esa-sceva/satcom-chunk-collection huggingface dataset:
# Ground Deformation Associated with Deep Excavations in Beijing, China
## 2 Typical Details concerning Deep Excavation
### Excavation Method  
The open cut method is a fast and efficient method, and widely used for excavating deep pits. It can be divided into two major types, including the sloped open cut and cantilever open cut. Some studies have shown that when the excavation depth is more than 5 m, sometimes the sloped scheme is not necessarily more economical. The cantilever open cut method has been widely used in the Beijing subway project. Over the past three decades, 210 stations have been constructed on 8 lines, including lines 4, 5, 6, 7, 8, 9, 10, and 14; of these, 126 stations have been constructed using the open cut method, which accounts for 60% of the total stations (Table 2).  
In Beijing, the maximum depth of practical projects that are constructed using the cantilever open cut method without support is up to 8 m, which can reach 15 m when using a sloped scheme. Figure 1 depicts a layered and segmented schematic diagram of an open-cut foundation pit in Beijing. Zonal sections are selected at a horizontal and vertical extent of 5-20 m, depending on the site's geometry and condition of the equipment. The excavation begins in one zone, while the rest of the area is left to support the wall of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Soil**} & **Elastic** & **Poisson’s** & **Cohesion** & **Friction** & **Density** \\  & **Modulus** & **Ratio** & **(kPa)** & **(’c)** & **Angle** & **(g/cm\({}^{3}\))** \\ \hline Backfill soil & 8–15 & 0.382 & 0 & 8 & 1.6 \\ Silt & 13–25 & 0.342 & 14 & 27.5 & 2.03 \\ Silty clay & 9–16 & 0.342 & 33.3 & 14.5 & 1.97 \\ Silty sand & 20–40 & 0.336 & 0 & 32 & 2.0 \\ Gravel & 70–120 & 0.263 & 0 & 38 & 2.15 \\ Sand & 25–45 & 0.336 & 0 & 40 & 2.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical characteristics of soils.  
excavation zone. Then, the support system in the excavation zone should be installed first, and excavation starts in the rest of the area. Temporary lateral support is provided by earth beams to maintain the stability of the retaining wall during construction. This process continues in stages as the excavation develops. Symmetrical excavation, the timely section of supports, and a reduction in exposure time without support are adopted. Several levels of horizontal elements (e.g., tie-backs or internal bracing) are used to brace the excavation, and they are arranged at a vertical spacing of 3-5 m side-by-side, which is installed in 16 h.
## 5 Results in Material Constants
\begin{table}
\begin{tabular}{c c c c c c} \hline
**LTE Mode** & \(f_{r}\) **(kHz)** & \(f_{a}\) **(kHz)** & \(k_{31}\) & \(\mathcal{S}_{11}^{E}\)(\(\times 10^{-12}\)**m\({}^{2}\)/N)** & \(\mathcal{S}_{11}^{D}\)(\(\times 10^{-12}\)**m\({}^{2}\)/N)** \\ \hline Sample 1 & 47.6 & 51.05 & 0.4019 & 14.2670 & 11.9626 \\ Sample 2 & 47.75 & 50.9 & 0.3851 & 14.0978 & 12.0070 \\ Sample 3 & 48.05 & 51.05 & 0.3756 & 14.1336 & 12.1402 \\ Sample 4 & 47.6 & 50.6 & 0.3772 & 14.3118 & 12.2758 \\ Sample 5 & 47.15 & 50.75 & 0.4114 & 14.4951 & 12.0416 \\ \hline Avg. Value & – & – & 0.3902 & 14.2611 & 12.0854 \\ \hline \end{tabular}
\end{table} 
Table 6: Resonant and anti-resonant frequencies and the converted material constants in length thickness extension (LTE) mode. \begin{table}
\begin{tabular}{c c c c c c} \hline
**TE Mode** & \(\mathcal{C}_{I}\)**(_nF_)** & \(\varepsilon_{33}^{T}\)(\(\times 10^{-9}\)**F/m**)** & **TS Mode** & \(\mathcal{C}_{L}\)** **(nF)** & \(\varepsilon_{11}^{T}\)(\(\times 10^{-9}\)**F/m**)** \\ \hline Sample 1 & 49.0810 & 28.1089 & Sample 1 & 4.7493 & 30.9323 \\ Sample 2 & 52.4846 & 29.9669 & Sample 2 & 4.7647 & 31.0326 \\ Sample 3 & 52.7355 & 29.9197 & Sample 3 & 4.8435 & 31.4987 \\ Sample 4 & 52.3802 & 29.8813 & Sample 4 & 4.2233 & 27.5341 \\ Sample 5 & 50.9098 & 29.1944 & Sample 5 & 4.2036 & 27.3714 \\ \hline Avg. Value & – & 29.4142 & Avg. Value & – & 29.6738 \\ \hline \end{tabular}
\end{table} 
Table 4: Capacitance and dielectric constant measured at 1 kHz in TE and TS mode. \begin{table}
\begin{tabular}{c c c c c c} \hline
**LE Mode** & \(f_{r}\) **(kHz)** & \(f_{a}\) **(kHz)** & \(k_{33}\) & \(\mathcal{S}_{33}^{E}\)(\(\times 10^{-12}\)**m\({}^{2}\)/N)** & \(\mathcal{S}_{33}^{D}\)(\(\times 10^{-12}\)**m\({}^{2}\)/N)** \\ \hline Sample 1 & 132.75 & 170 & 0.6631 & 20.1927 & 11.3143 \\ Sample 2 & 130 & 163.5 & 0.6453 & 20.9192 & 12.2074 \\ Sample 3 & 134.5 & 169.25 & 0.6459 & 19.4678 & 11.3468 \\ Sample 4 & 128.5 & 170.5 & 0.6946 & 21.7746 & 11.2705 \\ Sample 5 & 133 & 172.5 & 0.6749 & 20.0189 & 10.9016 \\ \hline Avg. Value & – & – & 0.6648 & 20.4746 & 11.4081 \\ \hline \end{tabular}
\end{table} 
Table 5: Resonant and anti-resonant frequencies and the converted material constants in length extension (LE) mode. vibration characteristics and performed experimental measurements to determine the accuracy of applying these material constants to dynamic analysis.
# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## 2 Materials and Methods
### CMIP6 GPP Data  
The monthly GPP data from 23 available CMIP6 models are used in the study, with the unit of kgCm\({}^{-2}\)s\({}^{-1}\). The period is over 1850-2100, including the historical and future projection period. More specifically, the projection period of 2015-2100 contains the four most interesting scenarios: SSP1-2,6, SSP2-4,5, SSP3-7.0, and SSP5-8.5. The model and GLASS GPP data are first uniformly interpolated to 2\({}^{\circ}\) due to the inconsistent spatial resolution. Meanwhile, it is necessary to unify the unit of model simulation data and GLASS GPP to gCm\({}^{-2}\)d\({}^{-1}\). The first member, r1i1p1f1, is selected if possible (r1i1p1f2 is used if unavailable). CMIP6 GPP can be downloaded from [[https://esgf-node.llnl.gov/projects/cmip6/](https://esgf-node.llnl.gov/projects/cmip6/)]([https://esgf-node.llnl.gov/projects/cmip6/](https://esgf-node.llnl.gov/projects/cmip6/)) (accessed on 9 June 2023). The information on the 23 models used is listed in the Table 1 below.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 3 Results
### Intensive Small Area Mapping by UAV
#### 3.1.4 Comparison of Classification Bands  
Here, the optimal bands to train the classifier with are investigated, again for the 2 July image of the C1 channel. The MicaSense RedEdge camera has red, green, blue, red edge and near-infrared bands. In addition to the raw bands, the classifier was also trained with common indices which are computed from these bands. The results are in Table 7. The vegetation indices taken in isolation do not perform well, probably because of the overlap between land and weed vegetation indices as seen in Figure 5. Taking four raw bands (B, G, R, NIR) is optimal, and is used in the following investigations. The addition of the red edge band is not necessary for this application, but may be more important for higher dimensional classification (for example, to classify different weed species).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Classifier** & **Kappa** \\ \hline Minimum Distance & 0.91 \\ Support Vector Machine & 0.91 \\ Random Forest & 0.93 \\ Naive Bayes & 0.95 \\ Classification And Regression Tree & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classifier accuracy for different classifiers using the kappa value.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Predicted**} \\ \cline{3-5}  & & **Water** & **Land** & **Weed** \\ \hline \multirow{3}{*}{Actual} & Water & 1000 & 0 & 0 \\  & Land & 23 & 953 & 24 \\  & Weed & 0 & 4 & 996 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Error matrix for the 2 July untreated channel (U) image using optimum classification parameters.
## 4 Analysis of Position-Holding Strategy of Constellation Stations
### Constellation Absolute-Station-Holding Strategy  
Based on the relationship between the semi-major axis deviation and the phase deviation, as well as the calculation method of the limit cycle control period and the semi-major axis change, the absolute-phase-holding strategy of the satellite constellation is presented in this section. Absolute phase holding is a method for the autonomous phase holding control of a single satellite, and it is suitable for the absolute station holding of large low-orbit constellations. The control flow chart of the satellite absolute control strategy is shown in Figure 2.  
The control process of the satellite absolute control strategy is as follows:  
1. The initial semi-major axis deviation relative to the reference star before satellite control \(a_{0}\) and the corresponding initial phase angle deviation \(\lambda_{0}\) are obtained.
2. The high -precision orbit propagator (HPOP) model was used to extrapolate the satellite orbit's average value of the semi-major axis and phase angle. The attenuation rate of the satellite's average value of the semi-major axis was calculated by curve fitting the variations of the average value of the semi-major axis attenuation rate \(\dot{a}_{d}\) and the range of the phase angle drift ring \(2\Delta\lambda_{0}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Control Strategy** & **Advantages** & **Disadvantages** \\ \hline Absolute control & Simple control, high reliability, mature control methods & Many orbital control tasks, large relative fuel consumption \\ \hline Relative control & Reduced frequency of control and lower fuel consumption & Measurement and control system is under great pressure, scheduling algorithm is complex, onboard autonomy is required to be high \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of control strategies.  
Figure 2: Flow chart of absolute satellite control.  
Based on the average value of the semi-major axis attenuation rate \(\dot{a}_{d}\) and phase angle drift ring range \(2\Delta\lambda_{0}\), the semi-major axis deviation target value of the phase-holding orbit maneuver within a phase-holding period \(\Delta a\) is calculated.
4. The change in the semi-major axis that maintains control is calculated: \(\Delta a_{w}=\Delta a+\Delta a_{0}\).
5. The disparity between \(2\Delta\lambda_{0}\) and \(\lambda_{0}\) is calculated. If the disparity is less than the tolerance value (\(d\lambda<\epsilon\)), the process proceeds to step 6; otherwise, the process returns to step 1 for iterative correction.
6. Orbit control (lifting or lowering the semi-major axis of the satellite) is carried out on the satellite at this moment to achieve the goal of satellite phase maintenance.
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## Appendix A
### Ternary Classification  
For ternary classification, any three of the six surfaces were compared with each other and classification accuracies were computed. This resulted in a total of 20 ternary combinations, as shown in the parallel coordinate chart (Figure 11). In most of the cases, the best average classification accuracies of around 96% were achieved with the features set computed from the lower back mounted sensor and both SVM and RF produced comparable results. The lowest average classification accuracies of up to 91% were observed with the features set computed from the chest sensor.  
Figure 12 shows the confusion matrices of all of the 20 ternary terrain combinations using SVM on all features computed form the lower back sensor data. The best ternary classification accuracy of above 96% was achieved among carpet, asphalt, and soil, whereas the lowest accuracy of 89% was observed among carpet, concrete, and tiles as well as grass, asphalt, and soil. In the case of RF, the average classification accuracy for the lower back sensor remained above 93% when trained with all features. For the chest sensor, both SVM and RF produced an average classification accuracy of 92%. The precision, recall, and F1-score remained above 89% in all cases. Detailed results are presented in Table 11.  
**Figure A1.** Parallel coordinate chart of _ternary classification_. The results were obtained with all features set (194 features) for each classifier and for each sensor location.  
**Figure A2.** Confusion matrices of _ternary classification_ with lower back sensor. All results were obtained with all features set (194 features) using SVM classifier. Classes: \(C_{T}^{Ca}=carpet\), \(C_{T}^{Ca}=concrete\), \(C_{T}^{Gr}=grass\), \(C_{T}^{As}=Asphalt\), \(C_{T}^{So}=soil\), and \(C_{T}^{Ti}=tiles\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Classification Category**} & \multirow{2}{*}{**Sensor**} & \multicolumn{10}{c}{**Accuracy (\%)**} & \multicolumn{10}{c}{**Statistical Analysis**} \\ \cline{3-14}  & & **All Features** & **Temporal Features** & **Spectral Features** & **3D Accelerations** & **3D Angular Velocities** & **Precision** & **Recall** & **f1-Score** \\ \hline \multirow{3}{*}{**Ternary**} & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF & SVM & RF \\ \cline{2-14}  & Lower Back & 93.77 & 93.55 & 93.31 & 93.87 & 89.4
# Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques
## 7 Experimental Findings and Results
### Focus Area Analysis with Explainable Model  
Various preprocessing and training were performed to train the time-frequency characteristics related to emotion in the input data. Explainable techniques were subsequently used to confirm the features' components used for classifying the emotions in the model. Figure 24 shows the characteristics of the frequency domain. The activation for each emotion can be determined based on the statistics of the entire dataset. In the case of anger and happiness, relatively high-frequency regions are commonly activated, whereas in the case of neutrals and sadness, lower-frequency regions are activated.  
Figure 23: Performance comparison with existing models (accuracy, recall, and F1 score).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Recall** & **F1 Score** & **Training Time** \\ \hline Before GDS & 86.54\% & 0.8681 & 0.8653 & 89.87 (min) \\ Results & 85.82\% & 0.8614 & 0.8583 & 70.62 (min) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of performance before and after Gaussian data selection (GDS).  
Figure 25 shows the activated area when the speech obtained using lines related to anger is converted into a spectrogram. The focused area is analyzed using the trained model and Grad CAM. The above method can be seen in the red area, where the time-frequency region influences the decision-making of the model. It is confirmed that the model focuses on areas with large values and combines areas with various values to classify emotions.  
Figures 26-29 show images that verify the model's decision-making process by applying Grad CAM, LIME, and occlusion sensitivity to the speech spectrograms. In images in which the explainable technique is applied, areas of the input spectrogram that influence the model's decision-making can be identified. Grad CAM uses gradient information through class scores in the model. Therefore, it has a high level of interpretation regarding which areas are important. In the case of LIME and occlusion sensitivity, by checking the extent to which a certain area influences the model classification, the areas that affect classification differently from Grad CAM can be determined.  
Figure 24: Emotional activation frequency region: (**A**) angry, (**B**) happy, (**C**) neutral, and (**D**) sad.  
Figure 25: Anger emotion spectrogram and Grad CAM focus area.
# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
## 7 Comparison with Other Mobility Models  
Furthermore, the performance of the proposed model was compared to the performance of another three mobility models, namely, the Random Waypoint mobility model, the Gauss Markov mobility model, and the Depth First-based mobility model, using the same performance metrics. The simulation scenarios presented in Section 5.1 were executed on all models to obtain results and compare the models under the same circumstances. Tables 5-7 compare the performance of the four models. The three mobility models used in the comparison are discussed in Sections 7.1-7.3.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Speed**} & \multicolumn{4}{c}{**Network Size**} \\ \cline{2-5}  & **26 Nodes** & **51 Nodes** & **76 Nodes** & **101 Nodes** \\ \hline
5 & 15.89 & 31.09 & 13.19 & 12.60 \\
10 & 14.99 & 32.07 & 13.28 & 13.32 \\
15 & 15.27 & 31.95 & 13.35 & 13.16 \\
20 & 14.35 & 31.94 & 13.20 & 16.20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Packet Delivery Ratio.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multirow{2}{*}{**Speed**} & \multicolumn{4}{c}{**Network Size**} \\ \cline{3-5}  & & **26 Nodes** & **51 Nodes** & **76 Nodes** & **101 Nodes** \\ \hline \multirow{4}{*}{Proposed Model} & 5 & 192.12 & 31.45 & 19.33 & 78.69 \\  & 10 & 166.56 & 28.58 & 26.65 & 151.50 \\  & 15 & 633.29 & 51.74 & 30.60 & 84.37 \\  & 20 & 262.34 & 73.93 & 267.47 & 140.39 \\  & 5 & 1070.78 & 3590.09 & 5344.26 & 7348.23 \\  & 10 & 1069.12 & 4247.96 & 4665.35 & 6904.05 \\  & 15 & 811.182 & 3507.06 & 5812.7 & 4860.76 \\  & 20 & 993.193 & 3535.66 & 6268.34 & 6077.27 \\  & 5 & 578.377 & 4642.27 & 6166.82 & 6084.23 \\ Gauss Markov Model & 10 & 231.716 & 3631.09 & 4142.33 & 7122.23 \\  & 15 & 461.982 & 2907.25 & 3492.46 & 4196.06 \\  & 20 & 579.801 & 3803.8 & 5322.32 & 6334.38 \\  & 5 & 153.439 & 2790.29 & 7334.31 & 12,043.8 \\ Depth First-Based Model & 10 & 116.511 & 3152.62 & 5937.32 & 11,173 \\  & 15 & 145.011 & 3335.92 & 6229.31 & 11,422.9 \\  & 20 & 392.171 & 3718.6 & 5352.08 & 11,506.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: End-To-End Delay Performance Comparison.
## 3 Results
### The Quantitative Changes GPP and ET with Summer Drought  
In the whole NADR, with a dryness increase (e.g., from humid to ED summer), the mean value of summer GPP and ET decreased by 0.36 g C m\({}^{-2}\) day\({}^{-1}\) for GPP and 0.18 mm day\({}^{-1}\) for ET. Additionally, the sensitivity of GPP and ET to summer drought also depends on biomes. As Figure 6 shows, GPP and ET for grassland and barren/sparse vegetation also decreased with increased dryness, while cropland and mixed forest presented opposite trends. Figure 6b,c quantified how much the percentage of GPP and ET changed from normal summers to dry summers. In the whole NADR, the difference of GPP (or ET) between normal and moderate summer (Normal-MD, \(-3.72\%\) for GPP; \(-5.95\%\) for ET) was less than that between normal and severe, extreme drought summer (Normal-SD: \(-6.85\%\) for GPP and \(-8.74\%\) for ET, Normal-ED: \(-10.54\%\) for GPP and \(-12.77\%\) for ET). Barren areas and grassland presented similar trends with NADRs by different change rate values, and the larger change rates (Normal-ED: \(-14.70\%\) for GPP and \(-17.58\%\) for ET) occurred in grassland, whereas cropland, ENF and MF have opposite trends. GPP and ET of MF increased \(11.89\%\) and \(11.34\%\) from a normal to extreme drought summer, respectively. Notably, although GPP (or ET) presented a positive and negative relationship with SPEI for shrubland and cropland, respectively, they first increased then decreased at shrubland (i.e., Normal-MD: \(+4.13\%\) for GPP, \(+3.59\%\) for ET; Normal-SD: \(-18.61\%\) for GPP, \(-13.24\%\) for ET) and decreased their amplification (i.e., Normal-MD: \(+5.85\%\) for GPP, \(+5.29\%\) for ET; Normal-SD: \(+2.10\%\) for GPP, \(+0.19\%\) for ET).  
Moreover, the impacts of abrupt dry-wet shifts (e.g., normal summer followed by a dry summer) on GPP and ET were also examined among different biomes (Figure 6e,f), which illustrated the lower change rates (Normal-ED: \(-4.44\%\) for GPP, \(-0.92\%\) for ET) but similar trends with multiple drought impacts (Figure 6c,d) in NADRs. Similar results also occurred in almost stable biomes except grassland of GPP (Figure 6e). To account for the difference, we classified the grassland into six sub-classes in China based on vegetation  
Figure 5: The linear slope between SPEI and (**a**) GPP anomaly, (**b**) ET anomaly for 2003–2015; the person coefficients boxplots between SPEI and (**c**) GPP anomaly, (**d**) ET anomaly among different biome types.  
species information and the vegetation map as we mentioned at Section 2.1. The statistics results suggested that the GPP of temperate meadow and alpine meadow increased when the ecosystem experienced a transition from a normal summer to an extreme drought summer (Normal-ED, Figure 6g), which may explain the not obvious changes of grassland GPP in Figure 6e. Notably, the reduction of GPP for MS, TS and DS reached the maximum for the case of transition from a normal summer to a severe drought summer (Normal-SD) and not for an extreme drought summer (Normal-ED).
## 3 Results
### Computational Efficiency  
To evaluate the computational efficiency of the involved methods, the number of trainable parameters and the floating point operations (FLOPs) are shown in Table 4.  
This represents the total number of floating point arithmetic operations performed by the model during inference or training, making it a key factor in measuring the computational resource requirements of a model. Higher FLOPs generally indicate more complex models that demand greater computational resources. The FLOPs of a convolutional layer can be calculated as:  
\[FLOPs=2\times k_{H}\times k_{W}\times C_{in}\times H_{out}\times W_{out}\times C _{out} \tag{6}\]  
Figure 6: Sinogram domain loss \(l_{sine}\) vs. image domain loss \(l_{image}\) in terms of (**a**) PSNR and (**b**) SSIM for reconstructed CT images, and (**c**) PNSR for predicted sinograms.  
where \(k_{H}\times k_{W}\times C_{in}\) is the size of the convolution kernel. The size of the output feature map is \(H_{out}\times W_{out}\). \(C_{out}\) is the output channel size.  
The results show that in comparison to the other two baseline networks, our network exhibits reduced parameter requirements and demands fewer floating point operations. Thus, our network is more computationally efficient.