esa-sceva/satcom-chunk-collection huggingface dataset:
# From Brown-Field to Future Industrial Networks, a Case Study
## 5 Discussion and Future Directions
The abstract performance dynamic formulation provides the opportunity to take the operational states of the network into account and reduce the number of parameters for network modeling proportional to the number of existing states. In other words, the regular workflow of the network can be divided into operational states, and the flows can be monitored based on the performance requirements of the associated state. This approach can reduce the computational complexity and improve the model accuracy by reducing the sample space and variance of the outcomes. However, further investigations are needed to identify and estimate the impacts of other important network parameters such as the number of transmissions, transmission intensity, acceptable delay and transmission intervals. There still is a need for formulating a KPI that can quantify the performance dynamics and the impact of the important parameters on the overall performance of the network. One of the difficulties when analyzing traffics of the existing networks is that the brown-fields have evolved during the years by the integration of new technologies and adopting new business models. Additionally, brown-fields are narrowly designed for specific production use-cases and scenarios. The proposed approach was evaluated and tested on the data collected from one brown-field, and the aim is not to generalize the model and the method at this stage to all scenarios. Nonetheless, it is a plausible discussion that due to similar features in traffic patterns and similarities between traffics of different classes, this approach can have relevance when applied to similar scenarios. For instance, it is an acceptable assumption to observe cyclic patterns in production networks with regular workflows, human intervention or planned maintenance. The performance dynamics can investigate these similarities to exploit insight about communication patterns and the configuration requirements to meet a set of functionality guarantees, even if not with the most optimized performance and highly detailed application-dependent configuration. For all the aforementioned discussion, more investigation into brown-field networks and analysis of real-data for profiling, characterization and modeling is needed to procure a path to the future industrial networks.
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
## 7 Performance Evaluation
### RUL Evaluation
This is the main reason for the high positive or negative error ratio of the adaptive sliding window method in Figure 7.  
The adaptive Bayesian method outperforms the adaptive sliding window method for five cases out of 17 cases. These five cases consist of four cases with condition A and a single case with condition B. In these five cases, the Bayesian estimator using the exponential degradation model performs better than the ones using the linear degradation model for three cases with condition A. It is an interesting observation since the linear degradation model outperforms the exponential one for 10 cases out of 17 cases. While the adaptive sliding window method generally outperforms the Bayesian method using the linear degradation model, it cannot fully replace the Bayesian method using the exponential degradation model. The main reason is the fundamental model limits of the degradation model of Equation (2) for specific cases of Bearing A_3, Bearing A_4, Bearing A_5. The overall trends of MAPE results are similar to those of the MAE results. The average value of all MAPE values of the adaptive sliding window is 0.3087, lower than that of the adaptive Bayesian method (0.4343). Note that the average values of all MAPEs are 0.6056 and 0.5229 for Bayesian methods using the exponential and the linear degradation models, respectively.
## 4 Results
### Scenario 2
#### 4.2.1 First Example Test  
The results of one of the tests in Scenario 2 (with parameters: \(K=5,N=13,L=2\), \(QBER=3\)) are presented in Table 3 and Figure 5. The number of victims' updates in this test, presented in the second column of Table 3 and in Figure 5a, is-as in the first test of Scenario 1-comparable in seven of the eight combinations of the update rules (\(\approx\)55 on average), while for all parties using hebbian its nearly two times lower (\(\approx\)33). The level of synchronization of the attacker (in %) is presented in the third column of Table 3 and in Figure 5b. Similarly to the observation from the first test of Scenario 1, again, the lower number of updates did not result in a more secure synchronization process. In this case, the difference in the attacker's synchronization percentage between tests in which the victims utilize either the dynamic_rows or dynamic_matrix algorithm and the test where all parties use the hebbian rule is even larger (\(\approx\)64% for both proposed solutions, versus \(\approx\)68%).  
In case of random walk-oriented tests, the attacker's synchronization percentage was nearly identical in all three cases-71.39%, 71.38% and 71.35%. These values are much higher than the results achieved by the attacker when they use random update rule, and the victims use any of the proposed textttdynamic solutions-\(\approx\)68%. As stated in the summary of the first test of Scenario 1, in this case, the communicating parties also benefit from attacker not knowing which update rule is used at every iteration.  
Figure 5: Scenario 2–\(K=5,N=13,L=2,QBER=3\).
## 3 Background
### Model Training
#### 3.1.3 Cross Validation  
Cross validation [57] is a widely used data resampling method to assess the generalizability of a model and to prevent over-fitting. Cross validation often involves stratified random sampling, meaning the sampling method retains the class proportions in the learning set. In leave-one-out cross validation, each sample is used in turn as the validation set. The test error approximates the true prediction error; however, it has high variance. Moreover, its computational cost can be high for large datasets. \(k\)-fold cross validation aims to optimise the bias/variance trade-off. In \(k\)-fold cross validation, the dataset is randomly split into \(k\) equal size partitions. A single partition is retained for test data, and the remaining \(k-1\) partitions are used for training. The cross validation steps are reiterated until each partition is used once for validation, as shown in Figure 1. The results are averaged across all iterations to produce an estimation of the performance of the model (Equation (4)). [PERSON] et al. highlighted risks of elevated Type I errors (false positives). With larger values of \(k\), variance is reduced. Moreover, bias also reduces because the model is trained on more of the dataset. We posit that resampling techniques could be used to improve robustness against adversarial examples.  
\[CV_{(k)}=\frac{1}{k}\sum_{i=1}^{k}MSE_{i} \tag{4}\]
## 5 Case Study
### The Main Prediction Results
#### 5.3.3 Ablation Study  
The Comparison of Different Concatenate Method  
The feature fusion of historical wind power and NWP is very important for the wind power prediction and there are three commonly used feature fusion methods. The prediction results of the three methods are listed in Figure 8.  
From Figure 8, it is obvious that the feature fusion method of bilinear and concatenate is better than the nonlinear Tanh method. The prediction error of the concatenate method is slightly lower than the bilinear method, especially in the interval of 0.5 h-3.5 h. Considering that the bilinear method is more complex and has lower training efficiency, the concatenate method is chosen as the feature fusion method in our network.  
Figure 7: Boxplot of prediction results for wind farms.  
The Comparison of Training Time Consumption under Different Wind Farm Numbers  
The training time of the M2 GSNet is crucial because it determines whether it can be utilized in the large-scale renewable energy cluster which includes hundreds, even thousands, of small wind farms. Therefore, we compare the training time of M2 GSNet under different wind farm numbers. The results are in Figure 9.  
According to the results, the training time for one wind farm is 66 min. So, if each wind farm uses one specific model, the training time is more than 1200 min in this case. However, when multi-task learning is used, the training time reduces to 271 min. Thus, it can be seen that by using multi-task learning, it saves a lot of training time and resources. Notably, when the wind farm number increases, the increase rate of training time is actually decreased. Therefore, when more wind farms are considered, the advantages of multi-task learning will be more remarkable.  
Figure 8: The comparison of different feature fusion method.  
Figure 9: The comparison of training time under different wind farm numbers.
## 7 Conclusions and Future Work  
Workflow scheduling in edge computing platforms aims to minimize resource consumption and response time for submitted workflows. With the advancements in artificial intelligence, leveraging AI techniques to address complex scheduling problems in various scenarios has become a focal point of research. In this paper, a workflow scheduling method is designed for edge workflow computing platform, where the scheduling is performed using a neural network trained with reinforcement learning, while the constraints are han  
Figure 10: Response Time in dynamic scheduling scenario.  
Figure 9: Resource Utilization in dynamic scheduling scenario.  
dled externally to the network, allowing the neural network to focus solely on scheduling the sub-tasks of the workflow without considering the constraints. Experimental results demonstrate that the proposed method exhibits good scalability. When the scheduling objective changes, only some modifications to the reword function are required to switch to the new scenario. Additionally, the computation time of the method enables its applicability in real-time environments.  
However, experiments show that the proposed method is slower in computation time compared to simple rule-based scheduling algorithms. Additionally, this approach cannot be directly applied to cluster environments, as cluster environments involve considerations beyond just the resource utilization of individual machines, such as load balancing among multiple devices and other factors.  
In future work, the focus will be on optimizing the aforementioned issues. Firstly, the multi-server environment will be modeled, and a two-stage scheduling algorithm will be designed. In the first stage, load balancing will be achieved based on the load and sub-task execution status of different servers, and sub-tasks will be allocated to specific machines. In the second stage, sub-task scheduling will be performed on specific servers. Secondly, in the first scheduling stage, a series of load balancing rules will be designed for the cluster environment, combining reinforcement learning with rule-based heuristic methods, enabling the neural network to self-learn scheduling strategies or select existing scheduling rules. Thirdly, more reinforcement learning algorithms will be compared and studied to improve the training results of the network.  
Conceptualization, [PERSON]; methodology J.Z.; software, J.Z.; validation, J.Z.; formal analysis, [PERSON]; investigation, [PERSON] and T.W.; resources, J.Z.; data curation, J.Z.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and T.W.; visualization, [PERSON]; supervision, T.W.; project administration, [PERSON]; funding acquisition L.C. All authors have read and agreed to the published version of the manuscript.  
This work was funded by National key R & D project under grant 2021 YFB3301802, the National Natural Science Foundation of China under grant U20A6003 and U1801263, Guangdong Provincial Key Laboratory of Cyber-Physical System under grant 2020B1212060069.  
Not applicable.  
The authors declare no conflict of interest.
# Asymptotic Analytical Solution on Lamb Waves in Functionally Graded Nano Copper Layered Wafer
## 2 Statement of the Problem
### Boundary Conditions  
The traction-free boundary conditions should be satisfied in the wave propagation in a simple or layered wafer. Moreover, the interface continuity conditions should be considered when investigating wave propagation in the latter. The boundary and the continuity conditions of a layered wafer composed of \(N\) single layers of wafer can be expressed as follows, where superscript \(\langle l\rangle\) represents the \(l\)th wafer.  
1. Lamb waves in a simple wafer Traction-free conditions: \(\sigma_{33}=0\) and \(\sigma_{13}=0\) at \(x_{3}=0,h\)
2. Lamb waves in a multilayered wafer Traction-free conditions: \(\sigma_{33}^{\langle 1\rangle}=0\) and \(\sigma_{13}^{\langle 1\rangle}=0\) at \(x_{3}=0\) and \(\sigma_{33}^{\langle N\rangle}=0\) and \(\sigma_{13}^{\langle N\rangle}=0\) at \(x_{3}=H\) Continuity conditions: \(\sigma_{33}^{\langle l\rangle}=\sigma_{33}^{\langle l+1\rangle}\), \(\sigma_{13}^{\langle l\rangle}=\sigma_{13}^{\langle l+1\rangle}\), \(u_{1}^{\langle l\rangle}=u_{1}^{\langle l+1\rangle}\), and \(u_{3}^{l}=u_{3}^{\langle l+1\rangle}\) at \(x_{3}=lh\)  
Figure 1: Two types of FG nano copper wafer. (**a**) Simple FG nano copper wafer; (**b**) multi-layered FG nano copper wafer.
## 3 Results
### Tensile Strength  
To analyze the influence of anisotropy on tensile strength and the differences between direct and indirect tensile strength, the tensile strengths at various loading angles were calculated using Equations (1)-(3), and the results are presented in Figure 5. It is evident that the indirect tensile strength was significantly higher than the direct tensile strength across all loading angles. As the loading angle increased, both the direct tensile strength and the indirect tensile strength calculated using Equation (3) showed a corresponding increase. The maximum tensile strengths were observed at \(\beta\) = 90\({}^{\circ}\), with values of 9.6 MPa and 10.45 MPa, respectively, while the lowest tensile strengths were observed at \(\beta\) = 0\({}^{\circ}\), with values of 4.06 MPa and 6.89 MPa, respectively. In contrast, the indirect tensile strength calculated using Equation (2) initially increased, then decreased, and subsequently rose again as the loading angle increased. The maximum tensile strength was 10.38 MPa at \(\beta\) = 90\({}^{\circ}\), while the lowest tensile strength was 7.00 MPa at \(\beta\) = 0\({}^{\circ}\).  
Figure 4: Load–displacement curves with different loading angles obtained by (**a**) DTT and (**b**) BST.  
The anisotropy index, defined as the ratio of maximum to minimum tensile strength, yielded values of 2.36, 1.52, and 1.48, respectively, indicating a significant degree of anisotropy. The direct tensile strength was most strongly influenced by anisotropy, whereas the indirect tensile strength calculated using Equation (3) showed a reduced effect of anisotropy due to the incorporation of elastic anisotropy. Nevertheless, the influence of anisotropy remained notable. The maximum discrepancy in tensile strength between BST and DTT reached 2.82 MPa, indicating a substantial divergence between these two testing methods.
## 2 Numerical Model Establishment and Validation
### Validation Result and Influence Mechanism of Lubricant Young's Modulus
To facilitate further analysis, the surface settlement at monitoring point O was recorded after the jacking of each steel pipeline, as shown in Figure 5. The simulation results for the two scenarios follow similar trends and can be broadly divided into two phases. In the first stage, the steel pipelines (1)-(0) at the same horizontal level were jacked, leading to a rapid increase in ground settlement. Conversely, in the second stage, the steel pipelines (1)-(9) on the same side were jacked, resulting in a slower settlement increase. This suggests that the disturbances induced by multiple jacking operations of adjacent steel pipelines at the same level are superimposed, resulting in greater surface settlement, whereas, when steel pipelines are arranged on the same side, those jacked earlier impede the ground movement induced by subsequent jacking operations. These phenomena, described in some articles as the repeated soil disturbance effect and the shielding effect, respectively [6], are further substantiated by the transverse settlement trough (Line 1) observed following the sequential jacking of each steel pipeline, as depicted in Figure 6. A comparison of the two simulation results reveals that the lubricant with high Young's modulus can more robustly suppress the superposition effect and enhance the shielding effect, thereby better controlling surface settlement. This effect becomes particularly noticeable in softer soil conditions (e.g., when the 18 th and 19 th steel pipelines travelled through the clay layer). Moreover, Figure 5 shows that the simulation results for the lubricant used on-site (black curve) align well with the field monitoring data (red curve), further validating the reliability of the established model. In summary, the calibrated model can accurately capture the soil disturbance and surface displacement mobilized by multiple adjacent pipe jackings. Furthermore, lubricants with high Young's modulus more significantly control surface settlement by improving the interactions among adjacent pipelines during pipe-roof construction, with this effect being especially evident in soft soil layers. Figure 4: Surface settlement from simulation and monitoring results: (**a**) comparison of surface settlement between simulation results and measured data at monitoring Line 1; (**b**) comparison of surface settlement between simulation results and measured data at monitoring Line 2.
# Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images
## 4 Experimental Evaluation
The considerable gap in recall between the metrics suggests that the models can locate changes well, but they occasionally do not perfectly overlap with the GT. Comparing the standard deviation of the models, we see that most have a 1-2% standard deviation. The results of the FC-Siam-conc, BIT, and TinyCD baselines could be more consistent regarding their initialization, which favors ChangeFormer and DTCDSCN. High standard deviation values come from our dataset's small number of samples and can be reduced by further data collection. Pretraining ChangeFormer on the LEVIR-CD dataset [17] barely improves the F1 score on the _pixel-based_ metric and even shows a slight decline in performance on the _object-based_  
evaluation. Generally, pretraining does not make much of a difference in this setting. We still argue that pretraining might be helpful in cases where little data are available, as in our study, or when a similar dataset can be used, preferably obtained from the same satellite. TinyCD and DTCDSCN perform the best on the _pixel-_ and _object-based_ metrics, respectively, followed by ChangeFormer, which places second on both metrics. Additionally, ChangeFormer has the best overall recall values, which is the most important metric for pipeline monitoring. Still, each of these three models would be suitable candidates as their performance differences are minor. In a deployment case in particular, the TinyCD model could be a good alternative to ChangeFormer due to its small model size, which could lead to savings in training the model. **Q3. Adjusting for higher recall**: In Tables 1 and 2, we observe that adjusting for higher recall yields positive results for both the _pixel-based_ and _object-based_ metrics and scales with higher values of \(\alpha\). At the \(\times 8\) scaling, we can achieve above 88% recall on the object-based metric while maintaining reasonably high precision at approximately 50%. Initially, adjusting for costs benefits the F1 score and IoU, but with higher \(\alpha\) values, we notice a small negative effect. In summary, using the weighted cross-entropy loss is a simple but effective way to adjust the model to the performance requirements of pipeline monitoring. **Key takeaway**: ChangeFormer has the highest recall and overall good performance, making it preferable for pipeline monitoring, where recall is the most critical metric.
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 4 Experiments
### Results
#### 4.6.3 Optimization Parameters for Focal Loss  
In order to test how focal loss [25] influences the model's performance in the SCD imbalanced situation, we predefined various values of parameters of focal loss, such as \(\alpha=[0.1,0.2,0.3,0.4]\) and \(\gamma=[0,1,2,3]\). The former \(\alpha\) is a weighting factor that balances class importance in imbalanced datasets. A lower \(\alpha\) (<0.5) gives more weight to the majority class (non-change points), while a higher \(\alpha\) (>0.5) gives more weight to the minority class (speaker change points). \(\gamma\) is a focusing parameter that reduces the weight of easy samples, making the model focus more on hard-to-classify examples. A higher \(\gamma\) increases this effect, but too high a value can cause training instability.  
We selected a highly imbalanced meeting from the training set to analyze different parameter combinations and reduce training time. Figure 4 presents the curves for different combinations of values of \(\alpha\) and \(\gamma\). Traditional binary cross-entropy (BCE) loss is also included for comparison and is denoted as BCEloss. Figure 3(a),b illustrate how the FAR and MDR change with different values of \(\alpha\) and \(\gamma\). It can be observed that \(\gamma=0\) and \(\gamma=1\), when using BCEloss, achieve comparably low FAR scores. Among them, the loss function, when \(\gamma=0\), becomes weighted BCE loss, which performs slightly better than the others. From Figure 3(b), it can be seen that \(\gamma=1\) with BCEloss results in a higher MDR compared to other settings. Figure 3(c) displays F1-scores across different settings, clearly showing that focal Loss outperforms BCEloss. When \(\gamma=1\) (orange curve) and \(\alpha=0.3\), the model achieves the highest F1-score. These values are chosen for focal Loss, and the final model is trained using this configuration, denoted as eMD\({}_{att}^{*}\).
# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 3 Results
### Pixel-Based Classification
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{4}{c}{**Salinas**} \\ \hline
**Technique** & **OA** & **AA** & **\(\kappa\)** \\ \hline None & 82.76 \(\pm\) 2.42 & 86.15 & 80.74 \\ Rotate 4\(\times\) & 87.74 \(\pm\) 1.21 & 89.07 & 86.39 \\ Inner-Rotate 4\(\times\) * & 87.96 \(\pm\) 0.35 & 89.75 & 86.60 \\ Dual-Rotate 4\(\times\) * & 87.12 \(\pm\) 0.70 & 89.58 & 85.65 \\ Dual-Rotate 16\(\times\) * & 91.02 \(\pm\) 0.71 & 93.38 & 90.00 \\ Flip 4\(\times\) & 88.84 \(\pm\) 0.91 & 91.14 & 87.58 \\ Inner-Flip 4\(\times\) * & 87.92 \(\pm\) 1.39 & 89.39 & 86.55 \\ Dual-Flip 4\(\times\) * & 87.73 \(\pm\) 1.77 & 90.01 & 86.35 \\ Dual-Flip 16\(\times\) * & **93.40 \(\pm\) 0.17** & 94.80 & 92.65 \\ PVS(+/-) 4\(\times\) [12] & 86.57 \(\pm\) 0.56 & 86.91 & 85.04 \\ PVS(+/-) 16\(\times\) [12] & 91.15 \(\pm\) 0.25 & 91.62 & 90.12 \\ Random Occlusion 4\(\times\) [13] & 85.24 \(\pm\) 0.64 & 86.95 & 83.54 \\ Random Occlusion 16\(\times\) [13] & 92.21 \(\pm\) 0.34 & 94.67 & 91.56 \\ \hline \hline \end{tabular}  
\end{table} 
Table 8: Pixel-based classification performance (in percent) for Salinas, PaviaU and PaviaC scenes using the training and testing sets detailed in Table 2. The best result for each scene is displayed in bold.