esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 3 Noise Model
### External Noise
#### 3.1.3 Fog Attenuation Model  
Let \(\xi\) (\(\rm{g/m^{3}}\)) be the density of liquid water in the fog. According to [25], the fog attenuation, denoted as \(\gamma_{\rm{fog}}\), is then determined using the Rayleigh approximation, which may be expressed as follows:  
\[\gamma_{\rm{fog}}=K_{l}(f,T)\xi, \tag{7}\]  
where \(K_{l}(f,T)\) (\(\rm{dB/km/(g/m^{3})}\)) is the specific attenuation coefficient based on the signal carrier frequency \(f\) (GHz) and cloud liquid water temperature \(T\) (K).  
The main relaxation frequency \(f_{p}\) and the secondary relaxation frequency \(f_{s}\) are defined in the following expressions:  
\[f_{p} =20.2-146(\frac{300}{T}-1)+316(\frac{300}{T}-1)^{2}, \tag{8}\] \[f_{s} =39.8f_{p}. \tag{9}\]  
\(\epsilon\) represents the dielectric permittivity coefficient, which can be computed by using the double Debye equation [26]:  
\[\epsilon=\frac{\epsilon_{0}-\epsilon_{1}}{1+\frac{f}{f_{p}}}+\frac{\epsilon_{ 1}-\epsilon_{2}}{1+j\frac{f}{f_{s}}}+\epsilon_{2}, \tag{10}\]  
where \(j=\sqrt{-1}\). Therefore, the imaginary and real components of \(\epsilon\) are below:  
\[\epsilon^{\prime\prime} =\frac{f(\epsilon_{0}-\epsilon_{1})}{f_{p}(1+(\frac{f}{f_{p}})^{ 2})}+\frac{f(\epsilon_{1}-\epsilon_{2})}{f_{s}(1+(\frac{f}{f_{s}})^{2})}, \tag{11}\] \[\epsilon^{\prime} =\frac{\epsilon_{0}-\epsilon_{1}}{1+(\frac{f}{f_{p}})^{2}}+\frac {\epsilon_{1}-\epsilon_{2}}{1+(\frac{f}{f_{s}})^{2}}+\epsilon_{2}, \tag{12}\]  
where  
\[\epsilon_{0} =77.66+103.3(\frac{300}{T}-1), \tag{13}\] \[\epsilon_{1} =0.067\epsilon_{0},\] (14) \[\epsilon_{2} =3.52. \tag{15}\]  
The specific coefficient of fog attenuation is determined using the following calculation:  
\[K_{l}(f,T)=\frac{0,819f}{\epsilon^{\prime\prime}\bigg{(}1+\bigg{(}\frac{2+ \epsilon^{\prime}}{\epsilon^{\prime\prime}}\bigg{)}^{2}\bigg{)}}. \tag{16}\]  
As a result, the fog attenuation is determined as follows:  
\[\gamma_{\rm{fog}}=\frac{0,819f\xi}{\epsilon^{\prime\prime}\bigg{(}1+\bigg{(} \frac{2+\epsilon^{\prime}}{\epsilon^{\prime\prime}}\bigg{)}^{2}\bigg{)}}. \tag{17}\]
## 4 Results and Discussion
### Gr-3  
The GF3 data included the FSII mode data on November 9 and UFS mode data on November 21.  
1. FSII mode localization accuracy analysis  
The positioning accuracy of the FSII data for November 9 obtained using the above method is shown in Figure 15.  
Figure 15 illustrates that the image from November 9 contains 14-point targets, and in the FSII mode image of GF-3, the positioning accuracy falls between \(-15\) and \(-11\) pixels in the range direction and between \(-2\) and \(0.5\) pixels in the azimuth direction.  
1. UFS mode localization accuracy analysis  
For the UFS data for November 21, the positioning accuracy obtained is shown in Figure 16.  
Figure 16: GF\(-3\) UFS mode data localization accuracy.  
Figure 15: GF\(-3\) FSII mode data localization accuracy.  
Figure 16 shows that the image from November 21 contains 13-point targets. In the UFS mode image of GF-3, the positioning accuracy is between \(-18\) and \(-11\) pixels in the range direction and between \(-11.5\) and \(-8\) pixels in the azimuth direction.
## 4 Parametric Study
### Effects of Changing Substrate Properties  
According to a study, two types of Rogers (RT 5880 and RT 6002) and an FR-4 (lossy) with the same dimension (\(9\times 9\times 1.6\) mm\({}^{3}\)) were used to observe the properties of different substrates materials. The loss tangent and paramagnetic constant for substrate RT 5880 were 0.0004 and 2.2, as for RT 6002 were 0.0037 and 3.48, respectively. The S21 depicted in Figure 5 indicated three sharp resonances with narrow bandwidths for RT 5880 and RT 6002. Substrate FR-4 (Dk 2.2 and loss tangent 0.025) demonstrated three resonances  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subdesign**} & **Resonance** & **Types Structure** & **Resonance Apex (dB)** & **Covering Bands** \\  & **Frequency (GHz)** & & & \\ \hline Design-1 & 2.9, 10 & SSR & \(-27.44\), \(-26.19\) & S, X \\ Design-2 & 3.9, 17.33 & SSR+ Heaagon & \(-25.76\), \(-20.83\), \(-28.28\) & S, X, Ku \\ Design-3 & 2.89, 10, 17.2 & Modified hexagon & \(-27.4\), \(-30.,-29\) & S, X, Ku \\ Proposed design & 2.89, 9.42, 15.16 & Formatted hexagon & \(-25\), \(-22.24\),\(-28\) & S, X, Ku \\ \hline \hline \end{tabular}
\end{table}
Table 2: S21 for various designs.  
Figure 4: S21 for the different sized unit cell.  
Figure 3: S21 for various designs.  
with satisfactory bandwidth covering S-, X-, and Ku-bands. A high EMR of 11.53 was calculated for the first resonance frequency (2.89 GHz). Therefore, FR-4 (lossy) was selected as a substrate material for the fabrication of the proposed unit cell.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Technical Considerations
#### 3.3.4 IoT-Based Heterogeneous Devices  
IoT devices are available in a wide range of sizes and designs; they operate on several operating systems and, of course, have varying performance characteristics. The variety of devices adds another layer of difficulty to the BIoT integration [85]. As low-cost, low-power devices grow increasingly common, the BIoT integration may become increasingly vulnerable to hardware flaws. Furthermore, because of the large degree of heterogeneity across IoT devices, the original BC architecture, which just employs an \"address\" to describe a node, is unsuitable for IoT devices [75]. Moreover, multilevel security systems must be created for heterogeneous devices [11; 76]. Before delivering services to users, the system should first adapt to current resources and make judgments about the security methods to apply at the IoT levels. Intelligence is required for such a dynamic system [77].
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 6 Conclusions  
Fault detection and prediction in industrial equipment is an important area that is subject to heavy research. To the best of the authors' knowledge, the analysis of the \(p\)-value from an ADF test of a time series is a novel approach, which has never been applied to industrial chip pump vibration analyses. It offers immense potential for monitoring and determining trends in the state of an equipment. The hypothesis was further investigated through the analysis of the \(p\)-value of three time series, using rolling windows of different sizes to calculate the \(p\)-value and also to apply noise-filtering techniques to stabilize the results obtained.  
The results showed a stable increase in their \(p\)-value, which was increasingly above the threshold of 0.05, when there were large variations in the vibration of the chip pumps. Equipment prognostic and fault prediction using the \(p\)-value is an appealing idea, because it can be a universal method that is simpler than defining limits for each variable, and the present study showed that vibration, although a stationary time series in the long term, is non-stationary during some periods.  
Future work requires the analysis of additional data, namely to study in deeper detail the behaviour of the \(p\)-value in situations of actual faults, both for vibration and other variables.  
Conceptualization, M.M. and N.L.; methodology, M.M. and N.L.; software, D.F. and F.R.; validation, M.M., N.L. and J.F.; formal analysis, M.M. and N.L.; investigation, D.F. and F.R.; resources, J.F.; data curation, D.F.; writing--original draft preparation, D.F.; writing--review and editing, M.M., N.L. and J.F.; supervision, M.M. and N.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets used to produce this article are not readily available because of non-disclosure agreements. Requests for more information about the datasets should be directed to the corresponding authors.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Advantages** & **Disadvantages** \\ \hline Innovative and simple method & Need to determine the best time windows \\ Universal approach & Need for more in-depth study \\ Effective in noisy data & Not immune to external factors \\ \hline \hline \end{tabular}
\end{table}
Table 8: Advantages and disadvantages of the proposed method.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Total Loss of Algorithm  
#### 2.7.1 Mean Square Error (MSE)  
The efficacy of machine learning algorithms can be analyzed through multiple approaches. Consequently, mean squared error (MSE) was chosen for the performance analysis, to yield definitive conclusions, as corroborated by references [11, 12]. This metric, frequently used in performance evaluation, requires calculating the error \(\widehat{X}\), which represents the difference from the actual X, and then averaging this error. Based on this difference, the gradient of loss is computed and utilized for backpropagating the weights. The next step involves applying gradient descent in the subsequent training phase to lower losses. The loss function used in regression is presented by  
\[MSE_{N}=\frac{1}{N}\!\sum_{i=1}^{N}\left(X-\widehat{X}_{i}\right)^{2} \tag{25}\]
## 4 Case Study
### Analysing Failure Modes for IVIS
#### 4.2.1 Evaluating Risk Parameters of Failure Modes for IVIS
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Failure Mode No.** & \multicolumn{5}{c}{**Fuzzy Evaluations of Team Members**} & \multicolumn{2}{c}{**Aggregation of**} \\ \cline{2-7}  & **TM1** & **TM2** & **TM3** & **TM4** & **TM5** & **TM6** & **TM7** & **Fuzzy Opinions** & **Defuzzification Value** \\ \hline
1 & H & VH & M & VH & M & H & H & (5.072, 7.572, 9.382) & 7.342 \\
2 & M & M & H & M & M & H & H & (3.428, 5.928, 8.428) & 5.928 \\
3 & VH & VH & H & VH & VH & VH & VH & (7.215, 9.715, 10.000) & 8.977 \\
4 & M & M & L & M & M & M & M & (2.227, 4.727, 7.227) & 4.727 \\
5 & M & M & M & M & H & M & M & (2.660, 5.150, 7.660) & 5.160 \\
6 & M & M & L & M & M & M & M & (2.227, 4.727, 7.227) & 4.727 \\
7 & M & H & M & M & H & M & M & (2.981, 5.481, 7.981) & 5.481 \\
8 & VH & VH & H & VH & VH & VH & H & (6.999, 9.499, 10.000) & 8.832 \\
9 & M & M & M & H & M & H & M & (3.065, 5.666, 8.066) & 5.566 \\
10 & M & M & H & M & M & M & (2.773, 5.273, 7.773) & 5.273 \\
11 & H & H & H & H & H & M & (4.798, 7.298, 7.989) & 7.298 \\
12 & H & M & M & H & H & H & H & (4.406, 6.906, 9.406) & 6.906 \\
13 & M & M & L & M & L & M & M & (1.962, 4.462, 6.962) & 4.462 \\
14 & M & H & M & M & M & M & M & (2.716, 5.216, 7.716) & 5.216 \\
15 & M & M & H & M & H & M & M & (3.038, 5.338, 8.038) & 5.538 \\
16 & H & H & M & H & H & H & (4.727, 7.272, 9.727) & 7.227 \\
17 & M & M & M & M & M & M & (2.500, 5.000, 7.500) & 5.000 \\
18 & H & H & H & VH & H & H & (5.271, 7.771, 10.000) & 7.681 \\
19 & H & H & H & H & H & H & (5.000, 7.500, 10.000) & 7.500 \\
20 & H & H & H & VH & H & H & (5.271, 7.771, 10.000) & 7.681 \\
21 & M & M & M & M & M & M & M & (2.500, 5.000, 7.500) & 5.000 \\
22 & VH & VH & VH & H & H & VH & H & (6.618, 9.118, 0.000) & 8.579 \\
23 & H & H & H & H & H & M & M & (4.589, 7.089, 9.589) & 7.089 \\
24 & H & H & H & H & H & M & M & (4.589, 7.089, 9.589) & 7.089 \\
25 & M & M & L & M & L & M & M & (1.962, 4.462, 6.962) & 4.462 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Fuzzy evaluations and aggregation of opinions for occurrence.
## 3 Parametric Study
### Effect of Stationary Vehicle Frequency
#### 3.1.3 Vehicle Frequency Higher Than Bridge's Fundamental Frequency  
The third case considered a stationary vehicle with a frequency of 3.18 Hz, which is slightly higher than the bridge's fundamental frequency. The mass of the vehicle remained at 200 kg, but the stiffness was increased to be 80,000 N/m. It is important to note that the stationary vehicle properties in this case, such as mass and stiffness, will be used in Sections 3.2-3.7.  
The stationary vehicle, contact-point, and bridge responses are shown in Figure 7a, and the acceleration spectra are shown in Figure 7b. Similar to the previous cases, the stationary vehicle response did not contain higher modes of vibration and the contact-point response was in good agreement with the bridge response. Unlike the 1.6-Hz stationary vehicle, the 3.18-Hz stationary vehicle response could detect the first mode of frequency of the bridge. Meanwhile, it is clear that the stationary signal was corrupted by its own frequency, which appears as the second peak in Figure 7b. This led to difficulties in identifying damage on the bridge because the signal was polluted by the vehicle frequency. Nevertheless, the frequency of the stationary vehicle was almost eliminated for the contact-point response.
## 3 Effects of Storage and Maintenance Modes on Battery Performance
### Effects of Charge-Discharge Modes on Battery Performance
#### 3.1.1 Effects of Charge-Discharge Modes on Battery Capacity  
Figure 5 demonstrates the charge retention rates and the capacity recovery rates of Ni-MH battery modules, maintained by different charge-discharge methods. It can be seen in Figure 5a that the charge retention rates maintained by the direct discharge and charge (_DDC_) mode were significantly reduced. However, when maintained by the full discharge and charge (_FDC_) + 10% mode, batteries from both manufacturers A and B presented the highest capacity retention rates. Moreover, Figure 5b shows that different charge-discharge maintenance modes had negligible effects on the capacity recovery rates.  
In short, the _FDC_ + 10% maintenance mode proved to be the best approach to maintain the charge retention of Ni-MH battery modules, and the three charge-discharge maintenance modes did not make a difference in the capacity recovery rate.
## 3 Results and Discussion
### Temperature Trends
Several investigations suggest that these differences in the increase in temperature are affected by several factors; some of them are anthropic activity, land use and the emission of greenhouse gases [9; 83]. Greenhouse gases appear to be related to the increase in average temperatures and the impact derived from this increase [73]. Research on the possible causes of the increase in temperature in the State of California shows that the existing changes in atmospheric teleconnection patterns have significantly altered the extreme temperature events that take place in the said region [20]. There is a concrete example in the North Pacific Ocean, where surface temperatures correlate highly with Californian temperatures [84]. Finally, a great deal of climatological research suggests that temperature variability can be related to variability within the atmospheric flow [22].
# Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation
###### Abstract  
The perception system has become a topic of great importance for autonomous vehicles, as high accuracy and real-time performance can ensure safety in complex urban scenarios. Clustering is a fundamental step for parsing point cloud due to the extensive input data (over 100,000 points) of a wide variety of complex objects. It is still challenging to achieve high precision real-time performance with limited vehicle-mounted computing resources, which need to balance the accuracy and processing time. We propose a method based on a Two-Layer-Graph (TLG) structure, which can be applied in a real autonomous vehicle under urban scenarios. TLG can describe the point clouds hierarchically, we use a range graph to represent point clouds and a set graph for point cloud sets, which reduce both processing time and memory consumption. In the range graph, Euclidean distance and the angle of the sensor position with two adjacent vectors (calculated from continuing points to different direction) are used as the segmentation standard, which use the local concave features to distinguish different objects close to each other. In the set graph, we use the start and end position to express the whole set of continuous points concisely, and an improved Breadth-First-Search (BFS) algorithm is designed to update categories of point cloud sets between different channels. This method is evaluated on real vehicles and major datasets. The results show that TLG succeeds in providing a real-time performance (less than 20 ms per frame), and a high segmentation accuracy rate (93.64%) for traffic objects in the road of urban scenarios.  
Keywords:graph structure; point cloud; cluster; real time; improved BFS +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Outlier Detection and Sample Set Split  
During the process of image acquisition and chemical composition measurement, the obtained spectral information and chemical composition content of samples might be interfered with by different factors and might have negative influences on calibration models. Thus, outlier samples detection and removal were conducted before splitting sample sets. The PLS model was firstly built for each chemical composition using all samples, and eight samples with the largest absolute prediction errors (the difference between reference value and predicted value) were identified as outlier samples [20]. It should be noted that different samples might be treated as outlier samples for difference chemical compositions. After the removal of 8 outlier samples, the remaining 172 samples were split into the calibration set and the prediction set at the ratio of 3:1 (129 for calibration and 43 for prediction). Firstly, the samples were ranked by chemical composition content from low to high. Then, one sample of the two samples in the middle of every four samples was selected for the prediction set, and the remaining three samples were selected for the calibration set. Table 1 shows the statistical summary of chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content. The sample distributions of the calibration set and the prediction set were similar, with samples distributed uniformly between the calibration set and the prediction set.
## Materials and Methods  
### Study Area  
The global arid and semi-arid regions were selected from the updated version of the Koppen-Geiger climate map [42], which shows five main climate groups, with sub-division of each group based on seasonal precipitation and temperature patterns. The five main groups are A (tropical), B (dry), C (temperate), D (continental), and E (polar). In this research, the subgroups starting with the letter \"B\" (dry) were identified as arid and semi-arid classes, namely, BWh (hot desert climates), BWk (cold desert climates), BSh (hot steppe climates) and BSk (cold steppe climates) (Figure 1a). The corresponding biome type distribution over these regions is also presented in Figure 1b using OpenLandMap Potential distribution of biomes dataset [43].  
Figure 1: (**a**) Global arid regions in Koppen–Geiger climate classification, including four arid types: BSh (hot semi-arid climate), BSk (cold semi-arid climate), BWh (hot desert climate) and BWk (cold desert climate); (**b**) biomes types distribution map over the global arid region.
## 2 Materials and Methods
### Experimental Method  
#### 2.3.1 Direct Shear Test  
To measure the shear strength of the soil samples, the shear apparatus uses a strain-controlled direct shear instrument. The normal stress used in the test is 50 kPa, 100 kPa, 200 kPa, and 400 kPa, and the horizontal load is 1.2 kN. The leverage ratio is 1:12, and the area of the specimen inside the shear box is 30 cm\({}^{2}\).  
According to the soil layer category, the shear tests of permeation grouting muddy clay and permeation grouting silty clay were carried out. In theory, at least two samples of each cement content should be prepared in case the cement slurry in one of the samples is not sufficiently mixed with the soil, resulting in a large deviation in the experimental results. A total of 64 sets of experiments were carried out according to different soil types and cement content, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Category**} & \multirow{2}{*}{**Group**} & \multicolumn{4}{c}{**Cement Content**} & \multirow{2}{*}{**Normal Stress**} \\ \cline{3-4} \cline{6-7}  & & **0\%** & & **5\%** & **10\%** & **15\%** & \\ \hline Muddy clay & M1 & M1-0 & M1-5 & M1-10 & M1-15 & \\  & M2 & M2-0 & M2-5 & M2-10 & M2-15 & 50 kPa; 100 kPa; \\  & S1 & S1-0 & S1-5 & S1-10 & S1-15 & 200 kPa; 400 kPa \\  & S2 & S2-0 & S2-5 & S2-10 & S2-15 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Samples of different soils and cement content.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Component** & **SiO2** & **K2O** & **SO3** & **CaO** & **Al2O3** & **Fe2O3** & **MgO** & **Na2O** \\ \hline Content (\%) & 20.33 & 0.42 & 2.15 & 65.50 & 4.83 & 4.90 & 1.30 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contents of main compounds in cement.  
After the test equipment was debugged, the prepared samples were subjected to shear testing as follows:  
1. After cleaning the shear box, the porous stone and filter paper were placed, the upper and lower boxes were aligned, and pins were inserted to connect them.
2. Then, the edge of the ring knife was made with the sample upward, and the sample was carefully pushed into the shearing box. Filter paper, porous stone, and pressurized cover plate were placed on the sample in sequence.
3. The transmission device is moved to make the steel ball on the upper box contact with the force ring, and the left tip of the lower box is just in contact with the transmission rod. The displacement measuring device is installed, and the dial indicator is adjusted to the zero position.
4. Adjust the pressure frame to make it fully in contact with the cover plate, and then apply normal stress to the cover plate in sequence.
5. The speed during shear is set to 0.08 mm/min; every time the specimen has a strain of 0.3-0.5 mm, the dynamometer and displacement measurements are recorded until the sample is sheared.  
Following the shear test, the sample is removed and used for the microstructure test. The preceding steps were repeated to shear the other samples after cleaning the shear box. Figure 2 depicts the testing procedure.