esa-sceva/satcom-chunk-collection huggingface dataset:
# Linear Vibration Analysis of Shells Using a Seven-Parameter Spectral/_hp_ Finite Element Model
## 6 Numerical Results
### Convergence Study
#### 6.1.2 Shells
For both models, the mesh is made using elements \(p=8\). Table 5 presents the convergence studies for these cylinders. It is concluded that good convergence is achieved in both cases using 25 elements. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**B.C.**} & \multirow{2}{*}{**Mesh Size**} & \multicolumn{8}{c}{**Natural Frequency**} \\ \cline{3-13}  & & \(f_{1}\) & \(f_{2}\) & \(f_{3}\) & \(f_{4}\) & \(f_{5}\) & \(f_{6}\) & \(f_{7}\) & \(f_{8}\) & \(f_{9}\) & \(f_{10}\) & \(f_{11}\) \\ \hline CC & 1 & 943.46 & 1113.20 & 1902.29 & 1902.82 & 1907.98 & 2049.30 & 3109.91 & 3229.87 & 3526.72 & 3888.11 & 3889.17 & 4391.09 \\  & 4 & 904.81 & 905.10 & 9130.40 & 932.30 & 1405.74 & 1416.29 & 1889.70 & 1889.79 & 1921.01 & 2026.41 & 2027.24 & 2059.93 \\  & 9 & 897.91 & 897.83 & 902.03 & 902.03 & 1933.46 & 1393.53 & 1510.55 & 1568.00 & 1685.00 & 1885.93 & 1885.93 \\  & 16 & 896.96 & 896.96 & 901.15 & 901.15 & 1392.09 & 1392.09 & 1500.38 & 1500.93 & 1675.61 & 1676.11 & 1884.77 & 1884.77 \\ \hline CF & 1 & 471.25 & 471.40 & 556.39 & 781.57 & 968.38 & 1515.96 & 1614.93 & 2031.64 & 2055.70 & 2056.21 & 2184.61 \\  & 4 & 316.28 & 317.29 & 469.22 & 469.22 & 786.62 & 808.72 & 930.65 & 4993.13 & 994.94 & 948.56 & 1462.88 \\  & 9 & 315.38 & 315.38 & 468.52 & 468.52 & 768.27 & 914.46 & 914.58 & 937.50 & 937.50 & 1451.27 \\  & 16 & 315.31 & 315.31 & 468.26 & 468.26 & 767.59 & 767.59 & 913.77 & 937.01 & 937.01 & 1450.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Convergence of natural frequencies of cylinder with constant thickness. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Mesh Size**} & \multicolumn{8}{c}{**Dimensionalless Frequency Parameter**} \\ \cline{3-13}  & & \(\Lambda_{1}\) & \(\Lambda_{2}\) & \(\Lambda_{3}\) & \(\Lambda_{4}\) & \(\Lambda_{5}\) & \(\Lambda_{6}\) & \(\Lambda_{7}\) & \(\Lambda_{8}\) & \(\Lambda_{9}\) & \(\Lambda_{10}\) \\ \hline \(r/h=5\) & 16 & 0.3334 & 0.3382 & 0.8548 & 0.8583 & 0.9198 & 0.9198 & 1.5180 & 1.5671 & 1.5671 & 1.8278 \\ (constant) & 36 & 0.3296 & 0.3296 & 0.8510 & 0.8510 & 0.8586 & 0.8621
## 4 Results
Besides, as shown in Figure 9a, unlike primary resonance, the 1/2-order subharmonic and 2-order super-harmonic resonances of the system can only be excited in the case of losing contact, because the system belongs to a linear continuous system (\(\alpha\) = 1) below the loss of contact boundary. Figure 6: For \(\alpha\) = 1.5, influence of \(\zeta\) on the frequency-response peak-to-peak curves of one-sided contact system, (**a**) \(W\) = 1 and (**b**) \(W\) = 2. Figure 7: For \(\alpha\) = 0.6, influence of \(\zeta\) on the frequency-response peak-to-peak curves of one-sided contact system, (**a**) \(W\) = 1 and (**b**) \(W\) = 2. Moreover, as shown in Figure 8, the period-doubling bifurcations at points \(A_{4}\) and \(A_{6}\) lead to the period-4 solution branches excited from the stable period-2 branch, and then period-8 response also emerges through period-doubling of the period-4 solution branch. The leading Floquet multiplier of the coming period-8 solution branch passes out of the unit circle from the +1 axis at \(A_{7}\) point of \(A=1.77983603\) as \(A\) changes (see Table 1), where the typical reverse tangent bifurcation (26; 29) is clear (see Figure 8b), and then the motion of type-I intermittency chaos is induced (see Figure 10a). As the excitation amplitude \(A\) increases, the loses contact characteristics of the system become stronger, and the typical crisis-induced (30) intermittency chaos (see Figure 10b) emerges in the interval a-a of Figure 8b. At this time, the type-I intermittency chaos attractor merges with another periodic orbit, and Figure 11 shows the process of the above involution. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(A\) & **1.7798** & **1.77982** & **1.779835** & **1.77983603** \\ \hline \multirow{2}{*}{\(\lambda_{\text{m}}\)} & 0.9920 & 0.9927 & 0.9991 & 1.0020 \\  & 0.1857 & 0.1854 & 0.1839 & 0.1833 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Period-8 motion Floquet multipliers \(A_{\text{m}}\) around the turning point \(A_{8}\). Figure 8: One-sided contact system for \(\omega=1\), (**a**) the periodic solution branch as the excitation amplitude \(A\) varies, and (**b**) numerical bifurcation diagrams of \(x(\tau)\) when \(A\) sweeps up (black dots) and down (red dots). Figure 9: One-sided contact system for \(A=1\), (**a**) the frequency-response peak-to-peak curves, and (**b**) phase portraits their Poincare mapping dots of co-existing period-\(n\) solutions (i.e., \(P_{n}\)) when \(\omega=1\).
## 1 Introduction
Nonetheless, this system required a spectrum resource of 500 MHz [23]. Hence, it is evident that the incorporation of NLFM technology alone with \(\varphi\)-OTDR systems still necessitates addressing concerns related to frequency response range and hardware resource utilization. This paper introduces an \(\varphi\)-OTDR technique that combines OFDM and NLFM time-series pulse modulation. It employs multiple orthogonal subcarriers to efficiently stack spectra, enhancing spectrum utilization and extending the system's frequency response range. Within the finite detector bandwidth, this approach allows for more frequency band multiplexing. NLFM technology is used to fine-tune frequency modulation nonlinearity, resulting in reduced sidelobe levels, improved sidelobe suppression ratios, reduced interference between neighboring signals, and an enhanced SNR. The combination of OFDM and NLFM in pulsed light generates wider pulse widths, enabling more optical energy injection into the sensing fiber and yielding higher backscattered optical power in long-distance sensing scenarios. Consequently, this approach enhances spatial resolution and SNR, extending sensing distances. The experimental results demonstrate that utilizing OFDM-NLFM time-series pulse modulation with a 65 MHz bandwidth achieves an 11 m spatial resolution and a 1-10 kHz frequency response range over a 16.3 km optical fiber by multiplexing four frequencies. This research has the goal of improving fiber optic sensing technology to make it more practical for applications in industries such as industrial automation, security, and aerospace. This study's objectives include expanding the frequency response range of \(\varphi\)-OTDR systems, enhancing spatial resolution, and extending sensing distances. The introduction of novel modulation techniques is intended to tackle the limitations of current technology, thereby driving progress in fiber optic sensing, increasing its utility, and fostering its utilization in engineering applications and scientific research.
## 5 Parameter Estimation
### Sensitivity Analysis  
Sensitivity analysis can be used to show which parameters play a bigger role in a model. The normalized sensitivity, \(S_{p}\), for parameter \(p\) and state variable \(x\) is given by:  
\[S_{p}=\frac{\partial x}{\partial p}\frac{p}{x}.\]  
Figure 3 shows the sensitivities of every parameter and state variable for Model 1. We observe that, with the exception of \(d\), no parameter has a much larger sensitivity than the rest. \(d\) is the only parameter that can dramatically affect cancer cell death rate, and we see a spike in the sensitivity figures. Cancer cell growth rate \(\mu\) has the greatest effect on androgen and cancer cells, whereas \(\sigma\) and \(\epsilon\) play the greatest roles in PSA production. Figure 4 shows the sensitivity of each parameter in Model 2. We see similarities with Model 1 in that \(\mu\) affects the production androgen and CR cells the most. In addition, PSA production is affected by the same parameters as in Model 1. Notice that CS cells are affected by \(d_{1}\) the most since they are the most susceptible to changes in the androgen concentration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Definition** & **Range** & **Units** & **Source** \\ \hline \(\mu\) & Maximum proliferation rate & 0.001–0.09 & day\({}^{-1}\) & [33] \\ \(q\) & Minimum cell quota & 0.1–0.5 & nM & [34] \\ \(q_{1}\) & Minimum CS cell quota & 0.1–0.5 & nM & [34] \\ \(q_{2}\) & Minimum CR cell quota & 0.1–0.3 & nM & [34] \\ \(b\) & Prostate baseline PSA & 0.1–2.5 & \(10^{-3}\)\(\mu\)g/L/nM/day & [9] \\ \(\sigma\) & Tumor PSA production rate & 0.001–0.9 & \(\mu\)g/L/nM/mm\({}^{3}\)/day & [28] \\ \(\epsilon\) & PSA clearance rate & 0.001–0.01 & day\({}^{-1}\) & [28] \\ \(d\) & Maximum cell death rate & 0.0001–0.09 & day\({}^{-1}\) & [33] \\ \(d_{1}\) & Maximum CS cell death rate & 0.001–0.09 & day\({}^{-1}\) & [33] \\ \(d_{2}\) & Maximum CR cell death rate & 0.0001–0.001 & day\({}^{-1}\) & [33] \\ \(\delta_{1}\) & Density death rate & 0.1–9 \(\times\) 10\({}^{-5}\) & 1/day/mm\({}^{3}\) & [31] \\ \(\delta_{2}\) & Density death rate & 0.01–4.5 \(\times\) 10\({}^{-4}\) & 1/day/mm\({}^{3}\) & [31] \\ \(R\) & Cell death rate half-saturation level & 0–3 & nM & [28] \\ \(R_{1}\) & CS cell death rate half-saturation level & 0–3 & nM & [28] \\ \(R_{2}\) & CR cell death rate half-saturation level & 0–3 & nM & [28] \\ \(c_{1}\) & Maximum CS to CR rate & \(10^{-5}\)–10\({}^{-4}\) & day\({}^{-1}\) & [12] \\ \(K\) & CS to CR half-saturation level & 0–1 & nM & [28] \\ \(r_{1}\) & Testes androgen production & 20 & day\({}^{-1}\) & ad hoc \\ \(r_{2}\) & Secondary androgen production & 0.001–0.01 & day\({}^{-1}\) & ad hoc \\ \(Q_{m}\) & Maximum androgen & 15–30 & nM & [9] \\ \(\
u\) & death rate decay rate & 0.01 & unitless & ad hoc \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter definitions, units, and ranges.
# Calibration of Model Parameters for Soda Saline Soil-Subsoiling Component Interaction Based on DEM
## 3 Results and Discussion
### Discussion
Through repeated experiments, it was found that the addition of bonds between soil particles to simulate the characteristics of high hardness and easy hardening of saline-alkali soil in Northeast China can obtain results closer to physical experiments. Notably, these bonds between soil particles can be disrupted during the DS test. However, even in the event of bond disruption during the shear process, the soil retains cohesion due to the surface energy. In order to faithfully reflect the shear and flow characteristics of the soil particle population, we conducted the DS test by amalgamating the JKR contact model and the HMB contact model, thereby modifying the discrete element simulation parameters. We used the modified parameter combination to carry out the repose angle simulation test and the direct shear simulation test, and the results are very close to those of the physical test. Therefore, future research could consider combining multiple contact models to simulate different mechanical properties of soil to obtain more accurate simulation results.
# Approximate Networking for Universal Internet Access
## 3 Context-Appropriate Approximate Networking Trade-Offs
### How Can We Visualize the Trade-Offs?  
An interesting approach to understanding trade-offs is to use visualization techniques. In approximate networking, the task for optimizing for one explicit parameter is easier than optimizing for multiple optimization variables (such as throughput, delay, and energy). The main problem arises when the various objectives are conflicting and have to compete for dominance, e.g., it is impossible to jointly minimize both bit error rate (BER) and transmit power simultaneously [51]. One approach to solving such a problem is to look for a solution on the so called 'Pareto Frontier' that defines the set of input parameters that define non-dominated solutions in any dimension. The use of a _tradeoff curve_[52] can be use to visualize bi-objective problems. The problem of visualizing high-dimensional trade-offs is more challenging. One approach that has been proposed is to utilize Pareto front, which defines the set of values that are each Pareto optimal. When considering multiple objectives, it is often useful to consider the _Pareto frontier_ or the _Pareto front_ that comprises of the set of choices that are Pareto efficient instead of considering the full range of every parameter. A _radar chart_ is another planar visualization technique that can be useful for visualizing multivariate network trade-offs. For example, [PERSON] et al. [53] have used radar chart for visualizing QoE in multiple QoS dimensions.
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Evaluating Security and Privacy
#### 5.2.4 Tracking Ransom Payments  
Ransomware groups use mixes of coin transactions across accounts to mask extorted payments. But network analysis reveals identifiable payment flows by correlating transaction times, values and links between groups of addresses [79].  
Figure 14 is a simplified representation, aimed at demonstrating the underlying structure of ransomware payment flows in a blockchain transaction network. The actual process  
Figure 13: Deanonymization.  
of unmixing coin transactions and tracking ransom payments involves a more complex and nuanced analysis of transaction attributes and connectivity patterns. The visualization serves to illustrate how ransomware groups move exotorted payments through various wallet addresses. The complexity and inter-connectedness of these transactions can be seen, showcasing the challenge in tracking and analyzing these flows. Nodes represent wallet addresses within the blockchain network. Directed edges (arrows) signify ransom-related transactions between these wallets.  
**Assumption 13**.:
* _The blockchain transaction network is a temporal graph_ \(G(V,E,T)\) _with wallet addresses as nodes_ \(v_{i}\in V\)_, transactions as edges_ \(e_{ij}eE\)_, and timestamps_ \(t_{ij}eT\) _on edges._
* _Ransomware groups mix coins across multiple wallets to hide payment trails._  
**Application Statement 13**.:
* _Correlating transaction timing, value flow patterns and connectivity trails between groups of addresses on temporal transaction graphs can reveal identifiable ransomware payment flows._  
Proof.:
* Apply the Modularity Optimization algorithm for community detection on transaction graph \(G\) to cluster related wallets \(A\) into campaigns.
* Construct subgraphs \(G_{A}\subseteq G\) induced by each ransomware wallet group \(A\subseteq V\).
* Apply network flow modeling and tracking on \(G_{A}\) based on transaction amounts, timings and unmixing of trails between clusters to trace laundered payments.  
This allows tracing obfuscated ransom transfers on blockchain networks via combined analysis of graphs, flows and temporal patterns. Multi-faceted network analysis techniques facilitate tracking ransomware payments on blockchain platforms despite coin mixing attempts.  
Figure 14: Tracking Ransom Payments.  
Thus, graph techniques enable enhanced the modeling, prediction and containment of security threats by uncovering hidden relationships and activity patterns.
###### Abstract  
In the domain of video recognition, video transformers have demonstrated remarkable performance, albeit at significant computational cost. This paper introduces TSNet, an innovative approach for dynamically selecting informative tokens from given video samples. The proposed method involves a lightweight prediction module that assigns importance scores to each token in the video. Tokens with top scores are then utilized for self-attention computation. We apply the Gumbel-softmax technique to sample from the output of the prediction module, enabling end-to-end optimization of the prediction module. We aim to extend our method on hierarchical vision transformers rather than single-scale vision transformers. We use a simple linear module to project the pruned tokens, and the projected result is then concatenated with the output of the self-attention network to maintain the same number of tokens while capturing interactions with the selected tokens. Since feedforward networks (FFNs) contribute significant computation, we also propose linear projection for the pruned tokens to accelerate the model, and the existing FFN layer progresses the selected tokens. Finally, in order to ensure that the structure of the output remains unchanged, the two groups of tokens are reassembled based on their spatial positions in the original feature map. The experiments conducted primarily focus on the Kinetics-400 dataset using UniFormer, a hierarchical video transformer backbone that incorporates convolution in its self-attention block. Our model demonstrates comparable results to the original model while reducing computation by over 13%. Notably, by hierarchically pruning 70% of input tokens, our approach significantly decreases 55.5% of the FLOPs, while the decline in accuracy is confined to 2%. Additional testing of wide applicability and adaptability with other transformers such as the Video Swin Transformer was also performed and indicated its progressive potentials in video recognition benchmarks. By implementing our token sparsification framework, video vision transformers can achieve a remarkable balance between enhanced computational speed and a slight reduction in accuracy.  
action recognition; transformer; efficient; token sparsification 2023
# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 2 Proposed System
### Modeling
#### 2.1.1 Pattern Pool Initialization  
For each different class detection target, the user needs to label at least one pattern. The system decides the aspect ratio and scaling range of detection window according to this pattern and generates a positive and negative pattern pool for the algorithm to learn. Figure 2 is an illustration of labeling the detection targets. The generation of patterns in a positive and negative pattern pool is decided by the overlapping area of the labeled target and sliding window in each input image, as given in (1)  
\[Overlap=\frac{area\Big{(}B_{sample}\cap B_{sliding}\Big{)}}{area\Big{(}B_{ sample}\cup B_{sliding}\Big{)}} \tag{1}\]  
The size of the sliding window starts from the minimum labeled target and zooms in equal magnification (1.2 in this research) until the size reaches the largest labeled size. The scanning stride is the short side of the sliding window times the fixed magnification (0.1 in this research) to scan through the whole image. Then, the pattern is generated by calculating the overlapping area. We used the same setting as in On-line Learning with Adaptive Model [24]. If the overlapping area is larger than 0.875, it is viewed as a positive pattern, as shown in Figure 3. Note that the overlap area of the positive sample is set to be less than 0.8, and it is not conducive to the selection of initial features. It will also affect the correctness of the object classification. If the overlapping area is less than 0.3, it is viewed as a negative pattern, as shown in Figure 4.  
Figure 1: The Proposed system flowchart.  
Figure 2: Illustration of labeling detection targets.  
The above steps are completed in the first labeled frame. As for the other labeled frames, the positive patterns are also chosen using the above steps. The negative patterns are taken where the overlapping area is between 0 and 0.3. At the same time, if the overlapping area between the positive pattern's position of the first labeled frame and the positive pattern of this frame is 0, this area would also be seen as a negative pattern to make up the background occluded by the positive pattern in the first labeled frame, as shown in Figure 5.
## 3 Processing Methods
### Structure of MDISE Model
#### 3.2.3 SE Module  
In a standard 1D-CNN model, the same weight update strategy is applied to each channel, ignoring the varying importance of feature information across different channels. By incorporating the SE module after each 1D Inception module, inter-channel dependencies are established, and the channel weights are adaptively calibrated. This selectiveattention to feature information helps suppress redundant features, thereby improving the model's performance. The structure of the SE module used in this paper is shown in Figure 6c.  
The working process of the SE module is as follows: First, for an input feature \(X=[x_{1},x_{2},x_{3},\ldots,x_{c}]\) with \(C\) channels and a feature length of \(N\), global average pooling is applied to each channel to compute the mean of all features within that channel, compressing the information into a channel descriptor z of size \(C\times 1\). The element of the \(c\)-th channel in the descriptor \(z\) can be expressed as follows:  
\[z_{c}=\frac{1}{N}{\sum_{i=1}^{N}x_{c}(i)} \tag{5}\]  
where \(i\) is the index of the \(c\)-th channel input feature \(x_{c}\). This operation generates a global summary for each channel.  
Next, the descriptor \(z\) is passed through two FC layers with weights \(W_{1}\) and \(W_{2}\) to capture the dependencies between feature channels. A ReLU activation function is applied after the first FC layer to enhance the model's non-linear representation capabilities, and a Sigmoid activation function is applied after the second FC layer to generate the calibration weights; the channel-wise attention weights \(s\) are then obtained. The computation process is as follows:  
\[s=\sigma[W_{2}\delta(W_{1}z)] \tag{6}\]  
where \(\sigma\) is the Sigmoid activation function, and \(\delta\) is the ReLU activation function. The first FC layer \(W_{1}\) has dimensions of \((C/r)\times C\), and the second FC layer \(W_{2}\) has dimensions of \(C\times(C/r)\), where \(r\) is the reduction ratio, used to reduce computational complexity and enhance generalization ability. To effectively balance the inversion accuracy and computational complexity of the MDISE model, the reduction ratio \(r\) is set to 16.  
The recalibrated channel-wise attention weights \(s\) are then applied to the input feature \(X\) to generate the final output \(Y=[y_{1},y_{2},y_{3},\ldots,y_{c}]\). where each channel is scaled according to its learned importance. The element of the \(c\)-th channel in the final output \(Y\) can be expressed as follows:  
\[y_{c}=s_{c}x_{c} \tag{7}\]