PFC Model and Experimental Validation
## 4 Experimental Design and Analysis
### Analysis of Variance
#### 4.2.2 Influence of Micro-Scale Parameters on [PERSON]'s Modulus  
Similarly, we can obtain the impact of these three micro-scale parameters on the macroscopic [PERSON]'s modulus. Figure 5 clearly demonstrates the one-factor and multiple factor interaction effects. The influence of \(B\) and \(C\) on [PERSON]'s modulus are extremely significant, while \(A\) does not affect it significantly. The [PERSON]'s modulus linearly changes from 22 to 70 GPa as the coded value of \(B\) increases from \(-1\) to 1. When \(C\) varies from \(-1\) to 1, the [PERSON]'s modulus decreases nonlinearly from 59 to 40 MPa, the reduction rate gradually decreases. Besides, it is found that the interaction between \(B\) and \(C\) has an extremely significant effect on the [PERSON]'s modulus, whereas the interactions of \(A\)-\(B\) and \(A\)-\(C\) are not significant. With the rise of \(B\) and the decrease of \(C\), the [PERSON]'s modulus gradually increases.  
Figure 4: Influence of micro-scale parameters on uniaxial compressive strength: (**a**) one-factor effect, (**b**) interaction effect between \(A\) and \(C\), and (**c**) interaction effect between \(B\) and \(C\).  
Figure 5: Influence of micro-scale parameters on [PERSON]’s modulus: (**a**) one-factor effect and (**b**) interaction effect between \(B\) and \(C\).
## 3 Underwater Routing Protocols
### Rrss
#### 3.2.4 Protocols Addressing Energy Consumption  
DRADS  
The depth- and reliability-aware delay sensitive (DRADS) protocol modifies the concept of opportunistic routing [62]. It considers the depth information in addition to the link state information when a forwarder sends packets to the sink. This reduces energy consumption and increases the throughput. However, the loads on low depth nodes increase due to opportunistic routing, and they die soon, deteriorating the system performance.
# The Development of an Optimal Control Strategy for a Series Hydraulic Hybrid Vehicle
## 3 Control System Development
### Thermostatic Control Strategy  
The thermostatic control approach is traditionally utilized for series hybrid electric vehicles (HEVs) and adopted for HHVs [6; 8]. The concept of a thermostatic control algorithm is shown in Figure 5. In this approach, the engine operates at a constant output power--the threshold--when the pressure of the accumulator is within [\(p_{\min}\), \(p_{\max}\)]. If the pressure drops below \(p_{\min}\) the engine produces greater power, as determined by a 1D-lookup table. Using the demand power, the engine speed and engine torque references are estimated. To prevent the unnecessary conversion of energy and to capture braking energy effectively, the engine is only activated if the pressure in the accumulator is within [\(p_{\min}\), \(p_{\max}\)] and when the vehicle is not being decelerated.  
### Thermostatic Control Strategy  
The term modulated-pressure originates from the need to maintain the pressure of the accumulator at a constant value to avoid the unnecessary energy conversion from the engine to the accumulator. [8] showed that by considering the reference value for the accumulator's pressure as a function of vehicle velocity demand, the modulated-pressure control strategy produces an improvement in fuel economy. A schematic for this control strategy is shown in Figure 6. Two PI controllers are used to estimate the power demand of the engine, to ensure that the vehicle's velocity and the accumulator pressure both track their corresponding reference values.
## 3 Metrics for Jamming Attack Detection
### Received Signal Strength (RSS)  
The received signal strength is defined as the power content of the radio signal received at the receiver. It is a measurable quantity and can either be measured by the RF power meter of the node or can be calculated using formulae as per the selected propagation model. The RSS by itself is not a logical metric to indicate jamming. However, when used in combination with metrics like the received jammer power (or noise power) or BER, it forms an effective combination to detect jamming.
# Revealing Coupled Periodicities in Sunspot Time Series Using Bispectrum--An Inverse Problem
## 1 Introduction
The index was compared with solar activity, and it was shown that the average 11-year periodicity in the index and in solar activity over a 1000-year time interval coincided to two decimal places. Finally, in [15], the authors applied the DCCA correlogram to study time-series memories (on time scale). The authors performed an analysis of the sunspot, and they identified the solar cycle as n = 132 months (11 years). However, this value disappears for large time scales, because of the solar phenomenon. The studies by [PERSON] et al. [16] as well as by [PERSON] et al. [17] and [PERSON] and [PERSON] [18] highlight the future challenges and research directions, emphasizing how regional-scale atmospheric circulation can aid in understanding solar-induced climate variability. Furthermore, [PERSON] et al. [19] conducted a study to explore the potential connection between the summer temperature distribution over Eurasian land and solar activity, while [PERSON] and [PERSON] [20] focused on investigating the periodicities of rainfall over Kerala, India, and sunspot numbers, and explored the possible associations between these factors. In the present work, we explicitly demonstrate that, using the power spectrum, the planetary periodicities as well as the synodic periodicities are mirrored by the sunspot activities on the Sun's surface. In a fine and detailed spectrum calculation procedure, it is possible to have all planetary periodic phenomena present on the surface of the Sun. The contribution of the present work is to demonstrate that simple periodicities obtained from planets' orbits are clearly present on the sunspots time series, probably manifesting the tidal effect (even too weak) of the planets on the Sun's plasma. Furthermore, coupled periodicities are also detected on the Sun's surface. The bispectrum [21; 22] is the best tool for this purpose. Periodicities (e.g., \((f_{1}+f_{2})\)) that are created based on the coupling of two distinct frequencies, \(f_{1}\) and \(f_{2}\), do not appear in the bispectrum. We have only a peak at the position \((f_{1},f_{2})\) without any indication at the summation of the frequencies. The amazing finding of our bispectral results is that a quadratic coupling mechanism bonds all the periodicities, which add up to specific summations. All the coupled periodicities on the sunspot time series are extensively discussed. This work is organized as follows. In Section 2, the employed data are analyzed and, simultaneously, the planetary periods and the synodic periods are presented according to the well-known astronomical data. The basic concepts of the bispectrum are presented in Section 3. The periodicities as extracted from the sunspot data are revealed in Section 4 using the power spectrum. The coupled periodicities are demonstrated using the bispectral representation in Section 5. Finally, the conclusions are drawn in Section 6.