esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Torsional Stiffness of Reducers and Its Testing Method
## 4 Experimental Procedure
### Torsional Stiffness Test of Reducer under All Operating Conditions  
In the previous test, it was observed that the torsional stiffness of the reducer varies with changes in speed. Consequently, it becomes imperative to investigate the alterations in the torsional stiffness of the reducer under real working conditions. To accomplish this, the transmission error method is utilized to examine the torsional stiffness at different speeds and torques.  
Figure 15 displays the test curve of the small reducer at the 180\({}^{\circ}\) position, while Figure 16 presents the average torsional stiffness test curve within one revolution range. These curves reveal that the torsional stiffness of small reducers decreases as the rotational speed increases. This behavior reflects the dynamic characteristics of the actual torsional stiffness of the reducer. The torsional stiffness curve takes on an approximately parabolic shape, illustrating the nonlinear changes in the internal system dynamics of the reducer.  
On the other hand, Figure 17 shows the test curve of the large reducer at the 180\({}^{\circ}\) position, and Figure 18 illustrates the average torsional stiffness test curve within one revolution range for this large reducer. Similar to the small reducers, the test results for the large reducer also show a decrease in torsional stiffness with increasing rotational speed. However, the torsional stiffness curve of the large reducer takes on an approximately linear shape, which is influenced by factors such as its structure and material properties.  
Figure 15: Evaluation curve of a small-size reducer at the 180\({}^{\circ}\) position.
## 4 Indoor Dynamic Beam Deflection Measurement Experiment and Validation
### Structural Full-Field Dynamic Displacement Field Measurement
#### 4.2.2 Spatiotemporal Deflection Data Processing  
The high-precision displacement of a large number of densely measured points on the surface of vertical beams at different times can be achieved by the method proposed, and the deflection variation in a measured point in the temporal field was calculated; as an example, the displacement-time history curve of the mid-span point on the beam is plotted in Figure 11.  
The spatial deflection along the beam at any time can also be calculated and extracted, and the deflection curve of the 20 th frame is plotted as illustrated in Figure 12, with an enlarged view of the local deflection curve with unevenness and discontinuity due to displacement measurement error in discrete data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & **Total** & \begin{tabular}{c} **Coarse** \\ **Mismatch** \\ **Removal** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Coarse** \\ **Mismatch** \\ **Rate \(m_{1}\)** \\ **Number** \\ \end{tabular} & \
\begin{tabular}{c} **Secondary** \\ **Mismatch** \\ **Rate \(m_{2}\)** \\ **Rate \(m_{3}\)** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Mismatch** \\ **Mismatch** \\ **Rate \(m_{4}\)** \\ \end{tabular} \\ \hline \(t=0.2\) s & & 4579 & 33.03\% & 3455 & 16.44\% & 49.47\% \\ \(t=0.4\) s & & 4325 & 36.75\% & 3053 & 18.60\% & 55.35\% \\ \(t=0.8\) s & & 4464 & 34.72\% & 3221 & 18.18\% & 52.90\% \\ \(t=1.2\) s & & 4444 & 35.01\% & 3186 & 18.40\% & 53.41\% \\ \(t=0.6\) s & & 4090 & 40.19\% & 2693 & 20.43\% & 60.62\% \\ \(t=2.0\) s & & 4319 & 36.84\% & 3095 & 17.90\% & 54.74\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mismatch removal at different times.  
Figure 11: Deflection variation at the mid-span in the entire temporal field.  
Figure 12: Deflection variation in the 20 th frame in the entire spatial field. (**a**) The global spatial deflection of the beam. (**b**) The local spatial deflection of the beam.  
After polynomial fitting, continuous and uniform deflection curves at five times were obtained and are plotted in Figure 13.
## 2 Optimal Design of the TID
### Optimization Method  
As shown in Equation (10) of Section 2.2, the optimization problem outlined above is equivalent to addressing the constrained optimization problem of the binary function. However, the complexity of the objective function makes it difficult to obtain the theoretical solution, so the use of numerical optimization methods to calculate the optimal values is a more feasible option. In this paper, the FMINCON function provided by MATLAB (2024a) Optimization Toolbox was adopted. The function descriptions of the FMINCON are  
\[\left\{\begin{array}{l}\underset{\mathbf{x}}{minf(\mathbf{x})}\\ s.t.\ \mathbf{l}\mathbf{b}\leq\mathbf{x}\leq\mathbf{ub};\ \mathbf{A}\cdot \mathbf{x}\leq\mathbf{b};\ \mathbf{Aeq}\cdot\mathbf{x}=\mathbf{beq};\ c(\mathbf{x})\leq 0;\ ceq (\mathbf{x})=0\end{array}\right. \tag{12}\]  
in which \(\mathbf{x}\) denotes a vector of variables. f(\(\mathbf{x}\)) represents a scalar function of \(\mathbf{x}\). The values of \(\mathbf{x}\) are constrained by \(\mathbf{lb}\) and \(\mathbf{ub}\), with \(\mathbf{lb}\) denoting the lower bound and \(\mathbf{ub}\) denoting the upper bound. The coefficient matrices and constant vectors for linear inequalities are denoted by \(\mathbf{A}\) and \(\mathbf{b}\), respectively. \(\mathbf{Aeq}\) and \(\mathbf{beq}\) represent the coefficient matrices and constant vectors corresponding to the linear equalities. The functions c(\(\mathbf{x}\)) and \(\mathrm{ceq}(\mathbf{x})\) take \(\mathbf{x}\) as input and return vectors. It is important to note that f(\(\mathbf{x}\)), c(\(\mathbf{x}\)), and \(\mathrm{ceq}(\mathbf{x})\) may be nonlinear functions.  
It can be seen from Equation (12) that the FMINCON function can be applied to the TID optimization problem described in Equation (10), where the matrix \(\mathbf{A}\) is defined as [1 0;0 \(-\)1], and the vector \(\mathbf{b}\) is defined as [0,0], which are used to specify the boundary conditions for the frequency ratio and damping ratio. The remaining parameters \(\mathbf{lb}\), \(\mathbf{ub}\), \(\mathbf{Aeq}\), \(\mathbf{beq}\), are not considered in this optimization problem. As a special case, [PERSON] et al. [34] deduced the theoretical solution of the optimal frequency ratio \(f^{opt}\) and optimal damping ratio \(\xi^{opt}\) of the TID under white-noise excitation on the premise that the damping ratio of the base-isolated structure is 0, as shown in Equation (13). Based on this, Figure 3 shows the numerical optimization results obtained by the FMINCON function and the calculation results of Equation (13). It can be seen from the results that the optimal parameters derived from both methods are in agreement, which verifies the accuracy of the numerical optimization method.  
\[f^{opt}=\frac{\sqrt{1+\mu/2}}{1+\mu},\ \xi^{opt}_{d}=\sqrt{\frac{\mu(1+3\mu/4)} {4(1+\mu)(1+\mu/2)}} \tag{13}\]
## 2 Material and Methods
### ATR-FTIR Test  
In this paper, solid bitumen samples were placed on reflectance crystal for ATR-FTIR tests to determine the functional groups of the HCRMB with different amounts of sulfur before and after aging [13,27]. The spectra were carried out in the range of 4000 to 600 cm\({}^{-1}\) with 32 scans. The aging of bitumen is mainly reflected in the change of the carbonyl group and, therefore, the degree of oxidation in the HCRMB with different amounts of sulfur can be determined by the carbonyl index (I\({}_{\text{CA}}\)) [28]. Aging can also lead to desulfurization of CR and degradation of polybutadiene in RMB [12]. The silica index (I\({}_{\text{Si-O-Si}}\)) and the polybutadiene index (I\({}_{\text{PB}}\)) in HCRMB during aging were chosen to analyze the degree of desulfurization of CR and degradation of polybutadiene. The calculation methods for I\({}_{\text{CA}}\), I\({}_{\text{Si-O-Si}}\), and I\({}_{\text{PB}}\) are described below:  
\[\text{I}_{\text{CA}} =\text{A}_{\text{1700 cm}}{}^{-1}/\text{A}_{\text{1376 cm}}{}^{-1} \tag{3}\]  
\[\text{I}_{\text{Si-O-Si}} =\text{A}_{\text{1100 cm}}{}^{-1}/\text{A}_{\text{1376 cm}}{}^{-1} \tag{4}\]  
\[\text{I}_{\text{PB}} =\text{A}_{\text{966 cm}}{}^{-1}/\text{A}_{\text{1376 cm}}{}^{-1} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Binder Type**} & \multicolumn{2}{c}{**Modification Plan**} \\ \cline{2-3}  & **Sulfur, \%** & **CR, \%** \\ \hline
70\# & 0 & 0 \\
20 TB\_0.2 Sul & 0.2 & 20 \\
20 TB\_0.4 Sul & 0.4 & 20 \\
40 TB\_0.2 Sul & 0.2 & 40 \\
40 TB\_0.4 Sul & 0.4 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the composition of RMB and HCRMB with different amounts of sulfur.
# Blockchain-Assisted QoS-Aware Routing for Software-Defined Wide Area Network
## 3 System Architecture
### QoS-Aware Route Selection (QRS) Model  
In the context of network-path selection to ensure QoS aspects of prioritized traffic from end-users, the considered model resembles the decision-making strategy outlined by [PERSON] et al. [27] for a different problem domain. The work in [27] originally proposed a feedback-based resource scheduling mechanism targeted at cloud computing environments. Its goal was to continuously refine resource allocation, such as CPU, memory, or storage needs in the cloud based on users' resource preferences, and also to optimize the cloud provider's resource utilization. In contrast, the current work adopts the same underlying preference matching model but applies it in a completely different context--QoS-aware routing decision-making in Software-Defined Networking (SDN) environments. Here, the model is used to select optimal routing paths by matching users' application-specific QoS requirements (such as bandwidth, delay, loss, etc.). Furthermore, this framework dynamically computes routing paths and adapts to real-time network changes without relying on continuous feedback cycles from users, as the feedback integration stage in [27] is not applicable in the routing context.  
Within the framework of QRS (QoS-aware route selection), the QoS preferences play a pivotal role in determining the optimal network path for traffic between the cloud user and the cloud server. While QRS introduces additional computational overhead and latency in scheduling decisions, it enables the simultaneous satisfaction of multiple QoS constraints, making it well-suited for QoS-aware routing. The QRS framework comprises two essential models: the cloud user's QoS Preference Model (QPM) and the router's Routing Capability Model (RCM), and both models are described below.
## Problem Formulation
### Mathematical Description of BESS
#### 2.3.1 Equivalent Loss of Cycle Life  
The cycle life of BESS is mainly effect by the charge/discharge depth [21]. According to the test results of the Lithium-ion battery system in literature [22], BESS has a corresponding cycle life at each depth of discharge, as listed in Table 1.  
Table 1 shows that shallow charge/discharge depth is conducive to extend the cycle life of BESS. Based on the power function method in Formula (6) [23], the fitting curve of charge/discharge depth with cycle life is illustrated in Figure 5.  
Based on the irreversible electrochemical loss for each charge/discharge process on the cycle life of BESS [24], the equivalent lifetime loss rate is used to obtain the equivalent service year of BESS, calculated as Equation (7):  
\[T_{life}=1/\sum_{i=1}^{N_{B}}\frac{1}{L_{cyc,Di}} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Discharge Depth (\%)** & **0.2** & **0.4** & **0.6** & **0.8** & **1.0** \\ \hline Cycle (time) & 9000 & 7200 & 5700 & 5200 & 4500 \\ \hline \end{tabular}
\end{table}
Table 1: Relationship between the charge/discharge depth and cycle life of Lithium-ion battery system.  
Figure 5: Depth of charge/discharge versus the cycle life of the Lithium-ion battery.
# A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm
## 4 Experimental Results and Discussion
### Comparison of Real-World Experiments in Narrow Environments  
In this study, more complex scenarios were set up to conduct comprehensive comparison experiments with the GA, Astra algorithm, and Dijkstra algorithm, and their performance in narrow spaces was tested. However, due to the limitations of the GA and Astra algorithm, they failed to successfully plan safe paths. They could only reduce the cost coefficient of the cost map, which increased the risk of collisions, resulting in paths that still collided with surrounding obstacles. Although the Dijkstra algorithm could generate safe paths, the large inflation radius significantly reduced map utilization, failed to plan a path through narrow areas, increased path length, and decreased path quality. In contrast, the proposed algorithm successfully navigated through narrow areas, as depicted in Figure 21. Meanwhile, it effectively balanced path length and cost map utilization, generating global paths that were both short and smooth, as shown in Figure 22.  
As shown in Figure 22, the path length of the proposed algorithm in this paper was significantly superior to the other two algorithms. Compared to the Astra algorithm, which failed to successfully plan paths, the paths generated by our algorithm were more effective. Moreover, in comparison to the Dijkstra algorithm, the path length of our algorithm was shortened by 5.35 m, representing a reduction of 30.1%, thereby significantly enhancing the performance of the algorithm and the utilization rate of the global cost map.  
Figure 19: Global cost Map 4.  
Figure 20: Comparison of the path-planning results using two algorithms in real environments.  
The experimental results demonstrate that, both in simulation and real environments, the proposed algorithm effectively avoids steering collisions in a dual-AGV-ganged system while considering the poses of the starting and goal points, thus successfully planning safe and collision-free paths. Traditional algorithms require setting a larger inflation radius to ensure path safety, but this significantly reduces the utilization of the cost map. In complex real-world environments, the GA and Astra algorithm fail to plan paths successfully, and although the Dijkstra algorithm can plan paths, the resulting paths are longer. In contrast, our algorithm successfully generates safe, collision-free, and efficient global paths, significantly improving path length and smoothness. Moreover, this algorithm efficiently utilizes map resources, enabling smooth passage through narrow areas, providing safer and more efficient global guidance for local path planning.
## 3 Results
### State-of-the-Art CNN Models for Automatic Screening of Athletes Osteopenia and Osteoporosis
#### 3.1.2 DenseNet  
DenseNet (Densely Connected Convolutional Network) is a deep neural network architecture, which is designed based on the concept of densely connecting each layer to every other layer in a feedforward manner. This connectivity pattern allows for efficient information flow and feature reuse throughout the network. The architecture of DenseNet-169 is depicted in Figure 5. DenseNet-169 consists of a conventional layer with a kernel size of 7 \(\times\) 7, a pooling layer with a size of 3 \(\times\) 3 for maximum pooling, four dense blocks with a transition layer between each pair of blocks, and a classification layer. Each dense block contains multiple conventional groups, which are composed of two conventional layers with kernel sizes of 1 \(\times\) 1 and 3 \(\times\) 3. The four dense blocks have different numbers of conventional groups, specifically 6, 12, 32, and 32. The transition layer is responsible for reducing computational complexity and consists of a conventional layer with a kernel size of 1 \(\times\) 1 and a pooling layer with a size of 2 \(\times\) 2 for average pooling. Finally, the classification layer includes a pooling layer with a size of 7 \(\times\) 7 for global average pooling, a fully connected layer, and a softmax function layer to obtain the final result.  
DenseNet has been applied in various studies for the automatic screening of osteoporosis using radiographs. For instance, researchers proposed a 3D DenseNet model for the detection of osteopororotic vertebral compression fractures. The model achieved high sensitivity (95.7%) and specificity (92.6%), as well as positive predictive value (PPV) of 91.7% and negative predictive value (NPV) of 96.2% [43]. Another study conducted by [PERSON] et al. utilized a novel convolutional neural network (CNN) model based on DenseNet to qualitatively detect bone mineral density for osteoporosis screening [44]. The classification precision for normal bone mass, low bone mass, and osteoporosis was reported as 80.57%, 66.21%, and 82.55%, respectively. The AUC exhibited a similar distribution pattern  
Figure 4: The backbone of ResNet101.  
as the precision. The dense connectivity in DenseNet enables direct access to feature maps from all preceding layers, facilitating feature reuse and enhancing the network's ability to learn and utilize feature information, thereby improving classification performance. However, the dense connection mechanism of DenseNet leads to increased computational complexity as each layer needs to connect with all previous layers. Moreover, DenseNet requires a significant amount of memory to store numerous feature maps, which can pose memory limitations when working with large medical datasets.
## 5 Discussion
Additionally, while approaches such as support vector machines (SVMs) and k-nearest neighbors (k-NNs) have demonstrated accuracies close to 80% [89], they are less effective at handling the temporal variability of fall risk data compared to LSTM models, which are capable of capturing long-term dependencies in time series data. Despite the promising results, this study has some limitations that should be considered. First, the accuracy of the model may be affected by variability in the placement of the mobile device on patients, as improper sensor positioning could distort the measurement of movement patterns. Second, the sample used in the study may not fully represent the heterogeneity of the older adult population, which could limit the generalizability of the results to other populations with different physical characteristics or health conditions. Additionally, the study was conducted in a controlled environment, which does not fully reflect real-life conditions, where environmental and behavioral factors could influence fall risk. Lastly, although the deep learning model demonstrated good performance, its \"black box\" nature makes it difficult for healthcare professionals to directly interpret the results, potentially limiting its clinical applicability without additional mechanisms to explain the model's decisions.
## 5 The Experiment and Analysis
### Fault Data from QPZZ-T
#### 5.2.1 Experimental Analysis  
Firstly, the KMVMD is employed to select the mode number. The fault signal is decomposed in different scales, and the kurtosis value and the kurtosis mean value are calculated, which are given in Table 2.  
According to the data in the table, IMF with a larger kurtosis value is chosen to reconstruct the signal under \(k=7\), which are IMF4, IMF5, and IMF6. The power spectrum of the reconstructed signal is given in Figure 8.  
It can be clearly found that 1/3 fault frequency harmonic (41.02 Hz) and rotating frequency (23.44 Hz) are obvious in Figure 8. Then the PGMCKD is applied to further deal with the reconstructed signal. The calculated theoretical value of \(T\) is 96 according  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**kurtosis**} & \multirow{2}{*}{**IMF1**} & \multirow{2}{*}{**IMF2**} & \multirow{2}{*}{**IMF3**} & \multirow{2}{*}{**IMF4**} & \multirow{2}{*}{**IMF5**} & \multirow{2}{*}{**IMF6**} & \multirow{2}{*}{**IMF7**} & \multirow{2}{*}{**Kurtosis**} \\  & & & & & & & & Mean \\ \hline
2 & **4.3737** & 4.2419 & & & & & & 4.3737 \\
3 & **4.3696** & 4.2406 & **4.8543** & & & & & 4.6119 \\
4 & 4.2420 & 4.3257 & **5.7181** & **4.8536** & & & & 5.2850 \\
5 & 3.6537 & 3.5656 & **4.4510** & **5.4256** & **4.8390** & & & 4.9052 \\
6 & 3.3937 & 3.5400 & 4.4029 & **5.7447** & **5.3062** & **4.8391** & & 5.2960 \\
7 & 3.3374 & 4.3578 & 4.3320 & **5.7153** & **5.6530** & **5.2321** & 3.4010 & **5.5334** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kurtosis and kurtosis mean value of each IMF with different \(k\).  
Figure 7: The obtained experiment results.  
to Equation (10), the search range of \(T\) is set [94; 98], the step size is 1, the search range of \(L\) is set [100, 500], its step size is 5. Based on the evaluation criterion of power spectrum entropy, the grid search is employed to obtain optimal parameter [\(T\), \(L\)] = [97; 175]. The power spectrum of the deconvoluted signal by PGMCKD is given in Figure 9.  
From Figure 10, it can be clearly found that \(1/4\) harmonic of the rotating frequency (5.859 Hz), the fault frequency \(f_{0}\) (140.6 Hz), and its second harmonic (281.3 Hz). It further proved that KMVMD-PGMCKD can accurately extract fault features of the rolling element signal.
## 2 Graph Notions  
### General notions  
All graphs in this work are undirected and finite. A graph \(G=(V,E)\) is a set \(V\) of vertices, \(|V|=n\), and a set \(E\) of edges which are pairs of distinct vertices, \(|E|=m\); an edge \(\{x,y\}\) is denoted \(xy\). The _complement_\(\overline{G}=(V,\overline{E})\) of graph \(G=(V,E)\) is the graph where, for any pair of vertices \(\{x,y\},x\
eq y\), edge \(xy\in\overline{E}\) if and only if \(xy\
ot\in E\). If \(X\subset V\) is a set of vertices of \(G=(V,E)\), \(G(X)\) denotes the _subgraph induced_ by \(X\) (which is a graph whose vertices are the elements of \(X\) and such that \(xy\) is an edge of \(G(X)\) if \(x,y\in X\) and \(xy\in E\)).  
**Neighborhoods:** The _neighborhood_ of a vertex \(x\) in \(G\) is \(N_{G}(x)=\{y\
eq x\mid xy\in E\}\); we omit subscript \(G\) when it is clear from the context which graph we work on. The extended neighborhood of a vertex \(x\) is \(N_{G}[x]=N_{G}(x)\cup\{x\}\). We say that vertices \(x\) and \(y\) are adjacent if and only if \(xy\in E\). The neighborhood of a set of vertices \(A\) is \(N_{G}(A)=\cup_{x\in A}N_{G}(x)-A\), and its extended neighborhood is \(N_{G}[A]=N_{G}(A)\cup A\).  
**Paths and cycles:** A _path_ is a sequence \((x_{1},x_{2},\ldots,x_{k})\) of different vertices such that for \(i<k\), \(x_{i}\) is adjacent to \(x_{i+1}\). A _chord_ in a path is an edge \(x_{i}x_{j}\) where \(j>i\) and \(j\
eq i+1\). A path is _chordless_ if it has no chord. A _cycle_ is a \"closed path\", _i.e._, a sequence \(x_{1},x_{2},\ldots,x_{k},x_{1}\) of vertices, with \(x_{1},x_{2},\ldots,x_{k}\) all different, such that for \(i<k\), \(x_{i}\) is adjacent to \(x_{i+1}\) and \(x_{k}\) is adjacent to \(x_{1}\). A _chordless cycle_ is a cycle with no chord. A chordless cycle with 5 or more vertices is called a _hole_, the complement of such a cycle is called an _antihole_.  
**Connexity:** A graph is _connected_ if there is a path connecting any pair of vertices, disconnected otherwise ; when the graph is disconnected, the maximal connected subgraphs are called the _connected components_ of the graph.  
**Cliques:** A _clique_ is a set of vertices which are pairwise adjacent. An _independent set_ is a set of vertices with no edges.  
**Perfection:** A graph is _perfect_ if it contains no induced hole or antihole with an odd number of vertices.  
**Example 2.1**: _In Figure 1, \((a,b,c,k)\) is a chordless cycle of length 4, \(\{c,d,j,k\}\) is a clique, \(N(c)=\{b,d,j,k\}\), \(N(\{a,b\})=\{c,k\}\)._  
Figure 1: Graph \(G\), our running example.