esa-sceva/satcom-chunk-collection huggingface dataset:
# Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information
## 2 Methodology
### Initial Two-Layer Sparse Graph Construction  
Given an RGB image and an aligned depth map, we first convert the RGB image to the CIELAB color space and segment it into \(N\) superpixels using mean shift [39] algorithm. The superpixel is a small region in the image composed of a series of adjacent pixels with similar features e.g., color, brightness, texture, etc. Then, we construct an initial two-layer sparse graph \(G=(V,E)\) such as [40], where \(V=\{v_{i}|1\leq i\leq N\}\) denotes the nodes and \(E=\{e_{ij}|1\leq i,j\leq N\}\) denotes the edges between nodes. The graph is generated by connecting each node to neighboring nodes and the most similar node sharing a common boundary with its neighboring nodes. It is worth to notice that the nodes on the four boundaries of the image are connected to each other to reduce the geodesic between the background nodes. As [40] proves, compared with the ordinary two-layer graph, the two-layer spares graph can effectively avoid the interference from surrounding redundant nodes.  
In this work, we utilize the pre-trained FCN-32s network [41] to extract the color feature vector, the Euclidean distance \(c_{ij}\) in RGB color space and depth difference \(d_{ij}\) between superpixels \(i\) and \(j\) are defined as  
\[c_{ij}=\parallel\textbf{x}_{i}-\textbf{x}_{j}\parallel \tag{1}\]  
and  
\[d_{ij}=\left|d_{i}-d_{j}\right| \tag{2}\]  
where \(\textbf{x}_{i}\) is the mean color feature vector of superpixel \(i\), and \(d_{i}\) denotes the mean depth value of superpixel \(i\). The similarity \(a_{ij}\) between superpixels \(i\) and \(j\) is defined as  
\[a_{ij}=a_{ij}^{c}\cdot\left(a_{ij}^{d}\right)^{\varepsilon} \tag{3}\]  
where the coefficient \(\varepsilon\) adjusts the weight of depth information and set as 0.5, \(a_{ij}^{c}\) and \(a_{ij}^{d}\) represent the color similarity and depth similarity respectively, and are defined as  
\[a_{ij}^{c}=e^{-\frac{c_{ij}}{\varepsilon^{2}}} \tag{4}\]  
and  
\[a_{ij}^{d}=e^{-\frac{d_{ij}}{\varepsilon^{2}}} \tag{5}\]  
where \(\varepsilon^{2}\) is a parameter to control strength of the similarity which is set to 0.1. The affinity matrix \(\textbf{W}=\left[w_{ij}\right]_{N\times N}\) of the graph is defined as the similarity between two superpixels,  
\[w_{ij}=\left\{\begin{array}{ll}a_{ij},&\text{if}j\in\Omega_{i}\\ 0,&\text{otherwise}\end{array}\right. \tag{6}\]  
where \(\Omega_{i}\) is the neighbors of superpixel \(i\) based on the initial two-layer sparse graph.
## 4 Toward Cognitive Connectivity: The Case of Dynamic Bandwidth Management  
One of COGNIFOG's primary objectives is to implement essential features to enable cognitive connectivity: that is, to ensure end-to-end connectivity between services deployed across the IoT-edge-cloud continuum with self-adaptability. The interaction between COGNIFOG's building blocks will empower the system to (1) _monitor_ the current state of the connectivity layer, (2) _determine_ the most efficient configuration of network resources, and (3) _apply_ the necessary changes to achieve an optimal configuration dynamically.  
In particular, one of the features of cognitive connectivity is to provide dynamic BW management across network links in a system that supports multiple applications with heterogeneous traffic patterns and priorities. This is especially useful, for instance, in scenarios such as emergency response operations, where network infrastructure is scarce, prone to congestion, or directly unavailable. In such cases, employing BW management techniques to delay the flow of certain types of network packets can effectively ensure network performance for higher-priority applications.
## 4 Future Research Directions
### QoS Control  
While a lot of contributions have been available on some important aspects of WSNs, supporting QoS in WSNs is still a largely unexplored issue. In mechanism design, each participating entity is often called an agent. In a mechanism, the participants can obtain the maximum benefit by reporting their parameters subject to a given algorithm. The mechanism of this kind is called a Strategyproof mechanism and also known as credible mechanism. In other words, in strategyproof mechanism, truth telling is a dominant strategy. A rational participant in order to maximize its interests will tell the truth. Strategyproof can well coordinate resource sharing among autonomous entities and collaboration to achieve the system's self-support and healthy development.  
Mechanism design is the most important application of the non-cooperative game theory. Although the system can achieve NE by mechanism design, but often only a sub-optimal result is obtained. Pareto Optimality can be derived from cooperative game theory. The theory will help protect the overall performance of WSN. However, the cooperative game theory only considers the ideal computing model. This simplification not only ignores the corresponding QoS constraints, but also ignores the diversity of operations, resources, heterogeneous and dynamic environments. Therefore, it is necessary to consider QoS with reference to constrained cooperative game theory and focus on the following questions:  
(1) QoS constrained algorithmic mechanism design.  
(2) QoS constrained and group strategyproof distributed algorithmic mechanism design.  
(3) QoS constrained cooperative game theory.  
Based on the ideal computing model for the specific application, one should study the mechanism designs of different characteristics by considering QoS constrained and in combination with the cooperative game theory.
# A Convex Hull-Based Machine Learning Algorithm for Multipartite Entanglement Classification
## 1 Introduction
In that case, we can establish an equivalence relation stating that two states \(|\phi\rangle\) and \(|\psi\rangle\) are equivalent if the parties have a no probability of success when trying to convert \(|\phi\rangle\) into \(|\psi\rangle\) and also \(|\psi\rangle\) into \(|\phi\rangle\) when \(|\phi\rangle\) is a GHZ state and \(|\psi\rangle\) is a W state. However, this conversion can happen when both states are GHZ or W states. This relation has been termed stochastic equivalence. Their equivalence under SLOCC indicates that both states are again suited to implement the same tasks of QIT, although this time, the probability of a successful performance of the task may differ from \(|\phi\rangle\) to \(|\psi\rangle\). For instance, in a three-qubit case, we have that \(|\psi\rangle\) can be locally converted into \(|\phi\rangle\) if an operator \(A\otimes B\otimes C\) exists, satisfying  
\[|\phi\rangle=A\otimes B\otimes C|\psi\rangle \tag{2}\]  
where operator \(A\) contains contributions from any round in which party A takes action on its subsystem and likewise for operators \(B\) and \(C\). To make sure the opposite conversion can happen, each of these operators is necessarily invertible, particularly such that  
\[|\phi\rangle=A^{-1}\otimes B^{-1}\otimes C^{-1}|\psi\rangle \tag{3}\]  
There has been an abundance of methods for classifying separable and entangled states. For instance, for \(2\times 2\) and \(2\times 3\) systems, the PPT criterion is a well-known method [19], the computable cross-norm or realignment (CCNR) criterion is simple and strong [20; 21], and the entanglement witness is a necessary and sufficient criterion in terms of directly measurable observables [22]. However, criteria for classifying GHZ and W states are relatively few at present. In this work, we employ machine learning techniques to tackle  
Figure 1: Schematic picture of the structure of mixed states for three qubits. The convex set of all fully separable states (fs) is a subset of the set of all biseparable states (bs). The biseparable states are the convex combinations of the biseparable states with respect to fixed partitions sketched by the three different leaves. Outside are the genuine tripartite entangled states, the W class and the GHZ class. There are many more GHZ states than W states. Reproduced with permission from [PERSON], [PERSON], Physics Reports; published by Elsevier, 2009.  
the GHZ and W states by recasting them as learning tasks. Namely, we attempt to construct a GHZ-W classifier. Our idea is to give the classifier a large number of sampled trial states and corresponding category labels and then train the classifier to predict the category labels of new states that it has not encountered before.
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 4 Control Methods of Servomotors
### Programmable Control  
Programmable control of a servomotor is based on the use of specialized software and external controllers to manage the speed, positioning, and torque of the motor. This method is associated with the principle of integration with Industry 4.0. Programmable control, depending on the task, provides adaptive control and tuning, allowing integration with other control systems. Accordingly, this method of servo motor control finds its application in automated production control systems and robotics [93; 94; 95].  
By using an external controller, programmable control allows a range of desired positions to be defined through corresponding commands and supports various motion variations, such as smooth start and stop, acceleration profiles, maintaining constant speed or torque, and so on [81; 96].  
The use of feedback in the programmable control system is essential, and the feedback can be either incremental or absolute. Measuring the position or rotational speed of the servomotor's output shaft allows for a clearer response to errors that may occur during positioning and a smooth response for their elimination. The flexibility of control is also supported using different coordinate systems (including relative or external) [97; 98; 99].  
Interaction with other devices in the control system through the software method is ensured by using various protocols, such as EtherCAT, Modbus, etc. Additionally, the software method allows the creation of Supervisory Control and Data Acquisition (SCADA) systems for interaction between machines and humans. The use of different programming languages, as well as proprietary servomotor libraries, allows optimizing costs for control and integration of the motor into existing control systems [100; 101; 102; 103; 104].  
Programmable control, based on its features, has such drawbacks as programming complexity and implementation due to the presence of many disparate control systems,  
Figure 4: The fuzzy logic algorithm structure.  
and depending on computational power, there may be a loss of real-time performance. It is also worth noting that this method is the only one among all those analyzed in this paper that requires constant debugging and maintenance of all involved systems.  
Programmable control is illustrated using the example of a digital twin of a wind turbine, presented at the beginning of the article. This method is ideal for integrating multiple different interfaces into a unified system and enables the quick analysis of input data from various sources, making it a versatile approach to software methods.
## 2 Problem Formulation
### Environmental Settings
#### 2.2.2 Unknown Obstacle Avoidance  
In practical operations, the HAUV is always subject to finite sensing resources; it cannot monitor the whole path at one time. Therefore, in this work whole path is divided into several subpaths. In the path \(P\)\(\{subP_{1},subP_{2},\ldots,subP_{n}\}\), each subpath contains more than 5 and less than 10 spots. Within the duration of \(subP_{i}\)\(\{p_{m},p_{m+1}\ldots,p_{m+n}\}\), randomly shaped obstacles can appear with a certain probability in path segment \([p_{m+i},p_{m+i+1}]\). We regard each obstacle as scattered points, and use k-means to find the center of each obstacle. For obstacles \(obs_{j}\) whose distances to the \(subP_{i}\) are less than the length of the  
Figure 4: Four types of trip according to the HAUV’s mission mode. A blue arrow illustrates an underwater path, and an orange one means an aerial one. Regarding when the trip crosses domains, the intersection point on surface will be inserted into the target spots set.  
HAUV, collisions happen. All the obstacles can be written as \(OBS\)\(\{obs_{1},obs_{2},\ldots,obs_{k}\}\). For the \(j_{th}\) obstacle \(obs_{j}\) in \(subP_{i}\), all points on \(obs_{j}\) are \([obs_{j}^{1},obs_{j}^{2},\ldots obs_{j}^{st}]\). During the path segment from \(p_{m+i}\) to \(p_{m+i+1}\), if obstacle \(obs_{s}\) exists, let the farthest distance \(r_{s}^{1}\) from \([obs_{s}^{1},obs_{s}^{2},\ldots obs_{s}^{k_{s}}]\) be the radius. Then draw a circle \(CIR_{s}^{1}\), such that \(CIR_{s}^{1}\) is free of obstacles. Then, extend \(CIR_{s}^{1}\) to \(CIR_{s}^{2}\), whose radius is denoted as \(r_{s}^{2}=veh_{L}+r_{s}^{1}\). Therefore, the space in \(CIR_{s}^{2}\) will be collision-free for the HAUV. The method in a two dimensional environment is as shown in Figure 5. Extended to three dimensions, the strategy also works.  
Finding the free point set on \(CIR_{s}^{2}\) and picking out one to insert into the path can be generalized to a KNN problem--a fixed-radius near neighbors problem: picking one lowest-cost spot from the set and inserting it into the original succession of path spots. If there is no solution on \(CIR_{s}^{2}\), the circle \(CIR_{s}^{2}\) must be extended again such that \(r_{s}^{2}=veh_{L}+r_{s}^{2}\). In Section 3.2, we present our method to finding a solution.
# Grammar Correction for Multiple Errors in Chinese Based on Prompt Templates
###### Abstract  
Grammar error correction (GEC) is a crucial task in the field of Natural Language Processing (NLP). Its objective is to automatically detect and rectify grammatical mistakes in sentences, which possesses immense application research value. Currently, mainstream grammar-correction methods primarily rely on sequence labeling and text generation, which are two kinds of end-to-end methods. These methods have shown exemplary performance in areas with low error density but often fail to deliver satisfactory results in high-error density situations where multiple errors exist in a single sentence. Consequently, these methods tend to overcorrect correct words, leading to a high rate of false positives. To address this issue, we researched the specific characteristics of the Chinese grammar error correction (CGEC) task in high-error density situations. We proposed a grammar-correction method based on prompt templates. Firstly, we proposed a strategy for constructing prompt templates suitable for CGEC. This strategy transforms the CGEC task into a masked fill-in-the-blank task compatible with the masked language model BERT. Secondly, we proposed a method for dynamically updating templates, which incorporates already corrected errors into the template through dynamic updates to improve the template quality. Moreover, we used the phonetic and graphical resemblance knowledge from the confusion set as guiding information. By combining this with BERT's prediction results, the model can more accurately select the correct characters, significantly enhancing the accuracy of the model's prediction correction results. Our methods were validated through experiments on a public grammar-correction dataset. The results indicate that our method achieves higher correction performance and lower false correction rates in high-error density scenarios.  
Chinese grammar error correction; prompt templates; pretrained models; bidirectional long short-term memory network; conditional random fields; confusion set Article
# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 3 Proposed System
### Feature Derivation and Selection
More importantly, each new feature is produced through the connection of several indicators, which is meaningful for improving the interpretability. Figure 2: Overview pipeline of our proposed feature engineering method. A feature derivation table is manually designed by filling with \(\mathcal{N}_{c}\) major categories, each with \(n_{i}\) sub-indexes. The feature size is enlarged into \(\mathcal{N}_{d}\) based on the table. Weakly influential features are filtered out during feature selection, and the feature size is reduced into \(\mathcal{N}_{s}\). After the process of feature derivation, the size of the input features is expanded from \(\mathcal{N}_{0}\) to \(\mathcal{N}_{d}\). To eliminate the existence of meaningless padding values and weakly influential features, feature selection is essential. The feature selection approach is introduced in Algorithm 1. The selecting pipeline can be divided into four parts: chiMerge, weight of evidence (WOE), Pearson correlation coefficient and LR. After the multi-step procedure of selecting influential features, the total volume of features decreases from \(\mathcal{N}_{d}\) to \(\mathcal{N}_{s}\). Selected features will be fed into the follow-up networks. ```
Input: derived features \(\lambda=\{\lambda_{i}\}_{i=1}^{\mathcal{N}_{d}}\), thresholds \(\theta_{\chi},\theta_{r},\theta_{p}\) Output: selected features \(\lambda^{\prime}=\{\lambda_{i}\}_{i=1}^{\mathcal{N}_{s}}\)
1 chiMerge [44] (\(\lambda\)) \(\rightarrow\) {\(\chi_{i}\)}\({}_{i=1}^{\mathcal{N}_{d}}\), {\(bin^{(j)}\)}\({}_{j=1}^{m}\)for\(i\gets 1\)to\(\mathcal{N}_{d}\)do
2 if\(\chi_{i}<\theta_{\chi}\)then
3\(\lambda\leftarrow\lambda-\{\lambda_{i}\}\)
4 WOE (\(\lambda,bin\)) \(\rightarrow\)
```  
**Algorithm 1**Feature Selection. Figure 3: An example to illustrate our proposed feature derivation method. We iteratively traverse all the feature collections in the feature derivation table to enlarge the feature size. At each iteration, one sub-index is selected from each major category to form a new feature.
# Safe and Sustainable Design of Composite Smart Poles for Wireless Technologies
## 3 Results
### Finite Element Analysis  
FEA was used to predict the effects of cutouts (maintenance doors) when the pole is subjected to the wind load and thermal load (e.g., due to radiation from the sun). For most of the design cases, the critical details are the cutouts designed for connections (cabling in and out from the pole) or maintenance, typically located at the shaft root with a high level of bending moment. Figure 10 shows the failure analysis for the diamond-shaped cross-section at the root cutout, where the maximum stress criterion predicts the onset of damage. The exact type and form of the failure criterion required analysis for the curved shape and different strain-rates [34]. Finally, the confirmation of the selection was made by comparing with the experiments (see Section 3.1). A 5 mm-wall (for the load-carrying layers) thickness represented a safe and low-weight solution.  
For the composite shaft, the thermal strains (per material's coefficients of thermal expansion) are governing the absolute deformation of the composite pole for the anticipated, average operation environment, i.e., non-storm weather. For this reason, additional thermal analyses were carried out together with the wind load analysis (Figure 11). The pole design with the circular cross-section experienced 15-64% higher deformations (51 mm compared to 84 mm with wind load and 64 mm compared to 73 mm with thermal load for the diamond-shaped and circular cross-section, respectively). Due to the anticipated services of the project (see Section 4.2), the level of deformations was not seen problematic and the lower costs of the circular cross-section overran the mechanical advantages of the diamond-shaped cross-section.  
Figure 9: The performance of the GFRP laminate and tube: the contact force-displacement response based on experiments and FEA (linear material [27]). The inset graph on the right shows the detailed data at the beginning of the tests/analyses and predicted displacement level for damage onset.  
**Shaft stress analysis:**  
Failure index (maximum stress criterion)  
Wall thickness 5.0 mm  
Wall thickness 8.0 mm  
Figure 11: FEA for the deformation when the pole shaft is subjected to the standard wind load (**left side image**) and a thermal load (**right side image**). Details of the highest deformation (pole top) are shown on the right.  
Figure 10: FEA for the failure index when comparing two different pole shaft laminates and a diamond-shaped cross-section (5 mm and 8 mm-wall thickness); corners (hotspot) at a root cutout are shown.
## 4 Three-Dimensional Fractal Model  
The modified normal stiffness fractal model for the joint is closer to reality after considering the elastic-plastic deformation and asperity interaction. Since the W-M function is a two-dimensional function, the fractal model is only a two-dimensional model, which does not match the actual three-dimensional morphology of the surface. Based on this, the two-dimensional model is extended into a three-dimensional model, and the friction factor is included. Therefore, the normal stiffness fractal model is further modified. [PERSON] et al. improved the traditional W-M function to obtain a three-dimensional fractal surface, and the modified W-M function is given as follows [20]:  
\[\begin{array}{rl}Z(x,y)&=L\bigg{(}\frac{G}{L}\bigg{)}^{D-2}\bigg{(}\frac{ \ln\gamma}{M}\bigg{)}^{\frac{1}{2}}\\ &\cdot\sum\limits_{m=1}^{M}\sum\limits_{n=0}^{n_{\text{max}}}\gamma^{(D-3)n} \cdot\Bigg{\{}\cos\varphi-\cos\Bigg{[}\frac{2\pi\gamma^{n}\big{(}x^{2}+y^{2} \big{)}^{\frac{1}{2}}}{L}\cdot\cos\big{(}\tan^{-1}(\frac{y}{x})-\frac{\pi m}{M }\big{)}+\varphi\Bigg{]}\Bigg{\}}\end{array} \tag{37}\]  
where \(D\) is the fractal dimension (\(2<D<3\)), \(G\) is the fractal roughness, \(\gamma\) is the scale factor, \(L\) is the sampling length, \(M\) is the number of surface ridges, \(\varphi\) is the random phase, and \(n\) is the frequency index.  
Simulation of the 3D W-M function, as shown in Figure 9, reveals that the surface is smoother as \(D\) increases, which is consistent with the known conclusions, indicating that the actual surface topography can be better simulated with the 3D fractal model.  
Figure 8: Modal verification.  
From Equation (37), the deformation of asperity can be derived as  
\[\delta=2^{\frac{11-30}{2}}G^{(D-2)}(\ln\gamma)^{0.5}\pi^{\frac{D-3}{2}}a^{\frac {3-D}{2}} \tag{38}\]  
From Equation (38), the equivalent radius of curvature of asperity can be derived as  
\[R=\frac{2^{\frac{30-11}{2}}\pi^{\frac{1-D}{2}}G^{2-D}a^{\frac{D-1}{2}}}{(\ln \gamma)^{0.5}} \tag{39}\]  
Considering the friction factor, the critical deformation of asperity is given as [21]  
\[\delta_{c}=\left(\frac{33\pi k_{\mu}\phi}{40}\right)^{2}R \tag{40}\]  
where \(k_{\mu}\) is the friction correction factor.  
When \(\delta=\delta_{c}\), the contact area \(a\) is the critical contact area \(a_{c}\), which can be calculated from Equations (38)-(40):  
\[a_{c}=2^{\frac{30-11}{2-D}}\left(\frac{33\pi k_{\mu}\phi}{40}\right)^{\frac{2 }{2-D}}\pi^{\frac{4-D}{2-D}}(\ln\gamma)^{\frac{1}{D-2}}G^{2} \tag{41}\]
## 4 Experimental Results
### Performance Analysis of the Proposed Model and Deep Learning Models
The accuracy and F1-score of the XmCNN model were 0.9018 and 0.8502, respectively, which were 7.56% and 23.16% higher than those of LightGBM, which had the best performance among the ML models. As a result, DL models predicted both positive and negative classes better than ML models, which means that meaningful information was extracted well from numerous features. Compared to the ML approach, the CNN model using the dropout regularization technique and the convolutional layer not only improved the representative capacity but also overcame the curse of dimensionality problem better by preventing overfitting.