esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithms for Calculating Generalized Trigonometric Functions
## 5 Algorithms for Calculation Space Transform Based on _Ateb_-Functions
### Method for Calculating Ateb-Transforms  
In this section, we describe how to realize algorithms for the calculation of continuous _Ateb_-transform.  
1. Define parameters \(n\) and \(m\) of _Ateb_-transform and function \(x(t)\), the spectrum of which we will calculate;
2. Control the periodic conditions ;
3. Calculate the period \(\Pi(n,m)\);
4. Define the interval \([-M;M]\) and step h for calculating integrals for \(A(n,m,\omega t)\) and \(B(n,m,\omega t)\) according to Formulas (37) and (38) and define interval \([-W;W]\) and step \(h_{w}\) for \(\omega\);
5. For current point w from the interval \([-W;W]\), calculate the values for \(sa(n,m,wt)\) and \(ca(n,m,wt)\), then calculate \(A(m,n,wt)\) and \(B(n,m,wt)\), and then calculate _Ateb_-transform according to Formula (36).  
The cosine and sine Fourier transforms are used for continuous functions. However, for problems related to information technology, it is more appropriate to use discrete functions and transforms. In this case, the discrete Fourier transform is used. Therefore, in the following sections, we will consider the construction of discrete _Ateb_-transforms.
## 2 Proposed Solution
### Overview of AmoebaNet Solution
When two systems are trying to send/receive the traffic to/from two physically isolated sites, then the service of AmoebaNet carefully identifies the corresponding p2p layer-2 circuits between sites and properly multiplexed and/or de-multiplexed the traffics accordingly. * AmoebaNet is able to provide a novel control plane logic, which consists of different sub-modules. Each sub-module of control plane architecture is dedicated for a specific task and interacts with each other. The entire control plane architecture is implemented inside ONOS SDN controller as a service and it uses north-bound and south-bound APIs to interact with applications and data plane, respectively. * To avoid the unsynchronized access and provides advanced reservation capability to network resources, we introduced the new locking/unlocking and scheduler mechanisms in AmoebaNet's solution. The more detailed information about AmoebaNet can be found in [12] for its design, implementation, and evaluation.
# Involvement of the Orexinergic System in Cancer: Antitumor Strategies and Future Perspectives
## 3 Orexins and Cancer
### Glioma  
It has been reported that orexin A, through a caspase-dependent mechanism, decreased the viability of rat C6 glioma cells [105]. C6 cells expressed OXR1 and OXR2, which did not mediate the proliferation of these cells, but orexin A (at a high concentration) decreased cell viability by increasing the activity of caspase 3, leading to the death of C6 cells [105]. However, orexin A did not affect cell viability at a physiological level (at a lower concentration) [105]. Moreover, compared with that found in normal individuals, a low level of orexin A has been observed in the cerebrospinal fluid of a patient with a hypothalamic tumor [106].
# Development of a Fire Risk Assessment Program for Submerged Tunnels
###### Abstract  
Submerged tunnels are an innovative infrastructure solution for connecting roads and railways, especially in areas where conventional bridge or overland tunnel construction is limited by deep waterways, narrow straits, or dense urban development. In such regions, submerged tunnels offer an efficient and less intrusive alternative that overcomes geographical constraints. However, unlike conventional ground-level or subsea tunnels, submerged tunnels have unique structural and environmental characteristics, which necessitate the development of a dedicated evaluation system for responding to fire and other disasters. In this study, a quantitative fire risk assessment program (SFT_QRA) was developed by reflecting the specific characteristics of submerged tunnels. The program was applied to both road and railway tunnels to obtain evaluation results. First, to more realistically reflect the fire risk within submerged tunnels, the latest statistical data were used to update fire occurrence probabilities and the proportion of vulnerable users. In addition, the optimal smoke control mode for structural stop zones in ultra-long tunnels was analyzed to derive strategies for establishing a safe evacuation environment. Second, an Excel VBA-based assessment program was developed to improve user convenience and was structured to enable fire analysis and evacuation simulations. Third, in order to verify the accuracy and reliability of the developed program, a comparative analysis was conducted against commercial quantitative risk assessment programs. As a result, the total risk error rate was 0.4% for road tunnels and within 5.0% for railway tunnels, showing similar levels of results. This study advances quantitative risk assessment methods by incorporating the unique features of submerged tunnels and implementing them in a validated program. Through this approach, it presents a practical solution that can contribute to the advancement of tunnel fire safety technologies and the overall enhancement of tunnel safety.  
submerged tunnels; fire risk assessment; quantitative risk assessment; road tunnel; railway tunnel 2025  
6798
# Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections
## 3 Model Predictive Intersection Control Design
### Operation of the MPC Controller  
The operation of the intersection MPC control is shown in Figure 2. The coordinator of the intersection collects the position and velocity data \(s_{i,ent}(k)\), \(v_{i,ent}(k)\)\(i\in[1...n]\) of each autonomous vehicle entering the intersection at a discrete time step \(k\) using a sampling time \(T_{s}\), along with their turning intention \(d_{i}\). For the newly entered vehicle, a decision is made based on the position of the preceding vehicle, whether to add the vehicle to the MPC optimization process, or switch to adaptive cruise control mode. Note, that for the MPC optimization method the control performance can be set by the scheduling variable \(\rho\). At each discrete time step \(k\), based on the predefined \(\rho\) value the solution of the time-optimal, energy-optimal or multicriteria vehicle ordering is calculated based on the analytical method detailed in Sections 3.3-3.5 for a time horizon \(T=\max(t_{i,fin})\ i\in[1...n]\). Note, that the time horizon depends on the vehicle states and the intersection geometry, and the optimization problem is solved in a single control problem for all vehicles. Moreover, newly entering vehicles only join the MPC optimization process when the preceding vehicle leaves the intersection, thus the optimization proceeds with maximum four vehicles at the same time. The solution of the optimization gives the control variables \(a_{i}(k+1)\ i\in[1...n]\), which the autonomous vehicles in the intersection follow until the subsequent time step, where the optimization is repeated with the forward shifted horizon. Since the vehicle ordering and prescribed accelerations are recalculated at every time step for vehicles in the entering zone, a sampling time \(T_{s}=0.1\) s is selected to avoid chattering of the control signals. Note that present MPC method is inherently robust against bounded initial errors in the measurement signals, since as the accuracy of the signals improve as the vehicles get closer to the intersection origin, the online recalculation of the MPC controller adjusts the vehicle order and control inputs to the punctual data.
# Compression of Vehicle Trajectories with a Variational Autoencoder
## 4 Methodology
### Bidirectional LSTM Autoencoder  
Based on LSTM networks' success in learning sequential data generation process's nonlinear, time-dependent dynamics [27; 28], the application of LSTM networks is evident. The structure of the model is described below. The Encoder is a Bidirectional LSTM RNN with an adjustable \(c\) context dimension--meaning that the LSTM [33] cell has a hidden state and cell state dimension equal to \(c\)  
Figure 3: Concept of the autoencoding scheme to learn latent features from the distribution of trajectories. The encoder and decoder are enforced to copy the data with the reconstruction loss as low as possible with an informational bottleneck.  
Coordinates \(x,y\) are fed in every time-step. The hidden and cell states are initialized randomly in the first time step, and the resulting new one is passed to the next step. The last ones are taken as the context vector and used as the initialization of the Encoder LSTM. During training mode, teacher forcing [34] is applied with a ratio of 0.5. The decoder in the first step takes the original trajectory's last time step \(x,y\) coordinates. The other steps take the previous output or the corresponding ground truth from the original trajectory. In the case of validation and test, teacher forcing is turned off. The context dimension is set to be \(c=10,12,15,20\) using three LSTM layers and 0.5 dropout.
## 4 Discussion
The absence of notable improvement in RMSE with the 4-segment model suggests that adding complexity to the IMU may lead to diminishing benefits. Future studies should examine the trade-off between increased model complexity and its practical applicability. There were several limitations to this study. First, movement artifacts in the IMU acceleration data may have occurred due to the fat, skin, and muscle on which the IMUs were attached. While the use of Fixomull(r) polyurethane film aimed to reduce such artifacts, alternative methods for securing the IMUs may prove more effective. Next, this study used [PERSON]'s [27] anthropometric data to weight IMU signals based on the mass proportions and center of mass locations of individual body segments. These measurements were derived from average values of a small sample of young adults, which may not perfectly represent all participants. A more tailored mass-weighting approach, one that does not rely on anthropometric norms, could be more optimal. Techniques such as DEXA scans or 3D imaging, while expensive and requiring specialized training, would offer more precise measurements for each participant compared to average center of mass data. Lastly, the weighting applied in this investigation across the various models may not have been optimal. Figure 1: Linear relationship between countermovement jump heights derived from a 2-segment IMU model and a force platform criterion. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Segments** & **RMSE** & **Acc. FP** & **\%** \\ \hline
1 a & Trunk, Thighs, & 2.2 (1.1) & 12.85 (0.74) & 17.1 \\
2 a & Trunk & 3.0 (1.4) & 12.85 (0.74) & 23.3 \\
7 a & Trunk, Shanks & 2.1 (1.3) & 12.85 (0.74) & 16.3 \\
13 a & Trunk, Thighs, Feet & 2.0 (1.2) & 12.85 (0.74) & 15.6 \\ \hline \hline \end{tabular}  
* Lowest root-mean-square error (RMSE) observed for 1-, 2-, 3-, and 4-segment models. Acc. FP = average force platform acceleration. Data are reported as mean (SD).  
\end{table} 
Table 9: RMSE as a percentage of average force platform acceleration.
# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
###### Abstract  
Public transportation systems face numerous challenges like traffic congestion, inconsistent schedules, and variable passenger demand. These issues lead to delays, overcrowding, and reduced patron satisfaction. Digital twin (DT) technology is a promising innovation for improving public transportation systems by offering real-time virtual representations of physical systems. By integrating real-time data from various sources, digital twins can enable predictive analytics, optimize operations, and improve the overall performance of public transportation networks. This work explores the potential of digital twins to optimize operational efficiency, enhance passenger experiences, and support sustainable urban mobility. A comprehensive review of the existing literature was conducted by analyzing case studies, theoretical models, and practical implementations to assess the effectiveness of DTs in transit systems. While the benefits of DTs are significant, their successful implementation in bus transportation systems is impeded by several challenges like scalability limitations, interoperability issues, and technical complexities involving data integration and IT infrastructure. This paper discusses ways to overcome these challenges, which include using modular designs, microservices, blockchain for security, and standardized communication for better integration. It emphasizes the importance of collaboration in research and practice to effectively apply digital twin technology to public transit systems.  
digital twin; public transportation; sustainable mobility; smart cities +
Footnote †: journal: Applied sciences
## Materials and Methods
### Optical Amplifier Characterization
Shorter fiber lengths help reduce nonlinear amplifier effects like Stimulated Raman Scattering (SRS), Stimulated Brillouin Scattering (SBS), and Four-Wave Mixing (FWM). The NuPhoton NP3000 series fiber amplifier (NuPhoton Technologies, Inc., Murrieta, CA, USA) is an existing commercial EDFA that has dimensions of 70 mm \(\times\) 90 mm \(\times\) 15 mm and weighs \(\sim\)0.17 kg. It is believed that the MOCT EDFA would be able to fit in a similar form factor for a nanosatellite mission.
# Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation
## 5 Experiments
### Prediction Performance (Rq1)  
In the section, we first focus on the performance of all methods. Table 3 presents the performance comparison results with the AUC and F1 of eight methods. The observations are as follows:  
* The proposed method KGET consistently outperforms these state-of-the-art baselines on all datasets. KGET exhibits improvements of 1.6% and 4.5% over the best algorithms in AUC on Last.FM and Book-Crossing, respectively, and F1 increased by 1.2% and 1.4% in Last.FM and Book-Crossing, respectively. The improvements of our method on Book-Crossing is higher than results on Last.FM. This may be due to the fact that Book-Crossing is sparser than Last.FM, and the distribution of users and items are more unbalanced in Book-Crossing, with the number of users far outnumbering the number of items. This demonstrates that direct learning of embedding unseen entities is difficult to capture comprehensive relationship information. However, our proposed method KGET is able to learn embedding more stably in training data sparsity and data imperfection by learning the connections between unseen entities (transductive learning).
* The performance of RippleNet, KGCN and KGNN in Last.FM is better than the performance in Book-Crossing, which indicates that the user's neighbor information is also useful in recommendations. Since RippleNet, KGCN and KGNN do not focus on the user's neighborhood information, but only on the neighborhood of the item with which the user interacts, and there are many more users than items in Book-Crossing, the user's neighborhood information becomes more important.
* We observe that CKAN and KGAT achieve better performance than other baselines, which illustrates that a method combining the collaborative signal with side information in KG might fully mine the feature information of users and items. The collaborative signal and higher-order connection information benefit the personalized recommendation.
* Compared with RippleNet, the results show that the performance of propagation-based methods outperform the path-based methods. Which may be because the relationships between entities in the real knowledge graph are complicated and it is difficult to extract high-quality paths.
* The AUC of the KG-free method BPRMF is better than that of KG-based methods including CKE, RippleNet, KGCN, and KGNN, indicating the advantages of the collaborative filtering method. It is not certain that good results are obtained by usingside information of the knowledge graph. It is also necessary to consider whether the advantages of the knowledge graph might be fully utilized and whether it is suitable for the dataset.
# SIV: Raise the Correlation of Second-Order Correlation Power Analysis to 1.00
## 3 Sparse Intermediate Value in AES
### Finding the SIV Based on Operational Characteristics
For the FOCPA of the MixColumns, the peak correlation coefficient of the General FOCPA is approximately 0.87, and the peak correlation of the SIV-based FOCPA is around 0.94, as shown in Figure 2. The peak correlation of the two versions of FOCPA is similar, demonstrating the existence of the SIV-related power consumption when performing xtime, as shown in Property 1. The power consumption of MixColumns is linearly related to the Hamming weight of SubBytes output because the SubBytes output is identical to the MixColumns input. In addition, the correlation of SubBytes is not zero because the Hamming weight of the SIV is linearly related to the MSB of the SubBytes output, as shown in Equation (6). To demonstrate Property 2, we compared each power consumption of \(S_{1}\) and \(S_{2}\). The power consumption is divided into two groups based on the SIV, and we verified the distributions of each group. Based on Equation (6), the effect of \(d_{7}\) on the power consumption \(P_{S_{2}}\) is four times greater than that on \(P_{S_{1}}\). Therefore, theoretically, the difference between the mean of the two distributions of \(S_{2}\) is four times larger than that of \(S_{1}\). We utilized the \(t\)-value of [PERSON]'s \(t\)-test [15] to measure the difference between the two distributions as follows:  
\[t=\frac{E\left[X\right]-E\left[Y\right]}{\sqrt{\frac{\sigma_{X}^{2}}{\left|X \right|}+\frac{\sigma_{Y}^{2}}{\left|Y\right|}}},\]  
where \(\sigma_{X}\) and \(\sigma_{Y}\) denote the standard deviation of \(X\) and \(Y\), respectively  
Figure 3 shows the experimental proof of Property 2. The difference between the mean of the two distributions of \(S_{2}\) is around 0.02084, which is approximately 3.5 times larger than that of \(S_{1}\) (0.005981). This is very close to the theoretical ratio of 4. The t-value of \(S_{2}\) is approximately 876.22 and that of \(S_{1}\) is 198.84. Owing to the substantial difference in the means of the two distributions of \(S_{2}\), the two distributions are completely separated despite the noise, as shown in Figure 3, i.e., the effect of noise can be relatively moderated, as demonstrated in Property 2. Figure 2: Results of General FOCPA and SIV-based FOCPA (0 th byte).
## 3 Joint Optimal Power Allocation and Relay Selection Scheme
```
1: Initialization: setting the value \(\alpha_{a}=0\), \(\alpha_{b}=1\), \(\delta\) is a positive real number close to 0;
2:While\(|\alpha_{a}-\alpha_{b}|\geq\delta\) do
3: Based on \(dR_{T}(\alpha)/d\alpha\), calculate \(\xi=dR_{T}(\alpha)/d\alpha\), where \(\alpha=(\alpha_{a}+\alpha_{b})/2\);
4:If \(\xi=0\), set \(\alpha_{a}=(\alpha_{a}+\alpha_{b})/2\) and go to 9;
5:Else if \(\xi>0\), set \(\alpha_{a}=(\alpha_{a}+\alpha_{b})/2\);
6:Else if \(\xi<0\), set \(\alpha_{b}=(\alpha_{a}+\alpha_{b})/2\);
7:endif
8:End while
9:\(\alpha^{*}=\alpha_{a}\) is the optimal solution. ```  
**Time allocation scheme**  
For optimal power allocation and optimal TS factor, the original problem in (16) is reduced to the following relay selection problem:  
\[i^{*}=\arg\max_{P_{S1}=P_{S1}^{*},\alpha=\alpha^{*},i\in[1,...N]}R_{T} \tag{25}\]  
The optimal relay selection method can be described by selecting the cooperative relay with the maximum instantaneous information rate in \(N\) relays. The original max-min bidirectional instantaneous information rate can be solved by the proposed joint optimal power allocation and relay selection algorithm. ```
1:Initialization parameters;
2:Obtain \(P_{S1}^{*}\) and \(P_{S2}^{*}\) for fixed \(\alpha\) and fixed relay node following the procedure in Equations (18)-(23);
3:Obtain the optimal \(\alpha^{*}\) for optimal \(P_{S1}^{*}\), \(P_{S2}^{*}\) and fixed relay node using the time allocation scheme;
4:Obtain the optimal cooperative relay node using the traversal method for optimal \(P_{S1}^{*}\), \(P_{S2}^{*}\) and optimal TS factor \(\alpha^{*}\) in \(N\) relays;
5:end. ```  
**Algorithm 1**Initialization of the optimal TS factor \(\alpha^{*}\) in \(N\) relay selection
# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
## 4 Experimental Study
### Data Complexity Measures  
To highlight the reason why the use of the CNN-features improves the classification performance, the complexity measures were also measured and compared. Figure 3 shows a comparison of the complexity measurements obtained by applying the four extractors of PCA, kPCA, AE, and a CNN (CNN-O2P or CNN-sum) to RealPatch, prepared for this experiment.  
In Figure 3, the CNN (CNN-O2P or CNN-sum) bar has the highest height for the \(F1\) complexity, while the PCA (or kPCA) bar has the highest for the \(Fv\), \(D2\), and \(L2\) complexities. Through this comparison, we can consider as follows: First, the result of comparing the data complexity shown in Figure 3 is consistent with the result of comparing the classification accuracy shown in Table 2. That is, compared to the accuracy of PCA and CNN-features in Table 2, CNN-based is superior to PCA. In Figure 3, when comparing the \(F1\) values for these two extractors, the CNN bar height is higher than that of PCA.  
Second, a comparison of the \(Fv\), \(D2\), and \(L2\) complexities is the opposite of \(F1\): the CNN-based bar height is lower than the PCA height. Specifically, the fact that the CNN-based accuracy rate in Table 2 is greater than that of the PCA is consistent with the fact that the CNN-based \(F1\) bar in Figure 3 is higher than that of the PCA. However, the \(Fv\) value is small when the accuracy rate is large. As reported in the relevant literature [13], the increasing value of \(F1\) reduces the overlapping feature space and, thus, allows for a better separation of the feature space of the two classes. Unlike \(F1\), the smaller the value measured in \(Fv\), the simpler the classification problem.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers** (**Types**)} & \multicolumn{4}{c}{**Feature Extraction Methods**} \\ \cline{2-6}  & **PCA** & **kPCA** & **AE** & **CNN-Sum** & **CNN-O2P** \\ \hline kNN (k = 1) & 92.47 & 93.10 & 86.33 & 95.67 & **99.03** \\ kNN (k = 3) & 84.52 & 86.56 & 74.59 & 93.19 & **98.47** \\ SVM (polyn.) & 80.20 & 86.69 & 72.69 & 85.88 & **96.21** \\ SVM (RBF) & 91.16 & 93.32 & 83.58 & 95.91 & **97.99** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification accuracy rates (mean) (%).  
Similar comparative analyses can be applied for the other complexity measures, \(D2\) and \(L2\). In general, for \(D2\) and \(L2\), the larger the bar height, the greater the overlap between classes, which makes classification difficult. The results of Figure 3 are very consistent with the above fact. From the observations made above, it can be argued that the CNN-based extractor is a good feature extractor when compared to the AE-based extractor, as well as when compared to the PCA (and kPCA). In particular, the extractor can be applied to situations where existing extractors do not work well because of the nature of the data. In various practices dealing with high-dimensional data, PCAs, which rely on covariance matrices, are known to be rarely used because they are not efficient.