esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Time Synchronization Principle
#### 2.1.2 Ka-Band Inter-Satellite Two-Way Time Synchronization  
It is similar to the satellite-ground two-way time comparison method. If satellite \(i\) and satellite \(j\) send time signals to each other at the clock \(T_{i}\) and \(T_{j}\), the signal sent by satellite \(i\) is received by satellite \(j\) after the delay from satellite \(i\) to satellite \(j\). On this basis, the pseudorange from satellite \(i\) to satellite \(j\) can be measured. Similarly, the signal sent by satellite \(j\) is received by satellite \(i\) after the delay from satellite \(j\) to satellite \(i\), so that the pseudorange from satellite \(j\) to satellite \(i\) can be measured. Next, satellite \(i\) and satellite \(j\) exchange their respective observation results, and finally calculate the relative clock offset between them. The detailed principle of Ka-band inter-satellite two-way time comparison is shown in Figure 2.  
Figure 1: Schematic diagram of satellite-ground time synchronization.  
Figure 2: Schematic diagram of inter-satellite time synchronization.  
Similar with the satellite-ground two-way time synchronization, the inter-satellite time synchronization of navigation satellites is based on pseudorange measurement of Ka-band two-way time synchronization, and the inter-satellite clock offset can be obtained as:  
\[\Delta T_{j}-\Delta T_{i}=\frac{1}{2c}\big{(}\rho_{ij}-\rho_{ji}\big{)}-\frac{1} {2c}\big{(}R_{ij}-R_{ji}\big{)}+\delta \tag{3}\]  
where \(\Delta T_{i}\), \(\Delta T_{j}\) represent the clock offsets of satellite \(i\) and satellite \(j\), respectively; \(\rho_{ij}\) refers to the pseudorange of satellite \(j\) receiving from satellite \(i\); \(\rho_{ji}\) refers to the pseudorange of satellite \(i\) receiving from satellite \(j\); \(R_{ij}\), \(R_{ji}\) respectively, represent the theoretical values of two-way geometric distances; \(\delta\) indicates the synthetic measurement noise of the inter-satellite two-way link; and \(c\) represents the velocity of light.  
The inter-satellite two-way time comparison method is used to obtain the observed inter-satellite clock offset, which can be expressed by a second-order polynomial [21]:  
\[\begin{array}{c}\Delta T_{ij}(t)+v_{ij}=a_{0}^{j}+a_{1}^{j}(t-t_{0})+a_{2}^{ i}(t-t_{0})^{2}-\\ \Big{(}a_{0}^{i}+a_{1}^{i}(t-t_{0})+a_{2}^{i}(t-t_{0})^{2}\Big{)}\end{array} \tag{4}\]  
where \(\Delta T_{ij}(t)\) represents the discrete point of inter-satellite clock offset of satellite \(j\) relative to satellite \(i\); \(t\) denotes the observation time of inter-satellite clock offset; \(t_{0}\) refers to the reference time of clock offset parameter; \(v_{ij}\) indicates the Ka-band inter-satellite link delay calibrated based on the L-band satellite-ground link; \(a_{0}^{j}\), \(a_{1}^{j}\), \(a_{2}^{j}\) represent the clock offset, clock rate and clock drift, respectively.
## 4 Improved Routing Protocol
### Basic Concept in Route Establishment  
If node S and node D are the source and destination, respectively, located at both ends of the transmission line, forward means the direction from node S to node D, and then backward means the direction from node D to node S.  
In route establishment, neighbor tables of forward and backward lists are executed by any node \(i\). The contents of route discovery neighbor table are shown in Table 1. The forward/backward list firstly describes the address of the next/previous nodes in a communication range. Distance between node \(i\) and neighboring nodes is calculated based on the GPS position information in route request messages (RREQs). Link quality indicator (LQI) describes the performance of wireless communication links. We use exponentially weighted moving-average algorithm (EWMA) [28] to calculate link quality (14):  
\[\left\{\begin{array}{l}PRR=\frac{N_{\rm r}}{N_{\rm r}+N_{\rm m}}\\ LQI_{k+1}=(1-\alpha)\cdot LQI_{k}+\alpha\cdot PRR\end{array}\right.^{\prime}, \tag{14}\]where \(PRR\) is the packet reception rate, \(N_{\rm r}\) and \(N_{\rm m}\) are the number of received and missing messages respectively, and \(\alpha\) is EWMA factor ranging from 0 to 1.
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 5 Simulation Results
\(C/N_{0}\) of all of the authentic signals are 31 dB-Hz, and those of the spoofing signals are 50 dB-Hz. ‘T’ represents the theoretical results. ‘NC’ represents corresponding noncoherent integration results. ‘EC’ represents the estimation-cancellation results. Figure 7: Spoofing validation probabilities versus \(C/N_{0}\) for different numbers of satellites and \(P_{FA}=10^{-6}\). comes from the integration of multiple signals. The ROC curves determined by Equations (21) and (22) can be viewed as the upper bound of the detection performance. This bound can be nearly reached when the correlation peaks of spoofing and authentic signals are separated completely.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
## 4 Establishment of LTE-R Time Synchronization SPN and MC Models
### Modeling Analysis of LTE-R Time Synchronization Vulnerability Based on Stochastic Petri Nets  
Due to the all-IP architecture of LTE-R and the broadcast sending address of the PTP, an ARP attack can easily be carried out at the data link layer during LTE-R time synchronization. The attacker becomes a middleman by forging the MAC frame structure and other means, imperceptibly inserting it into the normal communication process between eNodeB and the OBC, and maliciously tampering with or delaying the transmission of normal interactive messages randomly. This has a great impact on the synchronization accuracy of the LTE-R time synchronization process, and it seriously endangers the train operation safety and affects the real-time performance of train control systems. However, because it is difficult to find the attacker and the tampering of the message by the attacker is random and uncertain, it is difficult for the general method to accurately describe the attacker's behavior dynamically.  
Stochastic Petri nets can build a complete LTE-R time synchronization process model under attack, restore the impact of the attack on the synchronization process, realize the  
Figure 4: MAC frame format of PTP message.  
vulnerability analysis of the attacker on the LTE-R time synchronization process, and then determine the most vulnerable nodes in the synchronization process. Through the special protection of these key nodes, the success rate of attacks can be reduced, and train operation safety can be better guaranteed.
## 4 Experiments
### Case Study
#### 4.4.1 Visual Result Comparing with Baselines  
In order to show the prediction results more clearly, we selected the optimum models in RNN baselines and Seq2 Seq baselines based on Tables 2 and 3. LSTM is shown in green lines and BiLSTM-LSTM in purple lines, respectively. We chose three trajectories with 15 random lengths, while 10 track points were for input and 5 for output. The image in Figure 5 shows the visual comparison results on three different trajectories, where the difficulty of the prediction increases gradually. The yellow lines with arrows are the predictions by PESO, while green and purple denote the results of LSTM and BiLSTM, respectively. Blue lines are input trajectory points, and red lines are real ones. As we can see from the images, the trajectories in Figure 5a,b are steady, where PESO is the closest to the real trajectories and performs better than others. Both prediction results by LSTM and BiLSTM in Figure 5a,b are in the wrong directions. In a much more complex scenario in Figure 5c, the other predictions are confusing, while ours is still close to the real one. In reality, incorrect predictions easily cause accidents. Note that the predicted results by PESO are superior to others in the task of vessel trajectory prediction and are able to avoid secure problems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ablation** & **RMSE** & **MAE** & **ADE** & **FDE** \\ \hline PESO & _0.000466_ & _0.000327_ & _0.000523_ & _0.000681_ \\ _w/o_ COG\&SOC\&DIS_ & 0.000801 & 0.000576 & 0.000921 & 0.001344 \\ _w/o COG_ & 0.000554 & 0.000385 & 0.000621 & 0.000841 \\ _w/o SOG_ & 0.000486 & 0.000342 & 0.000540 & 0.000728 \\ _w/o DIS_ & 0.000474 & 0.000343 & 0.000542 & 0.000711 \\ _w/o SLV_ & 0.000492 & 0.000355 & 0.000563 & 0.000719 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ablation studies under the metrics of RMSE, MAE, ADE, and FDE.
# An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks
## 3 The Key Features of the SOVANET Algorithm
* [1] Register each UE and update the UE map accordingly
* [2] Create a LC and assign it to an LCG
* [3] Update the BSR
* [4] Calculate UL and DL CQI
* [5] **For UL LCs*
* [5] **The UL network load is calculated*
* [5] **The weight factor alpha for critical LCs is calculated according to the network load*
* [5] **For non-critical LCs the weight factor beta is calculated according to the network load*
* [5] **The number of bytes per LC is calculated and assigned*
* [6] **For DL LCs*
* [6] **The DL network load is calculated*
* [6] **The weight factor alpha for critical LCs is calculated according to the network load*
* [6] **For non-critical LCs the weight factor beta is calculated according to the network load*
* [6] **The number of bytes per LC is calculated and assigned*
* [7] **Activate UL and DL flows*
* [8] **UL Ctrl Symbol Allocation*
* [8] **Schedule UL/HARQ data*
* [9] **Indicate to MAC the decision for that slot*
* [10] **Retrieve UL Allocation for the slot*
* [11] **DL Ctrl Symbol Allocation*
* [12] **Schedule DL/HARQ data*
* [13] **DL Scheduling*
* [14] **Indicate to MAC the decision for that slot*
* [15] **Finishing allocation**  
Figure 1: SOVANET scheduling algorithm. Figure 2: Critical services weight factor (alpha) adaptation.
## 3 Results and Discussion
### Changes in Velocity of Longitudinal Wave  
It can be seen from Table 3 that the velocity of the longitudinal wave of two sandstones decreases with the increase in the number of freeze-thaw cycles. There are apparent differences in the varying amplitudes of the two rocks. The average velocity of the longitudinal wave of the fine sandstone specimen decreased from 3821 m/s to 2990 m/s after 120 freeze-thaw cycles, with a decrease of 21.8%, while coarse sandstone specimens decreased from 3642 m/s to 3116 m/s, with a decrease of 14.4%.  
The propagation velocity of sound waves in the rock mass is closely related to the elasticity of the medium, the degree of hard density and the integrity. In general, fresh and complete rock has a relatively large longitudinal wave velocity. With an increase in the degree of weathering, the wave velocity is bound to decrease. The continuous decrease in wave velocity also reflects the fact that rock porosity increases as the number of freeze-thaw cycles increases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sandstone Variety**} & \multirow{2}{*}{**Specimen No.**} & \multicolumn{6}{c}{**Time of Freeze–Thaw Cycles**} \\ \cline{3-6}  & & **0** & **30** & **60** & **90** & **120** \\ \hline \multirow{6}{*}{Fine} & 2-5 & 2.113 & 2.438 & 2.892 & 3.127 & 3.583 \\  & 2-20 & 1.628 & 1.904 & 2.451 & 2.897 & 3.328 \\  & 2-22 & 1.449 & 1.678 & 1.874 & 2.213 & 2.551 \\  & 2-23 & 1.321 & 1.574 & 1.987 & 2.215 & 2.46 \\  & Mean & 1.628 & 1.899 & 2.301 & 2.613 & 2.981 \\ \hline \multirow{6}{*}{Coarse} & 1-19 & 1.916 & 2.379 & 2.984 & 3.172 & 3.494 \\  & 1-21 & 2.444 & 2.954 & 3.523 & 4.005 & 4.809 \\ \cline{1-1}  & 1-23 & 1.727 & 2.687 & 3.014 & 3.289 & 3.464 \\ \cline{1-1}  & 1-32 & 1.606 & 2.089 & 2.712 & 3.145 & 3.721 \\ \cline{1-1}  & Mean & 1.923 & 2.527 & 3.058 & 3.403 & 3.872 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Porosity of sandstone specimens (%) subjected to different number of freeze–thaw cycles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sandstone Variety**} & \multirow{2}{*}{**Specimen No.**} & \multicolumn{6}{c}{**Time of Freeze–Thaw Cycles**} \\ \cline{3-6}  & & **0** & **30** & **60** & **90** & **120** \\ \hline \multirow{6}{*}{Fine} & 2-5 & 3818 & 3704 & 3571 & 3111 & 2900 \\  & 2-20 & 3705 & 3607 & 3448 & 3297 & 3022 \\ \cline{1-1}  & 2-22 & 3949 & 3871 & 3654 & 3304 & 3096 \\ \cline{1-1}  & 2-23 & 3812 & 3726 & 3448 & 3160 & 2942 \\ \cline{1-1}  & Mean & 3821 & 3727 & 3530 & 3218 & 2990 \\ \hline \multirow{6}{*}{Coarse} & 1-19 & 3639 & 3548 & 3448 & 3296 & 3198 \\ \cline{1-1}  & 1-21 & 3648 & 3561 & 3271 & 3105 & 3005 \\ \cline{1-1}  & 1-23 & 3520 & 3404 & 3314 & 3171 & 3087 \\ \cline{1-1}  & 1-32 & 3759 & 3661 & 3571 & 3304 & 3174 \\ \cline{1-1}  & Mean & 3642 & 3544 & 3401 & 3219 & 3116 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sandstone specimen velocity of longitudinal wave (m/s) after undergoing the different times of freeze–thaw cycles.
# Modern MultiPort Converter Technologies: A Systematic Review
## 2 General Classification and Operation Principles
### Isolated Multiport Converters
Table 3 contains some of the research results regarding the implementation of various isolated multiport topologies. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Isolation Type** & **Main Results** \\ \hline Isolated & In order to reduce component count and cost, the study suggested an application that uses just one switch-mode conversion stage to replace multiple separate DC converters and inverters, making it an excellent option for low-cost, low-power standalone PV systems when interfacing photovoltaic panel and battery energy storage. \\ \hline Isolated & A novel soft-switching technique for multiport isolated bidirectional DC/DC converters that are supplied with energy storage systems and renewable energy sources is proposed. The converter that is being described benefits from a non-complex topology, low-price, galvanic isolation, and small packaging. For this converter, only a suitable control strategy can enable zero voltage switching (ZVS) of the semiconductor switches. \\ \hline Isolated & No additional equipment was used for speed regulation. For the simultaneous power control of numerous renewable energy sources, a new inductor-capacitor-inductor (LCL) resonant, isolated, multiport DC-DC converter has been proposed. Three separate photovoltaic panels have been tested under simultaneous MPTP control using the suggested converter. \\ \hline Isolated & A thorough analysis of the dual transformer-based asymmetrical triple port active bridge (DT-operating ATAB) multiport converter’s principles, steady state analysis, and the closed-loop controller is offered. The suggested DT-ATAB provides a number of benefits, including isolation between the ports, ZVS switch activation, bi-directional power flow operation, lower circulating powers, reduction of magnetic short-circuiting circumstances, etc. With DT-ATAB, it is possible to operate the multiport converters at a variety of different phase shift ratios to provide a wide range of output voltage and power options. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Application of different isolated MPC topologies for RES and EV systems.
# The Variation of Surface Shape in the Gas Jet Forming
## 3 Numerical Simulation
### Grid Independence Test  
The simulation's mesh quality directly determines the numerical simulation's prediction accuracy. Therefore, a grid independence test is required before calculation. This paper uses a rectangular orthogonal grid to partition the model. In the the gas jet forming process, two cases of surface profiles are calculated at different grid densities, namely, no-point and two-point, as shown in Figure 5.  
There are many methods for grid independence tests, such as [PERSON]'s method [24], [PERSON]'s method [25], and LCSS [26]. However, these methods are only suitable for judging straight-line trajectories and cannot achieve the expected results in the case of judging similar curves. For this reason, this paper proposes a new method for determining the similarity of specular contours for the surface profile characteristics formed by the gas jet forming. Its similarity formula is as follows:  
\[\delta=\frac{\mathrm{C}}{\mathrm{A+B-C}}, \tag{18}\]  
where A is the area of intersection of curve 1 with the \(X\) axis, B is the area of intersection of curve 2 with the \(X\)-axis, and C is the common area of A and B.  
The grid size was set to 0.7 mm, 0.6 mm, 0.5 mm, 0.4 mm, 0.3 mm, 0.2 mm, 0.1 mm, and 0.09 mm to mesh the model. The number of meshes corresponding to the above mesh sizes was 3565, 4850, 6917, 10,911, 19,110, 42,596, 169,091, and 209,038, respectively. A curve similarity equation (Equation (18)) was applied to calculate the similarity of two adjacent curves, and the point line diagram was drawn based on the calculation results, as shown in Figure 6.  
According to the results of the calculations, the similarity values fluctuate considerably when the grid (or grids) size is between 0.7 mm and 0.3 mm. Between 0.3 mm and 0.09 mm, the fluctuations in the curve similarity values became smooth and reached over 95%. The similarity value did not reach 100%, due to a slight error caused by the time step adjustment. Based on the grid independence experiments, 0.3 mm was chosen as the grid size for the numerical simulations.  
Figure 5: Prediction of curves occurring at different grid densities.  
Figure 6: The results of the grid independence test.
## 3 Methods
### Mamba Fusion Block
#### 3.2.2 Feature Fusion Mamba  
In image segmentation tasks, contextual information plays a crucial role in helping models resolve ambiguities and improve edge detection. To effectively capture contextual information, it is necessary to have a comprehensive grasp of features enhanced by attention at various scales. As shown in Figure 3, the data processing in the Feature Fusion Mamba (FFM) involves two steps: Hilbert curve scanning and Mamba-2 [11] processing. Firstly, the features at various scales are encoded in parallel using the Hilbert curve, which expands them into a one-dimensional sequence based on coordinate encoding. Then, the one-dimensional sequences from each scale are stacked to form a new sequence. Finally, this new sequence is input into Mamba-2 for selective scanning.  
During the process of transforming two-dimensional features into a one-dimensional sequence, simple row-first or column-first interleaved scanning [13] is prone to causing information loss, especially for spatial proximity information. The Feature Fusion Mamba (FFM) adopts the Hilbert curve as the traversal path, which can maintain a certain degree of spatial proximity. Moreover, the Hilbert curve can reduce the distortion of information caused by serialization, allowing Mamba-2 to better simulate the way the human visual system pays attention to high-dimensional data when scanning the one-dimensional sequence. This will help the model to better capture contextual information.  
Additionally, the non-causal nature of image data constrains the effective reasoning of the Mamba series framework [13; 14; 19]. The FFM achieves single-pass multiple selective scanning by stacking multi-scale features, effectively integrating information at different scales and establishing a global receptive field in two-dimensional space.
## 2 Background
### Abstract Syntax Tree  
An AST is a tree representation of a code fragment. Since this tree represents the actual structure of a code fragment, studies have used ASTs for code clone detection [16; 17; 18]. To extract the syntactic representation of a code fragment, code is converted into a set of tokens, and the list of tokens is turned into an AST. Figure 1a is a code fragment of a method signature part. Figure 1b is an AST for the method signature part. Each node of the AST tree structure has a type specifying what it is representing. For example, a type could be a \"MethodDeclaration\" representing a method definition or a \"FormalParameter\"representing a parameter for a method declaration. There are two \"FormalParameter\" subtrees, each with a \"ReferenceType\" of \"str\", that is, String.  
A typical clone detection approach usually represents the code structure into different abstractions, such as AST, CFG, and PDG, and then compares code similarity between two code fragments. Graph-based techniques that generate CFGs or PDGs from code perform better in detecting code clones. However, PDG-based techniques are not scalable due to the complexity of graph isomorphism [19]. [PERSON] et al. [19] improve accuracy and scalability simultaneously in detection clones, but they do not validate their technique for Type 4 clones. [PERSON] et al. [17] improve scalability by mapping the subgraphs in PDG back to AST forest and comparing syntactical feature vectors extracted from AST, but the results are imprecise due to the approximations [20]. In this paper, we use AST as our code representation because we find that AST can represent code patterns with significantly lower effort compared to CFG and PDG, and it is scalable to large amount of code.
## 5 Implementation
### Technology Selection  
The search of technologies for implementing the system was based on a set of predefined conditions related to the main requirements of a pedestrian movement patterns detection system, including: (i) using a cross-platform programming language; (ii) develop over a programming language without technical complexities (not hard to type, has an automatic memory handler, etc.) and having a broad and active community; (iii) using free libraries and preferably open source; (iv) achieving good performance on all tasks covered by the system: image processing, pattern detection, message passing and management, etc.  
After a literature and technology research [30; 31], a group of configurations were selected for a deeper study: Matlab, OpenCV over C/C++, and OpenCV over Python. Both OpenCV and Python are free and open source. In addition, Python is a dynamically typed language and counts with an automatic memory manager. Python has a wide variety of free and open source scientific libraries and the community is broad and active. Regarding performance, Python is also an efficient option. The study allowed to conclude that the best choice for implementing the system was using OpenCV library (version 3.0.0 was selected) over Python (version 3.4.3).