esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study proposed a TLS-based crack detection technique on concrete surfaces using improved image processing associated with data compression. For the data compression, an octree-based shape information model was constructed using laser scanning data. The octree shape information model first transforms the scan data from pixels to voxels in 3D pixels, and the voxel model is converted into an octree data structure to generate an octree-based shape information model. For crack detection using the TLS data, a combination of image processing algorithms is proposed to minimize the false recognition of cracks in diverse operation environments. The proposed algorithm can automatically identify the existence of cracks against stains, sediment, and structural joints with the help of K-means clustering. Furthermore, the concrete crack pattern is visualized through other image processing algorithms such as improved subtraction with median filtering, [PERSON]'s method, and morphological operations.  
A validation test was carried out on a spillway wall in a concrete dam using a Leica ScanStation C10 model located 30 m distance from the wall, and the results are summarized as follows:  
1. The original scan data 19.56 MB was compressed using the octree data structure up to the 31.6%, 50.3%, and 80.2% corresponding to the data size of 13.36 MB, 9.7 MB, and 3.86 MB, respectively.  
Figure 16: The comparison of the proposed method with [PERSON]’s method and the crack maps by visual inspection. (**a**) Crack pattern map; (**b**) [PERSON]’s method; (**c**) Proposed method.  
Most major cracks as well as some minor cracks due to drying shrinkage of concrete could be identified successfully even in the 50% compressed image of the proposed method.
3 The identified cracks by the proposed method had good agreement with the cracks obtained by visual inspection.
4 The proposed method could minimize the false recognition of cracks on the structural joints and sediments with the help of K-means clustering, compared to [PERSON]'s method.  
For further research, the proposed algorithm needs to be modified and improved with the better accuracy and generality for various cases and other optimization methods can be applied to determine the parameters required in the image processing. The imaging processing associated with the TLS measurement is expected to have an important role in the visualization of cracks and the acquisition of the exact crack information.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed device/analysis tools; [PERSON] and [PERSON] wrote the paper.  
This research was funded by the Incheon National University.  
This work was supported by the Incheon National University Research Grant in 2015.  
The authors declare no conflict of interest
###### Abstract  
Wavelet analysis (WA) decomposes laser Doppler (LD) microcirculatory signals into characteristic frequency intervals related to endothelial nitric oxide (NO)-independent, endothelial NO-dependent, neurogenic, myogenic, respiratory, and cardiac physiological influences. Since LD signals have a finite length, the WA results suffer from spectral leakage due to edge effects. The cone of influence (COI) delineates the regions of the wavelet scalogram where these effects become important. We aimed to determine whether accounting for the COI leads to significant differences in the WA results. Two typical patterns of LD signals were analysed: a baseline and a post-occlusive reactive hypereemia (PORH) signal. The WA spectra were constructed without and with excluding data affected by the COI. The relative power (RP = median power of each frequency interval/median power of the total spectrum) of the spectral components obtained without and with the COI was compared. Applying the COI correction did not significantly affect the baseline signals. On the contrary, in PORH, accounting for the COI resulted in significant differences in the RP of the endothelial NO-independent (\(p\) = 0.0005; Wilcoxon signed-rank test), endothelial NO-dependent (\(p\) = 0.0005), neurogenic (\(p\) = 0.0038), myogenic (\(p\) = 0.001), respiratory (\(p\) = 0.0002), and cardiac frequency bands (\(p\) = 0.0002). The results suggest that applying the COI correction to the WA results obtained from the LD signals is desirable, especially for transient signals.  
c 2024 14 11736 1
## 6 Discussions on Future Research Directions and Challenges
### The Joint Optimization of Reliability, Security and Resource Allocation  
An optimal global design for system security, reliability and secure resource allocation is an important research idea that has not yet been well discussed in the literature. Newly introduced features and technologies in the core 5G network make the design of secure transmission approaches necessary and challenging. To enhance the optimal adaptability of user experience and network performance in wireless transmission systems, the reliability, security and allocation of necessary resources should be considered jointly in secure system design [12; 184]. However, in many existing studies, except for some studies that have investigated the trade-off between energy consumption and security performance, most methods have been considered separately and individually to minimize the complexity of security design [9]. Therefore, achieving a joint optimization between reliability, security performance and resource consumption with low latency is a challenging problem. In this case, when the three optimization factors interact with each other, the proposed security mechanisms become potentially optimal and trustworthy. In order to achieve global optimization performance, some researchers [34] proposed an AI-based integrated scheduling system that can realize an optimal power allocation scheme and improve the security and reliability of legitimate transmission. The proposed scheme was designed based on the integration of AI and edge computing to reduce the computation latency within transmission channels. The proposed framework can be applied to the PLS paradigm to design signal processing techniques and secure resource allocation schemes. However, the energy dynamics within the storage capacities of smartphone devices and the distributed resource management in edge cloud computing make this framework challenging to offload tasks for users. Therefore, this area of research is still in its infancy and many issues need to be investigated in future research.
# Analysis and Improvement of Oversize Goaf Backfill Engineering Based on Fuzzy Theory
## 3 Fuzzy Analysis of Backfill Quality
### Measures to Improve Backfill Quality  
The quality of the goaf backfill is affected by multiple factors. As a complex engineering system, its operation effectiveness is affected by the material supply, the preparation method in the surface backfill workshop, and the underground goaf environment. Necessary measures should be taken to solve the practical problems revealed in the fuzzy analysis of the backfill engineering system. (1) In the backfill process at #17R, it was found that a single feeding point may cause slurry segregation. However, because blasting was implemented on both sides of the mine and the construction was near the end, the feeding pipe could not be installed on two sides of the upper drilling cavern. This issue resulted in the entire mine only having one slurry feeding location. Therefore, it is necessary to use two or three scattered feeding points in the continuous backfill of other goaves. (2) The particle size grade and the chemical composition of the tailings are critical to the transportation performance of the backfill slurry. For the tailings of the CaolouIron Mine, coarse particles were abundant: the diameter of particles exceeding 20 \(\upmu\)m was approximately 77.89%. Because the SiO\({}_{2}\) content was 71.06%, the hardness of the tailings particles was high, and the wear on the equipment was greater than the wear produced by the fine tailings particles in other mines. Therefore, the local loss of very fine tailings should be reduced as much as possible when the tailings enter the tailings silo. A flocculation sedimentation study was carried out in the Caolou Iron Mine and was applied in the production. These efforts improved the grading properties of the tailings particles. (3) A suspension corrugated filter tube was used to achieve drainage. A hole was drilled at the top of the rock cavern on the side opposite the feeding location. A rebar bolt of \(\phi\)28 mm was installed. The top of the filter tube was firmly fixed to the anchor rod. The bottom part of the filter tube passed through the V-shaped trench and a reserved hole in the retaining wall. Usually, there are two filter tubes. Because the tailings were coarse and settled fast, the water precipitated quickly. Through field observations and practice, 2-4 corrugated filter pipes must be added in appropriate positions in order to discharge the water from the slurry as quickly as possible. (4) The slurry concentration control is crucial to ensuring the quality of the backfill. According to a computer record and a manual verification test, when the concentration was 68-72%, the slurry flowed smoothly, and the corresponding slump was 27.5-24.5 cm. When the concentration of the backfill slurry was high, the bleeding of the slurry decreased. Therefore, the concentration of the backfill should be increased as much as possible to ensure acceptable fluidity when another goal is backfilled in the future. Moreover, the water used to clean the pipes before and after the backfill should not be discharged into the
# Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs
## 3 Methodology
### Footprint Estimation Approach
As previously noted, the infrared MODIS SST data are used as a proxy for the ground truth. Given that real observations are being used for the 'ground truth', they will include measurement errors, hence they are not quite the 'truth'. However, as long as the errors of the MODIS SST values are randomly distributed from matchup to matchup, they will only increase the \(\epsilon\) term in Equation (3). Specifically, the MODIS SST measurement model for the machup data used here can be expressed as:  
\[M_{775\times N}=M_{775\times N}^{true}+\phi_{775\times N}, \tag{8}\]  
where \(M^{true}\) are the true values of SST and \(\phi\) is the measurement noise associated with individual MODIS pixels. The weighted average of these values contribute to \(\epsilon\) through the \(M_{775\times N}H_{775\times 1}\) term. This term includes contributions from the retrieval algorithm, noise in the sensor and misclassification of cloud-contaminated pixels. It also includes any geophysical differences between AMSR-E SSTs and MODIS SSTs resulting from the fact that they are sampling over slightly different depth ranges; the upper 1 mm for the microwave retrievals and the upper 10 um for the infrared retrievals. Given that there is no reason to expect any of these errors to have a preferred location in the AMSR-E footprint, the only effect of MODIS errors or of the slight differences in the geophysical variable being measured is to increase the noise term in Equation (2) with a concomitant increase in the uncertainty of the estimated footprints. Because measurement noise and errors are unavoidable in practice, we adopt the uniform subsampling bootstrap procedure [20] to solve Equation (7). Let \((\tilde{A}_{n,1}^{1},\tilde{M}_{775\times n}^{1})\), \((\tilde{A}_{n,1}^{2},\tilde{M}_{775\times n}^{2})\),\(\cdots\), \((\tilde{A}_{n,1}^{R},\tilde{M}_{775\times n}^{R})\) be sets of samples obtained from \((A_{N,1},M_{775\times N})\) with \(n\ll N\). The elements of each set are drawn uniformly and independently, without replacement, from \(A_{N,1}\). For each of the bootstrap samples, we solved Equation (7) using the reduced matchup dataset. The resulting footprint was then obtained by averaging the individual bootstrap solutions. The following Algorithm 1 illustrates the scheme. ```
Input \(A_{N,1}\), \(M_{775\times N}\), \(R\), \(n\) Output \(H_{25\times 35}\)(footprint)
1:\(H\gets 0\)
2:for\(i\gets 1,R\)do
3: Sample \((\tilde{A}_{n,1},\tilde{M}_{775,n})\) from \((A_{N,1},M_{N,775})\)\(\triangleright\) uniform subsample bootstrap
4: solve \(H^{(1)}=\text{argmin}\|\tilde{M}_{775\times n}H_{775\times 1}-\tilde{A}_{n \times 1}\|_{2}^{2}\) subject to \(\sum_{j=1}^{775}H_{j}=1\quad\&\quad H\geq 0\)
5:\(H\gets H+H^{(i)}\)
6:endfor
7:\(H\gets H/R\)
```  
**Algorithm 1** Advanced microwave scanning radiometer-Earth observing satellite (EOS) (AMSR-E) footprint algorithm.
## 4 Experiment
### Time Complexity  
We have many different-scale features in our proposed method. In order to verify the efficiency of the method, we performed the time complexity analysis experiment to study its computational load.  
In the document-scale feature, since the Glove dimension is smaller than the hidden layer dimension of the LSTM, the main time complexity of the model is produced by LSTM, which is \(O\left(KE(D_{1})^{2}\right)\) where \(K\) is the number of LSTM layers; meanwhile, \(E\) is the sequence length, which is the length of the essay, and \(D_{1}\) is the number of the hidden layer units in LSTM.  
In the sentence-scale feature, we utilize the pre-trained SBERT model; additionally, the number of hidden layer units is greater than the length of the input sequence. Its main time complexity is produced by SBERT, which is \(\left(PLS(D_{2})^{2}\right)\) where \(P\) is the number of sentences in the essay, \(L\) is the number of layers in SBERT, \(S\) is the length of the sentence, and \(D_{2}\) is the hidden dimensions of the model in BERT.  
Figure 6: The influence of sentence vectorization on the final score.  
In the prompt-related features, Doc2 vec has been trained in advance and its time complexity is similar to the lookup table layer. The time complexity after cosine operation is \(O(D_{3})\) where \(D_{3}\) is the dimension of the essay and the prompt vectorization.  
In the shallow linguistic features, the main time complexity is \(O(D_{4}D_{5})\) where \(D_{4}\) is the input dimension that is the number of manual features and \(D_{5}\) is the dimension of the output.  
On the whole, since the size of \(D_{2}\) is much larger than \(D_{1}\), the sentence-scale feature has the largest time complexity and the document-scale features come next. The shallow linguistic features and prompt-related features are very small. In the sentence-scale features, BERT has a large count of parameters which has more layers and a big size of hidden layer nodes. Secondly, LSTM cannot be calculated in parallel and requires loop calculations of \(E\) sentences. Therefore, the extraction of sentence-level features requires a large time complexity. On the other hand, compared with other pre-trained models, our sentence-level feature extraction structure is already lighter.  
It can be shown from the time complexity analysis that the time complexity of the shallow linguistic features and prompt-related features is very small and they are easily extended to the distributed representations of other DNN-AES models; therefore, they will achieve a great performance boost with little cost, which shows the effectiveness of these two parts features.
## 3 Results and Discussion
### Chemical Properties of WPC  
The chemical structures of WPC and poplar were analyzed by ATR FT-IR and XRD. As shown in Figure 8a, the absorption peak at 1035 cm\({}^{-1}\) was ascribed to C-O symmetric stretching in cellulose and hemicellulose [31]. The bending deformation absorption peak of C-H in methyl and methylene appeared at 1456 cm\({}^{-1}\), and the stretching of C-H in methyl and methylene appeared at 2924 cm\({}^{-1}\)[32]. The broad peak near 3339 cm\({}^{-1}\) belongs to the O-H stretching of the hydroxyl groups of lignocellulosic [31]. It was noticed that the peak intensity at 3339 cm\({}^{-1}\) decreased to a different extent in WPC, hence indicating that the amount of hydroxyl groups decreased significantly after the combination of wood flour and plastic powder.  
Due to the crystalline structure of cellulose (Figure 8b), two obvious diffraction peaks of WPC and poplar were observed near the 20 values of 15.6\({}^{\circ}\) and 22.4\({}^{\circ}\) that represent (101) and (002) crystal faces, respectively [9; 18]. The CrI = (1 - I\({}_{\mathrm{Am}}\)/I\({}_{\mathrm{002}}\)) \(\times\) 100% was used to calculate the relative crystallinity [18]. The obtained relative crystallinities of WF/PVC-9, WF/ABS-8, WF/HDPE-9, WF/PE-9 and WF/PP-9 were 62.54%, 55.53%, 63.37%, 64.23% and 58.41%, respectively, which were lower than the relative crystallinity of poplar (68.31%).  
Figure 7: Microscopic images of the cross-section of (**a**) poplar and (**b**) WPC. Microscopic images of the surface of (**c**) poplar and (**d**) WPC.
## 3 Methodology
### Ensemble Classification
#### 3.3.4 Voting Algorithm  
A voting algorithm is a meta-model that performs the decision process by implementing several classifiers. It considers the factors influencing the decision and applies a combination rule to perform the final step. For instance, the algorithm combination rules are the product of probabilities, maximum, minimum, and average of probabilities.  
Due to the number of classes in a classification, majority voting cannot be performed due to the complexity of the task. This paper introduces the average classifier of probabilities approach to perform the decision. The average of the predicted probabilities can determine the class label.  
Suppose we have \(l\) classifiers \(C\), and \(c\) classes \(\Omega=\{\omega_{1},\ldots\ldots,\omega_{c}\}\). For instance, due to the classifiers considered in our experiment, \(l\) can be set to 3, and the value of \(c\) depends on the attack types. A classifier \(C\): \(R^{n}\)\(\rightarrow[0,1]\) accepts an object \(x\in R^{n}\) and outputs a vector:  
\[[p_{c_{i}}(\omega_{1}|x),\ldots p_{c}(\omega_{c}|x)] \tag{3}\]  
where \(p_{c}(\omega_{j}|x)\) is the probability set by the classifier to determine which object \(x\) belongs to a class \(\omega_{j}\). For each class \(\omega_{j}\), let \(m_{i}\) represent the mean of the probabilities assigned by the \(l\) classifiers, which can be calculated as  
\[m_{i}=\frac{1}{l}\sum_{i=1}^{l}p_{c_{i}}(\omega_{j}|x) \tag{4}\]
# Medical Gesture Recognition Method Based on Improved Lightweight Network
## 3 Model
### Establishment of the E-MobileNetv2 Model  
Because aiming for the MobileNetv2 network structure feature extraction ability is not enough, and it is easy to ignore negative-valued feature information, this paper proposes an improved MobileNetv2 network structure gesture recognition method, namely E-Mobilenetv2. E-MobileNetv2 takes MobileNetv2 as the network skeleton, adds the ECA module into the inverted residual structure, and improves the original activation function ReLU6, which is replaced by the improved activation function R6-SELU. The specific operation is as follows: Insert the ECA module after the depthwise convolution of a linear bottleneck structure of stride = 1. R6-SELU activation function is added after the first convolution and the second depthwise convolution of the two linear bottleneck structures, respectively, and the nonlinear activation functions at the remaining positions remain unchanged. The ECA module is added to enhance the network's capability of feature refinement and reduce the interference of irrelevant information. The improved R6-SELU activation function retained the diversity of feature information and enhanced the expression ability of the target features. The improved inverted residual structure is shown in Figure 7. It can bring a significant performance gain and significantly reduce the model complexity while keeping the network lightweight.  
The improved E-MobileNetv2 network structure is shown in Table 2. The operator indicates the processing method of the input feature map, t indicates the extension factor, c indicates the number of channels, n indicates the number of repetitions, and s indicates the size of the step. The linear Bottleneck structure with the ECA module is named Bottleneck_1. When n is greater than 1, the step size of the convolution in the first Bottleneck of each group is the value in the table, and the other step size is 1.  
Finally, the transfer learning method is used to learn the weights in similar tasks to avoid the model learning from zero, so that the model can obtain better initial weight parameters. In this study, the pre-training weights of MobileNetv2 in the ImageNet dataset were used to optimize the initial weight parameters of the model and enhance the generalization performance of the model to improve the recognition accuracy of the model.  
Figure 7: The overall framework of the network.
# External Slot Relationship Memory for Multi-Domain Dialogue State Tracking
## 5 Experimental Results
### Ablation Study  
The purpose of this ablation study was to evaluate the impact of the external slot relationship memory component on the model's performance and observe its effect on capturing slot relations. We designed four methods to evaluate the model's performance: (1) Removing the slot relationship component. (2) Using self-attention alone to capture slot relations. (3) Incorporating layer normalization in the external slot relationship memory component. (4) Incorporating double-normalization in the external slot relationship memory component. We used the MultiWOZ 2.1 for our experiments and employed the same train-validation-test split. The model architecture and hyperparameter settings remained consistent.  
As shown in Table 5, we observed the effectiveness of the external slot relationship memory in our model. On the other hand, the use of double-normalization to filter out irrelevant slot information had a significant impact on the external slot relation memory component.