esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 4 Experiments
### Implementation Details  
We use Pytorch on the Ubuntu Linux 20.04 platform with NVIDIA RTX 3060 to implement our proposed method and other methods for comparison. In the training procedure of autoencoder, we train 3000 epochs using Adam optimizer with a learning rate of \(1\times 10^{-3}\), then we freeze the autoencoder network and train the reconstruction module. We also use the Adam optimizer with a learning rate of \(1\times 10^{-3}\) and cosine annealing learning rate decay with the gradient clipping. The balancing weighted parameter \(\lambda\) in \(\mathcal{L}_{reg}\) is set to 0.9 and the batch size is fixed to 100. To verify that our method can scale to large-scale data, we uniformly and randomly sample \(N\) points in each dataset to perform different experiments. For the Fashion-MNIST dataset, we set \(N\in\{200,500,1000,1500,2000\}\) uniformly at random to train our model. For COIL-20 dataset, we set \(N\) to \(\{200,500,1000,1440\}\). For COIL-100 dataset, \(N\) is set to \(\{200,500,1000,2000,5000,7200\}\). For YTF dataset, \(N\) is in \(\{200,500,1000,2000,5000,10,000\}\).
## 3 Results
### Artificial Neural Network (ANN) Results  
Table 3 shows the results of the training, validation, and testing processes for the internal bond test. \begin{table}
\begin{tabular}{c c c c c} \hline
**Variable** & **Mean** & **Std. Deviation** & **Min** & **Max** \\ \hline Particle temperature (\({}^{\circ}\)C) & 43.76 & 6.21 & 30 & 57 \\  Resin temperature (\({}^{\circ}\)C) & 27.9 & 10.3 & 7.5 & 40.4 \\  Mat moisture content (\%) & 4.7 & 0.6 & 6.6 & 3.6 \\  Resin percentage (\%) & 8.90 & 0.27 & 8.15 & 9.75 \\  Additive percentage (\%) & 0.30 & 0.03 & 0.16 & 0.48 \\  Mat velocity (mm/s) & 455.8 & 119.7 & 185 & 755 \\  Press temperature (\({}^{\circ}\)C) & 230.4 & 9.8 & 184.4 & 268.4 \\  Internal bond (N/mm\({}^{2}\)) & 0.56 & 0.11 & 0.30 & 0.98 \\ \hline \end{tabular}
\end{table} 
Table 2: Production and internal bond parameters. \begin{table}
\begin{tabular}{c c c c c} \hline
**Network** & **Phase** & **Structure** & **R\({}^{2}\)** & **R** & **\(p\)-Value** \\ \hline \multirow{4}{*}{\(1\)} & Training & & 0.82 & 0.91 & 0.96 \\  & Validation & & 0.80 & 0.89 & 0.82 \\  & Testing & & 0.57 & 0.75 & 0.72 \\  & Total & & 0.76 & 0.87 & 0.76 \\ \hline \multirow{4}{*}{\(2\)} & Training & & 0.81 & 0.90 & 0.95 \\  & Validation & & 0.73 & 0.85 & 0.29 \\  & Testing & & 0.55 & 0.73 & 0.97 \\  & Total & & 0.71 & 0.84 & 0.70 \\ \hline \multirow{4}{*}{\(3\)} & Training & & 0.71 & 0.84 & 0.87 \\  & Validation & & 0.69 & 0.83 & 0.24 \\  & Testing & & 0.60 & 0.77 & 0.93 \\  & Total & & 0.66 & 0.81 & 0.72 \\ \hline \multirow{4}{*}{\(4\)} & Training & & 0.82 & 0.91 & 0.59 \\  & Validation & & 0.69 & 0.83 & 0.72 \\  & Testing & & 0.63 & 0.79 & 0.39 \\  & Total & & 0.74 & 0.86 & 0.50 \\ \hline \multirow{4}{*}{\(5\)} & Training & & 0.78 & 0.88 & 0.92 \\  & Validation & [7 1 1] & 0.69 & 0.83 & 0.41 \\  & Testing & & 0.50 & 0.71 & 0.72 \\  & Total & & 0.69 & 0.83 & 0.61 \\ \hline \multirow{4}{*}{\(6\)} & Training & & 0.68 & 0.83 & 0.76 \\  & Validation & & 0.67 & 0.82 & 0.24 \\ \cline{1-1}  & Testing & & 0.63 & 0.79 & 0.47 \\ \cline{1-1}  & Total & & 0.62 & 0.79 & 0.61 \\ \hline \end{tabular}
\end{table} 
Table 3: Results of the artificial neural network design process. R\({}^{2}\): determination coefficient, R: correlation coefficient, \(p\)-value: statistic to identify significant differences between simulated and experimental values.
## 2 Experimental Setup
### Probabilistic Roadmap Planner  
Motion planning is a basic problem in many engineering applications, such as robotics [39; 40; 41; 42], navigation, and autonomous driving [43; 44; 45]. The essential problem in motion planning is to avoid obstacles and find a path to connect the start and target locations. Previous papers have proposed algorithms such as A* [46]. Algorithm D* [47] is an improvement on A* that supports incremental repetitive planning and has a lower calculation cost. However, the calculation cost of planning by D* is still very high and is not conducive to real-time path planning by robots. To improve the cost problem, a PRM [28] based on probability is proposed (Algorithm 1):  
```
1:while\(\text{len}(v_{i})<\text{propints}\)do
2:\(\text{rand}(v_{i})\)
3:if\(v_{i}\in\text{C}_{free}\)
4:\(\text{V}\leftarrow\text{V}\cup v_{i}\)
5:i\(\leftarrow\text{i}+1\)
6:end
7:for all\(v_{i}\in\text{V}\)do
8:\(N\leftarrow\text{the closest neighbors of }v\) chosen from V according to disthresh
9:for all\(v_{i}\in N\)do
10:if\(((v_{i},v_{j}))\
otin\Pi\) and \(\text{LP}(v_{i},v_{j})\
eq\text{NIL}\)then
11:\(\Pi\leftarrow\Pi\cup(v_{i},v_{j})\)
12:end
13:end
```  
**Algorithm 1** The probabilistic roadmap planner (PRM).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Distance** & **Object** & \begin{tabular}{c} **Accuracy** \\ **M(SD)** \\ \end{tabular} & **Object** & \
\begin{tabular}{c} **Accuracy** \\ **M(SD)** \\ \end{tabular} & **Object** &
\begin{tabular}{c} **Accuracy** \\ **M(SD)** \\ \end{tabular} \\ \hline \multirow{4}{*}{40} & O1 & 0.62(1.10) & O2 & 0.41(0.31) & O3 & 0.99(1.83) \\  & O4 & 0.41(1.00) & O5 & 0.78(0.83) & O6 & 0.97(0.46) \\  & O7 & 0.69(1.05) & O8 & 1.26(0.58) & O9 & 0.99(0.44) \\  & O1 & 0.39(2.48) & O2 & 0.78(0.54) & O3 & 0.70(1.04) \\  & O4 & 0.79(1.32) & O5 & 1.00(1.08) & O6 & 1.09(0.66) \\  & O7 & 0.63(1.89) & O8 & 0.70(2.10) & O9 & 1.00(0.91) \\ \hline \hline \end{tabular} Note: Distance: distance between the eye and screen, M: mean offset between the interaction and target, and SD: standard deviation. All are reported in cm.  
\end{table}
Table 1: The eye track table.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Distance** & **Object** & \begin{tabular}{c} **Accuracy** \\ **M(SD)** \\ \end{tabular} & **Object** & \
\begin{tabular}{c} **Accuracy** \\ **M(SD)** \\ \end{tabular} & **Object** &
\begin{tabular}{c} **Accuracy** \\ **M(SD)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Original} & O1 & 0.83(0.11) & O2 & 0.42(0.13) & O3 & 0.44(0.21) \\  & O4 & 0.98(0.32) & O5 & 0.50(0.20) & O6 & 0.87(0.46) \\  & O7 & 0.72(0.22) & O8 & 0.72(0.17) & O9 & 0.45(0.17) \\  & O1 & 0.39(0.21) & O2 & 0.65(0.23) & O3 & 0.22(0.14) \\ \cline{1-1}  & O4 & 0.44(0.18) & O5 & 0.46(1.13) & O6 & 0.55(0.27) \\ \cline{1-1}  & O7 & 0.37(0.26) & O8 & 0.20(0.13) & O9 & 0.56(0.12) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hand track table.
# The Combined Compact Difference Scheme Applied to Shear-Wave Reverse-Time Migration
## 3 Analysis of CCD and CSCD  
### Analysis of Truncation Error  
In this section, the truncation errors of the second derivative calculated by these schemes are compared. The results are shown in Table 1. \(\alpha,\beta,a,b,c\) are the difference coefficient matrices. It can be seen from Table 1 that, although the three methods can achieve a certain order of spatial accuracy, the truncation error is relatively different. The truncation error of the traditional difference scheme (central difference scheme) in calculating the second-order partial derivative is greater than that of the CCD and CSCD.
# Design Dimensions of Co-Located Multi-Device Audio Experiences
## 5 Design Dimensions
### Distribution  
The _distribution_ dimension captures different approaches for the distribution of audio to the devices in an experience. This can be linked to the previous dimension, where audio distribution can be determined based on the roles of each of the devices in the experience. There are three categories.  
1. **Mirrored--**The simplest case is where the same audio content is allocated to every device in the system. This mirrored approach is useful when a simple amplification or duplication of audio is required, e.g., in a silent disco or silent film [56], or in commercial multi-room speaker ecosystems such as Sonos [57].
2. **Distinct--**Here, the audio assets on each device are unique, where no two devices are playing the same audio. This approach is prevalent in interactive, music-based experiences where each device is allocated a unique instrument or sample, allowing each participant to have an equal yet distinct contribution to the experience [39].
3. **Hybrid--**In this instance, devices are allocated a mixture of shared audio material as well as unique assets for individual or groups of devices. Audio allocation here is determined through a pre-defined set of rules, which can take into account many factors such as device numbers, types of devices, and device locations. Examples of this include larger audio performances with many devices, where there are a number clusters of devices playing different content [13; 38].
## 2 Results and Discussion  
### Isolation and Identification of Compounds from the Leaves of Ailanthus Altissima  
Using phytochemical methods, 21 compounds were isolated and identified from the ethyl acetate extract of _A. altissima_ leaves (Figure 1). These compounds included steroids (compound **4**), phenols (compounds **5-17**), terpenoids (compounds **17-19**), coumarins (compound **20**), and alkaloids (compound **21**). The compounds were identified as stigmast-5-ene-3\(\beta\),7\(\alpha\),20\(\zeta\)-triol (compound **4**) [16], cinnamic acid (compound **5**) [17], benzoic acid (compound **6**) [18], (E)-3-(4-hydroxyphenyl)acrylic acid (compound **7**) [19], 4-hydroxybenzoic acid (compound **8**) [20], methyl gallate (compound **9**) [21], (Z)-3-(4-hydroxyphenyl) acrylic acid (compound **10**) [19], methyl caffeine (compound **11**) [22], 4-hydroxy-3-methoxybenzoic acid (compound **12**) [23], caffe acid (compound **13**) [24], methyl-3,4,5-trihydroxybenzoate (compound **14**) [25], luteolin (compound **15**) [26], quercetin (compound **16**) [27], (-)-loliolide (compound **17**) [28], (+)-dehydrocomifoilol (compound **18**) [29], (35,5R,65,7E)-3,5,6-trihydroxy-7-megastigmen-9-one (compound **19**) [29], scopoletin (compound **20**) [30], and indazole (compound **21**) [31] (Figure S1).
# MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking
## 1 Introduction
In recent years, the visual prompt learning paradigm has been introduced in target tracking, which adapts specific downstream tasks through prompt modules to fully utilize pre-trained knowledge and reduce dependence on downstream data. [PERSON] et al. [29] proposed ProTrack, which applies prompt learning methods to multi-modal tracking. This model utilizes visual prompts to convert RGB-T and RGB-D data into a unified RGB space. With the diversification of tracking tasks, some researchers are focusing on a universal visual prompt framework. [PERSON] et al. [30] proposed a OneTracker framework, which freezes the basic tracking model and only fine-tunes the cross-modal prompt module, achieving the unified processing of multiple tracking tasks, enabling the model to quickly adapt to diverse tracking scenarios. Due to target tracking being a temporal task, some methods rely on prompt learning to fuse spatio-temporal information. [PERSON] et al. [31]proposed an EVPTrack method, which effectively conveys spatio-temporal information to the current frame by generating an explicit visual prompt, avoiding frequent template update operations. This method enhances the model's adaptability to scale changes through a multi-scale prompt mechanism. Although various methods have achieved great success in target tracking, they mainly adopt full-parameter fine-tuning strategies, which have some limitations: (1) Low transfer efficiency: The introduction of multi-modality further increases the complexity of a model, resulting in a significant increase in the resources required for fine-tuning. (2) Catastrophic forgetting of pre-trained knowledge: Unbalanced data sizes can easily lead to the over-fitting of downstream noise in a model. (3) High cost of cross-task deployment: Each downstream task model needs to be stored separately and cannot share common capabilities. This paper focuses on the issues of multi-modal tracking, combining thermal infrared (T), depth (D), event (E), or other data from different sensors with RGB images, and makes full use of the complementary characteristics between modalities to enhance tracking performance. In this paper, a multi-modal efficient fine-tuning model based on prompt learning, MPVT, is proposed to address the problems of low transfer efficiency, catastrophic forgetting, and weak multi-modal correlation modeling in the traditional full-parameter fine-tuning transfer paradigm. The remainder of this paper is organized as follows: In Section 2, the multi-modal prompt vision tracker (MPVT) is proposed based on an efficient prompt-tuning paradigm. Decoupled input enhancement, dynamic adaptive prompt fusion, and fully connected head network modules are included in the model. Furthermore, a joint loss function is introduced so as to train the model efficiently. The experimental settings, datasets, details of the experimental results, and analysis are presented in Section 3. Finally, conclusions are summarized in Section 4.
## 2 Materials and Methods
### Data Preparation and Analysis  
#### 2.3.1 Landslide Inventory Map  
The most crucial step in the landslide susceptibility mapping is to identify landslide locations and determine when the landslide occurs. Therefore, a detailed and reliable landslide inventory map is the premise of an accurate assessment of landslide susceptibility. This study constructed the landslide inventory map from high-resolution remote sensing image data, field investigation, and historical landslide data, and a total of 165 landslides were identified in the study area (Figure 1). The total disaster area of the study area was 12.65 km\({}^{2}\), and the area of single landslide ranged from 1664 m\({}^{2}\) to 1.06 km\({}^{2}\). Most of the landslides in this study area occurred on the bank of the Yangtze River and the gully.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### Coverage and Range Improvement
#### 7.1.5 D2D Communications for BackCom  
D2D communications can be used for extension of the network coverage in BackCom. As D2D is an essential part of IoT technology that helps in the design, deployment, and maintenance of a sustainable IoT ecosystem. Researchers from both industry and academia are focussing  
Figure 37: Relaying in BackCom.  
on implementing energy-efficient IoT networks, which can work on BackCom. Intelligent D2D communication using passive IoT devices is among the challenges faced. A wirelessly powered BackCom architecture was proposed to allow D2D communications between passive nodes by integrating BackCom and wireless power transfer together [181]. Furthermore, [PERSON] et al. analyzed routing algorithms for D2D communications in IoT and suggested four different approaches to attain D2D communications with a routing algorithm [182].  
The authors in [183] proposed hybrid D2D communication. The communication technique integrates Amb-BackCom with WPCN. Figure 38 shows the concept of hybrid D2D communication for two groups of coexisting ambient transmitters \(X\) and \(Y\), which can work on separating frequency bands in WPCN. This approach considers two modes of operation: (i) the ambient backscatter mode and (ii) the HTT mode. The RF energy harvester of the hybrid transmitter operates on the frequency band of \(X\). If the mode of the hybrid transmitter is ambient-backscattered, load modulation is performed on incident signals from the frequency band of \(X\) transmitters. If hybrid transmitter is in HTT mode, the energy can be harvested using the frequency band of ambient transmitters \(X\), and information can be transmitted using the frequency band of \(Y\) transmitters. D2D communication is performed during HTT mode. Received signals at the hybrid receiver are impaired by the interference of \(Y\). [PERSON] et al. addressed this interference issue with the \(\alpha\)-Ginibre point process [183].
# Correlation of Microstructure and Mechanical Properties of Metal Big Area Additive Manufacturing
## 3 Results
### Microstructure Characterization  
Figure 9 shows the microstructure at four locations in the build height and Figure 10 shows high magnification images of the same regions. Starting from Figure 9d, or the bottom of the build, the fusion line between one of the weld beads and the substrate can be observed. After the fusion line, a traditional columnar microstructure (denoted by the direction/morphology of the grain boundary allotriomorphic ferrite), consisting of a mixture of acicular ferrite, bainite, and allotriomorphic ferrite, is present. The average width of the columnar structure of grain boundary allotriomorphic ferrite, representing the prior austinite grain boundaries, is approximately 50 \(\upmu\)m. The dark regions of Figure 9d are acicular ferrite and bainite, and the white regions are allotriomorphic ferrite that was transformed along the prior austenite grain boundaries. The region of the build that is in the region of 25 mm to 100 mm from the interface region is represented in Figure 9c. Here, alternating patterns of coarse- and fine-grained austenite heat affected zones (HAZs) are observed. The coarse grain HAZ in this region contains coarse allotriomorphic ferrite and small amounts of acicular ferrite and bainite [32]. The fine grain HAZ contains mostly refined polygonal ferrite, with some pearlite on triple points of grains. The pearlite can be seen in Figure 10b,c as the larger black features. The ferrite grain size in this region is approximately 10 \(\upmu\)m to 15 \(\upmu\)m. Most of the build fabricated under the quasi-steady state heat flow conditions is shown by Figures 9b and 10b. The major phase represented here is polygonal ferrite a small volume fraction of pearlite. This region has a more homogenous microstructure than the bottom of the build, which is noted by the absence of the alternating coarse- and fine-grained HAZs. This is due to a high interplays temperature (approximately 400 \({}^{\circ}\)C) and lower subsequent interpass cooling rates (estimated to be between 1 \({}^{\circ}\)C/s and 5 \({}^{\circ}\)C/s from thermal imaging and past experiments [24]), resulting in a more uniform grain structure. The average ferrite grain size from the middle of the build is approximately 15 \(\upmu\)m to 20 \(\upmu\)m. The last layers of the build are shown in Figure 9a. Here, a homogeneous region of 20 \(\upmu\)m polygonal ferrite transitions to a mixed microstructure in the last layer deposited. The last layer consists of the traditional mixture of acicular  
Figure 8: Hardness maps from different locations in the build height. (**a**) The last layer, top. (**b**) The middle of the build. (**c**)Bottom of the build. (**d**) interface between base plate and beginning of the build. Images show the Z–Y plane (thickness) of the build.  
ferrite, bainite, and allotriomorphic ferrite. This mixture is similar to the first few layers of the build. However, the microstructure is less refined than the interface region seen in Figure 10.
## 4 Results and Discussion
### Effect of Crosswind on Performance with Wind Deflectors  
From the above result, the crosswind with 5 m/s velocity has a significant effect on the air flow rate and heat transfer rate. To improve heat transfer performance, a numerical simulation was carried out with crosswind at 5 m/s. The results were observed and analyzed with various numbers of wind deflectors: 4, 8, 12, 16, 20, and 24, respectively. The velocity vectors of air at the tower inlet with varying wind deflectors under a crosswind speed of 5 m/s are shown in Figure 15. Figure 16 illustrates the contours of static pressure of air at the tower inlet with various wind deflectors under a crosswind speed of 5 m/s.  
Figures 15 and 16 show that different numbers of wind deflectors exert different effects on the air flow field. As the number of wind deflectors increases, the wind static pressure in the center of the tower begins to climb, presumably because the wind at the bottom of the cooling tower starts to gather  
Figure 16: Contours of static pressure (unit: Pa) of air at the tower inlet with various wind deflectors under a crosswind speed of 5 m/s.  
Figure 15: Velocity vectors (unit: m\(\cdot\)s\({}^{-1}\)) of the air at the tower inlet with various number of wind deflectors under the crosswind speed of 5 m/s.  
toward the center, and the wind speed on the leeward side gradually increases. The inlet air velocity of the leeward side with 24 wind deflectors is significantly higher than that with other numbers of wind deflectors.  
Figure 17 shows the variations in air flow rate and heat transfer rate with different numbers of wind deflectors obtained by the numerical simulation. Ventilation and heat transfer are respectively maximized with eight and four wind deflectors.  
The comparison of temperature contours at the tower inlet with four and eight wind deflectors is shown in Figure 18. The local high temperature and ventilation with eight wind deflectors is weaker and more homogeneous than with four wind deflectors because the former allows heat transfer from the radiators to be removed quickly from the cooling tower to avoid heat accumulation at the bottom of the tower. Moreover, the radiator is protected. The numerical results show that the air flow rate and heat transfer rate of the tower can be increased maximally by 61.7% and 15.1%, respectively, when eight wind deflectors are arranged at the bottom of the cooling tower.
# Parallel Complex Quadrature Spatial Modulation
## 1 Introduction
* The PCQSM increases the spatial spectral efficiency linearly with the number of antenna subgroups. As such, the PCQSM system extensively reduces the required number of transmit antennas to achieve a certain spectral efficiency. For example, to achieve 24 bits/s/Hz, the PCQSM requires 992, 224, and 32 less transmit antennas in contrast to QSM, CQSM, and PQSM, respectively. * For the same number of transmit antennas, the PCQSM uses lower modulation order, and therefore achieves better bit error rate (BER) performance. Compared to the PQSM, our proposed scheme has a signal-to-noise ratio (SNR) gain of 4 dB, while using two and four subgroups. The rest of this paper is structured as follows. Section 2 describes the system model and related works. The proposed parallel complex quadrature spatial modulation is discussed in Section 3. The performance analysis of the proposed scheme is analytically derived in Section 4. The receiver complexity of PCQSM is analyzed in Section 5. Simulations results are presented and discussed in Section 6. Last, Section 7 concludes the paper. _Notations_: Italicized letters represent scalars, bold capital letters represent matrices, and bold small letters represent vectors. \((\cdot)^{H}\) denotes the Hermitian transpose of a vector/matrix, \(|\cdot|\) indicates the absolute value of a scalar and \(\|\cdot\|_{F}\) denotes the Frobenius norm of a vector/matrix. \(Q(\cdot)\) denotes the tail distribution function of a standard normal distribution.