esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Analysis
### Results and Analysis  
In the experiment, the proposed method can correctly recognize the types of all components every time, i.e., the categorization accuracy is 100%. Table 2 exhibits the matching result of one of our test scenes. In Table 2, the maximum matching errors of different components are presented. It is indicated that the maximum error of the posture is (0.42, 0.41, 0.43)\({}^{\circ}\), and the maximum position error is (0.17, 0.16, 0.15) mm (as shown in Table 3), which proves the high precision of the proposed method.  
To verify the effectiveness of the proposed method, ten different scenes with all components randomly placed were tested. The matching errors of multiple repetitive tests of the proposed method, the NDT, the Photoneo localization method, as well as our 2D method, are shown in Table 3. In addition, since there is no field depth achieved by the 2D method, we measured its errors only in the 2D position (\(x,y\)) and posture \(\alpha\) domains. The results indicate that Photoneo localization achieves higher precision than NDT, and our 2D method obtains higher precision than NDT and Photoneo localization. Our 3D method achieves the highest precision, of which the absolute matching errors are lower than 0.2 mm and 0.5\({}^{\circ}\), significantly superior to the other three methods.  
We also computed the processing time of all methods to verify whether they can meet the efficiency requirement. The proposed 3D method takes 1.43 s to process a scene on average, and there is an additional time of approximately 2 s for the scene point cloud acquisition by the stereo camera. The total time is longer than the NDT and the 2D method with the same processing computer. However, since the operation performed on a set of components by the robotic assembly system takes more than 20 s, the additional processing time has no influence on the performance and efficiency of the entire assembly operation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{4}{c}{**MAE of Postures (\({}^{\circ}\))**} & \multicolumn{4}{c}{**MAE of Positions (mm)**} \\ \cline{2-9}  & **NDT** & **Photoneo** & **2D** & **Proposed** & **NDT** & **Photoneo** & **2D** & **Proposed** \\ \hline \hline \multirow{2}{*}{1} & 2.79 & 1.22 & 0.47 & 0.48 & 3.26 & 1.53 & 0.28 & 0.20 \\
2 & 2.54 & 1.09 & 0.37 & 0.32 & 1.88 & 1.10 & 0.34 & 0.13 \\
3 & 2.22 & 1.41 & 0.41 & 0.45 & 2.86 & 1.15 & 0.33 & 0.16 \\
4 & 3.63 & 1.14 & 0.40 & 0.39 & 2
# Simulation of an Indoor Visible Light Communication System Using Optisystem
## 7 Conclusions  
This article used an Optisystem simulation tool to present an indoor VLC system. The study demonstrates how to examine a VLC system using mathematical and computer simulation methods. The parameters needed for an effective VLC system can be estimated using the results of this investigation. For an indoor VLC system, our simulation looked at the LOS and NOLS propagation models. In an LOS propagation model, we examined the impact of the incidence and irradiance angles. The effect of the FOV on the bitrate estimate was also discussed. The investigation calculated the relationship between the FOV and bitrate for VLC systems using a single LED NLOS propagation model based on mathematical formulations. The data recovery rate for LOS and NLOS propagation models is estimated, and the VLC transmitter and receiver hardware implementation is tested in real-time. Future research should frame a universal strategy for assessing RMS delay spread for the n-LED system. Similarly, it is possible to think of post-processing the signal using many receivers-based VLC methods to reduce the deformation brought on by the delayed spread of numerous reflected optical signals.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 22: VLC hardware test results.
## 3 Results  
### Radiometric Calibration for the Infrared Thermal Imager  
In the process of radiometric calibration for the infrared thermal imager, two-point NUC is applied to ensure the uniformity of images from the blackbody source. In addition, we use a DalSA X64-CL iPro image acquisition card to grab 100 image frames for each temperature, and set temperatures of the blackbody increasing from 35 to 115 degrees C at interval of 5 degrees C, with DN value of pixel (320,256) collected. The radiance calculated according to [PERSON]' law of radiation and corresponding DN value are shown in Table 3.  
Figure 4: Workflow for the proposed method.  
Table 3 indicates that the DN value is of little increase during the temperature increasing from 110 degrees C to 115 degrees C. Considering the 14-bit output of the thermal imager, it can be concluded that the DN value has reached a saturation point around 110 degrees C. Therefore, data from 110 to 115 degrees C is removed for the least square, and only data from 35 to 105 degrees C is used. The result of the radiometric calibration under the condition of 2 ms integration time, 300 mm focal length, is given:  
\[G_{s}=679f(T_{0})+194 \tag{19}\]  
with the fitted straight line using least square method shown in Figure 5.
# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 3 Data Collection and Training Parameters
#### Ground Truth Development  
The training set consists of original images (such as Figure 5a) and expected output, i.e., ground truth (Figure 5e). As SHCC cracks are complex and numerous, labeling each pixel manually would require an overwhelming effort. A novel stage-wise labeling technique was introduced to reduce human effort. The details are as follows. At first, the image in which cracks were present was roughly brushed out manually (Figure 5b); then, the area within the brushed region was binarized based on the optimal color filter selected manually (Figure 5c). However, such a binary image is often populated with dot noise, which was removed through region-based filters (Figure 5d). Such an output looks reasonably good except for very thin sections of the crack. These sections were again manually improved to create the ground truth (Figure 5e).
## 4 Mobile Peer-to-Peer Architectures
### Architecture
#### 4.2.3 Cross Layered  
Besides layered and integrated approaches, cross-layered architectures were proposed (as shown in Figure 6(d)). By providing network layer information to the P2P overlay, routing maintenance overhead can be reduced and the performance of the MP2P network can be improved.  
CROSSRoad [20] combines a proactive MANET routing like the OLSR algorithm with a DHT overlay. As proactive routing algorithms have routes to all nodes of the network readily available, a cross layer approach could provide a complete knowledge of the network topology for the DHT. This way, each lookup may be accomplished after a single overlay hop. Yet, on the other hand, using a proactive routing protocol in a highly mobile, large scale network would result in a very high overhead.  
Similar to the layered approach presented in [15], MADPastry [21] builds upon a landmark-based geographical node-ID distribution. In order to reduce the maintenance overhead, MADPastry truncatesPastry's routing table. MADPastry uses a routing table with a single row and, thus, provides only the first hop in the same way as Pastry in order to route to the correct landmark cluster. The rest of the routing is based on the leafset routing table. Besides the direct next and previous hop neighbor which are maintained proactively, routing tables are maintained by cross-layer information obtained from overheard messages. This way, overhead generated by maintaining the routing tables can be reduced by harnessing cross-layer information. Further, the assignment of node-IDs based on geographical information reduces the number of required underlay hops per request similar to the approach presented by [PERSON] _et al_. [15].
# Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network
###### Abstract  
The traditional methods of structural health monitoring (SHM) have obvious disadvantages such as being time-consuming, laborious and non-synchronizing, and so on. This paper presents a novel and efficient approach to detect structural damages from real-time vibration signals via a convolutional neural network (CNN). As vibration signals (acceleration) reflect the structural response to the changes of the structural state, hence, a CNN, as a classifier, can map vibration signals to the structural state and detect structural damages. As it is difficult to obtain enough damage samples in practical engineering, finite element analysis (FEA) provides an alternative solution to this problem. In this paper, training samples for the CNN are obtained using FEA of a steel frame, and the effectiveness of the proposed detection method is evaluated by inputting the experimental data into the CNN. The results indicate that, the detection accuracy of the CNN trained using FEA data reaches 94% for damages introduced in the numerical model and 90% for damages in the real steel frame. It is demonstrated that the CNN has an ideal detection effect for both single damage and multiple damages. The combination of FEA and experimental data provides enough training and testing samples for the CNN, which improves the practicability of the CNN-based detection method in engineering practice.  
structural damage detection; real-time vibration signal; convolutional neural network; finite element analyses; steel frame
## 2 Methods
### Hourly Updated Ultrarapid POD Strategy
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aspect** & **Summary** \\ \hline Precession and nutation & IAU 2010 model \\ Geopotential & EGM2008 12 \(\times\) 12 \\ Solid Earth tides, ocean tides and solid Earth pole tides & IERS Conventions 2010 [31] \\ N body gravitation & DE405 ephemeris from JPL \\  & GPS/GLONASS/QZSS: seven-parameter ECOM2 (D1B1) \\ Solar Radiation Pressure (SRP) model & BDS-2: five-parameter ECOM1 \\  & BDS-3/Galileo: cuboid a priori plus five-parameter ECOM1 \\ Pseudo-stochastic orbit parameters & Every half day; constrained to \(1\times 10^{-6}\) m/s in the radial, \(1\times 10^{-5}\) m/s in the along-track and \(1\times 10^{-8}\) m/s in the cross-track direction \\ Ambiguity & Fixed for GPS, GLONASS, Galileo and BDS NONE GEO satellites \\  & GPS/GLONASS/Galileo maneuver information from GFZ ([[https://semisys.gfz-potsdam.de/semisys/scripts/satellites/mauever.php](https://semisys.gfz-potsdam.de/semisys/scripts/satellites/mauever.php)]([https://semisys.gfz-potsdam.de/semisys/scripts/satellites/mauever.php](https://semisys.gfz-potsdam.de/semisys/scripts/satellites/mauever.php))) (accessed on 25 July 2022) \\  & BDS/QZSS detected in real-time mode [32; 33] \\ Ultrarapid orbit update interval & Hourly updated based on five-system POD processing within one hour \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Due to the lack of detailed surface geometric and optical information for GNSS satellites, the empirical CODE Orbit Model (ECOM1) [34], and its extended version ECOM2 [35], developed at the Center for Orbit Determination in Europe (CODE), are widely used in POD activities of IGS/iGMAS analysis centers. As to Galileo and BDS-3 satellites with the rectangular shapes, the most pronounced orbit errors caused by the inappropriate SRP model have been distinguished by the most recent researchers. A prior analytical SRP model for the Galileo constellation called the cuboid model or the DLR model has been established [36; 37], which could be used a priori together with the empirical ECOM model to improve the Galileo orbit. [PERSON] et al. [3], [PERSON] et al. [38], [PERSON] et al. [39] and [PERSON] et al. [40] have also conducted some research on using an adjustable box-wing (ABW) model [41] a priori to strengthen the ECOM model. In this contribution, a priori cuboid model is introduced to enhance the five-parameter ECOM1 model for BDS-3 CAST (China Academy of Space Technology) satellites, BDS-3 SECM (Shanghai Engineering Center for Microsatellites, CAS) satellites as well as Galileo satellites. For BDS-3 satellite cuboid modelling, the parameters of the cuboid (\(a_{C}\), \(a_{S}\), \(a_{A}\)) are estimated and adjusted in the post-POD processing mode using the a priori cuboid+ECOM1 model, while ECOM1-only parameters are taken as the reference value, which is similar to Galileo cuboid modelling. All BDS-3 satellite metadata are obtained from China Satellite Navigation Office (CSNO) [42].
## 5 Results
### Results of Signal Preprocessing, Wavelet Entropy Decomposition, and ICMLD-CFAR  
The two-dimensional pseudocolor image of the raw and preprocessed radar data for the experimental scenario in Figure 5 is shown in Figure 7.  
At 2.3 m and 6.8 m, the values of many range points of the flipped wavelet entropy are larger than those of the corresponding values of ICMLD-CFAR. Thus, the points with the maximum wavelet entropy values were considered as the target positions.  
After preprocessing, the size of the radar data is compressed from [2048, 40 \(\times\) 64] to [200, 40 \(\times\) 64]. The wavelet entropy decomposition and ICMLD-CFAR are then converted to the preprocessed radar data, as illustrated in Figure 8.  
Figure 6: Typical energy spectral density comparisons of construction machinery in the idling, digging, and moving modes of operation. Periodic frequency of healthy adult respiration (0.2-0.4 Hz) was marked between the two red dotted lines.  
Figure 7: Raw and preprocessed radar data when a human stands stationary at approximately 2.5 m from the UWB radar and when the digging wheel loader is approximately at 6 m. (**a**) Raw data, and (**b**) preprocessed data.
## 5 Conclusions  
This study conducts a long-term trend analysis of wind speeds at various heights in the urban areas of Beijing over the past 30 years. Building upon the optimal model, a non-stationary GEV model with time-varying functions is established. The obtained key parameters of design wind loads are compared and discussed in contrast to the standards. The main conclusions are as follows:  
1. During the process of urbanization, the near-surface wind speed in the urban areas of Beijing has shown a decreasing trend over the past 30 years, while at higher altitudes, there has been a slight increasing trend. Apart from the winter high-altitude wind speeds, the wind speeds in all seasons generally exhibit a decreasing trend.
2. The trend of the wind shear index is inconsistent with the overall wind speed trend, showing an increasing trend. This promotes the dispersion of ground pollutants into the atmosphere and indicates that more extreme wind speed events occur at higher altitudes.
3. The overall evaluation metrics of the non-stationary GEV model are superior to those of the stationary GEV model. The introduction of time-varying functions enhances the model's predictive capabilities, albeit with a stricter penalty on the number of parameters shown by the BIC criterion.
4. The stationary GEV model performs poorly at higher quantiles, yielding basic wind speed values lower than the standards. The results of the model considering only changes in the location parameter closely match those of the standards, but incorporating changes in the scale parameter yields better evaluation metrics, suggesting that the standards underestimate basic wind speed values in this scenario.
* In the urban areas of Beijing, the wind pressure height variation coefficients fall within the range specified for Class C and Class D terrain features in the standards. At lower heights, they closely resemble the values for Class C terrain features, while at higher heights, they exceed the values for Class C. The coefficients obtained using the calculation formula for Class D terrain features are generally higher than the measured wind pressure height variation coefficients.
* Conceptualization, X.J. and Z.Z.; Methodology, S.Y., X.J., Z.Z. and L.J.; Software, S.Y., Y.T., Z.Z. and L.J.; Validation, S.Y.; Formal analysis, X.J. and J.J.; Investigation, Y.T. and J.J.; Resources, X.J., Y.T. and J.J.; Data curation, S.Y., Y.T., J.J. and L.J.; Writing--original draft, S.Y.; Writing--review & editing, S.Y., X.J., Y.T., Z.Z. and L.J.; Visualization, S.Y.; Supervision, X.J.; Project administration, J.J.; Funding acquisition, X.J. All authors have read and agreed to the published version of the manuscript.
* The investigation was supported by the National Natural Science Foundation of China (No. 31570708).
* The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
## 2 Methodology
### Evaluation Metric of Prediction  
In order to evaluate the performance of the proposed model, four different indicators, including RMSE (see Equation (19)), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (denoted by \(R^{2}\)), are adopted as evaluation metrics.  
(1) MAE  
MAE is the average of the absolute error and can reflect the error between the predicted value and the actual value well. The smaller the MAE is, the higher the accuracy the prediction achieves. The formula is as follows.  
\[MAE=\frac{1}{s}\sum_{i=1}^{n}|\hat{y}_{i}-y_{i}| \tag{24}\]  
Figure 3: The proposed prediction framework.  
(2) MAPE  
MAPE represents the ratio between the error and the true value. The smaller the MAPE is, the closer the predicted value is to the true value. The formula is as follows.  
\[\mathit{MAPE}=\frac{1}{s}\sum_{i=1}^{s}\frac{\left|\bar{y}_{i}-y_{i}\right|}{y_ {i}} \tag{25}\]  
(3) Coefficient of determination  
The coefficient of determination (\(R^{2}\)) is the square of the correlation between predicted values and true values. The range of \(R^{2}\) is specified within [0,1]. The closer \(R^{2}\) is to 1, the more perfect fit the prediction model has. Therefore, \(R^{2}\) can be used as an important indicator. The formula is as follows.  
\[R^{2}=\frac{\sum_{i=1}^{s}\left(\bar{y}_{i}-\overline{Y}\right)^{2}}{\sum_{i=1 }^{s}\left(y_{i}-\overline{Y}\right)^{2}} \tag{26}\]  
where \(\overline{Y}\) is the mean of the observed value.
## 2 Materials and Methods
### Measurement Platform Configuration and Correction Principle
This is illustrated in Figure 3. In such a case, the reference distance values will be as follows: For \(L_{1}\), \(L_{2}\) and \(L_{3}\):  
\[L_{\rm wI}=L_{\rm I}\text{-}\cos\beta \tag{5}\]  
For \(L_{4}\), \(L_{5}\), \(L_{6}\) and \(L_{7}\):  
\[L_{\rm wII}=L_{\rm II}\text{-}\cos\alpha \tag{6}\]  
For \(L_{8}\) and \(L_{9}\):  
\[L_{\rm wIII}=L_{\rm III}\text{-}\cos\alpha \tag{7}\]  
For \(L_{10}\) and \(L_{11}\):  
\[L_{\rm wIV}=\sqrt{\left(L_{\rm I}\text{-}\cos\beta\right)^{2}+\left(L_{\rm III }\text{-}\cos\alpha\right)^{2}} \tag{8}\]  
For \(L_{12}\), \(L_{13}\), \(L_{14}\) and \(L_{15}\):  
\[L_{\rm wV}=\sqrt{\left(L_{\rm I}\text{-}\cos\beta\right)^{2}+\left(L_{\rm II} \text{-}\cos\alpha\right)^{2}} \tag{9}\]  
where  
\(\alpha\) is the transverse inclination angle of the measuring platform;  
\(\beta\) is the longitudinal inclination angle of the measuring platform. The platform's inclination angles are dictated by the track's geometric profile. At the installation site, the track was essentially level--both transverse and longitudinal inclinations were negligible compared to the tachymetric measurement uncertainty. Nevertheless, incorporating the effect of inclination angles \(\alpha\) (longitudinal) and \(\beta\) (transverse) on inter-antenna vector lengths is crucial when the track geometry includes significant gradients or cant. In extreme cases, the transverse cant angle may reach approximately \(6^{\circ}\), yielding a length correction coefficient \(L\) of about 0.99. Platform angles were recorded continuously by an IMU, and precise time synchronization with the GNSS allowed computation of the corresponding distance corrections at each measurement epoch. \[L_{1}=L_{2}=L_{3}=L_{1} \tag{2}\]0 Apparent reduction in distances between GNSS receivers due to the inclination of the measuring platform: (**a**) angle \(\alpha\) resulting from the longitudinal inclination of the track, (**b**) angle \(\beta\) resulting from the transverse inclination of the track. By comparing these corrected reference distances with the continuously acquired GNSS data along the route, one can rigorously assess the resulting measurement uncertainty.
# Integration of LTE 230 and LTE 1800 in Power Wireless Private Networks
## 2 High-Level Design of the PWPN
### LTE 230
\begin{table}
\begin{tabular}{c c c c} \hline
**Technical Index** & **LTE 1800** & **LTE 230** & **Summary** \\ \hline Carrier Frequency Band & 1785–1805 MHz & 223–235 MHz & LTE 1800: need to apply to government departments for spectrum resources \\  & & & LTE 230: power private frequency point, without additional application \\ \hline Working Bandwidth & 5–20 MHz & 1 MHz (40 discrete frequency points, each accounting for 25 KHz) & LTE 230: has small bandwidth and supports narrowband \\  & & & LTE 1800: has large bandwidth and supports broadband \\  & & 1 MHz (40 discrete and supports broadband \\  & & frequency points, each accounting for 25 KHz) & high-speed services \\ \hline Single Station & Under 5 MHz & Under 1 MHz bandwidth, the uplink peak rate is 21 & LTE 1800: supports broadband \\ Peak Rate & downlink peak rate is 21 Mbps; the uplink peak rate is 5 Mbps & the downlink peak rate is 1.76 Mbps; \\  & & rate is 5 Mbps & 0.5 Mbps \\ \hline Time delay & When the station height is 45 m, it covers an area of 2–4 km in urban areas and an area of 5–7 km in suburbs & When the station height is 45 m, it covers an area of 3–5 km in urban areas and an area of 6–8 km in suburbs & LTE 230: the coverage radius is relatively large \\ \hline  & & & LTE 1800: the coverage radius is relatively small \\  & & & LTE 230: the coverage radius is relatively large \\  & & and an area of 5–7 km in suburbs & 1800: the coverage radius is relatively small \\ \hline Time delay & Millisecond level & Millisecond level & \\ \hline  & & Based on LTE standard encryption mechanism and bi-directional & Based on LTE standard encryption mechanism and bi-directional & \\  & & identity authentication, it & identity authentication, it & \\  & & adopts a three-layer & adopts three-layer \\  & & security encryption & security encryption & Both have the same security \\ Security & system to realize & system to realize & policy \\  & & authentication, & authentication, \\  & & encryption, non-access & encryption, NAS \\  & & stratum (NAS) signaling & signaling encryption, \\  & & encryption, and & and end-to-end \\  & & end-to-end encryption & encryption & \\ \hline  & & & LTE 1800: meets the global unified standard, upstream and downstream industry chain are mature. \\ Industry Chain &
\begin{tabular}{c} Mature (Huawei, ZTE, \\ Dingqiao, Beijing Xinwei, \\ Potevio, etc.) \\ \end{tabular} & Potevio & LTE 230: does not meet the global unified standard, the supporting industrial chain is inferior. \\  & & & LTE 1800: video, voice, data, multimedia trunking services & LTE 230: multimedia trunking \\  & & & LTE 230: data \\  & & & LTE 230: data \\  & & & LTE 230: data \\ \end{tabular}
\end{table} 
Table 1: Comparison of long-term evolution (LTE) 230 and LTE 1800.