esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
## 3 Experiment
### Experimental Results
#### 3.3.3 Point Cloud Registration
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Original**} & \multicolumn{2}{c}{**ICP (Iter 50)**} & \multicolumn{2}{c}{**ICP (Iter 100)**} & \multicolumn{2}{c}{**FR-ICP**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-9}  & **RMSE** & **Time** & **RMSE** & **Time** & **RMSE** & **Time** & **RMSE** & **Time** \\ \hline (a) & 0.808 & 31.82 & 0.518 & 61.84 & 0.488 & 36.75 & **0.487** & **18.87** \\ (b) & 1.092 & 32.16 & 0.627 & 62.29 & 0.556 & 33.71 & **0.532** & **21.04** \\ (c) & 1.523 & 32.74 & 0.807 & 62.36 & **0.591** & 41.49 & **0.591** & **28.39** \\ (d) & 1.721 & 32.68 & 0.834 & 63.08 & **0.580** & 56.17 & **0.580** & **36.51** \\ (e) & 27.067 & 41.19 & 26.972 & 79.26 & **0.575** & 46.70 & **0.575** & **32.07** \\ \hline \hline \end{tabular}  
* The best performance figures are highlighted in bold fonts.  
\end{table} 
Table 4: RMSE (\(\times 10^{-3}\)) and computation time (in seconds) for different registration methods on 5 sets of example point clouds. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Original Data** & **ICP (Iter 50)** & **ICP (Iter 100)** & **FR-ICP** & **Ours** \\ \hline \hline \multirow{2}{*}{(a)} & 0.808, 31.82 s & 0.518, 61.84 s & 0.488, 36.75 s & 0.487, 18.87 s \\  & 0.808, 31.82 s & 0.518, 61.84 s & 0.488, 36.75 s & 0.487, 18.87 s \\ \hline \hline \end{tabular}  
* The registration results using various methods on 5 sets of example point clouds, with RMSE (\(\times 10^{-3}\)) and computation time (in seconds) shown below.  
\end{table} 
Table 3: The registration results using various methods on 5 sets of example point clouds, with RMSE (\(\times 10^{-3}\)) and computation time (in seconds) shown below.
# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 2 Materials and Methods
### Methods
#### 2.2.1 A Brief Review of Used Deep Learning Models for Transfer Learning
[50] who proposed the ResNet-RS family of models. These models were designed to be faster than EfficientNets on cloud Tensor Processing Units (TPUs), achieving similar accuracies on the ImageNet dataset while utilizing advanced training and scaling techniques. According to the authors, the ResNet-RS models are 1.7\(\times\)-2.7\(\times\) faster than EfficientNets on TPUs [50]. In summary, each of these architectures has its own strengths and weaknesses. VGG is a deep learning architecture with a deep structure consisting of 16 or 19 convolutional layers [51], while ResNet, which introduced residual learning, is capable of being trained with a depth of up to 152 layers, making it 8 times deeper than the VGG network, while still having lower complexity. When evaluated on the ImageNet dataset, an ensemble of these ResNet models achieved an impressive error rate of 3.57% on the test set, winning first place in the ILSVRC 2015 classification task [29]. MobileNet is considered a lightweight deep neural network due to its fewer parameters and higher classification accuracy. To enhance its performance, MobileNet has integrated dense blocks from DenseNets, which further reduce the number of network parameters while improving classification accuracy [40]. Compared to MobileNet, DenseNet has a higher number of parameters and is more computationally intensive, making it less suitable for mobile and embedded devices. However, DenseNet can achieve state-of-the-art performance on many computer vision tasks and is often used in applications such as object detection and image segmentation. EfficientNet is a recent architecture that achieves state-of-the-art performance with fewer parameters and FLOPS compared to other models [41; 52]. The choice of which architecture to use depends on the specific requirements of the application, such as accuracy, speed, memory, and hardware constraints.
## 3 Results and Discussion
### Water Pressure
The displacements and velocities at the vault of super-large-diameter tunnels are significantly larger and exhibit distinct patterns compared to other monitoring points, necessitating focused investigation from Figures 11a and 12a. Figure 14a compares the vertical strain at the vault under different water pressures. The vertical strain curves oscillate with metro train-induced vibrations but remain positive, indicating vertical elongation of particles and soil loosening. This phenomenon arises because the surroundings at the vault tend to slide laterally due to the structural features of the liners under vibrations. Under C\({}_{2}\) and C\({}_{3}\) conditions, the vertical strains at the tunnel crown are similar and smaller than those at the C\({}_{1}\) condition, likely due to higher confining pressure in this region, which restricts particle movement and reduces strain. Figure 14b compares the maximum and minimum values of vertical strain at the vault. Under C\({}_{1}\)-C\({}_{3}\) conditions, the maximum vertical strain differences occur at 90\({}^{\circ}\) (0.015%, 0.014%, and 0.014%), while the minimum differences appear at 180\({}^{\circ}\) (0.004%, 0.002%, and 0.002%). This is likely due to the structural constraints of the twin tunnels at 180\({}^{\circ}\)  
Figure 14: Comparison of vertical strain at characteristic points of surroundings: (**a**) vertical strain at 90\({}^{\circ}\); (**b**) difference between maximum and minimum vertical strain. Figure 13: Comparison of coordination at characteristic points of surroundings under C\({}_{1}\)–C\({}_{3}\) conditions: (**a**) average coordination; (**b**) coordination variation rates. which limit lateral particle movement, resulting in minimal vertical strain. At 135\({}^{\circ}\), strain differences are slightly smaller than at 90\({}^{\circ}\), possibly because this region is closer to both the vibration source (T1) and the vault. These points are experiencing stronger disturbances and weaker resistance to sliding, leading to higher vertical strain. As shown in Figure 14a,b, the structural features of the vault liners cause significant positive vertical strain in the surroundings. Larger differences in maximum-minimum strains at 90\({}^{\circ}\) and 135\({}^{\circ}\) indicate lower stability in these regions. As shown in Figures 12-14, increasing water pressure from C1 to C2 condition reduces the static-dynamic responses in super-large-diameter tunnels reduced when water pressure increase from C1 to C2 condition, while the static-dynamic responses increase when the water pressure further increases to C3 condition. Optimal water pressure (\(P_{2}\) = 149 kPa) is critical for maintaining stability. Displacement and velocity in the surroundings depend on proximity to the vibration source, with the central tunnel region (especially 180\({}^{\circ}\)) most affected. Vertical strain peaks at 90\({}^{\circ}\) and 135\({}^{\circ}\) require special attention due to their instability.
## 1 Introduction
When faults occur in an actuator, the control allocation scheme uses the effectiveness level of the actuator to redistribute the control signals to the remaining fault-free actuators. In another famous study using a sliding mode fault-tolerant controller, a fault detection unit was generated from a linear observer [18]. When a fault was detected, a reconfiguration technique was used by scarifying the yaw control. The simulation results showed that the reconfiguration scheme could conduct a good tracking operation in the presence of partial loss in nominal thrust. The advantage of this method is that it was able to reject disturbances and uncertainties in fault-free operation. Another study proposed the sliding mode observer and sliding mode control for the AFTC scheme [35]. In this work, the fault estimation from the sliding mode observer was injected into the sliding mode controller for reconfiguration. The results show the effectiveness of this method in the presence of disturbances and uncertainties. Recently, there was a study on tracking control for a quadrotor using sliding mode and backstepping control [15]. The results showed that the proposed method could handle the chattering phenomenon and uncertainties, but this approach was complicated because it used the fuzzy method, which is a trial and error method. Moreover, that work did not focus on an actuator fault in a quadcopter model, which is what motivates us to conduct this study. An active FTC method for actuator fault control in the presence of disturbances and uncertainties is proposed in this paper. This method offers robust control for time-varying actuator faults and saturation, which was limited in previous studies. The main contributions of this study, over the previous ones, can be summarized as follows:  
* The fault diagnosis based on \(H_{\infty}\) technique [36] is introduced to estimate the time-varying faults. * The AFTC based adaptive sliding mode backstepping [15] was improved here to ensure that the tracking performance converges to zero in spite of time-varying, actuator faults and disturbances. * A modified AFTC is proposed to handle the input saturation. * Stability analysis in presented to verify the robustness of the proposed controller. The remaining article is organized as follows. Section 2 introduces the quadcopter dynamics. The active fault-tolerant control based on adaptive sliding mode backstepping control and \(H_{\infty}\) technique for attitude system is discussed in Section 3. Section 4 presents the simulation results. The conclusions and recommendations for future work are provided in Section 5.
# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 1 Introduction
Users tend to use informal language when writing their tweets; for example, users use \"4U\" to mean \"for you\", \"AMA\" for \"ask me anything\" and \"BFN\" for \"bye for now\". Spelling and grammatical mistakes are not checked or corrected. Short texts are therefore more difficult to analyze than long texts. The facts that tweets are short texts and are noisy add extra complication to the data. Furthermore, hashtags can be acronyms, shortened or misspelled words or a combination of words, numbers and punctuation marks. Thus, using hashtags as keywords does not necessarily convey the meaning of the discussion. The lack of control over the creation of hashtags has resulted in hundreds of hashtags being associated with a single discussion topic and different discussion topics being associated with a single hashtag. Hashtag recommendation can be either general, when the suggested hashtags are obtained based on the data of all users, or personalized, when the suggested hashtags incorporate the user's preferences and data. The hashtags from all the tweets in a dataset form a space known as the \"hashtag space\". The suggested hashtags are said to be novel if they are not in this hashtag space (i.e., not previously used by other users). Otherwise, they are said to be predefined. Although researchers have investigated various techniques and factors that affect the performance of hashtag recommendation methods for Twitter/Sina Weibo data [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23], there is a lack of a systematic review of these methods. [PERSON] et al. [30] listed only 11 methods in their survey on hashtag recommendations. Our paper aims to present a systematic review of research on hashtag recommendation for tweets and present insights from previous research papers together with their methodologies and factors to provide future perspectives on hashtag recommendation. Our paper comprises the following sections:  
* Section 2 presents the methodology that we adopted for our literature search;
* Section 3 provides the results of the literature review based on the research questions defined in Section 2;
* Section 4 provides a taxonomy of the selected research papers on hashtag recommendation for tweets based on the methodologies adopted in the papers;
* Sections 5 and 6 give a critical overview of each class in the taxonomy and provide a detailed review of the selective methods in each class;
* Section 7 outlines our research limitations;
* Section 8 summarizes the whole paper and highlights future research directions.
## 3 Results
### Brainon Improves SCO-Induced Spatial Memory Deficiency in the Morris Water Maze Task  
To examine the effects of Brainon on the spatial memory impairment of mice, escape latency, swim distance, and the platform crossing numbers were assessed in all test groups. Figure 3A revealed that the escape latency of the SCO-treated group was significantly prolonged for 4 days (days 24-27) compared to that in the Normal group (\(p<0.01\)). In contrast, 100 mg/kg Brainon and 50 mg/kg GBE shortened (\(p<0.05\)) the escape latency on days 26 and 27; 100 mg/kg Brainon also decreased (\(p<0.05\)) the escape latency compared to that in the SCO-treated group from day 25. Swimming distance to find the platform in the MWM task showed similar results to those of the escape latency (Figure 3B). The mice in the Brainon 30 (\(p<0.05\)), 100 (\(p<0.01\)), and GBE 50 (\(p<0.05\)) mg/kg groups swam shorter distances to locate the platform in comparison with the SCO-treated groups on day 26. We confirmed that the dose of 30 mg/kg (\(p<0.05\)) and 100 mg/kg (\(p<0.05\)) Brainon on day 27 reduced the swimming distance in mice when finding the platform. In the probe trials (Figure 3C) on day 28, the platform crossing numbers of the mice in the SCO-treated group was considerably lower comparison with that of the mice in the Normal group (\(p<0.01\)). On the other hand, administration with 30 and 100 mg/kg of Brainon (\(p<0.01\)) and 50 mg/kg GBE (\(p<0.01\)) increased the platform crossing numbers comparison with that observed in the SCO-injected group. Our results demonstrate that Brainon administration enhanced spatial, learning, and memory functions in SCO-treated memory-deficient mice.  
Figure 3: Effects of Brainon scopolamine treated spatial memory deficiency in the MWM trial. (**A**) The escape latency (s) and (**B**) swim distance of mice was investigated for 4 days (**C**) The platform crossing number was carried out on probe trial. Values are represented as means \(\pm\) SEM (\(n=8\)). ## \(p<0.01\) vs. Normal group; * \(p<0.05\) and ** \(p<0.01\) vs. SCO-treated group.  
Figure 2: Effects of Brainon scopolamine-treated memory deficit in passive avoidance performance. Values are represented as means \(\pm\) SEM (\(n=8\)). ## \(p<0.01\) vs. Normal group; * \(p<0.05\) vs. SCO-treated group.
## 3 Results and Discussion
### Effects of Pore Size  
Figures 3 and 4 present the physical variances, i.e., the reservoir pressure and pore volume, in the carbonate reservoirs during the CO2 injection process, specifically targeting the limestone and dolostone formations with different pore radii of 5, 10, and 50 \(\upmu\)m. The constant CO2 injection rate of 0.1 mol/min serves as a controlled parameter to elucidate the effects of pore size on reservoir behavior as a result of the introduced CO2. Analysis of these two figures reveals a consistent trend across both types of carbonate reservoirs, whereby a diminution in pore radius correlates with an increase in reservoir pressure and a more pronounced augmentation in reservoir pore volume. The underlying mechanisms driving this observed phenomenon can be attributed to the increased surface area afforded by smaller pore sizes, since for a fixed pore volume, a smaller pore radius results in a larger surface area. The larger surface area in the carbonate reservoir with a smaller pore radius significantly enhances the interaction between the CO2-saturated carbonate brine and the carbonate rock matrix, which accelerates the dissolution process of the rock matrix  
Figure 2: The comparison of (**a**) pore volume (PV) increase and (**b**) pressure buildup during CO2 injection in limestone and dolostone reservoirs.  
and leads to a faster pore volume increment and slower pressure buildup. Moreover, the residual brine saturations are higher in the reservoir with smaller pore radii due to the hypothesis in this study that no free brine exists in the reservoir and all the residual brine adheres to the pore walls. This is exemplified by the calculation showing a dramatic difference in residual water saturation levels--75% in reservoirs with a pore radius of 5 \(\upmu\)m versus only 8.5% in those with 50 \(\upmu\)m pores. The higher residual brine saturation levels in smaller pores facilitate a larger CO2 dissolution capacity in the brine, leading to a more pronounced and rapid increase in pore volume. A comparative analysis between limestone and dolostone reservoirs reveals that with identical pore sizes and no matter what the pore radius is, limestone reservoirs exhibit lower reservoir pressures and more significant increases in pore volumes compared to dolostone reservoirs. This distinction is attributed to limestone's inherently greater reactivity with CO2-saturated carbonate brine compared to dolostone, which has been stated in Section 3.1.  
Figure 3: The comparison of (**a**) pore volume (PV) increase and (**b**) pressure increment in the limestone reservoir with different pore sizes during CO2 injection.
# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 4 Experiments and Analysis
### Image Quality Comparison
#### 4.2.1 CT-MRI Image Fusion Comparative Experiments  
The 'brain' images are a pair of CT and MRI images shown in Figure 6a,b respectively. CT image shows the bone information and MRI image shows soft issues. BSD (Block-based Spatial Domain), DWT, DT-CWT, CS-DCT, KSVD-OMP, MFR, SRDL, JPC, MST-SR, and the proposed method (IDLE) are employed to merge CT and MRI images into a clear one with bone and soft issues information respectively. The corresponding fusion results are shown in Figure 6c-I respectively.  
As BSD is a spatial-domain method, it is easy to figure out block effects in fused image (c). Fused image (d) has good performance in details of high-frequency components, but details of low-frequency components are darker than source images. The contrast and sharpness of the fused image (d) are good. Image (e) fused by DT-CWT has low contrast and sharpness. CS-DCT keeps details of CT and MRI images in the fused image (f), but the contrast and sharpness are low. The fused image (g) of KSVD has good overall performance, but the sharpness of details is not sufficient. MFR keeps the details of source images in the fused image (h). The details in the Fused image (i) of SRDL have low contrast and sharpness. The fused image (j) of JPC shows good detailed information. MST-SR causes the details of low-frequency components to be darker than the source images in the fused image (k). The fused image (l) of proposed IDLE not only contains complete details, but also has good contrast and sharpness.  
Then it compares the details of each fused image. [PERSON] and blue frame in image (a) and (b) show the important details of bone and soft issues. Block effect affects the corresponding details in fused image (c). In fused image (d), the bone part is too bright and the soft issue part is too dark. The corresponding details of fused image (e) are blurred. This is not good for medical diagnosis (e). The contrast and sharpness of fused image (f) are low. The fused image (f) also has some blurs. The fused image (g) has good brightness, but the contrast is low. Details of source images are shown in the fused image (h) clearly. In the fused image (i), the details of bone part are blurred. The fused image (j) shows clear and complete detailed information. The soft issue part is too dark in the fused image (k). The proposed IDLE generates a fused image (l) that contains complete and clear details and also has good contrast and sharpness.  
Table 1 shows the objective evaluations. Compared with the rest of image fusion methods, the proposed IDLE gets the largest value in \(Q^{AB/F}\), MI, and VIF. BSD gets the largest value in AG and EI, but it has block effects in the fused image. So the proposed IDLE has the best overall performance in CT and MRI 'brain' image fusion among all 10 methods, according to the quality of fused image, contrast, sharpness and objective evaluations.
## 3 Proposed Method
### Lightweight Convolution and Attention Network for Damage Detection
#### 3.2.3 MHSA Module  
In the proposed LCANet, we use group convolution and depthwise convolution to design the LFE module by replacing the traditional convolution layers to make the model lightweight. However, the reduction of convolution operations still can affect the efficiency of feature extraction. Therefore, MHSA is further introduced to enhance the overall performance of the model by extracting global features. Then, we can obtain the local features of signal data using a conventional convolution layer and multiple LFE modules. Hence, the MHSA module can extract global contextual information based on them. As shown in Figure 4, the input features \(\textbf{Input}\in\mathbb{R}^{C\times W_{1}\times H_{1}}\) are divided into \(H\) groups along the channel dimension. Moreover, each group of features is generated as \(\textbf{Q}_{h},\textbf{K}_{h},\textbf{V}_{h}\in\mathbb{R}^{\widehat{h}\times W _{1}\times H_{1}}\), \(1<h<H\) through one-dimensional convolution. The attention operation is as follows  
\[\textbf{Att}_{h}=Softmax(\frac{\textbf{Q}_{h}\textbf{K}_{h}{}^{T}}{\sqrt{d_{k} }})\textbf{V}_{h}, \tag{11}\]  
where \(d_{k}\) is the dimension of the feature \(\textbf{K}_{h}\) which is used to prevent excessive dot product values. \(Q_{h}\) and \(K_{h}\) compose an attention matrix containing global contextual information, which is multiplied by \(V_{h}\) to realize the attention operation. The \(\textbf{Att}_{h}\) is the feature map after the attention operation, which is summed along the channel dimensions to obtain the attention branch output, which is usually expressed as follows:  
\[\textbf{Att}=Concat(\textbf{Att}_{1},\cdots,\textbf{Att}_{h}), \tag{12}\]  
where \(Concat(\cdot)\) indicates the concatenating features along the channel dimension. **Att** and **Input** have the same dimension and it can be obtained by summing them up at the end, which can be expressed as follows:  
\[\textbf{Out}_{MHSA}=\textbf{Att}+\textbf{Input}. \tag{13}\]  
where \(\textbf{Out}_{MHSA}\) is the output of the MHSA module. In the MHSA module, the correlation between different parts of the feature map can be obtained by calculating the attention weight of information at one location and other locations. In addition, the multi-head design of attention can learn different behaviors based on the same attention, which helps to capture the dependencies of signals in various ranges. Finally, \(\textbf{Out}_{MHSA}\), which contains local features and rich global context, will be fed into the global average pooling layer and the fully connected layer for the recognition of the damaged region.