esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic-Aware Optimization of Task Offloading and Content
## 2 Related Work
For traffic flow prediction, in order to enhance real-time traffic prediction in various scenarios, [PERSON] et al. [24] proposed two incremental learning methods and compared them with three existing methods to determine the suitable scenarios for these methods. To address the limitations of current approaches in long-term prediction performance, [PERSON] et al. [25] introduced a novel prediction framework called SSTACON, which utilizes a shared spatio-temporal attention convolutional layer to extract dynamic spatio-temporal features and incorporates a graph optimization module to model the dynamic road network structure. In order to tackle the issue of parameter selection and improve prediction accuracy, [PERSON] et al. [26] incorporated the Artificial Bee Colony (ABC) algorithm into the ABC-SVR algorithm. [PERSON] et al. [27] addressed the service switching challenge among adjacent roadside units, introducing cooperation between vehicles, vehicle-to-roadside-unit communication, and implementing trajectory prediction to minimize task processing delays. [PERSON] et al. [28] recognized the significance of traffic flow prediction, and, building upon this foundation, introduced the ST-ResNet network for traffic prediction, complemented by the NSGA-III algorithm for multi-objective optimization. Their approach demonstrated superior performance in terms of latency and energy consumption, outperforming existing methodologies. Based on these findings, we observed a relative scarcity of research focusing on joint optimization of computation offloading and content caching based on traffic flow prediction. Hence, our study aims to address the problem of joint optimization of task offloading and edge content caching. We employ an enhanced traffic-based multi-objective evolutionary algorithm to achieve this. The overarching objective is to minimize both transmission and computation latency while reducing energy consumption. Table 1 displays a concise synopsis of the scrutinized literature, delineating pivotal aspects including the application environment, addressed challenges, and optimization objectives.
## 4 Simulation Environment
### Simulation Scenario  
To evaluate the considered UCCF M-MIMO network, we utilized an urban scenario developed for the purpose of the METIS project named the _Madrid Grid Model_, which included both a Line-of-Sight (LoS) park area and narrow street canyons with non-LoS (NLoS) radio conditions [28]. Within this area, we placed six RUs. The RU of index \(l=0\) corresponds to a macro-RU installed at a height of 45 m, with a maximal transmit power of \(P_{0}=46\) dBm (128 PAs with \(P_{\max,0}=312.5\) mW) and an antenna array of \(M_{0}=128\) elements (8 rows \(\times\) 16 columns). The remaining RUs are micro-RUs installed at a height of 6 m, with a maximal transmit power of \(P_{l}=30\) dBm (32 PAs with \(P_{\max,l}=31.25\) mW) and equipped with \(M_{l}=32\) antennas (two panels with 8 rows \(\times\) 2 columns). The remaining simulation parameters are listed in Table 2.
## 3 Results
### Segment Onset Latencies  
The MM ANOVA revealed that that there were no significant interactions (\(p<0.05\)) between groups and turn amplitudes for all segment onset latencies (Table 1). Significant main effects were seen for the group and turn amplitude. The significant main effects between the groups were; head (F\({}_{(2,40)}\) = 9.29, \(p=0.003\), \(\eta_{\text{p}}\)\({}^{2}\) = 0.22), leading foot (F\({}_{(2,40)}\) = 36.59, \(p<0.001\), \(\eta_{\text{p}}\)\({}^{2}\) = 0.24) and trailing foot (F\({}_{(2,40)}\) = 94.34, \(p<0.001\), \(\eta_{\text{p}}\)\({}^{2}\) = 0.85), with the older group showing longer onset latencies. The significant main effects for turn amplitude were the mean onset latencies for all segments with the exception of the trailing foot, specifically; for the head (F\({}_{(2,40)}\) = 56.24, \(p<0.001\), \(\eta_{\text{p}}\)\({}^{2}\) = 0.07), thorax (F\({}_{(2,40)}\)= 52.35, \(p<0.001\), \(\eta_{\text{p}}\)\({}^{2}\) = 0.55), pelvis (F\({}_{(2,40)}\) = 9.85, \(p<0.001\), \(\eta_{\text{p}}\)\({}^{2}\) = 0.21), and leading foot (F\({}_{(2,40)}\) = 6.84, \(p=0.002\), \(\eta_{\text{p}}\)\({}^{2}\) = 0.15). Further post hoc pairwise comparisons found significant differencesin amplitude for the head onset latency between 90\({}^{\circ}\) and 180\({}^{\circ}\), 135\({}^{\circ}\) and 180\({}^{\circ}\), and 90\({}^{\circ}\) and 135\({}^{\circ}\). For the thorax onset latency, significant differences were seen between turn amplitudes at 90\({}^{\circ}\) and 180\({}^{\circ}\), 135\({}^{\circ}\) and 180\({}^{\circ}\), and 90\({}^{\circ}\) and 135\({}^{\circ}\), and for the pelvis onset latency between turn amplitudes at 90\({}^{\circ}\) and 135\({}^{\circ}\), and 90\({}^{\circ}\) and 135\({}^{\circ}\), with the leading foot also showing significant differences between 90\({}^{\circ}\) and 180\({}^{\circ}\) (Table 2 and Figure 2). Onset latencies were shortest during the smallest amplitude (90\({}^{\circ}\)) and longest during the largest amplitude (180\({}^{\circ}\)).
## Problem Description and Evaluation Metrics
### Evaluation Metrics
#### 2.2.1 Area under the Receiver Operation Characteristics Curve (AUC)  
A receiver operating characteristics (ROC) curve is a two-dimensional depiction of classifier performance [17]. In the field of link prediction, the ROC curve abscissa represents the probability of nonexistent links i.e., the false positive rate (FPR), when the link prediction score is greater than some threshold, \(\mu\), and \(\text{FPR}=\int_{\mu}^{\infty}g_{Y}(x)dx\). The ordinate represents the probability of missing links,  
Figure 1: Combination methods.  
i.e., the true positive rate (TPR), when score \(>\mu\), and TPR \(=\int_{\mu}^{\infty}f_{X}(x)dx\), TPR is equivalent to Recall. According to [18], AUC can be derived as  
\[\begin{array}{ll}P(X>Y)&=\iint\limits_{X>Y}f_{X}(x)g_{Y}(y)dxdy\\ &=\frac{1}{2}\iint\limits_{X>Y}f_{X}(x)g_{Y}(y)dxdy+\frac{1}{2}\bigg{(}1- \iint\limits_{X\leq Y}f_{X}(x)g_{Y}(y)dxdy\bigg{)}\\ &=\frac{1}{2}\iint\text{sgn}(x-y)f_{X}(x)g_{Y}(y)dxdy+\frac{1}{2}\\ &=\frac{1}{2}\mathbb{E}[\text{sgn}(X-Y)+1],\end{array} \tag{1}\]  
where  
\[\text{sgn}(x)=\left\{\begin{array}{ll}1,&x>0\\ 0,&x=0\\ -1,&x<0\end{array}\right. \tag{2}\]  
In the real network, original data is randomly divided into training set and the probe set. Equation (1) means that for \(n\) independent comparisons, if there are \(n^{\prime}\) comparisons where the missing link returns a higher score and \(n^{\prime\prime}\) comparisons where the missing and nonexistent links return the same score, we can obtain the algorithm expression of AUC:  
\[\text{AUC}=\frac{n^{\prime}+0.5n^{\prime\prime}}{n} \tag{3}\]
# Hybrid Precoding Design for Wireless Fading Channels
## 1 Introduction
* We proposed a hybrid precoding scheme utilizing null space information about the channel to prevent interference between the received signals for users applying linear precoding and those applying non-linear precoding. The proposed hybrid precoding consists of OMMSE precoding and the OSLP scheme. * Using the proposed hybrid precoding scheme, it is possible to adjust the number of users in each user group based on the hardware performance of the base station transmitter. This allows for the adjustment of the overall system's SER performance and computational complexity. The structure of this paper is as follows. Section 2 describes the system model considered in this paper. Section 3 explains the proposed hybrid precoding technique. Section 4 presents the simulation results, and Section 5 concludes. _Notations:_ Matrices and vectors are denoted by boldface capital- and lower-case letters, respectively. The notations \((\cdot)^{*}\), \((\cdot)^{T}\), and \((\cdot)^{H}\) represent the complex conjugate, transpose, and Hermitian of a matrix or vector. Also, \(\text{tr}(\cdot)\) and \(||\cdot||_{F}\) represent the trace and the Frobenius norm of a matrix, respectively. The notation \(E[\cdot]\) denotes the statistical expectation operation.
## 3 Results and Discussions
### Economic Analysis: Cost and Benefit
Other references, such as [PERSON] et al. [25] (55.5 \(\upxi\)/tonne of waste), [PERSON] et al. [28] (98.85 \(\upxi\)/tonne of waste), and [PERSON] [26] (82.76 \(\upxi\)/tonne of waste) were consistent with this range. The operation and maintenance costs for Scenario 3 ranged from 7.56 to 35.53 \(\upxi\)/tonne of waste per year (Table 1) according to [PERSON] et al. [12] and [PERSON] et al. [28]. Additional studies performed by [PERSON] et al. [27] and [PERSON] et al. [30] showed costs of 14.53 and 15.14 \(\upxi\)/tonne per year, respectively. Regarding the benefits carried out in this assessment, this could range from 13.57 to 47.16 \(\upxi\)/tonne per year (Table 1). According to [PERSON] et al., the annual gains by selling biomethane could be 13.57 \(\upxi\)/tonne of waste [31]. Other surveys performed by [PERSON] et al. [32] and [PERSON] et al. [27] showed a revenue of, respectively, 21.74 and 23.48 \(\upxi\)/tonne per year. Finally, the assessment carried out by [PERSON] et al. [28] evidenced an annual profit of 47.16 \(\upxi\)/tonne of waste. As done for the previous scenario, for biomethane production it is mandatory to consider in the assessment the social and environmental annual benefit represented by CO\({}_{2}\) emissions avoided. According to the studies proposed by [PERSON] and [PERSON] [33] and [PERSON] et al. [32], these could range from 1.47 to 4.39 \(\upxi\)/tonne per year (Table 1). Consequently, the initial costs carried out for Scenarios 2 and 3 are consistently higher than the initial costs reported in Scenario 1. This could be attributed to the different technologies needed for the anaerobic digestion process as compared with the aerobic digestion process. In relation to operation and maintenance costs, the highest value of Scenario 3 could be imputable to the technologies adopted, followed by an increase in personnel costs that arise from the increased technological component of a plant design; in fact, for Scenario 3 highly skilled personnel are needed for a comparison with the other two scenarios. The same reasons apply for the revenues that rise with an increase in the final quality of the product, due to the more advanced technological components in Scenarios 2 and 3 as compared with Scenario 1. In fact, according to [PERSON] et al., the economic favorability of anaerobic digestion (Scenarios 2 and 3), as compared with composting (Scenario 1), was highly dependent on plant scale and valorization of the end products [25].
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 3 Preliminary  
### Markov Decision Process Theory  
The Markov Decision Process (MDP) is constructed based on Markov theory and is used for optimal decision-making in dynamic environments [24]. The core is Markovian,which means that the future state is only determined by the current state and has nothing to do with history. The MDP can use methods such as dynamic programming to find the optimal strategy, thereby maximizing the cumulative reward.  
MDP is generally represented by a five-part tuple (S, A, P, R, \(\gamma\)):  
S represents the state space; it represents the set of all possible states in the system.  
A represents the action space; it defines the set of actions that can be executed in each state.  
P represents the state transition function; it refers to the probability that the system will transition to the next state given a specific current state, d, and action.  
R stands for the reward function; it is used to measure the immediate reward or punishment obtained after performing a specific action in a certain state.  
\(\gamma\) denotes the discount factor, which assesses the importance of future rewards to current decisions, and generally takes values within [0, 1].  
In practical applications, the MDP is widely used in fields such as artificial intelligence decision-making, resource allocation, and path planning, providing strong modeling tools. Common solutions include value iteration, strategy iteration, and deep reinforcement learning (DRL). During the interaction with the environment, the system observes the state, selects actions, obtains rewards, and continuously optimizes its decision-making strategy.  
The decision-making process of the MDP has discreteness. In each time step, the system will select an action based on the current state. The environment will transition from the current state to the next state based on the probability of state transition and then provide feedback rewards. This process will continue until it reaches the termination state or the task is completed.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 3 A New Risk Measurement Method Considering Multiple Driving Behaviors
### Validation of the Effectiveness of \(R_{total}\)  
The NGSIM dataset includes complete vehicle trajectories from the point of entry to the exit, meeting the duration requirements for following vehicle trajectories. We selected the US-101 highway data to validate the effectiveness of the newly proposed risk assessment model \(R_{\text{total}}\). The camera's field of view covers a road length of 640 m with five highway lanes. A schematic diagram of the US-101 detection section is shown in Figure 5. Currently, empirical microscopic analysis largely relies on NGSIM data.  
The NGSIM dataset, derived from image recognition technology, inherently contains errors and noise. As a result, many trajectory data points do not adhere to vehicle dynamics or kinematics principles. Three primary types of errors are identified in the NGSIM dataset: position, velocity, and acceleration errors. Existing trajectory data processing methods include wavelet analysis, Kalman filtering, local polynomial regression, and moving average techniques.  
To address these errors, we conducted experiments using Kalman filtering, moving average, and Symmetric Exponential Moving Average (SEMA) methods. Taking the lateral displacement of each vehicle as an example, we applied these methods to smooth the data and compared their processing effects. The evaluation metrics included mean square error (MSE), Signal-to-Noise Ratio (SNR), and correlation coefficient (\(r\)). The performance metrics derived from our experiments are presented in Table 1.  
As shown in Table 1, SEMA achieves the lowest MSE and the highest values for both SNR and \(r\), indicating that the data processed with SEMA aligns most closely with the trend of the original dataset. Therefore, we selected the Symmetric Exponential Moving Average (SEMA) method for processing. The results of the data smoothing process are presented in Figure 6.  
Figure 4: Sensitivity Analysis of \(R_{\text{total}}\).  
Figure 5: Schematic diagram of the US-101 detection section.  
Figure 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **SEMA** & **Kalman Filter** & **Moving Average** \\ \hline MSE & \(2.97\times 10^{-4}\) & \(1.4\times 10^{-2}\) & \(1.5\times 10^{-3}\) \\ SNR & 47.02 & 32.38 & 45.64 \\ \(r\) & 0.996 & 0.919 & 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance metrics for each method.  
As depicted in Figure 6a, the smoothing process successfully removes outliers from the vehicle's lateral displacement curve, producing a smoother and more consistent trajectory. Additionally, Figure 6b,c illustrate that the post-processed speed and acceleration curves exhibit no abrupt fluctuations, aligning closely with realistic driving conditions. This confirms the capability of the SEMA method to effectively mitigate noise and errors in trajectory data while preserving the underlying trends.
# Weight Adjustment Framework for Self-Attention Sequential Recommendation
## 3 Methodology
### Prediction Layer  
The prediction layer is responsible for converting the learned item representation into a specific recommendation output. Select the last elements \(F_{n}^{p}\) and \(F_{n}^{c}\) from \(F^{p}\) and \(F^{c}\), respectively. Now, two types of item probabilities can be obtained:  
\[\widehat{y}_{i}^{p}=\textit{softmax}\Big{(}F_{n}^{p}M^{\top}\Big{)} \tag{24}\]  
\[\widehat{y}_{i}^{C}=\textit{softmax}\Big{(}F_{n}^{c}M^{\top}\Big{)} \tag{25}\]  
where \(M\in\mathbb{R}^{|I|\times d}\) is the item-embedding matrix. Then, calculate the perturbation loss, \(\mathcal{L}_{p}\), and the calibration loss, \(\mathcal{L}_{C}\):  
\[\mathcal{L}_{P}=-\sum_{i=1}^{|\mathcal{I}|}\ y_{i}\textit{log}\Big{(}\widehat {y}_{i}^{p}\Big{)} \tag{26}\]  
\[\mathcal{L}_{C}=-\sum_{i=1}^{|\mathcal{I}|}\ y_{i}\textit{log}\Big{(}\widehat {y}_{i}^{C}\Big{)} \tag{27}\]  
The performance of attention affected by perturbation should be worse. Therefore, the loss can be defined as follows:  
\[\mathcal{L}_{P_{\textit{grad}}}\Big{(}\theta^{P}\Big{)}=-\mathcal{L}_{P}( \theta)+\delta\mathcal{L}_{norm}\Big{(}\theta^{P}\Big{)} \tag{28}\]  
\[\mathcal{L}_{norm}\Big{(}\theta^{P}\Big{)}=\sum_{l=0}^{L}\ \parallel 1-m^{l} \parallel_{2} \tag{29}\]  
where \(\theta^{P}\) and \(\theta\) are parameters of the model, \(L\) is the number of layers in the transformer, and \(\delta\) is a hyperparameter for balancing the loss function. \(\mathcal{L}_{\text{norm}}\) is used to ensure that the perturbation is not too large and avoids the severe degradation of the model performance.  
In addition, the logical representation models the variability and uncertainty of user tastes. In order to deepen the model's understanding of user preferences, we splice \(F_{n}^{p}\), \(F_{n}^{c}\) and the logical representation \(H_{l}^{u}\) together, respectively. The logical representation \(H_{l}^{u}\) is calculated from \(\overline{\alpha}\) and \(\overline{\beta}\):  
\[H_{l}^{u}=\frac{\overline{\alpha}}{\overline{\alpha}+\overline{\beta}} \tag{30}\]The predicted probability \(\widehat{p}^{a}\) and \(\widehat{p}^{c}\) of the item can be calculated:  
\[\widehat{p}^{a}=\Big{(}F_{n}^{p}\oplus H_{l}^{u}\Big{)}\Big{(}M\oplus E\Big{)}^{\top} \tag{31}\]  
\[\widehat{p}^{c}=(F_{n}^{c}\oplus H_{l}^{u}\Big{)}\Big{(}M\oplus E\Big{)}^{\top} \tag{32}\]  
where \(E=\frac{\alpha}{\alpha+\beta}\), uses the cross-entropy loss function to approximate the true value \(p\):  
\[\mathcal{L}_{\widehat{p}^{c}}=-\sum_{i=1}^{|I|}p_{i}log(\widehat{p}_{i}^{a})+ (1-p_{i})log(1-\widehat{p}_{i}^{a}) \tag{33}\]  
\[\mathcal{L}_{\widehat{p}^{c}}=-\sum_{i=1}^{|I|}p_{i}log\Big{(}\widehat{p}_{i} ^{b}\Big{)}+(1-p_{i})log\Big{(}1-\widehat{p}_{i}^{b}\Big{)} \tag{34}\]  
Finally, the total loss of our model is as follows:  
\[\mathcal{L}_{\text{final}}=\mathcal{L}_{\widehat{p}_{\text{final}}}+\mathcal{ L}_{C}+\gamma\Big{(}\mathcal{L}_{\widehat{p}^{c}}+\mathcal{L}_{\widehat{p}^{c}} \Big{)} \tag{35}\]  
where \(\gamma\) is a hyperparameter that balances various loss functions.
## 3 Static Analysis
### Fatigue Analysis Method  
The fatigue life commonly refers to the number of cycles of stress or strain that parts endure when they fail due to fatigue.  
Current fatigue analysis methods are divided into three main categories [24]:  
Figure 14: Stress nephogram of the rudder stock mechanism at four temperatures. (**a**) 0 \({}^{\circ}\)C; (**b**) \(-\)20 \({}^{\circ}\)C; (**c**) \(-\)40 \({}^{\circ}\)C; and (**d**) \(-\)60 \({}^{\circ}\)C.  
1. The first is the S-N method, which is used to calculate the stress of a member that does not reach the yield strength of the material. It is mainly used to calculate the high cycle fatigue life. This method studies the life of the member before fatigue failure;
2. The second is the E-N method, which is used to calculate the local stress-strain of a member when its stress reaches the yield strength but does not reach the tensile strength. It is mainly used for the calculation of low cycle fatigue life, which studies the life from local strain to crack initiation;
3. Third, the method of crack propagation life is used to study the fatigue life of components after cracks occur, which is based on the theory of fracture mechanics. This method reveals the essence of fatigue life from a micro perspective. However, due to the high theoretical requirements for fracture mechanics, it is difficult to apply this method widely in engineering practice.  
The rudder stock mechanism belongs to the high cycle fatigue type, so the S-N method is selected for the fatigue life analysis. Its theoretical basis is the fatigue cumulative damage hypothesis (Miner criterion). It is assumed that fatigue damage and failure are linear cumulative processes that ultimately reach the failure limit value, which leads to fatigue failure.  
The fatigue characteristic parameters of the materials of the submarine rudder stock mechanism have a decisive influence on the fatigue life. Therefore, only by obtaining accurate fatigue characteristic parameters of the materials, the exact fatigue life can be obtained. The conventional group test method for obtaining S-N curve is a time-consuming and expensive test program [25]. In fatigue research, it is a common method to infer the S-N curve of materials through static stress data and modify the existing curve. [PERSON] provided an overview of how to account for temperature effects on fatigue strength when designing steel structures [26]. The method proposed by [PERSON] [27] is used to modify the S-N curve of naval steel, and the S-N curve of a naval steel at low temperature is obtained (Figure 15).  
Figure 15: S–N curve of a naval steel at low temperature.