esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs
## 2 Research Background
### Brief Summary of References  
A high dropout rate, low completion rate, and poor learning effects are problems that the MOOC platforms needs to solve [29,30]. Forming a customized learning sequence based on learners' characteristics is not a new idea. However, the learning paths generated at once based on learners' feature or learning data are not adapted to the reality of the learning situation [31]. It is necessary to consider the learners' changing state to construct a personalized learning path.  
This study proposes a dynamic learning path planning algorithm for the online learning scenario. Following previous research [6], this study also pays attention to the significant role of learners' changing states in learning. In contrast to the strategy of manually labeling the knowledge difficulty used in the previous study, the method proposed in this study can calculate the difficulty of knowledge points automatically based on a data-driven scoring model.  
Most current personalized learning path recommending systems are applied to a specific course [32]. Moreover, MOOC platforms, such as XuetangX, cannot provide personalized learning sequences for learners even in a specific course. Thus, this study proposes a novel method that can provide personalized learning path planning based on the timely diagnosis of students' learning states in an MOOC learning scenario. This algorithm is a generic method that can be utilized in various courses.  
Based on the above analysis, this study focuses on the following research questions:  
* [leftmargin=*] RQ 1: Is a personalized learning path based on learning states beneficial to MOOC learners' learning efficiency?* [leftmargin=*,noitemsep,topsep=0 pt]
* Is a personalized learning path based on learning status conducive to the continuous learning of MOOC learners?  
Moreover, the research highlights and contributions of this work are summarized as follows:  
* A personalized learning path planning algorithm based on learners' dynamic learning state and the difficulty of knowledge points.
* A data-driven scoring model that measures the difficulty level of specific knowledge points for general students. A knowledge difficulty model is established based on the scoring model. The knowledge difficulty model is more accurate and convenient when compared to the previous study on manually marking knowledge difficulty levels by subject teachers [6].
* A knowledge mastery model based on learners' learning behavior data and exercise data, such as MOOCCubeX [33], to dynamically evaluate students' learning states.
* A feedback strategy to dynamically arrange learning paths by following a circular learning list based on their real-time state and the knowledge difficulty level. The importance of \"mastering learning\" is also emphasized.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
[11] combined the empirical mode decomposition method with the multi-scale entropy method to analyze guided wave signals and quantify the number of internal defects in anchor bolt anchoring systems. [PERSON] et al. [12] studied resin bolts with different anchorage defects (disbonding length and notch size). The influence of the L-mode defect detection ability and defect size parameters on the amplitude of guided wave signal was analyzed. The experimental results showed that the L-mode can be used to detect bolt defects, and the amplitude of the defect back wave represents the defect size. [PERSON] et al. [13] trained six machine learning models using ultrasound detection data and predicted the influence of the deterioration and corrosion levels of rubber concrete on ultrasound features. However, research on the corrosion identification of bolt anchoring systems using ultrasonic guided waves is limited, and current NDT data analysis primarily relies on digital signal processing techniques such as fast Fourier transforms (FFTs), wavelet transforms, correlation analyses, and adaptive filtering. Neural networks, on the other hand, possess strong learning and generalization capabilities and can automatically learn the internal structural features of anchor bolts, obtaining more accurate prediction results through sample training [14]. In the process of actual bolt detection, the neural network exhibits the capability to handle extensive amounts of data simultaneously, enabling the real-time evaluation of bolt quality, minimizing the requirement for manual intervention and adjustments, and enhancing problem-solving efficiency. The main contributions of this study are reflected in two aspects:  
(1) The analysis of the corrosion mechanism of anchor bolts and the propagation mechanism of ultrasonic guided waves in anchor bolts through practical experiments;  
(2) The design of a multi-scale convolutional neural network (MS-CNN) model for classifying and identifying ultrasonic guided wave signals, capturing local features of different degrees and improving the expressive ability of the model for features in ultrasonic guided wave data. The structure of the rest of this paper is as follows: Section 2 introduces the theoretical background, Section 3 presents the propagation mechanism of guided waves in anchor bolts and non-destructive testing experiments, and Section 4 discusses the experimental results.
## 2 Reservoir Rock Characteristics and Engineering Problems
### Rock Mechanical Parameters  
The triaxial compression tests on rock samples were conducted using a specialized apparatus (GCTS Ltd., RTR-1500, Tempe, AZ, USA), adhering to the method specified in the \"Regulation for testing the physical and mechanical properties of rock--Part 20: Test for determining the strength of rock in triaxial compression\" (Chinese Standard, Standard ID: DZ/T 0276.20-2015) [37]. The tests were carried out in accordance with the procedures detailed by [PERSON] et al. [38], using the samples extracted from the 5182 m to 5187 m depth interval of Well GS10, as shown in Figure 2. The results of these tests are listed in Table 1. A notable observation is the increase in rock compressive strength and elastic modulus with elevated confining pressure. However, the variation in the Poisson's ratio with confining  
Figure 1: Reservoir geographical location and geological feature map.  
pressure is not evident. A significant disparity in compressive strength is observed across different rock samples tested under identical confining pressure conditions. For instance, at a confining pressure of 50 MPa, the highest compressive strength recorded is 53.63% greater than the lowest. This substantial variation emphasizes the profound impact of cracks and solution cavities on the mechanical characteristics of carbonate rock, thus highlighting the distinct variability of the rock's mechanical parameters.
# Quality Parameters of Wheat Bread with the Addition of Untreated Cheese Whey
###### Abstract  
The present study was carried out to evaluate wheat bread of three different flour compositions prepared by replacing water with untreated cheese whey (WCB). Bread prepared with water was taken as the control (CB). All breads were stored at 24 \(\pm\) 1 \({}^{\circ}\)C for up to 6 days. Microbiological, physicochemical, and sensory analyses were determined as a function of storage time. WCB had lower total viable counts (TVC) (3.81 log cfu/g for CB and 2.78 log cfu/g for WCB on day 2 of storage) and showed delayed mold growth by 1 day (day 4 for CB and day 5 for WCB). WCB also had lower pH (5.91 for CB and 5.71 for WCB on day 0), higher tittatable acidity values (TTA) (2.5-5.2 mL NaOH/10 g for CB and 4.5-6.8 mL NaOH/ 10 g for WCB), and higher protein content (PC) (PC 7.68% for CB and 8.88% for WCB). WCB was characterized by a more intense flavor, reduced hardness but similar cohesiveness, springiness, and adhesiveness compared to CB. Based primarily on sensory (appearance/mold formation) data, the shelf life of WCB was 4-5 days compared to 3-4 days for CB stored at 24 \(\pm\) 1 \({}^{\circ}\)C. The proposed use of whey in bread preparation contributes decisively to the environmentally friendly management of whey.  
Keywords:wheat bread; untreated cheese whey; bread quality parameters; shelf-life of bread +
Footnote †: journal: Applied Chemistry
## 2 Materials and Methods
### Evolution and Advantages of DL in Object Detection  
The DL algorithm can overcome feature subjectivity in the feature extraction method of the traditional algorithm and improve the universality of model application scenarios. In particular, the application of CNNs can not only significantly improve the accuracy of detection but also make a qualitative leap in processing speed and efficiency [19]. CNNs are DL models that belong to a branch of ANN. In the field of computer vision, CNNs are widely used in image classification, image generation, target detection, and other tasks and have achieved good results. The application of CNNs in the field of object detection marks a major revolution in the field of DL.  
CNNs comprise several layers, including the input layer, convolutional layer, pooling layer, activation layer, and fully connected layer. By using local perception, weight sharing,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Train** & **Val** & **Test** & **Total** \\ \hline Sound & 274 & 112 & 105 & 491 \\ Mild & 534 & 152 & 171 & 857 \\ Severe & 272 & 96 & 84 & 452 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample numbers of maize data set.  
Figure 6: Maize external matrix.  
and spatial context, they can effectively extract image features. The advantages of CNNs include the following: The use of convolutional layers and pooling layers to extract local image features reduces the number of model parameters and the amount of calculation, thereby reducing the complexity and degree of overfitting in the model and optimizing it. Moreover, CNNs can better extract all kinds of features in image processing and improve the robustness of the model. It is generally believed that in the past 20 years, target detection has experienced two stages: The first is that of the traditional target detection method before 2014. In the second, in the period of object detection based on DL after 2014, there are two major branches, i.e., the CNN represented by Faster R-CNN and the single-stage object detection algorithm represented by YOLO.  
Two-stage object detection algorithms usually include two main stages. The first stage is the generation of candidate target regions, usually through a Region Proposal Network (RPN) or other methods. The second stage consists of classifying and refining these candidate regions. Two-stage algorithms generally excel in the accuracy of target detection tasks. They can provide high-quality target detection results and are especially suitable for complex scenarios and applications requiring high precision. However, due to the need for two independent phases, two-phase algorithms generally require more computational resources and time. As a result, their reasoning speed is relatively slow.
# Influence of Vegetation Filter Strip on Slope Runoff, Sediment Yield and Nutrient Loss
## 2 Materials and Methods
### Data Analysis  
#### 2.3.1 Multiple Regressions  
Multiple regression analysis was used to analyze the relationship between rainfall intensity, slope gradient, vegetation and erosion indexes. The vegetation coverage types including bare land (L), one filter zone (Z) and two filter zones (Z\({}^{\prime}\)) in this research belong to qualitative variables, which need to be converted into dummy variables and then introduced into equations, so that the regression results obtained had clear explanatory significance [25]. Usually, it is necessary to set \(n^{-1}\) dummy variables for qualitative variables with \(n\) types [25]. In this research, dummy variables \(V_{0}\) and \(V_{1}\) were used to assign values to vegetation.  
\[V_{0}=\left\{\begin{array}{ll}1,\;\text{L}\\ 0,\;\text{not L}\end{array}\right.\;\;V_{1}=\left\{\begin{array}{ll}1,\; \text{Z}\\ 0,\;\text{not Z}\end{array}\right. \tag{1}\]  
The multiple linear regression model was established as follows:  
\[Y=a_{0}+a_{1}\times R+a_{2}\times S+a_{3}\times V_{0}+a_{4}\times V_{1} \tag{2}\]  
where \(Y\) is the erosion index, \(R\) is the rainfall intensity (mm/h), \(S\) is the slope gradient (\({}^{\circ}\)), \(a_{i}\) (\(l\) = 1, 2, 3, 4) is regression coefficient of the corresponding factor and \(a_{0}\) is a constant term.  
Describing the relationship between erosion and rainfall intensity and slope gradient under different vegetation coverage types can reflect the vegetation benefits more clearly  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Rain intensity mm/h & 30 & 60 & 90 & 30 & 60 & 90 & 30 & 60 & 90 \\ Vegetation coverage type & L & L & L & Z & Z & Z & Z\({}^{\prime}\) & Z\({}^{\prime}\) & Z\({}^{\prime}\) \\ \hline \hline \end{tabular} Note: L stands for bare slope, Z stands for VFSI, and Z\({}^{\prime}\) stands for VFS2.  
\end{table}
Table 1: The experimental combinations.  
and explain the difference in the impact of two vegetation setting modes on erosion control. The following regression equation was used under different vegetation coverage types:  
\[Y=a_{0}+a_{1}\times R+a_{2}\times S \tag{3}\]  
where the explanation of the symbols is the same with Equation (2).
# Research on Tracking Control of Urban Rail Trains Based on Improved Disturbance Observer
## 2 Problem Statement
### The Multiple Point-Mass Model of the URTs
\begin{array}{rl}\dot{\tilde{x}}_{i}(t)&=\tilde{v}_{i}(t),\quad i=1,2,\cdots n\\ m_{1}\dot{\tilde{v}}_{1}(t)&=\tilde{u}_{1}(t)-\kappa(\tilde{x}_{1}(t)-\tilde{x }_{2}(t))\\ &-m_{1}b_{1}\tilde{v}_{1}(t)-2(\sum\limits_{i=1}^{n}m_{i})c_{1}v_{r}\tilde{v}_ {1}(t)+d_{L1}(t)\\ m_{i}\dot{v}_{i}(t)&=\tilde{u}_{i}(t)-\kappa(\tilde{x}_{i}(t)-\tilde{x}_{i+1}(t ))+\kappa(\tilde{x}_{i-1}(t)-\tilde{x}_{i}(t))\\ &-m_{i}b_{i}\tilde{v}_{i}(t)+d_{Li}(t),\quad i=1,2,\cdots n-1\\ m_{n}\dot{v}_{n}(t)&=\tilde{u}_{n}(t)+\kappa(\tilde{x}_{n-1}(t)-\tilde{x}_{n}(t ))\\ &-m_{1}b_{n}\tilde{v}_{n}(t)+d_{Ln}(t)\end{array} \right. \tag{10}\]  
Let \(X(t)=[\tilde{x}_{1}(t),\cdots,\tilde{x}_{n}(t),\tilde{v}_{1}(t),\cdots,\tilde {v}_{n}(t)]^{T}\) and \(U(t)=[\tilde{u}_{1}(t),\cdots,\tilde{u}_{n}(t)]^{T}\) be the new state variables and control inputs. The following dynamics equations in state space form are further obtained  
\[\dot{X}(t)=AX(t)+BU(t)+Bd_{L} \tag{11}\]  
where \(A=\left[ \begin{array}{cccc}0_{n\times n}&I_{n\times n}\\ A_{21}&A_{22}\end{array} \right]\), \(B=\left[ \begin{array}{c}0_{n\times n}\\ B_{21}\end{array} \right]\). More precise,  
\[A_{21}=\left[ \begin{array}{cccc}-\frac{x}{m_{1}}&\frac{x}{m_{1}}&0&\cdots &0\\ \frac{x}{m_{2}}&-\frac{2x}{m_{2}}&\frac{x}{m_{2}}&\cdots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&-\frac{2x}{m_{n-1}}&\frac{x}{m_{n-1}}\\ 0&0&\cdots&\frac{2}{m_{n}}&-\frac{2}{m_{n}}\end{array} \right],\]  
\[A_{22}=\left[ \begin{array}{cccc}-b_{1}-2(\sum\limits_{i=1}^{n}m_{i})c_{1}v_{ r}/m_{1}&0&0&\cdots&0\\ 0&-b_{2}&0&\cdots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&-b_{n-1}&0\\ 0&0&\cdots&0&-b_{n}\end{array} \right]\]\[B_{21}=diag\{1/m_{1},1/m_{2},\cdots,1/m_{n}\},\,d_{L}(t)=[d_{L1}(t),d_{L2}(t),\cdots,d_{Ln}(t)]^{T}.\]  
Actuator faults are common during the operation of the URTs, which can also seriously affect the safe train operation. Therefore, it makes sense to consider actuator faults in the URTs. Combining (11) with actuator faults  
\[\dot{X}(t)=AX(t)+B_{\rho}U(t)+Bd_{L} \tag{12}\]  
where \(B_{\rho}=B\rho\). \(\rho=diag\{\rho_{1},\rho_{2},\cdots,\rho_{n}\}\) indicates the actuator faults matrix. \(\rho_{i}\) is the actuator health factor of the \(ith\) carriage. \(\rho_{i}\) satisfies \(0<\rho_{i}\leq 1\). When \(\rho_{i}=1\), it means that the power unit is faultless. **Remark 1**. : _The above modeling approach of treating two carriages as one power unit is well mature in the high-speed trains field [24; 26]. Since the train formation and basic structure are similar to high-speed trains, it is reasonable to borrow the modeling approach from the above literature._  
**Remark 2**. : _The ramp resistance, tunnel resistance, and curve resistance suffered by the URTs are all related to the fixed running route. In addition to that, the unknown external disturbances suffered cannot be infinite, so \(d_{L}(t)\) is norm bounded._
## 2 Results
### KEGG Enrichment Analysis of the DEGs  
We used KEGG pathway analysis to map the DEGs to the biochemical pathways involved in the plant response to _C. ciliata_ damage. \"Monoterpenoid biosynthesis\", \"Linoleic acid metabolism\" and \"alpha-Linolenic acid metabolism\" were prominent pathways between the treatments and controls in response to _C. ciliata_ feeding damage after 24 and 48 h (Tables S4 and S5). In addition to these three pathways, \"Phenylpropanol biosynthesis\", \"Phenylalanine, tyrosine and tryptophan biosynthesis\", \"Flavonoid biosynthesis\", \"Terpenoid backbone biosynthesis\", and \"Sesquietreponid and triterpen biosynthesis\" are five of 26 pathways that were enriched at 24 h (Table S4). In the 48 h treatment, 22 pathways were enriched, including \"Photosynthesis-antenna proteins\", \"Photosynthesis\", \"MAPK signaling pathway-plant\", \"Cutin, suberine and wax biosynthesis\", and \"Pentose and glucuronate interconversions\" (Table S5). For the 303 unigenes that were up-regulated at both timepoints, there were 17 enriched pathways. Of these 17 pathways, the first three prominent pathways of significance are \"Monoterpenoid biosynthesis\", \"Linoleic acid metabolism pathway\" and \"alpha-Linolenic acid metabolism\" (Table S6). The results of the above analyses indicate that these pathways may be involved in the interaction between _P. acerifolia_ and _C. ciliata_.
# OCRL1 Deficiency Affects the Intracellular Traffic of ApoER2 and Impairs Reelin-Induced Responses
## 1 Introduction
Mutations in \(RELN\), as well as the reduction of Reelin expression, are associated with intellectual disability, learning, and memory deficits [58; 59; 60], and neurodegeneration [61; 62; 63]. Moreover, homozygous null mutations of \(RELN\) lead to lissencephaly, a disease characterized by the absence of regular convolutions in the cerebral cortex and by cerebellar hypoplasia, neonatal hypotonia, and mental retardation [64]. Interestingly, the CNS abnormalities in LS include hypotonia, seizures, and cognitive problems, such as intellectual disability, maladaptive behaviors, and developmental delay [4; 7; 65]. Therefore, there are similarities in many of the manifestations associated with a decrease in Reelin signaling and the phenotypes described for LS patients. Remarkably, the inhibition in ApoER2 recycling and surface expression induced by SNX17 knock-down significantly impairs Reelin function [41]. Accordingly, the link between ApoER2 intracellular trafficking and its ability to respond as a Reelin signaling receptor is essential for the pathway functioning. Thus, as with Megalin and other receptors, OCRL1 activity as a modulator of endosomal recycling to the plasma membrane could positively regulate the ApoER2 pool capable of responding to Reelin. In this work, we proposed that an impairment in Reelin-induced responses due to a defect in ApoER2 trafficking would partially explain the neuronal phenotype of LS patients. We found that the loss of OCRL1 affected ApoER2 intracellular trafficking, leading to reduced protein total and surface levels, directly impacting Reelin-induced responses measured in human cortical neurons.
## 6 Results and Discussions
### 118-Bus System
#### 6.3.2 Results with Reducing the Search Space by SBDMT
The effectiveness of the SBDMT is validated since it reduces the OPSC problem's search space extensively and at the same time keeps the most suitable buses for SC placement. In addition, this technique is easy to implement due to the simple formulation of TOPSIS as well as the SBIs. 4. The utilization of both PODESCA and SBDMT to solve the OPSC problem is very effective especially for large-scale RDSs, where they guarantee obtaining the optimal solutions and further increase the convergence rate and standard deviation of the obtained solutions. Nevertheless, some disadvantages and recommendations can be outlined as follows:  
1. Due to the hybridization between three mechanisms and applying the \"greedy selection\" several times, PODESCA suffers from a slightly higher computational time comparing to some other methods in the literature. However, this issue can be managed using a more powerful PC. Besides that, in offline planning applications, it is more essential to obtain much better solutions than suffer from a slightly higher computational time. 2. The SBDMT effectively keeps the most potential locations for SC placement in the search space, but it could be further tested for identifying the suitable locations DGs as well. As a future work, the proposed algorithm can be considered and adopted for other fields, which are innovative and new, like that of micro-nanofluidics networks [49]. The proposed algorithm can be adopted for solving problems in different aspects and fields in engineering and sciences.
## 2 Problem Definition
### Cost Analysis
#### 2.2.3 Vehicle Transportation Cost  
The travel speed and load changes caused by loading and unloading during distribution affect both the carbon emissions rate and the fuel rate. Therefore, it is more realistic to use the methodology for calculating transport emissions and energy consumption (MEET) to calculate the vehicle transportation cost (fuel cost) [20; 21; 22; 23; 41]. First, the carbon emission rate (g/km) of the MCV under the current road congestion degree can be calculated according to Equation (4). \(\tau_{0\sim 6}\) are predefined parameters, and the values of different types of trucks differ. \(\
u\) represents the travel speed of the MCV on the road with the current congestion degree. Second, the load correction factor corresponding to the carbon emission rate can be calculated according to Equation (5). \(\varpi_{0\sim 7}\) are predefined parameters, and the values of different types of trucks differ. \(r\) represents the ratio of the sum of the actual load of all compartments of the MCV on the current road to the maximum capacity of the entire vehicle. Third, the carbon emission rate (kg/km) of MCV \(k\) on the current road section \(d_{ij}\) under traffic congestion situation can be calculated according to Equation (6). Then, the fuel consumption rate (kg/km) of MCV \(k\) on the current road section \(d_{ij}\) under traffic congestion situation can be calculated according to Equation (7). For reference, 2.32 kg/L is the carbon emission coefficient of gasoline. Finally, the transportation fuel consumption \(TF\) and vehicle transportation cost \(C_{2}\) generated during the driving process can be calculated according to Equations (8) and (9). \(\theta\) represents the unit fuel cost.  
\[\xi_{v}=\tau_{0}+\tau_{1}\cdot v+\tau_{2}\cdot v^{2}+\tau_{3}\cdot v^{3}+ \frac{\tau_{4}}{v}+\frac{\tau_{5}}{v^{2}}+\frac{\tau_{6}}{v^{3}} \tag{4}\]  
\[\kappa=\varpi_{0}+\varpi_{1}\cdot r+\varpi_{2}\cdot r^{2}+\varpi_{3}\cdot r^{3 }+\varpi_{4}\cdot v+\varpi_{5}\cdot v^{2}+\varpi_{6}\cdot v^{3}+\frac{\varpi_ {7}}{v} \tag{5}\]\[C_{ij}^{k}=\frac{\xi_{\
u}\cdot\kappa}{1000} \tag{6}\]  
\[F_{ij}^{k}=\frac{C_{ij}^{k}}{2.32} \tag{7}\]  
\[TF=\sum\limits_{i\in N}\sum\limits_{j\in N}\sum\limits_{k\in V}x_{ij}^{k}\cdot t _{ij}^{k}\cdot v_{ij}^{k}\cdot F_{ij}^{k} \tag{8}\]  
\[C_{2}=\theta\cdot TF=\theta\cdot\sum\limits_{i\in N}\sum\limits_{j\in N}\sum \limits_{k\in V}x_{ij}^{k}\cdot t_{ij}^{k}\cdot v_{ij}^{k}\cdot F_{ij}^{k} \tag{9}\]