esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 2 Collaborative Channel Perception Algorithm
### Channel Perception Algorithm  
To overcome the dependence of local channel perception on single-node devices, enhance the accuracy of collaborative channel perception, and reduce the collision probability in data link network message exchange, this paper proposes a channel state perception algorithm based on the Dempster-Shafer theory (DST) [21; 22; 23]. In this algorithm, each node in the network calculates the number of channel traffic pulses within a statistical time window and derives its normalized characteristic parameters, such as receiving sensitivity, antenna VSWR, SNR, the number of operating months, and MTBF. The count and the normalized characteristic parameters are periodically broadcast to other nodes in the network through broadcast messages.  
Within a one-hop range of the data link network, each node receives broadcast messages from other nodes. It obtains the number of channel traffic pulses and characteristic parameters of node devices. A comprehensive evaluation of the traffic pulse statistical capability of node devices is conducted by employing the Dempster-Shafer theory (DST). Additionally, by calculating the data credibility and falsity of the current node and its neighboring nodes, a weight allocation strategy is formulated to reflect the traffic pulse statistical capability of the node devices. Based on the weights assigned to each node device, the channel occupancy statistics (\(Cos\)) is calculated as follows:  
\[Cos=\frac{\sum_{l=1}^{N}\left\{\sum_{l=1}^{M}\left(Rn_{f_{i}}^{j}+Sn_{f_{i}}^{j }\right)\alpha_{j}\right\}\tau+\sum_{l=1}^{M}\left\{\left(Rn_{f_{i}}+Sn_{f_{i} }\right)\beta\right\}\tau}{MT_{s}} \tag{4}\]  
where \(M\) represents the number of frequency hopping points in the data link network. \(N\) represents the number of neighbor nodes within one-hop range of the current node. \(Rn_{f_{i}}^{j}\) and \(Rn_{f_{i}}\) represent, respectively, the number of pulses on the frequency hopping point\(f_{i}\) received by neighbor node \(j\) and the current node. \(Sn_{f_{i}}^{j}\) and \(Sn_{f_{i}}\) represent, respectively, the number of pulses on the frequency hopping point \(f_{i}\) transmitted by neighbor node \(j\) and the current node. \(\alpha_{j}\) represents the weight of the neighbor node. \(\beta\) represents the weight of the current node. \(\tau\) denotes the time period of the pulse and \(T_{S}\) represents the duration of the statistical time window.  
The weights of node device \(\alpha_{j}\) and \(\beta\) satisfy the following relationship:  
\[\sum_{j=1}^{N}\alpha_{j}+\beta=1 \tag{5}\]  
where \(N\) represents the number of nodes within one-hop range of the current node.  
For detailed algorithms describing the methods, please see Appendix A.
## 4 Analysis of RT Solutions  
### Antenna Types  
Two kinds of experiments were performed in this study in order to verify the effect of antenna calibration on the RT solutions. In Phase 1, mRTN (multiple-epoch network RTK) and sRTN (single-epoch network RTK) solutions were collected for short baselines (\(\sim\)2.1 km) using the same receiver/antenna combinations. The RT solutions were compared with the final solutions determined by the OPUS-Project static surveys (Figure 7). All RT solutions are biased positively (4 to 9 mm), which seems to be related to the error modeling for the tropospheric delay. Two antennas located at station NORTH (smaller ground plane) and SOUTH (Choke ring) provides slightly large biases, which are certainly related to the surrounding environment (signal blocking by trees, see Figure 1). On the other hand, all four antennas perform equivalently under favorable circumstances (no obstructions of reflective objects in close vicinity).  
It is generally known that the accuracy of the RT solution is inversely dependent on the baseline lengths. The RT solutions from Phase 2 were not analyzed in terms of the baseline lengths because there are 20 different baseline lengths and no significant dependence was observed. In addition, several GPSBMs were obstructed by the surrounding objects (mostly trees), and so it is not easy to separate the effect by baseline lengths from other impacts. Therefore, the RT solutions in Phase 2 were analyzed only by antenna type. Three different types of antennas were used in Phase 2, but one type of antenna was used for two GPSBMs only, therefore, those two GPSBMs were excluded from the analysis. As can be seen in Figures 8 and 9 (mRTN with both GPS+GLONASS and GPS), the differences of the UP component with respect to the mean OPUS-Projects solution were plotted together with the distance to the common base station (COLA). All baselines are less than 25 km, and the reference ellipsoidal heights were obtained from either NGSIDB (NGS Integrated Data Base) or static processing of Trimble Business Center (TBC) software with the ver  
Figure 6: Differences in ellipsoidal heights by site (Phase 1) with respect to the mean OPUS-Projects solution.  
taken into account. The built-in antenna showed the most unstable performances, that is, large bias and dispersion were observed even for a relatively favorable surrounding environment.  
Figure 8: Errors and baseline lengths by antenna type (TRM57971.00 NONE) for Phase 2.  
Figure 7: Probability density function (pdf) of RT solutions by antenna types for Phase 1.
## 3 Control Algorithm Design
### Attitude Controller Design
#### 3.3.3 Spin-Up Phase  
During this phase, since the sun sensor's optical axis is already roughly aligned with the sun, the satellite is controlled so that the designated plane of the satellite (e.g., solar panel plane) is perpendicular to the solar vector, and the satellite begins to spin-up around the normal vector of the designated plane. The desired control torque \(\mathbf{T}_{c3}\in\Re^{3}\) outputted by the controller is given by the following equation:  
\[\mathbf{T}_{c3}= \mathbf{J}\Big{[}K_{5}\mathbf{e}_{p}^{b}\times S^{b}+K_{6}\mathbf{S}_{prez}^{b} \times S^{b}-K_{7}\mathbf{S}^{b}\times\mathbf{\omega}_{bi}^{b}\times\mathbf{S}^{b}-K_{8} \Big{(}\mathbf{\omega}_{bi}^{b}-\omega_{spin}\mathbf{S}^{b}\Big{)}\Big{]} \tag{17}\] \[+\mathbf{\omega}_{bi}^{b}\times\Big{(}\mathbf{I}\mathbf{\omega}_{bi}^{b}\Big{)}\]  
where \(K_{5}\), \(K_{6}\), \(K_{7}\), and \(K_{8}\) are positive control coefficients; \(\mathbf{e}_{p}^{b}\) represents the unit normal vector of the designated plane, and \(\omega_{spin}\) is a given spinning angular rate. In contrast to \(\mathbf{T}_{c2}\), whose control objective is to align \(\mathbf{e}_{s}^{b}\) with the solar vector, the control objective of \(\mathbf{T}_{c3}\) is to align \(\mathbf{e}_{p}^{b}\) with the solar vector and make the satellite spin around \(\mathbf{e}_{p}^{b}\). Similar to the sun-aligned phase, if the angle between the vectors \(\mathbf{e}_{p}^{b}\) and \(\mathbf{S}^{b}\) is larger than \(90^{\circ}\), the first part of \(\mathbf{T}_{c3}\) can be modified by referring to Equation (16). In particular, in order to obtain a better control effect, when the angle between \(\mathbf{e}_{s}^{b}\) and \(\mathbf{e}_{p}^{b}\) is large relative to the field of view of the sun sensor, the spin-up phase can be divided into two subphases. In the first subphase, the normal vector of the designated plane is controlled to align with the solar vector. Then, the satellite is controlled to spin up in the second subphase.  
The control scheme designed in this paper, which first makes the optical axis of the sun sensor align with the sun and then makes the normal vector of the designated plane align with the sun, can significantly improve the reliability and stability of satellite attitude control in the case of limitation of the field of view of the sun sensor.  
Then, the desired control torque \(\mathbf{T}_{c3}\) will be outputted by the 3-axis MTQs. When the angle between the vectors \(\mathbf{e}_{p}^{b}\) and \(\mathbf{S}^{b}\) is close to \(0^{\circ}\), and the deviation between the estimated value of the satellite's 3-axis angular rates and \(\omega_{spin}\mathbf{S}^{b}\) is near to \(0^{\circ}\)/s, the controller enters the next phase, namely the spin-stabilized phase. However, if the satellite flies into the shadow zone of the orbit during this phase, it needs to return to the sun-aligned phase.
# Track Line Recognition Based on Morphological Thinning Algorithm +
###### Abstract
However, the detection speed was still slow and the prerequisite for the application of the object detection algorithm in the mobile terminal, autonomous driving, and embedded fields was to achieve lightweight processing. Deep neural networks are usually accompanied by problems such as large model size and high resource consumption. Implementing lightweight processing of object detection algorithm based on deep learning is still a key issue to be urgently solved. Sample data set: Against the background of deep learning, the object detection range is increasingly widely used because of the lack of various training sample data. At present, the mainstream method for obtaining sample data involves manual methods for large-scale corpus labeling. For example, the ImageNet database established by [PERSON] and others, and the general large-scale labeling datasets such as PASCAL VOC and MS COCO. However, there is no clear sample of a railway track dataset. Our application scenario is  
Figure 2: PEI contrast. a modern freight railway of nearly 10 km in length. On the one hand, using supervised learning methods makes the cost of manual labeling too large, and it is impossible to label all scenarios. On the other hand, the complicated road conditions such as straight roads, curves, and turnouts, as well as material and color are different. The marked data cannot adaptively and accurately identify new categories of objects in different scenarios. Therefore, in recent years, methods such as transfer learning or reinforcement learning based on weak supervision [11] or unsupervised [12; 13] were proposed to train object detection models; however, combining prior knowledge to achieve adaptive object recognition in different scenarios is still a problem. To address these issues, in our scenarios, we consider the shortcomings of existing deep learning-based object detection technology. Our requirement is to show a clear track line, rather than directly giving the track area, which means that it cannot be segmented by a semantic method. We, therefore, propose an improved ZS thinning theory of the railway track line recognition algorithm. This method refines the track line into a single pixel, and there are no overlapping pixels, which makes the connection ability of the track line stronger. In addition, this method does not require sample data and only relies on the shallow features of the image to complete the track line extraction. It does not require much computing power and has good real-time performance. It can simultaneously perform track detection on straight roads, curves and turnouts, and is suitable for changing weather conditions. The remainder of this paper is organized as follows. In Section 2, we review the related works on track line detection. In Section 3, we describe our method, introducing the concrete steps of track line identification in the improved ZS thinning algorithm, and expounding the contour-based denoising algorithm. After showing the experimental results in Section 4, we finally draw our conclusion in Section 5.
# The Condition Evaluation of Bridges Based on Fuzzy BWM and Fuzzy Comprehensive Evaluation
## 1 Introduction
[21] enhanced sustainability management through three-stage FBWM-Fuzzy Cognitive Mapping (FCM), Decision-Making Trial and Evaluation Laboratory (DEMATEL), demonstrating faster consensus-building in multi-stakeholder environments compared to conventional Delphi techniques. In manufacturing quality control, [PERSON] et al. [22] synergized FBWM with Fuzzy Bayesian Networks (FBNs) for failure mode analysis, attaining higher diagnostic precision than traditional FMEA methods through probabilistic risk propagation across multiple failure pathways. Despite these cross-domain successes, bridge engineering remains conspicuously underdeveloped in systematic FBWM applications, particularly in synthesizing multi-level fuzzy relationships for comprehensive condition evaluation. This study addresses these critical knowledge gaps through the development of an innovative FBWM-FCE integrated bridge evaluation model, establishing four fundamental advancements in bridge condition evaluation methods. The model introduces a comprehensive bridge evaluation indicator system compliant with JTG 5120-2004 [23] specifications, incorporating safety, durability, suitability, and reinforcement economics, along with 46 indicators derived from extensive field surveys and historical maintenance records. By employing fuzzy triangular numbers to quantify expert linguistic preferences, the model reduces cognitive bias compared to the conventional BWM. The integrated FCE method converts qualitative evaluations into quantitative metrics, enabling comprehensive evaluations of objects influenced by multiple factors based on the actual situation, thereby implementing graded output for bridge condition evaluation. Furthermore, the system's compatibility with legacy inspection records ensures practical deployability across uninstrumented aging bridges. These innovations carry profound implications for sustainable infrastructure management by providing maintenance engineers with a scientifically robust yet operationally feasible decision-support tool.
# HAIPO: Hybrid AI Algorithm-Based Post-Fabrication Optimization for Modern 3D NAND Flash Memory
## 5 Evaluation
** To better understand the effectiveness of HAIPO, we analyze the reliability and performance across different WLs. Figure 15 shows the average VSum (the larger, the better reliability) and tPROG (the lower, the better performance) of each WL in a 3D TLC/QLC NAND flash block. To simplify the results, we sort the WLs in ascending order according to the VSum and tPROG values. As shown in Figure 15, HAIPO enhances reliability over HAIPO(-) for _all_ WLs in both TLC and QLC NAND flash chips. In addition, we can also identify that HAIPO significantly improves the performance of the worst WLs in a block (i.e., lower the tPROG difference between the best and worst WL). Uniform tPROG between WLs is important because deterministic I/O performance is increasingly critical in various modern data-intensive applications [27; 28]. **Optimization TAT. ** Finally, we compare the optimization speeds of the three techniques. Figure 16 shows how the three evaluated techniques improve the reliability (left) and performance (right) over time (until 100 generations) for the TLC and QLC NAND flash chips. Please note that all three techniques terminate post-fabrication optimization when all requirements are met or when the given time (e.g., 100 generations) is over. From the evaluation results, we observe that HAIPO significantly accelerates the entire optimization process over HAIPO(-). HAIPO can improve the reliability close to the optimization target within less than 30 generations, while HAIPO(-) fails to achieve the target reliability  
Figure 14: Optimization behavior on reliability and performance. Figure 15: Reliability and performance across WLs for TLC and QLC 3D NAND flash chips. until 100 generations. We also observe that Bayesian simulation critically impacts the optimization TAT. At the 50 th generation for the QLC NAND flash chips, HAIPO meets both the reliability and performance requirements, but the reliability and performance levels achieved by HAIPO\({}_{\text{DN}}\) are quite far from the optimization target. Figure 17 shows the comparison of the optimization TAT of different techniques in optimizing real 3D TLC NAND flash chips. HAIPO efficiently reduces the total optimization TAT up to 70% compared with the manual-based optimization. As mentioned above, it is not easy to find other research work to be compared with our technique. Therefore, we compare the effectiveness of our work with the conventional manual-based approach. We obtained the optimization TAT of the manual-based approach from two representative NAND manufacturers. Based on our evaluation results and observations, we conclude that HAIPO is an effective post-fabrication optimization method for modern 3D NAND flash memory.
# Elastic Hop Count Trickle Timer Algorithm in Internet of Things
## 6 Results of Random Topology
### Power Consumption  
#### 6.3.1 Power Consumption of 20 Nodes  
As illustrated in Figure 10, the proposed algorithm has the lowest consumed power compared with the original, e-trickle, and dynamic algorithms. More precisely, because controlling the listen-only period in the suggested algorithm depends on the number of hop counts, the elastic hop count algorithm consumes less power, about 5% less compared with the original algorithm.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 4 Blockchain System Architecture for Safety Supervision of Special Equipment
### System Function
#### 4.3.3 Recording Sampling Check Information  
The sampling check of special equipment is the main work of primary supervisors. According to the Chinese Special Equipment Safety Supervision Regulations [51], the update for recording sampling check information in the system also requires the verification of the equipment users, which means that the equipment users must accept the check result, or this work cannot be completed. The implementation of this function is risky for primary supervisors. If they make any mistakes, they will be queried during the accident responsibility tracing.
# Sentence Graph Attention For Content-Aware Summarization
###### Abstract  
Neural network-based encoder-decoder (ED) models are widely used for abstractive text summarization. While the encoder first reads the source document and embeds salient information, the decoder starts from such encoding to generate the summary word-by-word. However, the drawback of the ED model is that it treats words and sentences equally, without discerning the most relevant ones from the others. Many researchers have investigated this problem and provided different solutions. In this paper, we define a sentence-level attention mechanism based on the well-known PageRank algorithm to find the relevant sentences, then propagate the resulting scores into a second word-level attention layer. We tested the proposed model on the well-known CNN/Dailymail dataset, and found that it was able to generate summaries with a much higher abstractive power than state-of-the-art models, in spite of an unavoidable (but slight) decrease in terms of the Rouge scores.  
summarization; knowledge graph; neural networks; pagerank; natural language processing +
Footnote †: journal: _Applied_  
focused on the relevant entities of the graph. These models have shown very high performance, taming the problems related to large graphs (e.g., millions of vertexes) and missing the connections between vertexes, for which the neural networks are able to exploit latent information.  
However, adopting an external KB in the summarization task was a challenge. We could construct it using the entities that were present in the document to summarize, but the neural network could have the above-described problem, selecting external entities that were neither relevant nor correct for the summary; thus, we decided to create the graph using the sentences present in the input document. The graph selected the sentences that were fundamental to constructing the summary and from which the salient words (i.e., entities) could be copied into the output.  
Our contribution is twofold:  
* a latent knowledge graph based on document sentences, which was changed according to the generated summary. In detail, the model calculated the sentence-level attention scores using the graph, which were used by the word-level attention to select the relevant entities from the sentences;
* an inference-time score function that removed repetitions and attention errors, integrating two existing techniques: coverage vectors [6,7,15] and reinforcement learning.  
We tested our proposed model on the CNN/Dailymail dataset, obtaining interesting results both in the selection of the relevant sentences and the creation of summaries that contained all the salient information of the input document.  
The remainder of the article is composed of the following: Section 2 describes the latest research works in neural-based knowledge bases and summarization; Section 3 describes the proposed model, the sentence-level attention, and the inference-time score function; Section 4 reports the results obtained using the proposed model. The article concludes with Section 5.
# Analysis and Suppression of Self-Excited Oscillations in Pressure Servo Valve System
## 5 Conclusions  
In this paper, the dynamic model of PSVS ha been established and the correctness of the model has been verified by experiment. On this basis, the mechanism of pressure oscillation has been analyzed and an oscillation suppression method for the pressure servo valve has been proposed. The following conclusions can be drawn.  
(1) The pressure oscillation of PSVS is the self-excited oscillation caused by backpressure positive feedback.  
(2) The backpressure positive feedback can be suppressed by the open cavity and the orifice between Port H and the backpressure feedback channel.  
(3) Based on the basic principle of weakening the positive feedback, the pressure servo valve structure optimization method has been proposed. The optimized PSVS has sufficient stability margin to maintain stable pressure output under certain external disturbance.  
This paper solves the oscillation problem in the brake pressure control system of large aircraft, and the results provide a clear technical method for the optimal design of this kind of pressure control system.  
Conceptualization, Q.Z. and F.Z.; methodology, J.H.; software, Q.Z.; dynamic model and self-excited oscillation analysis J.H.; oscillation suppression, Q.Z. and T.W.; stability margin analysis J.H. and X.L.; writing-original draft preparation, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) under grant number 52175038, 51907009 and 52105049.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.