esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Label Correlation in Multi-Label Classification: A Survey
## 6 Analysis and Discussion  
The paper explores the application of label correlations in MLC, with a focus on analyzing both positive and negative correlations between labels and their impact on classification performance. It examines the applicability of both global and local label correlations, the granularity of shared correlations, and methods for integrating label correlations within MLC models. Additionally, it highlights the beneficial effects of incorporating label correlations on classification performance. Moreover, it is crucial to flexibly adjust the application strategies of label correlations according to the characteristics of different tasks and datasets. Furthermore, when balancing global and local label correlations within models, it is essential to consider both computational complexity and classification performance. Despite significant progress in the application of label correlations in MLC, several limitations remain. Firstly, many methods rely on external knowledge sources (such as WordNet or ConceptNet), which may not be applicable to domain-specific datasets, leading to the introduction of incorrect label relationships. Additionally, large-scale datasets often face challenges related to high-dimensional feature data and noise. Secondly, when addressing label imbalance, the computation of label correlations can be biased and may not accurately reflect the true situation. Furthermore, current methods encounter high computational complexity when dealing with a large number of labels, with classifier chain methods being particularly sensitive to label order, which can lead to error propagation issues. Finally, existing research into label correlations remains limited in depth and breadth, often considering only simple co-occurrence or hierarchical relationships, and lacking in-depth exploration of complex label interactions. Therefore, while MLC theoretically holds broad application potential, current algorithms still exhibit notable limitations in handling complex label relationships and large-scale data.
# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 3 Results
### MCDA-Based Decision-Making Framework for PM Implementation
#### 3.3.2 Define Decision Variables and Criteria  
Creating a decision-making framework for implementing PM of aircraft components requires MCDA approach. We can define a model based on key criteria, each represented as a decision factor that can be quantified and evaluated.  
Let \(C=\{c_{j}\},j=\overline{1.4}\) represent the set of aircraft components being evaluated for predictive maintenance, and \(K=\{k_{i}\},\ i=\overline{1.14}\) represent the criteria used to assess each component for predictive maintenance in accordance with Figure 10.  
Each criterion \(k_{i}\) is assigned a weight \(0\leq w_{i}\leq 1\) based on its importance, where:  
\[\sum_{i=1}^{14}w_{i}=1\text{ and }w_{i}\geq 0\]  
Weights can be determined by expert judgment, regulatory priorities, or through AHP to ensure that each criterion's influence aligns with organizational goals.  
Each component \(c_{j}\) receives a score \(S_{ij}\in[0,1]\) for each criterion \(k_{i}\).  
This score indicates how well the component \(c_{j}\) aligns with each criterion \(k_{i}\). For example, reliability predictability score \(k_{1}\) shows the degree to which degradation patterns can be forecasted, reliability predictability score \(k_{2}\) shows the degree to which degradation patterns can be forecasted and so on in accordance with Figure 10.  
For each component \(c_{j}\), calculate an overall predictive maintenance suitability score \(P_{j}\) as a weighted sum across all 14 criteria:  
\[P_{j}=\sum_{i=1}^{14}w_{i}S_{ij} \tag{1}\]  
The higher the \(P_{j}\) score, the more suitable component \(c_{j}\) is for predictive maintenance. Define a threshold \(T\), such that:  
* If \(P_{j}\geq T\), then PM is recommended for component \(c_{j}\).
* If \(P_{j}<T\), then PM may not be beneficial or feasible for component \(c_{j}\).  
This threshold can vary depending on organizational goals, budget constraints, or safety considerations.  
Conduct sensitivity analysis by varying weights \(w_{i}\), scores \(S_{ij}\), or the threshold \(T\). This analysis will reveal the following:  
* The criteria that most influence the final decision.
* How robust the PM decision is to variations in scoring and weighting.  
* Potential adjustments to the model to reflect changing operational needs or new information. This expanded model provides a robust mathematical framework, allowing aviation maintenance teams to make data-driven, multi-criteria decisions on predictive maintenance for each aircraft component.
## 3 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Object** & **Object** & **Object** & **Object** & **Overall** & **No.** & **Object** & **Object** & **Object** & **Object** & **Overall** \\  & **Seg.** & **Range** & **Image** & **Info.** & **Acc.** & **Seg.** & **Range** & **Image** & **Info.** & **Acc.** \\ \hline \multirow{2}{*}{1} & & & & 3514 cm & & & & & & & 1611 cm & \\  & & & 432 cm & & 972 cm & & & & & 378 cm & 0.901 & \\  & & & & 972 cm & & & & & & 582 cm & \\ \hline \multirow{2}{*}{2} & & & & 6442 cm & & & & & & 5522 cm & \\  & & & 738 cm & & & & & & 517 cm & 0.868 & \\  & & & 779 cm & & & & & & 927 cm & \\ \hline \multirow{2}{*}{3} & & & & 4295 cm & & & & & & 1017 cm & \\  & & & 732 cm & & & & & & 65 cm & 0.772 & \\  & & & 644 cm & & & & & & 130 cm & \\ \hline \multirow{2}{*}{4} & & & 6442 cm & & & & & & & 4295 cm & \\  & & & 630 cm & & & & & 60 cm & 0.758 & \\  & & 1081 cm & & & & & & 207 cm & \\ \hline \multirow{2}{*}{5} & & & 1933 cm & & & & & & 3514 cm & \\  & & & 729 cm & & & & & 49 cm & 0.873 & \\  & & & 327 cm & & & & & 132 cm & \\ \hline \multirow{2}{*}{6} & & & 4832 cm & & & & & & 2577 cm & \\  & & & 222 cm & & & & & 46 cm & 0.840 & \\  & & & 248 cm & & & & & 810 cm & \\ \hline \multirow{2}{*}{7} & & & 4295 cm & & & & & & 1611 cm & \\  & & 822 cm & & & & & & 162 cm & 0.800 & \\  & & & 831 cm & & & & & 397 cm & \\ \hline \multirow{2}{*}{8} & & & 568 cm & & & & & & 2973 cm & \\  & & 169 cm & & & & & 577 cm & 0.807 & \\  & & 166 cm & & & & & & 130 cm & \\ \hline \hline \end{tabular} (Object Info. in order of Distance, Width, and Height).  
\end{table} 
Table 1: Object segmentation results and 3D information of test image 1. 1, respectively. From the original image, it can be observed by the naked eye that object 6 appeared to be an extension of the leaves with object 8. However, it can be observed from Table 2 that the distances of objects 1, 6, and 8 are 15.46 m, 29.73 m, and 11.04 m, respectively. Based on the distance of the objects, we can realize that object 8 is the tree in front of object 1, and object 6 is another tree behind object 1. Therefore, the proposed algorithm can effectively avoid the misjudgment of 2D images. The Middlebury dataset includes test images captured in indoor scenes by Canon DSLR cameras (EOS 450D). The focal length and baseline are different for each set of the test images, and the provided focal length is converted to the pixel unit of each image. The first set of test images selected from the Middlebury dataset is Test image 3. The experimental results of Test image 3 are shown in Figure 18. Figure 18a is the image pair. The image resolution is 2964 \(\times\) 2000 pixels, the focal length is 3979.911 pixels, and the baseline is 193.001 mm. Regarding relevant parameters, the maximum detection distance is 5.9 m, the separation distance is 10 cm, and the minimum reserved area is 25 cm\({}^{2}\). Figure 18b shows the segmentation result of the object edge contour. The object segmentation result is shown in Figure 18c. The background image is shown in Figure 18d.
## 2 Related Work
### Intent Detection and Slot Filling Joint Framework  
In early research, the tasks of intent detection and slot filling were traditionally handled separately. Intent detection was typically regarded as a text classification task, while slot filling was commonly viewed as a sequence labeling problem. For example, [PERSON] et al. [19] proposed a gate mechanism that integrates intent information to explore slotfilling. [PERSON] et al. [20] developed a SFID modeling framework that enables the two tasks to interact with each other in a mutually reinforcing way. [PERSON] et al. [21] designed a joint model with a stack propagation mechanism that is able to incorporate intent information, thus guiding slot filling. [PERSON] et al. [22] enhance semantic features by combining intention and slot information to dynamically guide text representation. Although the above studies have taken into account the relevance of the two tasks of intent detection and slot filling and have shown promising results, most of the studies have been conducted in English; however, Chinese text is more complex and diverse than English.  
On Chinese utterances, [PERSON] et al. [23] introduced a variant of the long short-term memory network, Lattice-LSTM, to encode all potential words into a character-based LSTM. [PERSON] et al. [24] proposed a more concise approach. They applied the idea of Lattice-LSTM to the character representation layer to achieve the encoding of the matching vocabulary. [PERSON] et al. [25] designed a word-character attention model for Chinese text categorization, combining word-level and character-level attention mechanisms to determine text categories. [PERSON] et al. [26] proposed to make full use of Chinese characters, words, and radicals to represent and classify Chinese texts more accurately. Recently, [PERSON] et al. [27] proposed a character-based approach to handle Chinese and English intent detection and slot filling tasks. [PERSON] et al. [28] proposed a multi-level word adaptation model for Chinese dialogue systems. [PERSON] et al. [29] proposed a multi-source information fusion network based on reading comprehension. They utilized a machine reading comprehension framework to explore tasks such as Chinese slot filling and intent detection. The above studies focus on intent detection and slot filling for Chinese sentences, involving applications related to characters and words. However, there are few intent detection and slot filling tasks applied to medical dialogues. Therefore, this paper investigates the joint task of intent detection and slot filling in medical dialogues by incorporating character and word information from contextual conversations, based on the respective characteristics of character and word information.
## 2 Materials and Methods
### Support Vector Machines (SVM)  
Given a training set of instance-label pairs \(\left(x_{i},\ y_{i}\right)\), \(i=1,\ \ldots,\ l\) where \(x_{i}\ e\ R^{n}\) and \(y_{i}\ e\ \{1,\ -1\}^{l}\), the SVM require the solution of the following optimization problem:  
\[\min_{w,\ b,\ \xi}\frac{1}{2}w^{t}\ w+C\ \sum_{i=1}^{l}\xi_{i} \tag{1}\]  
\[y_{i}\big{(}w^{t}\ \phi(x_{i})+b\big{)}\ \geq 1-\xi_{i} \tag{2}\]  
subject to \(\xi_{i}\geq 0\). Where \(w\) are the weight and the support vector is there are different zero, \(C\) is the cost function, \(b\) is the bias, \(\phi(x_{i})\) is the kernel of the input, \(y\) is the output and \(\xi_{i}\) is the value of the plane.  
Here training vectors \(x_{i}\) are mapped into a higher (maybe infinite) dimensional space by the function \(\phi\). SVM finds a linear separating hyperplane with the maximal margin in this higher dimensional space. \(C>0\) is the penalty parameter of the error term. Furthermore, \(K\ \big{(}x_{i},\ x_{j}\big{)}\ \equiv\ \phi(x_{i})^{T}\ \phi(x_{j})\) is called the kernel function [31].  
To obtain a system able to reach the smallest error, it has been tested using diverse kernel functions with the aim to obtain a better forecasting approach. Year 2006 was used in the training mode, the reason is explained at the end of this section. During training and test modes, the kernel functions used in this supervised classifier were as follows:  
Linear kernel:  
\[K\ \big{(}x_{i},\ x_{j}\big{)}=\ x_{i}^{T}\ x_{j} \tag{3}\]  
Radial basis function (RBF) kernel:  
\[K\ \big{(}x_{i},\ x_{j}\big{)}=\exp\Bigl{(}-\gamma\|x_{i}-\ x_{j}\|^{2}\Bigr{)}, \ \gamma>0 \tag{4}\]  
Polynomial kernel:  
\[K\ \big{(}x_{i},\ x_{j}\big{)}=\ \phi\big{(}\gamma x_{i}^{T}\ x_{j}+r\big{)}^{d}, \ \gamma>0 \tag{5}\]  
being \(\gamma\), \(r\) and \(d\) the parameters of the kernel.  
As a result of the training function, MATLAB returns a structure called SVMStruct, which contains information about the trained SVM classifier.
# Contribution of Etalon Observation to Earth Rotation Parameters under a New Observation Scenario
## 2 Data and Methods
### Methodology
In accordance with the recommendations of the ILRS data handling file, range biases were estimated only for some selected stations as constants in the weekly solution, which was station- and satellite dependent. For the LAGEOS-1/2 and Etalon-1/2 satellites, six Keplerian orbit elements and five empirical parameters were estimated. The empirical orbit parameters comprised a constant acceleration in the along-track and once-per-revolution sine and cosine accelerations in the along-track and cross-track directions, respectively. The predicted orbit provided by ILRS in the consolidated prediction format (CPF) was used as a prior orbit. The ERPs, including the polar motion Xp and Yp and UT1-UTC, were estimated as piecewise constants with a one-day interval. Then, the LOD parameter could be obtained by calculating the differences in the estimated UT1-UTC values. The BULLET_A time series of the ERP was used as a prior ERP, and the fourth UT1-UTC parameter was fixed to the BULLET_A series to provide the absolute orientation due to its linear correlation with the right ascension of satellite orbit nodes. The parameters of Xp, Yp and LOD were loosely constrained to the BULLET_A series with a sigma of 1 m.  
The options of the dynamic model considered for the LAGEOS+Etalon solutions are described in Table 4. The earth gravity model EGM2008 [38] was used up to degree and order 30 for the LAGEOS-1/2 and Etalon-1/2 satellites for modeling the earth nonspherical perturbation. The ocean tide model CSR 4.0 [39] up to degree and order 30 was employed for four satellites. The N-body perturbation caused by the Sun, Moon, Venus, Mars and Jupiter was calculated by the solar system ephemeris DE405 [40], which was provided by  
Figure 4: Number of weekly observations for LAGEOS-1 and LAGEOS-2. the Jet Propulsion Laboratory (JPL). The perturbation caused by general relativity, namely, Schwarzschild orbit perturbation, was modeled according to the 2010 IERS convention. In terms of nongravitational forces, the direct solar radiation pressure and Earth radiation pressure were included. The radiation pressure coefficients of Cr = 1.13 and Cr = 1.20 were applied to the LAGEOS-1/2 and Etalon-1/2 satellites for direct solar radiation pressure correction, respectively. The equations of motion for LAGEOS-1/2 and Etalon-1/2 were integrated using the method of [PERSON]'s collocation with an order of 12 and a step size of 120 s.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 1 Introduction
Second, based on the scenario, we analyzed fifteen combinations of three representative routing protocols (AODV, DSDV, and OLSR) with five reconnaissance mobility models (SRP, RDPZ, EGM, MP, and DPSR) in terms of PDR performance. Third, with consideration of a reconnaissance scenario, we measured the connectivity of nodes as well as the reconnaissance rate of a mobility model in our analysis. Based on these metrics, we could identify a protocol and a mobility model for the reconnaissance scenario. Last, for a fair evaluation of routing protocols, we developed and used our own mobility model simulator to generate the trace file of each mobility model. With the trace file, we could analyze each protocol under the same condition of a mobility model. Furthermore, the repeated simulation of the same trace file helped us to analyze the effect of node connectivity on the PDR performance of routing protocols and to analyze the effect of GCS location on the PDR performance of routing protocols. This paper is organized as follows: Section 2 discusses the relevant studies and their limitations. Section 3 describes the routing protocols, mobility models, and a reconnaissance scenario. Section 4 describes research questions, experimental environment, performance parameters, and experimental procedures to evaluate routing protocols. Section 5 reports the experimental results. Section 6 discusses the additional metric, end-to-end delay, and an additional factor, speed of nodes. The section then summarizes all experimental results and other issues. Section 7 concludes the paper.
## 4 Classification and Analysis of the Input Data  
### Types and Sources of Input Data  
Input data commonly refer to physical values such as voltage amplitude, frequency, active power, current, etc. Input data related to physical values are most often taken locally, near DG or on the PCC. Figure 7 shows the input data used for islanding detection using ANN. It also shows that the number and type of input data are determined based on one's knowledge and experience. Some methods and authors use only one type of input data, mostly voltage amplitude. The other authors use several types of input data, most of which are voltage amplitude and current. The introduction of rotor stability into the detection of islanding operation by monitoring the rotor angle [44; 45] should be specially highlighted, with the help of which it is much easier to conclude whether the generator is in synchronism with the grid or has gone into islanding.  
Figure 6: Flow chart of artificial neural network method in islanding detection.  
Different types of input data with associate research papers are shown in Table 1.  
Different types of input data types and associated literature.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input data** & **Reference** \\ \hline voltage amplitude & [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56] \\ \hline current & [57] \\ \hline rotor angle & [44; 45] \\ \hline frequency & [58] \\ \hline active power & [59] \\ \hline voltage and current amplitude & [45; 60; 61; 62; 63; 64; 65] \\ \hline voltage, current and frequency & [66] \\ \hline voltage, current, frequency and power factor & [67] \\ \hline voltage, frequency and active power & [68] \\ \hline voltage, current and rotor angle & [45] \\ \hline voltage amplitude and frequency & [69] \\ \hline voltage, frequency, current, active and reactive power & [70; 71; 72; 73] \\ \hline voltage, frequency, active and reactive power & [74] \\ \hline frequency and active power & [75] \\ \hline voltage, active and reactive power & [76] \\ \hline active and reactive power & [77; 78] \\ \hline \end{tabular}
\end{table}
Table 1: Review of input data types and associated literature.  
Figure 7: Different types of input data for artificial neural network.  
The sources of input data used for training and testing the IDMs are extracted into data obtained by measurement and downloaded from relays that can record data or fault recorders, or they are extracted in dynamic analysis real-time simulation using programs such as PSCAD-EMTDC, DlgSILENT, ATP, PSIM, and MATLAB. Table 2 shows the sources of input data, and most papers use measurements as a source of the input data.
applied sciences  
In Vitro Study Comparing Retention of Custom Post and Cores Fabricated Using Conventional, CAD/CAM Milling and 3D-Printing Techniques  
[PERSON] 1,2,*(c), [PERSON] 3, [PERSON] 4, [PERSON] 4, [PERSON] 5,6,*(c), [PERSON] 5(c), [PERSON] 7,8 and [PERSON] 9(c)  
1 Restorative and Prosthetic Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 14611, Saudi Arabia 2 King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia 3 Department of Prosthetic Dental Sciences, College of Dentistry, King Saud University, Riyadh 11545, Saudi Arabia 4 Advanced Specially Education Program in Prosthodomics, Loma Linda University School of Dentistry, Loma Linda, CA 92350, USA 5 Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia 6 Rutgers School of Dental Medicine, Rutgers University, Newark, NJ 07103, USA 7 Advanced Specially Education Program in Endodontics, University of California School of Dentistry, Los Angeles, CA 92350, USA 8 Private Practice, Newport Beach, CA 92600, USA 9 School of Nursing, Loma Linda University, Loma Linda, CA 92354, USA *Correspondence: [EMAIL_ADDRESS] (H.A.); drsayed2030 gmail.com (M.E.S.)  
This study aimed to evaluate the differences in the retention of custom-cast non-precious post and cores (CCNPPCs) (control group), custom-milled titanium post and cores (CMTPCs), custom-printed titanium post and cores (CPTPCs), and custom-milled zirconia post and cores (CMZPCs), and to evaluate their mode of failure. The tested null hypothesis was that there were no differences in the retention of the various custom post and cores tested. A total of 80 post-and-core patterns were made using pattern resin and were divided into four groups: Group 1--fabricated via conventional casting using a non-precious casting alloy; Group 2--fabricated using a computer-aided design/computer-aided manufacturing (CAD/CAM) subtractive technique using titanium; Group 3--fabricated using a CAD/CAM additive (3D printing) technique using titanium; and Group 4--fabricated using a CAD/CAM subtractive technique using zirconia. The post and cores were cemented with resin cement and a universal pull-out test was used to check the retention. The data were statistically analyzed using one-way ANOVA tests, post hoc tests, and [PERSON]'s adjustment for multiple comparisons. The pull-out test revealed frequent values for CPTPCs and CMTPCs. When compared with CMZPCs, the conventional CCNPPCs revealed significantly better retention values (\(p<0.05\)). Cohesive failure was observed in Groups 1, 2, and 4. However, Group 3 revealed a mixed type of failure. The CCNPPCs revealed clinically acceptable values, while the CPTPC and CMTPC groups revealed better overall values of retention and time to failure. The titanium alloy was assessed to be a promising choice for fabricating dental post-and-core restorations.  
titanium alloy; zirconia; retention; CAD/CAM; additive manufacturing; subtractive manufacturing; custom post and core; 3D printing; non-precious alloy