esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Coupled Bending-Torsional Flutter Mechanism for Ideal Plate
## 4 Ideal Flat Plate Flutter Analysis
### Stiffness Parameter Analysis  
The phase difference \(\theta_{1}\) of torsional displacement self-excitation force and vertical vibration displacement is closely related to the stiffness parameters \(\gamma_{ha}\) and \(\Gamma_{ha}\) of the vertical vibration system, so it is necessary to further analyze the stiffness parameters \(\gamma_{ha}\) and \(\Gamma_{ha}\) of the vertical vibration system.  
Figure 7 shows the stiffness parameters of the vertical vibration system and the curve with wind speed. From the graph, the following can be observed:  
1. In the stiffness parameter expression \(\gamma_{hu}\), the value of \(-(C_{h0}-H_{1})\zeta_{sa}\omega_{sa}\) is close to zero relative to the other two stiffness parameters in \(\gamma_{hu}\). Therefore, the numerical value of stiffness parameter \(\gamma_{hu}\) depends on the sum of \(K_{h0}-H_{4}\) and \(m_{h}\big{(}\xi_{sa}^{2}-1\big{)}\omega_{sa}^{2}\). Among them, because the torsional mode damping ratio of square \(\xi_{sa}^{2}\) in term \(m_{h}\big{(}\xi_{sa}^{2}-1\big{)}\omega_{sa}^{2}\) is significantly less than 1, the value of this term is approximately equal to \(-m_{h}\omega_{sa}^{2}\), and \(-m_{h}\omega_{sa}^{2}\) is the negative stiffness generated by the torsional displacement self-excited force during the excitation of vertical vibration. The stiffness parameter \(\gamma_{hu}\) has a negative value because the negative stiffness \(-m_{h}\omega_{sa}^{2}\) is significantly greater than the vertical vibration system's positive stiffness \(K_{h0}-H_{4}\).
2. In the stiffness parameter expression \(\Gamma_{hu}\), \(-2m_{h}\xi_{sa}\omega_{sa}^{2}\) is always negative before flutter, and it changes from negative to positive as it approaches the critical flutter wind speed; whereas \((C_{h0}-H_{1})\omega_{sa}\) is always positive, with its value increasing linearly with wind speed \(-2m_{h}\xi_{sa}\omega_{sa}^{2}\), being significantly greater than at all wind speeds, resulting in a positive value for stiffness parameter \(\Gamma_{hu}\).
3. Negative stiffness \(-m_{h}\omega_{sa}^{2}\) results in a negative value for stiffness parameter \(\gamma_{hu}\), while positive stiffness \((C_{h0}-H_{1})\omega_{sa}\) leads to a negative value for stiffness parameter \(\Gamma_{hu}\). Therefore, the cosine function \(\cos\theta_{1}\) is negative, causing the phase angle between torsional displacement self-excited lift and vertical displacement response to be in the (90\({}^{\circ}\) 180\({}^{\circ}\)) interval.  
Figure 7: Stiffness parameters of vertical vibration system.
## 5 Challenges and Open Directions
### Open Directions for Enhancing Traceability and Copyright Protection  
Traceability and copyright protection are essential for combating the misuse of generative content. Safe-SD [26] introduces a watermark-trigger mechanism using text prompts to enhance traceability, while ProMark [53] provides proactive watermarking strategies aimed at causal attribution. The work by [PERSON] et al. [54] proposes tracing mechanisms that do not require artificial watermarking, offering a different perspective on generative model traceability. Current watermarking approaches need to evolve to handle increasingly sophisticated attacks that aim to remove or obscure watermarks, such as diffusion-based adversarial attacks or latent variable manipulations. Methods like watermark-embedded adversarial examples [55] have shown promise in enhancing copyright protection, but their implementation is currently limited by scalability and computational efficiency concerns. Future research could explore hybrid techniques combining watermarking with other traceability methods like model fingerprinting [56] or blockchain-based logging [57] to ensure a secure and transparent trail of content ownership.
## 1 Introduction
[26] applied THz-TDS technology to qualitatively and quantitatively detected four pesticides and three food powders in the frequency range of 0.5-1.6 THz. It was showed that the limits of detection (LOD) of imidacloprid was lower than 8% and the relative error was less than 5% in predicting the weight ratio of imidacloprid in these two mixtures by the partial least squares (PLS) model. [PERSON] et al. [27] found that the THz spectral of caffeine and 3-acetylmorphine in the range of 0.2-2.6 THz were in good consistency with the spectra calculated by density functional theory (DFT) and Hartree-Fock (HF). [PERSON] et al. [28] explored THz spectral properties of eleven antibiotics and found that eight of the antibiotics had THz fingerprint peaks. Moreover, two antibiotics in three kinds of food were detected by the THz system, which indicated the feasibility of THz technology for detection of antibiotics in food. These researches illustrated the existence of molecular terahertz fingerprint peaks and the potential of terahertz technology for quantitative detection of matters. In this paper, terahertz time-domain spectroscopy (THz-TDS) was used to investigate 2,4-D spectral characteristics, including analysis of time-domain waveform, frequency-domain spectrum, and absorption spectrum. Additionally, DFT was applied to 2,4-D geometry optimization and vibration frequency calculation, and the generation mechanism of fingerprint peaks was explored. Then, feasibility of quantitative detection of 2,4-D in Zizania latifolia was explored by a terahertz system. The linear relationship between terahertz absorbance intensity and 2,4-D content was established. Overall, it is believed that this work provides a theoretical and experimental basis for fingerprint peaks analysis of 2,4-D and a rapid method to detect 2,4-D in Zizania latifolia.
## 4 Experiment and Analyses
### Objective Evaluation
#### 4.2.2 The Structural Similarity Index (MEF-SSIMc)  
The structural similarity index, MEF-SSIMc, represents the structure correlation between fused images and source images. The larger the MEF-SSIMc value, the closer the structure correlation. The formula of MEF-SSIMc is defined as:  
\[Q(\{X_{k}\},Y)=\frac{1}{M}\sum\limits_{j=1}^{M}S(\{x_{k}(j)\},y(j)) \tag{16}\] \[S(\{x_{k}\},y)=\frac{2\sigma_{y}+C_{2}}{\frac{C_{2}}{C_{2}}+C_{2 }^{2}+C_{2}},\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{5}{c}{**Method**} \\ \cline{2-7}  & **Mertens10**[15] & **Vonikakis11**[16] & **Li13**[18] & **Liu15**[12] & **Ma18**[14] & **The Proposed Method** \\ \hline balloons & 0.6210 & 0.5493 & 0.5940 & **0.6333** & 0.6137 & **0.6340** \\ recorder & 0.6784 & 0.4483 & 0.6720 & **0.6970** & 0.6479 & **0.6923** \\ Belgium & 0.6500 & 0.5563 & **0.6650** & 0.6608 & 0.6447 & **0.6724** \\ mask & 0.7484 & 0.7201 & 0.7451 & **0.7646** & 0.7396 & **0.7636** \\ Studio EV & 0.6748 & 0.6297 & **0.6812** & **0.6870** & 0.6712 & 0.6802 \\ landscape & 0.7530 & 0.6805 & 0.7307 & **0.7874** & 0.7496 & **0.7749** \\ lighthouse & 0.7037 & 0.6860 & 0.6823 & **0.7326** & 0.7012 & **0.7245** \\ Kluki & 0.7604 & 0.7558 & 0.7539 & **0.7815** & 0.7435 & **0.7883** \\ desk & 0.6188 & 0.5324 & 0.6179 & **0.6201** & 0.6088 & **0.6314** \\ farmhouse & 0.7334 & 0.7409 & 0.7486 & **0.7855** & 0.7411 & **0.7758** \\ office & 0.7500 & 0.7449 & 0.7331 & **0.7665** & 0.7268 & **0.7727** \\ Madison & 0.7289 & 0.4884 & 0.7154 & **0.7316** & 0.6934 & **0.7389** \\ \hline Average & 0.7017 & 0.6277 & 0.6949 & **0.7207** & 0.6901 & **0.7208** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of different methods on the 12 image sequences, using \(Q^{AB/F}\)[37]. Top two values are shown in bold.  
where \(\{x_{k}\}=\{x_{k}|1\leq k\leq K\}\) denotes the set of co-located image patches in the source image sequence, \(y\) is the fused image. \(\delta_{\overline{x}}^{2}\), \(\delta_{y}^{2}\), and \(\sigma_{\overline{y}y}\) denote the local variances of the source image \(\overline{x}\) and the fused image \(y\), and the local covariance between \(\overline{x}\) and \(y\), respectively. \(C_{2}\) is a small positive stabilizing constant, \(j\) is the spatial patch index and \(M\) is the total number of patches.  
The performance of the different methods on the 12 image sequences, using MEF-SSIMc, is listed in Table 2, where the top two values are shown in bold. According to the fusion metric, MEF-SSIMc, the performance of the proposed method was better than the other three compared methods and near to the Ma18 method. However, it was not as good as Mertens10, which uses contrast, saturation, and exposure quality to evaluate the source image exposure quality. However, this method may also lead to loss of details in the brighter regions, as shown in Figure 8a.
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 4 Performance Evaluation
### Performance of the MOTSO Model
#### 4.2.1 Maximizing the Number of Completed Tasks  
According to the first objective, i.e., maximizing the number of completed tasks, Table 10 shows the results of the number of tasks achieved using our proposed model in the synthetic dataset (with two cases) and the Gowalla dataset; \(|V|\) and \(|T|\) indicate the number of completed tasks and the total number of tasks in the task set, respectively. Indeed, there are no differences in the performance of MOTSO between the synthetic dataset (in two cases) and the real dataset, that is, the performance of our proposed solution was compared by the different algorithms mentioned in related works [2,10], in terms of the number of completed tasks using the Gowalla dataset.  
The percentages of the completed tasks are presented in Figure 7, where our MOTSO model achieved the highest percentage of completed tasks, reaching approximately 98% compared to the other algorithms (i.e., baseline algorithm (A&S), global assignment and local scheduling algorithm (GALS), naive local assignment local scheduling based on location (NLALA-L), naive local assignment local scheduling based on task-oriented partitioning (NLALS-T), bisection-based local assignment and local scheduling based on K-means  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Total Travel Cost Using MOTSO** \\ \cline{2-3}  & **TTCs** \\ \hline
**Case 1** & 637.2025 \\ \hline
**Case 2** & 917.3457 \\ \hline
**Gowalla** & 1292.929 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The result of the total travel costs (TTCs) using our MOTSO model.  
Figure 6: The percentages of completed tasks using the proposed MOTSO model for case 1 (**a**), case 2 (**b**), and Gowalla (**c**).  
(BLALS-K), bisection-based local assignment and local scheduling based on task-oriented partitioning (BLALS-T), baseline algorithm (BLA), load-balancing algorithm (LBA), and the divide-and-conquer algorithm (DCA)) in [2; 10]. In other words, the performance of the proposed MOTSO was better than that of the other methods, as illustrated in Figure 7. Based on this, our MOTSO model achieved effective performance regarding the first objective of the MOTSO model, i.e., maximizing the number of completed tasks, because we adapted the MOPSO based on a fitness function that considers the number of completed tasks in each iteration, which helped to improve and increase the number of completed tasks. Additionally, consideration of the task entropy in the ranking strategy algorithm helped to maximize the number of completed tasks.
## 3 Preliminary of Millimeter Radar
### The Principle of Speed Measurement of FMCW Radar  
If there is no Doppler frequency, the frequency difference during the rising edge is equal to the measured value during the falling edge. For moving targets, the frequency difference during the rising or falling edge is different. We can measure the distance and speed through these two frequency differences. Therefore, we can draw the curve of the beat frequency versus time, as shown in Figure 2, where \(f_{b}\) refers to beat frequency.  
Figure 1: The curves of the sent signal frequency and the received signal frequency versus time.  
From the Doppler effect, we know:  
\[f_{b1}=f_{b}-f_{d} \tag{5}\]  
\[f_{b2}=f_{b}+f_{d} \tag{6}\]  
\[f_{d}=\frac{2f\cdot v_{r}}{c} \tag{7}\]  
\(f\) is the central frequency of the transmitted signal, and from Equation (4), we can obtain:  
\[f_{b}=\frac{2\cdot R\cdot f_{c}}{c\cdot t_{c}} \tag{8}\]  
Substituting Equations (5)-(7) into Equation (8) yields Equations (9) and (10):  
\[f_{b1}=\frac{2\cdot R\cdot f_{c}}{c\cdot t_{c}}-\frac{2f\cdot v_{r}}{c} \tag{9}\]  
\[f_{b2}=\frac{2\cdot R\cdot f_{c}}{c\cdot t_{c}}+\frac{2f\cdot v_{r}}{c} \tag{10}\]  
Subtracting Equation (9) from Equation (10), then we can obtain the formula of the relative speed \(v_{r}\) (11):  
\[v_{r}=\frac{c}{4\cdot f}\cdot(f_{b1}-f_{b2})=\frac{\lambda}{4}\cdot(f_{b1}-f_ {b2}) \tag{11}\]  
Thus, FMCW radar can realize ranging and speed measurement.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
### Challenge 1: Lack of Labeling  
When providing anomaly detection services for a single business scenario, the anomaly detection system will be faced with handling millions of time series. At this point, it is not possible for operators to manually tag each time series, and if all of them are manually tagged, they will also be affected by the efficiency and will not be applicable to the big data operation and maintenance environment. In addition, an effective anomaly detection system not only needs to be able to accurately identify known types of anomalies, but also needs to have the ability to detect anomalies in unknown situations. Currently, although supervised anomaly detection methods based on deep learning have emerged, they are unable to effectively detect and cope with the ever-changing and complex forms of the current network environment because they are trained with training datasets of already-labeled anomaly types [4]. In summary, in the face of anomaly detection in the current network environment, traditional supervisory methods not only fail to obtain sufficient labels to ensure accuracy, but also fail to address the challenges of new types of anomalies.
# An Approach of Path Optimization Algorithm for 3D Concrete Printing Based on Graph Theory
## 2 Materials and Methods
### Optimal Path  
The geometry is divided into n areas after the partition is completed. Create n arrays to store the geometric points in each partition geometry; each array is denoted as \(R_{n}\). The geometric point in each array is denoted as \(S_{ti}\), and mark its Cartesian seat as \(\left(x_{S_{ti}},y_{S_{ii}}\right)\). Extract a geometric point from each array and apply the ant colony algorithm to get the shortest path between them. This process needs to be repeated several times until the geometric points in each array are traversed to find the final optimal path. However, such a process is complicated and time-consuming. Therefore, constraints are introduced in this paper (the constraints are as follows: Equations (1)-(3)). Use the ant colony algorithm to find the shortest path, as the judgment result is F = 1. On the contrary, discard the extracted geometry point combination if the judgment result is F = 0.  
\[d=\sum_{t=1}^{n}\sqrt{\left(x_{(t+1)i}-x_{ti}\right)^{2}+\left(y_{(t+1)i}-y \right)^{2}}\,,i\in\text{max}R_{n} \tag{1}\]  
\[t=\sqrt{\left(\text{max}(x_{i})-\text{min}(x_{i})\right)^{2}+\left(\text{max }(y_{i})-\text{min}(y_{i})\right)^{2}} \tag{2}\]  
\[\begin{cases}d<t,\,\,\,F=1\\ d\geq t,\,\,\,F=0\end{cases} \tag{3}\]  
Figure 5: Partitions found in the geometric model.  
The following are the critical steps of the ant colony algorithm used in this section:  
Step (1): Initialize the various parameters, such as the number of ants \(\mu\), the influence of pheromone concentration on the direction of ant transfer \(\alpha\), the relative importance of visibility \(\beta\), the number of volatiles \(\rho\), and the maximum number of iterations.  
Step (2): Construct a solution space, place each ant randomly at a different starting point, and calculate the next printing point to be visited for each ant k (k = 1, 2, 3... n) until all ants have visited all the printing points.  
Step (3): Update the pheromone, calculate the path length \(d_{k}\) of each ant (k = 1, 2, 3... n), and record the optimal solution (shortest path) in the current iteration times. At the same time, the pheromone concentration on the connecting path of each printing point is updated.  
Step (4): Determine whether to terminate; if iterations < max(iterations), set iterations = iterations + 1, clear the record table of the path that the ant passes, and return to step (2); otherwise, terminate the calculation and output the optimal solution (Figure 6).
## 3 WebAssembly and IoT
### WebAssembly and IoT Performance Optimization  
Performance optimization is a critical aspect of IoT application development. IoT devices often operate on limited resources and have power efficiency requirements, making it essential to maximize computational efficiency and minimize resource usage. With its high-performance characteristics, Wasm serves as an ideal choice for optimizing IoT applications. This section delves deeper into how Wasm contributes to performance optimization in IoT, and explores how the performance benefits of Wasm can be leveraged to optimize IoT applications. It discusses aspects such as lower resource usage, reduced latency, and faster execution times, which are crucial for the smooth functioning of IoT applications.  
* **Fast Execution Speed** Wasm is designed to offer near-native performance, which is crucial for IoT devices that often have to process complex tasks within strict time constraints. With its efficient binary format, Wasm instructions can be executed quickly and effectively, which results in faster application performance. This speed is particularly valuable in IoT environments, where real-time data processing can be critical.
* **Efficient Use of Memory** Wasm implements a linear memory model, which means all memory is managed in a single, contiguous block. This approach reduces memory fragmentation and allows more efficient memory usage. Furthermore, Wasm's compact binary format requires less memory for code storage. These memory management practices can greatly benefit IoT devices, which often operate with limited memory.
* **Streamlined Parsing and Compilation** Wasm's binary format is designed to be fast to parse and compile. Compared to traditional text-based languages, binary formats are significantly quicker to process. This feature can greatly speed up the time from fetching the code to executing it, an important factor in IoT devices, as they often need to start quickly and perform their tasks in real-time.
* **Parallelism and Concurrency** Wasm is designed with parallelism and concurrency in mind. With the proposal of Wasm threads, developers can leverage multicore processors more effectively by parallelizing their computations. This feature can be especially beneficial in the IoT context, where devices equipped with multi-core processors can utilize Wasm to run computationally intensive tasks more efficiently.
* **Performance in Real-world IoT Applications** Wasm's performance features are not just theoretical; they have practical implications in the world of IoT. For instance, in edge computing, where latency is a critical factor, Wasm's fast execution speed and efficient memory usage can greatly improve application responsiveness. Similarly, in the realm of smart devices and wearables, where battery life and responsiveness are crucial, the efficient use of resources that Wasm allows can lead to significant improvements.
## 2 Materials and Methods
### Parallel Intersection Calculation  
In three-dimensional space, a ray cross the whole computational domain and intersects with some of triangular facets in geometries. Through the lattice method, most triangles that are impossible to intersect with the specific ray are excluded. In order to calculate the intersection point between rays and facets, we just need to find the triangle in the candidate triangles. An intersection test method without division calculation is used, which can reduce the machine error. For a ray \(r\) and a facet \(t\), \(\mathbf{v_{0}}\), \(\mathbf{v_{1}}\) and \(\mathbf{v_{2}}\) are three vertexes of \(t\). The orientation \(O_{i}\) representing the relative location between \(r\) and each edge of \(t\) can be calculated through Equation (16):  
\[O_{i}=\left|\begin{array}{cc}A_{x}^{i}&A_{y}^{i}\\ A_{x}^{j}&A_{y}^{j}\end{array}\right| \tag{16}\]  
with  
\[A_{x}^{i}=v_{i,x}-r_{x} \tag{17}\]  
\[A_{y}^{i}=v_{i,y}-r_{y} \tag{18}\]  
where \(0\leq i\leq 2\) and \(j=i\ mod\ 3+1\).  
The \(O_{i}\) represents the relative location of the edge \(\overline{v_{i}v_{j}}\) and the ray \(r\). If \(O_{i}>0\), \(r\) is located on the left side of edge \(\overline{v_{i}v_{j}}\). Whereas, If \(O_{i}<0\), \(r\) is located on the right side of edge \(\overline{v_{i}v_{j}}\). If the \(O_{i}\) for three edges satisfy the condition: \(\cap O_{i}>0\) or \(\cap O_{i}<0\), the ray must cross through the triangular facet \(t\). There are some special cases of the location relationship, such as the ray cross through a vertex of the facet. All cases will be analyzed in Section 2.5.  
If a facet pass the intersection test for a ray, then the intersection point of \(z\) coordinate of ray can be calculated using Equation (19). All the intersection points in ray direction are arranged in ascending order forming an ordered array. In the last step of mesh generation, the property of substance need to be mapped to the mesh cells. A mesh cells in a ray is divided into some columns by the intersection points. The number of columns must be an even number and can be expressed as \(2n\). In the ray direction, the \(i\)-th cell can be mapped using Equation (20).  
\[r_{z}=v_{0,z}+A_{x}^{0}\cdot\frac{n_{x}}{n_{z}}+A_{y}^{0}\cdot\frac{n_{y}}{n_{z}} \tag{19}\]  
where \(r_{z}\) is the \(z\) coordinate of the intersection point.  
\[cell_{r}^{i}=\begin{cases}1,&r_{z}^{2n-1}<i\cdot s_{z}\leq r_{z}^{2n}\\ 0,&else\end{cases} \tag{20}\]  
where \(cell_{r}^{i}\) is the property flag of cells in \(r\) direction.
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 3 System Description  
The assistive device reported in this paper was designed to facilitate blind pedestrian outdoor navigation and follows the three-module concept of Figure 1. Commercial hardware components and software modules were used to implement a fully operational prototype with special attention on wearability, reliability, efficiency, and low cost.