esa-sceva/satcom-chunk-collection huggingface dataset:
# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 1 Introduction
In the realm of convolutional neural network-based methods, significant strides have been made in various aspects of stenosis localization and detection. For instance, a fundamental CNN architecture, with slight modifications within its layers, was employed for the detection of stenotic lesions in angiographic images in 2018 [11]. Experimental outcomes revealed that networks trained on artificial data and subsequently fine-tuned with clinical datasets achieved a remarkable accuracy of approximately 90%, surpassing the performance of traditional feedforward neural networks. Transfer learning has also been explored, where pretrained CNNs are fine-tuned using artificial data. It was observed that a pretrained CNN, with only the top three blocks serving as feature extractors, exhibited promising performance in stenosis detection [12]. Within the domain of blood vessel segmentation, researchers have contributed a spectrum of model- and tracking-based approaches in recent years. These include techniques like multiscale Gabor filtering [13], Hessian filtering with flux flow measurement [14], and active contour-based segmentation methods [15]. Furthermore, deep learning-based approaches, such as the utilization of long short-term memory recurrent neural networks [16], have yielded notable performance improvements, contributing significantly to the advancement of this field. These multifaceted methodologies collectively constitute a rich tapestry of techniques aimed at enhancing our understanding and capabilities in the realm of medical image analysis, particularly in the context of coronary artery stenosis detection and blood vessel segmentation. In this study, we propose a preprocessing method for coronary artery image enhancement that combines Hessian matrix multiscale filtering and image feature fusion. In this method, we used the image fusion method to retain as many features as possible in the image based on image enhancement. These features can help strengthen the algorithm's ability to learn and detect small or distal vessels. HFV filtering was used to enhance the blood vessels in angiographic images, and an image fusion step was performed on the enhanced images to accentuate the blood vessels. Then, we employed various deep learning networks for model training and compared their performances. In comparisons, we not only evaluated the performance of different deep learning networks under identical conditions but also compared the performance of images with and without preprocessing across these networks. The experimental results showed that the proposed method can obtain satisfactory object detection results.
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
## 3 Load-Sharing Based Voltage Redistribution Mechanism for Modules in Each Level
### Exponential Load-Sharing Rule  
Similar to the linear load-sharing rule, in the exponential rule, the load on the failure module is distributed to the remaining normal modules according to the distance between them. However, the voltage variation is subject to an exponential relationship. In this condition, we have:  
\[\Delta V_{p}(x)=\begin{cases}0&p\in S_{1}(x)\\ c\times d^{D_{pq}}&p\in S_{0}(x)\\ \end{cases} \tag{17}\] \[s.t.\;\sum\Delta V_{p}(x)=\delta\cdot V_{q}(x-1)\] \[0<d<1,\;c>0,\;x\geq 1\]According to Equation (17), we can obtain:  
\[c=\frac{\delta\cdot V_{q}(x-1)\cdot d^{D_{pq}}}{\sum_{p\in S_{0}(x)}d^{D_{pq}}} \tag{18}\]  
Thus, the voltage variation \(\Delta V_{p}(x)\) can be given by:  
\[\Delta V_{p}(x)=\begin{cases}0&p\in S_{1}(x)\\ \frac{\delta\cdot V_{q}(x-1)\cdot d^{D_{pq}}}{\sum_{p\in S_{0}(x)}d^{D_{pq}}}&p \in S_{0}(x)\end{cases} \tag{19}\]  
where \(0<d<1\) represents the adjustment factor.
# Effect of Rotating Cylinder on Mixing Performance in a Cylindrical Double-Ribbon Mixer
## 4 Results and Discussions  
### Effect of Ribbon Rotational (RR) Speed  
In industrial applications, VCR mixers operate with various ribbon rotational speeds. It is crucial to optimize the rotation speed (rpm, rotation per minute) of the ribbon in order to achieve effective mixing in a specific duration. In order to optimize the rotation speed and determine the appropriate time interval to ensure uniform mixing, DEM simulations were performed with ribbon rotational speeds of 100, 200, and 300 rpm for 60 s. The simulation results are shown in Figure 4. By general observation of the particle mixing simulation result, such as those at 10 s, we show that the mixing at 200 rpm is more uniform than that at 100 rpm.  
Figure 5a shows the variations of the mixing index over time. An SMI of 0.58 was achieved at a ribbon rotation speed of 100 rpm, and when the rotation speed was increased to 200 and 300 rpm, SMIs of 0.92 were achieved. Figure 5b marks the approach of a stable SMI over time. A stable SMI of 0.58 was attained at 3 s at a rotation speed of 100 rpm, and a stable SMI of 0.92 was attained at 59 and 56 s at rotation speeds of 200 and 300 rpm, respectively. Though a stabilized SMI was attained faster at 100 rpm than at the higher rotation speeds, the SMI value was very small and the mixing was non-uniform. Figure 6 shows a single particle path for various ribbon rotation speeds for 60 s. The particle motion does not indicate effective mixing performance when the ribbon is rotating at 100 rpm, as shown in Figure 6a, but as the rotation speed increases, the enhanced mixing performance is evident by the particle movement, as shown in Figure 6b,c.  
Figure 3: Interaction between DEM–DEM particles.  
Figure 4: Simulation results for particle mixing at various ribbon rotation rates at different times.  
Figure 5: (**a**) SMI plot and (**b**) stable SMI vs. time for different ribbon rotation speeds.
# On the Role of Matrix-Weights Elements in Consensus Algorithms for Multi-Agent Systems
## 2 Problem Formulation
### Consensus Control Design  
The consensus control protocol for the \(n\) agents is given by  
\[u_{i}(t)=\sum_{v_{j}\in\mathcal{N}_{i}}W_{ij}(x_{j}(t)-x_{i}(t)). \tag{10}\]  
where \(W_{ij}\in\mathbb{R}^{m\times m}\) is the weight matrix and \([W_{ij}]_{kl}=w_{kl}^{ij}\) for \(k=1,\ldots,m\), and \(l=1,\ldots,m\). This control law can be written in a compact matrix form as  
\[u(t)=-\mathcal{L}_{\mathcal{W}}(\mathcal{G})x(t), \tag{11}\]  
where  
\[u(t) =\begin{bmatrix}u_{1}&\cdots&u_{i}&\cdots&u_{n}\end{bmatrix}^{T}, \quad u_{i}=\begin{bmatrix}u_{i}^{[1]}&\cdots&u_{i}^{[m]}\end{bmatrix}^{T}\] \[x(t) =\begin{bmatrix}x_{1}&\cdots&x_{j}&\cdots&x_{n}\end{bmatrix}^{T}, \quad x_{j}=\begin{bmatrix}x_{j}^{[1]}&\cdots&x_{j}^{[m]}\end{bmatrix}^{T}\]  
**Remark 4**.: _For the case when \(m=1\), the consensus control law becomes:_  
\[u_{i}(t)=\sum_{v_{j}\in\mathcal{N}_{i}}w_{ij}(x_{j}(t)-x_{i}(t)),\]  
_and it can be written in a compact matrix form as:_  
\[u(t)=-\mathcal{L}_{w}(\mathcal{G})x(t).\]  
**Remark 5**.: _For the case when \(m>1\) and_  
\[W_{ij}=w_{ij}l_{m},\]  
_the consensus control law becomes:_  
\[u_{i}(t)=\sum_{v_{j}\in\mathcal{N}_{i}}w_{ij}l_{m}(x_{j}(t)-x_{i}(t)),\]  
_and it can be written in a compact matrix form as:_  
\[u(t)=-\mathcal{L}_{W}(\mathcal{G})x(t)=-(\mathcal{L}_{w}(\mathcal{G})\otimes l _{m})x(t).\]  
In order to observe the evolution of \(x_{i}^{[k]}\), we need to study the dynamics of \(x_{i}^{[k]}\), which obeys  
\[x_{i}^{[k]}=u_{i}^{[k]}(t).\]Here  
\[u_{i}^{[k]}(t) =\sum_{v_{j}\in\mathcal{N}_{i}}\sum_{l=1}^{m}w_{kl}^{ij}(x_{j}^{[l]}( t)-x_{i}^{[l]}(t))\] \[=\sum_{v_{j}\in\mathcal{N}_{i}}w_{kk}^{ij}(x_{j}^{[k]}(t)-x_{i}^{[k ]}(t))+\sum_{v_{j}\in\mathcal{N}_{i}}\sum_{l=1,l\
eq k}^{m}w_{kl}^{ij}(x_{j}^{[ l]}(t)-x_{i}^{[l]}(t)), \tag{12}\]  
is the \(k\)th entry of the control input of agent \(v_{i}\) defined in Equation (10). The coefficient \(w_{kl}^{ij}\) is the element on the \(k\)th row and the \(l\)th column of the matrix weight \(W_{ij}\). When \(k\
eq l\), \(w_{kl}^{ij}\) is an off-diagonal element on the \(k\)th row of the matrix weight \(W_{ij}\), and it qualifies the effect of the \(l\)th states of the neighbors of agent \(v_{i}\) on its \(k\)th state. When \(k=l\), \(w_{kk}^{ij}\) is a diagonal element of the matrix weight \(W_{ij}\), and it qualifies the effect of the \(k\)th states of the neighbors of agent \(v_{i}\) on its \(k\)th state.
## 4 Structural Analysis of the APTS' L-Shaped Bracket Structure
### Loading and Boundary Conditions of the APTS's Bracket Structure  
This study considers the static loads and boundary conditions of the bracket structure for the acquisition, pointing, and tracking system (APTS) to conduct innovative topology optimization under three models. The boundary conditions include fixed constraints on the supports, preventing displacement and rotation to ensure stability and precision. The three optimization models focus on minimizing structural compliance, accounting for displacement constraints, and addressing both displacement and stress constraints. These designs enhance the load-bearing capacity and vibration resistance of the bracket while reducing material usage, achieving a lightweight design that meets the requirements of high-precision tracking systems.  
Figure 3 presents an oblique view of the initial reinforced bracket design for the APTS. The outer side plate of the support structure incorporates straight reinforcements, while the inner side is a flat plate. The loads and boundary conditions: the bottom surface of the lower bearing circular ring of the APTS support structure is fixed with triangle symbols, while a concentrated load of 100 N is applied perpendicular to the surface of the upper bearing circular ring.
# Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
## 4 Improved Ant Colony Algorithm
### Steps of the IACA  
The specific steps of the IACA are shown in Algorithm 3.  
\(path_{bb}\) denotes the best path length of the current iteration, and \(path_{gb}\) denotes the historical global best path length. Lines 12 to 17 denote that customers are selected based on different policies. Lines 18 to 22 denote updates to vehicle capacity and customer demands. Lines 30 to 32 denote that if the conditions are met, the 2-opt strategy will be used to optimize the solution. Lines 33 to 36 denote that if the conditions are met, the pheromone backtracking mechanism will be used and the pheromone volatilization factor will be updated.  
```
1:Initialize parameters \(\alpha\), \(\beta\), \(\rho\), \(\rho_{max}\), \(Z\), \(\lambda\), \(\omega\), \(A\), ant_num, max_iter;
2:Initialize \(\tau_{ij}(0)\) according to Equation (14) for all node pairs;
3:Set iteration counter \(iter=0\), Set \(N=\) number of customers;
4:for\(iter=1\) to max_iterdo
5:for each ant \(k=1\) to ant_numdo
6: Set demand = demand of nodes;
7: Set capacity \(v=Q\);
8:while\(\sum_{j=1}^{N}demand_{j}\
eq 0\)do
9:if\(v>0\)then
10: Put nodes with demand\({}_{j}>0\) into allow;
11:endif
12:if allow is not empty then
13:if the node to be selected is the first node then
14: Select the next node \(j\) according to Equation (12), then add \(j\) into the path;
15:else
16: Select the next node \(j\) according to Equation (13), then add \(j\) into the path;
17:endif
18:if\(v-demand_{j}\geq 0\)then
19:\(demand_{j}=0\); \(v=v-demand_{j}\);
20:else
21:\(demand_{j}=demand_{j}-v\); \(v=0\);
22:endif
23:else
24: Return to depot 0; Reload set \(v=Q\);
25:endif
26:endif
27:endif
28: Save the shortest paths of all ants in this iteration;
29:Update the pheromone on the path according to Equations (15), (16) and (18);
30:if\(iter\) MOD 10 = 0 or \(path_{bb}<path_{bb}\)then
31: Execute Algorithm 1; Execute Algorithm 2;
32:endif
33:if\(path_{gb}\) has not changed for \(A\) consecutive iterations then
34: Set the pheromone on the path to the value before \(A\) iterations;
35: Update \(\rho\) according to Equation (17);
36:endif
37:\(iter=iter+1\);
38:endfor
39:Output the shortest paths;
```  
**Algorithm 3**IACA
# Experimental Study on Dynamics of Wooden House Wall Panels with Different Thermal Isolation
###### Abstract  
Wood frame buildings are very popular in regions that are exposed to different dynamic excitations including earthquakes. Therefore, their seismic resistance is really important in order to prevent structural damages and human losses. The aim of the present paper is to show the results of experimental tests focused on the dynamic response of wall panels of a wooden frame building with thermal isolation made of mineral wool and polyurethane foam. Firstly, the static and the dynamic mechanical analysis (DMA) tests were conducted so as to determine the basic thermomechanical properties of the analyzed isolation materials. Then, the elements of the exterior walls with two types of thermal insulation were tested under harmonic excitation for different amplitudes of displacement. The results of the static material tests indicate that the polyurethane foam behaves in a highly nonlinear way both during compression and tension. Moreover, the results of the DMA tests show that the storage and loss modulus of the polyurethane foam are significantly larger in relation to the values obtained for the mineral wool. The results of the dynamic tests on wall panels show that the use of polyurethane foam as thermal isolation leads to a substantial increase in stiffness and damping properties, as compared to the case when the mineral wood is used.  
wood-frame buildings; seismic resistance; thermal insulation; polyurethane foam; mineral wool +
Footnote †: journal: Applied Physics Letters
## 5 Experimental Results and Analysis  
In this section, experiments of the RH scheduling strategy using the DPGA (Dwyer Instruments, Inc., Michigan, IN, USA) with a local search strategy for the aircraft carrier operations are described in detail. The experiments were performed using a PC with two Intel(R) Core(TM) i5-4258U CPU @ 2.4 GHz processors and 4 GB RAM (Intel cooperation, Santa Clara, CA, USA). The algorithm has been coded in MATLAB 2017(a) software (The MathWorks, Inc., Natick, MA, USA). All of the instances were solved by the general genetic algorithm (GA), improved genetic algorithm with local search named Memetic algorithm [22], different evaluation algorithm (DE) [23], and DPGA. The computational results were compared and analyzed to test the algorithms' performance. Finally, a comparison was conducted between the proposed approach RH scheduling strategy in this paper and the static reactive rescheduling strategy after distributions occurred in the support operations.
## 2 Materials and Methods
### Histological Analysis  
For histological analysis, young and aging WT (\(n=4\) and \(n=4\), respectively) and PACAP HZ (\(n=3\), \(n=7\), respectively); PAC1-R WT (\(n=9\)); and PAC1-R KO (\(n=3\)) mice were used. Mice were sacrificed under isoflurane anesthesia (AErrane, Baxter, Budapest, Hungary). Removed organs and tissues were fixed in 4% buffered paraformaldehyde and embedded in paraffin. Three-um-thick sections were made and stained with routine histological staining (hematoxylin and eosin, H&E) as well as with Congo red, a pathological marker for amyloid. Amyloid deposits appear as an eosinophilic homogenous mass with H&E staining. The appearance of amyloid was confirmed with Congo staining, which shows deposits as a red-orange mass under normal light, and apple-green birefringence under polarized light. An amyloid index described in our previous study was used in the present investigation to provide a semi-quantitative scoring of Congo red-positive amyloid deposits. The amyloid scoring was the following [28]: 0: no amyloid; 1: slight focal deposits; 2: presence of moderate/severe focal or slight diffuse amyloid; and 3: with massive diffuse amyloid deposit. Previously, we presented that male and female scores combined as no gender differences had been observed [28]. A statistical comparison between groups was made with a non-parametric Mann-Whitney test.
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Implementation of Image-Based Devices and Systems
#### 4.2.1 2D Vision System for DIC  
A series of digital photographs taken of the surface of a structural component at various loading stages are used in the full-field, non-contact structural sensing technique known as DIC, to reveal deformation patterns between the photos [47]. This setup is a non-destructive method for SHM that utilizes image and video capturing devices.  
According to the study of [58], the 2D system, shown in Figure 3a, is placed near a ground floor window of a building adjacent to the region of interest and capturing it from the side of the pedestrian bridge. The camera for the 2D vision system was located 13 m away from the monitored area to capture 140 cm of the bridge's length. Note that the location of the camera for such a 2D vision system must be established properly, so the line of sight is not obstructed by any means, before proceeding to image acquisition. Additionally, the camera can also be adjusted with different angles, distances, and frame-erates, as conducted in the study of [10]. A Canon VIXIA HF R42 camcorder can be used to begin measuring the deformations of an object that was perfectly still for a series of predetermined distances. Using a 2D vision system, the result is nearly indistinguishable from using vibration devices to track the displacement of a particular structure.
# Snap-Through Buckling Mechanism for Frequency-up Conversion in Piezoelectric Energy Harvesting
###### Abstract  
This paper describes a piezoelectric energy harvester employing a snap-through buckling (STB) mechanism for frequency-up conversion (FuC). The harvester consists of two main components: a bistable mechanical structure and one piezoelectric cantilever beam. The device is designed by means of analytical methods and numerical simulations. A proof-of-concept prototype is manufactured and tested under low frequency mechanical excitation. Experimental results show that, if the STB is induced, from the second stable configuration back to the undeformed one, the FuC is obtained and the response of the beam presents frequency components in a wide range, even though the resonant frequency of the cantilever beam is not excited. The results are hence in agreement with the expected behavior: if the device, forced in the second stable configuration, is subject to a low-frequency excitation whose amplitude exceeds a threshold, STB is triggered and the ensuing FuC provokes a widening of the beam vibrations frequency range and consequently a significant effectiveness in terms of power output. A maximum power of 4 mW is obtained by using an optimal resistive load as STB from a stable configuration of the bistable mechanism to the other one is triggered; a maximum energy of 4.5 \(\upmu\)J is obtained in case of a rectifier circuit with storage capacitor.  
p ezoelectric converters; energy harvesting; snap-through buckling; frequency-up conversion Article
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
## 2 Related Work
### Canonical Ordering  
The idea of applying Canonical Ordering as a speedup technique for real-time pathfinding systems that operate on regular grids was proposed by [PERSON] [24] and [PERSON] [25]. As mentioned in the literature, searching in grids often becomes overwhelmed by a high degree of path symmetry, which accounts for a major part of the computational costs. Two paths are viewed as symmetric if (1) they have the same start and goal cells; (2) they are of the same length; and (3) their respective sequences of moves (i.e., cardinal or diagonal moves) can be reordered into the other. With symmetries in the grids, a search task will explore multiple cells for multiple times from those symmetric paths and this severely undermines the efficiency.  
To break such symmetries, an online algorithm called Jump Point Search (JPS) [26] was presented by [PERSON] et al. to apply Canonical Ordering to recursively prune redundant successors and selectively expand only certain cells, called jump points. Canonical Ordering is essentially a special case of partial orderings among all the symmetric paths and prefers the diagonal-first ones to other alternatives. We say that a path has the diagonal-first property if there is no straight-diagonal turning point can be mutated into a diagonal-straight one of the same length constrained by the obstacles. By its virtue, JPS visits much fewer cells than traditional searching strategies; therefore, it answers a path query averagely faster than A* by an order of magnitude. After that, this algorithm's performance was further improved by a preprocessing based strategy and addition of Bounding Boxes, resulting in the algorithms JPS+ [26] and JPS+BB [27]. As an automatic move pruning technique for single-agent search [28], Canonical Ordering can not only be used in grids, but can also be built on general graphs and considerably reduce the number of cells generated by an A* search [29].  
The outstanding performance of the Canonical Ordering strategy in compressing search space for real-time pathfinding algorithms provides us with a novel method to guide the direction of the wavefronts which propagate the distance changes, making it possible to speed up the construction of grid-based DMs.