esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Earthquake-Induced Pipe Damage in Liquefiable Soils
## 4 Evaluation of Pipeline Damage Caused by Earthquake in the Iskenderun Region
### Discussion of Results with Numerical Analysis with 3115 Station Data for Study Area 2
All the drainage lines, especially the stormwater and sewer lines located on the coast, as shown in the plan obtained from HATSU (Hatay Water Authority) [57] (Figure 7), were damaged due to settlements, and repairs have not been completed yet. The entire sewer and stormwater infrastructure serving the city center collapsed (Figure 7). Fractures occurred in the manhole, as shown in Figure 8a, similar to the expected outcome of the settlement behavior suggested by the analysis results. The effect of the thickness of the liquefiable layers on the pipe uplift behavior was clearly demonstrated. After the earthquake, displacement along the coastline resulted in flooding in the streets and avenues. The analysis results confirm occurrence of significant displacements (Figure 24). The ground displacements and the collapse of stormwater and sewage pipelines led to groundwater contamination in the damaged areas, resulting in environmental pollution and flooding. Drinking water pipelines also suffered similar settlement problems, as suggested by the analyses. Figure 11 shows plan views of three localities where drinking water pipelines were damaged due to liquefaction-induced settlements. In the field, fractures have occurred, particularly at the connections of pipelines to buildings, due to different structural behaviors and boundary conditions. Figure 23: (a) Ru value of DN \(\mathcal{O}\) 1500 mm steel pipe for (3.2 m depth) far field (H = 1.2D); (b) vertical displacement for DN \(\mathcal{O}\) 1500 mm steel pipe H = 1.2D.
# Dynamic Modeling of McKibben Muscle Using Empirical Model and Particle Swarm Optimization Method
## 1 Introduction
In addition, [PERSON] in 2012 improved the hysteresis modeling using spring, viscous damper, and dynamic coulomb friction model as proposed by [PERSON] [34]. Meanwhile, [PERSON], in 2014, investigated the effect of strand on strand and bladder on bladder friction models for the dynamic modeling [35]. The above mentioned approaches without a doubt were the pillar in hysteresis modeling of McKibben muscle on both static and dynamic modeling. Through some review analysis and simulation of the existing empirical models, significant research gap has been found to improve dynamic modeling of McKibben muscle. As suggested by [PERSON] and [PERSON] on dynamic modeling of hysteresis, the hysteresis behavior is characterized using both velocity's dependence coulomb and viscous friction models. However, in vertical load system it is found that friction on the McKibben muscle depends on both input excitation frequency and vertical load. Thus, additional correction functions \(\gamma(F)\) and \(\gamma(f)\) for compensating hysteresis behavior were introduced to improve adaptability of the proposed empirical model while considering quasi-static, quasi-rate, and historic dependencies. The critical parameters of the proposed empirical model were optimized using particle swarm optimization (PSO) method.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.4. Dense VNet  
In 2018, [PERSON] et al. developed a dense VNet deep-learning-based registration-free multi-organ segmentation. The targeted organs include the pancreas, parts of the gastrointestinal tract (esophagus, stomach, and duodenum), as well as adjacent organssuch as the liver, spleen, left kidney, and gallbladder. This study thoroughly assessed the segmentation accuracy of the proposed approach by comparing it to existing methods, including traditional deep learning and multi-atlas label fusion (MALF) techniques, through cross-validation on a dataset from multiple centers involving 90 subjects. This method demonstrated clear improvements, achieving higher Dice similarity scores and reduced mean absolute distances across most organs. For example, Dice scores reached 0.78 for the pancreas, 0.90 for the stomach, and 0.76 for the esophagus [15].
## 3 Results and Discussion
### Comparison with Experiment
We believe that general conclusions derived in this paper (in particular, in Section 3.2) are generally valid. However, there may be refinements necessary as far as alternative realizations of PECs are concerned. In particular, it would be interesting to investigate how the damping factor and the effective constants of different PEC realizations may change as the magnitude of the tip mass increases and what the implications are on the achieved frequency bandwidth.
## 3 Resource Allocation Based on a Multi-Agent Reinforcement Learning Algorithm
### Multi-Agent Environment Modeling
#### 3.1.1 State Space  
At a specific time slot t, the CV acting as an agent observes the current state \(\mathcal{S}\) and the observation comprises multiple factors as follows:  
1. The present channel gain knowledge between the CV and RSU indicated as \(h_{i,\mathcal{L}}^{t}[b]\);
2. The channel gain knowledge between the CV and its FV indicated as \(h_{i,j}^{t}[b]\);
3. The prior knowledge of interference to the CV from another platoon member indicated as \(I_{i}^{t-1}[b]\);
4. The AoI of \(\
abla\)VI indicated as \(AoI_{i}^{t}\);
5. The dissemination of residual CAM messages in the specified time limit \(T\leqslant 100\) ms indicated as \(\aleph_{i}^{r}\);
6. The residual estimated time indicated as \(T_{i}^{r}\).  
Consequently, the CV \(i\)'s agent state space, where \(\mathsf{s}_{i}^{t}\in\mathcal{S}\), is given by  
\[\mathsf{s}_{i}^{t}=\{h_{i,\mathcal{L}}^{t}[b],h_{i,j}^{t}[b],I_{i}^{t-1}[b], AoI_{i}^{t},\aleph_{i}^{r},T_{i}^{r}\}\qquad i\in\mathcal{P}\]
# MIMO Signal Detection Based on IM-LSTMNet Model
## 2 Methods
### MIMO-OFDM Signal Detection Model Based on FrFT
#### 2.2.2 Squeeze-and-Excitation Network  
The attention mechanism can filter out irrelevant information and highlight key information to improve neural network performance. In this study, an attention mechanism module is designed based on the Squeeze-and-Excitation Network proposed by [PERSON] et al. to clearly model the interdependencies between channels and adaptively recalibrate the channel intelligent feature response [19].  
The extrusion excitation block is a unit of calculation that can construct any given transformation \(Ftr:X\to U\). To enhance the network's sensitivity for the information features, the filter response is recalibrated in two steps: squeeze and excitation. Squeeze is the process of global information embedding. The purpose of the excitation is adaptive recalibration. To take advantage of the information aggregated during the squeeze operation, a second operation follows it with the goal of fully capturing the channel-related dependencies. In order to achieve this, two requirements must be meet in the function. Firstly, it must be flexible. In particular, it must be able to learn nonlinear interactions between channels. Secondly, it has to learn the non-exclusive relationship because it wants to ensure that multiple channels are able to be emphasized rather than one-hot activation.  
The Squeeze-and-Excitation Network in our proposed IM-LSTMNet model is shown in Figure 6. The global spatial information is compressed into the channel descriptor, which is achieved by generating channel statistics through the global average pool. The extrusion process uses a linear rectification function (ReLU function); in order to limit the model complexity, the gating mechanism is parameterized by forming a bottleneck of two fully connected layers around the nonlinearity, and the final output is transformed by re-scaling the output using the sigmoid function.  
Figure 5: Convolutional neural network module diagram.
# Robust Full-Screen Wavelength Calibration Algorithm
## 3 The Proposed Calibration Method
### Spectral Wavelength Calibration Algorithm at Central Pixels  
We used the wavelengths of standard light sources (i.e., mercury lamps and lasers) to calibrate the spectrometer. The linear model obtained in Equation (4) was used for the rough calibration of wavelength (please refer to Appendix A.1 for a detailed introduction). We can obtain data pairs for the central pixels that we need using rough calibration. Through experiments, we designed a sinusoidal model, using the Levenberg-Marquardt algorithm (LMA) to correct the wavelength calibration. The new model is better than the model constructed in Equation (4). Through this model, a sinusoidal curve is obtained to predict the relationship between wavelengths other than standard wavelengths, and the corresponding feedback values.  
The Levenberg-Marquardt Algorithm to Fit the Sinusoidal Function  
To implement accurate calibration, we require more feedback value and wavelength data pairs. With the method described in Appendix A.1, we can obtain data pairs \((x_{c}^{1},\lambda_{c}^{1})\), \(\cdots,(x_{c}^{n},\lambda_{c}^{n})\) at central pixels.  
According to the results of the experiment, the sinusoidal model is more realistic than the linear model. Assume that the relationship can be represented as  
\[\lambda_{c}=a\text{sin}(bx_{c}+c)+d, \tag{5}\]  
where \(x_{c}([x_{c}^{1},x_{c}^{2},\cdots,x_{c}^{n}]^{\top})\) and \(\lambda_{c}([\lambda_{c}^{1},\lambda_{c}^{2},\cdots,\lambda_{c}^{n}]^{\top})\) are the feedback value and wavelength at the central pixel, respectively. \(a,b,c,d\) are unknown parameters. To simplify discussions, we denote \(\theta=[a,b,c,d]\).  
Suppose the fitting non-linear function to be \(f(X\mid\theta)\), and the initial value of \(\theta\) is \(\theta_{0}=[a_{0},b_{0},c_{0},d_{0}]\). Therefore, we can find the optimal values for \(\theta\) by minimizing  
\[F(\theta)=\sum_{i=1}^{n}(f(x_{c}^{i}\mid\theta)-\lambda_{c}^{i})^{2}=f(\theta) ^{\top}f(\theta),\;f(x_{c}^{i}\mid\theta)=a\sin(bx_{c}^{i}+c)+d, \tag{6}\]  
where \(f(\theta)=f(x_{c}\mid\theta)-\lambda_{c}\). As this is a non-linear, least-square problem, the LMA is used to provide a solution.  
The LMA is based on the trust region theory because the Taylor expansion in the Gauss-Newton method can only have a good effect near the expansion points. Therefore, to ensure approximation accuracy, we need to set a region with a certain radius as the trust region. In LMA, the size of the trust region is determined by the gain ratio, which is defined as  
\[\rho=\frac{f(\theta+\Delta\theta)-f(\theta)}{J(\theta)^{\top}\Delta\theta}, \tag{7}\]  
where \(J(\theta)\) is \(f(\theta)^{\prime}\).
## 4 Experiments
### Technology Comparison
The performance of the AYOLO algorithm was evaluated based on several key metrics, including mean Average Precision (mAP), box loss, class loss, and object loss. The results of the training process are depicted in the figure attached. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Normalization** & **Accuracy (\%)** & **mAP (\%)** & **Inference Time (ms)** & **Parameters (Million)** & **FLOPs (Billion)** \\ \hline BatchNorm & 92.3 & 94.1 & 25 & 8.5 & 45 \\ LayerNorm & 91.8 & 93.7 & 28 & 8.5 & 45 \\ RMSNorm & 92.0 & 93.9 & 27 & 8.5 & 45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Normalization technique comparison. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Input Size** & **FPS** & **Latency** \\ \hline YOLOv5-M & 640 & 235 & 4.9 ms \\ YOLOX-M & 640 & 204 & 5.3 ms \\ PPYOLOE-M & 640 & 210 & 6.1 ms \\ YOLOv7 & 640 & 135 & 7.6 ms \\ YOLOv6-3.0-M & 640 & 238 & 5.6 ms \\ YOLOv8 & 640 & 236 & 7 ms \\ AYOLO-M & 640 & 239 & 6.1 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of latency and throughput in YOLO series models. In this study, AYOLO demonstrated effective performance in detecting poppy plants within a small, custom dataset of 80 images. The model achieved a competitive mAP@50:95 score, with steady decreases in box, class, and object loss over the training period. When compared to other popular object detection algorithms, AYOLO performed favorably, particularly in balancing accuracy and computational efficiency. YOLOv5 and YOLOv8 provided strong benchmarks in terms of precision, while YOLOX excelled in speed. However, AYOLO's performance was well-suited for small-scale datasets, outperforming more complex algorithms like DETR in this context.
# Numerical Study on the Evolution Mechanism of the Crater under a Millisecond Laser
## 3 Results and Discussion
### Effect of Peak Power Density on Morphology  
The microstructure morphology and melted zone under the peak power density of \(3\times 10^{9}-5.5\times 10^{9}\) W/m\({}^{2}\) were compared, and the melted zone area was calculated by integrating the closed area composed of 1700 K isotherm and the surface curve (as shown in Figure 14) at the end of laser heating. It can be seen from Figure 15 that the experimental and simulation results show the same variation trend, and the difference in the morphology may be attributed to the differences between the thermophysical parameters of the material and laser parameters in the numerical simulation and the experiment. The simulation results also show that with an increase in the laser peak power density, the height of the edge bulge increases from 1.5 \(\upmu\)m to 3.4 \(\upmu\)m, the center depth increases from 1.78 \(\upmu\)m to 9.95 \(\upmu\)m, the diameter increases from 110 \(\upmu\)m to 170 \(\upmu\)m, and the melted zone increases from 0.00198 mm\({}^{2}\) to 0.00648 mm\({}^{2}\). On the other hand, the growth rate of central depth is more rapid than that of the edge bulge height.  
Figure 14: Integral diagram of the melted zone area.  
Figure 15: Variation in the molten pool under different peak power densities for \(\tau=1\) ms, \(t=10\) ms. (**a**) Radius of the crater, (**b**) Height of the crater, (**c**) Depth of the crater, (**d**) Melted zone area of the crater.  
At the end of the heating process, the surface morphology of the molten pool was compared, as shown in Figure 16. It can be seen that the growth rate of the molten pool depth was also more rapid than that of the edge bulge height. This can be attributed to the fact that the bulge at the edge was formed by the outward flow and accumulation of molten metal in the molten pool, under the influence of recoil pressure and the thermo-capillary stress, and the amount of molten material accumulated at the edge was reduced due to the existence of ablation rate. The increase of center depth was finally more evident than edge bulge height after cooling.
# Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
## 3 Verification and Analysis Based on Numerical Simulation  
Set the computational domain of a 3D temperature field to 4 m \(\times\) 4 m \(\times\) 4 m. Set the X-axis and Y-axis as 0.1 m as the step length; select four different heights, respectively, 0.5 m, 1.5 m, 2.5 m, and 3.5 m, for the two-dimensional temperature distribution section to characterize the three-dimensional temperature field distribution data. Calculate 4 \(\times\) 40 \(\times\) 40 temperature values to establish the three-dimensional temperature distribution data. The Tucker decomposition reconstruction optimization algorithm proposed in this paper is used to reconstruct and calculate the temperature profiles under different experimental conditions and different flame offset situations with data loss and noisy signals. The reconstruction result is analyzed to demonstrate the effectiveness of the Tucker decomposition optimization algorithm and the feasibility of applying the Tucker decomposition algorithm to the reconstruction of three-dimensional temperature distribution with different conditions and different flame deflection.
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
###### Abstract  
Developing spherical sector harmonics (SSHs) benefits sound field decomposition and analysis over spherical sector regions. Although SSHs demonstrate potential in the field of spatial audio, a comprehensive investigation into their properties and performance is absent. This paper seeks to close this gap by revealing three key limitations of SSHs and exploring their performance in two aspects: sector sound field radial extrapolation and sector sound field decomposition and reconstruction. First, SSHs are not solutions to the Helmholtz equation, which is their main limitation. Then, due to the violation of the Helmholtz equation, SSHs lack the ability to conduct sound field radial extrapolation, especially for interior cases. Third, when using SSHs to decompose and reconstruct a sound field, the shifted associated Legendre polynomials and scaled exponential function in SSHs result in severe distortion around the edge of the sector region. In light of these three limitations, the future implementation of SSHs should focus on processing and analyzing the measurement sector region without any extrapolation process, and the measurement region should be larger than the target sector region.  
spherical sector harmonics; spherical harmonics; sound field reconstruction; sound field synthesis +
Footnote †: journal: Applied sciences  
0
Footnote 0: Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).**
## 5 Discussion  
### Ablation Study  
#### 5.1.1 Effects of Filtering Strategy  
In order to verify the effectiveness of the filtering strategy, we evaluated the performance of the filtering strategy on \(\mathrm{B}\rightarrow\mathrm{C}\), \(\mathrm{C}\rightarrow\mathrm{B}\), \(\mathrm{A}\rightarrow\mathrm{C}\), \(\mathrm{C}\rightarrow\mathrm{A}\), \(\mathrm{A}\rightarrow\mathrm{B}\), and \(\mathrm{B}\rightarrow\mathrm{A}\). The experimental results in Tables 11-16 prove that adding filtering strategy helped filter out wrongly detected trees effectively as it improved the F1-score by 0.85%, 0.25%, 0.07%, 0.58%, 0.57%, and 0.18% on six transfer experiments, respectively.  
Moreover, there was a slightly decrease in TP and precision after filtering since a few individual trees were correctly detected but filtered by mistake. Despite all of this, Tables 11-16 reveal that 972, 814, 377, 504, 569, and 269 detected trees were dropped through the filtering process, of which 588, 636, 288, 459,
## 2 Related Works
### Deep Learning Approaches for HAR  
Advanced deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have demonstrated remarkable effectiveness in various recognition tasks, including HAR. These models leverage hierarchical representations from raw sensor data, enabling them to extract relevant features autonomously and understand intricate temporal dependencies within sequences of activities. Researchers have developed deep learning frameworks for HAR [28; 29; 30; 31; 32; 33], achieving accuracy and robustness across diverse user groups and environments.  
[PERSON] et al. [28] constructed a dataset from body movement sensors using the Boulic kinematic model. They proposed a deep convolutional generative adversarial network (DCGAN) and a pre-trained deep CNN architecture, VGG-16, for identifying three distinct categories of walking actions based on velocity. Although this approach effectively broadens the training set to prevent overfitting, it is limited to three categories of walking activities (fast walking and extremely fast walking). [PERSON] et al. [29] introduced CNN models with three distinct regularizations, achieving high accuracy in classifying four datasets--ActivMiles, WISDM v1.1, Daphnet FoG, and Skoda. Their focus on sensor orientation invariant and discriminative features maintained consistent accuracy for real-time classification with low power consumption. However, accuracy and computational durations were comparable to previous state-of-the-art approaches. [PERSON] et al. [30] examinedthree datasets--Opportunity, PAMAP2, and Dolphin Gait--developing five deep models for each. They achieved high accuracy but failed to demonstrate consistent success across all benchmark HAR datasets, and their recommendations were limited to a few deep models. [PERSON] et al. [31] utilized Inception, GoggLeNet, and GRU on three datasets, achieving enhanced generalizability. However, they did not address the issue of class imbalance in data relevant to practical HAR applications. [PERSON] et al. [32] introduced Ensem-HAR, a stacking meta-model consisting of random forest and various deep learning models. Although the approach outperformed other works, the cumulative training time of four distinct models made it unsuitable for real-time HAR implementations.
## 4 Performance Analysis for Different ERSR Radius Sets  
Considering that not all SUs are willing to help USUs, from the perspective of system, the proposed RDUR scheme aims to select as few URs as possible. This is achieved by different ERSR allocations to AAs. For different ERSR radius sets, the performances of UR selection ratio and the second-stage coverage ratio are analyzed in this section.
# GNSS Accuracy Under White Gaussian Noise Jamming +
## 1 Introduction
### Goal of This Paper  
The main goal of this paper is to provide insight into the effects of jamming on the accuracy of GNSS receivers. The main message is that the effective range of a jammer is not equal to the range at which the receiver loses its lock; this range is potentially much larger, depending on the required accuracy of the position solution. This paper focuses on the pseudo-range accuracy under additive white Gaussian noise (AWGN) jamming conditions. Biases are not considered. If the accuracy can be predicted well, this prediction can be used to determine the expected effective range of a jammer, in this case, for an AWGN jammer, for applications where accuracy is critical.
## 3 Results
### Durability Tests
#### 3.2.2 Adhesion Tests  
Figure 10a shows the Zwick Roll Z010 machine employed to perform the adhesion test. Some pictures of samples before and after the test are also displayed (Figure 10b,c, respectively). Tests performed at room temperature revealed that failure at adhesive-coating interfaces occurred for SR and S1 samples, at an adhesion strength of 8.1 and 9.9 MPa, respectively; whereas, failures at adhesive-substrate interfaces (see the schematic drawing in Figure 11b) occurred for S2 (10.7 MPa), CR (19.9 MPa), C1 (7.2 MPa), and C2 (10.2 MPa), meaning that the adhesion strength of the interface adhesive-coating was higher than the reported strength. At \(-30\)\({}^{\circ}\)C, only C samples were tested. It was found that also at low temperatures for CR, C1, and C2 sample failures occurred at the adhesive-substrate interface at adhesion strengths of 2.9 MPa per CR, 2.2 MPa for C1, and 1.8 for C2. So, the adhesion strength of the adhesive-coating interfaces was higher than the measured values. The measured adhesion strength decreased as the temperature decreased, as shown in Figure 11a, which may be due to the more brittleness of adhesive at low temperatures [35]. In Figure 11b, a schematic drawing of the failure at the adhesive-substrate interface is shown for clarity.
# Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning
## 3 The Proposed Deep Multi-User Reinforcement Learning
### DMRL Training
The last indicator is cumulative reward, which represents the cumulative amount of the total reward value as the time slot increases. The cumulative amount has been explained above, so the cumulative bonus is also well understood. It can be calculated by:  
\[\textit{Cum\_Rewards}=\sum\sum_{i=1}^{N}r_{i}. \tag{20}\]