esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate Prediction and Key Feature Recognition of Immunoglobulin
## 3 Results and Discussion
### Key Feature Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Selection** & **Classifier** & **TPR** & **FPR** & **Precision** & **MCC** & **auROC** & **ACC** \\ \hline \multirow{8}{*}{CC–PSSM} & \multirow{3}{*}{MRMD1.0} & NB & 0.890 & 0.113 & 0.892 & 0.781 & 0.950 & 0.8904 \\  & & MLP & 0.904 & 0.101 & 0.908 & 0.810 & 0.946 & 0.9035 \\  & & SVM & 0.868 & 0.139 & 0.877 & 0.744 & 0.865 & 0.8684 \\ \cline{2-10}  & \multirow{3}{*}{MRMD2.0} & NB & 0.846 & 0.158 & 0.849 & 0.694 & 0.875 & 0.8465 \\  & & MLP & 0.825 & 0.177 & 0.825 & 0.648 & 0.877 & 0.8246 \\  & & SVM & 0.803 & 0.202 & 0.804 & 0.605 & 0.801 & 0.8026 \\ \cline{2-10}  & \multirow{3}{*}{MRMD 1.0+2.0} & NB & 0.886 & 0.121 & 0.895 & 0.780 & 0.955 & 0.8859 \\  & & MLP & 0.921 & 0.081 & 0.921 & 0.842 & 0.934 & 0.9211 \\  & & SVM & 0.895 & 0.112 & 0.902 & 0.796 & 0.891 & 0.8947 \\ \hline \multirow{8}{*}{MonoTriKGap} & \multirow{3}{*}{MRMD1.0} & NB & 0.535 & 0.439 & 0.576 & 0.119 & 0.563 & 0.5351 \\  & & MLP & 0.526 & 0.461 & 0.537 & 0.068 & 0.505 & 0.5263 \\  & & SVM & 0.531 & 0.509 & 0.562 & 0.052 & 0.511 & 0.5307 \\ \cline{2-10}  & \multirow{3}{*}{MRMD2.0} & NB & 0.491 & 0.500 & 0.496 & –0.009 & 0.514 & 0.4804 \\  & & MLP & 0.491 & 0.499 & 0.497 & –0.008 & 0.501 & 0.4912 \\  & & SVM & 0.522 & 0.522 & 0.522 & 0.000 & 0.500 & 0.5219 \\ \cline{2-10}  & \multirow{3}{*}{MRMD 1.0+2.0} & NB & 0.539 & 0.442 & 0.561 & 0.108 & 0.572 & 0.5395 \\ \cline{1-1}  & & MLP & 0.522 & 0.454 & 0.550 & 0.081 & 0.514 & 0.5219 \\ \cline{1-1}  & & SVM & 0.522 & 0.522 & 0.522 & 0.000 & 0.500 & 0.5219 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results of different two–dimensional key feature recognition methods.
## 4 System Evaluation and Analysis
### Effect of Decision Factors on Distinct Network Density  
According to Equation (1), we try to find the optimum decision factor to maintain the system balance between reference ratio and energy status. Hence, we applied our methods to \(\alpha\) = 0, 0.5 and 1 respectively. The system is evaluated with the distinct network density (from 0 to 50), and the network times are gathered in Figure 11. In Figure 11, we collect all the network executing time till the network shutdown. According to the data, we can distinguish that the decision factor with \(\alpha=0\) and 0.5 retained the longer network execution time. On the contrary, the decision factor with \(\alpha=\)1 is with the less performance. Hence, the system balance should be addressed with the consideration with reference ratio and energy status. When the network density is small, the factor with energy status is more important than the reference ratio. Furthermore, when the network density increases, the system should focus on the reference ratio to retain the structure's balance, instead of the energy status. The intermediate nodes could be wasting energy when the system counts to the same nodes to connect with other nodes. Thus, in our system, we all adopted \(\alpha=0.5\) as the optimum value.  
Figure 10: The computation time on different device densities.
## 3 The Process of Calculating One-Dimensional Consolidation Settlement  
This section provides a step-by-step procedure for calculating the one-dimensional consolidation settlement using the proposed equations.  
1. Fit the experimental consolidation data to the VCL using the AJOP equation (Equation (6)) to obtain four parameters \(\Gamma,R,\alpha\), and \(\theta\).
2. Fit the experimental consolidation data using Equation (8) to obtain three parameters for the upward-facing parabola (unloading portion) \(C\), \(h\), and \(k\).
3. Fit the experimental consolidation data using Equation (18) to obtain a parameter \(\Psi\ \geq 0\) for the downward-facing parabola (reloading portion). (\(\Psi=0\) for symmetric hysteresis).
4. Divide the soil to be analyzed for settlement into the sublayers.  
Determine the initial \(\sigma^{\prime}_{ini}\) and the final \(\sigma^{\prime}_{f}\) stresses resulting from construction at the middle point of each layer. The elastic solution can be used for simplification.
6. For the overconsolidated clay, it is necessary to calculate more steps as follows: (6a) Calculate the maximum past stress \(\sigma^{\prime}_{p}=\)\(\mathit{OCR}\cdot\sigma^{\prime}_{ini}\) which will be used as the unloading point \(\sigma^{\prime}_{\theta}\) on the VCL of each layer. (6b) Calculate the void ratio of the unloading point \(e_{\theta\mathit{new}}\) at \(\sigma^{\prime}_{p}\) in the step (6.1) using AJOP equation. (6c) Determine the parameters (\(C^{\prime}\), \(h^{\prime}\), and \(k^{\prime}\)) = \(f\)(C, \(h\), and \(k\)) of the unloading of each midpoint. (6d) Calculate the void ratio of the initial stress \(e_{int}\), which will be the same value of the void ratio of the starting point of reloading process \(e_{\omega}\). (6e) Determine the parameters (\(C_{1}^{\prime}\), \(h_{1}^{\prime}\), and \(k_{1}^{\prime}\)) = \(f\)(C, \(h\), and \(k\)) of the reloading of each mid-point. (7) Calculate \(\Delta e\) between the initial and final stresses mentioned above in step (5) as follows: (7a) For NC (normally consolidated) clay, use Equation (7). (7b) For OC (overconsolidated) clay, use Equation (19) or (21). (8) Calculate the consolidation settlement of the \(i^{th}\) layer using \(\rho_{i}=H_{i}\Big{[}\frac{\Delta e_{i}}{1+\rho_{i}}\Big{]}\). (9) The total consolidation settlement can be calculated as the sum of the settlements for each individual layer; i.e., \(\rho=\sum\limits_{i=1}^{n}(\rho_{i})\) when n is the number of soil layers.
# X-ray Free Electron Laser Accelerator Lattice Design Using Laser-Assisted Bunch Compression
## 4 Bunch Compressors
### First Bunch Compressor
#### 4.1.1 BC1 Using a Conventional C-Chicane  
The design of the conventional C-chicane of four bending magnets is very straightforward, as used in lattice 1. The design parameters are shown in Table 3. Two weak quadrupoles, 0.05 m in length, were used to reduce the dispersion functions at the end of the bunch compressor, and they were inserted between the first and the second, as well as between the third and the fourth, bending magnets. The magnetic field gradients of all weak quadrupoles concerned in this paper were limited to below 5 T/m.  
Due to the CSR effect and the dispersion caused by the quadrupoles interacting with a beam with an energy chirp, the projected horizontal normalized emittances at the end of the bunch compressor increased to 81.7 nm rad while the projected vertical normalized emittance was unchanged, at 65.8 nm rad.  
The performance of the BC1 using a conventional C-chicane is shown in Figure 5b.i-iii. The central section of the output beam carried a current of 500 A while the beam emittance  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{3}{c}{**Value**} \\ \hline  & \(\alpha_{x}\) & 16.15 & \\  & \(\beta_{x}\) & 679.6 & m \\  & \(\alpha_{y}\) & 16.15 & \\  & \(\beta_{y}\) & 679.7 & m \\ Horizontal norm. emittance & \(\epsilon_{n,x}\) & 65.9 & nm rad \\ Vertical norm. emittance & \(\epsilon_{n,y}\) & 65.9 & nm rad \\ Horizontal rms beam size & \(\sigma_{x}\) & 174.6 & \(\mu\)m \\ Vertical rms beam size & \(\sigma_{y}\) & 174.6 & \(\mu\)m \\ Unco. frac. energy spread & \(\sigma_{\delta,\partial}\) & \(2.91\times 10^{-6}\) & \\ Co. frac. energy spread & \(\sigma_{\delta}\) & \(1.12\times 10^{-2}\) & \\ Energy chirp coefficient & \(h\) & 24.3 & m\({}^{-1}\) \\ rms beam length & \(\sigma_{z}\) & 477.3 & \(\mu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Twiss parameters and other beam properties at L1 exit. Beam energy 750 MeV.  
Figure 5: Performance comparison of BC1 configurations in a conventional C-chicane (lattice 1) and a double C-chicane (lattice 2). The electron beam is divided into 1000 slices (**a**) at the exit of L1, (**b**) at the exit of BC1 using a conventional C-chicane, and (**c**) at the exit of BC1 using a double C-chicane. (**a**).**i, (**b**).**i, and (**c**).**i show the normalized emittances and the currents of each slice along the beam; (**a**).**ii, (**b**).**ii, and (**c**).**ii show the distributions of the transverse centroids; (**a**).**iii, (**b**).**iii, and (**c**).**iii show the distributions of the average transverse momentum shifts.  
of each slice was preserved. The comparison between Figure 5a.iii and b.iii indicated that CSR exerted transverse kicks on the beam as it traversed the bending magnets of BC1. However, the comparison between Figure 5a.ii and b.ii showed that there was little change to the beam transverse centroid shift profile as the beam transited from the L1 exit to the BC1 exit.
# Safety, Security and Privacy in Machine Learning Based Internet of Things
## 5 Research Challenges, and Future Directions  
Research issues, challenges, solutions and future directions in the field of IoT security are as follows.  
* Data Security and integrity: In machine learning techniques, reliable data for datasets and training data are very important to develop an accurate machine learning technique. A training dataset with low-quality data may interrupt the implementation of a particular learning technique. Thus, authenticated training datasets are crucial in ML techniques to secure the IoT network [74]. Furthermore, it is very easy for any hacker to learn the attack type and device vulnerabilities, and be able to manipulate the dataset used in the ML technique. Hence, it is a significant challenge to ascertain how the date can be secured and to detect different types of attacks and their probability of occurrence in any IoT environment. In other words, data security and integrity is challenging the future research field of IoT security.
* Backup Security Mechanisms: Usually, it is difficult to accurately state and estimate the network attack in an IoT environment in case of a \"bad\" defense policy in the learning process. Sometimes, this can cause disaster and drastic loss for IoT networks. Backup security mechanisms may also solve this difficulty protecting IoT systems from the exploration of the learning process. More mechanisms need to address this issue by incorporating ML-based security schemes to provide reliable, resilient and secure IoT services by reducing the risks of selecting bad policies.
* Privacy Problems: Privacy is a common issue in IoT environments. In IoT environments, Smart devices, such as sensors and wearable devices, are used to exchange data and information, and the users are not fully aware of where and how their personal information is shared via these devices. IoT smart devices carry the private and personal information of the clients and users, which may be misused. Every IoT device has security protocols to communicate with other devices, i.e., encryption and authentication. Privacy disclosures, leakage, and threats are crucial challenges that make users hesitate to adopt these technologies [75].
* Computational Cost: Many ML-based techniques require a substantial amount of training datasets and a complicated process for feature extraction, creating high computational costs and increasing the complexity of the system. Therefore, it is challenging to find new ML techniques with low communication and computational costs [76].
* Infrastructure Issues: A weak infrastructure always makes it easier for attackers to hack through the software. This is also known as a zero-day attack and is very difficult to determine using traditional security suits and schemes. It is, therefore, essential to build a strong and smart infrastructure to develop a secure IoT system [77]. Safety and Security features must be considered and need to be included in every phase of the IoT system.
## 3 Dataset Description
### HFAVD Dataset Structure
#### 3.3.2 Heart Rate  
The heart rate estimation approach proposed by [40] was employed in this study. The methodology involved the following steps: (a) Extraction of the Region of Interest (Face): Landmarks obtained from the 3 DDFA_V2 framework [41] were utilized to extract the Region of Interest, specifically the face, from the input data. (b) Processing through a Vision Transformer: The extracted face region was then processed through a Vision Transformer model, incorporating multi-skip connections. This process generated features from five different levels. (c) Block processing: The output from each level of the Vision Transformer was passed through a block structure. This block structure consisted of a Bidirectional Long Short-Term Memory (BiLSTM) layer, batch normalization, 1D convolution, and a fully connected layer. (d) Averaging of block outputs: The outputs from the five different blocks were averaged to obtain the minimum, maximum, and mean heart  
Figure 2: Models used to label the videos.  
rates. (e) Weighted averaging: The minimum, maximum, and mean heart rates were then combined by using a weighted averaging scheme to estimate the final heart rate. This model demonstrated superior accuracy in heart rate estimation compared with previously published methods, as evidenced by its performance on the LGI-PPGI and V4V datasets, achieving mean absolute errors (MAEs) of 2.68 and 9.996 bpm, respectively. Nevertheless, it is important to note that the dataset utilized for training the model exhibited a lack of samples representing both very low and very high heart rates. Consequently, the model may produce inaccurate estimations when presented with subjects exhibiting specific medical conditions characterized by extreme heart rate values.
# Seismic Stability of Dual Tunnels in Cohesive-Frictional Soil Subjected to Surcharge Loading
###### Abstract  
In this study, a self-developed adaptive finite element limit analysis (AFELA) code was adopted to explore the stability of dual tunnels in cohesive-frictional soil subjected to NADG and seismic action. Parametric studies of different influential factors, including the depth of tunnels, horizontal distance between tunnels, seismic acceleration coefficient, unit weight, cohesion and internal friction angle of soils, were conducted using the AFELA code. An adaptive meshing technique was adopted for optimal accuracy and efficiency, and a pseudostatic method was used to simulate the seismic action. Strict upper bound (UB) and lower bound (LB) results with relative errors of less than 7% were acquired. Detailed design tables were presented to facilitate the engineering design, and three typical failure patterns, including single side-wall failure, half-cross-shaped failure and cross-shaped failure, corresponding to different stable levels, were summarized for a deeper insight into how the failure mechanism evolved under different conditions. The results indicated that the variations in soil unit weight and void depth affected the seismic bearing capacity almost linearly. Furthermore, the dual tunnel system is vulnerable to seismic actions, and the stability of tunnels was further undermined by the adverse effects of additional seismic-caused interactions between two adjacent tunnels.  
seismic bearing capacity; adaptive finite element limit analysis; tunnel stability; dual tunnels Article
## 2 Dataset and Study Area
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Planting Conditions**} & **Growing Seasons** & **Training/Testing** \\  & & **Over a Full Year** & **Dataset (Pixels)** \\ \hline
1 & Spring corn \(\rightarrow\) Vegetables (SC-Veg) & 2 & 1620/1000 \\
2 & Peach & 1 & 2036/1000 \\
3 & Forest & 1 & 1014/1000 \\
4 & Greenhouse (GH) & - & 780/500 \\
5 & Vineyard & 1 & 2122/1000 \\
6 & Winter wheat \(\rightarrow\) Corn (WW-Cron) & 2 & 3656/1000 \\
7 & Vegetables & 3 & 1426/1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of planting conditions and the training/testing datasets. Figure 1: Geolocation of the study area. series (as shown in Figure 2). Atmospheric correction was performed using the Sen2 Cor tool, which is a processor for Sentinel-2 Level-2A product generation and formatting. Following atmospheric correction, three commonly used vegetation indices (VIs) were calculated for each of the original time series because they have been confirmed to be highly related to the growth cycle and physical features of crops. The three VIs are normalized difference vegetation index (NDVI) [44], green chlorophyll vegetation index (GCVI) [45], and enhanced vegetation index (EVI) [46], and their formulas are as follows:  
\[\mathrm{NDVI}=\frac{\mathit{Nir}-\mathit{Red}}{\mathit{Nir}+\mathit{Red}}, \tag{1}\]  
\[\mathrm{GCVI}=\frac{\mathit{Nir}}{\mathit{Green}}-1, \tag{2}\]  
\[\mathrm{EVI}=\frac{2.5*(\mathit{Nir}-\mathit{Red})}{(\mathit{Nir}+6.0*\mathit{ Red}-7.5*\mathit{Blue}+1)}, \tag{3}\]  
where _Nir_, _Red_, _Green_, _Blue_ is the spectral reflectance of the band, respectively. NDVI is usually interpreted as a useful indicator of LAI and photosynthetic capacity of vegetation; however, a saturation phenomenon appeared at high leaf area biomass. The development of GCVI and EVI is largely aimed at solving the saturation problem in NDVI. Additionally, GCVI has been found to have the most linear relationship with leaf area index (LAI) when compared with other VIs, and EVI is capable of reducing the influence of some atmospheric effects by including blue bands and is proportional to the vegetation biomass. These three VIs were selected to express the variation trend of each kind of vegetation among a full year in the aspects of photosynthetic capacity, vegetation biomass, and LAI, and then reveal the difference between a vineyard and other classes.
## 4 Research Results
### Influence of Rainwater on Measurement Using the UPV Testing Method
#### 4.1.1 Testing Slab V1 SFRC  
The results of the ANOVA calculation are summarized in Table 3 and in the graphical form in Figure 9, where are depicted boxplots of the ultrasonic wave transmission with respect to the factor level represented by the number of rain days (r0 = no rain, r1 = one day of rain, r7 = seven days of rain).  
The ANOVA showed that the hypothesis of the same means was rejected (\(p\)-value is smaller than the significance level 0.05). Therefore, the Tukey post hoc test was conducted. Its results are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **df** & **SS** & **MS** & & \\  & **Degrees of** & **Sum of** & **Mean Sum of** & **F-Statistics** & \(p\)**-Value** \\  & **Freedom** & **Squares** & **Squares** & & \\ \hline factor (rain) & 2 & 2.330 & 1.1649 & 13.26 & 1.25\(\cdot 10^{-5}\) \\ \hline residuals & 72 & 6.326 & 0.0879 & & \\ \hline \multicolumn{5}{c}{Bartlett test of homogeneity of variances: K = 0.4492, \(p\)-value = 0.7989} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the analysis of variance (ANOVA) for Slab V1.  
Figure 9: Boxplots of the ultrasonic wave transmission time for Slab V1 and all rain levels (r0, r1 and r7).
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 3 Results
### Impacts of Urban Topographic Change on Surface Water Runoff  
The results of the surface water flow simulation revealed that the flow patterns within the USGS NED DEM (Figure 15A) were significantly modified at Site 1 (Figure 15B). The fused DEM (Figure 15C) shows that the new flow patterns seamlessly transition from the fused area into the existing landscape without artificial accumulation along the fused edges. The simulation highlights the impact of grading on water flow, with water pooling on the graded construction site and capturing surface water runoff in the stormwater control ponds decreasing both the depth and flow rate into Walnut Creek.  
Micro basins derived from the USGS NED DEM and the fused DEM revealed that the development preserved the existing basins' flow directions even with some modifications to the shape of the basins. The basins derived from the fused DEM outline the contributing areas for each of the stormwater control ponds and areas draining into Walnut Creek or the road on the southern side of Site 1 (Figure 16).  
Figure 14: Elevation and slope maps at 3 m resolution: (**A**) USGS NED DEM, (**B**) Fused DEM, (**C**) USGS NED DEM slope, and (**D**) Fused DEM slope.
# Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
## 3 Results
### Correlation Analysis of Remote Sensing Factors and Biomass  
Because the optical remote sensing band information records the information received by the sensor through electromagnetic radiation, the vegetation index is a measure of surface vegetation status. Texture feature information is the structural feature formed by the regular distribution or change in color in an optical image. In optical images, texture reflects the change law of an image's gray value or color. The SAR backscattering coefficient contains abundant information on ground objects. Therefore, these remote sensing parameters were extracted and three variables, namely forest elevation, slope and canopy, were added. There were 86 variables in total. Their correlation statistics with forests are shown in Table 1.  
Figure 2: Biomass distribution map of various tree species.  
The results showed that 35 feature factors extracted from Landsat 8 OLI images were correlated with the forest biomass of the plot (\(p<0.05\)), and 22 texture features were correlated with the forest biomass of the plot (\(p<0.05\)). The Landsat 8 band2 had the highest correlation, with a correlation coefficient of \(-0.342\). Among the vegetation index, NDPI had the highest correlation with biomass, and the correlation coefficient was 0.323. Among the characteristic factors extracted from Sentinel-1A radar images, seven factors were correlated with biomass (\(p<0.05\)), and the other five factors were negatively correlated, except entropy. In other aspects, the correlation coefficients of altitude and canopy density with biomass were 0.404 and 0.328, respectively.  
In summary, a total of 44 factors were correlated with biomass, among which 21 factors were strongly correlated with biomass at the (\(p<0.01\)) level. Because the following three models need appropriate variables, too many variables will increase the amount of calculation and affect the stability of the model, but too few variables will not be conducive to the correct construction of the model. At the same time, a too strong or too weak correlation between variables will also affect the construction of the model. Therefore, factor 21, which showed a strong correlation at the level of 0.01, was screened and put into three models.