esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
To address the trade-off between biofidelity and the cost associated with anthropomorphic test devices, a novel test methodology for evaluating head-neck injuries in blast environments was developed, leveraging a synergy of virtual and physical testing paradigms. A low-cost simplified dummy was designed to test human body responses, and a mapping test model based on 1D-CNN was proposed to calibrate the measured data.  
(1) A head-neck physical model is proposed, replicating the segmental mass and center of gravity distribution of the Hybrid III 50 th percentile dummy. Its cervical spine is composed of stacked simulated vertebral bodies and intervertebral discs. By calculating the correlation between the chest and head acceleration peak of the simplified dummy and those of the Hybrid III 50 th FEM under the same loads, it is demonstrated that the head-neck response of the simplified dummy physical model exhibits a strong positive correlation with the response of the Hybrid III 50 th FEM. This indicates that the head-neck physical model of the simplified dummy possesses the capability to identify response variation.  
(2) The mapping relationship between the responses of the simplified dummy and the Hybrid III FEM was established through NSGA-II to train the parameters of 1D-CNN. The training data were acquired by employing the pendulum collision to simulate the under-body blast impact on the dummy. The explosion test verification was conducted, and the responses of the Hybrid III 50 th numerical model under the same load were taken as a benchmark. The relative error of HIC output by this test method is 10.9%, and the relative error of maximum axial compression force of the upper neck is 2.4%, affirming the efficacy of the combined physical dummy and surrogate model in assessing blast-induced trauma.  
Conceptualization, C.D.; methodology, X.S.; formal analysis, X.S. and C.X.; investigation, X.S.; resources C.D. and P.P.; data curation, X.S.; writing--original draft preparation, X.S.; writing--review and editing, C.D. and P.P.; visualization, X.S.; supervision, C.D. and P.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable for studies not involving humans or animals.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
We sincerely thank the School of Mechanical Engineering, Nanjing University of Science and Technology, for providing access to their test site.  
**Conflicts of Interest:** The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article.
## 5 Potential Use Cases  
In this section, three potential use cases are described which have been selected because they are highly representative in terms of system performance, scalability, and impact in the future 5G mass market. Moreover, the peculiarities of these applications will permit to characterize the efficiency of the edge agility paradigm within different mobility patterns, slice types, deployment requirements, and triggering events.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 3 Proposed Methodology: DCBA Fault Diagnosis  
As shown in Figure 1, the DCBA (digital-CNN-BLSTM-attention) fault diagnosis model proposed in this study is primarily composed of two core modules: the digital twin model and the integrated fault diagnosis model.  
Specifically, in this study, we first constructed a high-fidelity digital twin model using Matlab 2023b, which was proportionally consistent with the actual system. By adjusting the model parameters, we injected operational parameters under different working conditions into the twin model, generating fault data that are relatively scarce in real operating environments, addressing the issue of insufficient fault data.  
To explain the results of the digital twin and support better maintenance decisions, we performed an in-depth analysis of the virtual twin model's operational state. Through an Internet of Things (IoT)-based data acquisition and transmission system, real-time operational data from the physical system were fed back into the virtual twin model, ensuring that the virtual model dynamically updated and stayed synchronized with the actual system. The virtual data generated by the digital twin model not only provided new training data sources for the fault diagnosis model based on the latest operating conditions but also integrated historical data and real-time feedback to help the system predict potential faults and performance degradation, thereby supporting intelligent maintenance decisions.  
Next, we performed feature engineering on the generated virtual data, including data normalization and resampling. After data cleaning, we applied a sliding window technique to fuse the data and input the processed data into the CNN-BLSTM-attention network for model training, ultimately resulting in a fault diagnosis pre-training model for transfer learning. Finally, the final diagnostic model was trained and optimized through transfer learning using a small amount of real data. The following sections will provide a detailed explanation of the design concepts, implementation methods, and specific applications of these two components in fault diagnosis.
## 3 Results
### Within-Group Differences  
The repeated measures ANOVA revealed that there were differences in the scores obtained between the six phases for both groups (G1: \(F_{(1,47)}=3.890,p=0.003\); MSE = 408.168; G2: \(F_{(1,47)}=2.392,p=0.042\); MSE = 527.579) (see Figure 2). The effect sizes are larger for  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **G1** & **G2** & **Total** & & \(p\) \\ \hline Ph2-A.F. & & & & \(\chi^{2}\)(2,49) = 1.653 & 0.438 \\ External A.F. & 13 (54.2) & 16 (64.0) & 29 (59.2) & & \\ Internal A.F. & 5 (20.8) & 2 (8.0) & 7 (14.3) & & \\ Mixed A.F. & 6 (25.0) & 7 (28.0) & 13 (26.5) & & \\ Ph3-A.F. & & & & \(\chi^{2}\)(2,49) = 3.991 & 0.136 \\ External A.F. & 13 (54.2) & 9 (36.0) & 22 (44.9) & & \\ Internal A.F. & 6 (25.0) & 4 (16.0) & 10 (20.4) & & \\ Mixed A.F. & 5 (20.8) & 12 (48.0) & 17 (34.7) & & \\ Ph4-A.F. & & & & \(\chi^{2}\)(2,49) = 1.780 & 0.411 \\ External A.F. & 10 (41.7) & 10 (40.0) & 20 (40.8) & & \\ Internal A.F. & 6 (25.0) & 3 (12.0) & 9 (18.4) & & \\ Mixed A.F. & 8 (33.3) & 12 (48.0) & 20 (40.8) & & \\ \hline \end{tabular} _Notes:_ Number and percentage of cases for categorical variables. Mean and standard deviation for continuous variables. G1: Attributional Negative NF group; G2: Non-Attributional Negative NF group; Ph: phase; pressure: perceived pressure to score; Exp. Darts: self-rating in darts experience; Recent Practice: recent practice in darts throwing in the last two years, A.F.: attentional focus.  
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **G1** & **G2** & **Total** & \(F_{(1,47)}\) & \(p\) & \(\eta^{2}p\) \\ \hline Session 1 & & & & & & \\ Phase 1 & 252.79 (38.90) & 235.92 (36.33) & 244.18 (38.18) & 2.464 & 0.123 & 0.050 \\ Phase 2 & 261.38 (21.73) & 246.16 (35.51) & 253.61 (30.26) & 3.240 & 0.078 & 0.064 \\ Phase 3 & 270.88 (37.86) & 237.88 (40.72) & 254.04 (42.35) & 8.610 & 0.005 & 0.155 \\ Phase 4 & 276.33 (40.99) & 247.84 (35.89) & 261.80 (40.70) & 6.715 & 0.013 & 0.125 \\ Session 2 & 262.63 (38.52) & 229.68 (40.98) & 245.82 (42.75) & 8.393 & 0.006 & 0.152 \\ Phase 6 & 265.33 (37.80) & 233.84 (43.98) & 249.27 (43.65) & 7.199 & 0.010 & 0.133 \\ \hline \end{tabular} _Note:_ G1: Attributional Negative NF group; G2: Non-Attributional Negative NF group; \(\eta^{2}p\) = partial eta square.  
\end{table}
Table 2: Means (M) and standard deviations (SD) for dependent variable (throwing score), and analysis of variance to study the main effects of the variable study group (manipulation of attribution) in each phase.  
G1 (\(\eta^{2}p=0.15\)) than for G2 (\(\eta^{2}p=0.09\)). Bonferroni post hoc tests showed that for G1 there were significant differences between phases first and four (\(p=0.049\)). In G2, although the ANOVA showed differences, these were so small that the post hoc analyses between phases revealed no significant differences. Figure 2 shows the evolution of the scores in the two groups over the two sessions (six phases).
# On the Influence of Data Imbalance on Supervised Gaussian Mixture Models
## 5 Results
### Simulation Studies
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Precision/** & **Recall/** & **F2** & **Sensitivity/** & **Specificity/** & **Balanced** \\  & **PPV** & **TPR** & **TPR** & **TNR** & **Accuracy** \\ \hline EDDA & 0.5539 (0.0024) & 0.1478 (0.0030) & 0.1726 (0.0033) & 0.1478 (0.0030) & 0.9866 (0.0003) & 0.5672 (0.0013) \\ EDDA+costSens & 0.2782 (0.0006) & 0.8508 (0.0099) & 0.6026 (0.0005) & 0.8508 (0.0009) & 0.7551 (0.0008) & 0.8029 (0.0003) \\ EDDA+adjPriorThreshold & 0.2784 (0.0009) & 0.8566 (0.0014) & 0.6015 (0.0005) & 0.8566 (0.0014) & 0.7488 (0.0014) & 0.8027 (0.0003) \\ EDDA+optThreshold & 0.2781 (0.0018) & 0.8498 (0.0030) & 0.6008 (0.0007) & 0.8498 (0.0030) & 0.7534 (0.0029) & 0.8016 (0.0003) \\ EDDA+downSamp & 0.2773 (0.0006) & 0.8523 (0.0010) & 0.6023 (0.0005) & 0.8523 (0.0010) & 0.7535 (0.0009) & 0.8029 (0.0003) \\ EDDA+upSamp & 0.2768 (0.0006) & 0.8542 (0.0009) & 0.6027 (0.0005) & 0.8542 (0.0009) & 0.7524 (0.0008) & 0.8033 (0.0003) \\ EDDA+smote & 0.2763 (0.0006) & 0.8547 (0.0009) & 0.6024 (0.0005) & 0.8547 (0.0009) & 0.7516 (0.0008) & 0.8032 (0.0003) \\ MCLUSTDA & 0.5753 (0.0030) & 0.1211 (0.0037) & 0.1430 (0.0041) & 0.1211 (0.0037) & 0.9897 (0.0004) & 0.5554 (0.0017) \\ MCLUSTDA+costSens & 0.2764 (0.0006) & 0.8551 (0.0009) & 0.6026 (0.0005) & 0.8551 (0.0009) & 0.7515 (0.0008) & 0.8033 (0.0003) \\ MCLUSTDA+adjPriorThreshold & 0.2740 (0.0008) & 0.8588 (0.0013) & 0.6017 (0.0005) & 0.8588 (0.0013) & 0.7473 (0.0013) & 0.8031 (0.0003) \\ MCLUSTDA+opThreshold & 0.2782 (0.0017) & 0.8506 (0.0029) & 0.6013 (0.0006) & 0.8506 (0.0029) & 0.7534 (0.0028) & 0.8020 (0.0003) \\ MCLUSTDA+downSamp & 0.2762 (0.0006) & 0.8550 (0.0010) & 0.6023 (0.0005) & 0.8550 (0.0010) & 0.7513 (0.0009) & 0.8031 (0.0003) \\ MCLUSTDA+upSamp & 0.2763 (0.0010) & 0.8497 (0.0016) & 0.6001 (0.0006) & 0.8497 (0.0016) & 0.7526 (0.0016) & 0.8011 (0.0004) \\ MCLUSTDA+smote & 0.2752 (0.0010) & 0.8490 (0.0017) & 0.5987 (0.0006) & 0.8490 (0.0017) & 0.7514 (0.0016) & 0.8002 (0.0004) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for the first simulated scenario with probability of the minority class \(\tau_{1}=0.1\) for various models and methods addressing class imbalance. Values reported are averages (with standard errors in parentheses) obtained from simulations.
# Development of Predictive Equations for Thermal Conductivity of Compost Bedding
## 3 Results and Discussion
### Thermal Properties of Composts in Different Moisture Contents and Dynamic Compaction Degrees  
As can be observed in Figure 6, \(k\) presented higher values with a 60% MC and a DC of 0.4 MPa (0.789 W m\({}^{-1}\) K\({}^{-1}\)). Theoretically, these values of \(k\) can be predicted, since the DC over time has a direct effect due to the increase in the apparent density of the bedding material, reducing the porosity and resulting in an increase in \(k\) values. The thermal conductivity of leaf compost increased linearly with the increase in water content and compaction degree, represented by volume fractions of air ([PERSON] et al., 2013).  
The ANOVA indicated that there is a significant correlation (\(p<0.05\)) between the values of DC and MC along the three depths evaluated (H1, H2, and H3). Thus, in the same way as with SC, there is a dependence of DC and MC on the values of \(k\), and a polynomial regression model was adjusted taking into account the interaction between DC and MC for each evaluated depth (H1, H2, and H3).  
The multiple regression analyses for compost materials showed a polynomial relationship between \(k\) and the dependent variables MC and DC for each depth evaluated (H1, H2, and H  
The magnitudes of the respective determination coefficients (R2) in Equations (5)-(7) confirm the good quality of fit obtained by the polynomial models in the description of \(k\) by the variables DC and MC. Thus, the models are appropriately adjusted based on the observation of high value F as well as a high coefficient of determination (R2).  
As shown in Figure 7, there was an increasing trend in the values of \(k\) in the composting material with increasing MC and DC. The values of \(k\) with different MC contents and degrees of DC varied from 0.010 to 0.789 W m\({}^{-1}\) K\({}^{-1}\). In this case, the standard deviation of \(k\) was \(\pm\) 0.015 W m\({}^{-1}\) K\({}^{-1}\), which is an acceptable precision to estimate the \(k\) when using a transient line heat source sensor (Decagon KD2-Pro, accuracy \(\pm\) 0.02 W m\({}^{-1}\) K\({}^{-1}\)).  
In Figure 7, contrary to what was observed in the behavior of \(k\) as a function of SC (Figure 5), the influence of DC on the values of \(k\) did not vary along the depths tested. Thus, Equations (5)-(7) have the same components (MC and DC), with only the estimates of the parameters varying (kH1, kH2, and kH3). This probably occurred because of the pressure variation applied by the plunger in the bed samples, which may have rearranged the particles of the materials, reducing the porosity and density over the entire depth tested. This behavior is consistent with the findings of other investigations [48] and occurs because as the material is dynamically compacted, there is more physical contact between the solid particles, which increase thermal conduction [49]. Additionally, the magnitude of the thermal conductivity is similar to that presented in the literature [48; 49; 50].
## 3 Experimental Results
### Analysis of Strain Response
#### 3.2.2 Characteristics of Strain Curve  
According to the strain curve of specimens, the SC-CO\({}_{2}\) jet fracturing can be divided into two stages, the pressurization of the perforation pressure stage and fracture propagation stage: In the stage of pressurization, the pressure in the perforation increased with the inletting and expanding of SC-CO\({}_{2}\), and when the perforation pressure reached breakdown pressure of specimen, fracture initiated, and the strain reached a peak value.  
Figure 11: (**a**) The AR of longitudinal fractures in group A, B and D; (**b**) the overall AR of different rock specimens.  
Figure 12: Strain differences between upper and lower layers of (**a**) specimen A1 and (**b**) specimen B1.  
When the fracture initiates, the strain energy released, so at the earlier stage of fracture propagation, the value of the strain would decrease immediately. With the SC-CO\({}_{2}\) entering into fractures and expanding, the pressure in the perforation and fractures increased, and then new fractures initiated, and old fractures propagated. According to the strain curve in the stage of feature propagation, the fractures initiation and propagation was intermittent.  
A parameter is defined to evaluate the rate of jet fracturing called fracturing time, which starts with a dramatic change in strain and ends with stabilization of the strain (see Figure 13). Comparing the fracturing times of specimens in group A and B (see Figure 14), it is indicated that the hard rocks (group A) need more time to be fractured than soft rocks (group B) due to their high rock strength. With the increasing of injection pressure, the fracturing time needed decreases, and the rate of jet fracturing increases. It is clear that both the injection pressure and the rock strength can influence the rate of jet fracturing.
## 4. SOP and Energy Storage Optimization Configuration Model
### Constraint Condition  
#### 4.2.1 System Power Flow Constraints  
In (23)-(28), \(r_{ij}\) and \(x_{ij}\) are the resistance and the reactance of branch \(ij\), respectively. \(P_{ij}\) is the active power flowing from node \(i\) to node \(j\) on the branch, and \(Q_{ij}\) is the reactive power flowing from node \(i\) to node \(j\) on the branch. \(P_{i}\) is the sum of the active power injected into node \(i\). \(P_{i}^{ES}\) is the active power injected by the energy storage at node \(i\). \(P_{i}^{SOP}\) is the active power injected by the SOP at node \(i\). \(P_{i}^{LOAD}\) is the active power consumed by the load at node \(i\). \(Q_{i}\) is the sum of the reactive power injected at node \(i\). \(Q_{i}^{ES}\) is the reactive power injected by the energy storage at node \(i\). \(Q_{i}^{SOP}\) is the reactive power injected by the SOP at node \(i\). \(Q_{i}^{LOAD}\) is the reactive power consumed by the load at node \(i\).  
\[\sum_{j\in\Omega_{\rm b}}\left(P_{t,ji}-r_{ji}l_{t,ij}^{2}\right)+P_{t,i}=\sum _{ik\in\Omega_{\rm b}}P_{t,ik} \tag{23}\]  
\[\sum_{j\in\Omega_{\rm b}}\left(Q_{t,ji}-x_{ji}l_{t,ij}^{2}\right)+Q_{t,i}=\sum _{ik\in\Omega_{\rm b}}Q_{t,ik} \tag{24}\]  
\[U_{t,i}^{2}-U_{t,j}^{2}+(r_{ij}^{2}+x_{ij}^{2})l_{t,ij}^{2}-2(r_{ij}P_{t,ij}+x _{ij}Q_{t,ij})=0 \tag{25}\]  
\[l_{t,ij}^{2}U_{t,i}^{2}=P_{t,ij}^{2}+Q_{t,ij}^{2} \tag{26}\]  
\[P_{t,i}=P_{t,i}^{\rm ES}+P_{t,i}^{\rm SOP}-P_{t,i}^{\rm LOAD} \tag{27}\]  
\[Q_{t,j}=Q_{t,i}^{\rm ES}+Q_{t,j}^{\rm SOP}-Q_{t,i}^{\rm LOAD} \tag{28}\]  
Equation (25) represents a quadratic nonlinear constraint, which can be transformed into a second-order cone constraint form under the conditions that the objective function is strictly increasing and the node load is not constrained by an upper bound [17].  
\[\begin{array}{c}2P_{ij}\\ \parallel 2Q_{ij}\\ i_{ij}-u_{i}\end{array}\parallel\begin{array}{c}2P_{ij}\\ \parallel\begin{array}{c}2Q_{ij}\\ 2\end{array}\parallel\begin{array}{c}2i_{ij}+u_{i}\\ 2\end{array}\end{array} \tag{29}\]
## 3 The Proposed Scheme
### Tree Construction
The flowchart of the GB-PEDAP tree construction algorithm is shown in Figure 2.  
```
Notations: Tree: The tree path for data aggregation  CellHeadNum: The number of cell heads not added to the tree  TreeNodeA: The tree node array  CellHeadA: The cell head array  TreeNode(i): The i-th element of TreeNodeA  CellHead(j): The j-th element of CellHeadA  TargetPNode: Parent node of the target node  TargetNode: Target node  TargetEdge: Target edge (TargetPNode, TargetNode)  Weight(p, q): The weight value of the edge between node p and node q  min: the minimum weight  node.check(r): Determine whether node r has been added to the tree Procedure: Tree \(\leftarrow\) BS; // initialize TreeNodeA \(\leftarrow\)\(\varnothing\); // initialize TreeNodeA  CellHeadA \(\leftarrow\) The members of cell heads not in the tree; // initialize CellHead  while (CellHeadNum!= 0) // do while-block until CellHeadNum is equal to 0  {  double min = DBL_MAX; // initialize min to the maximum value of the double type for(i = 1; i! TreeNodeA.length; i++) // do for-block for each member of TreeNodeA  {  if (node.check(CellHeadA(j))==0\&g  Weight(TreeNodeA(i), CellHeadA(j)) < min)  // if the j-th node does not in the tree and the weight of the current edge < min  {  min = Weight(TreeNodeA(i), CellHeadA(j)); // update the value of min  TargetPNode = TreeNodeA(i); // assign TargetPNode  TargetNode = CellHeadA(j); // assign TargetNode  TargetEdge = (TargetPNode, TargetNode); // assign TargetEdge  }  node.check(TargetNode) = 1; // assign node.check(TargetNode) to 1  Tree \(\leftarrow\) Tree \(\cup\) TargetEdge; // add the TargetEdge to Tree  TreeNodeA \(\leftarrow\) TreeNodeA \(\cup\) TargetNode; // add TargetNode to TreeNodeA  CellHeadA \(\leftarrow\) CellHeadA - TargetNode; // remove TargetNode from CellHeadA  CellHeadNum = CellHeadNum - 1; // CellHeadNum decreased by 1 }
```  
**Algorithm 1** GB-PEDAP Tree Construction Algorithm.
# Electromagnetic and Thermal Phenomena Modeling of Electrical Discharges in Liquids
## 7 Modeling of Temperature Distribution in the Time between Discharges
Such approach enables to consider the temperature dependence of material parameters that are particularly important for electromagnetic simulations (\(\varepsilon\), \(\rho\)). Based on the presented approach, it was possible to compute break time between the discharges, to guarantee random propagations of streamers and new discharge channel. In Figure 20 electric field intensities (a, b) and the gradients of electric field intensity (c, d) in vicinity of electrodes (1) are shown for the break time value of 0.5 s (a, c) and 1 s (b, d) from previous discharge. In the figure geometry of previous discharge channel is shown (2). Figure 19: Temperature distribution (\(t\)) and fluid velocity vectors (\(v\)) in analyzed sample in different time steps. Figure 20: Electromagnetic field distribution (**a,b**) and electromagnetic field gradients (**c,d**) distribution for nonlinear parameters (\(\varepsilon\), \(\rho\)) after 0.5 s (**a,c**) and 1 s (**b,d**) from previous discharge. 1—electrode; 2—geometry of previous discharge channel. Simulations results, presented above, show that break time of 0.5 s is too short for initiation of next discharge. In this case, heterogeneous distribution of temperature in the analyzed fluid leads to the conclusion that the next discharge will propagate in the channel of previous one (Figure 20c). After 1 s, temperature distribution and electric field intensity distribution are more homogeneous, and random distribution of the discharge channel is probable in this case.
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 4 Results  
### Datasets and Metrics  
#### 4.1.1 DAVIS  
DAVIS 2016 [32] is a benchmark dataset for single-object video segmentation, comprising a total of 50 videos. Among these, 30 are used as the training set, and 20 are allocated to the validation set. DAVIS 2017 [13] serves as a benchmark dataset for video multi-object segmentation, including a total of 120 videos. Its validation set and test-dev set each consist of 30 videos.