esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Analysis of Different Failure Modes  
Section 3.2.2 analyzes the possible degradation of constellation performance due to the failure of three satellites in the Walker 24/3/1, and it is necessary to analyze the distribution of the geometric positions of the three failed satellites in space.  
The correlation between 10 distinct satellite failure modes and constellation health status is illustrated in Table 4. As shown in Table 4, the constellation's health status is excellent or good for the 1 st-3 rd-satellite failure modes and mostly good overall. This ensures that the constellation can continue to provide users with high-quality navigation services. The 4 th-satellite failure mode has a low probability of affecting navigation services negatively, representing the only instance among the 10 failure modes where \"poor\" health is observed. For the 5 th- to 10 th-satellite failure modes, the constellation's health status is generally good or excellent, resulting in a moderate reduction in navigation service quality compared to the 1 st-3 rd modes.  
In order to analyze the minimum CV data of the constellation, 512 simulations were conducted for a single failure within each orbital plane, and Table 5 presents the failure modes of the top 10 with the most severe performance degradation. According to Table 4, the minimum CV corresponds to a failure mode of 14-22-35, with a CV of 83.9751 indicating a decline in the performance health status of the constellation to poor.  
The space phase of a single failure within each orbital plane is shown in Figure 15. The failure mode characterized by the occurrence of a single-satellite failure within each orbital plane and the phase of adjacent satellites is relatively close, which exerts a significant influence on constellation performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Failure Mode** & **Excellent** & **Good** & **Fair** & **Poor** & **Failure Mode** & **Excellent** & **Good** & **Fair** & **Poor** \\ \hline
1-3 & 48.00\% & 52.00\% & — & — & 1-2, 3-1 & 11.67\% & 65.83\% & 22.50\% & — \\
2-3 & 45.45\% & 54.55\% & — & — & 1-1, 2-2 & 16.67\% & 69.79\% & 13.54\% & — \\
3-3 & 41.67\% & 58.33\% & — & — & 1-1, 3-2 & 27.43\% & 65.49\% & 7.08\% & — \\
1-1, 2-1, 3-1 & 13.83\% & 44.66\% & 32.02\% & 9.49\% & 2-2, 3-1 & 27.93\% & 60.36\% & 11.71\% & — \\
1-2, 2-1 & 11.11\% & 77.78\% & 11.11\% & — & 2-1, 3-2 & 29.06\% & 63.25\% & 7.69\% & — \\ \hline \hline \end{tabular}
\end{table}
Table 4: Satellite failure modes and constellation health status.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Failure Mode** & **Minimum CV** & **Number** & **Failure Mode** & **Minimum CV** \\ \hline
1 & 14-22-35 & 83.9751 & 6 & 15-23-36 & 84.2006 \\
2 & 12-28-33 & 84.0952 & 7 & 16-21-37 & 84.2084 \\
3 & 11-24-32 & 84.1053 & 8 & 17-25-33 & 84.2085 \\
4 & 15-23-31 & 84.1111 & 9 & 14-27-35 & 84.2088 \\
5 & 18-26-34 & 84.1176 & 10 & 18-23-31 & 84.2182 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Failure modes of the top 10 with the most severe performance degradation.
# A Formula-Structure-Embedding-Based GNN for Formula Equivalence Determination
## 4 Experiments and Analysis  
### Dataset  
To the best of our current knowledge, most of the existing research focus on the matching of formula symbols, and less on formula equivalence determination. Therefore, we construct a dataset from equivalent formulas in calculus, as calculus is not only an important part of higher mathematics and has a wide range of applications, but also involves a large number of equivalent transformations, i.e., derivative and integral transformation, Taylor expansion, trigonometric identity transformation, etc. These advantages allows us to easily obtain a large number of equivalent calculus formulas.  
To obtain positive and negative samples for formula equivalence determination, a large number of mathematical expressions from several widely used calculus textbooks, i.e., _Calculus and Its Applications_[21] by [PERSON] et al., _Single Variable Calculus: Early Transcendentals_[22] by [PERSON] et al., and _CALCULULUS EARLY TRANSCENDENTALS_[23] by [PERSON] et al. are curated.  
Figure 8: Graph–graph interaction module.  
Figure 9: Formula determination score calculation module.  
To generate positive samples, we applied four common mathematically equivalent transformations: algebraic expansions and simplifications, derivatives and integral calculations, substitution of variables, and trigonometric constant transformations to the extracted expressions, and used the Python's SymPy tool (version 1.12) to generate equivalent formulas. These transformations were chosen because, firstly, algebraic expansion and simplification operations, e.g., \((x+y)^{2}=x^{2}+2 xy+y^{2}\), are the common equivalence operations in elementary algebra, and are suitable for assessing the model's understanding of the underlying transformations; secondly, derivative and integral calculations, e.g., \(\frac{d}{dx}(x^{2})=2x\), help to test the model's grasp of the semantics of core operations in calculus; thirdly, variable substitution, e.g., \(\int xdx=\frac{x^{2}}{2}+C\) and \(\int tdt=\frac{t^{2}}{2}+C\), can verify whether the model has the ability to deal with the generalization of variable change without semantic change; lastly, the triangular constant transformations, e.g., \(\sin^{2}(x)+\cos^{2}(x)=1\) and \(\sin^{2}(x)=1-\cos^{2}(x)\), are highly structurally variable, which help to enhance the robustness of the model to complex structure deformations. These operations cover the most common cases of equivalent transformations in the field of calculus, reflecting both equivalence relations in the mathematical sense and structure diversity, which helps to train the model to maintain stable semantic comprehension under different structures.  
To construct negative samples, we selected formulas with completely different mathematical meanings, e.g., \(\int xdx=\frac{x^{2}}{2}+C\) and \(\int\sin(x)dx=-\cos(x)+C\), which may have structure similarities, but are unequivivalent.  
By the above method, a dataset of formula equivalence determination from calculus is constructed (see in Table 2).
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 4 Training Strategy for Data Domain Transfer
### Low Light Enhancement  
In order to enhance the color features of vehicle and pedestrian targets at night, this paper uses the Zero-Reference Deep Curve Estimation (Zero-DCE) algorithm to achieve low-light enhancement. The algorithm processing process is shown in Figure 4.  
Figure 3: General training and testing framework for algorithms.  
The algorithm iterates itself continuously to approach pixel-level higher-order curves by designing specific curves associated with the image, resulting in images with strong brightness and contrast.  
The curve equation for the brightness enhancement is shown in Equation (5) below.  
\[LE_{n}(x)=LE_{n-1}(x)+A_{n}(x)LE_{n-1}(x)(1-LE_{n-1}(x)) \tag{9}\]  
\[\text{Here }n=1, \tag{10}\]  
where \(n\) is the number of iterations, \(A\) is a matrix of curve parameters of the same size as the input image, \(I(x)\) is the input low-illumination \(x\) image, and \(LE(x)\) is the enhanced image.  
Furthermore, this algorithm has the advantages of fewer model parameters and faster processing speed compared to other enhancement algorithms such as Low-Light Net (LLNet) [68] and EnlightenGAN, which are suitable for deploying low-end devices while ensuring the enhancement effect. In practical tests, the algorithm consumes only 2 ms of inference time for the experimental dataset in this paper, and the model size accounts for only 313 K. The performance of different low-light enhancement algorithms is shown in Table 1.
## 4 Numerical Results and Analysis
### Evaluation of Resource Allocation  
Besides path planning of the UAV base station, communication resource allocation also matters. Transmission power and bandwidth allocation for all ground users at different time slots are derived via our designed optimization algorithm.  
It can be seen in Figures 9 and 10 that as time passes, the bandwidth allocation varies as the power of the UAV increases because the number of users in the valid area decreases. It is worth noting that there are 10 significant changes in P of the UAV base station, which is equal to the number of moving ground users, making it consistent with the logic of algorithm optimization.  
Figure 10: **Power control illustration.** UAV transmission power control is influenced by the positions of UAV and users, as well as the current bandwidth allocation, also adhering to the minimum throughput constraint.  
Figure 9: **Bandwidth allocation illustration.** Bandwidth allocation scheduling is influenced by the positions of UAV and users, as well as the power of UAV, also adhering to the minimum throughput constraint.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 4 Proposed Model
### Detection of Anomalies  
The training dataset \(x^{A\times C}_{\text{train}}\), test time series \(x^{B\times C}_{\text{test}}\), \(A\) and \(B\) are the maximum length of the timestamp, and \(C\) is the feature dimension. To better capture the hidden information in the time series, a sliding window is used to divide the multivariate time series into sub-sequences. That is, \(X^{\text{train}}=\{\text{x}_{1},\text{x}_{2},\text{x}_{3},\dots,\text{x}_{ \text{a}}\}\), \(a=(A-t_{w})+1\). \(X^{\text{test}}=\{\text{x}_{1},\text{x}_{2},\text{x}_{3},\dots,\text{x}_{ \text{b}}\}\), \(b=(B-t_{w})+1\). In the same way, \(Z\) is the random hidden space,  
Figure 5: Generator and discriminator of TGAN-AD.  
\(Z=\{\mathrm{z}_{1},\mathrm{z}_{2},\mathrm{z}_{3},\ldots,\mathrm{z}_{\mathrm{a}}\}\). Then put \(X\) and \(Z\) into TGAN-AD; the TGAN-AD model trains the generator and discriminator in _mini-max_ games:  
\[\min_{G}\max_{D}V(G,D)=\varepsilon_{z\sim p_{z}(x,z)}[\log(1-D(G(x,z)))]+ \varepsilon_{x\sim p_{x}(x)}[\log D(x)] \tag{2}\]  
After many iterations, the fake samples generated by the TGAN-AD generator can fool the discriminator, indicating that the training is completed. Then the reconstruction loss and discrimination loss of the test sample are calculated to obtain the AD-Score by using the sum of the training.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 5 The Prototype
### Results and Discussion
#### 5.4.3 Evaluation of Response Time  
In smart domains (home, city, health, tourism, etc.), the services recommendation and adaptation processes involve analyzing trade-offs between response time and number of mobile devices. Moreover, once a user's situation has been recommended it will be important to determine its applicability across the available devices. Thus, we evaluated the response time (e.g., total of recommendation and adaptation time) of the proposed approach by varying the numbers of mobile devices such that the number of situation rules is fixed to 80. As shown in Table 8, we observe that the increased number of mobile devices incurs service adaptation delay.
## 4 Mission Reliability Modeling, Allocation, and Analysis Method Design
### Reliability Allocation Methodology Design
#### 4.2.2 Constraint Establishment  
In order to meet the reliability requirements of the mission of reconfigurable ship electronic information system and the reliability requirements of the unit itself, this paper investigates the constraints of reliability allocation, which need to consider two constraints: the mission constraints and the unit constraints. Mission reliability refers to the probability that the ship will complete the operational mission within the specified time and under the specified conditions. The unit reliability constraint is shown in Equation (11) as follows:  
\[R_{\text{imin}}\leq R_{i}\leq R_{\text{imax}} \tag{11}\]  
where \(R_{\text{imin}}\) is the lower limit of unit reliability constraint for ship electronic information system; \(R_{\text{imax}}\) is the upper limit of the unit reliability constraint; \(R_{i}\) is the value of unit reliability. The mission reliability constraints are presented in Equation (12) as follows:  
\[R_{mi}\geq R_{mi}^{*} \tag{12}\]  
where \(R_{mi}^{*}\) is the target value of the mission reliability of the ship electronic information system; \(R_{mi}\) is the allocation value of the mission reliability.  
In summary, the reliability constraints of the mission reliability of the ship electronic information system as well as the objective function are associated, and the reliability allocation model of the ship electronic information system is established by Equation (13).  
\[\begin{array}{l}\text{min}C=\sum\limits_{i=1}^{n}C(R_{i})=\sum\limits_{i=1}^ {n}k_{i}(1-f_{i})\ln\biggl{(}\frac{1}{R_{i,\text{max}}-R_{i}}\biggr{)}e^{ \sigma_{i}}\\ s.t.\biggl{\{}\begin{array}{l}R_{\text{imin}}\leq R_{i}\leq R_{\text{imax}} \\ R_{mi}\geq R_{mi}^{*}\end{array}\end{array} \tag{13}\]
## 3 Results and Discussion
### Fiber Matrix Interaction
Therefore, the greater friction between the matrix and the coated fibers can be considered the results of mechanical interlocking that arises during ongoing pull-out due to the removed coating material that remains between the fiber surface and the matrix. According to the results obtained for SB and CSB (Table 4), the addition of the methylol melanin cross-linking agent does not have significant effect on the pull-out behavior of the modified fibers. Therefore, it can be concluded that the increase in the fiber surface roughness observed in the AFM investigation (Table 3) plays a minor role in the quasi static pull-out for the fiber-matrix system investigated. On the contrary, the addition of nanoclay particles in both the SB and CSB polymers determine an increase of both the IFSS and W\({}_{total}\). This can be attributed to the ability of the MMT particles to accelerate and enhance the formation of hydration products on the fiber surface, as shown during the storage in cement solution in Section 3.4. The results confirm the hypothesis that nanoclay particles can increase the interaction between organic coating and cementitious matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Name** & **IFSS [MPa]** & **W\({}_{total}\) [mN*mm]** & \(l_{e}\) [mm]** \\ \hline PTMO & \(4.2\pm 2.4\) & \(15.2\pm 7.6\) & \(663\pm 245\) \\ SB & \(1.9\pm 0.3\) & \(25.3\pm 5.6\) & \(894\pm 163\) \\ CSB & \(1.5\pm 0.5\) & \(20.3\pm 7.5\) & \(847\pm 106\) \\ SB-MMT & \(2.2\pm 0.5\) & \(28.8\pm 10.1\) & \(782\pm 117\) \\ CSB-MMT & \(1.9\pm 0.5\) & \(34.4\pm 11.3\) & \(883\pm 62\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of SFPO depending on the surface treatment of the ARG fibers.
## 3 Proposed Yielding Protocol
### Identification of the Communicating Vehicle
It represents the angle of direction in which the other vehicle is moving, \(\theta\)dir, in the counterclockwise rotation with the direction in which the own vehicle is moving defined as \"0.\" As shown in Figure 3, the other vehicle is moving forward (the same direction as the own vehicle) if its direction is within the \(90^{\circ}\) of \(0\leq\theta\)dir \(\leq\pi\gamma 4\) and \(7\pi\gamma 4<\theta\)dir \(\leq 2\pi\), is moving left if its direction is within the \(90^{\circ}\) of \(\pi\gamma 4<\theta\)dir \(\leq 3\pi\gamma 4\), is moving backward (opposite to the direction of the own vehicle) if its direction is within the \(90^{\circ}\) of \(3\pi\gamma 4<\theta\)dir \(\leq 5\pi\gamma 4\), and is moving right if its direction is within the \(90^{\circ}\) of \(5\pi\gamma 4<\theta\)dir \(\leq 7\pi\gamma 4\). The own vehicle can determine whether the other vehicle is moving toward  
Figure 2: Determining the relative position of the communicating vehicle. The red vehicle is the own vehicle and the white vehicle is the other vehicle. (**a**) The own vehicle is the requesting vehicle; (**b**) the own vehicle is the responding vehicle. the intersection by combining the information about the relative direction with the information about the relative position. For example, consider a case where the own vehicle is a responding vehicle. If the relative position of the other vehicle is ahead and its relative direction indicates that it is moving left, it is approaching from the right side. If the relative direction indicates that it is moving forward, it is a vehicle ahead of the own vehicle. If the relative direction indicates that it is moving right, it is approaching from the left side. Thus, a combination of the relative position and the relative direction provides information about whether the other vehicle is moving toward the own vehicle. For example, if the other vehicle is located ahead and is moving backward, it is heading toward the own vehicle. On the other hand, if the other vehicle is ahead and is moving forward, it is not coming this way. The own vehicle tries to identify the other vehicle only when the other vehicle is moving toward it. A requesting vehicle can determine the direction from which the responding vehicle is approaching from the latter's relative position and relative direction. Since it needs to recognize the vehicle model of only those vehicles that are moving in that direction, it is easy for it to identify the responding vehicle even when there are a number of vehicles of the same vehicle model. A responding vehicle can determine whether the requesting vehicle is a competing vehicle from the latter's relative position and relative direction and the yielding pattern. Since it needs to recognize the vehicle model of only competing vehicles, it can reduce the processing load for vehicle model recognition. In addition, this paper does not consider how the method of recognizing a vehicle model in Step 4 is implemented. Because, there are already methods that can recognize a vehicle model with a high level of accuracy [11; 12].
# Formation Control and Distributed Goal Assignment for Multi-Agent Non-Holonomic Systems
## 10 Simulation Results for Goal Exchange  
This section presents numerical simulation of the algorithm extended with goal exchange procedure. The initial conditions are exactly the same as in Section 4 (results for the algorithm without goal exchange). The parameters of the controllers were also the same. The initial value of the permutation matrix was the identity matrix \(P(0)=I\).  
Figure 12a shows paths of robots on the \((x,y)\) plane. As in the previous experiment signals of 3 robots (out of 48) are highlighted in black. Figure 12b,c present graphs of \(x\) and \(y\) coordinates as a function of time. The robots converge to the desired values in less than 20 s, a significantly better result than 115 s (without goal exchange). This experiment was completed at 28 s due to faster convergence. Figure 12d shows a time graph of the orientations. In Figure 12e,f linear and angular controls are plotted. They reach constant values in less than 20 s. Figure 12g presents a time graph of the freeze procedure (refer to Assumption 2). It can be seen that the last collision avoidance interaction ends at 13 s. In Figure 12h relative distances between robots are shown. No pair of robots reaches inter-agent distance lower than or equal to \(r=0.3\) m (dashed line). It means that no collision has occurred.  
Figure 12: _Cont._Notice that in the presented simulation no communication delay of the goal exchange procedure was taken into account. Depending on the quality of the network single goal exchange may take even hundreds of milliseconds. On the other hand, as the procedure involves only a pair of agents, many of such pairs can execute goal exchange at the same time. Of course another limitation was the bandwidth of the communication network. These issues will be investigated by the author in the near future. In the presented numerical simulations the number of goal switchings was 238, which is a significant number.  
Visualizations of the exemplary experiments are available on the website [[http://wojciech.kowalczyk.pracownik.put.poznan.pl/research/target-assignment/ts.html](http://wojciech.kowalczyk.pracownik.put.poznan.pl/research/target-assignment/ts.html)]([http://wojciech.kowalczyk.pracownik.put.poznan.pl/research/target-assignment/ts.html](http://wojciech.kowalczyk.pracownik.put.poznan.pl/research/target-assignment/ts.html)).
## 3 Results
### Features Selection  
At the beginning, the 163 ions were directly used as features for classifier training. Each ion represents a compound and had a detection rate over 95% in malignant group. Table 3 listed the accuracy, sensitivity, specificity, and cross-validation of the trained classifiers. The accuracy of classifiers is from 66.0-80.0%, the sensitivity from 66.7-74.1%, and the specificity from 65.2-87.0%. The highest accuracy is 80.0% with the random forest (RF) method.  
Afterwards, 13,203 new features were generated from the 163 ions by pairwise coupling, and 3630 features were used for classifier training. The accuracy of classifiers is from 74.0-92.0%, the sensitivity from 63.0-88.9%, and the specificity from 87.0-95.7%. The use of new features significantly increased the performance of the MLP and SVM classifiers.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Features** & \multicolumn{3}{c}{**163 Ions, \%**} & \multicolumn{3}{c}{**3630 Ion Ratios, \%**} & \multicolumn{3}{c}{**Improvement, \%**} \\ \hline method & SVM 1 & RF 2 & MLP 3 & SVM & RF & MLP & SVM & RF & MLP \\ accuracy & 72.0 & 80.0 & 66.0 & 84.0 & 74.0 & 92.0 & 12.0 & \(-\)6.0 & 26.0 \\ sensitivity & 70.4 & 74.1 & 66.7 & 74.1 & 63.0 & 88.9 & 3.7 & \(-\)11.1 & 22.2 \\ specificity & 73.9 & 87.0 & 65.2 & 95.7 & 87.0 & 95.7 & 21.8 & 0 & 30.5 \\ cross-validation 4  & 87.9 \(\pm\) 1.2 & 73.0 \(\pm\) 3.6 & 84.0 \(\pm\) 0.8 & 89.5 \(\pm\) 1.2 & 77.0 \(\pm\) 89.5 \(\pm\) 1.2 & 89.5 \(\pm\) 4.3 & 1.6 \(\pm\) 4.0 & 4.5 \\  & & & & & 6.0 & 4.3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracy, sensitivity, specificity, and cross-validation of classifiers.  
Figure 2: Representative PESI-MS spectra of malignant and benign nodules in positive mass mode.