esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Works  
This paper introduces an MLSTM-based congestion-aware EFL, namely MLSTM-CEFL, as a prediction handler and controller to orchestrate the aggregation scheduling policy. By leveraging the MLSTM-CEFL model, congestion prediction activates the capability for the controller to proactively measure the next-state QoS metrics and allocates the resource on edge aggregators efficiently for multi-service model averaging. Our network architecture is divided into participants, edge aggregators, and controllers, which enables scalable IIoT model aggregation. Integration of MLSTM-CP and the controller as a VIM enhances adaptability and facilitates policy decision making to obtain the optimal scheduling flows. The simulation results showcased the precision and stability of MLSTM-CEFL throughout various congestion network states in order to ensure multi-class QoS expectations and efficient resource placement. Our proposed approach minimized the E2E delays and optimized the reliability in terms of model drop/delivery ratios.  
In future studies, the E2E network slicing based on the EFL framework for IIoT applications will be extended. Another perspective involving multi-convolutional neural networks for various industrial sensing image datasets will be explored to capture the differentiation of computing latencies. The slicing framework aims to enhance the multi-class handling of time-sensitive model communications in EFL-based real-time image processing applications.  
Conceptualization, P.T. and S.K. ([PERSON]); methodology, P.T.; software, P.T. and S.K. ([PERSON]); validation, S.K. ([PERSON]), and S.R.; formal analysis, S.K. ([PERSON]), and S.R.; investigation, S.K. ([PERSON]); resources, S.K. ([PERSON]); data curation, P.T.; writing--original draft preparation, P.T.; writing--review and editing, S.K. ([PERSON]) and S.R.; visualization, S.K. ([PERSON]); supervision, S.K. ([PERSON]); project administration, S.K. ([PERSON]); funding acquisition, S.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by an Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2022-00167197, Development of Intelligent 5G/6G Infrastructure Technology for The Smart City), in part by the National Research Foundation of Korea (NRF), Ministry of Education, through the Basic Science Research Program under Grant NRF-2020R1I1A3066543, in part by BK21 FOUR (Postering Outstanding Universities for Research) under Grant 5199990914048, and in part by the Soonchunhyang University Research Fund.
# RFID Authentication System Based on User Biometric Information +
## 4 System Design
### Feature Extraction  
After data preprocessing, we obtain a continuously available tag phase signal, and further extract the phase features related to user identity information. We find that the signal changes are relatively complex and chaotic at the moment when the user touches the tag, and the phase characteristics are also unstable. However, during touch, the phase signal of the tag tends to be stable. So, we propose an anomaly detection algorithm to extract the stable part in the middle of the tag phase signal. We first empirically set up a fixed-size window to detect the abnormal part. The average amplitude in the \(k\)-th window [2] of tag \(i\) can be expressed as  
\[A_{i}(k)=\frac{\sum\limits_{i=1}^{l}\left|\theta_{j}-\theta_{m}\right|}{l}\quad and \quad\theta_{m}=\frac{\sum\limits_{i=1}^{l}\left|\theta_{j}\right|}{l}, \tag{3}\]  
where \(l\) represents the data volume of the phase in the window, \(\theta_{j}\) represents the phase value, and \(\theta_{m}\) represents the average phase value of the tag obtained from the stable tag signal in the first window and is used as a metric to evaluate the anomaly. In Equation (4), the amplitude function \(G(k)\) is obtained [2] by summing the amplitudes of all tags in the same window.  
Figure 5: The dashed rectangle represents the window of abnormal phase signal, and the red rectangle represents the feature window. The line between the two blue circles indicates that these windows are more stable feature information. After phase unwrapping, some phase values will exceed the range of 0 to 2\(\pi\).  
\[G(k)=\sum_{i=1}^{9}A_{i}(k). \tag{4}\]  
After sliding window anomaly detection and comparison with the set threshold value, an anomaly sequence can be obtained. The first exception window detected is taken as the left exception window, which is caused by the user just touching the tag. The last one in a continuous set of exception windows starting from the left exception window is taken as the right exception window, which is caused by the user's finger leaving the tag surface. We take the middle window between the left exception window and the right exception window as our feature window. The purpose is to avoid using the more unstable signal caused by the user just touching the tag, and to obtain the relatively stable signal brought by the user's continuous touch. Figure 5 shows the feature window of the tag phase signal. The mean value of the nine tag phases in this window is  
\[V=[A_{1}^{\prime},A_{2}^{\prime},A_{3}^{\prime},A_{4}^{\prime},A_{5}^{\prime },A_{6}^{\prime},A_{7}^{\prime},A_{8}^{\prime},A_{9}^{\prime}]. \tag{5}\]  
Then, the phase difference between any two of the nine tags is calculated to form an eigenvalue:  
\[\Delta A_{ij}=\big{|}A_{i}^{\prime}-A_{j}^{\prime}\big{|}. \tag{6}\]  
Since \(\Delta A_{ij}\) and \(\Delta A_{ji}\) are equal, and \(\Delta A_{ii}\) is equal to 0, we can obtain 36 effective eigenvalues from a feature window to form the eigenvector  
\[F=[\Delta A_{12},\cdots,\Delta A_{19},\Delta A_{23},\cdots,\Delta A_{29}, \Delta A_{34},\cdots,\Delta A_{89}]. \tag{7}\]
## 2 Proposed Synchronization Algorithm Based on LPA  
### System Model with Tilted Phase Transformation  
The general baseband CPM signal [8] is modeled as  
\[s(t;\mathbf{\alpha})=\sqrt{\frac{E_{s}}{T}}e^{i\phi(t;\mathbf{\alpha})}, \tag{1}\]  
where \(T\) is the symbol interval, and \(E_{s}\) is the energy per transmitted symbol. The phase of the CPM signal is defined as  
\[\phi(t;\mathbf{\alpha})=2\pi\underset{i}{\sum}h_{i}\alpha_{i}q(t-iT), \tag{2}\]  
where \(h_{i}=k_{i}/p\) is the modulation index at \(i\)th symbol interval, while \(k_{i}\) and \(p\) are integers. \(h_{i}\) is selected periodically from a set as \(\{h_{0},h_{1},.....,h_{N_{h}-1}\}\) with a symbol duration, and \(N_{h}\) is the number of modulation index. \(\mathbf{\alpha}\) with \(\alpha_{i}\in\{\pm 1,\pm 3,...,\pm(M-1)\}\) is the sequence of \(M\)-ary information symbols. The phase pulse response \(q(t)\) is determined by the \(L\)-length frequency pulse \(g(t)\) as \(q(t)=\int_{-\infty}^{t}g(t)dt\). Rectangular and raised cosine are commonly used as the frequency pulse shapes with the denotations \(L\)REC and LRC, respectively.  
Using TPT, the symbol sequence is mapped to \(\mathbf{u}\in\{0,1,...,M-1\}\) with the element \(u_{i}=(\alpha_{i}+M-1)/2\). Then, the number of phase states \(N_{state}\) can be reduced from \(2 pM^{L-1}\) to \(pM^{L-1}\) without performance loss [16]. The number of MFs is still \(N_{h}M^{L}\). For ARTM CPM as a 4-ary \(h=\{\frac{4}{16},\frac{5}{16}\}\) CPM, the trellis using TPT has 256 states and 64 MFs.  
We rewrite the phase \(\phi(t;\mathbf{\alpha})\) as  
\[\begin{split}\phi(t;\mathbf{\alpha})&=2\pi\underset{ i}{\sum}h_{i}\alpha_{i}q(t-iT)\\ &=\theta_{n}+\eta(t;I_{n},\alpha_{n}),\qquad nT\leq t<(n+1)T, \end{split} \tag{3}\]  
where \(\theta_{n}\) is the cumulative phase of the modulator:  
\[\theta_{n}=\pi\sum_{i=0}^{n-L}h_{i}\alpha_{i}\qquad\text{mod}\ 2\pi. \tag{4}\]  
Using TPT, \(\theta_{n}\) becomes  
\[\theta_{n}=\theta_{n}+\phi_{n}\qquad\text{mod}\ 2\pi, \tag{5}\]  
where \(\theta_{n}\) is the updated cumulative phase:  
\[\theta_{n}=2\pi\sum_{i=0}^{n-L}h_{i}u_{i}\qquad\text{mod}\ 2\pi \tag{6}\]  
and \(\phi_{n}\) is the tilted phase:  
\[\phi_{n}=-(M-1)\pi\sum_{i=0}^{n-L}h_{i}. \tag{7}\]  
Note that \(\phi_{n}\) is independent of the \(\mathbf{\alpha}\) or \(\mathbf{u}\). Thus, the number of phase states is reduced by half.  
\(\eta(t;I_{n},\alpha_{n})\) is the correlative phase of the modulator  
\[\eta(t;I_{n},\alpha_{n})=2\pi\sum_{i=n-L+1}^{n}I_{i}\alpha_{i}q(t-iT),\]  
\[I_{n}=[\alpha_{n-L+1},...,\alpha_{n-2},\alpha_{n-1}]. \tag{8}\]  
In Equations (3) and (8), \(I_{n}\) is the correlative state vector and \(\alpha_{n}\) is the current symbol.  
The multi-\(h\) CPM signal is transmitted over an additive white Gaussian noise (AWGN) channel, and the carrier phase \(\varphi\) and timing offset \(\tau\) are unknown to the receiver. Hence, the received signal can be modeled as  
\[r(t)=e^{-j\varphi}s(t-\tau;\mathbf{\alpha})+w(t), \tag{9}\]  
where \(w(t)\) is a complex baseband AWGN with zero mean and single-sided power spectral density \(N_{0}\).
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 6 Performance Evaluation
### Evaluation
This is because charging time becomes a major factor that mainly affects goodput in  
Figure 9: Performance comparison under the conditions \(U_{\max}=6\) V and \(\tau=2\): (**a**) The goodput performance under different values of \(E_{b}/N_{0}\), (**b**) frame length under different values of \(E_{b}/N_{0}\), and (**c**) number of redundant frames under different values of \(E_{b}/N_{0}\). the case of better channel conditions and poor energy-harvesting conditions. Therefore, under this condition, obtaining a larger transmission frame length at the cost of a longer charging time may cause a decrease in goodput. Figure 11 shows the communication energy consumption of our scheme and fixed frame length strategy under \(U_{\max}=5\) V and \(\tau=3\). It can be seen from Figure 11 that, in the case of low \(E_{b}/N_{0}\), our scheme consumes more energy than the fixed frame length strategy, while in the case of high \(E_{b}/N_{0}\), our scheme consumes less energy. This is because, when \(E_{b}/N_{0}\) is low, our scheme guarantees a successful packet-receiving rate by increasing the number of redundant packets, so that the energy consumption is increased. Also, when the signal-to-noise ratio is better, the number of redundant packets is reduced and our scheme eliminates the need to return an acknowledgment to per frame to save energy. It is worth mentioning that our paper does not consider the calculation energy consumption because it is of a smaller order of magnitude compared to communication energy consumption. Moreover, the core algorithm of the method used in this paper runs on the reader side, and the calculation amount of the CRFID is small. Figure 10: Performance comparison under the conditions \(U_{\max}=5\) V and \(\tau=3\): (**a**) The goodput performance under different values of \(E_{b}/N_{0}\), (**b**) frame length under different values of \(E_{b}/N_{0}\), and (**c**) number of redundant frames under different values of \(E_{b}/N_{0}\).
# A Coordinated Supply Contract for a Two-Echelon Supply Chain Considering Learning Effects
## 4 Coordinated Supply Contract under the Learning Curve (LC)
### The Identification of the Feasible Range of \(\phi\) with Bargaining Power \(\alpha\)  
By observing the equation, it can be deduced that an increase in \(\phi\) leads to an increase in \(w\) in the coordinated contract. The optimal set of \((w^{*},\phi^{*})\) can have infinite combinations, and each combination affects the profit structure of both the manufacturer and the retailer. As the values of \((w^{*},\phi^{*})\) increase, the retailer's profit increases while the manufacturer's profit decreases. The selection of appropriate \((w,\phi)\) can be determined based on the bargaining power of supply-chain members. The total profit generated in the supply chain is distributed among the members based on their relative bargaining power. Let \(\alpha\)represent the expected retailer's profit share in the total supply-chain profit, i.e., \(\alpha=\frac{\pi_{L_{c}}}{\pi_{L_{c}}}\). From expressions (7) and (12), we can derive the following (note that \(I(Q)=\int_{0}^{Q}F(x)dx\)):  
\[\alpha=\frac{(\phi p-w)Q-(\phi p-s)I(Q)}{\big{(}p-c_{Q}\big{)}Q-(p-s)I(Q)} \tag{17}\]  
When we restate Equation (17) for \(\phi\), we have the following expression.  
\[\phi=\frac{s}{p}+\alpha\frac{\big{(}p-c_{Q}\big{)}Q-(p-s)I(Q)}{p[z^{*}Q-I(Q)]} \tag{18}\]  
From expression (18), we can see that \(\phi\) increases as \(\alpha\) increases in the coordinated contract. Similarly, from Equation (16), \(w\) also increases as \(\alpha\) increases. To be feasible in industries, \(\alpha\) should lie between zero and one, and so should \(\phi\). When \(\alpha=0\) (all the SC profit goes to the manufacturer), we have:  
\[\phi_{\alpha=0}=\frac{s}{p} \tag{19}\]  
Since \(p>s\) is assumed, the range of \(\phi\), \(\phi\in(0,1)\) is feasible. Equation (19) indicates that the revenue-sharing ratio \(\phi\) should be at least \(\frac{s}{p}\) for the channel coordination. In case of \(\alpha=1\) (all the SC profit goes to the retailer), we have:  
\[\phi_{\alpha=1}=\frac{s}{p}+\frac{\big{(}p-c_{Q}\big{)}Q-(p-s)I(Q)}{p[z^{*}Q- I(Q)]} \tag{20}\]  
We prove that \(\phi_{\alpha=1}<1\) (the proof is given in Appendix A). Since \(\phi\) is an increasing function in terms of \(\alpha\), we can have the possible range of \(\phi\) under the coordinated supply contract as follows:  
\[\frac{s}{p}\leq\phi\leq\frac{s}{p}+\frac{\big{(}p-c_{Q}\big{)}Q-(p-s)I(Q)}{p[ z^{*}Q-I(Q)]} \tag{21}\]  
Expression (21) shows that all bargaining-power structures, that is, \(0\leq\alpha\leq 0\), can be satisfied under the presented coordinated supply-contract scheme.
## 5 Conclusions  
The GP algorithm for solving the multi-class TAP with mixed modes was developed in this paper. It proposed the use of asymmetric travel time functions to represent the effect of vehicle interaction between HVs and CAVs. Two numerical experiments were performed to investigate the proposed model's features. The numerical results indicated the following: (1) when the proportion of CAVs was increased, travel time decreased significantly; (2) travel time was reduced nonlinearly although the percentage of CAVs was raised linearly; (3) there was a considerable change in the assigned traffic pattern with and without CAV demand; (4) the flow pattern on freeways was more affected by CAV driving; and (5) computational time was more efficient when CAV driving was allowed on more roads. These findings have the potential to have a considerable impact on various fields of research, including road construction, safety, and environmental impact studies. In the future, the issue of CAV performance in terms of travel time function and route choice behavior should be investigated. Another possibility is to consider other types of CAV, such as trucks (medium and heavy trucks) to improve the realism as each type of CAV has unique characteristics. Additionally, computational experiments should be conducted on a variety of network topologies, congestion levels, and zonal densities to determine the route-based GP algorithm's effectiveness and efficiency.  
Conceptualization, S.R.; methodology, S.R.; formal analysis, S.R.; investigation, M.K.; writing--original draft preparation, S.R.; writing--review and editing, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science, ICT, Republic of Korea (K-22-L04-C06-S01).  
Not applicable.  
The data presented in this study are available, upon request, from the corresponding author.  
Figure 9: Flow difference between scenario 1 and other scenarios. (**a**) Scenario 1 and scenario 2; (**b**) scenario 1 and scenario 3; (**c**) scenario 1 and scenario 4; (**d**) VKT and VHT for scenarios.
## 5 Stability Evaluation of Three-Dimensional Numerical Simulation
### Numerical Simulation Results Analysis
#### 5.3.3 Numerical Simulation Results under Condition III  
Figure 10 shows the contours of the transverse component of displacement of the slope under condition III. The maximum value of the transverse component of displacement is 13.9 mm, which appears at the front of the slope, and the slope shows a similar displacement distribution trend to under condition II. Compared with condition II, the rise of water level leads to further increase in deformation of the front part of the slope in the simulation results under condition III.  
Figure 11 shows the point factor of safety distribution of the slope under condition III. Compared with the distribution of point factor of safety under condition II, the minimum value of point factor of safety of the slope under condition III is 1.35, which appears at the fault. The point factor of safety at the fault of the leading edge of the slope is decreased. The results show that the front edge of the slope is affected by the uplift of reservoir water, which leads to a decrease in safety coefficient and an increase in displacement.  
Figure 10: Transverse riverward displacement distribution under condition III. (**a**) Profile 5; (**b**) profile 10; (**c**) profile 13; (**d**) entire body.
## 3 Methods
### The Edge Feature Extractor Module  
Due to the variable shape of crack features, the accuracy of the model largely depends on the effectiveness of edge feature extraction. Therefore, it is necessary to strengthen the network's ability to extract edge feature information. We considered adding edge detection operators Sobel, Laplacian, and Canny to the network to enhance the image segmentation performance of the model; thus, we designed an Edge Feature Extractor Module (EFEM), which is shown in Figure 4.  
Figure 3: The structure of our proposed Mix Global Attention Module.  
In EFEM, the input feature \(G_{1}\) is input into the convolution layers to obtain the output feature \(G_{2}\) and \(G_{6}\). At the same time, \(G_{1}\) is input into the Sobel operator, Laplacian operator and the Canny operator to extract more detailed edge features: \(G_{3}\), \(G_{4}\), \(G_{5}\). The Sobel operator could calculate the grayscale values of the top, bottom, left, and right pixels for each pixel in the image, and find the extreme value in images to detect edges. The Laplacian operator is a derivative operator, which has rotational invariance and could meet the requirements of edge enhancement of images in different directions. The Canny operator is used to find strong edges in the image and eliminate as much noise as possible. Therefore, by inputting the output features of the convolutional layer into these three edge detection operators, we can enhance the edge feature information of high-level features, thereby enhancing the model's ability to extract edge feature information. Here, we also assign three adjustable weight parameter to the feature maps output by the three edge detection operators, and this weight parameter could be adjusted during the model training process. Finally, feature \(G_{3}\), \(G_{4}\), \(G_{5}\) are added to feature \(G_{6}\) to generate the final output \(G_{7}\). The complete process is as follows:  
\[\begin{split} G_{7}&=G_{6}+\alpha G_{3}+\beta G_{4}+ \gamma G_{5}\\ G_{6}&=Conv(Conv(G_{1}))\end{split} \tag{3}\]  
where \(\alpha\), \(\beta\), \(\gamma\) are the adjustable weight parameters; \(Conv(x)\) represents the convolution operation.
## 3 Results and Discussion
### Suggestion of SOC Calculation Algorithm  
To improve accuracy for determining SOC, a complex experimental method is needed. To solve this problem, we propose an algorithm as shown in Figure 7 that reduces error due to current integration by measuring and integrating charge current of the secondary battery by the SOC at each braking deceleration section (low speed, medium speed, high speed).  
So, two charging states are defined for the secondary battery, that is, the charging lower limit value and the charging upper limit value. When charge state is between upper limit value and lower limit value, the secondary battery is charged with increased voltage during braking, and the secondary battery is no longer charged in the driving phase.  
First, during braking, the battery SOC is estimated at corresponding deceleration per deceleration section, and battery current is measured at predetermined time intervals during braking period.  
Thereafter, the SOC value is calculated based on accumulated current value measured for a predetermined time, and compared with the SOC of the immediately preceding same condition, it is determined if the SOC value is equal to or smaller than a particular value (a).  
If it is not less than the particular value (a), it is judged as an error. If it is less than the particular value (a), this value is compared with the SOC value at the start of braking.  
If it is not less than the specified value (a), it is judged an error. If it is less than the specified value (a), this value is compared with the SOC value at the start of braking. If the value is less than the specific value (b), the actual SOC calculation result is transmitted to determine the final SOC value. If it is more than the specific value (b), it is judged an error.  
When it is determined as an error, the arithmetic mean value of the two values is calculated and stored as the final SOC value.  
Figure 7: Algorithm flowchart for SOC determination.
# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
## 4 Experiments
### Overall Performance Comparison  
Table 1 list the overall performance of all models on the MovieLen 20M and MovieLen Latest datasets. This includes the pure multi-layer perceptron models and the two-layer matrix factorization and multi-layer perceptron model. We use the accuracy and MSE loss as the metrics for the binary classification task and use accuracy and CE loss for the multi-classification task. We bold the test results of the TMMNN model to show the difference.  
From the results in the tables, we can draw the following conclusions:  
1. In this paper, the accuracy of our model on all datasets and all tasks far exceeds that of the fixed model, and our model has a lower loss value as well. Meanwhile, compared to the fixed model, each multi-size embedding model on any dataset and any task has a higher accuracy and a lower loss value. The above conclusion shows that multi-size embedding can improve the performance of the model.
2. In terms of accuracy, the model in this paper exceeds all models on all datasets and all tasks, and our model also has the lowest loss value, which indicates that the added matrix factorization technique can improve model performance through the capture of linear interactions.
3. The improvement effect of the TMMNN on the ml-latest dataset is not as high as that on the ml-20m. The reason for this difference is that ml-latest can provide more data for the training of the multi-layer perceptron, which further indicates that when the amount of data is insufficient, the linear capture technology can really provide linear capture capability for deep learning models.
4. The test results show that our model improves the prediction accuracy in both binary classification and multi-classification tasks. Although the performance improvement in two tasks is numerically similar, the performance on the multi-classification task is better with smaller benchmark values. This shows that in the face of complex evaluation indicators, the simultaneous introduction of linear models and multi-dimensional embedding vectors plays a significant role in capturing implicit interactions.