esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Investigating the Influence of Gradient Parameter
As shown in Figure 5, the temperature was inversely proportional to the natural frequency with any gradient parameter. The reason is that with the increase of temperature, the increase of thermal stress \(N_{x}^{CT}\) and \(N_{y}^{CT}\) decreases the coefficient of bending stiffness \(K_{33}\), which leads to the decrease of the natural frequency. At the same temperature, with the increase of positive gradient parameter, the natural frequency increases in varying degrees. At the temperature of 25 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 55 \({}^{\circ}\)C, the natural frequency was relatively stable as the gradient parameter increased. However, when the temperature was higher than 70 \({}^{\circ}\)C, the natural frequency was gradually significantly affected by the gradient parameter. Especially when the temperature was elevated and the gradient parameter decreased, the natural frequency decreased until it approached zero, at which time the sandwich plate began to buckle. In a certain temperature range and a positive gradient parameter range in Figure 5, buckling occurred only when the temperature is at 100 \({}^{\circ}\)C and the positive gradient parameter takes 0.2, which indicates that a higher temperature and lower gradient parameter are more prone to buckling. Figure 4: Dimensionless natural frequencies vs. temperature subjected to different gradient parameter (\(p\)) value.
## 6 Conclusions  
This paper addresses the problem of low resource utilization and high system latency in dynamic satellite networks concerning resource allocation and task offloading. The innovation lies in proposing a collaborative optimization framework based on dynamic programming, combined with a multi-objective optimization model and Gradient Surgery techniques, ensuring fairness and efficiency in resource distribution. Experimental results demonstrate that the proposed D-GTOM algorithm outperforms existing comparative algorithms in terms of objective function value and resource utilization while also exhibiting high computational efficiency. Specifically, D-GTOM achieves an average reduction of 22.56% to 56.56% in objective function values across varying satellite configurations. The algorithm also demonstrates a significant improvement in resource utilization, ranging from 5.21% to 54.68%, and exhibits lower running times, with reductions up to 96% compared to other algorithms. Although the task delay is slightly higher than some comparative algorithms in smaller-scale datasets, D-GTOM maintains competitive delays of 0.185 s for 5 satellites and 0.192 s for 200 satellites, ensuring efficiency and fairness in larger-scale and high-dimensional problems.  
For future research, we will focus on the following aspects: further optimizing the D-GTOM algorithm to significantly reduce task delays in small-scale datasets; expanding the algorithm to adapt to more complex and heterogeneous satellite network environments, including multiple types of satellite nodes and diverse task requirements; and exploring the scalability and performance optimization of the algorithm in larger satellite constellations, ensuring its widespread applicability and practicality in future large-scale satellite networks.  
Conceptualization, Y.L.; methodology, Y.W. and Y.L.; software, Y.W.; validation, Y.W. and S.W.; formal analysis, Y.L.; investigation, Y.W.; resources, Y.L. and S.W.; data curation, Y.W.; writing--original draft preparation, Y.L. and Y.W.; writing--review and editing, Y.L. and S.W.; visualization, Y.W. and S.W.; supervision, S.W.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China under Grant U1911401 and the National Natural Science Foundation of China under Grant U1435215.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflicts of interest.
## 4 Benchmark Databases and Performance Evaluation Metrics
### Evaluation Metrics
#### 4.2.1 Detection Accuracy  
Any method used for attack detection should be able to accurately detect attacks. This metric determines the percentage of accurate predictions across all the cases considered. The detection accuracy (\(D_{A}\)) is expressed using (26):  
\[D_{A}=\frac{T_{P}+T_{N}}{T_{P}+F_{P}+T_{N}+F_{N}} \tag{26}\]  
In (26), \(T_{N}\) stands for true negative and signifies the number of instances of normal traffic that the detection method correctly classifies as belonging to the normal class; \(F_{N}\) denotes false negative, indicating the number of instances of attack traffic that are identified as normal traffic; true positives (\(T_{P}\)) signifies the number of attack instances accurately categorized; and false positives (\(F_{P}\)) signifies the number of instances of normal traffic that are wrongly classified as attack instances.
## 2 Materials and Methods
### Reducing Fe\({}^{3+}\) to Fe\({}^{2+}\)  
First, a calibration curve for Fe\({}^{2+}\) ions was created using aqueous solutions of VI iron II sulphate (FeSO\({}_{4}\)). To reduce any Fe\({}^{3+}\) ions in the test sample, the following were added to 100 mL volumetric flasks: suitable quantities of FeSO\({}_{4}\) (such that the resultant concentrations of Fe\({}^{2+}\) ions are in the range of 0.5 to 5 mg/L) and 1 mg of ascorbic acid. The flasks are filled to the mark with distilled water, then closed snugly with a stopper and agitated to produce uniform solutions [3].  
First, 1000 \(\mu\)L of an aqueous FeSO\({}_{4}\) solution with ascorbic acid is put into a test tube. Then, 1000 \(\mu\)L of an aqueous ferrozine solution (with a concentration of 1 g/L) is added. The tubes are tightly sealed with a stopper and incubated at room temperature for 10 min, and then the absorbance of the test solutions is measured using a spectrophotometer at a wavelength of 562 nm. Spectrophotometric analyses are carried out in triplicate using a Thermo Scientific GENESYS 50 instrument, obtaining a calibration curve for Fe\({}^{2+}\) ions (y = 0.4888x + 0.0064; R\({}^{2}\) = 0.999).  
Assessment of the ability to reduce iron III ions to iron II by the ferrozine method was carried out as follows: 1000 \(\mu\)L of aqueous FeCl\({}_{3}\) solution (with an Fe\({}^{3+}\) concentration of 0.5 g/L), 1 \(\mu\)L of FPE, and 1000 \(\mu\)L of aqueous ferrozine solution (with a concentration of 1 g/L) were introduced into glass tubes. The tubes were closed tightly with a stopper and incubated at room temperature for 10 min, and then the absorbance of the test solutions (\(\lambda\) = 562 nm) was measured using a spectrophotometer. First, the instrument was zeroed using 1000 \(\mu\)L of aqueous FeCl\({}_{3}\) solution, 1 \(\mu\)L of distilled water, and 1000 \(\mu\)L of aqueous ferrozine solution as a reference.  
The reduction activity (\(RA\)) of Fe\({}^{3+}\) to Fe\({}^{2+}\) was estimated using the following Formula (6):  
\[RA=\frac{[(C_{Fe2+r.s.}-C_{Fe2+t.s.})\cdot V_{s}]}{V_{FPE}}.100\% \tag{6}\]  
where  
\(RA\)--reduction activity of Fe\({}^{3+}\) [mg/L],  
\(C_{Fe2+r.s.}\)--the concentration of Fe\({}^{2+}\) ions in the reference sample [mg/L],  
\(C_{Fe2+t.s.}\)--the concentration of Fe\({}^{2+}\) ions in the tested sample [mg/L],  
\(V_{s}\)--total volume of solution introduced into volumetric flasks [L],  
\(V_{FPE}\)--volume of FPE introduced into volumetric flasks [L].
# Adaptive Feedback-Driven Segmentation for Continuous Multi-Label Human Activity Recognition
## 2 Related Work
### Multi-Label Classification  
Continuous HAR with multi-label classification, which aims to more accurately mirror real-world scenarios, was first introduced by [9] (see Figure 1). This pioneering approach is crucial in radar-based HAR as it allows for the assignment of multiple activity labels to a single instance of radar data, essential for recognizing continuous or concurrent human motions. In their seminal study, they segmented continuous radar streams into fixed windows with overlapping, subsequently applying a classifier to these segmented windows. Utilizing multi-representation inputs (range-time, range-Doppler, and spectrogram), their methodology achieved notable outcomes, including 95.8% accuracy and a 92.08% F1-score on a public dataset, thus demonstrating the efficacy of multi-label classification in managing continuous activities.  
In a follow-up study [28], they extended their research by introducing significant technical enhancements. The team employed both signal-level and decision-level fusion techniques to process three distinct data representations. The refined approach greatly enhanced the integration and interpretation of sensor data, maintaining a high accuracy of 95.65% while substantially reducing computational demands. Additionally, the researchers undertook a comparative analysis of two neural network architectures.  
Although these authors' innovations have enhanced continuous HAR with their multi-label classification, challenges remain. Fixed window segmentation is typically pre-determined based on known activity durations, limiting adaptability in scenarios where activity durations and transitions vary, potentially leading to suboptimal recognition performance. Additionally, their studies emphasize the ongoing need for improved data stream segmentation methods. In response, our work proposes a feedback-driven segmentation approach as a solution to these issues, aiming to advance HAR precision and adaptability. This method is designed to dynamically adjust segmentation, aligning with the complex nature of human activities in dynamic environments and driving significant progress toward robust HAR systems.
# Vibration Response Analysis of Overhead System Regarding Train-Track-Bridge Dynamic Interaction
## 2 System Dynamics Theory of Train-Track Overhead System  
### Vibration Analysis Model of Overhead Structures  
The train bridge coupling system assumes no relative displacement between the track and the beam, as well as no elastic deformation of fasteners such as the base plate and fastener under the track. Then, the differential equations of motion of the nodes in the overhead system are as shown in Equations (3) and (4) [17; 18; 19; 20].  
\[M\ddot{X}+C\dot{X}+KX=F \tag{3}\]  
\[F=F_{e}+F_{w} \tag{4}\]  
where: \(F_{e}\) = External forces on bridge joints. In the paper, only the interaction between the train rail bridge system is considered, so \(F_{e}\) = 0.  
\(F_{w}\) = Wheel rail force transmitted from the vehicle to bridge through track structure.
## 1 Introduction
To the best of our knowledge, no studies have conducted a comprehensive biomechanical dynamic analysis of the VBBT that includes not only hand trajectories but also hand kinematics and muscular activation. Prior research has not captured hand movement during the use of the VBBT, aside from incidentally obtaining wrist range of motion for the evaluation of tracking capacities of LMC [8]. Due to the limitations of the LMC hand-tracking system, using external devices to measure hand kinematics with accuracy while performing RBBT and VBBT could provide valuable insights into differences in kinematic parameters and patterns due to the game environment and health condition. Furthermore, thorough dynamic analysis should extend beyond kinematics alone. When comparing the RBBT and VBBT, differences in muscle activation are expected due to the lack of strength feedback in virtual environments. Interestingly, training with LMC has been shown to improve pinch strength [4; 6; 6], suggesting that muscle activations are worth analysing to determine if they differ due to health conditions. Despite the growing interest in the VBBT across various platforms, no comprehensive dynamic studies have yet been conducted to examine the full scope of hand kinematics and muscle activation, particularly in SCI patient populations, to better understand the implications of using these tests in virtual environments. This study aims to fill this gap by comparing detailed biomechanical signals, including hand trajectories, hand joint kinematics, and forearm muscular activation, of healthy participants and four patients with SCI, while performing RBBT and VBBT. By comparing these multidimensional signals, this research seeks to provide a more complete understanding of how virtual reality affects motor control and muscle engagement compared to real-world tasks. Understanding these differences is essential for refining virtual rehabilitation tools and ensuring they accurately mimic real-world motor demands.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 3 Path Planning Design
### Search Path for UAV
#### 3.1.4 Hilbert Curve Path  
The Hilbert curve is a continuous fractal space-filling curve, which was first described by the German mathematician [PERSON] in 1891 [45]. It is capable of filling a planar square with Hausdorff dimension 2 and has filling properties similar to those of a Z-curve. The Hilbert curve is constructed by copying four copies of the curve of the previous order, flipping the lower left and lower right curves along the diagonal, and adding three  
Figure 7: Peano curve route map.  
line segments to connect these four copies. The nth-order Hilbert space-filling curve is a continuous fractal path that traverses all cells divided into \(2^{n}\times 2^{n}\) square regions and does not intersect itself during the traversal. This shows that the Hilbert curve has three important advantages. First, it can map a multidimensional (mainly two-dimensional) space into a one-dimensional space, making it possible to traverse the multidimensional space without duplicating any subspace. Second, any subspace can be further partitioned without breaking the continuous curve. Third, the maximum number of contiguous cells in a Hilbert curve is three. Therefore, in almost all cases, skipping cells can reduce the total distance of the Hilbert curve. Due to these characteristics, Hilbert curves have been widely used [46]. In our collaborative UAV search scenario, we use Hilbert curves as the path planning strategy to achieve efficient traversal of all devices in the target area.  
When the target region is divided into \(4^{n}(n\geq 1)\) by square cells with side length \(D\), the UAV enters the cell near the starting point \(S\) on the left side of the spatial region from the starting point \(S\), moves in the shape of a door, and connects all the cell centroids in the target region with a non-crossing and non-overlapping curve by rotation or translation according to the Hilbert transformation rule, and, finally, enters the cell near the starting point \(S\) from the right side of the spatial region cells near the starting point \(S\) and returns to the starting point position \(S\), as shown in Figure 8. The pseudo-code to generate the Hilbert curve path is shown in Algorithm 3.  
```
0: Matrix A = \([C_{0,0},\ldots,C_{m-1,0};\ldots;C_{0,n-1},\ldots,C_{m-1,n-1}]\)
0: P
1: P: The path of UAVs
2: A: Explore points within a region
3: X: \(m\) Columns
4: Y: \(n\) Rows
5: if X and Y are 2 to the Nth power then
6: N \(\leftarrow\)\(\log_{2}(X)\)
7: //Stage 1: Initialize starting point and direction
8: Hilbert = HilbertCurve(N)
9: //Stage 2: Generate Path P based on the Hilbert curve rule
10: P = Hilbert.HilbertRule(\(C\))
```  
**Algorithm 3** Make Hilbert Curve Path  
Figure 8: Hilbert Curve Route Map.
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 3 Fusion of Lane Location Information and SLAM Positioning Algorithm
### Result Processing and Output
It has great advantages when there is a small displacement or the position itself does not change significantly, especially when the convergence speed is greatly improved, which is suitable for real-time unmanned vehicle positioning. It is worth noting that the algorithm proposed in this paper needs to adjust the parameters adaptively in different applications. The SLAM system used in this paper has a release frequency of 20 Hz, and the update frequency is equal to the release frequency of the SLAM system. There are points stored in the directed graph in the map. The interval is selected as 5 cm, and this value is the resolution of the positioning longitudinal error at different speeds and update frequencies. The value of K and the initial covariance matrix need to be adjusted. If the value of K is too large, the amount of calculation will increase. If the value of K is too small, it is easy to be limited to a specific area when updating, and the prediction incentive is not enough to break the local optimum. Similarly, the selection of the covariance matrix also affects the positioning results. In this paper, the value of K is selected as 10, and in the covariance matrix, \((d_{x},d_{y})\) is set to 1, \(d_{\theta}\) is set to 0.2, and \(d_{l}\) is set to 2. With a selection interval of 5 cm, each update can affect about 20 map points, and the calculation speed is 5 ms. When the movement speed is significantly reduced or the publishing frequency is greatly increased, the K value needs to be adjusted. This paper provides an empirical conclusion. When the number of map points that can be affected by each update is about twice the value of K, the positioning effect is improved.
# A Modeling Method of Agents and SOA in Advanced Avionics System Based on AADL
## 1 Introduction
Therefore, the combination of a traditional software architecture modeling method and a dynamic behavior description method, based on pattern components, provides a more accurate description of the dynamic behavior of the agent and SOA. At the same time, this model has very good compatibility with the existing avionics models based on AADL, so that the same model can describe the common software architecture and the agent and SOA at the same time, and accurately describe these complex features of the agent and SOA. Based on the above content, this paper proposes a novel modeling method of agent and SOA based on AADL, aiming to improve the ability of model to describe agent and SOA by describing the dynamic behavior and attributes of agents and SOA in different states. Firstly, based on the change in the agent working state, the environmental input feedback and multi working states modeling method of the agent are proposed, aiming to describe the transition mechanism of the working state of the agent with external input and the attributes of the agent in different working states. In addition, considering the service-oriented encapsulation of SOA and the invocation of services by applications, an application and service modeling method of SOA is proposed, aiming to describe the encapsulation and reuse of services in SOA and the flexibility of software architecture, when executing different applications. Moreover, this modeling method is used to model an avionics system, including SOA and agents. Based on this model, the delay time of different agent working states and SOA execution of different applications are analyzed. The analysis results show that the system has different delay times under different inputs and different application execution, which accurately describes the attributes of agents in different states and SOA execution of different applications and proves the effectiveness of this method in practice. In the remainder of this paper, Section 2 briefly reviews the agents, SOA, and AADL, and Section 3 presents the agents and SOA modeling method based on AADL. In Section 4, a case study is introduced and the last section provides conclusions and future work.
## 3 System Model
### Communication Range Judgment Model
#### 3.2.2 Forward Transmission Nodes  
To avoid routing loops, the selected node should be closer to the destination than the previous one. Therefore, it is necessary to control the direction of each hop forwarding to the destination node. For these, we define a forward transmission region as follows:  
As shown in Figure 5, the two circles are made with node \(i\) as the center, \(d_{0}\) as the radius, and the node \(D\) (destination node) as the center, \(d_{iD}\) (the distance from node \(i\) to node \(D\)) as the radius, respectively. The area where the two circles intersect is the forward transmission region. Assuming the coordinate of \(D\) is \((x,y)\). If node \(j\) is a forward transmission node of \(i\), it should satisfy two conditions: firstly, \(j\) is within the transmission range of \(i\); secondly, node \(j\) is closer to the destination \(D\) than \(i\), that is:  
\[\left\{\begin{array}{l}\left(x_{j}-x_{i}\right)^{2}+\left(y_{j}-y_{i}\right) ^{2}\leq{d_{0}}^{2}\\ \left(x_{j}-x\right)^{2}+\left(y_{j}-y\right)\right)^{2}\leq{d_{iD}}^{2}\end{array}\right. \tag{11}\]  
**Definition 2**.: _The forward transmission nodes set of node i is:_  
\[FN(i)=\left\{j\big{|}\left(x_{j}-x_{i}\right)^{2}+\left(y_{j}-y_{i}\right)^{2 }\leq{d_{0}}^{2},\left(x_{j}-x\right)^{2}+\left(y_{j}-y\right)^{2}\leq{d_{iD}} ^{2}\right\} \tag{12}\]
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## Appendix A Alternative Anomaly Detection Methods  
Figure 11 shows the anomaly detection results of implementing the Forecast model on the original BT network traffic data. Since most anomaly detection methods work better with stationary data, we have applied this method on both the original BT non-stationary  
Figure 11: Anomaly detection and clustering for SKAB.  
traffic time series and the detrended residual series from the NARX neural network for a better comparison. The first plot is the anomaly detection results based on the original BT network traffic data and the second plot summarises the anomaly detection results based on the on deflated residual time series.  
The Forecast model is a widely used model estimation method for anomaly detection in many applications due to its simplicity and interpretability. It can be found that compared to the results of the NARX-SCAPA, the Forecast algorithm was too conservative with the original observations (e.g., anomalies with low intensity) and too sensitive with the residuals (e.g., the normal region around 12,240 min). It is worth noting that the method is based on decomposing the target time series and dividing normal and anomaly groups for the remaining time series. The detection results of this method may vary with different decomposing ratios. However, in this case, such tuning options did not show a significant impact on the detection performance. The detection only focuses on the outliers outside the baseline or the curve so the data in the split anomaly regions which are aligned with the normal pattern cannot be identified with any decomposing ratio we choose. Meanwhile, varying the decomposing ratio to its extremes can make the detection oversensitive. As is shown in Figure A1, a decomposing ratio that ensures correct labelling of split anomaly can also result in a high false positive rate. This is due to the fact that the anomalies are not evenly distributed over the series and a fixed threshold cannot adapt to it.  
Figure A2 shows the anomaly detection results of HDoutlier package for the original BT network traffic data. Similarly, the first plot represents the detection results based on the original observations and the second plot is based on the residual series from the NARX neural network. This anomaly detection method comes with a threshold \(\alpha\) for determining the cutoff for anomalies. It can be found in Figure 5 that there is a major anomalous segment around 12,960 min. Such a segment was considered to tune the \(\alpha\) values for HDoutlier on both the original time series and residual time series. In order to identify the most anomalous data within that segment, the \(\alpha\) value for HDoutlier based on the original BT network traffic time series needed to be 1 (the maximum setting), and with such a setting the algorithm became over sensitive (e.g., labelling the normal region between 10,100