esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### _Furnace Space Particle Combustion_  
#### 3.1.1 Particle Combustion Mechanisms  
Combustion of single wood particles follows three possible mechanisms depending on the air flow velocity. The first mechanism is realized at an air flow velocity of 20 m s\({}^{-1}\) and lower. It is characterized by stage-wise nature. After the particle ignition, a flame of burning volatiles is formed around it (Figure 4a). This flame obstructs air penetration to the particle surface and prevents oxidation of a char layer. The front side of a particle remains cold and there is no flame near it.  
Volatiles are released and burned at the first stage. A flame envelope is torn off from the particle surface when the temperature in its center is as high as 330-350 \({}^{\circ}\)C (Figure 4b). The flame base is located at the back face of a particle. After termination of the intensive release of volatiles the carbon residue of a particle starts to incandesce, and a stage of carbon residue combustion commences (Figure 4c).  
A single-stage mechanism of particle combustion occurs at air velocity in the tuyere above 125 m s\({}^{-1}\). At the initial stage of particle combustion, its front edges and corners start incandescing, which evidences the burning of a char layer in those places (Figure 5a). A flame is not observed during burning. Incandescent areas gradually embrace the entire particle, but not its back face (Figure 5b). Particle center remains relatively cold. Its temperature is about 200 \({}^{\circ}\)C, which is not sufficient for the wood to be intensively decomposed. The center of a particle is heated to the temperature of pyrolysis commencement only when the burning front approaches it directly (Figure 5c). In a single-stage mechanism of combustion, the stages of volatile release and of char residue burning occur simultaneously.  
With the velocity of air flow growing from 20 to 125 m s\({}^{-1}\), a two-stage conversion gradually changes over to a single-stage one. It is worthwhile mentioning that the temperature impact on the ranges in which some or other type of combustion mechanism occurs has not been observed. This impact is most probably irrelevant.
## 3 Results and Discussion
### Modeling \(H_{2}\) Effect under Altitude and Sea Level Conditions
The selectivity towards \(\cdot\)OH, which is shown in Figure 3c, indicated that at temperatures below 160 \({}^{\circ}\)C for low H2 concentrations and below 200 \({}^{\circ}\)C for high H2 concentrations. Above these temperature thresholds, introducing H2 led to a decrease in CO selectivity from \(\cdot\)OH to the O2 oxidation pathway. Regarding the selectivity of HC oxidation, which is represented in Figure 3b,d, it showed the same type of trend as observed with CO upon the introduction of H2. However, it did not reach the selectivity levels for the \(\cdot\)OH pathway observed in CO. The influence of H2 on HC conversion is weaker at low temperatures due to the adsorption role. In parallel, the HC selectivity to the \(\cdot\)OH pathway decreases as temperature does, but this trend is more gradual and less affected by H2 concentration than for CO.  
As a result of this calibration, the kinetic constants collected in Table 1--Appendix A were obtained. These constants include the activation energies and pre-exponential factors of the reactions involved and the accumulation capacity of HC and \(\cdot\)OH in each monolith channel.
Sensors_**2008**, \(8\), 7753-7782; DOI: 10.3390/s8127753  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
An Energy-Efficient Secure Routing and Key Management Scheme for Mobile Sinks in Wireless Sensor Networks Using Deployment Knowledge  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1,*, [PERSON] 1 and [PERSON] 2**  
1 Dept. of Computer Engineering, Kyung Hee University, Seocheon, Giheung, Yongin, Gyeonggi, Korea; E-Mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
2 Dept. of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]; Tel.: +82-31-201-2514; Fax: +82-31-202-2520  
_Received: 7 October 2008; in revised form: 10 November 2008 / Accepted: 3 December 2008 / Published: 3 December 2008_  
**Abstract:** For many sensor network applications such as military or homeland security, it is essential for users (sinks) to access the sensor network while they are moving. Sink mobility brings new challenges to secure routing in large-scale sensor networks. Previous studies on sink mobility have mainly focused on efficiency and effectiveness of data dissemination without security consideration. Also, studies and experiences have shown that considering security during design time is the best way to provide security for sensor network routing. This paper presents an energy-efficient secure routing and key management for mobile sinks in sensor networks, called SCODE_plus_. It is a significant extension of our previous study in five aspects: (1) Key management scheme and routing protocol are considered during design time to increase security and efficiency; (2) The network topology is organized in a hexagonal plane which supports more efficiency than previous square-grid topology; (3) The key management scheme can eliminate the impacts of node compromise attacks on links between non-compromised nodes; (4) Sensor node deployment is based on Gaussian distribution which is more realistic than uniform distribution; (5) No GPS or like is required to provide sensor node location information. Our security analysis demonstrates that the proposed scheme can defend against common attacks in sensor networks including node compromise attacks, replay attacks, selective forwarding attacks, sinkhole and wormhole, _Sybil_ attacks, HELLO flood attacks. Bothmathematical and simulation-based performance evaluation show that the SCODE_plus_ significantly reduces the communication overhead, energy consumption, packet delivery latency while it always delivers more than 97 percent of packets successfully.  
Keywords:Secure routing, key management, sensor networks, sink mobility, deployment knowledge +
Footnote †: journal: Journal of Network Science
## 4 Discussion
### Joint and Pore Size Development Characteristics
As seen in the figure, with an increase in pore pressure, the contact aperture gradually enlarges. When the pore pressure increases from 0 MPa to 1 MPa, some vertical contact apertures within the model expand, and due to the application of stresses on both  
Figure 10: Joint development law in the process of pore-compression. sides, the lateral contact apertures within the model almost entirely close. As the pore pressure increases from 1 MPa to 2 MPa, the connectivity of the contact apertures expands, resulting in more apertures that traverse the entire model. When the pore pressure increases from 2 MPa to 3 MPa, most of the joint contact apertures reach their maximum size. These observations indicate that there is a certain regularity in the changes in apertures as the pore pressure difference increases. Especially when the pore pressure difference reaches a certain value, the connectivity of the apertures significantly improves. As the normal stiffness is a critical factor influencing the stress response of rocks, we conducted an in-depth analysis of its effects. When rocks possess a higher normal stiffness, their response to stress becomes stiffer, meaning they deform less under pressure. This high level of stiffness response results in more noticeable closure of the internal rock pores, consequently reducing permeability. By varying the normal stiffness parameter, our study revealed its varying impact on permeability. Specifically, as shown in Figure 12, when the normal stiffness increases from 100 GPa to 500 GPa, the permeability rapidly decreases. Subsequently, as the normal stiffness increases from 500 GPa to 1000 GPa, the rate of permeability reduction slows down. Finally, increasing the normal stiffness from 1000 GPa to 2000 GPa results in a slight decrease in permeability, but with minimal further change. Therefore, we recommend selecting a normal stiffness value of 1000 GPa or higher for model studies to minimize its impact on the overall model properties, which is more in line with real-world conditions. Figure 11: Changes of pore size under different pore-pressure. Figure 12: Influence of normal stiffness on permeability.
## 2 Material and Methods
### Dataset 3: Fault dataset generated using Simulink  
The Simulink of the power system was modeled to generate the dataset for fault analysis in MATLAB depicted in Figure 4. Three 25,000 MVA generators are used in the model power system to generate alternative energy. These generators are located at the start and end of the transmission line. Through the use of transformers that convert 120 kV to 25 kV, the generators' 120 kV voltage is sent to a 28 km transmission line. Two 30 MW loads are present on the transmission line. The middle of the transmission line, at km 14, is where multiple faults are simulated to occur at different times. The voltage and current values of the line at the power system's output were recorded while the circuit was simulated under both normal and different fault scenarios. The fault types included in the dataset are shown in Table 2.  
The study evaluated the results of symmetrical three-phase short-circuit faults as well as a single phase to ground, phase to phase, and phase to phase to ground asymmetric faults. Every failure type's allowed occurrence time was within 0.1 s during the simulation's total runtime of 1 s. Voltage and current graphs of nominal and fault conditions obtained during 1 s of operation are given in Figure 5.  
Figure 4: A Simulink model was developed to generate the dataset.  
For the dataset, approximately 19,999 data points were recorded and labeled. A total of six inputs (Ia, Ib, Ic, Va, Vb, and Vc), consisting of the voltages and currents of three phases, were applied to the input of the created machine learning models.
## 4 Experiments and Analysis
### Experiment 1: DAH Pattern Performance
To evaluate the detection performance in the experiment, we used three metrics: the data capacity, detection rate, and average detection time. As all images in the dataset included one valid marker, the images in which no valid marker was found were considered failed detections, while those in which a valid marker was found were considered successful detections. The detection rate was calculated by dividing the number of successful image detections by the total number of images. Table 1 summarizes the results of the first experiment. We observed that compared to the ArUco, the marker including the DM has a larger data capacity. In the original image dataset, the DAH pattern showed the highest detection rate and detection time compared to the other markers. In particular, the DAH pattern significantly outperformed the ArUco, DM, and DM with Box in terms of detection rate, demonstrating that the DAH pattern is more robust and reliable than any of the individual markers in actual industrial environments.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 2 Theory and Method
### Network Architecture
#### 2.2.3 Separation  
The separation module estimates the masks of the direct and reflected waves for each segment. Then, the encoded representation \(\mathbf{w}\) of the full wave segment is multiplied by the mask \(\mathbf{m}_{i}\in\mathbf{R}^{1\times N},i=1,2\) to obtain separate encoded representations \(\mathbf{d}_{i}\in\mathbf{R}^{1\times N},i=1,2\) of the direct and reflected waves.  
\[\mathbf{d}_{i}=\mathbf{w}\odot\mathbf{m}_{i}, \tag{10}\]  
where \(\mathbf{m}_{i}\in[0,1]\), \(\mathbf{m}_{1}+\mathbf{m}_{2}=1\). In the mask of reflected wave, the larger the proportion of energy expressed by the reflected wave after encoding, the closer the mask is to 1. The mask of the direct wave is constructed in a similar way. The separation module is consistent with Figure 1B in [22]. It mainly contains three fully convolutional separation modules. Each full convolution separation module consists of eight stacked 1-D dilated convolution blocks.
## 4 Results
### Validation of Fusion Wind  
The ASCATs onboard the Metop-A/B/C satellites, the HY-2B scatterometer, and the WindSAT winds during the period November 2019 to October 2020 were fused by using the above interpolation method. Figure 6 shows the fusion wind map for 6 September 2020 at 12:00 UTC, where there were maximum wind speeds of up to 45 m/s, and some gaps and swath patterns were observed. This problem could be solved using products from more scatterometers. The 12 h average wind speeds from the fusion data for June\(-\)September 2020 were collected and compared with the measurements from the NDBC buoys that were marked in Figure 3. We noted that the distance between the grids and the available  
Figure 5: The flowchart of the research framework.  
NDBC buoys was less than 5 km. The statistical analysis showed a 1.66 m/s RMSE, with a 0.81 COR for the wind speed, as depicted in Figure 7. Although the temporal resolution of the fusion wind was coarser than that of the atmospheric numerical simulation (i.e., it was 1 h for the ERA-5), the accuracy of the fusion wind was very satisfactory. Moreover, the swath patterns and the spatial resolutions of the fusion data could be further improved by using the multiple satellites in orbit, i.e., the ASCATs, HY-2C/2D, and Sentinel-3 satellites, such that the wind structure could be observed in detail. Thus, it was believed that the fusion wind field was reliable for this study.  
Figure 6: The fusion wind map on 6 September 2020 at 12:00 UTC.  
Figure 7: The comparison between the daily average wind speeds from the fusion data and the measurements from the NDBC buoys for June\(-\)September 2020.
# Switchable Broadband-to-Tunable Narrowband Magnetic Probe for Near-Field Measurements
## 5 Isolation and Sensitivity  
One of the critical steps in magnetic probe design is to measure the ability of the probe to suppress an undesirable electrical field. The transmission coefficient S21 parameter is utilized to represent the electrical field or the magnetic field, based on the direction of the sensor related to the reference microstrip trace. Thus, the magnetic field can be estimated when the sensor and the reference trace are in a parallel orientation. On the other hand, the orthogonality case offers the electrical field.  
In this work, the first and fourth layers are made of copper and connected through vias to shield the magnetic probe against the electrical field. The vias-connected ground planes impact the isolation and field distribution and make the stripline equipotential, where any point has the same potential [34]. Moreover, the top and bottom layers have a gap in the middle of the loop at the top side, which splits the shield symmetrically and creates an open circuit for eliminating the electrical current passing on the surface due to the symmetric distribution. An unsymmetrical gap will make the probe sensitive to the tangential electrical field due to it not canceling the electrical currents flowing on the outer surface. Figure 23a presents the final geometrical shape of the switchable magnetic probe, while Figure 23b shows the isolation results for different switching states. The via and fence technique offers an isolation value of about 13 dB.  
To verify the enhancement in sensitivity and isolation using the via fence technique, Figure 24a,b present the sensitivity and isolation with and without using this technique for different biasing voltage values. It is evident that the via fence technique considerably impacts isolation and sensitivity. For instance, a 5 dB isolation enhancement has been achieved using the via fence technique. On the other hand, a sensitivity enhancement of about 5 dB is obtained through critical applications.  
Figure 23: Modeling and simulation results of the via fence isolation technique: (**a**) The final geometrical shape of the magnetic probe; (**b**) isolation for the multi-narrow bands.  
The sensitivity measures the level of the magnetic field distribution above the device under test. Comparing the achieved sensitivity with the previous results in [24; 25; 26; 28] shows the main contribution of this work. The proposed probe has summed up the benefits of a large bandwidth with high sensitivity compared to the probes described in the literature reviewed, as illustrated in Table 3.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 4 Issues in the Evolution of the Inter-Domain Routing Control Plane  
There has been a myriad of publications on the inter-domain routing field, and despite the success of the Internet, it still has some crucial issues and research challenges regarding its operation and design that need to be addressed. This section presents the most evident issues described in related works that impact the evolution of the control plane inter-domain routing systems. Each issue is explained and systematized into the following types: ossification; backward compatibility; distributed configuration; complexity routing policies; coordination among ASs; traffic engineering.
## 3 Results  
### Classical Statistical Features  
Classical statistics can directly reflect the volatility of variables. In general, the coefficient of variation (Cv) (Cv = standard deviation/mean) is used to measure the degree of variation. When \(0\leq\text{Cv}\leq 30\%\), it can be called weak variation; \(30\%<\text{Cv}\leq 100\%\) can be called medium variation; and \(\text{Cv}>100\%\) can be called strong variation. It can be seen from Table 2 that the content of silt, bulk density and saturated water content have weak variation; clay content has medium variation; and sand content and saturated hydraulic conductivity have strong variation. However, classical statistics cannot establish the relationship between variables and spatial location; therefore, geostatistics and the fractal dimension are used for further analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Soil Property** & **Minimum** & **Maximum** & **Mean** & **Standard Deviation** & **Cv (\%)** \\ \hline Sand (\(\%\)) & 0.10 & 66.70 & 7.05 & 10.54 & 149.58 \\ Silt (\%) & 33.20 & 95.70 & 81.42 & 10.50 & 12.89 \\ Clay (\%) & 0.10 & 42.90 & 11.40 & 9.16 & 80.32 \\ \(\gamma\)1 (N-m\({}^{3}\)) & 12,055.20 & 16,056.00 & 14,049.35 & 948.95 & 6.75 \\ \(\theta_{\text{s}}\)2 (cm\({}^{3}\)cm\({}^{-3}\)) & 34.47 & 53.24 & 42.53 & 4.00 & 9.41 \\ K\({}_{\text{s}}\)3 (10\({}^{-4}\)cm\(\cdot\)s\({}^{-1}\)) & 0.01 & 28.31 & 4.70 & 5.76 & 122.63 \\ \hline \hline \end{tabular}  
* \(\gamma\)—Bulk density; 2 \(\theta_{\text{s}}\)—Saturated water content; 3 \(K_{\text{s}}\)—Saturated hydraulic conductivity; Cv—coefficient of variation.  
\end{table}
Table 2: Basic statistical characteristic values of soil properties.
## 4 Discussion of the Results
### Parametric Study
By definition, in these dashed curves, the positive values mean that the target object increases in absolute value with the increase of the magnification factors, while the negative values mean opposite tendencies. These tendencies are hereafter represented by \(\uparrow\) and \(\downarrow\), respectively. The values in the dashed curves represent the change rate (positive or negative), i.e., the level of sensitivity to the relative parameter. Target objects which are very sensitive to a certain parameter are indicated using \(\uparrow\uparrow\) or \(\downarrow\downarrow\). Based on the calculation results, the tendencies and the levels of sensitivity to the parameters are summarized in Table 8.  
According to the summary, optimizing the structural performance by adjusting only one parameter is difficult, as increasing the value of one parameter never decreases the absolute values of all target objects. A combination of the choices for the parameters is significant for practical design in order to obtain a better structural response. As shown in Table 8, the shear stiffness and the width of the column play an important role in the design of quasi-rectangular tunnel linings. Shear stiffness does not affect the behavior of circular shield tunnels significantly as smaller shear forces exist in circular tunnels. However, when it comes to quasi-rectangular tunnels, due to the shape of the section and the existence of the interior column, the shear forces influence a lot both the structural deformations as the maximum bending moments. The column is the unique characteristic in the design of this type of tunnel, and its width influences the bending moments to a large extent. Therefore, considerable attention needs to be paid to the shear stiffness and the size of the interior column. It is worth mentioning that a change of the size of the rigid zone results in a difference of 10% of maximum bending moments. As such, a choice for the suitable area of the rigid zone also needs due attention.
# Peak Age of Information Optimization in Cell-Free Massive Random Access Networks
## 3 Peak Age of Information
### Successful Transmission Probability in Single-AP Scenarios  
Consider the group \(\mathcal{G}_{\{1\}}\) in the single-AP network scenario. The probability that the buffer queue of an MTD is empty is given by \(1-\rho^{\{1\}}\), where \(\rho^{\{1\}}=\frac{\lambda}{\lambda+p^{\{1\}}q^{\{1\}}}\) is the offered load [18]. For \(n^{\{1\}}\) MTDs in the group, the number of MTDs competing for channel access is \(n^{\{1\}}\rho^{\{1\}}\). The successful transmission probability \(p^{\{1\}}\) for a target MTD is the probability that all other \(n^{\{1\}}-1\) MTDs either have empty buffers or do not access the channel. This is expressed as follows:  
\[p^{\{1\}}=\left(1-\rho^{\{1\}}+\rho^{\{1\}}(1-q^{\{1\}})\right)^{(n^{\{1\}}-1)}, \tag{1}\]  
where \(q^{\{1\}}\) is the channel access probability. In the scenario of massive access, we have \(n-1\approx n\) and \(\left(1-x\right)^{n}\approx\exp(-nx)\). Based on this, the probability of successful packet transmission can be obtained as follows:  
\[p^{\{1\}}=\exp(-\frac{n^{\{1\}}\lambda q^{\{1\}}}{\lambda+p^{\{1\}}q^{\{1\}}}). \tag{2}\]