esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Key Factors Affecting the Thermal Property of HWHE
### Influence of plate's Parameters on the Thermal Property
#### 4.3.2 Influence of Plate's Thickness on Heat Exchanger's Thermal Property  
The influence of plate thickness on the heat transfer of HWHE is shown in Figures 14 and 15. It can be seen that when the pipe distance is short, there is little variation in the heat flux and thermal resistance which means that the plate's thickness has little influence on the heat exchanger's thermal property. While, with the increase of pipe distance, the difference in the heat flux and thermal resistance become obvious, meaning that the thickness of the plate becomes an important role in the heat exchanger's thermal property. For example, when the pipe distance is 0.19 m, the conductivity  
Figure 12: Heat exchanger’s heat flux vs. plate’s conductivity and pipe distance.  
Figure 13: Heat exchanger’s thermal resistance vs. plate’s conductivity and pipe distance.  
is 50 W/(m\({}^{\circ}\)C), the thickness is 0.3 mm, and the surface heat flux is \(\sim\)67.6 W/m\({}^{2}\). While, when the thickness is increased to 0.7 mm, the heat flux on the surface increased to 94 W/m\({}^{2}\), which is \(\sim\)40% higher than the case with 0.3 mm thickness. The thickness of the plate should be considered as an important factor in strengthening heat exchanger's heat transfer performance, especially when the pipe distance is large.
# Classification of Marine Vessels with Multi-Feature Structure Fusion
## 3 Multi-feature Structure Fusion Based on Linear Discriminant Analysis
### Linear Discriminant Analysis  
LDA [29] is a supervised dimensionality reduction technology. The idea of LDA minimizes the variance within a class and simultaneously maximizes the variance between classes. After projecting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**Dimension**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{3-4}  & & **Visible** & **IR** \\ \hline HOG & 31,248 & 72.40\% & 57.18\% \\ LBP & 256 & 76.27\% & 56.67\% \\ VGG-16(relu5-3) & 100,352 & 84.93\% & 51.64\% \\ VGG-16(relu6) & 4096 & 82.13\% & 59.03\% \\ VGG-19(relu5-4) & 100,352 & **86.53\%** & **67.71\%** \\ VGG-19(fc6) & 4096 & 85.60\% & 63.16\% \\ VGG-19(relu6) & 4096 & 81.87\% & 63.16\% \\ GoogLeNet(cls3\_pool) & 1024 & 79.73\% & 54.62\% \\ ResNet-50(pool5) & 2048 & 84.27\% & 64.30\% \\ ResNet-101(pool5) & 2048 & **86.67\%** & 64.58\% \\ ResNet-152(pool5) & 2048 & 84.93\% & **69.13\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy of different input features.  
Figure 2: The location of the extracted features from VGG-19 or ResNet-152.  
the data into a low dimensional space, the same category data are as close as possible, and the different category data are as far as possible. By eigenvalue decomposition of the divergence matrix of the given training data, the optimal projection function of LDA can be solved. A brief introduction of LDA and spectral regression discriminant analysis (SRDA) [29] is shown below.  
Suppose \(m\) samples \(x_{1},x_{2},\ldots,x_{m}\), the optimization function of LDA is shown in Equation (1):  
\[a^{*}=\underset{a}{argmax}\frac{a^{T}S_{b}a}{a^{T}S_{w}a} \tag{1}\]  
\[S_{b}=  
To solve the eigenvector problem of LDA in Equation (8) more effectively, the literature [29] has proposed spectral regression discriminant analysis (SRDA). Let \(\overline{X}^{T}a=\overline{y}\), and then Equation (8) can be transformed into:  
\[W\ \overline{y}=\lambda\ \overline{y} \tag{9}\]  
Since the eigenvalue \(\lambda\) of Equations (8) and (9) are the same, the eigenvector \(a\) of \(\overline{X}^{T}a=\overline{y}\) is the same as the eigenvector \(a\) in Equation (8). For the solution of \(\overline{X}^{T}a=\overline{y}\), a possible solution is to use the least squares method, as shown in Equation (10):  
\[a=\underset{a}{\text{argmin}}\sum_{i=1}^{m}\left(a^{T}\overline{x}_{i}- \overline{y}_{i}\right)^{2} \tag{10}\]  
By solving Equations (9) and (10), the mapping matrix can be obtained as \(A=[a_{1},a_{2},\ldots,a_{c-1}]\). Thus, the features after dimension reduction is obtained by \(Y=A^{T}X\), where \(Y\) is a \(c-1\) dimensional vector through projection.
## 2 System Description
### Traffic Model  
The IoRT and video streaming traffic have been considered in this paper. In a mixed traffic setup, for every 50 IoRT devices, 10 video streaming users are considered. This number increases proportionally.  
The IoRT traffic is modelled using a Constant Bit Rate (CBR) traffic model with a packet size of 400 Bytes and time interval of 40 ms [34], while the realistic video trace files that are provided in [35] are used to model the video streaming traffic. The obtained video sequence of 25 frames per second has been compressed using the H.264 standard [34]. The mean bit-rate of the video source is 242 kbps.
## 3 Experiments and Results
### Model Performance Test  
To further test the performance of the proposed method, different sample sizes, different sampling times and different sampling frequencies of each sample are investigated. The detailed experimental design is shown in Table 6. Ten test experiments are conducted under each design, and the training set and test set are randomly assigned to each experiment in a fixed proportion. The experimental results are shown in Figure 9.  
Combined with Table 6 and Figure 9, in general, a smaller sample size of the training set, shorter sampling time, or reduced sampling frequency affects the model performance. Still, the average accuracy is no less than 98%. Above a specific threshold condition, the average recognition accuracy of the model is more excellent than 99.70%, and experiment C is the best, with an average recognition accuracy of 99.90% and a standard deviation of only 0.01%. In addition, according to experiments A, B, C and D, when the sample size of the training set is less than 180, the accuracy decreases obviously. According to experiments C, E and F, when the sampling time is less than 0.05 s, the accuracy decreases obviously. According to experiments E, G and H, accuracy is decreased when the sampling frequency is lower than 4 kHz. In other words, there are only 60 samples in each fault type and the sampling time is only half of the rotation cycle of the lowest speed signal. And when the sampling length only includes 200 sampling points, the model can still maintain good performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Serial** & **Sample Size of** & **Sample Size of** & **Sampling Time** & **Sampling** \\
**Number** & **Training Set** & **Test Set** & **(s)** & **Frequency** \\ \hline A & 2160 & 720 & 0.1 & 8 \\ B & 1800 & 1800 & 0.1 & 8 \\ C & 180 & 3420 & 0.1 & 8 \\ D & 108 & 3492 & 0.1 & 8 \\ E & 180 & 3420 & 0.05 & 8 \\ F & 180 & 3420 & 0.025 & 8 \\ G & 180 & 3420 & 0.05 & 4 \\ H & 180 & 3420 & 0.05 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental design of model performance test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Fault Types** & **Accuracy (\%)** \\ \hline TFCF+RTFANet (Proposed) & 3 & **99.86** \\ FRFT+SSA-DBN [4] & 3 & 95 \\ STFT+CNN [32] & 3 & 96 \\ WPT-MVSVD+SVM [5] & 3 & 87.8 \\ CNN-BLSTM [30] & 3 & 99.2 \\ ResNet-STAR-tanh [31] & 3 & 90.77 \\ RCMFE+SOF [6] & 3 & 95.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with other rolling bearing fault diagnosis methods.
## 3 Results and Discussion  
### Effect of Pyrolysis Temperature on Biochar Yield and Physicochemical Characteristics  
#### 3.1.1 Yield and Ash Content of the Biochar Samples  
The biochar yield obtained decreased from 62.1% to 48.8% with increasing pyrolysis temperature (Table 1). This tendency is consistent with that observed in other studies; however, the weight loss at each pyrolysis temperature differs according to the source material [15; 17; 26]. During production, two decomposition events occur: the first step is carbohydrate decomposition (180-270 \({}^{\circ}\)C) and is followed by protein degradation (320-450 \({}^{\circ}\)C) [27].  
In this temperature range, an exothermic carbonization process occurs by the decomposition of inorganic matter into metallic carbonates, generating considerable weight loss [28]. From 450 \({}^{\circ}\)C upward, the process of the volatilization of organic and inorganic matter continues, but at a slower rate [27; 28; 29]; this explains the similarity in the yield of BC450 and BC600.  
In general, biochar production from seaweeds is greater than that from lignocellulosic materials [12; 17; 27], and this provides an economic advantage. The biochar generated from _M. pyrifera_ showed a higher yield compared to other algal biochars [12; 15; 27], which could be related to its high content of ash (57.5-71.1%) (Table 1) and the difference in the pyrolysis operating conditions (i.e., the rate of pyrolysis temperature increase). Although a high ash content is characteristic of brown algae [30], the content generated in this study was even higher due to the absence of an intensive washing pretreatment or deashing post-treatment given the prioritization of productivity for commercial purposes.
# High-Precision Noncontact Guided Wave Tomographic Imaging of Plate Structures Using a DHB Algorithm
###### Abstract  
The safety diagnostic inspection of large plate structures, such as nuclear power plant containment liner plates and aircraft wings, is an important issue directly related to the safety of life. This research intends to present a more quantitative defect imaging in the structural health monitoring (SHM) technique by using a wide range of diagnostic techniques using guided ultrasound. A noncontact detection system was applied to compensate for such difficulties because direct access inspection is not possible for high-temperature and massive areas such as nuclear power plants and aircraft. Noncontact systems use unstable pulse laser and air-coupled transducers. Automatic detection systems were built to increase inspection speed and precision and the signal was measured. In addition, a new Difference Hilbert Back Projection (DHB) algorithm that can replace the reconstruction algorithm for the probabilistic inspection of damage (RAPID) algorithm used for imaging defects has been successfully applied to quantitative imaging of plate structure defects. Using an automatic detection system, the precision and detection efficiency of data collection has been greatly improved, and the same results can be obtained by reducing errors in experimental conditions that can occur in repeated experiments. Defects were made in two specimens, and comparative analysis was performed to see if each algorithm can quantitatively represent defects in multiple defects. The new DHB algorithm presented the possibility of observing and predicting the growth direction of defects through the continuous monitoring system.  
tomographic imaging; DHB algorithm; noncontact; air-coupled transducer +
Footnote †: journal: Applied Physics Letters
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 7 Results
### Association Process  
To show the performance and the coherence of the proposed association process and its underlying routing, all STAs were forced to repeatedly renew their association to the network every two _primary beacons_ (\(N_{\text{pr}}=2\)) and compute their best parent according to Equation (1) with \(P_{\text{TX}_{max}}=14\) dBm and the following empirical parameters: \(a_{1}=a_{2}=10\), \(a_{3}=1\), and \(a_{4}=5\). In turn, GW association parameters were set to values depicted in Table 8.  
In addition, the number of children per STA was artificially limited to five to guarantee multiple paths towards the GW. Interspersed _data primary beacons_ were used to check the reliability of routing paths and to allow not yet associated STAs to have another opportunity to join the network.  
The selected RDC sublayer for all STAs was X-MAC and no error was introduced in the network (i.e., \(E_{0/0}\) error configuration was used). Under these premises, and after 200 repetitions, an average number of 11.97 STAs were associated to the network after the _data primary beacon_ of the given sequence (i.e., 99.75% of success). As for the PDR, it achieved 100% in all the associated STAs.  
Routing tables compiled by the GW were processed and adapted to graphical representation in Figure 10, where line's thickness is proportional to link's frequency appearance. Preference of STAs  
Figure 15: Nodes’ placement at Testbed #3.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \(\mathbf{a_{t}}\) & \(\mathbf{a_{s}}\) & \(\mathbf{T_{a}}\) & \(\mathbf{T_{g}}\) \\ \hline _Network association mechanism_ & 5 & 6 & 2 & 8 \\ _STA association mechanism_ & 1 & 4 & 2 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: GW association parameters.  
for establishing paths with closer neighbours in their way to the GW becomes evident, just like the importance of _clear paths_ (i.e., without obstacles) such as the formed by the corridor walls.  
The limitation of five children can be clearly appreciated in STAs #6, #8, #9, #10, and #11 being almost always directly connected to the GW in ring 1. The rest of STAs (principally #7) could only access to that ring when circumstantially having better channel conditions than the aforementioned ones.
## 3 Results and Discussion
### Case Study of Measured Data  
This study describes the application effects of jointly applying the BP and the RPCA algorithms on measured data. The measured data were obtained from Qiyue Mountain Tunnel, China, as shown in Figure 7. There was a push of water at the bottom of the tunnel. After emptying out the accumulated water, a GPR was used to detect the tunnel floor, aiming to define the location of the seepage channel and carry out the targeted repairs. Data from this case was utilized in the study carried out by [14] but using completely different processing approaches.  
The detection line is parallel to the tunnel axis and its length is 20 m. The GPR device used here is a GSSI 100 MHz (center-frequency) ground coupled antenna. The time window is about 140 ns. The raw GPR data are shown in Figure 8a. In the raw data, five areas are marked as the leakage passages, in which A1, A2 and A5 have the characteristics of phase axis faults, A3 and A4 have the characteristics of hyperbolic events.  
Figure 6: (**a–d**) represent the lining voids and lining-rock separation model, waveform, BP imaging, and extracted target, respectively; (**e–h**) represent the lining fractures model, waveform, BP imaging, and extracted target, respectively; (**i–l**) represent the concrete honeycomb model, waveform, BP imaging, and extracted target, respectively.  
Figure 7: The positions of Qiyue Mountain tunnel and its longitudinal geological sections from west to east of the studied sections [14].  
The corresponding back-projected image is shown in Figure 8b. Even though the responses of the seepage channels have been inverted into points, the interference caused by noise and residual direct waves is still severe in this image. Figure 8c is the RPCA-extracted sparse matrix, and it is shown that the five anomalies were clearer than Figure 8b. Of course, there are still some ghosts in the extracted imaging by RPCA (Figure 8c) and our method cannot completely solve the problem of interpretation of lining defects detection data. Compare the extracted imaging (Figure 8c) and the original BP imaging (Figure 8b), we can say that the RPCA algorithm could make the imaging of lining defects more obvious and clearer to some extent.  
We can estimate the interior structure of the tunnel lining, as shown in Figure 9, using Figure 8b,c, which is considerably more accurate than processing data directly. Ref. [14]. We dug a hole along the measurement line at ZK329+622 (5.5 m distant from the water entrance site) to check the accuracy of our method's forecast findings, and discovered water flowing out of the hole, proving the method's correctness. As a result of the foregoing, the application of our geophysical method in a real scenario was realized.  
Figure 8: (**a**) actual detection data; (**b**) back projection imaging; (**c**) robust principal component analysis anomaly extraction.  
Figure 9: GPR recognition of the geology below the tunnel floor and the actual situation of water inrush from the drill hole [14].
## 5 Simulation and Analysis
### System Performance with Different Numbers of Terminals  
Figure 10 shows the results for the end-to-end delay using the NB-IoT parameter configuration of the 3 GPP standard parameters, PSO algorithm optimized parameters, and proposed ACE parameters in the NS-3 simulator. From this figure, with a growing number of terminals, end-to-end latency is on the increase. For configurations with the 3 GPP R17 standard, an increasing delay can be noted during the whole communication process. This is because the maximum number of repetitions of the 3 GPP standard parameters can reach 128, and excessive repetitions have caused an avalanche effect which results in a significant decrease in RACH success probability. With the proposed ACE parameters, there is a significant decrease in end-to-end delay, which is due to the appropriate size of repetitions and back-off window sizes. Both of these reduce the occurrence of repeated collisions.  
Figure 8: The total number of RA procedures and battery life of NB-IoT terminals with different CE Method.  
Figure 7: RACH success probability and power consumption performance vary with the demodulation threshold SINRs [24].  
Figure 11 shows the throughput comparison of the 3 GPP standard parameters, PSO algorithm optimized parameters, and NSGA-II algorithm parameters. In order to make the simulation more realistic, we utilized the NS-3 system-level simulator. By modifying the LTE-LENA module and Channel module, the NB-IoT protocol stack and LEO satellite channel model was implemented. Finally, through date packet simulation, the overall system throughput within the beam coverage time was obtained. It was observed that as the number of terminals increased, the system throughput showed an initial increase followed by a decrease. When the number of NB-IoT terminals is in the range [150; 300], the throughput of the system is on the rise and is always increasing. However, the performance  
Figure 10: End-to-end delay with different NB-IoT terminal parameter configurations [22; 24].  
Figure 9: RACH success probability and power consumption performance vary with the NB-IoT terminal load [22; 24].  
of system throughput gets worse in the range [300; 600]. This trend is attributed to the fact that NB-IoT has only 48 subcarriers. The collision of preamble leads to more repetitions and the avalanche effect. However, the four-step random access NB-IoT terminals with NSGA-II optimized parameters exhibited an improvement of approximately 34-62% in system throughput compared to the NB-IoT terminals with the 3 GPP R17 parameters.  
Furthermore, combined with the two-step random access proposed in 5G NR, the overall system throughput was further enhanced by reduced delay. However, due to the fact that two-step random access has stricter requirements in the RSRP and requires modifications to a LEO satellite, it is unsuitable to be considered a viable solution now. Therefore, this approach can be considered as a research direction for future LEO satellite-based IoT networks.
## 4 Implementation Details
### CORDIC Down-Converter and Phase Generation for CORDIC Blocks  
**Figure 11.** Variable phase generation for CORDIC units. Such units are used in NCO to downconvert the IF signal to baseband, In interference estimators to down-convert interferences to baseband and in adaptive FIR unit to generate sinusoid coefficients. Input to this unit is only \(T_{s}\), which is fed from LUT.  
CORDIC is used in rotation mode to down-convert the signal from IF to baseband [21]. Change in sampling frequency requires variable phase generation for CORDIC unit. Figure 11 shows the variable phase generator for various CORDIC/NCO units mentioned above. Input to this block is sampling frequency provided by LUT. We have used 11 pipelined stage of CORDIC for IF to baseband conversion. The phase is represented by \(32\) bits. Number of pipeline stages and word length for phase representation are optimized based on analysis in [22], with the constraint that errors introduced by quantization in above two parameters should not corrupt a full length packet. \(\hat{\Omega}\) is the estimated frequency error generated by FEE. \(\hat{\theta}\) is the phase error estimated by PhEE.
## 3 Experimental Results: Baseline Data
### Destructive Testing  
As is said in Section 2.1, three specimens from each batch were tested according to C469. This standard is traditionally followed to estimate the static modulus of elasticity. The results are presented in Table 4. Interestingly, the values of the elastic moduli are closer to those predicted with the HNSWs whereas the UPV data underestimated the elastic properties of the cylinders. Nonetheless, the destructive testing confirmed that the modulus of elasticity decreased with an increase in the \(w/c\) ratio. On average, the moduli relative to the \(w/c=0.45\) samples and the \(w/c=0.50\) samples were 7.5% and 9.2%, respectively, smaller than the \(w/c=0.42\) sample.
# Study on Microseismic Monitoring of Landslide Induced by Blasting Caving
## 4 Microseismic Monitoring Analysis of Landslide
### Microseismic Moment Tensor Analysis for Types of Mountain Body Slide
\begin{table}
\begin{tabular}{c c c c c} \hline
**Event** & **R** & **Rupture Type** & **Event Number** & **R** & **Rupture Type** \\
**Number** & **R** & **Rupture Type** & **Event Number** & **R** & **Rupture Type** \\
1 & 34.67 & Tensile rupture & 21 & \(-\)31.17 & Compaction rupture \\
2 & \(-\)28.14 & Shear rupture & 22 & 15.90 & Shear rupture \\
3 & \(-\)38.20 & Compaction rupture & 23 & 28.28 & Shear rupture \\
4 & \(-\)31.59 & Compaction rupture & 24 & 24.05 & Shear rupture \\
5 & 37.41 & Tensile rupture & 25 & 22.97 & Shear rupture \\
6 & \(-\)42.78 & Compaction rupture & 26 & 15.68 & Shear rupture \\
7 & 1.50 & Shear rupture & 27 & \(-\)24.67 & Shear rupture \\
8 & 33.30 & Tensile rupture & 28 & \(-\)37.02 & Compaction rupture \\
9 & 34.51 & Tensile rupture & 29 & 10.28 & Shear rupture \\
10 & 20.88 & Shear rupture & 30 & 19.43 & Shear rupture \\
11 & \(-\)32.21 & Compaction rupture & 31 & 9.59 & Shear rupture \\
12 & \(-\)46.01 & Compaction rupture & 32 & 1.73 & Shear rupture \\
13 & \(-\)34.29 & Compaction rupture & 33 & \(-\)8.17 & Shear rupture \\
14 & 36.23 & Tensile rupture & 34 & \(-\)23.12 & Shear rupture \\
15 & 42.72 & Tensile rupture & 35 & 29.65 & Shear rupture \\
16 & \(-\)35.27 & Compaction rupture & 36 & 33.31 & Tensile rupture \\
17 & 31.24 & Shear rupture & 37 & 15.12 & Shear rupture \\
18 & \(-\)42.59 & Compaction rupture & 38 & 0.12 & Shear rupture \\
19 & \(-\)27.35 & Compaction rupture & 39 & \(-\)1.27 & Shear rupture \\
20 & 34.93 & Tensile rupture & & & \\ \hline \end{tabular}
\end{table} 
Table 5: R parameters and rupture type classification.