esa-sceva/satcom-chunk-collection huggingface dataset:
# Timing Performance Testing and Regularity Analysis of eLoran System
## 5 Conclusions  
The following conclusions were derived from the study of the timing performance indices of two receiver types using real measurement methods.  
The timing performances of the different receivers under various signal sources were quantified. Relevant data from multiple timing tests, including the received time signals and other pertinent information, were presented. Timing metrics were visualised using charts and images, and the test results were analysed to identify patterns. Different subsections explored performance variations under various conditions and examined the stability and accuracy of time synchronisation.  
The eLoran receiver has basically the same timing accuracy in the morning, midday, and evening time slots. Receiver 1 had a timing error accuracy of 329.2 ns and a stability of 38.3 ns, whereas Receiver 2 had a timing error accuracy of 802.5 ns and a stability of 56 ns. The eLoran receiver demonstrated superior timing monitoring capabilities and exhibited high stability as well as accurate time synchronisation. The eLoran receiver provides reliable temporal information under diverse environmental conditions, making it suitable for a wide range of applications.  
eLoran receivers require less than 100 nanoseconds for high-precision applications, 100 nanoseconds to 1 microsecond for moderate precision applications, and 1 to 10 microseconds for low-precision applications. Receiver 1 is best suited for telecommunications, financial transactions, and power grid management owing to its superior timing accuracy and stability. Receiver 2 is suitable for navigation, positioning, general scientific research, and industrial timekeeping, where slightly larger timing errors and less stability are acceptable.  
The eLoran system has high signal stability and an anti-interference capability, which enables it to maintain consistent timing accuracy in different time periods. Although there may be variations in environmental interference from one time period to the next, the overall environment at the receivers' location was relatively stable, with no significant changes in signal propagation paths or conditions, so their timing accuracy remainedconsistent. eLoran receivers are equipped with high-performance equipment and antennas that can effectively capture and process signals to maintain high timing accuracy even in different time periods. Future studies should conduct long-term time monitoring to further explore and utilise the potential of eLoran receivers.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments, analysed the data, and wrote the paper; [PERSON] and [PERSON] helped in the discussion and revision; [PERSON], [PERSON] and [PERSON] completed the software code that analysed the data. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China, grant number 42174051.  
Not applicable.  
The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# FPE-Transformer: A Feature Positional Encoding-Based Transformer Model for Attack Detection
## 2 Literature Review
### Background of Transformer Models
This model succeeds in text understanding tasks with the bidirectional encoder structure of BERT and generates text effectively with the autoregressive decoder structure of GPT. BERT2 GPT is mainly introduced in tasks where complex language understanding and production processes are integrated. It aims to bridge the gap between natural language understanding (NLU) and natural language generation (NLG). The model excels in tasks such as text completion, Q&A systems, dialogue generation, and text summarization. Consequently, the evolution of Transformer models has significantly propelled advancements in natural language processing and has facilitated the creation of various architectures designed for specific tasks. Table 1 compares the various Transformer models examined according to their types and preferred application areas. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Type** & \
\begin{tabular}{c} **Pre-Training** \\ **Architecture** \\ \end{tabular} & **Applications** \\ \hline BERT & Auto-encoding & Encoders &
\begin{tabular}{c} Text classification, \\ sentiment \\ analysis, NER \\ RE, Q&A, NER \\ Text classification, \\ sentiment analysis \\ Text classification, \\ NLP tasks \\ Text classification, \\ dialogue systems \\ Text classification, \\ NLP tasks \\ Text generation, \\ dialogue systems \\ Text generation, \\ dialogue systems \\ Language modeling, \\ translation, \\ summarization, \\ classification, Q&A \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Transformer models. Auto-encoding Transformers, such as BERT and its derivatives, leverage bidirectional training approaches to capture contextual nuances effectively, making them suitable for various applications, including text classification and named entity recognition. In contrast, autoregressive Transformers, exemplified by the GPT series, focus on sequential text generation, enabling coherent and contextually relevant output. The ongoing enhancements in these models, such as dynamic masking in RoBERTa and decoupled attention in DeBERTa, highlight the continuous quest for optimizing performance while reducing computational requirements. Encoder-decoder Transformer models are architectures that can establish complex relationships between input and output sequences. Models such as T5, BART, and mBART use a powerful encoder to understand input sequences and work with a decoder to produce consistent outputs using this information. This architecture is particularly effective in machine translation, text summarization, and Q&A. In addition, models such as PLBART and Code-T5 offer practical solutions for software development processes by processing natural language and programming languages in the same structure. Developments made in encoder-decoder models provide versatile use by optimizing both understanding and production processes, unlike models such as BERT and GPT, which focus solely on encoders or decoders.
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 5 Formation Flying Nanosatellite Bus
### Subsystems Design  
As in the aforementioned bus architecture, bus subsystems are designed by applying the COTS products to establish precise formation flying. The details of each bus subsystem are as follows.
### Path Nodes  
Figure 18 randomly shows 10 path node results of the PELCR-TP algorithm with and without BAS, and the PLCR algorithm at different bad node rates from 1,000 simulations in which the power upper limit is 0.0001 w, bad note rate is 0.1 and the node density is 0.005. The source node and destination node are located at (250, 0) and (250, 500). The middle axis coincides with the line which represents \(k=3\). The area on the left of the middle axis is the region with a better transmission environment whose \(k\) is lower, while the other side is the region with a worse transmission environment whose \(k\) is  
Figure 17: Retransmission rate _vs._ Bad node density.  
higher. The path nodes of the PELCR-TP algorithm with BAS are symmetrically distributed on both sides of the middle axis. On contrast, for the other two algorithms the path nodes on the left side of the middle axis are more than on the right. This indicates that the PELCR-TP algorithm with BAS can work well wherever \(k\) is higher or lower with the help of BAS. The path nodes of PLCR algorithm is more concentrated near the source node than that near the destination node, while the path nodes of PELCR-TP algorithm with and without BAS both have symmetrically vertical distributions. This means the new algorithm has more reliable performance with few failures. This figure visually displays the reliable performance of the PELCR-TP algorithm and the high adaptability BAS can provide.
## 3 Results and Discussion
### Optimal Wavelength Selection  
To reduce the amount, collinearity, and redundancy of data, successive projections algorithm (SPA) was used to select the optimal wavelengths for chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid prediction. The number of wavelengths to be selected was limited to 5-30. Table 4 shows the selected optimal wavelengths. The selected optimal wavelengths varied among different compositions, and varied between fresh and dry flowers. Selected optimal wavelengths were different for the same chemical composition, but it should also be noted that the selected optimal wavelengths were close with slight shift.  
As shown in Table 4, the wavelengths in the range of 975-989 nm and the wavelength at 995 nm might be related to the second overtone of N-H stretching; the wavelengths at 1025 might be related to water; the wavelengths at 1029 nm might be related to O-H bending and C=O stretching; the wavelength at 1315 nm might be due to the first overtone of the O-H stretch and OCO bending; the wavelengths in the range of 1463-1484 nm might be attributed to the first overtone of N-H stretching; and the wavelengths in the range of 1645-1675 might be attributed to the first overtone of the C-H stretch [21]. The wavelengths in the range of 1090-1260 nm might be related to the second overtone of C-H stretching, and the wavelengths in the range of 1350-1450 nm might be attributed to the C-H combination [22].
# The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks
## 2 System Models
### Transmission Model  
We adopt the physical model [1] as the channel capacity between two nodes. Define \(R_{ij}\) as the rate between transmitter node \(i\) to receiver node \(j\). The data rate can be expressed as follows:  
\[R_{ij}=\log\left(1+\frac{P_{i}\ell(i,j)}{N_{0}+\sum_{\xi\
eq i}P_{\xi}\ell( \bar{\zeta},j)}\right)\text{bit/s}, \tag{4}\]  
where \(\ell(i,j)\) represents the path loss between \(i\) and \(j\), and we assume \(\ell(i,j)=\min\{1,1/d_{ij}^{\alpha}\}\) with \(\alpha>3\). We also assume the physical link is over a unit bandwidth. \(P_{i}\) is the transmission power of node \(i\), \(N_{0}\) stands for noise power of the channel at the receiver \(j\), and \(\zeta\) is the node which can transmit simultaneously with node \(i\). \(d_{i,j}\) is the transmission distance from node \(i\) to node \(j\). In our strategy, we allocate different power for different nodes.  
The per-node throughput capacity \(R(n)\) is defined as the number of bits per second that, with high probability, (w.h.p.), all nodes can transmit to their intended destinations.  
We summarize the notations in Table 1.  
Figure 1: (**a**) belongs to the cluster-dense model if \(\gamma<v\), while (**b**) is a cluster-sparse model if \(\gamma>v\).
# LAAVOS: A DeAOT-Based Approach for Medaka Larval Ventricular Video Segmentation
###### Abstract
Their STAVOS (Spatiotemporal Attention-based Ventricular Object Segmentation) model introduced a frame-matching mechanism to establish temporal associations across frames, significantly improving segmentation stability during phases of intense ventricular deformation. They also released the first publicly available pixel-level annotated video dataset of medaka embryonic hearts, providing a valuable benchmark for subsequent research. However, the dataset was recorded using the same imaging device under identical conditions, resulting in high visual similarity across videos. This led to overfitting during training and limited generalization to videos with varying illumination, equipment, or ventricular scale. Furthermore, as a matching-based approach, STAVOS involves a complex network with a large parameter set, resulting in slow inference speed, which restricts its applicability in real-time scenarios. To overcome these limitations, we propose LAAVOS (Local Attention Asymptotic Video Object Segmentation), a video segmentation model designed to enhance accuracy, robustness, and efficiency across diverse imaging settings. First, we constructed a more complex medaka ventricular video dataset that includes 19 sequences recorded under varied conditions of color, brightness, and equipment, totaling over 4200 pixel-level annotated frames. Second, we introduce a DeAOT-based architecture that features two key innovations: (1) an attention-enhanced encoder, LA-ResNet, which incorporates local attention into ResNet bottlenecks to emphasize ventricular features and suppress artifacts such as blur and occlusion; and (2) a progressive multi-scale decoder, AFPViS, which constructs hierarchical feature fusion paths and a gradual decoding mechanism to resolve misalignments in semantic detail across spatial scales. Benefiting from the memory-efficient propagation design of DeAOT, LAAVOS not only achieves improved segmentation accuracy but also significantly enhances inference speed compared to matching-based models like STAVOS, making it more suitable for real-time and large-scale screening applications. The main contributions of this paper are as follows: (1) We created a diverse, pixel-level video segmentation dataset for medaka ventricular segmentation. It comprises 19 videos captured under various imaging environments, containing over 4200 precisely annotated frames. (2) We proposed LA-ResNet, a novel attention-enhanced encoder that integrates local attention into the ResNet bottleneck structure, enabling the model to focus more effectively on ventricular features while resisting image degradation and organ occlusion. (3) We designed AFPVis, a new progressive multi-scale decoder. By introducing hierarchical multi-scale feature fusion and constructing cross-layer feature interaction pathways, this decoder addresses the limitations of conventional FPN architectures in aligning semantic detail, thereby enhancing segmentation performance across ventricular scales.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 5 Results and Validation
### Performance Comparison  
#### 5.3.1 Baselines  
The TBPPO algorithm was evaluated in comparison to the DRSIR [21], PPO [22], and Dijkstra [13] algorithms. Furthermore, we integrated the trust mechanism introduced in our paper with DRSIR to form TBDRSIR and conducted comparative evaluations.  
1. Dijkstra Algorithm [13]: The Dijkstra algorithm is based on the weights of a graph to select paths. This algorithm is simple and feasible and is an important component of the OSPF protocol. We hope to use this algorithm to demonstrate the deviation of our algorithm's delay portion from the theoretical optimal value and to evaluate its security performance.
2. DRSIR Algorithm [21]: The DRSIR algorithm is based on DQN's deep reinforcement learning algorithm and its effectiveness has been thoroughly demonstrated in [21]. However, it does not explore multiple objectives. We aim to use this algorithm to showcase our algorithm's optimization ability for multiple objectives.
3. TBDRSIR (Trust-Based DRSIR): TBDRSIR is a variant of our ablation experiment introduced to explore the performance of TBPPO. It draws inspiration from DRSIR and combines the dynamic security assessment capability we designed. We aim to test the performance of our security mechanism on other algorithms.
4. PPO Algorithm [22]: The PPO algorithm has been used to solve routing optimization problems in SDNs in [22]. Its advantage lies in its adaptability to diverse environments and good performance in terms of delay. However, the authors lacked consideration for security capabilities. Additionally, we have made improvements to PPO, enabling it to achieve a superior performance. We hope to use this algorithm as a baseline to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**DDoS Attack Proportion** & **KL Trust Value** \\ \hline
20\% & 0.069 \\
40\% & 0.167 \\
60\% & 0.298 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The relationship between KL trust value and DDoS attack ratio of tested node.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SIP Parameter \(\mathbf{\beta_{1}}\)** & **DIP Parameter \(\mathbf{\beta_{2}}\)** & **SYN Parameter \(\mathbf{\beta_{3}}\)** & **DP Parameter \(\mathbf{\beta_{4}}\)** \\ \hline
0.8 & 1.0 & 2.0 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(\beta\) of advanced KL-Divergence.  
demonstrate our algorithm's superior security performance and better performance in terms of delay variation.
# Grammar-Supervised End-to-End Speech Recognition with Part-of-Speech Tagging and Dependency Parsing
## 4 Experiments
### Experimental Setup  
All experiments in our study were performed using the WeNet toolkit [28] and run on a server equipped with 8 Tesla V100 GPUs.  
Based on the conformer ASR system described in Section 2, an 80-dimension log-Mel filter bank was used as input features with a 25 ms Hamming window with a 10 ms fixed frame rate, and all the training features were processed with mean and variance normalization. Speed perturbation and SpecAugment [29] were used simultaneously for additional data augmentation. Specifically, speech was perturbed on speed with the factors of 0.9, 1.0, and 1.1; SpecAugment was implemented, with two frequency masks with the maximum frequency mask (F = 10) and two times masks with the maximum time mask (T = 50). The architecture of the encoder model is composed of a convolutional subsampling module and 12-layer conformer blocks. The convolutional subsampling module contains 2D convolutional layers with stride 2, resulting in a 40 ms frame-rate output. For the conformer blocks of the encoder module, each layer is configured with an 8-head attention of 512-dim and 2048 feed-forward hidden nodes. For the transformer blocks in the decoder module, each layer is also configured with an 8-head attention and 2048 feed-forward hidden nodes. Finally, 6728 tokens served as the decoder outputs, including the Chinese characters in GB2312, a start symbol, an end symbol, and an unknown symbol used to indicate 'out of vocabulary'.  
In order to prevent over-fitting, label smoothing [30] is applied in the training process, and the penalty is set to 0.1. The Adam algorithm [31] is adopted to avoid falling into the local minimum and ensure the algorithm's stability with regularization terms. The learning rate firstly warms up linearly to 0.002 in 25,000 steps and then decreases proportionally to the inverse square root of the step number. To the framework of the ASR system, the CTC weight is set to 0.3 in training while it is set to 0.5 in decoding.
## 4 Practical Formula for Bearing Capacity and Reliability Analysis
### Reliability Analysis
#### 4.5.2 Reliability Analysis Steps  
1. Variable Identification for Reliability Index: This includes cross-sectional dimensions (\(D\)), tube wall thickness (\(t\)), load effects (\(S_{Gk}\), \(S_{Qk}\)), model uncertainty (\(k_{p}\)), concrete strength (\(f_{c}\)), and steel strength (\(f_{y}\)). Statistical analysis of \(k_{p}\) is performed.
2. Resistance Calculation (\(N_{u,r}\)): Varying material strength values are substituted into Equation (10) to determine \(N_{u,r}\).
3. Load Effect Determination: Based on the formula \(N_{u,r}\geq S=\gamma_{G}S_{Gk}+\gamma_{Q}S_{Qk}\) and the load effect ratio \(\rho_{L}=S_{Qk}/S_{Gk}\), calculate the standard value of the dead load effect \(S_{Gk}=N_{u,r}/(\gamma_{G}+\rho_{L}\gamma_{Q})\) and the standard value of the live load effect \(S_{Qk}=\rho_{L}S_{Gk}\).
4. MATLAB Random Sampling: Random sampling of \(D\), \(t\), \(S_{Gk}\), \(S_{Qk},k_{p},f_{c},f_{y}\), and \(N_{u,r}\) is performed \(N\) times (\(N\geq 100/p_{f}\)) in MATLAB. These values are then used into limit state Equation (15).
5. Reliability Index Calculation (\(\beta\)): The number of instances where \(Z<0\) is counted, and \(\beta\) is calculated using Equation (17). Error estimation is carried out with Equation (18).
## 2 Related Work
### Supervised Monocular Depth Estimation  
Supervised MDE methods rely on large-scale datasets that provide ground truth depth information to train deep neural networks. One of the earliest works in this domain is [9], which introduced a multi-scale convolutional neural network (CNN) architecture that predicts depth at different resolutions, improving global depth understanding. Since then, CNNs have become the dominant architecture for depth prediction. The authors extended this approach using residual networks, achieving better optimization and improved depth prediction accuracy [10]. They also introduced a scale-invariant loss to address the inherent scale ambiguity in MDE. The success of residual networks prompted several researchers to explore even deeper architectures, such as ResNet-101 and ResNeXt, to further enhance depth prediction accuracy [11]. However, these supervised methods depend heavily on labeled data, which is costly and difficult to acquire at scale, particularly for outdoor scenes with large objects and varying lighting conditions. This limitation motivated the development of self-supervised approaches, which aim to reduce the dependency on ground truth labels by using unlabeled video sequences.
## 3 Historical Review to State-of-the-Art
### Application to Other Instruments
Anisotropic factors from the CERES-Terra/Aqua ADMs have been applied to EPIC imagery, where shortwave radiances were derived using relationships between similar narrowband channels on MODIS and the CERES shortwave radiance, and scene type was determined using low-Earth orbiting and geostationary satellite imager retrievals [65]. The global-mean daytime anisotropic factors from the EPIC analysis have also been applied to the NISTAR unfiltered radiances [66]. These studies concluded that daily mean EPIC and CERES shortwave irradiance agree to within 2%, which is within calibration and algorithm uncertainties, indicating that the calculation of global anisotropic factors from the CERES-Terra/Aqua ADMs is robust, while annual-mean NISTAR shortwave irradiances are about 6% larger than those from CERES but their diurnal variations are highly correlated.