esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 5 Simulation Results and Discussion  
In this section, we show the results of evaluating the performance of the proposed algorithm through simulation experiments. We provide a detailed evaluation and analysis of the convergence of the algorithm under different hyperparameter settings. We also compare the performance of the proposed algorithm with other Deep Reinforcement Learning (DRL) algorithms in terms of system cost, communication coverage, and delay performance. The experimental results show that our algorithm enables more accurate route prediction and can achieve about 95% user coverage in marine environments while ensuring that the average latency of user response is around 1 s. The algorithm is implemented in the Visual Studio Code (VSCode) environment, using the Python programming language and integrating TensorFlow and PyTorch, two popular deep learning frameworks, to improve the execution efficiency and performance of the algorithm.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 4 Methodology
### Proposed Stacked Convolutional Autoencoder for Data Reconstruction
#### 4.3.2 Step 2: Use the Preinitialized Weights from Step 1 to Train the Entire SCAE Network  
After weights \(W_{\text{conv}}\), \(W_{\text{deconv}}\), \(W_{12}\), \(W_{23}\), \(W_{34}\), and \(W_{45}\) are pretrained, they serve as the starting points for training the entire network. Step 2 uses a training procedure similar to that of the CAE model presented above.
## 4 Results and Discussion
### Influence of the Operational Parameters
#### 4.2.2 Effects of HTF Inlet Mass Flowrate  
Figure 10 shows the effect of the HTF mass flowrate of ethylene glycol on the liquid fraction for the shell and coil tube ice storage system. Four different mass flowrates including 2, 4, 6, and 8 L/min are considered. As it can be seen, increasing the mass flowrate from 2 to 8 L/min decreases the melting time by about 2% at long times (typically \(t=1250\) min). The influence is even less significant at short times.  
Figure 8: Temporal evolution of the liquid fraction for four different HTF (heat transfer fluid) inlet temperatures of ethylene glycol.  
Figure 9: Contours of the liquid fraction at four different times and two HTF inlet temperature of ethylene glycol: (**a**) T\({}_{\text{inlet}}\) = 7.5 \({}^{\circ}\)C and (**b**) T\({}_{\text{inlet}}\) = 10 \({}^{\circ}\)C.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 5 Performance Evaluation
### End-To-End Delay  
It can be observed in Figure 10 that all of the routing schemes follow the same decreasing trend in end-to-end delay. This is because in a sparse network, there are less potential neighbors. On the other hand, in a dense region, a node finds more potential neighbors, which is beneficial for the reduction of propagation delay. This eventually reduces end-to-end delay. End-to-end delay primarily comprises propagation delay, holding time, transmission delay and calculation time. Accumulated propagation distance increases while considering two hops for routing decision in 2 hop-AHH-VBF. That is why 2 hop-AHH-VBF bears more end-to-end delay as compared with AHH-VBF and WDFAD-DBR. Up to node Number 350, 2 hop-AHH-VBF follows the same trend as AHH-VBF and WDFAD-DBR. After that as the node number increases, the number of hops taken to reach the destination also increases. Holding time for packets is also introduced in both schemes. Successful transmissions towards the destination cause transmission delay, which is also added to overall end-to-end calculations along with holding time. In AHH-VBF, qualified node selection criteria are based on the distance from the virtual vector. Without considering residual energy and two-hop information, this causes packets to forward towards the void region. Transmission failure increases end-to-end delay due to retransmissions of the same packet. Therefore, in the QF-2 hop-AHH-VBF scheme, selection of the qualified node and holding time calculation are based on the composite function as in Equation (10). This reduces the propagation delay due to avoidance of the overage hole and efficiently minimizes holding time. Hence, QF-2 hop-AHH-VBF performs 30% better than AHH-VBF scheme. Similarly, utilizing the two-hop information along with the node position with respect to the virtual vector results in less hop traversing in QF-2 hop-AHH-VBF. Hence, it performs 28% more efficient in reducing APD as compared to the AHH-VBF scheme as shown in the Figure 11.  
Figure 9: Energy tax comparison.
# AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
## 3 Results
### Comparison with Methods
#### 3.3.3 Experiments on FC100  
The experimental results of our model compared with other state-of-the-art algorithms on FC100 are shown in Table 4. Calculate the average accuracy of each model within a 95% confidence interval under the settings of 5-way 1-shot and 5-way 5-shot. Due to the lower resolution and the way superclasses are divided in the FC100 dataset, the difficulty of the classification task is increased. This results in relatively low prediction accuracies for most algorithms. Our model outperforms the majority of models listed in Table 4, but in the 5-way 1-shot task, the model with the highest classification accuracy is SIB. SIB models the meta-learning problem as a Bayesian model and designs an Amortized Variational Inference method to learn the variational posterior of all tasks. This method includes a synthetic gradient network and an initialization network. In low-sample scenarios such as 1-shot task, the function mapping that the model needs to learn is relatively simple. The synthetic gradient can effectively approximate the real gradient, thereby updating model parameters and improving model performance. Compared to AFGN, SIB has become the most effective method on the FC100 dataset with a gap of 0.7%. When the support set is relatively large, the initial values generated by the initialization network may already be close to a local optimum. Subsequent synthetic gradient update steps have a limited impact on the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline ProtoNet [12] & \(51.31\pm 0.91\) & \(70.77\pm 0.69\) \\ MAML [10] & \(55.92\pm 0.95\) & \(72.09\pm 0.76\) \\ MatchingNet [11] & \(61.16\pm 0.89\) & \(72.86\pm 0.70\) \\ RelationshipNet [19] & \(62.52\pm 0.34\) & \(76.11\pm 0.69\) \\ [PERSON] et al. [21] & \(63.30\pm 0.94\) & \(81.35\pm 0.67\) \\ FEAT [24] & \(68.87\pm 0.22\) & \(82.90\pm 0.15\) \\ TEAM [31] & \(69.39\pm 0.00\) & \(82.78\pm 0.00\) \\ TriNet [7] & \(69.61\pm 0.46\) & \(84.10\pm 0.35\) \\ BML [32] & \(76.21\pm 0.63\) & \(90.45\pm 0.63\) \\ DC [8] & \(78.29\pm 0.22\) & \(88.92\pm 0.12\) \\ afrasiyabi et al. [33] & \(79.60\pm 0.80\) & \(90.48\pm 0.44\) \\ FGFL [15] & \(80.77\pm 0.90\) & \(92.01\pm 0.71\) \\ AFGN & \(\mathbf{81.10\pm 0.37}\) & \(\mathbf{92.30\pm 0.41}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison to prior work on CUB. 5-way 1-shot and 5-way 5-shot average classification accuracy(%) with 95% confidence intervals. The best results in the table are highlighted in bold.  
model parameters, resulting in limited improvement in model performance. However, in the 5-way 5-shot task, as the number of support set samples increases, the information of frequency components aggregated by graph networks from neighboring nodes also increases, leading to an improvement in model performance. The classification accuracy of AFGN is improved by 7.37% compared with SIB and becomes the best-performing model on the FC100 dataset.
## 3 Results and Performance Evaluation
### Performance Metrics for Comparisons  
In this study, we used a hybrid framework designed for fault diagnosis in milling machines, combining CNN and BiLSTM networks with a GA. This hybrid approach capitalizes on CNNs for feature extraction from CWT scalograms of AE signals, followed by temporal feature processing through BiLSTM. The final classification step is optimized using a GA, ensuring the most important and relevant features are utilized for accurate fault classification. To evaluate the effectiveness of this approach, we considered several performance metrics, including accuracy, precision, recall, and F1-score, as shown in Equations (8)-(11). These metrics are useful for assessing the model's ability to correctly classify different fault conditions within the milling machine. The mathematical expressions for these metrics are detailed below  
\[\mathbf{Accuracy}=\frac{(\mathbf{TN}+\mathbf{TP})}{(\mathbf{TP}+\mathbf{TN}+\mathbf{FP}+\mathbf{FN})}\times 1 00\% \tag{8}\]  
\[\mathbf{Precision}=\frac{\mathbf{TP}}{\mathbf{TP}+\mathbf{FP}}\times 100\% \tag{9}\]  
\[\mathbf{Recall}=\frac{\mathbf{TP}}{\mathbf{TP}+\mathbf{FN}}\times 100\% \tag{10}\]  
\[\mathbf{F1}-\mathbf{Score}=\frac{2\mathbf{TP}}{2\mathbf{TP}+\mathbf{FP}+\mathbf{FN}}=\frac{2\times( \mathbf{Precision}\times\mathbf{Recall})}{\mathbf{Precision}+\mathbf{Recall}} \tag{11}\]  
Here, TP (true positive) refers to the instances correctly classified as faulty conditions, while TN (true negative) indicates instances correctly identified as non-faulty. FP (false positive) represents cases where the model incorrectly classifies a non-faulty instance as faulty, and FN (false negative) indicates faulty instances incorrectly identified as non-faulty by the model. These metrics provide a comprehensive evaluation of the model's classification performance, ensuring that the hybrid CNN-BiLSTM-GA approach is both robust and accurate in detecting and diagnosing faults in milling machine operations. The use of a GA for feature selection enhances the model's ability to distinguish between different fault types effectively, leading to improved diagnostic accuracy and reliability.
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 4 Modeling Description
### Incipient Failure Threshold Determination
#### 4.4.2 Similarity Threshold  
As a supplement for direct vibration monitoring, information contained in the other component in \(\mathbf{X}_{obs}\) and \(\mathbf{X}_{est}\) are applied to calculate a comprehensive similarity, which is significant for detecting faults reflecting in indirect variables. Furthermore, as shown in Figure 1 and Table 1, there are no redundancies for vibration measurement, therefore, similarity judgement is an effective way in situations where vibrating measurement fails, and it is significant for improving the accuracy of condition monitoring. The similarity threshold could be formulated as,  
\[T_{s}=\frac{S(X_{obs},X_{est})_{\text{min}}}{c_{2}} \tag{20}\]  
where \(S(X_{obs},X_{est})\text{min}\) is the minimum of similarity in a sliding window, and coefficient \(c_{2}\) is set as 1.1 in this paper.
## 3 Analysis of Suspension Damping Coefficient
### Calculation of Equivalent Damping Coefficient
\(F_{ox}\) and \(F_{dx}\) are the horizontal forces at points o and d, and \(F_{oy}\) and \(F_{dy}\) are the vertical forces at points o and d. The forces acting on rod2 are analyzed as follows:  
\[ \begin{array}{l}(\mathrm{a}):\sum X=0,F_{cx}-F_{ox}+F_{dx}=0\\ (\mathrm{b}):\sum Y=0,F_{cy}-F_{dy}-F_{oy}-F_{k}=0\\ (\mathrm{c}):\sum M_{O}=0,F_{cx}l_{2}\sin\alpha-F_{cy}l_{2}\cos\alpha-F_{dx}l_ {1}\sin\alpha-F_{dy}l_{1}\cos\alpha=0\end{array}  \tag{7}\]  
Figure 4: The local force analysis of the seat. The following formula is obtained by sorting through Equations (1)-(9)  
\[ \begin{array}{l}\{\frac{1}{l_{1}+l_{2}}[\frac{1}{\cos\alpha}\sin(\alpha+\theta) l_{1}+\frac{\mu\tan\alpha-1}{\mu\tan\alpha+1}\sin(\alpha-\theta)l_{2}]mc\}\dot{y}+ \frac{\ddot{\beta}k}{(l_{1}+l_{2})^{2}}y\\ =\{\frac{1}{l_{1}+l_{2}}[\frac{1}{\cos\alpha}\sin(\alpha+\theta)l_{1}+\frac{\mu \tan\alpha-1}{\mu\tan\alpha+1}\sin(\alpha-\theta)l_{2}]mc\}\dot{y}_{0}+\frac{l _{2}^{2}k}{(l_{1}+l_{2})^{2}}y_{0}\end{array}  \tag{8}\]  
The simplified form of the differential equation of motion for the vibration system caused by harmonic excitation is shown in Equations (9) and (10), where \(\zeta\) is the damping ratio, \(\omega_{0}\) is the excitation frequency, and \(m\) is the system mass. \[\ddot{y}+2\zeta\omega_{0}\dot{y}+\omega_{0}^{2}y=2\zeta\omega_{0}\dot{y}_{0}+ \omega_{0}^{2}y_{0} \tag{9}\]  
\[c_{s}=2m\zeta\omega_{0} \tag{10}\]  
The expression for the equivalent damping \(c_{s}\) is derived from Equations (8)-(10) as follows:  
\[c_{S}=\frac{1}{l_{1}+l_{2}}[\frac{1}{\cos\alpha}\cos(\alpha+\theta)l_{1}+\frac {\mu\tan\alpha-1}{\mu\tan\alpha+1}\sin(\theta-\alpha)l_{2}+1]c \tag{11}\]  
From the expression for the calculated equivalent damping, it can be seen that the equivalent damping of the scissor frame is related to the lengths \(l_{1}\) and \(l_{2}\) of the scissor bars of the suspension, the damping coefficient \(c\) of the damper, the damping angle \(\theta\) of the damper, the scissor bar angle \(\alpha\), and the coefficient of friction \(\mu\) at the suspension balance position.
# Robust Quadratic Optimal Control for Discrete-Time Linear Systems with Non-Stochastic Noises
## 3 Robust Optimal Control Strategy
### Analysis of State Estimation Error  
The center \(\hat{x}(k)\) of true state feasible ellipsoid set is adopted as the estimated state in this paper, then state estimation error can be defined as follows,  
\[\eta(k)=x(k)-\hat{x}(k).\]  
Based on the set-membership filtering results, it yields that at instant \(k\) the estimation error \(\eta(k)\) belongs to the following ellipsoid set,  
\[\eta(k)\in\Omega(0,P(k)),\]  
namely,  
\[\eta(k)\in\{\eta(k)|\eta^{T}(k)P^{-1}(k)\eta(k)\leq 1\},\]  
the shape matrix \(P(k)\) of error ellipsoid set can be refreshed according to Equations (9)-(13), which avoids solving complex convex optimization problem.
## 3 Results
### Ability of the Proposed Method to Distinguish Impervious Surfaces from Vegetation and Bare Soil  
Figure 9 shows the differences between typical samples of impervious surfaces, bare soil, and vegetation in terms of the coastal band, blue band, green band, red band, ratio vegetation index (RVI) [37], NDVI, and difference vegetation index (DVI) [38], where all features underwent a 0-1 transformation. Of these three vegetation indices, the NDVI gave the greatest difference between impervious surfaces and other ground objects. Besides, the values for impervious surfaces in the coastal band were higher than those for vegetation and bare soil, and the contrast between impervious surfaces and bare soil gradually decreased in the coastal, blue, green, and red bands.  
Therefore, the ratio of the coastal band to the NDVI can maximise the information on impervious surfaces and increase the difference between impervious surfaces and other ground objects, and thus effectively distinguish impervious surfaces from bare soil, which is the main advantage of our method in comparison with the other five methods.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 2 Method
### Frequency Domain FWI  
Inversion is the process of obtaining geophysical parameters by inverting the wave-field based on the actually observed seismic data. Actually, inversion is a process of local optimization. By continuously updating the initial model, the real model can be approximated to the maximum to obtain the global optimal solution.
# Context-Aware Caching Distribution and UAV Deployment: A Game-Theoretic Approach
## 1 Introduction
In the proposed scenario, users need to be served in a disaster area when the communication facilities are destroyed. UAVs with caching contents fly to the destination to provide communication services. Considering the cooperation among UAVs, the caching contents of neighbor UAVs can be shared by one hop. Thus, the optimal arrangement of UAVs' caching contents and service locations are the key points to solve. However, the problem is challenging. Firstly, the transmission overhead may be affected by both UAVs' caching contents and service locations, i.e., the source of the request file and the neighbor UAV sets. Secondly, when considering the locations of UAVs, the probability that the users can communicate with UAVs successfully will be influenced by the propagation groups, i.e., _LOS_ and _NLOS_, with corresponding probabilities of occurrence. Motivated by these observations, we consider both the probability that the users can communicate with UAVs successfully and the transmission overhead that the users use to obtain the requested files. Therefore, we apply the ratio of users' service probability and transmission overhead as the performance indicators of UAVs' actions to balance the tradeoff. Meanwhile, we formulate the challenging problem as a UAV caching game applying game theory and prove the existence of the Nash equilibrium (NE). In addition, a log-linear caching algorithm (LCA) can achieve great performance. The main contributions are as follows:  
* We first investigate the problem of joint optimization of UAVs' caching contents and service locations with the goal of balancing the tradeoff between user's service probability and transmission overhead under the consideration of the share of neighbor UAVs' caching contents. * The problem is modeled as a UAV caching game, which intends to find the optimal solution that maximizes UAVs' performance indicators, i.e., the ratio of user's service probability and transmission overhead. In addition, the proposed game is proved to be a potential game with at least one pure-strategy NE. Meanwhile, the optimal solution can be achieved by its optimal NE. * The log-linear caching algorithm is proposed to achieve the desirable solution in joint caching contents and service locations optimization problem. The simulation results show that the proposed algorithm can converge to a great NE solution and guarantee the great performance of UAV-assisted networks, which demonstrates the algorithm's validity and effectiveness. The remainder of this paper is organized as follows. In Section 2, we introduce the system model and formulate the problem. In Section 3, we propose the UAV-assisted caching game and apply the log-linear caching algorithm to achieve the desirable solution. We present the simulation results and give an expansion and discussion in Section 4. Finally, we draw a conclusion in Section 5.
## 3 Results
### Stand Biomass and CUE Estimations  
As shown above, the total increase in biomass was determined starting from the point after the rehydration period (Figure 6a). EC-derived GPP for the vegetation period ranged from the beginning of March to the end of September for both years (Figure 6b). The estimates of NPP and GPP allowed the calculation of the CUE values. It should be pointed out that GPP summarized for the vegetation period represents the total CO\({}_{2}\) amount absorbed from the atmosphere which could be then potentially be built into the tree's tissues as carbon (C).  
Figure 6: Specific moments and periods of tree growth based on: (**a**) dendrometers and (**b**) eddy covariance measurements. SG—Stem increment over the year, related to seasonal growth, MWC—maximum winter stem contraction, B—wood growth period, P0—the zero-point of the current year that corresponds to the respective culmination point of the past year, NEP-net ecosystem productivity, CO\({}_{2}\) sink-ecosystem productivity over the year.  
Using the first method of the allometric equation with the general formula (M1), an absolute increase in dry biomass of 0.9 kg per average tree was estimated in 2019, and 1.2 kg in 2020 (Table 3). Considering the number of trees estimated at our study site (4262 per hectare), the total increase in dry biomass was approximately 4.3/4.4 t C ha\({}^{-1}\) in 2019, and 5.5/5.7 t C ha\({}^{-1}\) in 2020 (calculated by M1/M2 method, respectively). Consequently, NPP was approximately 2.1/2.2 t C ha\({}^{-1}\) in 2019, and 2.7/2.8 t C ha\({}^{-1}\) in 2020. A possible uncertainty was also given, considering an error that could occur when determining the phases of the biomass increase (\(\pm\)0.01 cm for initial increment measurement; Table 3). This also reveals how the 0.01 cm of the average DBH increment for each tree translates into the estimation of the total forest biomass during the year (the difference of 0.01 mm, depending on the method, gives an error of ca. 0.3 kg of the total dry biomass of the stand unit area).  
The total amount of absorbed CO\({}_{2}\) (GPP) during the vegetation period was higher in 2019 than in 2020 (Table 3). However, the sum of inspired CO\({}_{2}\) (R\({}_{\rm eco}\)) during the same period was slightly higher in 2020 (10 t C ha\({}^{-1}\)) than in 2019 (9.7 t C ha\({}^{-1}\)). Hence, the difference between GPP and R\({}_{\rm eco}\), which essentially constitute Net Ecosystem Productivity (NEP), was higher in 2019. Nevertheless, CUE, which is a key indicator of how much absorbed CO\({}_{2}\) was fixed and converted into fresh wood growth, was higher in 2020. CUE has been calculated both for the vegetation period (different periods for total annual NPP and total annual GPP) and the wood growth period (B) (Figure 6; Table 3). It was found that CUE for the vegetation period remained fairly low in both years, although it was higher in 2020; depending on the method, it was estimated to be 0.19/0.20 (M1/M2). In 2019, it was estimated to be 0.15 using both methods. For the B period, CUE values were 0.18/0.19 and 0.23/0.24 in 2019 and 2020, respectively (Table 3).
## 4 Methodologies and Implementation
### Comparison with Other Segmentation Methods
#### 4.2.2 Handling Noisy Medical Images  
Traditional deep learning methods might struggle when confronted with noisy medical images. CNNs, while powerful, may inadvertently amplify noise during training, leading to suboptimal segmentation outcomes [1,77].  
The incorporation of DDPM in the pre-processing stage allows DeepSegDDPM to effectively mitigate noise, providing clean input images for subsequent deep learning layers. This denoising capability enhances the robustness of the algorithm in handling noisy medical images [13,75].
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 3 Buffer Management
### Evaluation Method
```
1:for each node \(A\), \(B\in V\)do
2:if\(B\) and \(A\) is connected and \(B\) is the sender then
3:for each message \(i\) in \(B\)BufferQueue send to \(A\)do
4:if\(s_{i}\leq A_{a}\)
5: Calculate the message \(i\) utility value and \(A\) receive \(i\) and store it
6:else
7: Calculates the \(A\) average utility value \(U_{(A)}\)
8:if\(U_{(i)}\leq U_{(A)}\)
9: abandon transfer message \(i\)
10:else
11: Select the message \(q\) with min utility value in \(A\)BufferQueue
12:for each node \(C\in V\)do
13:if\(B\
eq C\) and \(\mu_{(C)}\geq\mu_{0}\)then
14: Calculate average utility value \(U_{(C)}\)
15:if\(U_{(q)}\geq U_{(C)}\)
16:\(C\) receives \(q\) and stores it
17:endif
18:else
19: Abandon transfer message \(q\)
20:endfor
21: Node \(A\) delete the message \(q\) until node \(A\) store message \(i\) in BufferQueue
22:endif
23:endif
24:endfor
25:endif
26:endfor
```  
**Algorithm 1** Message forwarding and migration strategy of CBM.