esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Surface Characterization  
#### 3.1.1 X-ray Photoelectron Spectroscopy (XPS)  
Polyethylene terephthalate (PET) has the chemical formula (C\({}_{10}\)H\({}_{8}\)O\({}_{4}\))\({}_{n}\), and is composed of carbon, hydrogen, and oxygen. To investigate the fibrous surface modification process, we employed the XPS technique, which is capable of detecting all elements except hydrogen and helium. In PET and PET-COO, we observed signals with binding energies of 292.4 eV and 537.3 eV, representing the presence of C1s and O1s, respectively (Figure 2). Following type I collagen modification, the XPS spectrum of PET-COL exhibited an induced N1s signal at 404.45 eV, indicating the introduction of nitrogen (N), an element undetectable in PET and PET-COO. Furthermore, the elemental composition analysis (Table 1) revealed that, compared to PET, the C content in PET-COL after collagen coating decreased from 71.34% to 62.78%, while the O content decreased from 27.91% to 19.47%. Notably, the N content reached 17.36%. XPS results further confirmed that crosslinking modification of PET with collagen successfully introduced nitrogen-containing groups onto the material surface, indicating that collagen had been effectively coated onto the PET fiber scaffold surface through the aforementioned step-by-step manipulations.
## 3 Proposed Self-Demodulation-Based Visualization Method  
### Overview of the Proposed Method  
In this paper, we propose the visualization of the demodulated sound of a PAL with the sequential ray-tracing method. Figure 2 shows the basic concept of sequential acoustic ray tracing in the proposed method, and Figure 3 gives an overview of the proposed method. A PAL, which is defined as the sound source, emits multiple acoustic rays from its propagation surface. The directions of the rays show the propagation direction and the directivity of the PAL. As the initial rays travel, new rays are generated sequentially along the directions of the initial rays. Each ray holds two indices: the ray index \(m\) (\(m=0,1,2,\cdots,M-1\), where \(M\) is the total number of initial rays) to identify the original position and direction, and the frame index \(i\) (\(i=0,1,2,\cdots\)) to mark the update time.  
The target sound reproduced by the PAL is set as an audible sound with frequency \(f_{\text{d}}\). Considering a case that uses a single sideband modulation [25], we can express the ultrasound emitted from the PAL with Equation (1). Here, the carrier wave at frequency \(f_{\text{c}}\) corresponds to the component indexed with \(n=1\) in Equation (1), and the sideband wave at frequency \(f_{\text{s}}\) corresponds to the component indexed with \(n=2\) in Equation (1).  
The following variables are defined for each acoustic ray, and they are initialized at the beginning and updated in each frame.  
* The ray index \(m\) (\(m=0,1,2,\cdots,M-1\)) is defined to identify the original positions.
* A position vector \(\mathbf{u}=\left[u_{\text{x}},u_{\text{y}},u_{\text{z}}\right]^{\text{T}}\) is defined as the starting position for each ray.
* The direction vector \(\mathbf{v}=\left[v_{\text{x}},v_{\text{y}},v_{\text{z}}\right]^{\text{T}}\) is defined as the direction of each ray, where \(v_{\text{x}}^{2}+v_{\text{y}}^{2}+v_{\text{z}}^{2}=1\).
* The sound pressure \(P_{\text{c}}\) is defined as the sound pressure of the carrier wave at frequency \(f_{\text{c}}\).  
Figure 3: Diagram of the proposed method.  
Figure 2: Basic concept of sequential acoustic ray tracing in the proposed method.  
* The sound pressure \(P_{\rm s}\) is defined for each sideband wave at frequency \(f_{\rm s}\).
* The sound pressure \(P_{\rm d}\) is defined as the demodulated sound pressure at frequency \(f_{\rm d}\).
* The virtual source intensity \(Q_{\rm d}\) is defined as the intensity of the virtual source at frequency \(f_{\rm d}\).  
In the simulation, each time frame has a duration of \(\Delta t\). The frame index \(i\) (\(i=0,1,2,\cdots\)) is defined for the update time of ray tracing; therefore, the position vector of the \(m\)-th ray in the \(i\)-th frame is denoted as \(\mathbf{u}(m,i)\) in the following description.
# A Lightweight Multi-Source Fast Android Malware Detection Model
## 2 Related Work
### Static Analysis  
Static analysis is widely used for Android malware detection. The code is examined without execution, and the results are generated by analyzing the code structure, the sequence of statements, and how variable values are handled in different function calls. An example is the AndroidManifest.xml file, which describes the permissions, API calls, package names, referenced libraries, and application components. Another one is the classes.dex file contains all Android classes compiled into dex file format [9]. Some static methods can represent the analyzed application code as abstract models such as opcodes in the form of n-grams or other information about the program such as metadata (application description, application ratings, number of application downloads) depending on the purpose of the study, which can be collected from other perspectives for static analysis [3]. [PERSON] et al. [10] proposed Drebin, a lightweight mathod for Android malware detection that enabled identifying malicious applications directly on smartphone, Drebin performs a broad static analysis, gathering features and embedding them in a joint vector space, finally, the features were classified by support vector machines, achieving 94% accuracy. [PERSON] et al. [11] looked at three aspects of static analysis (i.e., signature-based detection, permission-based detection and Dalvik bytecode detection), proposed methods for Android malware detection, and discussed the advantages and limitations of these methods. HybriDroid [12] extracted permissions, API calls, the number of users downloading the application and the rating of the application, and built a malware detection model using a nonlinear integrated decision tree forest (NDTF) approach with a detection rate of 98.8%. [PERSON] et al. [13] used seven feature selection algorithms to select permissions, API calls, and opcodes, and then merged the results of each feature selection algorithm to obtain a new feature set. subsequently, they used this to train the base learner, and set the logical regression as a meta-classifier to learn the implicit information from the output of base learners and obtain the classification results and the F1-Score reached 96%. Although static analysis has some problems resisting malicious deformation techniques such as java reflection and dynamic code loading, static analysis is not only scalable and usable when facing batch unknown APKs detection, but also can traverse all possible execution paths of the APKs [14]. Moreover, static analysis can detect malware quickly and prohibit malware before installation, which is one of the key factors that will enable us to achieve our goals. so it is essential to use static analysis in Android malware detection.
# A Percentile Method to Determine Cold Days and Spells in Bangladesh
## 3 Results
### Determination of Cold Spells  
Administrative-division-level monthly and seasonal averages of cold spells during the winters from 2000-01 to 2020-21 are presented in Figure 6a-d. The results reveal that the highest number of average cold spells was found in January (Figure 6b). In December (Figure 6a), January (Figure 6b), and the winter season (Figure 6d), higher numbers of cold spells were found in Rajshahi division, decreasing gradually towards Sylhet division. In contrast, a lower number of average cold spells was observed in Dhaka division and the surrounding areas, in the central part of Bangladesh (Figure 6c). On the seasonal scale, the average number of cold spells was higher in Rajshahi division (3.24 spells/year) and lower in Mymensingh (2.05 spells/year) and Sylhet (2.14 spells/year) divisions.  
Figure 6: Spatial distribution of the average number of cold spells in the winter months: (**a**) December, (**b**) January, (**c**) February, and (**d**) the whole winter season, during the period 2000–2001 to 2020–2021. Black dots are meteorological stations with their IDs in blue numbers.  
Figure 7a-e present five different categories of average cold spells from 2000-01 to 2020-21 winters. High values of the mild (Figure 7a) and moderate (Figure 7b) categories were mostly observed in the southern coastal stations. In contrast, high numbers of the very (Figure 7c) and severe (Figure 7d) categories were prominently found in the northeast, north and northwest, and west. Moreover, the highest average spells in the extreme (Figure 7e) category were noticed in the northwest, which gradually decreased towards the east, south, and southeastern regions. The extreme category was the highest in Rangpur (1.29 spells/year) and Rajshahi (1.0 spells/year) divisions, and the lowest in Barishal and Chattopgram, with 0.19 spells/year.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 3 Methodology  
In this study, it is considered that a UAV is moving in a 3D workspace, \(\mathcal{R}^{3}\), where a set of BSs, obstacles, and no-fly zones are distributed over the workspace. Given the initial and destination and the locations of BSs, obstacles, and no-fly zones, the problem consists of finding an optimal path in the workspace that avoids collision with the objects, avoids passing from no-fly zones, minimizes the total traveling distance, and maximizes communication link reliability. Figure 1 depicts a schematic diagram of the considered problem. Figure 2 depicts the general flowchart of the methodology, and the following subsections describe each step in detail.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Optimal Decision
#### 3.3.2 DBSCAN  
Apart from the power-relevant nonlinear effect, a random jitter in the time domain is also a detrimental factor in VLC systems. Decision schemes based on Euclidean distance or K-means ignore the chronological order of the received sequence, so these methods are not suitable to deal with random jitter. Meanwhile, density-based spatial clustering of applications with noise (DBSCAN), as one of the clustering unsupervised ML algorithms, can divide clusters according to density, and thus has great potential in mitigating the impairment from random jitter. In [66], a DBSCAN-based decision scheme is demonstrated in VLC systems. The received one-dimensional sequence with random jitter is converted into a two-dimensional sequence with the time-axis. The key point of applying DBSCAN in the VLC system is the normalization of amplitude and the time-axis, because it is closely relevant to the density. Experimental results prove that the sequence with random jitter can still be divided into the appropriate cluster according to the density of the two-dimensional sequence.
## 3 Experimental Results
### Results of Experimental Setup  
The LINAC sterilization application is controlled by the FLC algorithm and Takagi-Sugeno method. The experimentation sets a cooling temperature of 40 \({}^{\circ}\)C and the vacuum pressure of the LINAC is less than 5 \(\times\) 10\({}^{-8}\) torr. The suitable parameters are measured by CMM and calculated using the equation of the Hull cut-off and Hartree conditions, determining an anode voltage and permanent magnetic field of 45 kV and 0.152 tesla, respectively. The X-ray responses of the LINAC are shown in Figure 19.  
In Figure 21, the results of the designed FLC controller under a duration time of 45 min indicate that the response of the X-ray level is constant at 0.8 \(\upmu\)SV/h within 12 min (see dot-rectangular #1) and 20 min (see dot-rectangular #1), respectively. During the experimentation, the X-ray dose rates decreased suddenly (see dot-circle #1 and #2) because the temperature changed by 1.5 Celsius, as illustrated in Figure 21d. The result of the resonant frequency generated by the magnetron shifted beyond the target frequency of 2.9982 GHz, and the frequency response converges at the desired boundary of the frequency if the system is controlled by the FLC, which compensates for the frequency difference by adjusting the tuner slot distance via a stepping motor, as shown in Figure 21c. This result of the X-ray response is constant. The compensation of the frequency depends on the AFC signal feedback or the difference in the forward and reflect signals compared with the reference frequency at 0 volts or 2.9982 GHz, and the motor position response is shown in Figure 21b.  
Figure 20: The spectrum of the resonant frequency of each cavity radius with tuner distance of 3.97 mm.
# A Seismic Phase Recognition Algorithm Based on Time Convolution Networks
## 3 Neural Network Structure
### Improved S-TCN Structure Based on TCNs  
Our S-TCN model mainly included four modules (the structure is shown in Figure 9):  
(a) Input representation: seismic waves that were sampled at 100 Hz were input into the network after data preprocessing using the linear band-pass filtering (continuous wavelet transform) normalization step;  
(b) Convolution block: to learn the inter- and intra-channel features, three consecutive 1D convolution blocks were input into the model. The size of the convolution kernel was set to 6*1 and the number of convolution kernels was set to 16, 32 or 64 to extract features at different scales. In addition, the output was batch normalized and the rectified linear unit (ReLU) activation function was used. The block dropout operation was used in the last convolution block;  
Figure 8: A schematic diagram of causal convolution.  
(c) TCN block: to learn the contextual information of the seismic phases, the output of the convolution block was reshaped into 1D and then sent to three consecutive TCN blocks. The TCN blocks could utilize the full context of the input sequence;  
(d) Time distributed module: the output of the TCN blocks was fed into two parallel branches of the time distributed module, each of which consisted of its own time distributed module, which performed the final phase identification and detection.  
Unlike neuron networks, which attempt to learn the underlying structures in sequences using dilated convolution, non-neuron networks can handle a wider range of inputs. In general, when d = 1, normal convolution is performed. On the other hand, the higher the inflation factor, the larger the receptive field, so the kernels expand over a more comprehensive input area. To obtain such a large exponential receptive field for TCNs, deeper networks are required, which suffer from unstable gradients during backpropagation. To overcome this limitation, residual connections are used to learn the modifications to the identity maps. Generally, TCNs utilize causal convolution, in which the output at time t depends on current and past elements. This step can be achieved using zero-padding. However, in this study, convolution within the TCN blocks was modified to be non-causal, with batch normalization employed immediately after dilated convolution to mimic the use of future knowledge by bidirectional neural networks. Next, the normalized output was passed to the ReLU function for activation. Finally, a dropout function with a loss rate of 0.5 was used for correction.
## 4 Results and Analysis
### Result of DEAP Dataset
#### 4.3.2 Results of Embedded-Based Feature Selection on the DEAP Dataset
Overall, while the embedded-based LASSO feature selection method effectively reduced the number of features from 160 to 34, it generally led to improved or consistent performance across the models tested. This indicates that LASSO can be beneficial in managing feature dimensionality, thus potentially enhancing the robustness and generalization of the models. Table 7 encapsulates these findings, highlighting the performance metrics of accuracy and F1 score for each model and label before and after applying the LASSO feature selection method.
# Planar Delaunay Mesh Smoothing Method Based on Angle and a Deep Q-Network
## 3 Parameter Setting and Data Preparation of the Constrained DQN Model  
### Quality Metric  
In the program, the aspect ratio [25] of the triangle is used as the quality metric, and the formula is as follows:  
\[q_{e}=\frac{4\sqrt{3}\text{-}S}{l_{1}^{2}+l_{2}^{2}+l_{3}^{2}} \tag{3}\]  
where \(l_{i}\) is the side length of the triangle, and \(S\) is the area of the triangle. Here, \(q_{e}\in[0,1]\), and the closer the numerical value is to 0, the worse the mesh quality (conversely, the closer the numerical value is to 1, the better the mesh quality).  
In the data statistics, the aspect ratio and maximum/minimum angle are used to evaluate the mesh quality, reflecting the optimization effect of the constrained DQN on poor element angles. Ref. [5] showed that the stiffness matrix condition depends on the element's shape, and elements with small angles will lead to poor matrix conditions, proving that the minimum angle can be used as an index to evaluate the mesh quality.
## 3 Experimental Results and Discussion
### Experimental Setup
Several widely used indices, including the spectral angle mapper (SAM), relative dimensionless global error in synthesis (ERGAS), and universal image quality index (UIQI), were adopted to evaluate the fusion performance in order to give a comprehensive assessment. For the sake of avoiding stochastic errors caused by random initialization of network weights, 5 independent runs were conducted for CNN network training and testing, and the average values were calculated and displayed in the following sub-sections.
## 3 Numerical Results
Figure 8 depicts the minimum transmit power versus the SINR threshold \(\gamma\) for the uplink when \(v=160\) km/h. From the Figure 8, one can see that the constructed DNN well approximates the solution to (P2\({}^{\prime}\)) when 0.1 dB \(\leq\gamma\leq\) 0.5 dB while it tends to slightly overestimate when \(\gamma<\) 0.1 dB. Figure 9 shows the performance of the proposed scheme in the uplink for the same simulation setup as in Figure 8 except that \(v\) is set to 300 km/h. From Figure 9, we can also observe that the constructed DNN precisely approximates the solution to (P2\({}^{\prime}\)) when 0.1 dB \(\leq\gamma\leq\) 0.5 dB, while it tends to slightly overestimate the solution to (P2\({}^{\prime}\)) otherwise. Figure 8: Minimum transmit power versus the SINR threshold for the uplink with \(v=160\) km/h. Figure 7: Learning curve of the proposed scheme for the uplink with \(v=160\) km/h.
# A Robust Timing Synchronization Algorithm Based on PSSS for LTE-V2X
## 3 Traditional PSSS-Based Time Synchronization Algorithm
### PSSS-Based Selective Summing Algorithm  
Considering that the PSSS is transmitted in two adjacent SC-FDMA symbols, a robust PSSS detection algorithm, namely the selective summing algorithm, is proposed in the literature [16]. This algorithm leverages the known relative distance between the two PSSS symbols at the receiver. Along with their respective detection thresholds, peaks detected in a subframe are summed with other peaks detected and their detection thresholds in the same subframe at the known relative distance, which is the relative distance between the two PSSS symbols. Subsequently, the new correlation result is compared with the new threshold value to obtain the final peak. Figure 6 illustrates the flow chart of the algorithm, where \(T\) represents the detection threshold.  
According to Figure 6, the calculated correlation and threshold are defined as  
\[C_{i}(\theta)=\left|\sum_{n=0}^{L-1}r^{*}(\theta+n)p_{i}(n)\right|^{2} \tag{6}\]  
\[C^{sum}(\theta)=C(\theta)+C(\theta\pm d) \tag{7}\]  
\[T^{sum}(\theta)=T(\theta)+T(\theta\pm d) \tag{8}\]  
where \(d\) is the relative distance of the two PSSS symbols and \(T\) represents the detection threshold.  
The main idea of the selective summing algorithm is to utilize the known distance between two PSSSs to perform selective peak addition processing on the calculated cor  
Figure 5: Block cross-correlation algorithm flow diagram.  
Figure 6: Selective summing algorithm flow diagram.  
relation, thereby improving the performance of the algorithm under the condition of low SNR. However, when the propagation condition is bad (large frequency offset and/or rich multipath), a \"secondary peak\" appears near the correct peak. When the distance between the \"secondary peaks\" is equal to the distance between two PSSSs, the selective summing algorithm also adds those peaks to the wrong position, causing time synchronization failure.
## 3 Results and Discussion
### Beach Analysis of the Plane Image Experiment Results
The corresponding subject feature curves (ROC curves) of all algorithms on the Beach_Plane experimental dataset are shown in Figure 7, and the AUC values of the area under the corresponding curve are shown in Table 1.  
Figure 5: Beach_Plane dataset detection results. Graph of true abnormal object (**a**); RX (**b**); LRX (**c**); CRD (**d**); WSCF (**e**); RPCA-RX (**f**); LRASR (**g**); KIFD (**h**); ours (i). Figure 6: Three-dimensional grid detection results of the Beach_Plane dataset. Graph of true abnormal object (**a**); RX (**b**); LRX (**c**); CRD (**d**); WSCF (**e**); RPCA-RX (**f**); LRASR (**g**); KIFD (**h**); ours (i). Figure 7: ROC curves of the Beach_Plane dataset detection results. ROC curves (**a**); Enlarged view of the upper left corner of ROC (**b**). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **RX** & **LRX** & **CRD** & **WSCF** & **RPCA-RX** & **LRASR** & **KIFD** & **Ours** \\ \hline AUC & 0.9852 & 0.9971 & 0.9943 & 0.9840 & 0.9787 & 0.9621 & 0.9910 & 0.9981 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: AUC values for Beach_Plane dataset. In the ROC curve result diagram in Figure 7, when the false alarm rate is less than 0.05, the difference between the algorithms, except the LRASR algorithm, is small. When the false alarm rate is close to 0.05, the detection probability of the algorithm of this paper is rapidly improved. The detection probability is larger than that of other comparison algorithms under the fixed false alarm rate. At the same time, the algorithm of this paper reaches the detection probability of one at the earliest time. Based on the summary of AUC values in Table 1, it can be seen that the AUC value of the algorithm of this paper is obviously higher than those of the RX, WSCF, RPCA-RX, and LRASR algorithms. LRX, CRD, KIFD and the algorithm of this paper show good detection performance, with AUC values above 0.99.
# Numerical Simulation Research on Plane Alignment Parameters of Desert Highway
## 3 Results and Analysis
### Analysis of the Results of the Angle Embankment Model
#### 3.1.2 Characteristics of Horizontal Wind Speed Distribution  
The near-surface wind speed has a significant impact on the movement of sand particles. The horizontal speed of the axis wind speed of 18 m/s at different heights from the embankment is analyzed, and the variation law of airflow speed around the embankment is studied, as shown in Figure 5.  
It can be seen from Figure 5 that, at the same height at different angles, the speed of the wind-sand flow is basically the same. When the angle \(\alpha\) between the wind direction and the line is 90\({}^{\circ}\), 50\({}^{\circ}\), and 30\({}^{\circ}\), and when the distance from the top of the embankment is 0.3 m and 0.5m, the maximum wind speeds are 16.45 m/s, 15.85 m/s, 14.29 m/s, and 17.46 m/s, 16.40 m/s and 14.65 m/s; they are 1.05, 1.00, 0.91, and 1.11, 1.03, 0.93 of the initial wind speed, respectively. With the decrease in the angle, the wind erosion effect of the airflow on the road shoulder gradually weakens.  
On the slope of the windward side of the embankment, the speed decreases. At the shoulder of the windward side of the embankment, the speed reaches the maximum due to the influence of the current-collecting effect, and the speed decreases to the minimum at the position of 1/3 of the embankment surface; the wind speed changes around the embankment. It gradually decreases as the angle between the wind direction and the line decreases.