esa-sceva/satcom-chunk-collection huggingface dataset:
# WEREC: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement
## 1 Introduction
Concepts also tend to appear in multiple subject areas (e.g., in deep learning and information technology). Rich linguistic understanding and semantic computation [18; 19] are needed to extract educational concepts effectively. Thirdly, an obstacle within this type of concept extraction entails the scarcity of extensive annotated data. Acquiring large-scale, high-quality annotated data is also labor-intensive. To allay problems in contextual awareness and semantic computation, scholars must examine how to fully represent educational concepts' semantic information. The unsupervised concept extraction methods that researchers typically use mostly rely on word frequency statistics, which can easily overlook low-frequency educational concepts and fail to capture underlying semantic information [16; 20; 21]. Fortunately, due to the increasing prevalence of pre-trained models, numerous scholars have started to use word embedding techniques for unsupervised concept extraction [18; 19; 22]. Even so, it is not desirable to directly incorporate pre-trained models into unsupervised concept extraction tasks: these models are trained on extensive corpora and may not cover every subject-specific concept. Therefore, how to refine pre-trained models to pinpoint more semantic information for educational concept extraction is another impressive research gap and merits attention. To bridge some of these gaps, we present an unsupervised method for extracting educational concepts from unstructured text. Our work makes the following contributions:  
1. A novel unsupervised method is proposed for educational concept extraction based on word embedding refinement. The proposed method, named word embeddingrefinement-based educational concept extraction (WERECE), efficiently integrates the semantic information of domain concepts. Its performance surpasses popular baselines and state-of-the-art algorithms in educational concept extraction. 2. We introduce a manifold learning algorithm to adapt pre-trained large language models to a downstream natural language processing (NLP) task. The algorithm fully considers geometric information in semantic computation and reinforces semantic clustering among educational concepts. 3. A discriminant function based on semantic clustering and Box-Cox transformation is developed to improve the accuracy and reliability of educational concept extraction. 4. Two real-world datasets are created for educational concept extraction and used to experimentally assess WERECE's effectiveness. We also evaluate how WERECE's parameter settings influence its performance. The rest of this paper is organized as follows. Section 2 gives an overview of studies on concept extraction and its educational applications. Section 3 describes WERECE and the method's main steps. Section 4 discusses our experiments and analysis of WERECE's performance in terms of precision, recall, and F scores. Finally, Section 5 presents conclusions and suggested future work.
## 3 The Fractal Model and the W-M Contact Model
### Deformation Stages of a Single Micro-Asperities  
The following studies the conditions for the existence of three types of deformation processes: elastic, elastoplastic, and plastic. Schematic diagram of the micro-asperity deformation process is shown in Figure 13.  
Figure 12: Schematic diagram of the fractal model base.  
1. Elastic contact In the [PERSON] contact theory, when \(\omega<\omega_{c}\), the micro-asperities are in an elastic contact state.
2. Plastic contact When \(\omega>6\omega_{p}\), the micro-asperities are in the stage of fully plastic contact deformation.
3. Elastoplastic contact When \(6\omega_{c}\leq\omega\leq 110\omega_{p}\), the micro-asperities is in the elastic-plastic deformation stage. The contact load and contact stiffness at the beginning and end of this stage should satisfy the conditions of continuous and smooth transition.  
According to [PERSON]'s contact theory, the relationship between the contact load \(f\) of micro-asperities and the deformation \(\omega\) can be known.  
Contact load:  
\[\begin{array}{ll}F_{ne}=\frac{16\varepsilon\pi^{1/2}G^{D-2}(ln\gamma)^{1/2}} {3L^{D-1}}a_{ne}^{3/2}\gamma^{n(D-1)}&a_{ne}\leq a_{nec}\\ F_{np1}=\frac{2}{3}KH\times 1.1282a_{nec}^{-0.2544}a_{np1}^{1.2544}&a_{nec}<a_{ np1}<a_{nepc}\\ F_{np2}=\frac{2}{3}KH\times 1.4988a_{nec}^{-0.1021}a_{np2}^{1.1021}&a_{nepc}<a_{ nep2}\leq a_{npc}\\ F_{np}=Ha_{n}&a_{ne}\leq a_{nec}&a_{np}\geq a_{npc}\end{array} \tag{25}\]
# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 3 Methods
### Methodology Overview
#### 3.2.2 Sign Correction Strategy  
Next, our approach follows the same procedure as Eigen-CAM: we perform SVD on \(O_{L=k}\) and extract its principal components, capturing the most significant variance in the feature activation. The localization map \(L_{Ours}^{c}\) is then obtained by projecting \(O_{L=k}\) onto the first principal component. The computation process is defined as follows:  
\[O_{L=k}=U\Sigma V^{T} \tag{11}\]  
\[L_{Ours}^{c}=O_{L=k}V_{1} \tag{12}\]  
However, a fundamental mathematical property of SVD results in the issue of sign ambiguity: When a matrix \(A\) is decomposed as \(U\Sigma V^{T}\), the column vectors of \(U\) and \(V\) can change signs simultaneously without affecting the final decomposition. This is because \((-1\cdot U)\cdot\left(-1\cdot V^{T}\right)=U\cdot V^{T}\). This phenomenon, known as sign ambiguity [21; 22; 23; 24], poses challenges in feature extraction and visualization by potentially changing the representation of salient regions, thereby affecting the stability, consistency, and interpretability of feature maps.  
To address this issue, we propose a Dynamic Sign Correction Mechanism as a post-processing step for the localization map. Specifically, we perform a directional analysis by comparing the absolute values of the extrema in the localization map, dynamically adjusting its overall sign. The correction mechanism is defined as follows:  
\[L_{Ours}^{c}=\begin{cases}\begin{array}{ll}L_{Ours}^{c},&\text{if }\left|\max(L_{Ours}^{c})\right|>\left|\min(L_{Ours}^{c})\right|,\\ -1\cdot L_{Ours}^{c},&\text{otherwise.}\end{array}\end{cases} \tag{13}\]  
Figure 4: 2D example demonstrating the effects of different gradient weighting strategies. The red part highlights the negative values, and blue part indicates positive (normal) values.  
This mechanism is based on the optimization principle of feature representation, which assumes that in a well-optimized saliency map, class-discriminative information should contribute positively. When the absolute value of the negative extrema significantly exceeds that of the positive extrema, it often indicates a suboptimal assignment of signs. By dynamically correcting the sign, our method ensures that the primary feature information remains aligned with the positive direction, thereby enhancing the consistency and interpretability of the localization map. Furthermore, this correction strategy not only rectifies potential directional bias but also enhances feature representation stability through a simple linear transformation.
## 2 Materials and Methods
### Data Preprocessing Methods
#### 2.2.1 find_peaksAlgorithm (Peak Search)  
The find_peaks algorithm (Algorithm 2) is designed to detect local maxima (peaks) in a one-dimensional numeric array. The main goal of the algorithm is to select point indices that correspond to peaks that meet the given criteria, particularly the \"prominence\" of the peak.  
Main Steps of the Algorithm  
Input data: The algorithm takes as input a one-dimensional array of numbers signal and an optional parameter prominence, which is a tuple (min_threshold, max_threshold). The prominence parameter specifies the minimum and maximum thresholds for \"highlighting\" the peaks to be considered significant.  
Local maxima: The first step of the algorithm identifies all local maxima in the input signal. A local maximum is defined as a point with index \(i\) whose value is greater than the values at the neighboring points \(signal[i-1]\) and \(signal[i+1]\) (\(signal[i-1]<signal[i]>signal[i+1]\)).  
```
1:Input:signal--one-dimensional array of numbers
2:Input:\(prominence=(min\_threshold,\ max\_threshold)\)--minimum and maximum excretion thresholds
3:Output:List of peaks indexes (maxima corresponding to a given prominence)
4:Initialize empty list peaks
5:Find all local maxima in signal (points where \(signal[i-1]<signal[i]>signal[i+1]\))
6:for each local maximum \(i\)do
7: Determine its prominence--the height of the peak relative to local troughs
8:if prominence \(\geq\) min_threshold and \(\leq\) max_threshold then
9: Add \(i\) to the peaks list
10:endif
11:endfor
12:return peaks
```  
**Algorithm 2** find_peaks  
Calculation of \"prominence\": For each local peak found, its \"prominence\" is calculated. The \"prominence\" of a peak is a measure of its height relative to the surrounding local troughs. The exact definition of \"prominence\" can vary, but, in general, it reflects the vertical distance from the top of the peak to the lowest contour point defined to the left and right of the peak until the higher peak or end of the signal is reached. (In the simplified scheme, the details of the prominence calculation are not disclosed, but it is assumed that such a calculation is made.)  
Threshold filtering prominence: After computing the \"prominence\" for each local peak, the algorithm applies threshold filtering. A peak is considered significant and is included in the list of results peaks only if its \"prominence\" is within the specified range, i.e., greater than or equal to min_threshold and less than or equal to max_threshold. If the prominence parameter is not set or thresholds are not specified, filtering may not be applied or may use default values.  
Output: The algorithm returns a list of peaks that contains the indices of local maxima that satisfy the prominence criterion (if given).  
Remarks  
This simplified find_peaks algorithm focuses on the basic steps of prominence-based peak detection. However, the presented scheme reflects the key idea of selecting significant peaks based on their prominence.
## 3 Research Case Study
### Experimental Tests
#### 3.1.3 Discussion  
In this research, seven RC bridges from the Tehran subway railway network were selected and checked. The riskiest bridge has been selected for detailed inspection and monitoring over time.  
To extract the probability of failure, it is possible to count the number of defects in each component divided by their approximate volume, and the probability of failure (\(P_{f}\)) is updatable in each time window. To find a critical case, seven RC bridges were checked during pre-posterior analysis. The probability of failure in each bridge was calculated based on their approximate volume according to Table 1.  
Based on the approximate volume and the quantity of the damaged area on the material in question, the probability of failure in each element was estimated during the visual inspection, as is shown in Tables 2 and 3A,B.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Bridge** & **Pier Type** & **Span** & **Average Span** & **Abtment** & **Bridge** & **Bridge** & **Deck** & **Material** \\  & & **Number** & **Length (m)** & **Height (m)** & **Width (m)** & **Length (m)** & **Area (m\({}^{2}\))** & **Volume (m\({}^{3}\))** \\ \hline
1 & - & 1 & 15.6 & 4 & 12 & 15.6 & 187.2 & 748.8 \\
2 & Wall, Single pier & 1 & 15.6 & 5 & 9.14 & 15.6 & 142.584 & 712.92 \\
3 & Several in each & 5 & 15.6 & 3 & 9.06 & 78 & 706.68 & 5653.44 \\
4 & - & 1 & 15.6 & 4 & 9.1 & 15.6 & 141.96 & 709.8 \\
5 & Several in each & 1 & 19.8 & 7 & 12 & 79.2 & 950.4 & 6652.8 \\
6 & Several in each section & 1 & 15.6 & 12 & 9.13 & 15.6 & 142.428 & 854.568 \\
7 & - & 1 & 12.6 & 6 & 9.06 & 12.6 & 114.156 & 684.936 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Approximate volume of elements for risk assessment.  
Figure 5: (**A**) Surface concrete with a minor defect. (**B**) Surface concrete with a major defect.  
Meanwhile, according to Figure 6, RC component degradation intensity status changes over an element's lifespan. This means that the durability index has to change in each period, not only based on repair activities but also according to a component's period of life.  
Therefore, in this research, it is desirable to estimate the tangent line of the function for the degradation model of an RC element in a short period of definite time [46]. To this end, the durability index estimated by the function of risk over time is as follows:  
\[D_{i}=\frac{dR_{t}}{dt} \tag{7}\]
## 3 The Proposed Pattern Recognition Method
### Feature Extraction Based on Time-Frequency Statistical Parameters  
When the shearer drum is cutting different types of coal seams, the time domain and frequency domain properties of the collected vibration signal will change. Hence, the time domain and frequency domain feature parameters are comprehensively utilized, in order to acquire more feature information. In this paper, seven time domain feature parameters (\(p_{1}\)\(\sim\)\(p_{7}\)) and seven frequency domain feature parameters (\(p_{8}\)\(\sim\)\(p_{14}\)), are used to extract the features of the original signal. Likewise,the 14 features (\(p_{15}\)-\(p_{28}\)) of the optimal PF component are also computed, presenting a total of 28 features. The formulas for calculating \(p_{1}\)-\(p_{14}\) are listed as follows:  
\[p_{1} =\frac{1}{N}\sum\limits_{i=1}^{N}x_{i} p_{6} =\frac{x_{max}}{\frac{1}{N}\sum\limits_{i=1}^{N}|x_{i}|} p_{11} =\frac{1}{K-1}\sum\limits_{j=1}^{K}\left(y_{j}-p_{8}\right)^{2}\] \[p_{2} =\frac{1}{N-1}\sum\limits_{i=1}^{N}\left(x_{i}-p_{1}\right)^{2} p_{7} =\frac{1}{N}\frac{\sum\limits_{i=1}^{N}x_{i}^{4}}{p_{3}^{4}} p_{12} =\frac{1}{K}\sqrt{\sum\limits_{j=1}^{K}\left[y_{j}\left(f_{j}-p_{ 9}\right)^{2}\right]}\] \[p_{3} =\sqrt{\sum\limits_{i=1}^{N}x_{i}^{2}} p_{8} =\frac{1}{K}\sum\limits_{j=1}^{K}y_{j} p_{13} =\frac{1}{K^{p_{12}}_{j}}\sum\limits_{j=1}^{K}\left[y_{j}\left(f_{j} -p_{9}\right)^{3}\right]\quad, \tag{21}\] \[p_{4} =\frac{1}{N}\sum\limits_{i=1}^{N}\left(x_{i}-p_{1}\right)^{3} p_{9} =\frac{1}{\sum\limits_{j=1}^{K}\left(f_{j}y_{j}\right)} p_{14} =\frac{1}{K\sqrt{p_{10}}}\sum\limits_{j=1}^{K}\left[y_{j}\sqrt{ \left|f_{j}-p_{9}\right|}\right]\] \[p_{5} =\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}^{4} p_{10} =\frac{1}{\sum\limits_{j=1}^{K}y_{j}}\sqrt{\sum\limits_{j=1}^{K} \left(f_{j}^{2}y_{j}\right)}\]  
where \(y_{j}\) denotes the frequency spectrum of the signal \(x(i)\), \(K\) is the number of spectrum lines, and \(f_{j}\) denotes the frequency value of the \(j\)th spectrum line.  
In these 14 features, the time domain feature parameters are mainly comprised of the mean, variance, root mean square, skewness, kurtosis, impulsion index, and kurtosis index. For the frequency domain feature parameters, \(p_{8}\) reflects the vibration energy in the frequency domain. \(p_{9}\) and \(p_{10}\) represent the change in the position of the dominant frequency. \(p_{11}\) and \(p_{14}\) characterize the degree of the spectrum's dispersion or concentration.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 1 Introduction
Existing systems often face limitations such as power inefficiency, reliance on wired connections, and installation complexity, which restrict  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Work** & **Measurement Method** & **Key Feature** & **Accuracy** & **Limitations** \\ \hline [PERSON] et al. [18] & IoT-based MEMS accelerometer system & Horizontal movement calculated from measured tilt & RMSE: 0.212\({}^{\circ}\) &  
real-time monitoring and scalability. For instance, the studies mentioned above utilized smartphones and CCTV systems for measuring bearing behavior over periods of 2 days and 17 days, respectively. These limitations highlight the need for a wireless, low-power, and energy-efficient solution that simplifies installation and maintenance while enabling prolonged and reliable operation in real-world environments. Moreover, existing methods often require the installation of multiple sensors or precise calibration processes to measure 3D displacement, which further increases the complexity of installation and operation. Therefore, the development of a simple and efficient system capable of reliably measuring multidimensional displacement is emerging as a critical research challenge. This paper proposes a method for the long-term measurement and monitoring of the 3D displacement of bridge bearing using low-power, CV-integrated IoT sensors and cloud servers for data management and processing. For displacement measurement, the method employs fiducial markers to provide reliable tracking and 3D displacement measurement. The precision and performance of the proposed method were verified through lab-scale tests. The developed system was applied to Dongbu Highway in Seoul, South Korea, collecting and analyzing actual behavior data over nine months. The contributions of the study are (1) the development of a low-power, CV-integrated IoT sensor for long-term bridge bearing monitoring, (2) implementation of a cloud server for efficient data management and processing, and (3) nine months of field validation on a bridge, demonstrating the system's performance in a real-world environment. The remainder of this paper is organized as follows: Section 2 describes the proposed approach, including the development of low-power hardware, software, and cloud-based data management systems. Section 3 focuses on experimental validation, covering lab-scale tests, power consumption analyses, as well as field deployment details such as experimental setup, raw data analysis, and anomaly detection using Gaussian Process Regression (GPR). Section 4 discusses the comparison between the proposed system and existing installed devices. Finally, Section 5 summarizes the key findings and contributions of this research, concluding the paper.
## 4 Communication Technologies  
As stated, trains will be responsible for collecting the information generated by the monitoring system. It is therefore necessary to study the different options to communicate the deployed WSN in the slab track with passing trains. Additionally, the network organization also has to be considered. For this task we have studied two main technologies: Radio transmitters and RFID (Radio-frequency identification). A third option, based on GPRS is briefly presented for the case where data mules are not available. Energy efficiency, the highest range with the least cost and fault tolerance are essential requirements to meet in the different architectures. The following subsections describe these alternatives of communication architecture. Due to cost limitations, GPRS and RFID communication architectures were not tested, but we think the study carried out could be useful.  
The knowledge of how much information can be sent to each of the data mule trains is essential to determine the feasibility of this system. This depends on two main factors:  
1. The train speed: Higher speed less data to communicate.
2. The antenna range of the radio transceivers in the train and slabs: Higher range, higher communication bandwidth.  
As an example, Table 1 shows the time that two radio transmitters (in the train and the slab track) are in range. In this example, we consider that both antennas have the same range and that the coverage is ideal.  
In a real scenario, the time each antenna has to transmit information to the train is determined by several factors such as the distance between radio transmitters on the track, train speed, amount of information you want to convey, etc. Let us note that at 320 km/h the time that the antennas remain in range is very small. Therefore, we must be sure that the communication is feasible and how it affects the rate of these results in a real scenario. Table 2 shows a summary of the different architectures presented in this section.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Train Speed(km/h)**} \\ \cline{3-10}  & & 50 & 100 & 150 & 200 & 250 & 300 & 320 \\ \hline \multirow{10}{*}{**Sensors**} & & 50 & 7.2 & 3.6 & 2.4 & 1.8 & 1.44 & 1.2 & 1.125 \\  & & 100 & 14.4 & 7.2 & 4.8 & 3.6 & 2.88 & 2.4 & 2.25 \\  & & 150 & 21.6 & 10.8 & 7.2 & 5.4
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 5 Proposed Coexistence Solutions for Uncoordinated LTE-U and Wi-Fi Networks  
The ultimate goal of this article is to propose a WiFi saturation based fair and efficient coexistence scheme for uncoordinated LTE and WiFi networks. In this section, we formulate and describe coexistence schemes for non-coordinated coexistence solutions. For comparison and validation purposes, we formulated simple rule based and classical Q-learning coexistence schemes. These coexistence schemes are formulated in such a way that they can be utilized for uncoordinated LTE-U and Wi-Fi networks. For this reason, the first two subsections of this section describe a simple rule based adaptive coexistence scheme and Q-learning based coexistence scheme for uncoordinated LTE-U and WiFi networks. The proposed ER and RER based Q-learning coexistence schemes are described in the last two subsections of this section.