esa-sceva/satcom-chunk-collection huggingface dataset:
# Multigraphene Prepared by One-Pot Pyrolysis of Diatomite/Polypropylene Composites
## 4 Conclusions  
Multigraphene (\(\leq\)10 layers with yield \(\sim\)95%) with a high degree of graphitization was successfully synthesized using Diatomite/Polypropylene composites as feedstock via a one-pot pyrolysis method for the first time. The graphene yield was greatly dependent on the DM content, while the structure of graphene was significantly affected by the pyrolysis temperature. The highest yield of 25.86% was gained via the pyrolysis of the DM/PP-30 composites at 1000 \({}^{\circ}\)C. The graphene growth on the DM surface could be explained by a catalytic effect of DM and infusible cross-linking structure formation. This study also provides a promising method for the large-scale production of graphene with a small number of layers, and the as-prepared graphene has potential for use in polymer composite, electrode material, wastewater treatment, and CO\({}_{2}\) adsorption applications.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12052687/s1](https://www.mdpi.com/article/10.3390/app12052687/s1)]([https://www.mdpi.com/article/10.3390/app12052687/s1](https://www.mdpi.com/article/10.3390/app12052687/s1)), Figure S1: SEM images of graphene flakes with inset showing a photograph of the final product.  
Conceptualization, Y.C.; methodology, Y.C.; formal analysis, Y.C.; investigation, Y.C. and B.W.; resources, B.W.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C. and B.W.; supervision, B.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Grant No.2020 YFB1505700).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Tensile Properties and Tensile Failure Criteria of Layered Rocks
## 3 Numerical Study of Layered Rocks
### Direct Tension Test on Layered Rock Samples by RFPA\({}^{2D}\)  
#### 3.3.1 Preparation of the Numerical Layered Specimens  
[PERSON] et al. used three model materials to prepare artificial transversely isotropic rock blocks [8]. In this study, the layered rock specimens are assumed to be made up of rock material and joint material, which determines the mechanical properties of the layered rocks. In this investigation, seven rock specimens with a dip angle, \(\theta\), of 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\), with respect to the loading direction, are made up of the two different materials. Furthermore, some extra specimens with a layer dip angle of 80\({}^{\circ}\) and 85\({}^{\circ}\) were prepared to verify the numerical results compared with [PERSON]'s experimental study [40]. The specimen size was 200 mm \(\times\) 100 mm, and it was discretized into 600 \(\times\) 300 (180,000 elements) meshes.  
It is much more difficult to obtain efficient satisfactory transversely isotropic rock samples in laboratory experiments. With RFPA\({}^{2D}\), it is more convenient to prepare layered rock samples with different rock materials. As shown in Figure 9, the light gray rock layers and the dark gray joints constitute the numerical specimens, respectively.  
Figure 8: Failure modes of the layered rock specimens under the Brazilian test.  
In all cases, the specimens underwent plane strain compression, imposed by the relative motion of the upper and lower loading plates by applying an external displacement at a constant rate of \(-0.0008\) mm/step in the axial direction. The stress, as well as the deformation and the energy released, in each element were computed in each step during loading. The external displacement in the axial direction was slowly increased step by step.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 5 Discussion
### Limitations of the Study
#### 5.1.1 Dataset Dependency  
The conducted research used two datasets: IoT-23 and CI-CIoT-2023.Although these are rich sources of information, it should be remembered that they represent only a fragment of the wide spectrum of potential threats present in IoT networks. Both sets were generated in laboratory conditions, which may not have fully reflected the complexity and dynamics of real network environments. Some types of attacks are underrepresented in the analyzed datasets. This may lead to a situation in which a developed model shows poor performance in the case of new, previously unknown threats. In addition, the limitation to two datasets may affect the generalization of the obtained results to other, more diverse environments.
## 4 Deconvolution
### Minimum Entropy Deconvolution
#### 4.1.3 Strength and Weakness  
* Enhances signal sparsity
* Sensitive to random pulses
* Sensitive to parameters  
In the field of structural health signal processing, minimum entropy deconvolution (MED) stands out as a unique signal processing method, demonstrating distinct advantages and disadvantages.  
Its advantages are mainly manifested in the following aspects: First, MED can effectively enhance the sparsity of signals, which is extremely beneficial for extracting fault features in structural health monitoring. This makes the impact components or fault pulses in the signal more prominent, facilitating the identification of structural damage or anomalies. Second, MED exhibits high sensitivity to random pulses in the signal, a characteristic that makes it excel at detecting non-periodic or random faults, aiding in the capture of sudden damage or transient events. By adjusting the filter to make the output signal sparse, MED effectively separates noise and recovers the original impulsive features. Furthermore, MED does not rely on the periodic characteristics of faults, thus offering greater flexibility in processing complex or irregular signals and being suitable for the processing of non-periodic fault signals. Finally, as a method based on signal processing theory, MED is not entirely data driven and can still effectively operate in the absence of large amounts of training data, demonstrating high practicality.  
However, MED also has some notable disadvantages. First, it performs poorly in processing periodic fault pulses, tending to enhance the sparsity of random pulses instead. This may render it ineffective in extracting fault features for periodic faults, such as periodic impacts in bearing faults. Second, the performance of MED is relatively sensitive to parameter selection. Improper parameter settings can lead to unsatisfactory signal decomposition results or even introduce spurious components, requiring users to possess a certain level of experience and skill. Additionally, the computational process of MED involves iterative optimization, resulting in relatively high computational complexity, which may pose a challenge for real-time monitoring or large-scale data processing. Moreover, although MED can enhance the impact components in the signal, its robustness to noise is limited. In environments with strong noise, noise may be mistaken for fault pulses, potentially reducing the accuracy of diagnosis. Finally, the results of MED may be influenced by the initial conditions of the signal. Improper selection of initial conditions may lead to unstable or unreliable decomposition results.
# Polarimetric Dehazing Method Based on Image Fusion and Adaptive Adjustment Algorithm
## 4 Conclusions  
To solve the problem of many polarimetric dehazing methods cannot operate effectively when the DOP of the background is very low, a new dehazing method is proposed and verified in this paper based on image fusion and adaptive adjustment algorithm. The main idea of the scheme is that the haze and the targets can be roughly separated by the low-pass filtering, which is helpful to obtain the polarization information accurately. A new atmosphere transmittance map that contains the distance relationship between the targets and the camera has been obtained based on image fusion technique, and this operation can enhance the visual range of the images evidently. Meanwhile, an adaptive adjustment algorithm is applied to adjust the illumination distribution of the dehazed images, which can enhance the quality of the dehazed images furtherly. Comparing with other polarimetric dehazing methods, the outdoor experimental results indicated that our method can make better improvement in both visual effect and quantitative evaluation when the DOP is very low, and it can still operate very well even though the DOP is less than 0.01, which means that the proposed method is effective, generalized and robust in many different hazy backgrounds. This paper can provide a reference for the optimization of the dehazing method.  
However, there is an obvious disadvantage that cannot be ignored in the improved operation mode, the current algorithm is very time-consuming and it takes about 10-11 s to finish a typical dehazing operation. After making statistics and analysis, it is found that most of the time is spent in low-pass filtering process and adaptively adjusting process, and the running time will be shorten by optimizing the steps of low-pass filtering process and adjusting process. It can also be expected that the polarimetric dehazing method will be improved in the next version to realize better dehazing effectiveness in shorter time.  
Conceptualizatio, B.L.; methodology, B.L. and Y.L.; validation, B.L., Y.L., C.G., Y.C. and F.W.; investigation, Y.L.; resources, B.L. and Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, B.L., Y.L. and C.G.; visualization, Y.L.; supervision, B.L.; project administration, B.L. All authors have read and agreed to the published version of the manuscript.  
This research is supported by National Natural Science Foundation of China (NSFC) (61975235) and Natural Science Foundation of Hunan Province (2019J40342).  
Not applicable.  
Written informed consent has been obtained from the patient(s) to publish this paper if applicable.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### The Proposed ECT Image Quality Assessment Methods  
#### 3.2.1 The Main Idea  
To bridge the gap between established raster image Quality Assessment (QA) methods and the unique challenges of graph-based 3D ECT, we introduce node grouping as a novel approach to addressing local node structure issues. This method creates a group of nodes for each node in the graph based on a user-defined depth parameter. The rationale behind node grouping is that each node in the graph has a set of neighboring nodes contributing to its overall information. For a given node, a group with depth 1 includes its immediate neighbors. Increasing the depth to 2 incorporates the immediate neighbors and their respective neighbors, capturing a broader local context.  
The schema of node grouping is illustrated in Figure 7, where \"1\" indicates a node group at depth 1 and \"2\" represents a node group at depth 2:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Shape** & **Number** & **Random Parameters** \\ \hline multiple balls & 259 & number (2–5), radius, position \\ H-shape & 262 & size, position, orientation \\ L-shape & 262 & size, position, orientation \\ rod & 259 & number (1–5), diameter, length, position \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of data used in this study.  
Figure 6: Visual quality comparison: (**a**) the H-shaped phantom; (**b**) 4 \(\times\) 4 volumetric view; (**c**) 4 \(\times\) 4 cross-sectional view; (**d**) 8 \(\times\) 4 volumetric view; (**e**) 8 \(\times\) 4 cross-sectional view. For subfigures (**c**,**e**) blue indicates lower electrical permittivity (less dense material), while red indicates higher electrical permittivity (denser material) and the location of objects.
## 2 Materials and Methods
### Experiment Specifications
#### 2.3.4 Compared State-of-Art Algorithms  
We compare our algorithm with the NFVDeep framework presented in [14]. We have created three progressive enhancements of the NFVDeep algorithm for an exhaustive comparison with E2-D4 QN. NFVDeep is a policy gradient DRL framework for maximizing network throughput and minimizing operational costs on general-case SFC deployment. [PERSON] et al. design a _backtracking_ method: if a resource shortage or exceeded latency event occurs during SFC deployment, the controller ignores the request, and no reward is given to the agent. Consequently, sparse rewards characterize NFVDeep. The first algorithm we compare with is a reproduction of NFVDeep on our particular Live-Streaming vCDN Environment. The second algorithm introduces our dense-reward scheme on the NFVDeep framework, and we call it NFVDeep-Dense. The third method is an adaptation of NFVDeep that introduces our dueling DDQN framework but keeps the same reward policy as the original algorithm in [14], and we call it NFVDeep-D3 QN. The fourth algorithm is called NFVDeep-Dense-D3 QN, and it adds our dense reward policies to NFVDeep-D3 QN. Notice that the difference between NFVDeep-Dense-D3 QN and our E2-D4 QN algorithm is that the latter does not use the backtracking mechanism: In contrast to any of the compared algorithms, we permit our agent to do wrong VNF assignations and to learn from its mistakes to escape from local optima.  
Finally, we also compare our proposed algorithm with a greedy-policy lowest-latency and lowest-cost (GP-LLC) assignation agent, based on the work presented in [57]. GP-LLC is an extension of the algorithm in [57], that includes server-utilization, channel-ingestion state, and resource-costs awareness in the decisions of a greedy policy. For each incoming VNF request, GP-LLC will assign a hosting node. This greedy policy will try not to overload nodes with assignation actions and always choose the best available actions in terms of QoS. Moreover, given a set of candidate nodes respecting such a greedy QoS-preserving criterion, the LLC criterion will tend to optimize hosting costs. Appendix B describes in detail the GP-LLC SFC Deployment algorithm.
## 3 The Non-Uniform Hybrid Sampling Technology  
### Timing of the Non-Uniform Hybrid Sampling Technology  
As shown in Figure 3, a complete echo is obtained at \(N\) PRPs with the proposed non-uniform hybrid sampling technology, and the equivalent-time pulse repeated period (E-PRP) should meet the oversampling requirement of the Doppler signal in the azimuth direction. The interval between two adjacent hybrid sampling, the number of hybrid sampling for obtaining one complete echo, and E-PRP are denoted as \(t_{\text{hs}}\), \(N\), and \(T_{\text{E-PRP}}\), respectively. The relationship among them can be expressed as  
\[T_{\text{E-PRP}}=\alpha_{\text{t}}\cdot N\cdot t_{\text{hs}},\alpha_{\text{t}}>1 \tag{2}\]  
where \(\alpha_{\text{t}}\) is the duty-cycle factor. Meanwhile, the time window for the reception of SAR echoes is estimated as  
\[t_{\text{hs}}>t_{\text{w}}=(M+1)T_{\text{ADC}}\geq\frac{2W_{\text{swath}}\cdot \sin\theta_{\text{in}}}{\text{c}}+t_{\text{P}} \tag{3}\]  
where \(T_{\text{ADC}}\), \(t_{\text{w}}\), \(M\), \(W_{\text{swath}}\), \(\text{c}\), \(\theta_{\text{in}}\) and \(t_{\text{P}}\) denote the period of ADC clock, the time window of receiver, the number of ADC sampling periods with the real-time sampling rate in the time window, the imaging swath along the range direction, the speed of the light, incident angle and pulse width of the transmitted signal, respectively. Thus, the total number of the sampling points for each echo with the non-uniform hybrid sampling technology is \(N_{\text{S}}=M\cdot N\), and the equivalent-time sampling interval is \(t_{\text{int}}=T_{\text{ADC}}/N\). For the space-borne SAR system, the ADC real-time sampling rate is 2.5 GSPS, the sampling period is 400 ps, and the time-delay step of 100 ps can be achieved through the programmable delay line. In other words, the equivalent-time sampling interval is 100 ps, and the equivalent-time sampling rate is 10 GSPS, which can meet the oversampling requirements of the echo with a bandwidth of 3.5 GHz. A complete wideband echo can be obtained after four consecutive periods of hybrid sampling.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Value** \\ \hline Quantization resolution of the ADC & 14 bits \\ Maximum input bandwidth of the high-speed ADC & 6.0 GHz \\ Equivalent-time sampling interval of the non-uniform hybrid sampling & 100 ps \\ Sensitivity of the radio frequency receiver & about \(-\)90 dBm \\ Equivalent PRF & 2 Hz\(-\)20 kHz \\ Local oscillator frequency & 7.5 GHz \\ Clock of the FPGA & 250 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: Timing of the non-uniform hybrid sampling technology.
## 5 Results
### Functional Validation
In addition, the system successfully stored a total of 97,914 records in MongoDB collections during the experiments, including sensor readings from all six scenarios. This confirmed the stability and completeness of the data flow, from transmission to storage. Table 6 details the key characteristics of each experimental scenario, including the connectivity type, protocol used, whether traffic was captured, and the main contribution of each configuration. Although this study focused on a fixed configuration for each experimental scenario, the laboratory was intentionally designed to support alternative setups. Thanks to its modular architecture, it is possible to reconfigure the network topology, change the communication protocols, and redistribute data processing tasks between edge nodes and centralized services. This flexibility enables future experiments to evaluate the performance, energy consumption, and security implications of different rural IoT system designs.
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 2 System Model  
In this section, the 5G baseband signal model and time delay estimate method based on conventional MUSIC will be presented.  
Figure 1: The conventional MUSIC spectral function in typical NLOS channel.
# Lung's Segmentation Using Context-Aware Regressive Conditional GAN
## 5 Results and Discussion
### Lung Segmentation  
The performance of the proposed CGAN and state-of-the-art segmentation models is shown in Table 4. It is noticed that UNet, Mask-RCNN, and GAN are the top-performers for the segmentation of lungs. However, it is shown that the FCN model did not perform well compared to the UNet network, which is state-of-the-art architecture for segmentation tasks. Mask-RCNN has indicated the acceptable DSC score for the segmentation of lungs. The proposed CGAN is the top-performer network for the segmentation of lungs with DSC and AJC of 96.77% and 95.59%, respectively. It has been observed that the top-three performers have very successfully segmented the tiny areas from the lung regions, as illustrated in Table 4. Even though the lungs are badly affected by COVID-19, the trained model could segment the lungs' boundaries correctly, as reflected in Figure 3, which highlights the robustness of the suggested lung segmentation model in this work. To further improve the performance of the applied CNN models, we have ensembled UNet+ FCN, UNet+ Mask-RCNN, and UNet+ FCN+ Mask-RCNN. We noticed a minute improvement in the performance, as listed in Table 4.  
In this research, various preprocessing steps were performed to enable us to assess the networks on the different datasets and utilize only slices where the lungs are visible. Segmentation of the lungs is conducted as part of the pre-processing. This procedure proved very effective in our preliminary experiments. We have demonstrated how the proposed method enhances performance by improving the stability of CGAN during training. We have implemented the proposed CGAN with the initial batch normalization  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Severity Level** & **Infection (\%)** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline Cr & 76–100\% & 100\% & 100\% & 100\% & 100\% \\ ST & 51–75\% & 98.63\% & 81.02\% & 98.77\% & 72.26\% \\ MT & 26–50\% & 96.29\% & 76.45\% & 98.94\% & 80.61\% \\ mT & 1–25\% & 97.32\% & 99.01\% & 94.11\% & 98.23\% \\ HT & 0\% (Healthy) & 99.10\% & 96.21\% & 98.51\% & 98.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Shows classification COVID-19 severity levels.  
Figure 3: Results of lung segmentation using proposed CGAN before (**b**) and after (**d**) preprocessing. (**a–d**) Preprocessing enables models to segment lungs regions robustly and ignore other regions along the boundary lines i.e., reflections, noise etc.  
setting instead of spectral normalization. A learning rate of 0.002 caused oscillation during training, and worse results are noted for CGAN. The initial GAN setting utilized crop entropy loss function and suffered from collapse; to avoid this, we have adopted the mechanism suggested by [34].
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 4 The Performance Analysis of Dual-Hop Single-Relay System
### Outage Probability  
The OP is a crucial performance metric to evaluate the quality and reliability of communication systems. It represents the probability that the instantaneous SNR falls below a predefined threshold due to the channel impairments. In the considered system, there is no transmission when the e2e SNR falls below \(\gamma_{1}\), which is represented as follows:  
\[P_{out}=P_{r}\{\gamma_{I}\,<\,\gamma_{1}\}=\int_{0}^{\gamma_{1}}f_{\gamma_{I}} (\gamma)d\gamma=F_{\gamma_{I}}(\gamma_{1}). \tag{18}\]
# Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks
## 7 Performance Metrics
### Instance-Level IoU  
It is well-known that the global IoU measure (1) is biased toward object instances that cover a large image area. This becomes troublesome in applications where there is a strong scale variation among instances and pavement distress detection is definitely one of such applications. To address the issue, an instance-level intersection over union (iIoU) metric has been proposed [30]  
\[\text{iIoU}=\frac{\sum_{i}w_{i}\text{TP}_{i}}{\sum_{i}w_{i}\text{TP}_{i}+ \text{FP}+\sum_{i}w_{i}\text{FN}_{i}}, \tag{5}\]  
where \(\text{iTP}_{i}\), \(\text{iFN}_{i}\) denote the numbers of True Positive and False Negative pixels per \(i\)-th instance, respectively, weighted by \(w_{i}=\hat{S}/(\text{iTP}_{i}+\text{iFN}_{i})\) where \(\hat{S}\) is the average instance size. The False Positive pixels, however, are not associated with any instance and thus do not require normalization.  
The iIoU aims to evaluate how well the individual instances in the scene are represented in the labeling, however, if iIoU < IoU, it simply means that smaller objects are not detected with the same accuracy than larger objects and vice versa.  
For example, Figure 9 depicts a fragment of an orthoframe in which there are two ground truth instances (one of which is predicted well and another poorly) and one false prediction. The distribution of pixels is as follows TP = 35,567, FP = 10,658, FN = 14,449, which yields 59% global IoU (1). When calculating iIoU, iTP\({}_{1}\) = 34,699, iFN\({}_{1}\) = 548, iTP\({}_{2}\) = 868, iFN\({}_{2}\) = 13,901 thus \(\hat{S}\) = (iTP\({}_{1}\) + iFN\({}_{1}\)+iTP\({}_{2}\)+iFN\({}_{2}\))/2 = 25,008, \(w_{1}\) = 0.71, \(w_{2}\) = 1.69, which yields 43% iIoU. iIoU is considerably smaller than IoU because the instance which gives most True Positives is larger than \(\hat{S}\) and the instance that yields most False Negatives is smaller than \(\hat{S}\).