esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Localization Algorithm
### ICP Based Position Correction  
The position correction can be obtained by matching the digital map with the detected lane and guardrail data, which is explained in Section 3. The high definition digital map utilized in this study is constructed by the Korean NGII in vector type. In Korea, the NGII is working on establishing a map standard by making maps available for autonomous driving.  
A two-dimensional ICP algorithm is used for map-matching in order to obtain position correction information. [PERSON] initially introduced the method of point-to-plane matching [13]. The point-to-plane ICP algorithm searches for the best transformation between the environment representation and the map by repeating itself iteratively until the alignment error is less than a threshold. Where there is a difference in the error metric function, the point-to-plane algorithm minimizes error along the surface normal vector instead of minimizing the Euclidean distance between the road feature and the digital map. The proposed error metric function is shown as  
\[J=\sum\
olimits_{i=1}^{N}\left\|\eta_{i:}(R\cdot p_{i}+T-q_{i})\right\|^{2} \tag{2}\]  
where \(p_{i}=\left[p_{i,x},p_{i,y}\right]^{T}\) shows the point clouds acquired from the environment sensors, vision, and radar, and \(q_{i}=\left[q_{i,x},q_{i,y}\right]^{T}\) indicates the points of the HD map with regard to the host vehicle's local frame. \(R\) and \(T\) represent the transformation matrices, rotation and translation, by using ICP matching. Additionally, \(\eta\) represents the normal surface at \(q_{i}\).  
Figure 4: Major characteristics of an HD map: estimate current vehicle position with a variety of road information such as road curvature.  
Since we used two-dimensional matching in this study, \(R\), \(T\) can be shown below:  
\[R=\left[\begin{array}{cc}\cos\left(r\right)&-\sin\left(r\right)\\ sin\left(r\right)&\cos\left(r\right)\end{array}\right]\text{, }T=\left[\begin{array}{c}t_{x}\\ t_{y}\end{array}\right] \tag{3}\]  
where the matching result is expressed as \(r,t_{x},t_{y}\). \(r\) is rotation angle in radian, and \(t_{x},t_{y}\) is the amount of translation in the direction of x and y of the local vehicle frame. In order to correct the vehicle position, this result will be applied. Let \((X_{V},\psi)\) represent the present global vehicle position. The result of ICP-matching obtained \((X^{*}\
u,y^{*})\) is shown below:  
\[\begin{array}{c}X^{*}_{V}=R_{\psi^{*}}T+X_{V}\\ \psi^{*}=r+\psi\end{array}\text{ \ \ where }R_{\psi}=\left[\begin{array}{cc}\cos (\psi)&-\sin(\psi)\\ sin(\psi)&\cos(\psi)\end{array}\right] \tag{4}\]
## 4 The Factors for Degrading Performance of Localization
### Signal Quality Deterioration  
The quality of the wireless signal can be degraded by various factors, including noise and the medium through which the signal travels. Noise can be produced by various sources, such as other wireless devices, and electrical equipment. The presence of noise can make it difficult for the receiver to distinguish between the desired signal and the unwanted noise, which can result in bit error, packet loss, and reduced signal strength in the estimation of the location.  
Moreover, atmospheric conditions can also have a significant impact on the performance of localization systems that rely on radio signals. This is because atmospheric conditions can cause the radio signal to be attenuated or refracted, leading to errors in signal timing or strength. For example, changes in temperature, humidity, and pressure can make the atmosphere more or less dense, which in turn affects the speed of radio waves. This can cause signal delays or phase shifts, leading to errors in range measurements.  
Additionally, atmospheric phenomena such as ionospheric scintillation, which is caused by charged particles in the ionosphere, can cause rapid fluctuations in signal strength and phase. This can lead to errors in position estimates or even complete signal loss.
## 4 Conclusions  
This study presents an innovative deep learning predictor, CSpredR, specifically designed for multi-site mRNA subcellular localization prediction. The method utilizes graph construction combined with two encoding techniques to perform fusion encoding on graph nodes, which are then input into a hybrid neural network model for feature extraction and final prediction. For feature encoding, CSpredR transforms sequences into an advanced graph structure and applies word2 vec and fasttext encoding techniques to process graph nodes. This approach enhances the richness of sequence features, providing high-quality inputs for the subsequent neural network. The neural network component employs a parallel architecture with CNN and Bi-LSTM to extract key features from sequences, which are then fused and fed into a multi-head attention mechanism to capture dependencies between sequences. The multi-head attention mechanism achieves parallel computation, improving efficiency and enhancing text processing performance. Finally, the output from the MLP is mapped to multiple labels, enabling multi-label classification. Evaluation results  
Figure 4: Performance comparison of prediction models, (**A**) Performance evaluation of the model in terms of ACC and Average precision. (**B**) Performance evaluation of the model in terms of one-error, Ranking loss and Hamming loss.  
show that CSpredR outperforms existing methods across most metrics on both benchmark and independent test datasets, demonstrating superior performance and robustness. By effectively integrating advanced techniques and neural network models, CSpredR has made notable progress in the field of mRNA subcellular localization. However, we believe there is still room for improvement. In the future, we will focus on developing new tools to further enhance the accuracy of mRNA subcellular localization prediction, aiming for a deeper understanding and more precise prediction of mRNA functions and roles.  
Conceptualization, W.S.; methodology, W.S.; supervision, X.W. and R.W.; validation, X.W.; writing--original draft, W.S.; writing--review and editing, X.W. and R.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by funds from the Key Research Project of Colleges and Universities of Henan Province (No. 22A520013, No. 23B520004), the Key Science and Technology Development Program of Henan Province (No. 232102210020, No. 202102210144), and the Training Program of Young Backbone Teachers in Colleges and Universities of Henan Province (No. 2019 GJS132).  
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare there are no conflicts of interest.
## 4 The Proposed Binary White Shark Optimizer
### Discretization Strategy
8:for\(i\ =\ 1\) to \(n\) to \(\mathbf{do}\)
9:\(v_{k+1}^{i}=\mu[v_{k}^{i}+p_{1}\Big{(}w_{gbest_{k}}-w_{k}^{i}\Big{)}\times c_ {1}+p_{2}\ \Big{(}w_{best}^{i}-w_{k}^{i}\Big{)}\times c_{2}]\)
10:endfor
11:for\(i\ =\ 1\) to \(n\) to \(\mathbf{do}\)
12:if\(rand<mv\)then
13:\(w_{k+1}^{i}=w_{k}^{i}\cdot\rightleftharpoons\oplus w_{o}+u.a+l.b\)
14:\(w_{k+1}^{i}=transferFunction\ \Big{(}w_{k+1}^{i}\ \Big{)}\)
15:else
16:\(w_{k+1}^{i}=w_{k}^{i}+v_{k}^{i}/f\)
17:\(w_{k+1}^{i}=transferFunction\ \Big{(}w_{k+1}^{i}\ \Big{)}\)
18:endif
19:endfor
20:for\(i\ =\ 1\) to \(n\) to \(\mathbf{do}\)
21:if\(rand<s_{s}\)then
22:\(\overset{\rightarrow}{D}_{w}=\left|rand\ \times\Big{(}w_{gbest_{k}}-w_{k}^{i} \Big{)}\right|\)
23:if\(i==1\)then
24:\(w_{k+1}^{i}=w_{gbest_{k}}+r_{1}\overset{\rightarrow}{D}_{w}sgn(r_{2}-0.5)\)
25:\(w_{k+1}^{i}=transferFunction\ \Big{(}w_{k+1}^{i}\ \Big{)}\)
26:else
27:\(w_{k+1}^{i}=w_{gbest_{k}}+r_{1}\overset{\rightarrow}{D}_{w}sgn(r_{2}-0.5)\)
28:\(w_{k+1}^{i}=\frac{w_{k}^{i}+w_{k+1}^{i}}{2\times r\wedge d}\)
29:\(w_{k+1}^{i}=transferFunction\ \Big{(}w_{k+1}^{i}\ \Big{)}\)
30:endif
31:endif
32:endfor
33: Adjust the position of the white sharks that proceed beyond the boundary
34: Evaluate and update the news positions
35:\(k=k+1\)
36:endwhile
37: Return the optimal solution obtained so far
```  
**Algorithm 2** Pseudo-code of binary WSO  
Figure 4 presents a block diagram illustrating the step-by-step workflow of the proposed bWSO algorithm. The process begins with the input of the dataset into the algorithm. Next, the algorithm parameters are initialized, and binary white sharks are generated randomly. Each search agent (white shark) is evaluated using a fitness function. The positions of the agents are then updated based on the relevant formulas, followed by the application of transfer functions for the discretization process. The fitness values are  
recalculated according to the new positions. This loop continues until the maximum number of iterations is reached. Once the iteration limit is exceeded, the best feature subset is returned as the output. This diagram aims to visualize how each component of the algorithm operates systematically.
# Base Pounding Model and Response Analysis of Base-Isolated Structures under Earthquake Excitation
## 2 Models and Equations of Motion  
### Base Pounding Model  
There are two methods to investigate impact behavior, the classical dynamics method and the contact element method. The classical one cannot reflect the change of impact force, deformation and collision duration and other elements. Furthermore, it is difficult to implement in finite element analysis. Therefore, it has limited scope of use [32; 33; 34]. The contact element method is easy to implement in software with high precision. Consequently, the contact element method is adopted in this paper. Research conducted by [PERSON] et al. [30] shows that linear viscoelastic model can provide enough accuracy in engineering. Thus, a linear spring-gap element was used in this base bounding theoretical model. Figure 1 presents the linear spring-gap element. Figure 2 and Equation (1) present its force-displacement relation.  
\[f_{p}=\left\{\begin{array}{cc}0&\left|x_{0}\right|<x_{gap}\\ k(\left|x_{b}\right|-x_{gap})&\left|x_{0}\right|\geq x_{gap}\end{array}\right., \tag{1}\]  
where \(f_{p}\) is the impact force, \(k\) is the stiffness of linear spring-gap element, \(x_{0}\) is the relative displacement, and \(x_{gap}\) is the initial width of clearance.  
Figure 1: Linear spring-gap element.
###### Abstract  
This paper presents a comparative study between two maximum power point tracking (MPPT) algorithms, the incremental conductance algorithm (InC) and the fuzzy logic controller (FLC). The two algorithms were applied to a low photovoltaic power conversion system, and they both use different PI controllers and grid synchronization techniques. Moreover, both InC and FLC methods have Clarke and Park Transformation. To some extent, the incremental conductance and fuzzy logic controller approaches are similar, but their control loops are different. Therefore, the InC has classic Proportional Integrative (PI) controllers with simple phase-locked loops (PLL). At the same time, the FLC works with fuzzy logic PI controllers linked with the Second Order Generalized Integrator (SOGI). The proposed techniques examine the solar energy conversion performance of the photovoltaic (PV) system under possible irradiance changes and constant temperature conditions. Finally, a performance comparison has been made between InC and FLC, which demonstrates the effectiveness of the fuzzy controller over the incremental conductance algorithm. FLC turns to convert photovoltaic power easily, decreasing fluctuations, and it offers a quick response to the variation of solar irradiance (shading effect). The simulation results show a superior performance of the controller with fuzzy logic, which helps the inverter convert over 99% of the power generated by the photovoltaic panels. In comparison, the incremental conductance algorithm converts around 80%.  
solar panel; single-phase inverter; MPPT; Fuzzy Logic Controller; incremental conductance; fuzzy PI controllers; SOCI-PLL
# A Review of the Evolution Characteristics and Argillization of Clay Interbeds in Rockslides
## 3 Parameter Changes during the Argillization Process
### Clay Minerals in the Process of Argillization
#### 3.5.1 Expansibility of Clay Minerals  
The expansiveness of clay minerals is not only related to their properties but also restricted by the orientation of the bedding plane in landslides. With the increase in the angle between the bedding surface and the end surface, the expansion angle decreased gradually. When the angle between the bedding surface and the end surface was 0 degrees, the maximum expansion rate was 3.54%. This explains the high probability of clay interpeds existing in gentle dip landslides [20]. The expansiveness of clay minerals under different cementing methods was also found to vary [45]. Calcareous cement would produce a greater expansion force than siliceous cement.  
The hydrophilic swelling of clay minerals mainly causes the hydration of groundwater. The mineral's crystal form changed after absorbing water. It further alters the bonding of clay minerals, causing the entire structure to become loose and destroyed. Among them, montmorillonite is the clay mineral with the strongest expansiveness. So, it plays a controlling and promoting role in the overall hydration process [46].
## 2 Experimental Setup  
The experimental setup is shown in Figure 1(a). The light source was based on a Ti:sapphire multi-pass amplifier system (790 nm, 30 fs, 0.85 mJ at 1 kHz, Femtopower compactPro, FEMTOLASERS) with a Dazzler (Fastlite) stretcher and a transmission grating compressor. The MIR generation scheme is basically the same as our previous experiments [18; 22]. However, one important difference is that a delay plate and a dual wave plate were inserted in the path of the co-propagating fundamental and SH pulses, and used to adjust the delay and the polarization of the fundamental and SH pulses inline [20; 30; 38; 39] instead of separating them at first and combining them together by using dichroic mirrors. SH (\(\omega_{2}\), 15 \(\mu\)J) was generated by Type-I frequency doubling the collimated fundamental beam in a 0.1 mm thick BBO crystal cut at 29\({}^{\circ}\). It was easy to achieve more than 10% efficiency of the SHG. However, we tilted the crystal from the optimal angle for two reasons. One is to reduce the conversion efficiency to avoid back conversion effects that distort the fundamental beam profile, and the other is to generate SH at slightly longer wavelengths, which helps to generate a broad MIR spectrum. Temporal walkoff in the doubling crystal and wave plate was compensated by a delay plate, which was a 1.7-mm-thick calcite crystal. A dual wave plate (\(\lambda\) at 400 nm and \(\lambda/2\) at 800 nm) was used to adjust the relative polarizations of the fundamental and SH pulses. The beam was focused into nitrogen at around atmospheric pressure by a concave mirror (\(r=-1\) m), and a bright filament was generated with a length of \(\sim\)3 cm around the beam focus. This filament generated an MIR pulse (\(\omega_{0}\)) through an FWM process (\(\omega_{1}+\omega_{1}-\omega_{2}\rightarrow\omega_{0}\)). The energy of this MIR pulse was measured as \(\sim\)0.5 \(\mu\)J by using a pyroelectric detector (J-10 MB-LE, Coherent). With this energy level, it is possible to apply the pulsesfor the nonlinear spectroscopy of condensed matter. The pulse-to-pulse intensity fluctuation was about 1.6% rms while that of the amplifier output was about 1.0% rms.
## 3 Methodology
### Threat Model: Addressing Security Attacks on ECUs
#### 3.1.3 Wireless Communication Attacks  
ECUs in CAVs are equipped with wireless communication technologies such as radio communication, Bluetooth, Wi-Fi, DSRC, cellular networks, and GNSS, transforming CAVs into open systems and creating potential threats [41; 42]. Wi-Fi has become an appealing target for a wide range of security threats, carrying more than 75% of the Internet traffic in the last mile [43]. Cybercriminals often engage in blocking and spoofing GNSS signals to compromise authenticity and integrity [44; 45; 46; 47]. Cellular networks face security threats like sniffing and jamming attacks [48].
# Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale
## 3 Results and Analysis
### Performance Analysis of Machine Learning Models  
Multiple machine learning models were selected for optimal training for regression prediction and compared, including the SVR model (Figure 4a), SA-SVR model (Figure 4b), GPR model (Figure 5), RFR model (Figure 6a), and MLP-RFR model (Figure 6b).  
Figure 4: Results of SVM model and its modified model. (**a**) Multiple regression results of SVR model on corn yield. (**b**) Multiple regression results of SA-SVR model on corn yield.  
Figure 3: Correlation graph between traditional improved model evaluation results and yield. (**a**) Correlation graph between MCI model productivity evaluation results and maize yield. (**b**) Correlation graph between MPI model productivity evaluation results and maize yield.  
Figure 4a shows that the coefficient of determination R\({}^{2}\) of the SVR model is 0.47, which is an improvement compared to the MPI model, but the MSE reaches 1743.24, which indicates that there is a large error when applying it to the test set to predict some of the yields; Figure 4b shows that the coefficient of determination R\({}^{2}\) of the SA-SVR model, which combines the SA algorithm, is 0.70, and the MSE decreases to 967.84. The SA algorithm optimizes the minimization loss function which, in this experiment, is specifically shown to effectively reduce the error between the predicted and actual yields in the prediction model. The SA algorithm optimizes the minimization loss function which, in this experiment, effectively reduces the error between the predicted yield and the actual yield in the prediction model; Figure 5 shows that the coefficient of determination R\({}^{2}\) of the GPR model is 0.71, and the MSE is 932.19, which is excellent for the small dataset of the experiment due to the nonparametric nature of the GPR model that does not require the function to be pre-specified; and Figure 6a shows that the coefficient of determination R\({}^{2}\) of the RFR model is 0.71, and the MSE is 932.19. The coefficient of determination R\({}^{2}\) of the RFR model is 0.57, and the MSE is 1391.06, which is also good for the small dataset with complex relationships in this experiment, and the prediction is more reliable than that of the MPI model; Figure 6b shows that the RFR model combined with the MLP can reduce the error by choosing the appropriate activation function and the loss function, and  
Figure 5: Multiple regression results of GPR model on corn yield.  
Figure 6: Results of RF model and its modified model. (**a**) Multiple regression results of RFR model on corn yield. (**b**) Multiple regression results of MLF-RFR model on corn yield.  
it is applied to the small dataset of the present experiment with the performance of MLP. The coefficient of determination R\({}^{2}\) of the RFR model is 0.62, which is improved compared with the RFR model, and the MSE is reduced from 1391.06 to 1234.58 compared with the RFR model.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 3 Methodology
### Preprocessing
#### 3.2.2 Differencing Non-Stationary Features  
The Time of Week (TOW) feature represents the timestamp, in seconds, assigned by the satellite at the moment the signal message is generated and transmitted. On the other hand, the RX feature represents the time recorded by the GPS receiver when capturing a GPS signal. Both features are correlated and exhibit a continuous upward trend [31], raising a non-stationary phenomenon.  
Since the values of mean, median, and variance may not remain constant, extracting meaningful patterns from TOW and RX becomes challenging for most machine learning algorithms that assume data following a stationary distribution. To mitigate this issue, we  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mission**} & \multirow{2}{*}{**Ranging Time**} & \multirow{2}{*}{**Period**} & \multirow{2}{*}{**Size**} & \multicolumn{4}{c}{**Class Distribution**} \\ \cline{5-8}  & & & & **Label 0** & **Label 1** & **Label 2** & **Label 3** \\ \hline
1 & [491,568–492,039.42] & 471.42 s & 95,027 & 57,599 & 10,940 & 9790 & 16,698 \\ \hline
2 & [173,640–174,233.86] & 593.86 s & 66,393 & 43,572 & 8072 & 11,977 & 2772 \\ \hline
3 & [262,704.02–264,109.68] & 1405.66 s & 282,256 & 239,553 & 9971 & 11,495 & 21,237 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of the extracted mission files.  
apply first-order differencing, as shown in Equation (1), to both features across the eight channels of each mission file:  
\[\Delta x_{i}=x_{i}-x_{i-1}, \tag{1}\]  
where \(\Delta x_{i}\) represents the first-order difference, \(x_{i}\) is a data sample of the non-stationary feature, and \(x_{i-1}\) is its preceding value. Furthermore, rows containing unlocked channels (i.e., rows that have only zero values) are removed to ensure data consistency.  
Consequently, the features TOW and RX were replaced by their corresponding derivatives DELTA_TOW and DELTA_RX, respectively. Here, DELTA_TOW represents the time step between successive signals transmitted by the satellites, while DELTA_RX describes the time step between successive signals collected by the GPS receiver.  
Tables 4-7 show the different modality occurrences for the new features classified by mission. We can notice that the DELTA_TOW feature has various modalities since the signals are broadcast from different sources. However, DELTA_RX has only two values for each mission. This can be explained by the fact that the signals are received by a single destination.