esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Accuracy Analysis of LSTM Network Models for Predicting Buildings' Settlement
### Analysis of the Effect of Construction Parameters on the Accuracy of Model  
When the soil body is in the state of equilibrium of geostress, the change in building settlement monitoring value is not obvious. In order to analyze the influence of different construction methods on the accuracy of building settlement prediction under different construction conditions, the settlement monitoring values of the buildings around the pit corresponding to the open excavation method and the full reversal method under conditions 2, 3, and 4 are selected as inputs, and the results obtained by using the LSTM model for prediction are shown in Figures 6 and 7. At the same time, the prediction models  
Figure 5: Predicted and monitoring of maximum settlements at measuring points.  
used for different conditions are also different, according to the aforementioned model hyperparameter optimization method to determine the parameters of the model for each condition, which are as follows: Condition 2 (batch size of 16, training set ratio of 80%), Condition 3 (batch size of 8, training set ratio of 60%), Condition 4 (batch size of 16, training set ratio of 70%).  
As can be seen from Figures 6 and 7, under different working conditions, the predicted building settlement corresponding to the open-cut method and the full reversal method basically coincides with the monitoring values. The average RMSE, MAE, \(R^{2}\), and relative errors of building settlement prediction under different working conditions are 0.81, 1.01, 0.78, and 18.06%, respectively, while the average RMSE, MAE, \(R^{2}\), and relative errors of building settlement prediction under full reversal method are 0.78, 0.85, 0.85, and 14.88%, respectively. This indicates that the settlement prediction of the surrounding buildings is more accurate when the pit is constructed using the full reversal method.  
Meanwhile, the prediction accuracy of the model varies under different working conditions, and the relative errors of the settlement predictions of the two construction methods are the largest in Working Condition 2 (19.86% and 15.87%, respectively), which may be due to the large disturbance to the soil body caused by grouting reinforcement, which causes the monitoring data to fluctuate greatly. This indicates that different working conditions have a large impact on the accuracy of building settlement prediction, and the  
Figure 6: Comparison of settlement monitoring values and predicted values for various conditions of the open-cut method.  
working conditions need to be taken into account when using the LSTM model to predict the settlement of buildings around the foundation pit.
## 2 Architecture Description  
The block diagram of the proposed switched 4-to-1 TCA is shown in Figure 5. It amplifies and combines the photocurrent from the four partitioned photosensitive cells into one voltage signal. The switched 4-to-1 TCA consists of four primary stages: (1) four over-current protection (OCP) circuits; (2) four switches, four current buffers; (3) a signal combiner; and (4) a post-amplifier. The OCP circuits prevent the fabricated chip from being damaged by a very high input signal. The switch is turned on when the reflected laser pulse arrives at the corresponding photodetector among the multiple detectors. The current buffer is a low impedance input stage intended to receive the optical current from a photosensitive cell. The signal combiner sums the outputs of all the current buffers. The post-amplifier is designed to preserve the bandwidth and to enhance the transimpedance gain. A balun is a differential amplifier with differential input signals biased at the same direct current (DC) level to convert the single-ended output of the signal combiner into a differential signal. The output buffer is a differential amplifier with resistor loads of 50 \(\Omega\) on both the positive and negative outputs. The schematic diagram of the designed circuit is illustrated in Figure 6.
## 3 Experimental Results
### Effect of the Opening and Penetrant Size
Meanwhile, for metallic pipes, where the heat-transfer path remains intact, the penetrant area and solid cross-section act as direct determinants of performance loss, showing a clearer correlation than only the opening size or firestop volume. This trend was consistently observed even under experimental conditions where only the pipe size varied while opening size remained unchanged.
## 4 Results  
As presented in Figure 6, the estimated electron number density on the axis of the plasma jet varies insignificantly, up to a distance \(d\) of about 3 mm downstream from nozzle exit when operating at a pulse frequency of 60 kHz. Thereafter, the values decrease for larger distances from the nozzle exit. The radial distribution of the electron number density increases somewhat unexpectedly by about 17% towards larger radial positions \(r\). Nonetheless, after consideration of the measurement error, the electron density can be assumed to be constant at \(0.52\times 10^{21}\) m\({}^{-3}\) for distances \(d<3\) mm and \(r<0.3\) mm.  
Similarly to the electron density, the temperature of the excited nitrogen molecule \(\overline{T}_{\mathit{SPS}}\) decreases from 6500 K to 5500 K across the analyzed distance of 7 mm downstream from nozzle outlet on the axis of the effluent jet. The temperature along the nozzle axis, i.e., for \(r=0\), can be assumed constant up to 3 mm and then gradually decreases with an approximate gradient of \(-250\) K/mm. A similar behaviour of the estimated temperature values is observed for the \((0,0)\)-transition of ionized nitrogen molecule, although the temperature difference is more significant within the analyzed distance range of 7 mm in comparison to the excited molecule. The \(\overline{T}_{\mathit{FNS}}\) values in the right-hand side of Figure 6 decrease from almost 8000 K to 4600 K on the nozzle axis, although a rather high deviation from the mean values is observed at a distance of 1 mm. This uncertainty is probably caused by imaging errors, as can be observed in Figure 7, and thus should be considered as an outlier. At \(d=2\) mm, the temperature falls radially from 7000 K on nozzle axis to about 6300 K at \(r=0.6\) mm, almost twice the value compared to a temperature difference of only 350 K in the case of SPS spectra. A zone of almost constant temperature of approx. 6600 K spreads between 3 mm to 5 mm from the nozzle exit. Nonetheless, considering the measurement errors and inaccuracies in the temperature estimation, the discrepancies of the \(\overline{T}_{\mathit{SPS}}\) and \(\overline{T}_{\mathit{FNS}}\) estimates appear to be insignificant and can be assumed to be equal in the analyzed distance range. Thus, the heavy particle temperature for the pulsed low-current discharge under study can be assumed to range between 6000 K and 7000 K in the proximity of the jet axis.  
Figure 6: Average electron density (\(\overline{n}_{e}\)) and heavy particle temperatures (\(\overline{T}_{\mathit{SPS}}\), \(\overline{T}_{\mathit{FNS}}\)) estimated on nozzle axis (first row of plots denoted with \(r=0\) mm) for different distances \(d\) downstream from nozzle outlet and for different lateral positions \(r\) at a distance \(d=2\) mm (second row of plots). In both cases, the experimental setup was triggered at peak current.
Using the spectral method, [PERSON] et al. [24,25] developed a set of algorithms for solving the dispersion characteristics of axisymmetric modes in multi-layer cylindrical structures. To date, there has been no research on the dispersion characteristics of non-axisymmetric modes in fluid-filled elastic tubes using the spectral method. In this paper, a new algorithm based on the spectral method is proposed, which obtains the dispersion curves of the axial propagation modes in fluid-filled elastic tubes. It can also calculate the displacement distribution and stress distribution of the axial propagation modes in the radial direction. The algorithm proposed here works well in spite of tube dimensions, wall thickness, layers and frequency ranges. The results of the algorithm are verified using a COMSOL simulation. We found that there existed some modes omission of the dispersion curves presented in the literature [26].
# Reversible Image Processing for Color Images with Flexible Control
## 3 Proposed Method
### Image Processing
#### 3.1.3 Recovery Information  
The proposed method embeds the recovery information to restore raw images. Three-bit data are first required to distinguish the five types of brightness controls: sharpening, smoothing, increase, decrease, and CE. One-bit data are further used to verify the numbers of the brightness controls to be conducted. This method can apply one or two functions for the brightness. When the CE function is selected, the other recovery information is the same as in the previous method [13]; otherwise, we need the following information:  
1. For Sharpening and Smoothing As described in Section 3.1.1, the sharpening and smoothing functions cause rounding errors in the \(Max^{\prime}\) and \(Min^{\prime}\) values. These errors prevent us from restoring the raw images. Thus, we need to record the rounding errors as recovery information. If a large number of rounding errors arises, then the amount of recovery information may exceed the hiding capacity. Two location maps are prepared for \(Max\) and \(Min\) to store the pixels with rounding errors. Our method compresses the maps and error values according to the JBIG2 standard [20] and Huffman coding, respectively. The above data are required for every single process. Additionally, the parameter values of \(I_{VSH}\) or \(I_{VSM}\) should be stored for reversibility.
2. For Brightness Increase or Decrease The bin data before image processing (hereafter the intact bin data) need to be stored. The intact bin data mainly consist of three types of data. The first is the eight-bit pixel value of the reference bin in Step 2. The second is the one-bit classification data for distinguishing whether the reference bin is empty or not in Step 3. In the case where the reference bin is not empty, extra one-bit data are essential for each integrated pixel to discriminate the original bin, namely the reference or adjacent bin. The above data are required for every single process.  
Figure 4: Histogram transition of \(Max\) and \(Min\) for brightness increase function. (**a**) Raw histograms and integrated histogram of \(Max\) and \(Min\) (**b**) In case the reference bin is empty. (**c**) In case the reference bin is not empty. (**d**) Separation into \(Max^{\prime}\) and \(Min^{\prime}\).  
The recovery information is embedded into the RGB components in the final step. In this paper, we use the PEE-HS method [19], which is one of the most typical methods in this area. The PEE-HS method can hide information in a prediction error histogram with the minimum perceptional distortion, so many extended methods have been proposed based on it. Note that we can use another RDH method on behalf of this method.  
To reduce the amount of recovery information, we further modified the preprocessing of an efficient RDH method [4] and introduced the extended one into our method. Here, the preprocessing is conducted on the integrated histogram of the \(R\), \(G\), and \(B\) components to restrain the hue distortion. In contrast, a PEE-HS method is applied to RGB components independently.
## 4 CL-APCC Protocol
### Network Congestion Control Methods  
Congestion has local relevance. That is, congestion would normally appear in a number of nodes in the local network. Furthermore, the control method of node-level congestion cannot entirely reflect the real status of a network. Therefore, CL-APCC respectively analyzes a single node and the data flow of the grid where the node lies. Based on the analysis, the rate control method is adopted in advance to avoid congestion occurring.  
Here, a simple description is made to introduce the congestion method of the node-level and system-level. Assume that the occupied node memory size is \(L\), the maximum node memory size is \(m\), and threshold value is \(L_{\max}\)[15]. Firstly, we describe the congestion control method of the node-level. If the occupied node memory size is \(L<L_{\max}\), CL-APCC identifies the node isn't congested. If the memory size is \(L_{\max}\!<\!L<m\), then CL-APCC identifies the congestion of node-level to have probably occurred. In this case, the sending rate of the node is adjusted to control the congestion. If the occupied node memory size reaches the maximum( \(L=m\)), the input rate of the node is adjusted to 0. Secondly, we describe the congestion control method of the system-level. At the beginning of a new period \(t\), CL-APCC protocol makes a prediction for the expected value \(E(\mathrm{t})\) of data quantity in period \(t\) using the average rate in period \(t-1\) in the grid. If \(E(t)<n\,\times L_{\max}(n\) is the number of nodes in the grid), congestion of system-level does not occur. On the contrary, if \(n\,\times m>E(t)>n\,\times L_{\max}\), congestion of system-level may occur. In this case, the average inputting and outputting rates of the network are adjusted to avoid system-level congestion (because we strictly control the margin between the inputting and outputting rate, it could not occur \(E(\mathrm{t})>n\,\times m\)).  
In practical applications, according to the status of network resource, CL-APCC separately sets a weight for node-level and system-level to obtain the real rate of each node. Assume that the total outputting rate of the upper nodes is equal to the inputting rate of the lower nodes. This means that the outputting rate of node \(i\) is decided by the inputting rate of node \(i+1\). Each node obtains its data retransmission rate according to the relationship between inputting and outputting (the model of the sending rate is shown in Figure 3).  
Now, the congestion methods of the node-level and system-level are described in detail below. Node \(i\) is analyzed in a certain grid \(k\).
## 3 Results and Discussion
### Inertia and Friction Coefficient  
Figure 8 shows the comparison of the simulated speed and the experimental one. According to Equation (45), the inertia and friction coefficient of the induction machine were \(J=0.38\) (g\(\cdot\)m\({}^{2}\)) and \(B=0.61\) (mN\(\cdot\)m/(rad/s)). This result also determined the objective function of Equation (44) as 8.388 (rad/s), which was a rather low value. It confirmed that the parameters obtained by this method were quite consistent with the actual situation.  
Figure 8: Comparison of speeds.  
Figure 7: Comparison of currents.  
However, it was found that the objective function of the mechanical system was larger than the objective function of the equivalent circuit. The reason was due to the non-linear relationship between the mechanical load and the rotor speed, where the well-known wind loss is the third power of the rotor speed. Therefore, fixing the operating conditions will inevitably result in errors.  
In addition, errors occur in the steady state. The reason is that the polynomial fraction optimization optimizes the curve segment, and if the optimization result has a more accurate estimation in the transient state, there will be a less accurate estimation in the steady state; similarly, if the steady-state signal is increased, that of the transient will have poor estimation results.  
This method uses polynomial fractions to calculate the curve parameters and will vary depending on the curve segment. It estimates the induction machine parameters in two stages: equivalent circuit parameters and mechanical system parameters. The equivalent circuit parameter estimation range was \(s=0\)-1, and the analysis scope was clear, so a stable result could be obtained. The mechanical system parameter estimation range was \(t=0\)-\(T\), where \(T\) is the time of the sampling section, which will vary due to the length of the steady-state selection period, and different \(T_{s}\) will produce different parameters. As a result, the estimation of the mechanical system parameter changes greatly. This is a common problem faced by the estimation methods.
## 4 Results and Analysis
### Stepwise Regression Model  
#### 4.2.1 Correlation Analysis between Meteorological Factors and the Occurrence of Forest Fires  
Meteorological factors play a crucial role in determining the occurrence of forest fires [53; 54]. As illustrated in Figure 6, there is a significant negative correlation between the annual average rainfall and wind speed and the frequency of forest fires in Yunnan Province (\(p<0.01\)). Similarly, a notable negative correlation exists between annual average rainfall and the area affected by fires. In contrast, there is no direct correlation between the annual average temperature and humidity and the occurrence and spread of forest fires.  
This can be attributed to the significant diurnal temperature variations and humidity fluctuations in Yunnan. With July's highest temperatures ranging from 19 \({}^{\circ}\)C to 22 \({}^{\circ}\)C and January's lowest temperatures remaining above 6 \({}^{\circ}\)C to 8 \({}^{\circ}\)C, the province experiences an annual temperature fluctuation of about 10 \({}^{\circ}\)C to 12 \({}^{\circ}\)C. This indicates that the average temperature and relative humidity in the province do not directly affect the rate of forest fires and fire damage area [55].
# Comparing End-to-End Machine Learning Methods for Spectra Classification
## 3 Data-Driven End-to-End Neural Network Methods
### Convolutional SCT Attention Network  
More generally, the spectral data can be regarded as typical 1D time series data. In this setting, several other classification models can be explored. As a powerful network, CNN can learn features from different scales, but unfortunately, it cannot capture feature dependencies in a global view. To solve this problem, some methods involving attention mechanisms have been proposed [5; 26; 47; 46; 63], which investigate attention across single or multiple dimensions. Inspired by the study [26; 63], we designed a lightweight convolutional Spatial-Channel-Temporal (SCT) attention network for the application of time-resolved spectra classification, as shown in Figure 3.  
Figure 3: Illustration of convolutional SCT attention network architecture. In this architecture, attention is calculated across spatial, channel and temporal dimensions.  
Figure 2: Illustration of end-to-end FCNN with automatically capturing weighting factors model.  
The framework consists of two branches. The upper branch models the global contextual information in the temporal dimension. The lower branch model captures global dependencies across the channel and temporal dimensions. These two branches are then aggregated together to better represent features. Finally, a Feed Forward module and a FC module are applied for the classification task.
## 4 Conclusions  
In this study, we investigated the effect of DCE addition on the electrochemical properties of graphite in a concentrated PC-based electrolyte solution. The viscosity drastically decreased in the concentrated electrolyte solution by the addition of DCE, which led to improved ionic conductivity. DCE has low electron-donating ability, so it did not coordinate to lithium ions in the concentrated electrolyte solution. Consequently, reversible intercalation and de-intercalation took place at graphite in the concentrated PC-based electrolyte solution containing DCE, in addition to the enhancement of electrochemical performance. This result indicated that an effective SEI could be formed at graphite despite the decrease in the total molality of LiPF\({}_{6}\) upon the addition of DCE. Raman spectra of the electrolyte solution confirmed that the solvation structure of the lithium ions, which enables the formation of an effective SEI, was maintained in the concentrated PC-based electrolyte solution containing DCE. This result suggests that the addition of a co-solvent having low electron-donating ability is an effective method for improving the electrochemical performance in concentrated electrolyte solutions. However, the reversible capacities gradually decreased in the concentrated electrolyte solutions containing DCE, because DCE also decomposed during the initial charge process. Moreover, DCE is unsuitable for application to commercial LIBs because of its high  
Figure 3: Raman spectra of (**a**) PC, (**b**) DCE, (**c**) PC/DCE = 3:1 (molar ratio), (**d**) PC/LiPF\({}_{6}\) = 3:1 (molar ratio), (**e**) PC/LiPF\({}_{6}\)/DCE = 6:2:1 (molar ratio), (**f**) PC/LiPF\({}_{6}\)/DCE = 3:1:1 (molar ratio), and (**g**) PC/LiPF\({}_{6}\)/DCE = 3:1:2 (molar ratio).  
toxicity and low anodic stability, which need to be resolved for practical LIBs. Despite these bottlenecks, we suggest a new strategy for forming an effective SEI and improving the ionic conductivity in concentrated electrolyte solutions by controlling the solvation structure of lithium ions. In future studies, it will be necessary to clarify the reason for the high ionic conductivity so that suitable co-solvents can be chosen to ensure high safety in practical LIBs. In addition, it will be necessary to prevent the decomposition of the co-solvent while improving the cycling ability, so that the electrochemical performance of LIBs will be substantially improved.  
Conceptualization, H.-Y.S.; methodology, M.-H.J. and H.-Y.S.; investigation, M.-H.J.; writing--original draft preparation, H.-Y.S.; writing--review and editing, H.-Y.S. and S.-K.J.; supervision, S.-K.J.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2017R1A2B4010544 and NRF-2019R1A4A1021237). This work was also supported by the Soonchunhyang University Research Fund.  
The authors declare no conflict of interest.
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 4 Results
### Comparison of Single-Wavelength and Multi-Wavelength LiDAR
The OA  
Figure 5: Results of Case 1. (**a**) Reference image; (**b**) the result of Case 1-1; (**c**) the result of Case 1-2; (**d**) the result of Case 1-3; (**e**) the result of Case 1-4. Figure 6: Completeness and correctness for different classes in Case 1-1 to Case 1-4.  
of classified results were similar to the features from 1064 nm and 1550 nm (90% and 92%) (Figure 7), but misclassifications still appeared for grass and soil (Figure 6).