esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Finite Element Simulation  
### Finite Element Modeling  
A 3D numerical model is developed consisting of a homogeneous clay layer, a new tunnel, an existing tunnel, and a shield excavation panel, as shown in Figure 9. The dimension of the model is 840 mm \(\times\) 220 mm \(\times\) 510 mm. The new tunnel and the existing tunnel have an outer diameter of 120 mm and an inner diameter of 108 mm. The depth of the overlying clay layer of the new tunnel is 2\(D\). The tunnel excavation surface and the soil around the existing tunnel are divided into a denser fine mesh, while the soil area away from the tunnel is divided into a coarse mesh to balance the accuracy and computational efficiency. The clay layer, the new tunnel, the existing tunnel, and the shield excavation panel are modeled using C3D8 cells, with a total number of 66,940 cells. The interactions between the soil and the tunnel are defined as 'hard contact' in the normal direction and as friction contact in the tangential direction. The boundary conditions of the soil model are fixed constraints at the bottom, normal constraints at the sides, and free constraints at the top. Normal constraints are set on the side of the existing tunnel close to the new tunnel, and fixed constraints are set on the side of the existing tunnel away from the new tunnel to minimize the influence of boundary effects. The excavated panels are loaded with displacement loads in the horizontal direction and fixed in the rest of the directions to prevent rotation during the simulated pullback instability. The soil is modeled using the Mohr-Coulomb ideal elastic-plastic model, and the two tunnel linings and shield excavation panels are modeled using ideal elastic materials. The structural material parameters are listed in Table 4. Because the stability of excavation faces is the main focus of the study, the coupling effect between groundwater and soil is not considered.  
Figure 8: Three-dimensional reconstruction of the instability zone (\(S=3\) mm).
# The Numerical Study for the Effect of Stiffness Matching on Wheel-Rail Curve Squeal Noise
## 3 Results
### Frequency Response Analysis of Wheel and Rail
#### 3.2.2 Frequency Response Analysis of The Wheel  
The Lanzos method, a feature of ABAQUS software's modal analysis capability, is harnessed to determine the natural frequencies of the wheel within the frequency range of 0 to 5000 Hz. Subsequently, the \"Steady-state dynamics, Modal\" method is employed to calculate the frequency response of the wheel. This analysis focuses on the wheel's behavior under transverse excitation occurring at the contact point between the wheel and rail. Given the high-frequency characteristics associated with curve squeal noise, the sweep frequency range is configured to span from 1000 Hz to 5000 Hz. In line with empirical findings regarding wheel damping characteristics, the modal damping for the wheel is  
Figure 4: Partial modal shapes of wheel: (**a**) (1,0) mode; (**b**) (2,0) mode; (**c**) (6,0) mode; (**d**) (1,1) mode; (**e**) (2,1) mode; (**f**) (3,1) mode; (**g**) 1 pitch diameter; (**h**) 2 pitch diameters.  
set to 0.0001. The outcome of the frequency response analysis yields the radial and axial displacement responses of the wheel at the contact point, as depicted in Figure 5.  
The observed response results reveal that when the wheel is subjected to lateral excitation at the contact point with the rail, several resonant frequencies emerge within the frequency range of 1000 Hz to 5000 Hz. Resonance tends to occur near the wave crest frequencies. The axial displacement exhibits peaks at frequencies of 1072.6 Hz, 1890.58 Hz, 2038.69 Hz, 2468.33 Hz, 2811.14 Hz, 3785.97 Hz, and 4786.55 Hz, respectively. The highest axial displacement response is observed at 1072.6 Hz, corresponding to the (3,0) mode. While the amplitude of the radial displacement response is relatively modest, it features a relatively higher number of wave crests. Due to the geometry of the spoke plate, it is evident that the axial displacement response of the wheel consistently incorporates a notable radial response component.  
To delve into the modal shapes during resonance, we have summarized the modal shapes of the axial response at the wave crest frequency, as illustrated in Figure 6. Notably, when resonance occurs, the axial modes of the wheel consistently exhibit a dominant presence of 0 pitch circles and 1 pitch circle. Vibration at the wheel rim becomes more pronounced, and there are discernible vibration components in both the axial and radial directions.
## Results
### Demographic Data and Type D Personality Stratified by the Presence Inflammatory Markers
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**HAM-D Items**} & **MDD Subjects with TDP** & **MDD Subjects with TDP** & \\  & **and Non-Significant** & **and Elevated Inflammatory** & \\  & **Inflammatory Markers a  & **Markers a**CRP and/or IL6** & **\(p\)-Value** \\  & **(n = 14)** & **(n = 7)** & \\ \hline
1. Depressed Mood & 10.61 & 11.79 & 0.649 \\
2. Feelings of guilt & 10.68 & 11.64 & 0.714 \\
3. Suicide & 10.50 & 12.00 & 0.568 \\
4. Insomnia—early & 12.14 & 8.71 & 0.162 \\
5. Insomnia—middle & 11.57 & 9.86 & 0.517 \\
6. Insomnia—late & 10.57 & 11.86 & 0.624 \\
7. Work and Activities & 10.57 & 11.86 & 0.628 \\
8. Retardation & 9.50 & 14.00 & 0.102 \\
9. Agitation & 10.18 & 12.64 & 0.349 \\
10.Anxiety—psychic & 9.82 & 13.36 & 0.183 \\
11.Anxiety—somatic & 8.46 & 16.07 & * 0.005 \\
12.Somatic symptoms—GI & 10.29 & 12.43 & 0.403 \\
13.Somatic & 8.99 & 15.21 & * 0.016 \\ symptoms—General & 14.61 & 12.07 & 0.532 \\
15.Hypochondaries & 10.29 & 12.43 & 0.405 \\
16. Weight loss & 11.96 & 9.07 & 0.264 \\
17. Insight & 11.00 & 11.00 & 1.000 \\ \hline \hline \end{tabular} Note: HAM-D-the 17-item Hamilton Depression Rating Scale; GI-gastrointestinal, a—the listed figures represent mean ranks; Mann–Whitney U Test was used to compare the mean ranks of patients without significant inflammatory markers and the two subgroups of elevated inflammatory markers (only CRP and both CRP and IL-6, respectively).  
*—The level of significance for all analyses was set at \(\alpha\) = 0.05.  
\end{table} 
Table 2: Depressive symptoms profile depending on the presence or absence of peripheral inflammatory markers (in comparison with subjects with normal blood levels) in MDD patients with type D personality \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Significantly Associated**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{\(p\)**-Value**} & \multirow{2}{*}{**Df**} & **Partial Eta** \\
**Risk Factors** & & & & **Squared** \\ \hline IL-6 & 5.577 & * 0.023 & 1 & 0.115 \\ CRP & 0.687 & 0.412 & 1 & 0.016 \\ Type D personality & 0.276 & 0.602 & 1 & 0.006 \\ IL-6*CRP & 0.180 & 0.673 & 1 & 0.004 \\ IL-6*Type D personality & 0.642 & 0.427 & 1 & 0.015 \\ CRP*Type D personality & 0.300 & 0.300 & 1 & 0.025 \\ IL-6*CRP*Type D personality a  & - & - & 0 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect size of peripheral pro-inflammatory markers and type D personality on the baseline depressive symptoms.
# Identification of Location and Camera Parameters for Public Live Streaming Web Cameras
## 4 Formulation of the Problem and Its Solution Algorithm
### Auxiliary Steps  
#### 4.2.1 Describing of the Area Q  
Different Q shapes help for varying tasks. We fix \(\hat{Q}_{im}\) as a quadrilateral on the image \(\hat{\Phi}\). The \(Q_{c}\) (source of the quadrilateral in space) in the plane \(\Pi\) may be a rectangle or a tetragon. We choose the four corners of \(g_{1},g_{2},g_{3}\), and \(g_{4}\) of the domain \(\hat{Q}_{im}\) as pixels in the \(\hat{\Phi}\). The \"tetragon\" \(Q_{im}=\Theta^{-1}(\hat{Q}_{im})\) is the ROI in the original image \(\Phi\), in which we use object detection to estimate transport flows statistics. The lines bound \(\hat{Q}_{im}\) are ([7] (p. 28)):  
\[l_{bl}^{h}=g_{1}^{h}\times g_{2}^{h},\ l_{tl}^{h}=g_{1}^{h}\times g_{3}^{h},\ l_{tr}^{h}=g_{3}^{h}\times g_{4}^{h},\ l_{br}^{h}=g_{2}^{h}\times g_{4}^{h}, \tag{41}\]  
where \(g_{i}^{h}=(\hat{a}^{i},\hat{\sigma}^{i},1)\) are the homogeneous coordinates of pixel \(g_{i}=(\hat{a}^{i},\hat{\sigma}^{i})\). Pixel \(g^{h}\in\hat{Q}_{im}\) must be the solution to a system of four inequalities:  
\[\begin{cases}l_{tl}^{h}\cdot g^{h}\geq 0\\ l_{tr}^{h}\cdot g^{h}\geq 0\\ l_{bl}^{h}\cdot g^{h}\leq 0\\ l_{br}^{h}\cdot g^{h}\leq 0\end{cases}. \tag{42}\]  
The \(Q_{im}\) is a continuous domain in \(\mathbb{R}^{2}\), but the whole image \(\Phi\) contains only a fixed number of actual pixels (\(N\times M\)). The same is true for \(\hat{Q}_{im}=\Theta(Q_{im})\). We can obtain real pixels that fall in \(Q_{lm}\) from \(\hat{Q}_{im}\) with the per-pixel correspondence \(Q_{im}=\Theta^{-1}(\hat{Q}_{im})\). We apply the mapping (39) and obtain the set of points in \(F_{c}\) which correspond to actual pixels in \(Q_{im}\). \(F_{c}\) is a metric coordinate system, so the distance in \(F_{c}\) can be used to estimate meters/second or objects/meter. The same is true for any rotations or shifts of the frame \(F_{c}\).
## 2 Fuzzy C-Means (FCM) Clustering Algorithm  
Let \(X=\{x_{1},\,x_{2},\,\ldots,\,x_{n}\}\) be \(n\) data samples; \(c\) (\(2\leq c\leq n\)) is the number of the divided categories for these data samples; \(\{\)A\({}_{1}\), A\({}_{2}\), \(\ldots\), A\({}_{c}\}\) indicates that the corresponding \(c\) categories, and \(\mathbf{U}\) is their similarity classification matrix, whose cluster centers are \(\{v_{1},v_{2},\,\ldots,\,v_{c}\}\); \(\mu(x)\) is the membership degree of \(x_{i}\) in the category \(A_{k}\) (abbreviated as \(\mu_{k}\)). The objective function \(J_{b}\) can be expressed as follows:  
\[J_{b}(\mathbf{U},v)=\sum_{i=1}^{n}\sum_{k=1}^{c}(\mu_{ik})^{b}(d_{ik})^{2} \tag{1}\]  
where \(d_{ik}\) is the Euclidean distance that is used to measure distance between the \(i\)-th sample \(x_{i}\) and the center point of the \(k\)-th category. It can be calculated as follows:  
\[d_{ik}=d(x_{i}-v_{k})=\sqrt{\sum_{j=1}^{n}\left(x_{ij}-v_{ij}\right)^{2}} \tag{2}\]  
where \(m\) is the number of characteristics of the data sample; \(b\) is the weighting parameter and its range is \(1\leq b\leq\infty\). The FCM clustering algorithm is to find an optimal classification, so that the classification is able to produce the smallest function value \(J_{b}\). It is required that the sum of the values of membership degree for a sample in terms of each cluster is 1. That is to say, it can be described as  
\[\sum_{j=1}^{c}\mu_{j}(x_{i})=1,i=1,2,\cdots,n \tag{3}\]  
As stated before, \(\mu\)\(\mu\) is the membership degree of \(x_{i}\) in the category \(A_{k}\), and it can be updated as  
\[\mu_{ik}=\frac{1}{\sum_{j=1}^{c}\left(\frac{d_{ik}}{d_{jk}}\right)^{\frac{2}{b -1}}} \tag{4}\]  
Subsequently, all the cluster centers \(\{v_{i}\}\) are calculated as  
\[v_{ij}=\frac{\sum_{k=1}^{n}\left(\mu_{ik}\right)^{b}x_{ij}}{\sum_{k=1}^{n} \left(\mu_{ik}\right)^{b}} \tag{5}\]  
here, we suppose \(\,I_{k}=\{i\,|\,2\leq c<n;d_{ik}=0\}\). For all of category \(i\), \(i\in I_{k}\), \(\mu_{ik}=0\).  
The updating process mentioned above is repeated by Equations (4) and (5) until the method converges. When the algorithm converge, in theory, various cluster centers for each sample and the membership degree in terms of each category are obtained at this time, thus fuzzy clustering partition has been done by now. Although FCM has a high search speed, it is essentially a local search algorithm, and is therefore very sensitive to initial cluster centers. If the cluster centers have the initial poor choice, it will converge to a local minimum.
# An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications
## 2 Quantum Linear Systems and the Implementation of the Solver  
### Overview of the Harrow-Hassidim-Lloyd (HHL) Algorithm  
Quantum information is encoded into the state of quantum systems. Here, we assume all relevant quantum states can be represented as statevectors. An \(n_{d}\)-qubit statevector \(\ket{x}=\sum_{j=0}^{2^{d}-1}\alpha_{j}\ket{\vec{j}}\) is a normalized complex vector, i.e., \(\alpha_{j}\in\mathbb{C}\) for all \(j\) and \(\sum_{j=0}^{2^{d}-1}\ket{\alpha_{j}}^{2}=1\), while \(\vec{j}\in\{0,1\}^{n_{d}}\) is the number \(j\) as a binary string. The set \(\{\ket{\vec{j}}\}\) forms the basis set of \(\mathbb{C}^{2^{d}}\), referred to as the computational basis. Specifically, \(\ket{\vec{j}}\) is the unit vector whose \((j+1)^{\text{th}}\) entry is \(1\) and other entries are \(0\). The notation \(\bra{\vec{j}}\) is the conjugate transpose of \(\ket{\vec{j}}\).  
**Definition 1** (A quantum linear-system problem).: _A quantum linear-system problem is to solve a system of linear equations with a normalized solution vector \(\ket{x}=A^{-1}\ket{b}/\left\|A^{-1}\ket{b}\right\|_{2}\) where coefficient matrix \(A\in\mathbb{C}^{N\times N}\) is Hermitian and \(\ket{x}\) and \(\ket{b}\) are both normalized vectors._Start with a classical complex linear system \(A\vec{x}=\vec{b}\), \(A\in\mathbb{C}^{N\times N}\), where the right-hand-side (RHS) vector \(\vec{b}\) is normalized to obtain \(\left|b\right\rangle:=\vec{b}/\|\vec{b}\|_{2}\); then, a quantum linear-system problem can be formed with \(A\) and \(\left|b\right\rangle\) if \(A\) is Hermitian. Otherwise, a larger linear system can be constructed as follows [3]:  
\[\left[\begin{array}{cc}\mathbf{0}&A\\ A^{\dagger}&\mathbf{0}\end{array}\right]\left[\begin{array}{c}\vec{0}\\ \left|x\right\rangle\end{array}\right]=\left[\begin{array}{c}\left|b \right\rangle\\ \vec{0}\end{array}\right], \tag{1}\]  
where \({}^{\dagger}\) is the conjugate transpose. Therefore, we assume coefficient matrices are Hermitian in the rest of this paper. Since the data is encoded into qubits, if the dimensions of \(A\) and \(\vec{b}\) are not in the power of 2, \(A\) and \(b\) must be expanded. Suppose there exists a quantum linear system solver that obtains \(\left|x\right\rangle=A^{-1}\left|b\right\rangle/\left\|A^{-1}\left|b\right\rangle \right\|_{2}\) from the circuit; then the original solution of the system can be recovered by \(\vec{x}=\left\|b\|_{2}\right\|A^{-1}\left|b\right\rangle\|_{2}\left|x\right\rangle\). While \(\left\|b\right\|_{2}\) is known from the previous computation, the solver needs to provide the value of \(\left\|A^{-1}\left|b\right\rangle\right\|_{2}\).
###### Abstract  
The rapid development and application of AMRs is important for Industry 4.0 and smart logistics. For large-scale dynamic flat warehouses, vision-based road recognition amidst complex obstacles is paramount for improving navigation efficiency and flexibility, while avoiding frequent manual settings. However, current mainstream road recognition methods face significant challenges of unsatisfactory accuracy and efficiency, as well as the lack of a large-scale high-quality dataset. To address this, this paper introduces IndoorPathNet, a transfer-learning-based Bird's Eye View (BEV) indoor path segmentation network that furnishes directional guidance to AMRs through real-time segmented indoor pathway maps. IndoorPathNet employs a lightweight U-shaped architecture integrated with spatial self-attention mechanisms to augment the speed and accuracy of indoor pathway segmentation. Moreover, it surmounts the challenge of training posed by the scarcity of publicly available semantic datasets for warehouses through the strategic employment of transfer learning. Comparative experiments conducted between IndoorPathNet and four other lightweight models on the Urban Aerial Vehicle Image Dataset (UAVID) yielded a maximum Intersection Over Union (IOU) of 82.2%. On the Warehouse Indoor Path Dataset, the maximum IOU attained was 98.4% while achieving a processing speed of 9.81 frames per second (FPS) with a 1024 \(\times\) 1024 input on a single 3060 GPU.  
road recognition; semantic segmentation; transfer learning +
Footnote †: journal: Applied Sciences
# Research on Non-Random Vibration Analysis of Concrete Pump Truck Boom Based on Dynamic Excitation
###### Abstract  
When pouring concrete overhead, a pump truck boom's vibration has a big effect on how accurately the concrete is poured. This is especially true during fixed-point pouring, where the boom's vibration is likely to cause the pouring position to deviate, which lowers the quality of the construction. It is difficult to forecast the dynamic reaction of the pump truck boom in a construction setting because of the constantly shifting external factors (wind speed, pipeline stress during pumping, etc.), which makes it difficult to guarantee casting accuracy. This study suggests a non-random vibration analysis technique for pump truck booms based on the interval process theory in order to address this issue. A dynamic excitation analysis method based on rigid-discrete coupling is proposed, taking into account the response influence of the material characteristics in the transportation process. The pumping process of concrete materials in the conveying pipeline is simulated using discrete element simulation technology to determine the system's stress conditions under pumping conditions. The dynamic response characteristics of the pump truck boom under operating conditions are revealed by using non-random vibration analysis with the mathematical model that has been created based on the particular specifications of the pump truck boom. This study employs the Newmark-\(\beta\) technique for numerical computation to solve the dynamic equations and characterize the displacement response envelope under uncertain system parameter settings. The experimental findings demonstrate that the suggested approach may accurately capture the upper and lower bounds of the boom dynamic response, offering a trustworthy way to assess the dynamic behavior while pumping. The technique can reliably predict the dynamic displacement boundary and control the casting position deviation within a predefined range by accurately predicting the dynamic displacement range of the pump truck's boom end and efficiently constructing the displacement envelope under uncertain dynamic excitation. For numerical computation, use the Newmark-\(\beta\) algorithm. This outcome confirms the substantial enhancement of the proposed method regarding pouring precision in construction settings, offering a novel solution and technical guidance for vibration control in engineering projects.  
p +
Footnote †: journal: Applied Sciences
# Impact of Intersection Left Turn Guide Lines Configuration on Novice Drivers' Behavior
## 3 Results
### Impact Analysis Based on Trajectory Characteristics
#### 3.1.2 Medium Intersection  
Based on the number of guide lines, Scenario 2 and Scenario 3 can be denoted as 2-A, 3-A (no guide line), 2-B, 3-B (one guide line), and 2-C, 3-C (full guide lines). The trajectory characteristics of vehicles in each sub-scenario are compared, as illustrated in Figure 7, where the \(X\)-axis represents the standardized lateral distance of the vehicle during a left turn, and the \(Y\)-axis represents the standardized longitudinal distance of the vehicle during a left turn.  
In Scenario 2, as vehicles enter the intersection, the lateral fluctuation range of vehicle trajectories is relatively small. However, as they proceed towards the exit lane, the range significantly increases, becoming notably larger in Scenario 2-A compared to 2-B and 2-C, with maximum ranges of approximately 0.115, 0.081, and 0.087, respectively. Compared to Scenario 1, lateral fluctuation range is significantly reduced in Scenarios 2-B and 2-C, indicating a noticeable impact of guide line settings on novice drivers' left-turn behavior at medium intersections.  
In Scenario 3, as vehicles enter the intersection, the lateral fluctuation range significantly increases. Scenario 3-A has the largest range, followed by 3-B and the smallest in 3-C, with maximum ranges of 0.171, 0.163, and 0.124, respectively. Guide line settings have a certain impact on novice drivers' left-turn behavior, becoming more pronounced with an increase in the number of guide lines in Scenario 3. Compared to Scenario 2, the lateral fluctuation range at the exit lane in Scenario 3 is significantly larger, particularly in 3-A  
Figure 6: Trajectory fluctuations of left-turn vehicles in Scenario 1.  
Figure 7: Trajectory fluctuations of left-turn vehicles in scenarios: (**a**) Scenario 2; (**b**) Scenario 3.  
and 3-B, emphasizing the superior guiding effect of full guide lines in scenarios with dual left-turn lanes.
## 3 Results and Discussions
### Thermogravimetric Analysis Results (TGA)
The thermal degradation was established by convention to be located at the temperature at which the sample lost 5% of its mass and was noted with \"\(T_{5\%}\)\", with the obtained values being tabulated in Table 2. EDI resin has a very good thermal stability, losing 5% of its mass at temperatures above 280 \({}^{\circ}\)C. Analysing the systems with HC, it is observed that the addition of filler slightly reduced the thermal stability of the materials, with \(T_{5\%}\) being between 262-280 \({}^{\circ}\)C. In fact, in the case of the material filled with 5 wt.% HC, the thermal stability was improved, being higher by about 10 \({}^{\circ}\)C than that of the neat epoxy resin (\(T_{5\%}\) = 282 \({}^{\circ}\)C). Instead, the SB filler led to a gradual decrease in thermal stability of the composites' materials, reaching up to \(T_{5\%}\) = 192 \({}^{\circ}\)C in the case of formulation with 30 wt.% spruce bark particles, showing the contribution of the lignocellulosic component.