esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper uses CFD analysis to study the close-formation flight for a MALE class UAV model with fixed wing. Using 3D wing wind tunnel test results, the mathematical model is first validated using data. Next, the region of the aerodynamic effect is examined, and a single UAV is evaluated. The primary focus of the study is the analysis of two UAVs for flights at different positions. The following outcomes of these analyses were attained for the case of a flocked cruise in the V-formation:  
1. The characteristics in the aerodynamic interaction region of the longitudinal distance behind the UAV do not change greatly.
2. Two UAVs flying in close formation should avoid flying inside the wing line to avoid entering the downwash area, as this can have a negative interaction.  
Figure 16: Histogram for aerodynamic efficiency variation for analysis numbers.  
Figure 15: \(C_{L}/C_{D}\) vs. lateral distances.  
* For the most efficient aerodynamic performance in close flight of swarm UAVs, they must fly at the same altitude, i.e., in the same vertical alignment.
* For \(b\) wingspan, the most aerodynamically efficient spots for close-formation flight are lateral distances of 0.875\(b\) and 1\(b\).
* Considering \(C_{L}/C_{D}\) as an aerodynamic performance parameter, in the most efficient flight positions, the trailing UAV has an efficiency increase of about 11.5% compared to a single flight.  
Conceptualization, A.T.I. and M.C.; methodology, M.C.; software, M.C.; validation, M.C.; formal analysis, M.C.; investigation, M.C.; resources, M.C.; data curation, M.C.; writing--original draft preparation, A.T.I. and M.C.; writing--review and editing, A.T.I.; visualization, M.C.; supervision, A.T.I.; project administration, A.T.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
This study is based on the unpublished dissertation of [PERSON] supervised by [PERSON] at Marmara University. The authors thank [PERSON] for his contribution.  
The authors declare no conflicts of interest.
# Video over DSL with LDGM Codes for Interactive Applications
## 2 Channel Codes
### Pro-MPEG COP #3  
Professional-MPEG code of practice #3 (Pro-MPEG COP #3) [23] is an industry standard for video transmission protection that is widely deployed. Incoming packets are arranged in a matrix on a row-by-row basis, assuming packet-sized symbols. Redundant packets are subsequently appended to each column of the matrix and optionally to each row of packets. The packets are transmitted column-by-column, _i.e.,_ orthogonally to the way they were read into the matrix. The redundant packets are created by a byte-wise XOR operation across the packets of each column/row. This simple interleaving scheme has the advantage of convenient hardware implementations. The standard restricts the number of columns and rows to a maximum of 20.  
In this paper, the one-dimensional version of Pro-MPEG COP #3 is tested in which redundant packets are only created for the columns. One-dimensional Pro-MPEG COP #3 was also selected in [10] for the reason that it is more widely deployed. The number of rows was set to four (the minimum) and the number of packets in a column to 20. This is the same configuration as employed in [24] as part of an unequal loss protection (ULP) scheme, according to the video frame type (I- B- and P-type) priority. In the current paper, for ease of comparison, ULP is not used.
###### Abstract  
In recent years, the world has seen a significant increase in extreme weather events, such as floods, hurricanes, and storms, which have caused extensive damage to infrastructure and communities. These events result from natural phenomena and human-induced factors, including climate change and natural climate variations. For instance, the floods in Europe in 2024 and the hurricanes in the United States have highlighted the vulnerability of urban and rural areas. These extreme events are often unpredictable and pose considerable challenges for spatial planning and risk management. This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. By utilizing data such as weather records, land use and land cover (LULC) information, topographic LIDAR data, and advanced predictive models, the study aims to identify the most vulnerable areas and provide recommendations for risk mitigation. The results indicate that integrating these technologies can improve forecasting accuracy, thereby supporting more informed decisions in land management. Given the effects of climate change and the increasing frequency of extreme events, adopting advanced forecasting and planning tools is crucial for protecting communities and reducing economic and social damage. This method was applied to the Calopinace area, also known as the Calopinace River or Fiumara della Cartiera, which crosses Reggio Calabria and is notorious for its historical floods. It can serve as part of an early warning system, enabling alerts to be issued throughout the monitored area. Furthermore, it can be integrated into existing emergency protocols, thereby enhancing the effectiveness of disaster response. Future research could investigate incorporating additional data and AI techniques to improve accuracy.  
climate change; flood susceptibility; machine learning; Markov chain; LIDAR; LSTM 2025157563  
1
## 5 Online Task Offloading Decision Algorithm
### Mobility-Aware UE-BS Algorithm  
This algorithm simultaneously has UEs with both the UE-side state information and the BS-side information. The BS remains constant during the offloading of one task, as previously noted. Having both sides' state information helps the UE to select the optimal BS for offloading and avoids any HOs. Within the UE-BS scenario, all sub-tasks of \(m\) will be served by a single optimum BS \(\eta_{m}^{opt}\). The main challenge for solving GP in an online algorithm is that without having future task information, \(m+1,m+2,\ldots,M\), the limited energy budget for current tasks will be spent and nothing will remain for upcoming tasks. To overcome this issue, the solution is to define an energy queue and store the used energy budget. By exceeding a specific amount of energy budget for task \(m\), future tasks will be allocated to another candidate BS \(\eta\in\mathcal{A}\). According to [43], the energy queue is obtained as (17):  
\[\beta_{m+1}=\max\{\beta_{m}+E_{m,\eta_{m}^{opt}}^{tot}-\rho/M,0\} \tag{17}\]  
where \(\beta_{0}\) is equal to zero. By using the Lyapunov optimisation, we can define the optimisation problem as a trade-off between time cost and energy cost of task offloading, represented as (18):\[Z_{m}^{tot}=\alpha T_{m,n}^{tot}+\beta_{m}E_{m,n}^{tot} \tag{18}\]  
where \(\alpha\) is a control parameter to adjust the trade-off between energy and time cost. No handover will occur as we have both sides' information in the UE-BS scenario and we selected the appropriate BS for the entire time of processing task \(m\). The total time cost can be reduced to \(T_{m,\eta}^{tot}=\sum_{k=1}^{k_{m}}(t_{m,\eta}^{e}+t_{m,\eta}^{o})\). As all sub-tasks will be served by a single optimum BS \(\eta_{m}^{opt}\), the optimisation function in (18), can be simplified as  
\[z_{m}=\alpha(t_{m,\eta_{m}^{opt}}^{e}+t_{m,\eta_{m}^{opt}}^{o})+\beta_{m}e_{m, \eta_{m}^{opt}}^{o}. \tag{19}\]  
To minimize the cost \(z_{m}\) in Equation (19) for each task \(m\), the online UE-BS algorithm presented in Algorithm 1 is proposed.  
```
Input:\(\mathcal{A}(m)\), \(d_{m}\), \(c_{m}\), \(f_{m,n}\), \(h_{m,n}\) and \(\alpha\)
1:if\(m=\iint+1,\forall f=0,1,\dots,F-1\)then
2:\(\beta_{m}\gets 0\)
3:endif
4: Choose \(\eta_{n}^{*}\) subject to (13), (15) by solving \(\min\limits_{n\in\mathcal{A}(m)}\alpha(t_{m,\eta_{m}^{opt}}^{e}+t_{m,\eta_{m} ^{opt}}^{o})+\beta_{m}e_{m,\eta_{m}^{opt}}^{o}\)
5: Update \(\beta_{m}\) according to (17).
```  
**Algorithm 1**Mobility-aware online UE-BS algorithm  
Because there is no handover within this scenario, there is no need to divide tasks into sub-tasks. Therefore, according to the optimization function in line 4, the Algorithm 1 complexity only relies on the number of candidate BS \(\mathcal{A}(m)\), which is \(\mathcal{O}(|\mathcal{A}(m)|)\).
## 3 Results and Discussion
### Analysis of Influence Factors on CO\({}_{2}\)-Wag
At the same time, an over-high injection rate will instantly increase the bottom hole pressure of the injection well. When it exceeds the fracture pressure of the formation, it will crush the formation, causing damage to the formation on the one hand. On the other hand, it may cause CO\({}_{2}\) to escape and pollute other formations. The model in this study has a low reservoir pressure due to a period of depleted extraction in the early stage. In addition, the fluid injection rate is low, which means it cannot restore the reservoir pressure to the initial pressure. The simulated reservoir is a low-pressure reservoir. For this type of reservoir, as shown in Figure 11, the higher the water-gas ratio is, the better the oil production will be. In addition, when the injection rate and the reservoir pressure are high, a lower water-gas ratio has a higher oil recovery. A higher water-gas ratio results in less CO\({}_{2}\) being buried because the proportion of injected CO\({}_{2}\) is smaller, but the storage efficiency is higher. A lower water-gas ratio allows more CO\({}_{2}\) to be buried because the proportion of CO\({}_{2}\) in the injected fluid is larger. Moreover, more CO\({}_{2}\) will be produced from the production well, leading to lower storage efficiency. Figure 9: Effect of WAG cycle on (**a**) cumulative oil production, (**b**) CO\({}_{2}\) storage amount, and (**c**) CO\({}_{2}\) storage efficiency. Figure 10: Effect of fluid injection rate on (**a**) cumulative oil production, (**b**) CO\({}_{2}\) storage amount, and (**c**) CO\({}_{2}\) storage efficiency. Figure 11: Effect of water–gas ratio on (**a**) cumulative oil production, (**b**) CO\({}_{2}\) storage amount, and (**c**) CO\({}_{2}\) storage efficiency.
# Quantum-Based Analytical Techniques on the Tackling of Well Placement Optimization
## 4 Results and Discussion
### Wilcoxon's Rank-Sum Test  
To validate whether the results of QPSO and QBA are statistically different from the other six algorithms, a non-parametric statistical test, the Wilcoxon rank-sum test [61, 62], with a significance level of 0.05, is used. Table 6 shows the \(p\)-value one tail, \(p\)-value two tails, and Z values for the Wilcoxon rank sums test. A \(p\)-value less than 0.05 and Z value higher than 1.96 is required to reject the null hypothesis. From Table 6 it can be inferred that there is a statistical difference between the performance of QPSO and QBA with other algorithms except in one case. In case study 1 and 2, the performance of the QPSO and QBA are not statistically different. Therefore, it can be inferred that quantum computation can bring improved results from the other six algorithms. However, QPSO and QBA's proved to be statistically different from its primary algorithm.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & \multicolumn{3}{c}{**Case Study 1**} & \multicolumn{3}{c}{**Case Study 2**} \\ \hline  & **Z Value** & \begin{tabular}{c} _p Value_ \\ **One Tail** \\ \end{tabular} & \begin{tabular}{c} _p Value_ \\ **Two Tails** \\ \end{tabular} & **Z Value** & \
\begin{tabular}{c} _p Value_ \\ **One Tail** \\ \end{tabular} &
\begin{tabular}{c} _p Value_ \\ **Two Tails** \\ \end{tabular} \\ \hline QPSO Versus GSA & 3.5033 & 0.00022979 & 0.00045958 & 3.3481 & 0.00040678 & 0.00081355 \\ QPSO Versus PSO & 3.0379 & 0.0011912 & 0.0023824 & 1.6418 & 0.050321 & 0.10064 \\ QPSO Versus CSA & 3.5033 & 0.00022979 & 0.00045958 & 3.2447 & 0.00058782 & 0.0011756 \\ QPSO Versus DE & 2.9867 & 0.0014101 & 0.0028202 & 2.1416 & 0.016113 & 0.032226 \\ QPSO Versus GA & 3.4516 & 0.00027868 & 0.00055736 & 3.5044 & 0.00022878 & 0.00045757 \\ QPSO Versus BA & 3.3999 & 0.00033711 & 0.00067422 & 3.428 & 0.00030402 & 0.00060805 \\ QBA Versus GSA & 3.5033 & 0.00022979 & 0.00045958 & 3.3999 & 0.00033711 & 0.00067422 \\ QBA Versus PSO & 2.9862 & 0.0014124 & 0.0028249 & 2.5216 & 0.0058404 & 0.011681 \\ QBA Versus CSA & 3.5033 & 0.00022979 & 0.00045958 & 3.5033 & 0.00022979 & 0.00045958 \\ QBA Versus DE & 2.5725 & 0.0050482 & 0.010096 & 3.0735 & 0.0010578 & 0.0021155 \\ QBA Versus GA & 3.3481 & 0.00040678 & 0.00081355 & 3.5044 & 0.00022878 & 0.00045757 \\ QBA Versus BA & 3.2964 & 0.0004896 & 0.00097921 & 3.3245 & 0.00044287 & 0.00088574 \\ QBA Versus QPSO & 1.3315 & 0.091512 & 0.18302 & 0.74132 & 0.22925 & 0.4585 \\ \hline \end{tabular}
\end{table}
Table 6: Statistical data of applied metaheuristic algorithms on well placement optimization for case study 1 reservoir.  
Figure 7: Box plot for PSO, GSA, CSA, and NCSA for case study 1 and case study 2.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Age**} & **NFL** & **HRL** & **IPL** & **INL** & **ONL** & **ELM-RPE** & **Inner** & **Outer** & **Whole** \\  & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **Retina** & **Retina** & **(\(\mu\)m)** \\ \hline P14 & \(12.6\pm 1.7\) & \(10.9\pm 1.1\) & \(45.3\pm 1.9\) & \(51.5\pm 1.5\) & \(77.4\pm 0.3\) & \(40.9\pm 0.7\) & \(120.3\pm 2.5\) & \(118.3\pm 0.9\) & \(238.5\pm 2.3\) \\ P17 & \(10.9\pm 1.8\) & \(8\pm 2.3\) & \(46.4\pm 2.4\) & \(46.2\pm 3.6\) & \(75.6\pm 2.2\) & \(45.4\pm 3.1\) & \(111.5\pm 6.2\) & \(121\pm 3\) & \(232.5\pm 8\) \\ P21 & \(8.9\pm 2.4\) & \(4.7\pm 1.5\) & \(46.1\pm 3.5\) & \(41.1\pm 3.2\) & \(72.7\pm 2.5\) & \(48.6\pm 2.7\) & \(100.9\pm 4.4\) & \(121.3\pm 1.9\) & \(222.1\pm 4.4\) \\ P28 & \(8.9\pm 1.3\) & \(1.7\pm 0.5\) & \(45.9\pm 1.8\) & \(34\pm 1.4\) & \(67.3\pm 2.9\) & \(54.4\pm 2\) & \(90.6\pm 3.1\) & \(121.7\pm 3.2\) & \(212.3\pm 6.2\) \\ P56 & \(9.1\pm 0.5\) & \(1\pm 0.6\) & \(46.9\pm 2.7\) & \(33.6\pm 1\) & \(67\pm 1.1\) & \(55.5\pm 1.4\) & \(90.5\pm 2.2\) & \(122.6\pm 1.9\) & \(213.1\pm 3.2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative thickness analysis of individual retinal layer changes with aging. Figure 2: Longitudinal OCT monitoring of developing retina. (**A**) Representative enface image of the dorsal quadrant of the mouse retina. The red line indicates the location for B-scan analysis. (**B**) Representative OCT B-scans of mouse retina from P14 to P56. The red arrows indicate the location of a hyporeflective layer (HRL). The green, purple, and yellow two-headed arrows indicate the thickness of INL, ONL, and ELM to RPE layers. (**C**) Line profiles of the image B-scans in B. The red arrows indicate the location of HRL, and the yellow arrows indicate the location of IZ. (**D**) The thickness distribution of HRL over time. The radius of the retinal area is 1 mm. (**E**) Layer thickness changes of inner retina layers (**E1**), outer retinal layers (**E2**), whole retina (**E3**). NFL, nerve fiber layer; HRL, hyporeflective layer; IPL, inner plexiform layer; INL, inner nuclear layer; ONL, outer nuclear layer; ELM, external limiting membrane; EZ, ellipsoid zone; IZ, interdigitation zone; RPE, retinal pigment epithelium; BM, Bruce membrane; Ch, choroid. In (**D**), each group was composed of 5-6 mice, and the error bars indicate the standard deviation.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 4 Experiment Analysis
### Forecast Error Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ModelsDatasets** & \multicolumn{2}{c}{**1036A**} & \multicolumn{2}{c}{**1037A**} & \multicolumn{2}{c}{**1038A**} & \multicolumn{2}{c}{**1039A**} \\ \cline{2-9}  & **E\({}_{\text{RMSE}}\)** & **E\({}_{\text{MAE}}\)** & **E\({}_{\text{RMSE}}\)** & **E\({}_{\text{MAE}}\)** & **E\({}_{\text{RMSE}}\)** & **E\({}_{\text{MAE}}\)** & **E\({}_{\text{RMSE}}\)** & **E\({}_{\text{MAE}}\)** \\ \hline PSO-SVR & 43.6978 & 32.8549 & 23.6843 & 16.2445 & 22.5831 & 17.4816 & 17.9638 & 13.3836 \\ BPTT & 42.1986 & 30.1981 & 23.2581 & 15.8677 & 24.1578 & 18.7674 & 16.4549 & 13.0542 \\ CNN-LSTM & 42.1453 & 29.7911 & 22.2376 & 16.3033 & 22.6298 & 17.4288 & 16.2873 & 13.9929 \\ GA-ACO-BP & 42.0361 & 30.1735 & **22.2159** & 16.4132 & 25.7076 & 18.3761 & 17.1207 & 12.8305 \\ CNN-Bi-LSTM & 41.2659 & 30.6524 & 22.8206 & 16.3292 & 22.4862 & 17.7012 & 16.0755 & 12.4518 \\ APSO-Bi-LSTM & 42.5511 & 30.1736 & 22.6436 & 16.1949 & 22.7092 & 17.2056 & **15.9328** & 12.4395 \\ APSO-CNN-Bi-LSTM & **38.9324** & **29.1980** & 22.2685 & **15.8166** & **22.4338** & **16.9921** & 15.9729 & **12.3634** \\ \hline \hline \end{tabular} Note: the model performance evaluation indicators (RMSE and MAE) represent the prediction accuracy for the next 1 h.  
\end{table} 
Table 5: Comparison of prediction errors of each model. Figure 9: **(a)** RMSE and **(b)** MAE of all the models at different prediction time steps. Subfigures a and b are the average RMSE and MAE of different models at different prediction steps. clearly outperforms other models. This indicates that APSO-CNN-Bi-LSTM is more adept at handling complex data as the prediction problem becomes more challenging. The CNN module effectively captures spatial dependencies in the data, while the Bi-LSTM module leverages past and future information for predictions. Additionally, the APSO optimization algorithm further refines model hyperparameters, minimizing the need for manual intervention. This combination of techniques enhances the model's ability to manage and predict complex data scenarios.
# Deep Learning-Based Postural Asymmetry Detection Through Pressure [PERSON]
## 3 Deep Learning Methods with Pressure Mat Data
### Object Detection Algorithms  
Object detection is a fundamental task in deep learning and computer vision, where the goal is the identification and localization of objects in an image or video. As described by [PERSON] et al. [51], in contrast to image classification, where the goal is to classify an entire image into one category, object detection aims at locating and classifying existing objects in any one image and labeling them with rectangular bounding boxes.  
Object detection based on DL models uses neural network architectures such as YOLO [52, 53] (You Only Look Once), SSD [54] (Single Shot Multibox Detector), RetinaNet, and RCNN [55, 56] (Region-based Convolutional Neural Networks) to detect and classify objects in images. These models are typically divided into two groups: one-stage detectors, such as YOLO and SSD, and two-stage detectors, such as RCNN [57].  
In this study, YOLO has been selected as the object detection algorithm due to its good results in speed and accuracy. YOLO is a well-known single-stage detection algorithm in which the tasks of region proposal and classification are performed simultaneously. Unlike  
Figure 4: Convolutional neural network architecture.  
two-stage detectors, where region proposal and object classification are performed in separate steps, YOLO uses an end-to-end neural network architecture to predict bounding boxes and class probabilities simultaneously.  
Simply described, YOLO divides the input image into a \(S\times S\) grid, and each grid cell is responsible for predicting the object centered in that grid cell (see Figure 5). Each grid cell predicts \(B\) bounding boxes and their corresponding confidence scores \(C\)[51]. The predictions are encoded as an \(S\times S\times(B*5+C)\) tensor [52]. Post-processing is then applied to keep only the most confident objects. This significantly improves the speed of inference, making YOLO suitable for real-time applications.  
Figure 5 shows the stages of the YOLO object recognition algorithm: On the one hand, in the left panel, the input image is divided into a grid of size \(S\times S\), in which each grid cell is responsible for detecting objects whose centers lie within its boundaries; on the other hand, the middle panel shows the prediction phase, where each grid cell generates multiple bounding boxes and associated confidence scores for potential objects; and finally, the final detection results are shown in the right panel, where post-processing techniques are applied to remove redundant bounding boxes and retain only the most reliable predictions.
# Double Deep Q-Network with a Dual-Agent for Traffic Signal Control
## 4 Double Deep Q-Network with Dual-Agent Algorithm
### Traffic Signal Control Algorithm  
To effectively control the traffic signal, we propose a DDQN with a dual-agent algorithm. Its pseudocode is indicated in Algorithm 1. Two agents have the same initial action, which keep the traffic phase sequence fixed. At each time step, the busy agent (e.g., the agent \(P\) first) perceives the traffic state, and adopts the \(\epsilon\)-greedy algorithm to select an action \(a_{p}\). After the action \(a_{p}\) is implemented, traffic states change from \(s_{p}\) to \(s^{\prime}_{p}\). Then, it obtains a reward \(r_{p}\) calculated by the (8). All this information forms a record (or a transition) \((s_{p},a_{p},r_{p},s^{\prime}_{p})\) that need to be put into experience \(MP\). At the end of the time step, the agent \(P\) randomly selects mini-batch data from the experience \(MP\) to train, then based on (11) which is shown in the follow to update evaluating network parameters \(\theta\).  
\[\theta_{i}\leftarrow\theta_{i-1}+r_{l}(y_{i}-Q(s,a;\theta_{i}))\
abla\theta_{i }Q(s,a;\theta_{i}) \tag{11}\]  
where \(a\) is agent action, \(r_{l}\) is the learning rate. At every training iteration, the agent \(P\) updates parameters \(\theta^{-}\) of the target network based on:  
\[\theta^{-}=(1-\beta)\theta^{-}+\beta\theta \tag{12}\]  
where \(\beta\) is the target network update rate. The agent \(A\) has the same training process as agent \(P\) except for the experience memory, parameters \(\epsilon\), and reward functions.  
Figure 6: The network structure of the dual-agent architecture.  
```
1:Initialize network parameters, \(\theta\) with random values
2:Initialize two agent replay memory \(MP\) and \(MA\) with capacity \(L\)
3:Initialize \(N\),\(T\),\(r_{p}\),\(t_{a}\)
4:for episode = 1,2,...,\(N\)do
5: initialize agent \(P\) historical states \(s_{p}\) and agent \(A\) historical states \(s_{a}\)
6:start a new time step and set the agent \(P\) state to busy
7:for\(t\) = 0 to 7 - 1 s do
8:if new time step begins then
9: busy agent chooses an action (\(a_{p}\) or \(a_{a}\)) according to \(\epsilon\)-greedy policy
10:if action changes then
11: execute the transition
12:endif
13:endif
14:if transition ends then
15: exchange two agent states
16: directly execute the busy agent selected action
17:endif
18: add a second, \(t=t+1\)
19:if busy agent (agent \(P\) or \(A\)) time step ends then
20: busy agent observes a new state (\(s^{\prime}_{p}\) or \(s^{\prime}_{a}\))
21: busy agent acquires a reward \(r\)
22: busy agent places a record (\(s,a,r,s^{\prime}\)) in memory
23:\(s_{p}\gets s_{p}\) or \(s_{a}\gets s^{\prime}_{a}\)
24: busy agent training
25: start a new time step
26:endif
27:endfor
28:endfor
```  
**Algorithm 1** Double Deep Q-Network with Double-Agent for Traffic Light Control
## 3 Results and Discussions
### Thermal Conductivity Analysis  
Figure 12 shows the thermal conductivity of GF-3/epoxy compounds in different weight fractions. The thermal conductivity grows as the weight fraction increases, and a sudden acceleration of growth occurs at 60%. This is because the epoxy matrix's thermal conductivity is very low compared to the filler itself. When the fraction of filler is low, the epoxy matrix acts as a thermal barrier positioned between fillers [46]. When the filler fraction reaches a certain point, a thermal channel is formed. The maximum result of \(1.6\;\mathrm{W}/(\mathrm{mK})\) was obtained when the weight fraction of GF-3 in epoxy compounds was 90%, which was 605% higher than that of pure epoxy, respectively.  
Figure 11: Schematic of the GO/FeSiAl epoxy compounds microwave absorption mechanism.  
Figure 10: Impedance matching Z of epoxy composites with (**a**) GF-1, (**b**) GF-2, (**c**) GF-3, and (**d**) GF-4.