esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 3 Experimental Results
### Experimental Environment and Validation  
The proposed algorithm is implemented on a Windows 7 machine running an Intel(R) Core(TM) i7-4790 CPU @ 3.60 GHz with visual studio 2015 by C++ language. The validation applied 51 cases which were generated for 10 to 60 messages: there are 100 examples in each case. The message parameters of FlexRay, such as \(T_{i}\), \(D_{i}\), \(PF_{i}\), and \(S_{i}\), are randomly generated. The periods and \(PF_{i}\) are varied between 2 ms and 40 ms while the deadline is the same as the period and 1% and 50%, respectively. The period of a FlexRay communication cycle is 5 ms, \(L_{\text{FC}}\) is 3 ms and \(N_{slot}\) is 450, and \(SR\) is 0.99. The above parameter configurations are according to the BMW specification and are also adopted in the literature [8].  
The validation of the proposed method uses three cases: message transmission with and without the proposed FRSA, with the other case unable to be scheduled. Data in Table 1 show the five messages which should be transmitted and their parameters. The global success reliability (\(GS\)) approaches 0 by applying Equation (5). \(GS\) does not meet \(SR\) because of being without a message re-transmission mechanism. In almost every case, the recovery mechanism is a necessary processing step to ensure the communication reliability of the FlexRay protocol. The test case applied the proposed FRSA to re-compute the \(SR_{mi}\) and \(RT_{i}\); we can see that FRSA increases the \(GS\) to 0.9987. The proposed FRSA re-computed each corresponding \(RT_{i}\), and then messages \(M_{1}\)\(\sim\)\(M_{5}\) are sorted by their \(GS_{mi}\) in descending order. The overhead of the re-transmission mechanism is an increase of bandwidth utilization. The utilization increase of bandwidth (slot) can be explained because of the recovery mechanism for the re-transmitted messages.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Recovery** & \multirow{2}{*}{\(M_{1}\) (\(T_{i}\), \(D_{i}\), \(S_{i}\), \(PF_{i}\))} & \multirow{2}{*}{\(RT_{i}\)} & \multirow{2}{*}{\(GS_{mi}\)} & \multirow{2}{*}{\(GS\)} & \multirow{2}{*}{\(\begin{array}{c}\text{\bf Slot}\\ \text{\bf Utilization}\end{array}\)} \\ \cline{5-6}
**Non** & & & & & & & \\ \cline{5-6}
**
\begin{tabular}{} \end{tabular}** & & & & & & & \\ \cline{5-6}
**
\begin{tabular}{} \end{tabular}** & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The validation of the proposed method.  
There are two conditions which are unable to be scheduled. In one, the \(GS\) does not meet the system reliability goal (\(SR\)). In the other, the slot utilization is over the upper bound (\(N_{\text{slot}}\)). This means that there is available bandwidth that can be used to re-transmit messages, meaning that the \(RT_{i}\) rule cannot be followed, causing lower reliability.
## 4 Implementation and Experimental Setup
### Depthwise Separable and Ensemble of Depthwise Separable CNN Models  
Using a heuristic approach and optimization, the proposed CNN model contains 21 layers. Figure 4 shows the comparison between the CNN model with and without depthwise separable CNN in terms of structure and number of train parameters. As shown in the figure, the total train parameters are less with the implementation of depthwise separable. Hence, the training time will be faster for  
The depthwise separable CNN as described in Table 2 was used to improve the training time, while the ensemble of depthwise separable CNN, as depicted in Figure 5, was used to improve further its classification accuracy. The input layer size is 256 representing the raw ECG beat waveform, while the output layer size is 16 representing the number of classes as described in the MIT-BIH database (see Table 1). There is a group of layers repeated three times, i.e., layer 5 to 9, layer 10 to 14, and layer 15 to 19. These five layers contain three convolution layers with filter size of 5, 1, and 5, respectively. Layer 5 to 7 is configured according to the DSC algorithm. Moreover, layer 2, 7, 12, and 17 were using stride 4 to replace the function of pooling layer according to the ACN algorithm. Suppose that we use an ensemble of three DSC, the total number of parameters will be three times larger, i.e., 34,719 train parameters, which is still less than the train parameters without DSC, i.e., 47,600. As is discussed in Section 5, the use of ensemble improves its accuracy.
## 4 Results and Discussion
### Test Results of Car Noise in the Tunnel  
Case II mainly studies the sound absorption performance of closed-cell aluminum foam under moving sound sources. Analysis of the measurement results in Case II can more accurately reveal the noise reduction effect of closed-cell aluminum foam boards on vehicle noise in tunnels. Since the impact of the vehicle model on the noise generated was considered in the test, when analyzing the possible noise reduction effect of closed-cell aluminum foam in tunnel applications, the analysis was carried out according to the three common vehicle models in the tunnel, as shown in Figure 11a-c.  
Figure 10: Influence of frequency.  
Figure 11: Test results of Case II.  
It can be seen from the above that each point in the figure is the representative value of the average of 10 test data. It can be seen from the figure that when the aluminum foam board is not installed, the noise generated by the large car in the tunnel is the largest, and the maximum instantaneous A-weighted sound pressure level reaches 95.1 dB(A). The sound pressure level of noise produced by small cars is slightly lower than that of mid-sized cars, at 87.7 dB(A) and 88.5 dB(A) respectively. After the single-sided aluminum foam boards were installed, the noise generated by small and middle-sized cars is decreased to a limited extent. The maximum instantaneous A-weighted sound pressure level in the tunnel when a bicycle passes through the tunnel is reduced by less than 2 dB(A). However, the single-sided aluminum foam board has a more obvious reduction effect on the traffic noise generated by large vehicles, which is reduced by 3.4 dB(A). The attenuation of the vehicle noise by the double-sided aluminum foam boards is greater than the vehicle noise when installed on single side or not installed. After installation on both sides, the noise attenuation for medium-sized cars is the strongest, reaching 4.9 dB(A). The vehicle noise reduction for small cars and large cars are 3.3 dB(A) and 4.5 dB(A). This may be due to the fact that the frequency of the noise generated by the medium-sized car is closer to the natural frequency of the aluminum foam board structure.
## 1 Introduction and Related Research
To improve recognition accuracy, we developed the LBP-CNNs model [11], which integrates LBP with CNNs. LBP is a widely adopted texture descriptor with rotation and grayscale invariance that can efficiently extract local texture features. This design not only reduces computational cost but also retains key image characteristics. The experimental results showed that the LBP-CNNs model achieved an average ranging error of 1.27 cm and an obstacle recognition accuracy of 92.3%. To further investigate this limitation, we conducted experiments evaluating laser reflections on surfaces made of different materials. As shown in Figure 1, the reflection intensity of the laser spots varied significantly across wall, metal, paper, and wood surfaces. Notably, obstacles (e.g., metal or wood) produced more prominent and consistent reflections compared to flat background walls. These findings confirmed that surface material characteristics and illumination angle play a critical role in visual-based ranging performance and motivated the development of more robust feature extraction and classification methods. Figure 1: Laser emissions on different materials. In addition, we extended the LBP-CNNs architecture by integrating FFTs, resulting in the LBP-FFT-CNNs model. FFT converts image data from the spatial domain to the frequency domain and extracts the parts that are critical for identifying textures, edges, and repeating patterns. This transformation improves the ability of feature discrimination, reduces the data dimension, and improves the classification efficiency. Experimental evaluations demonstrated a recognition accuracy of 98.6%, with average indoor positioning errors reduced to 0.91 cm. The key contributions of this paper are summarized as follows:  
1. Device Optimization: We improved the original MC4L design by integrating an IMU module to form the MC4L-IMU device, thereby increasing adaptability in complex indoor environments. 2. Obstacle Recognition Accuracy: By introducing FFT-based feature extraction, we significantly reduced computational overhead while improving recognition accuracy to 96.3%. 3. Model Efficiency: The proposed LBP-FFT-CNNs model features a simplified architecture with consistently low prediction standard index (PSI) values (<0.02), indicating high robustness. 4. Hybrid Positioning Algorithm: We developed an inertial-visual fusion algorithm that achieves sub-centimeter positioning accuracy, even in low-light environments. The remainder of this paper is structured as follows: Section 2 describes the module structure and provides a connection diagram for the MC4L-IMU device. Section 3 introduces the proposed LBP-FFT-CNNs model and detail processing pipeline. Section 4 describes the experimental environment and fusion-based indoor positioning algorithm. Section 5 provides a comprehensive performance evaluation, including regression and classification metrics, and a discussion of the experimental results. Finally, Section 6 summarizes our conclusions and outlines future research directions.
## 2 Methodology
### Proposed Model Orga-Dete
#### 2.2.2 Multi-Path Coordinate Attention  
In organoid microscopy object detection, attention mechanisms significantly improve the accuracy of small target detection by optimizing multi-level feature representations. This is particularly critical for organoid images, where high-density small objects and low-contrast micro-morphological variations make discriminative feature extraction difficult [34]. To address these challenges, we introduce a multi-path coordinate attention (MPCA) [35] mechanism after the C2 PSA module in the Backbone. MPCA enhances small target feature extraction by refining the attention mechanism to better distinguish subtle morphological differences.  
Figure 2: Feature fusion network architecture (**a**) FPN; (**b**) FPN + PAN; (**c**) BiFPN.  
The MPCA module is an enhanced version of the coordinate attention (CA) model [36], integrating a multi-path architecture to extract more comprehensive features across varying scales and orientations. It splits the input feature map into four separate pathways, each tailored to capture scale- or orientation-specific information through coordinate attention. This spatial decoupling allows the parallel analysis of distinct spatial characteristics. Each pathway performs bidirectional global average pooling: horizontally, by compressing the feature map along rows to encode horizontal spatial relationships, and vertically, by compressing along columns to capture vertical distributions. This dual-path pooling enables each stream to gather global contextual information from the entire H \(\times\) W-dimensional space. The resulting encoding vectors from all four pathways are concatenated channel-wise and processed through a 1 \(\times\) 1 convolution to enable cross-path interaction and dimensionality reduction. These features are then passed through a multi-layer perceptron (MLP) to generate channel attention weights. These weights are element-wise multiplied with the original feature map to recalibrate and emphasize critical regions. Finally, the outputs from all pathways are fused via weighted summation. The architecture of the MPCA module is shown in Figure 3.  
Given the abundance of small targets with nuanced micromorphological features in pulmonary organoid images, conventional methods often fail to distinguish them effectively. The MPCA module, through its three-stage optimization--path decoupling, bidirectional encoding, and dynamic weighting--significantly improves micro-scale organoid detection accuracy while maintaining computational efficiency.
# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 4 Methodology
### Time Complexity of Model  
In this section, we analyze the time complexity of DEISI by focusing on its primary components: the inter-session module and the intra-session module. The process of learning factor-level embeddings through inter-session dependency modeling with iterative information propagation yields a computational complexity of \(O(P|M|^{2}Kd)\), where \(P\), \(|M|\), \(K\), and \(d\) represent the number of propagation layers, the number of sessions, the number of factors, and the embedding size, respectively. For the intra-session module, the time cost primarily comes from Graph Gated Neural Network (GGNN) operations across K factor-level channels, resulting in a complexity of \(O(T|M|L^{2}Kd)\), where \(T\) and \(L\) represent the number of GGNN layers and the average session length, respectively. Therefore, the overall time complexity of DEISI is \(O(P|M|^{2}Kd+T|M|L^{2}Kd)\). While this theoretical complexity is higher than simpler baseline models, several optimization strategies are employed in practice, including mini-batch training and parallel processing of factor channels, which help maintain computational efficiency while preserving the model's superior recommendation performance.
## 4 A Case Study of the LW7434
### Identification and Weighting of Main Impact Factors
#### 4.2.3 Determination of the Degree of Dynamic Hazard in Each Area of LW7434  
The evaluation matrix for the degree of factors on the dynamic risk in each region of the longwall is constructed by selecting five factors: depth, coal seam thickness, coal dip, mining speed, and fault. This matrix is shown in Table A2 of Appendix B. The weights of each main impact factor were calculated according to Equations (5)-(8), as shown in Table A3 of Appendix B.  
\begin{table}
\begin{tabular}{c}  
The entropy value \(H_{j}\) and indicator weight \(w_{j}\) of each risk factor are calculated based on the weights of the impact factors in each region and according to Equations (9) and (10), with \(n=6\) representing the number of dividing regions (illustrated in Figure 9). These values are then normalized, and the results are presented in Tables A4 and A5 of Appendix B.  
After calculating the weights of the influence degree of each factor, a comprehensive evaluation of the impact factors on different areas is conducted according to Equation (14), with the findings presented in Table A6 of Appendix B. It is evident that the D and E areas exhibit the largest degree of influence, while the B area has the smallest.  
The synthesis of the analysis indicates that coal depth, coal seam thickness, coal dip, and mining speed are significant overall factors affecting the dynamic risk in LW7434. The first three predominantly influence local geological anomalies, whereas mining speed's impact extends across the entire longwall. Fault acts as a local factor with a particularly pronounced effect during specific mining stages of the longwall. Analysis of the parameters associated with each factor and the seismic event distribution characteristics reveals a stronger influence of fault and mining speed on rockburst incidents compared to the relatively weaker impact of coal depth, coal seam thickness, and coal dip.
# TEDL: A Text Encryption Method Based on Deep Learning
## 3 TEDL Overview  
It seems that security and efficiency are usually contradictory. Increased encryption algorithm complexity probably means strengthened security and reduced efficiency, which motivates people to search for the best trade-off. In this paper, our TEDL method provides a novel way to deal with this problem. As Figure 1 shows, TEDL contains two stages: (1) communication preparation and (2) communication process.  
At the first stage, both parties in the communication get copies of the public corpus and modify them under the instruction of the key, completing the construction of confidential synthetic corpora, respectively. Furthermore, the synthetic corpora mastered by both parties are expected to be consistent. Afterward, the hyperparameters in the key instruct the training on the synthetic corpora. Hence word vector tables are established, followed by a further process on them with the SHA-256 function to obtain codebooks. To date, the first stage called communication preparation ends.  
At the second stage, when a word requires transmitting, the sender refers to the codebook at hand and uses the plaintext as an index unit to obtain the corresponding ciphertext. Furthermore, then the ciphertext is sent to the receiver. In turn, the receiver decrypts the ciphertext based on the mapping in the codebook, which is equivalently an inverted indexing operation. After completing the transmission of a word, both ends adjust the codebook in a certain way. Therefore, when the next word needs to be transmitted, it is encrypted based on the new codebook.
## 2 Analysis of VRET Tools Used for the PTSD Treatment
### Virtual Vietnam System  
VR technologies were first used to treat PTSD in the late 1990s. Based on the positive results of treating acrophobia with VRET, [PERSON] et al. hypothesized that prolonged exposure to trauma in a safe VE would significantly reduce PTSD symptoms [1]. Thus, they created the \"Virtual Vietnam\" system to treat Vietnam War veterans with chronic PTSD [1]. This system immersed the patients in a 3D world with content that simulated two situations prevalent in the Vietnam War: a jungle clearing and a helicopter. These two environments presented the user with visual stimuli such as fog and flying or landing helicopters accompanied by auditory stimuli, including sounds of the jungle, firearms, and helicopters. In the virtual helicopter environment, the subject could see the interior of the aircraft as well as shots of other flying helicopters, the jungle, and a river through the helicopter's side door. These VEs could be explored using an HMD supporting both visual and auditory renderings of the 3D scene presented to the user. To improve the patients' immersion, the authors added vibrotactile stimuli using a woofer attached to the patient's seat to simulate the helicopter's vibrations according to the scene presented in the 3D VE. The sound transmitted through the headphones was similarly intended to match the scene shown on the screens, while the armchair vibrations corresponded to the shaking of the VE. The scenario was designed to be manually updated by the therapist according to the patient's narration during the sessions.  
Several studies involving a total of 33 Vietnam War soldiers or veterans have reported using the Virtual Vietnam tool for the treatment of PTSD [1; 36; 37; 38]. Overall, there was a 15-67% reduction in PTSD symptoms after the treatment. These results were maintained at the 3-month and 6-month follow-ups. However, one study indicated a substantial increase in anxiety levels in two patients after treatment [36].
## 5 Results and Discussion
### Usability Evaluation  
#### 5.3.1 Objective Performance Evaluation  
The objective performance indicators in the usability evaluation reflect the actual results that occur during the user's interaction with the product. As a quantitative analysis method for user operation behavior, it typically includes the analysis of task completion accuracy, task completion rate, task completion time, error frequency, and other metrics to assess the product's usability level. These are presented in the form of visualized data, directly reflecting the interaction results between the user and the product. Furthermore,  
* The average age of the participants is 35.6 years old.  
\end{table}
Table 2: Cont.  
it often garners more attention from corporate executives and project stakeholders as a quantitative usability evaluation indicator.  
Three prefabricated residential buildings from the above-mentioned case study were selected, having identical structural forms, standard floor areas, and other design elements. This study invited the design team of the case project to conduct an objective performance evaluation, including designers from five specialties: architecture, structure, plumbing, electrical, and HVAC. They used three different design methods for these three prefabricated residential buildings through controlled experiments, and at the same time, they recorded and summarized the collision detection and drawing review results of each profession. As shown in Table 3, the design method using BIM and VR-integrated technology increased the number of identified design collisions by 28.35% compared to using BIM technology alone. Regarding to drawing reviews, compared to the traditional 2D drawing design method, the number of design issues was reduced by 41.35% with BIM and VR-integrated technology, and it was dropped by 15% with the BIM-based design method alone.  
The results showed that using BIM technology alone and VR-integrated technology could reduce the amount of feedback on design errors or queries during the drawing review phase across the five major disciplines, especially in plumbing, electrical, and HVAC specialties. However, the BIM and VR-integrated method could further reduce these numbers, especially in architecture. It validates the advantages of this method's high degree of visualization and enhanced architectural space experience in reducing the number of design errors or queries. By utilizing this design method, participants, including customers, are invited to engage in the design proposal at an early stage, keep continuous communication, and provide their feedback and experience throughout the design phase. It will be beneficial to understand the designer's intent and avoid collisions and design errors during the architectural design and project implementation, ensuring the overall integrity of the design.