esa-sceva/satcom-chunk-collection huggingface dataset:
# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 4 Experiment  
Our experiment was carried out on a personal computer with i7-6500 CPU and 16 GB RAM. The environment used in the experiment is Python 3.7 combined with pyTorch framework (version 1.11.0), and the cvxopt tool [31] on MATLAB 2016b is used to solve the convex optimization problem.  
For the experiment in GF-1_WHU remote sensing images [32] and datasets of Cloud-38 [33], the detailed description is shown in Table 1. The original high-pixel image is divided into sub-images of \(400\times 400\times 3\), and only their visible light channels are used.  
In the process of setting the level of segmentation parameters of super pixel segmentation, considering that the super pixel object needs to have certain information inclusion ability, in the image of \(400\times 400\times 3\), the classification levels are 1000, 1600, 2000, 2400, and 3000, which can be divided into about 160, 100, 80, 67, and 54 pixels in the super pixel block. In order to explore the optimal parameters of the super pixel partition level, we construct a parameter optimization dataset from the original datasets with the size of 1000. A total of 80% of the parameter optimization dataset is used for training, and the rest is used for testing. The average accuracy in the test process is used as the evaluation index. The experimental results are shown in Figure 3. The optimal result is obtained when the super pixel level is 2000, which shows that the number of super pixels in the super pixel is about 80, which is suitable for the smallest unit of the cloud recognition task. Therefore, for the super pixel segmentation method using convenient and efficient SLIC method, the segmentation level is 2000, that is, an image contains about 2000 super pixels, and each super pixel contains about 80 pixels. By resize operation, the number of rectangular pixels is about 256 pixels (\(16\times 16\)). The resize process uses the imresizee function of MATLAB software, and imresizee uses bicubic interpolation by default. The super pixel blocks with cloud content exceeding 45% are automatically labeled as cloud super pixels, and the others are labeled as non-cloud super pixels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Number of** & **Resource Acquisition** & **Remarks** \\  & **Images** & & \\ \hline \multirow{4}{*}{GF-1\_WHU} & \multirow{2}{*}{108} & [[http://sendimage.whu.edu](http://sendimage.whu.edu)]([http://sendimage.whu.edu](http://sendimage.whu.edu)). & GF-1\_WHU includes 108 GF-1 \\  & & cn/en/mfc-validation-data/ & wide field of view (WFV) \\  & & (accessed on 15 July 2022) & level-2A scenes and its \\  & & (accessed on 15 July 2022) & reference cloud and cloud \\  & & (accessed on 15 July 2022) & shadow masks. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Public dataset description.
## 2 Materials and Methods
### Output Feature Extraction  
The output feature of the proposed LF/HF model is Roll Angle (\(\varphi\)), which needs to be predicted using the proposed model. To train the model, ground truth data for \(\varphi\) is required, for which the human-in-the-loop labeling (HITLL) method was utilized. The length of the black area of the ICE catheter tip on the AP view can be extracted by calculating the Euclidean distance (D) between two tracked points at the edges of this region (\(q_{1}\), and \(q_{2}\)), as shown in Figure 5a. These points can be tracked using the Kinovea open-source software, but with manual effort. This step is performed only once to extract ground truth data and is not required later when using the trained model. A visual inspection of the video in the AP view reveals a direct relationship between D and \(\varphi\). While D is highly correlated with the magnitude of \(\varphi\), it does not contain information about its direction, which needs to be manually determined. By analyzing the D graph and observing the recorded video in the AP plane, it can be determined that in some frames, D is equal to \(\varphi\), while in the rest of the frames, \(\varphi\) is equal to the vertically flipped version of D. As a result, as shown in Figure 5b,  
Figure 4: Horizontal and Vertical Displacement of P\({}_{1}\) and P\({}_{2}\) in AP and LAO90 planes.  
Figure 3: The procedure for extracting these features for three samples (the first, mth and nth frames) at point P1 of both AP and LAO90 planes. Red and blue lines represent the catheter tip movement.  
the human-in-the-loop feature extraction process involves vertically flipping the D graph in selected samples to obtain ground truth data for \(\varphi\).
# Research on the Influence of Loading Frequency on the Dynamic Response of Concrete Sleopers
## 3 Dynamic Responses of Sleepers at Different Loading Frequencies  
### Influence of Loading Frequency on Particle Displacement in Sleepers  
Under the same number of cycles (five) and different loading frequencies, the particle displacement cloud diagram and vertical displacement cloud diagram in the sleeper are shown in Figures 8 and 9.  
Comparing the sleeper-displacement cloud map in Figure 8, it is seen that, under the same loading times, the distribution of sleeper particle displacement with different loading frequencies was similar: the particle displacement in the center of the sleeper was the smallest, and the particle displacement on both sides of the edge of the sleeper was the largest. The maximum particle displacement value of the sleeper increased with increasing loading frequency, but all of the change ranges were within 1 mm.  
The sleeper was loaded by applying a vertical load to the rail. Under the vertical load, the internal particles of the sleeper mainly produced vertical displacement, and the lateral displacement was almost negligible. Therefore, Figure 9 only shows the vertical-displacement cloud diagram of the particles in the sleeper corresponding to different loading frequencies. Figure 9 shows that under the action of cyclic loading, the sleeper area under the rail had a large vertical displacement, while the sleeper particle displacement in other areas was small. Under the cyclic loading times, the maximum displacement value of the sleeper increased with increasing loading frequency.  
Figure 8: Sleeper-displacement cloud map corresponding to the same number of cycles at different loading frequencies. (**a**) 5 Hz. (**b**) 10 Hz. (**c**) 20 Hz. (**d**) 50 Hz.
# Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
## 3 System Model
### Task Model  
The QoS requirement is an important metric for actual tasks. In actual scenarios, different types of tasks have different QoS requirements. Previous researchers have also stated that real-time tasks, video tasks, audio tasks, and image tasks have various QoS requirements [20; 21]. Table 1 lists the QoS requirements for different task types.  
The data transmission rate and data delivery latency are the most commonly used parameters to describe the QoS demands of tasks [22]. Since transmission energy consumption is directly proportional to the transmission rate [20], the transmission energy consumption and data transmission delay are chosen to determine the QoS demands of the tasks. As shown in Table 2, the QoS levels of the two metrics are set from \(l_{1}\) to \(l_{4}\) according to their corresponding values described in Table 1, which indicate the QoS preference level is from strong to weak. For example, since the image data in Table 1 have the lowest latency requirement among all data types, the QoS latency level of the image task is set to \(l_{1}\).  
Further, the QoS vector of a task is proposed. For instance, the QoS vector of task \(t_{i}\) is set as \(\eta_{i}=\{\eta_{i,1},\eta_{i,2}\}\), where \(\eta_{i,1}\) and \(\eta_{i,2}\) denote the weight of energy consumption and the weight of data delivery delay, respectively. The calculation method for \(\eta_{i,1}\) and \(\eta_{i,2}\) is described in Algorithm 1.  
In Algorithm 1, \(P\in R^{5\times 2}\) represents the QoS level matrix, which is mapped from the matrix in Table 2. \(A_{i}\) denotes the type of task. First, the information of the task queue (\(T\)) is input, and the matrix correlation variables are initialized (lines 1-2). Then, the two weight coefficients corresponding to different variables are calculated separately (lines 3-6). Afterwards, we normalize the two weights again to obtain the final weight coefficients (lines 7-8). Finally, the resulting set of QoS weighting coefficient values \(\eta_{i}\) are added to the list of weighting coefficients (\(W\in R^{I\times 2}\)) (line 9).  
Each UAV-generated task can be described by a vector. For instance, task \(t_{i}\) is explained as vector \(\{S_{i},D_{i},A_{i},\eta_{i}\}\), where \(S_{i}\) represents the data size; \(D_{i}\) is the maximum latency requirement of the task; and \(\eta_{i}=\{\eta_{i,1},\eta_{i,2}\}\) represents the QoS requirements of the task, which are calculated with Algorithm 1.  
Moreover, the SPs are given incentives for executing the tasks. For task \(t_{i}\), the base incentive (\(R_{i}\)) is given as  
\[R_{i}=\alpha S_{i} \tag{4}\]  
where \(\alpha\) is a constant.  
The reward penalty (\(l_{i}\)) is described as  
\[l_{i}=\frac{D_{i}}{D_{i}-T_{i}^{al}} \tag{5}\]  
where \(T_{i}^{al}\) is the total delay for completing the task, which can be calculated as shown in Section 4.  
Hence, the incentive for SP \(m\) to perform task \(t_{i}\) can be given as  
\[re_{m,i}=\begin{cases}\frac{R_{i}}{l_{i}}&T_{i}^{al}<D_{i}\\ \frac{R_{i}}{D_{i}}&T_{i}^{al}=D_{i}\\ 0&T_{i}^{al}>D_{i}\end{cases} \tag{6}\]
## 4 Experimental Results
### MSSA Results
The PSECT method provides the best results, as shown in Figure 11. The Gabor filter and MSSA filter have similar performance over the WT filter on the extraction of the corrosion signal. However, the Gabor filter required to choose a specific cutoff frequency, while the MSSA filter is a nonparametric method. Figure 8: Decomposition of the multivariate magnetic scanned signal into trend signal, noise signal and corrosion signal by the MSSA. Figure 10: Magnetic scanned signal by the ECT method with the wavelet, Gabor and proposed MSSA filters. Figure 9: Magnetic scanned signal by the MFLT method with the wavelet, Gabor and proposed MSSA filters. Further advantages of the MSSA filter over the wavelet and Gabor filters could be observed in Figure 12. The magnetic scanned signal, including the end of the MSR tube, where the fins height is decreasing to zero, and the tube profile is changed. There is large corrosion with a diameter of 4.5 mm and a depth of 100% of the wall thickness. The signals from the large corrosion and end of the tube effect were much higher than the other corrosions. Thus, the retained principal components in the wavelet filter contain much information of these large signals, but less information of the small corrosions; then, all the small corrosion signals were eliminated from the wavelet filter. This phenomenon has not appeared in the Gabor filter because the Gabor filter analyzes the signal in the frequency domain. However, the significant signals have appeared at the start and end of the signals. This could be the result of the leakage frequencies in the Gabor filter. However, the effects have not happened in the MSSA filter. Thus, the MSSA filter is more stable than the other two filters. Figure 11: Magnetic scanned signal by the PSECT method with the wavelet, Gabor and proposed MSSA filters. Figure 6:0 Magnetic scanned signal by the PSECT method with the end of the tube effect. Figure 13 shows the magnetic scanned signal at different speeds of 20, 40, 60, 80 and 100 mm/s. The raw signals are on the left column, and the processed MSSA filter signals are on the right column. The intensity of the corrosion signal decreased as the speed increases. This could be the result of the noise signal increasing. The MSSA filter shows good filtering of the noise signal and the trend signal in all the experimented speeds that help to detect the corrosion more clearly.
# Trunk Borer Identification Based on Convolutional Neural Networks
## 3 Data and Processing  
### Vibration Signal Collection of Pests  
In the process of collecting the vibration signal of _Agrilus planipennis_ Fairmaire larvae, it is necessary to embed the probe into the branches of the tree. The characteristics of the larvae are different from the worms which generally have vocal organs. The vibration signals generated when the pests act in the wood segments are collected by piezoelectric sensors, recorded as audio and stored. Coupling the collection probe with the tree trunk, not only reduces the difficulty of information acquisition, but also improves the purity of the signal.  
In order to simulate the natural situation to a great extent, it is more realistic to approximate the actual application environment. In this study, two types of data were collected, one was the vibration sound of _Agrilus planipennis_ Fairmaire larvae's activity in the wood segments of ash tree, and the other was the outdoor noise audio.  
The collection of pest vibration signals was carried out in a soundproof room, and the collection equipment was SP-1L probe and self-developed sensor probe. The acquisition equipment is shown in Figure 1. SP-1L is a piezoelectric sensor probe with a sampling frequency of 44.1 kHz and a sampling accuracy of 16 bit. The front side is connected to a 6 mm metal probe, and the vibration signal of the pest is recorded directly after being embedded in the trunk, as shown in Figure 2. The cylinder is the schematic diagram of the wood segment. According to the working principle of SP-1 L, the self-developed sensor was made of piezoelectric ceramics, drive amplification circuit, AC and DC amplification circuit. The recording effect of the self-developed sensor is the same as that of SP-1L, which reduces the cost of acquisition equipment and improves the autonomy of technology.  
Figure 1: Acquisition equipment diagram.  
Selecting 18 ash tree segments with a length of about 30 cm in the forest farm, we grouped them according to the growth state of the trees. In this case, we investigated trees in different damaged situations. We drilled holes in the middle of each wood section to facilitate insertion of the probes. During the period of frequent activity of the pest (around July), activity vibration signals were recorded using acquisition equipment, and the sound was recorded purely. The entire process took place in a closed and quiet environment. We stopped recording when the pests' activity was reduced to being undetectable. In this case, the effective vibration signal cannot be monitored. Keeping the consistency of the acquisition equipment, this research recorded the sound of the control group in an outdoor open environment, such as woods and roadsides. These include birdsong, pedestrian sounds and car sounds. All the sounds recorded were saved in.wav format.
# Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO
## 3 Design of the Speed Controller Based on Improved SMC
### Controller Stability Proof  
The stability proof of the novel convergence law is performed using a Lyapunov function. The Lyapunov function is obtained as follows [38]:  
\[V=s^{2}/2 \tag{25}\]  
When the Lyapunov stability theorem, \(\dot{V}=s\dot{s}\leq 0\), is satisfied, the system is stable. In this system, the following holds true:  
\[\dot{V}=s\dot{s}=[-\varepsilon\text{arctan}|x_{1}|sat(s)\cdot s]/[\eta\exp(- \delta|s|)]-qs^{2} \tag{26}\]  
In the formula, \(\varepsilon\) and \(\eta\) are constants greater than \(0\), and \(sat(s)\) has the same sign as \(s\), so \(\dot{V}=s\dot{s}\leq 0\) is valid. This improved convergence law satisfies the [PERSON] stability theorem and the sliding mode arrival condition, which enables the system to arrive near the origin from any position in the phase space in finite time, i.e., convergence is always achieved.
# Differences in Performance of Models for Heterogeneous Cores during Pulse Decay Tests
## 3 Dual-Porosity Model with Different Matrix Shape
### Effect of Storativity Ratio  
The numerical modeling results of pulse decay tests for the dual-porosity medium at different storativity ratios are shown in Figure 4. The pressure and pressure derivative curves of both the early (\(t_{\rm D}<10\)) and equilibrium stages (\(t_{\rm D}>10^{5}\)) are completely coincident, and only slightly different in the interporosity flow stage (\(10<t_{\rm D}<10^{5}\)). The pressure in the interporosity flow stage decreases as the storativity ratio increases, but the pressure derivative is less affected by it. As the storativity ratio increases, the pressure derivative decreases slightly. The difference in pressure derivatives of different matrix shapes during the interporosity flow stage is almost unaffected by the storativity ratio.  
Figure 3: Effect of vessel volume on the pulse decay test for dual-porosity cores. (**a**) Pressure history. (**b**) Pressure derivative.
# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
###### Abstract  
In the context of rapid industrialization, efficiently detecting metal corrosion areas has become a critical task in preventing material damage. Unlike conventional semantic segmentation targets, metal corrosion characteristics vary significantly in color, texture, and size. Traditional image segmentation methods need improvement in scenarios involving occlusions, shadows, and defects. This paper proposes a convolution and sequence encoding combined network, MCD-Net, for metal corrosion area segmentation. First, a visual Transformer sequence encoder is introduced into the convolutional encoder-decoder network to enhance global information processing capabilities and establish long-range feature dependencies. A feature fusion method based on an attention module is proposed to enhance the model's ability to recognize corrosion boundaries, thereby enhancing segmentation accuracy and model robustness. Finally, in the model's decoding stage, a score-based multi-scale feature enhancement method is employed to emphasize significant features in the corrosion areas. Experimental results indicate that this method attained an F1 score of 84.53% on a public corrosion dataset, demonstrating the model's deeper understanding and reasoning capabilities for shadow and defect features, as well as excellent noise resistance performance.  
deep learning; metal corrosion area detection; Transformer; attention mechanism; feature fusion +
Footnote †: journal: Applied sciences
## 3 Discovering Polysaccharides in Africa
### A Focus on Bioactive and/or Functional Polysaccharides from Arid and Semi-Arid Lands
#### 3.3.1. Acacia  
More than 1000 of _Acacia_ species were described around the world, but the gummy exudates are produced from specific acacia trees growing in a large belt of semi-arid land across sub-Saharan Africa. It extends through northern central Africa, from east Africa to southern Africa. The world largest producer is Sudan with production achieving forty thousand tons in 1996 followed by Nigeria then Chad, Mali and Senegal [115]. Polysaccharides extracted from _Acacia_ species are often described as \(\beta\)-(1,3)-galactan, and the presence of other monosaccharides such as Ara, Rha and GlcA are often also reported (Table 3). The polysaccharide isolated from _A. glomeras_ gum was very close to those reported from _A. senegal_. It was mainly consisting of a \(\beta\)-(1,3)-D-Galp backbone, and Ara residues can be branched up to four units along to the galactan backbone in O-6 position. Terminal residues were often described as Rha unit, and GlcA and its 4-O-Me derivative seemed to represent the two kinds of uronic acids for this gum [116]. Water-soluble light brown gum was produced by _Acacia_ _macraantha_ gum, found close to Madagascar [121]. This gum species was characterized for its atypical features, such as negative specific rotations and a Gal/Ara ratio > 1 [122]; in comparison to few reports for other _Gummijferae_ spp. gums [121]. Regarding the literature, it was reported as a complex arabinolactan-protein [123]. Gal, Ara and GlcA, with a molar ratio around 3:2:1, were the major monosaccharides described for _A. macraantha_ gum polysaccharide. The main backbone was essentially a \(\beta\)-(1,3)-galactan, with GlcA, Ara as well as Rha residues [117; 123]. Arabic gum (GA), also known as Acacia gum, is obtained from exudates of _A. senegal_ and _A. seval_ trees, which can be found across the Sahelian area of Africa [124]. GA was described as a naturally complex polysaccharide, used especially in food industry as emulsifier and stabilizer agent [125]. The main backbone was described as a \(\beta\)-(1,3)-galactan with uronic acids and Rha in terminal positions all along the structure and some side chains composed of uronic acids, galactan or both [126]. Regarding the species, some differences can be found in the composition of these \(\beta\)-(1,3)-galactan from _A. senegal_ and _A. seval_ (Table 4).
## 3 Results  
### Systematic Literature Search  
The search covered all articles published between 1971 and June 2021. After duplicates were removed, 953 articles were identified (Figure 1). Finally, a total of three articles that contained data regarding the number of the root-canal-treated teeth with confirmed VRF that presented the combination of deep periodontal pocket and presence of sinus tract were included for the systematic review [8; 9; 10].  
The identified suitable articles were subjected to data extraction as presented in Table 1.  
Gender was available for 251 VRF cases [8; 9]. There were 156 women (62%) and 95 men (38%).  
The tooth location was available for 292 VRF cases [8; 9; 10] there were more posterior teeth 226 (77%) than anterior 66 (23%).  
Radiographic appearance of \"halo\" was found in 67 VRF cases (44%) out of 151 cases available [9; 10].  
The presence of deep periodontal pockets was reported in 179 of 351 VRF cases (51%) [8; 9; 10].  
The presence of sinus tract was reported in 161 of 351 VRF cases (46%) [8; 9; 10].  
The combination of the presence of deep periodontal pocket and sinus tract was reported in 100 of 351 VRF cases (28%) [8; 9; 10].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & **Number** & **Gender** & **Tooth** & **Radiographic** & **Sinus** & **Deep** & **Pathognomic** & **VRF** \\  & **of Cases** & **Type** & **Appearance** & **Tract** & **Pocket** & **Combination** & **Confirmation** \\ \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} [PERSON] et al., \\ 1999 \\ \end{tabular} } & \multirow{2}{*}{92} & Anterior-13 & \multirow{2}{*}{Halo-36} & \multirow{2}{*}{32} & \multirow{2}{*}{62} & \multirow{2}{*}{22} & \multirow{2}{*}{Extraction} \\  & & Posterior-79 & & & & & \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} [PERSON] et al., \\ 2014 \\ \end{tabular} } & \multirow{2}{*}{200} & Male-69 & Anterior-53 & \multirow{2}{*}{} & \multirow{2}{*}{39} & \multirow{2}{*}{82} & \multirow{2}{*}{53} & \multirow{2}{*}{Extraction} \\  & Female-123 & Posterior-147 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The characteristics of the included studies.