esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Property Profile
#### 3.2.3. Ignition Properties  
The results of ignition temperature testing are shown in the schematic in Figure 6. The ignition temperature increases with the addition of B4C nanoparticles. There is a sharp increase in ignition temperature from E21-1.5B4C to E21-2.5B4C. A maximum ignition temperature of 798 \({}^{\circ}\)C was observed in E21-2.5B4C and the ignition temperature increased by about 57 \({}^{\circ}\)C (7.7%) in the nanocomposite, which indicates the effectiveness of B4C particles in enhancing the ignition properties of magnesium.  
Figure 6: _Cont._  
E21 alloy has a high ignition temperature with delayed flammability and, hence, is approved by FAA to be used in in-cabin applications [5]. This is due to the presence of a class of elements which have a positive influence on enhancing the ignition temperature. Rare earths like Nd and Gd are found in E21, raising magnesium's ignition temperature from 590 to 741 \({}^{\circ}\)C, as shown in Figure 6[10]. Further, Figure 6a demonstrates that ignition temperature increases with the addition of B4C nanoparticles. There is a sharp spike in ignition temperature from E21-1.5B4C to E21-2.5B4C; due to the additional 1% B4C added in the nanocomposite. B4C has excellent thermal properties, with a high specific heat capacity, low thermal conductivity, and a low thermal coefficient of expansion; it brings about thermal stability to E21. Figure 6b gives the relation between the thermal conductivity of the material and the ignition temperature and it has always been observed that the ignition temperature is higher for materials with lower thermal conductivity. Therefore, it is probable that the increased weight fraction of B4C nanoparticles in E21-2.5B4C can bring about a higher ignition temperature as it is more thermally stable as compared to the other compositions. This is essential, as it is an improvement in the thermal and ignition properties of E21 alloy, one of the few magnesium-based alloys to meet the survivability model of the Federal Aviation Administration, allowing E21 to be more reliant and suitable in the aerospace industry.  
E21-2.5B4C is an example of a nanocomposite, another emerging class of materials with extremely good mechanical properties coupled with thermal integrity owing to the presence of dimensionally stable ceramic or metallic reinforcements, which provide high mechanical strength as well as ignition resistance.
# A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G
## 4 Framework Proposal
### UPF Placement Stages  
By placing the UPFs at the network edge not only the network response time but also the bandwidth consumption can be significantly reduced. However, placing a UPF in every available EN results in a rise of costs and UPF relocations. Therefore, the main objective of the UPF placement stages is to determine the optimal number of UPFs and their location in the given EN infrastructure, so that 5G services requirements can be satisfied while costs are minimized. Please note that, unlike the ENPP, where the access nodes can be simplified as TGs, the UPFs placement is strongly dependant on the behaviour of their underlying end-users (e.g., service demand and mobility patterns).  
The UPF placement consists of two main stages: **Placement Analysis** and **UPF VNF Placement**. The former is in charge of processing and preparing all the data required for the UPFs placement. It is a compound of three modules: _Service Classification_, _Placement Criteria_ and _Candidate Placement Selection_. The _Service Classification_ module aims at clustering the services into categories with similar placement requirements such as mobility, latency and reliability. For each one of these parameters, different thresholds or levels can be established. The demands of those services that belong to the same category are considered as a whole and the placement priority for each category is established.  
Taking into account the service classification, _placement criteria_ such as optimization objective and placement considerations are determined within the namesake module. For instance, the required number of backup UPFs for each service category is calculated according to its reliability level. The last module is responsible for selecting the candidate locations (i.e., ENs) for the UPF placement by taking into account available resources within the underlying infrastructure as well as the UPF maximum capacity and latency requirements of the service category. As a result, we obtain a set of UPF candidate locations for each access node (\(N_{nc_{r}}\)) and a set of near access node for each candidate (\(N_{nr_{c}}\)). To determine \(N_{nc_{r}}\) and \(N_{nr_{c}}\), clustering techniques, i.e., Fuzzy C-means (FCM), are used. This method has been widely used in the area of allocation by various researchers [28; 29]. Namely, FCM is used to determine the membership preferences between access nodes-candidate and candidate-access nodes.  
Finally, the **UPF VNF Placement** stage solves the UPFP by considering service demands, placement requirements, candidate locations and UPF capacity. To address the UPFP, we propose two strategies: an ILP model called Optimal UPF Placement (OUP) and a Near-Optimal UPF Placement (NOUP) algorithm. Table 2 summarizes the notation used in the UPF problem formulation.
## 4 Experimental Results
Consequently, even if there is no significant difference for the prediction models based on a variety of FS methods considered in this study, FFR proves to be more stable than FSS methods having lower CV values. The best prediction model developed using each classifier is illustrated in Figure 2. In Table 15, prediction models with FS methods outperform models developed without features selection. This indicates the importance of FS methods while developing the SDP model regardless of which family (characteristics) the classification algorithm belongs. Figure 3 shows the positive gain, that is, the variation of the prediction models with FS methods to prediction models without FS methods. Figures 4 and 5 show the performance stability of prediction models developed with FS methods respectively. Both figures depict the standard deviation (SD) and Co-efficient of Variation (CV) values for each FS method. Lastly, Figure 6 pictorially presents the performance FS method based on average accuracy values on each dataset. Figure 2: Average Accuracy for Prediction Models. In conclusion, listed below are a summary of our findings from this comparative study:  
* FS methods are very important and useful as it improves the performance of prediction models. Figure 4: Performance Stability of Prediction Models (SD). Figure 5: Performance Stability of Prediction Models (CV). Figure 3: Prediction Models with the Highest Variation. Figure 6: Performance of Feature Selection Method on each Dataset. * Based on the individual performance accuracy values, FS methods had the highest improvement on the predictive performance of NB classifier. * CFS based on (AS, BAT, GS, FS, PSO, BFS, and GSS) selects (automatically) the minimum number of features. * On the average as presented in Table 15, CFS based on BAT had the highest positive variation on NB and DT while CNS based on GS and BFS had the highest positive variation value on LR and KNN. * FFR had the lowest C.V. values which make it more stable than other FSS methods (See Table A1 in Appendix A).
## 2 Theory of TRM-SBDC UWA Communication
### Principles of PDS  
The PDS scheme used in this paper belongs to the pulse position coding (PPC) scheme. Digital information is typically modulated in the code waveform, while the PDS scheme modulates the digital information in the time delay shift value of the pattern pulse. The different time delay shift values represent different information [23]. Figure 2 shows the symbol structure of the PDS scheme.  
As shown in Figure 2, \(\tau_{di}(i=1,2,3,\ldots)\) represents the time delay shift value, which is the position where the pattern pulse appears in the encoding window. \(T_{0}\) is the length of one symbol, \(T_{p}\) denotes the pattern pulse width, and \(T_{c}=T_{0}-T_{p}\), which is the encoding time window for information coding.  
Figure 1: The flow chart of TRM-SBDC UWA communication system.  
When each symbol carries \(n\) bits of information, the communication rate can be calculated as \(n/T_{0}\). The quantization unit of time delay shift can be expressed as \(\Delta\tau=T_{c}/(2^{n}-1)\), and the time delay shift value can be expressed as \(\tau_{d}=k\cdot\Delta\tau\), where \(k=0,1,\ldots,2^{n}-1\). In this paper, each symbol carries 4 bits of digital information, so \(k=0\) represents the digital information \"0 0 0 0\", and \(k=10\) represents the digital information \"1 0 1 0\".
# Experimental Study on the Influence of Low Temperature on the Gas Permeability of Granite
## 5 Engineering Implications  
1. The significant increase in gas permeability with decreasing freezing temperature, particularly for fine-grained granite, indicates that temperature fluctuations in cryogenic environments must be carefully considered when designing underground LNG storage facilities. Fine-grained granite's higher sensitivity to freeze-thaw damage could lead to higher permeability and potential leakage risks over time. Thus, temperature control and monitoring strategies are critical to mitigate such risks. (2) The distinct patterns of freeze-thaw damage between coarse-grained and fine-grained granite emphasize the need for tailored materials in LNG storage construction. Coarse-grained granite, with its focus on macropores, may be more resilient to freeze-thaw cycles, while fine-grained granite, with its pre-existing micropores, may undergo more significant damage. This should influence the selection of materials for long-term underground storage, particularly in regions where temperature cycling is a concern.
## 5 Results and Discussion
### SINR Analysis  
Figures 9 and 10 show the SINR values of the LoRa link as our research vehicle travels along the predetermined route. Figure 9 shows that the SINR values have dropped rapidly to below 40 dB as the signal passes through the container where it is diffracted. As we can see, the LoRa receiver successfully decoded the data over most of the specified distance. With a standard deviation of 7.95, the overall average LoRa SINR achieved, covering the entire specified path, is equivalent to 27.80 dB, as shown in Figure 10. The weak signal and spotty coverage were responsible for the subsequent drop in performance. The path of the main diffracted signal includes trucks, containers, and natural obstructions, in addition to the main buildings. There is also a significant gap between the transmitter and the receivers.  
Figure 8: Receiver side.  
Figure 9: LoRa SINR results, measured while the vehicle is moving across the defined path.  
The color bar to the right of the plot in Figure 9 shows the values of each point shown on the map. The purple points with white outlines on the map describe the loss of connectivity at the receiver end.
## 3 Results and Discussion
### Band Structure of the Supercell  
To further confirm that Pb\({}_{2}\)BiBrO\({}_{6}\) and Pb\({}_{2}\)SeTeO\({}_{6}\) are topological insulators, Figure 6 shows the electronic band structures of the Pb\({}_{2}\)BiBrO\({}_{6}\) and Pb\({}_{2}\)SeTeO\({}_{6}\) supercell. We constructed a supercell containing 12 layers of Pb\({}_{2}\)BiBrO\({}_{6}\) or Pb\({}_{2}\)SeTeO\({}_{6}\) (6 formula units) with a vacuum of 20 A. The calculations were done without structure relaxation, including surface atomic reconstruction. There are two kinds of surface layers in our Pb\({}_{2}\)BiBrO\({}_{6}\) or Pb\({}_{2}\)SeTeO\({}_{6}\) supercell, one is the PbO layer and the other is the BiBrO\({}_{4}\) (SeTeO\({}_{4}\)) layer. We denote these two surface layers as top PbO and top BiBrO\({}_{4}\) (SeTeO\({}_{4}\)). We also denote the second nearest BiBrO\({}_{4}\) (SeTeO\({}_{4}\)) layer to the surface as top2 BiBrO\({}_{4}\) and top2 SeTeO\({}_{4}\). The calculated electronic band structure of the Pb\({}_{2}\)BiBrO\({}_{6}\) and Pb\({}_{2}\)SeTeO\({}_{6}\) supercell are shown in Figure 6a,e. Clearly, we observe metallic bands between \(\Gamma\)-X and M-\(\Gamma\). It theoretically confirms that Pb\({}_{2}\)BiBrO\({}_{6}\) and Pb\({}_{2}\)SeTeO\({}_{6}\) are topological insulators. It is also important to verify the origin of these metallic bands. To investigate, we illustrate the project band structures in Figure 5b-d,f-h. In Figure 6b,f, the red solid circles represent the energy band due to top PbO layer. Very interestingly, the top PbO layer gives no contributions to the metallic surface bands in Pb\({}_{2}\)BiBrO\({}_{6}\). We found that the surface BiBrO\({}_{4}\) layer, Figure 6c, and nearest surface BiBrO4 layers, Figure 6d, give contributions to the metallic surface bands. Unlike Pb\({}_{2}\)BiBrO\({}_{6}\), both PbO and SeTeO\({}_{4}\) surface give contribution to the metallic bands. The surface energy can be calculated from the equation  
\[\gamma=\frac{E_{slab}-NE_{bulk}}{2A} \tag{13}\]  
where \(E_{slab}\) is the total energy of the supercell structure, \(N\) is the number of bulk Pb\({}_{2}\)BiBrO\({}_{6}\) in the supercell, and \(E_{bulk}\) is the bulk total energy. Our calculated surface energy for Pb\({}_{2}\)BiBrO\({}_{6}\) is 0.56 J/m\({}^{2}\). In Pb\({}_{2}\)BiBrO\({}_{6}\), although the PbO layer gives no contribution to the surface conduction bands, the surface PbO layer is still useful because the surface PbO layer can act as a protective layer when building the actual devices. It can prevent the topological protected surface metallic bands damaged from the experimental defects, such as gas and atom adsorption, surface disorders, and dangling bonds.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Network Simulator
#### 3.3.3 Routing Algorithm  
Routing algorithms can be categorized into three types based on their use of network information: Topology-based Routing, Location-based Routing, and Cluster-based Routing. Among these, Location-based Routing algorithms leverage the geographical position of nodes for data forwarding, with Greedy Perimeter Stateless Routing (GPSR) serving as a quintessential example. GPSR achieves efficient data transmission without maintaining a global routing table by integrating greedy forwarding and perimeter routing strategies.  
Building upon the strengths of GPSR, SatScope employs an enhanced algorithm as its default routing protocol to improve path search efficiency in satellite networks. This improved algorithm incorporates distance pruning techniques, whereby only the two nearest neighbor nodes to the target node are considered for path expansion during the selection process. Consequently, this approach significantly reduces the search space and computational overhead. Like GPSR, the enhanced algorithm does not require a global routing table, thereby alleviating storage and computational burdens on nodes and adapting seamlessly to frequent topological changes in satellite networks. This results in an efficient routing solution that operates independently of global information. The detailed routing algorithm is described in Algorithm 1.  
```
0: Starting node \(s\), target node \(t\)
0: Path from \(s\) to \(t\), if exists
1: Initialize a queue \(Q\leftarrow\{s\}\)
2: Initialize an empty set \(V\leftarrow\emptyset\)
3:while\(Q\) is not empty do
4: Dequeue \(P\) from \(Q\)
5: Let \(u\) be the last node in \(P\)
6:if\(u\in V\)then
7: continue
8:endif
9:if\(u=t\)then
10: return\(P\)
11:endif
12: Add \(u\) to \(V\)
13: Retrieve valid neighbors of \(u\)
14: Calculate the distance to \(t\) for each neighbor
15: Sort neighbors by distance and retain the two closest
16:for each neighbor \(v\) not in \(V\)do
17: Form a new path \(P^{\prime}\) by appending \(v\) to \(P\)
18: Enqueue \(P^{\prime}\) into \(Q\)
19:endfor
20:endwhile
21:return an empty list
```  
**Algorithm 1** Improved GPSR Algorithm
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 3 Proposed Method
### Fault Location with ELM and MLP  
The data samples used in the fault detection are combined with the additional data collected for faults at a different distance (target output) for each type of fault. The data was grouped into two: the training and testing data. Training and testing were done using either MLP or ELM. The results were then compared.  
For MLP, a single layer network was selected. Levenberg-Marquadt was used as the training algorithm while hyperbolic tangent sigmoid function was used as the activation function of the hidden layer for the MLP (as shown in Figure 4). For ELM, on the other hand, a zero type algorithm with a regulation coefficient and radial basis function (RBF) were selected as optimal kernel parameters.  
The performance of machine learning tools in detecting, classifying, and locating the faults are discussed for each scenario. To inspect the effectiveness of locating the faults in the distribution grid, the following performances measures were considered: measurements, including root mean squared error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (\(R^{2}\)), Nash-Sutcliffe efficiency coefficient (NSEC), and Willmott's index of agreement (WIA), which were evaluated for the testing dataset.
## 2 Results and Discussion
### Phase Composition Analysis (Fourier Transform Infrared Spectroscopy (FT-IR))  
FT-IR spectroscopy supports the analysis of multi-component systems to provide information concerning the material's phase composition and types of interactions existing among various kinds of polymers. In the current study, FT-IR measurement was employed to study the removal of PVP covalent bonds and the ionic bonds formation of TiO\({}_{2}\) nanoparticles. The spectra showed the organic and inorganic content of the sample before and after calcination within a wave number range of 280-4000 cm\({}^{-1}\) (Figure 2).  
Before calcination, at a room temperature of 30 \({}^{\circ}\)C, the spectrum reveals all absorption peaks that are credited to PVP, the absorption peaks at wave numbers 3371, 2954, and 1641 cm\({}^{-1}\) were assigned to N-H, C-H, and C=O stretching vibrations, respectively. In addition, the absorption peak found at 1432 cm\({}^{-1}\) was detected due to the C-H bending vibration originating from the methylene  
Figure 1: Thermogravimetric (TG) and thermogravimetric derivative (DTG) curves for polyvinyl pyrrolidone (PVP) at a heating rate of 10 \({}^{\circ}\)C/min.  
Figure 2: Infrared (IR) spectra of PVP and titanium dioxide (TiO\({}_{2}\)) nanoparticles before and after calcination with different temperatures in the range of 280–4500 cm\({}^{-1}\).  
group, while 1274 cm\({}^{-1}\) was related to the C-N stretching vibrations [28]. The calcination at 500 \({}^{\circ}\)C resulted in the disappearance of broadband absorption peaks that belong to the organic composition of PVP [29; 30]. The remaining peak between 800 and 250 cm\({}^{-1}\) observed for calcined samples at 500, 600, 700, and 800 \({}^{\circ}\)C was assigned to the Ti-O stretching bands, which were attributed to the formation of TiO\({}_{2}\) nanoparticles with an anatase structure [31; 32].
# Perturbation-Based Explainable AI for ECG Sensor Data
## 5 Model Explanations
### Evaluation and Discussion
Limiting the data to only ECG records can cause experts to distrust prediction algorithms. Finally, we discuss a number of issues with perturbation-based approaches and their implications for our method. Our method uses the perturbation principle at two levels (selection of the most relevant channel and selection of the most important parts of the ECG signal within that channel), and the issues discussed below can be influential at both of them. In general, perturbation-based methods select features that are important to the model; these are not necessarily the ones with the highest intrinsic explanatory power, as they are based on the model's behavior and not on the underlying structure of the data. As a result, there might be other intervals of the ECG signal that are more relevant, even for more sophisticated models. Further testing of the proposed perturbation-based method should be performed using more sophisticated models in order to verify the robustness of our method. This issue is less critical for the first stage, where the most significant ECG channel is selected, because the decision there is based on a set of examples from a particular class, with the most frequently voted channel being selected. Moreover, there is always the possibility of visualizing more channels with significant numbers of votes. Another drawback of the perturbation-based methods is that it can devalue correlated features. When two or more features are highly correlated, perturbing or removing one feature can result in similar changes in the model's output. This can lead to an underestimation of the importance of correlated features. This effect can be worse for more complex models, as they can learn more sophisticated relationships between features and rely more heavily on correlated features. One way to address this problem is to use different kinds of perturbations. In our method, we optimized only one parameter, the length of the perturbed interval. Another possibility could be to combine our approach with other interpretation methods. Our experimental design presents human experts with the model's output, then asks them about the extent to which they agree. This may cause an anchoring bias, where the human expert's default response might be to agree with the model and to only disagree in extreme situations. To prevent this potential bias, experts could be asked to first label the important regions based on their opinion without showing them the model's predictions, then compare the selections of the expert with those of the model. This would require a completely different experimental design, however, which could be a possibility for future research.
## 4 Case Studies
### Case Study of Custom IC System
[36] is a fully integrated platform that processes heart rate detection and ECG for 445 nW and 895 nW, respectively. It lowers its power profile through the ability to utilize a low power, less precise clock or a higher power, more accurate clock as well as power-efficient biasing analog components. [37] is designed to be used in fabric to monitor vital signs, utilizing only 12 \(\upmu\)W. [37] selects their topologies of their low-drop out  
Figure 11: Die photograph of the ECG SoC. The analog front end (instrumentation amp (IA) and A/D) and microcontroller (\(\upmu\)C) comprise only 0.0633 mm\({}^{2}\) of active area [7]. regulator, analog front end, and A/D carefully to remain under their power budget. As can be seen, design efforts for energy efficiency and very small form factor are two features common for custom platforms.