esa-sceva/satcom-chunk-collection huggingface dataset:
### Contributions of this Study  
This study aims to overcome such limitations of the think-aloud protocol by using natural language processing (NLP). NLP is a field of artificial intelligence that focuses on developing computational models for understanding human language [8]. One of the important NLP techniques used in this study is text embedding, which involves representing a word or sentence as a fixed-length vector [9; 10; 11; 12; 13; 14; 15; 16]. This numerical representation is used for downstream tasks. Recently, neural network-based embedding models that take contextual information into account are being trained using a large corpus [11; 12; 13; 14; 15; 16], which has greatly advanced NLP applications.  
The key contributions of this study are as follows. First, we propose a method to represent the relationships between the stimulus text and think-aloud responses (Figure 1). For this, sentences of stimulus texts and subjects' responses are first encoded to high-dimensional vectors using sentence embedding techniques. Then, similarity scores between input sentence and readers' responses are calculated. Differences in these similarity scores between the two sentence embedding models are investigated.  
Second, we propose to use those embedding-based similarity scores to automatically identify poor comprehenders using the think-aloud protocol. Machine learning models are trained to classify poor comprehenders and normal students from their responses to stimulus texts. The classification accuracy of the proposed method is compared with the diagnoses of human experts.  
Therefore, the proposed method aims to address the challenges associated with the think-aloud protocol mentioned above. First, the proposed method improves replicability by providing objective criteria for quantifying readers' responses to text. Second, it could automate the think-aloud protocol and efficiently identify poor comprehenders at a lower cost.  
The remainder of this paper is organized as follows. Section 2 describes data collection, sentence embedding-based feature extraction, and classification of normal and abnormal. In Section 3, we examine the differences in the sentence embedding-based features between poor comprehenders and normal readers, and classification accuracies of the classifiers are measured. In Section 4, we discuss the results and their implications. In Section 5, we draw general conclusions with future research directions.
## 2 Time Series Modeling
### Extend ARMA to ARIMA Model  
In practice, the collected TS might not satisfy the stationary conditions, where the ARMA estimation in Equation (12) does not apply. However, in many cases, it is found that a TS may achieve stationarity after several differencing applications. Thus, the AR series models, e.g., ARMA, may fit an arbitrary TS after \(I\) differencing. In other words, the raw TS can be estimated by \(I\) times integration of the fitted TS, as reflected by \"integrated\" in the name of the ARIMA model. In the implementation of the ARIMA(\(p,I,q\)) model, the raw TS is subjected to \(I\) differencing (differentiation) first, and then fitted with an ARMA(\(p,q\)) model. Suppose \(x\) satisfies the stationarity condition after the \(I\) differencing of \(y\), i.e., \(x=\
abla^{I}y\). Therefore, \(y\) follows an ARIMA(\(p,I,q\)) model, and \(x\) follows an ARMA(\(p,q\)) model, where, for each time step, \(t\), \(x(t)\) can be expressed as follows,  
\[x(t)=\
abla^{I}y(t)=\sum_{i=1}^{p}\alpha_{i}x(t-i)+\epsilon(t)+\sum_{j=1}^{q} \beta_{j}\epsilon(t-j)+c. \tag{13}\]  
Therefore, differencing can be made to Equation (11) to form an ARIMA(\(2n,I,2n\)) model, where the TS is stationary after the \(I\) differencing. Taking one step back from \(y(t)\) in Equation (11), we have  
\[y(t-1) = \alpha_{1}y(t-2)+\alpha_{2}y(t-3)+\cdots+\alpha_{2n}y(t-2n)+ \epsilon(t-1)+ \tag{14}\] \[\beta_{1}\epsilon(t-2)+\beta_{2}\epsilon(t-3)+\cdots+\beta_{2n} \epsilon(t-2n-1).\]  
Subtracting Equation (14) from Equation (11), and dividing by the time interval \(\Delta_{t}\), we obtain  
\[\dot{y}(t) = \alpha_{1}\dot{y}(t-1)+\alpha_{2}\dot{y}(t-2)+\cdots+\alpha_{2n} \dot{y}(t-2n)+\dot{\epsilon}(t)+ \tag{15}\] \[\beta_{1}\epsilon(t-1)+\beta_{2}\dot{\epsilon}(t-2)+\cdots+\beta _{2n}\epsilon(t-2n)\]  
Similarly, the \(\beta\)-trick can be applied to Equation (15), and the weighted noise part can be replaced by the random Gaussian noise \(\epsilon\). If \(\dot{y}(t)\) satisfies the stationary condition, it can be estimated by an ARMA(\(2n,2n\)) model, Equation (16). In other words, \(y(t)\) follows an ARIMA(\(2n,1,2n\)) model. Therefore, if \(y(t)\) achieves stationarity with \(I\) differencing, \(y(t)\) can be estimated by an ARIMA(\(2n,I,2n\)) model.  
\[\dot{y}(t)=\widehat{y}(t)\approx\sum_{i=1}^{2n}\alpha_{i}\dot{y}(t-i)+\epsilon (t)+\sum_{j=1}^{2n}\beta_{j}\epsilon(t-j)+c. \tag{16}\]  
Previous studies [2; 3; 23; 26] showed that the coefficients of the AR part, \(\alpha_{i}\), are more important than the coefficients of the MA part, \(\beta_{i}\). Thus, the above ARIMA(\(2n,I,2n\)) model can be simplified to an ARI(\(2n,I\)) model. Finally, the authors in [23] provided details to estimate and map the TS coefficients to physical parameters, e.g., modal frequencies and damping ratios.
## 4 Si Transporters
### Brief Introduction of Si Transporters in Some Other Plants
#### 4.2.4 Horsetail  
Horsetail, as a pteridophytic plant, has extremely high Si accumulation ability and is a very important model plant, but it has been neglected in Si research [45]. Two putative Si-efflux transporter genes, _EaLsi2-1_ and _EaLsi2-2_, have been identified using the sequenced horsetail transcriptome. The sequences of these genes have very low similarity to their homologues in higher plants. The localization of EaLsi2-1 to the plasma membrane has been verified by subcellular localization. It was confirmed that EaLsi2-1 is an effective Si-efflux transporter by heterologous expression experiments in which it was injected into _X. laevis_ oocytes [23].  
Figure 3: Schematic model of the Si transport system in cucumber.
# Physarum-Inspired Bicycle Lane Network Design in a Congested Megacity
## 3 Physarum-Inspired Bicycle Lane Network Design
### Physarum-Inspired BLN Design Method
#### 3.2.2 Conductivity Update of Individual Links  
While the pressure of individual nodes and the initial conductivity of links are set, the main task of Physarum-inspired method is to update the conductivity of individual links (\(D_{ij}\)) considering the flux flow through each individual link (\(Q_{ij}\)) iteratively. Flux resembles protoplasmic flows, and there is a protoplasmic flow (\(Q_{ij}\)) in each link \(e_{ij}\) in this model. In general, plasmodium flows through a link from one node to another (and vice versa) based on the pressure differences between the two nodes connected to the link. In this study, pressures of individual nodes are maintained fixed, and the amount of flux \(Q_{ij}\) in link \(e_{ij}\) is considered proportional to the amalgamation of pressures between the two terminals of this link, i.e., \(Q_{ij}\propto\left(P_{i}+P_{j}\right)\). It is logical for BLN design because the flow might be bidirectional; \(Q_{ij}\propto P_{i}\big{(}P_{j}=0\big{)}\) and \(Q_{ji}\propto P_{j}(P_{i}=0)\). Thus, flux \(Q_{ij}\) in link \(e_{\bar{y}}\) is considered following Equation (8) instead of Hagen-Poiseuille (Equation (1)) used in existing studies.  
\[Q_{ij}=\frac{D_{ij}}{L_{ij}}\big{(}P_{i}+P_{j}\big{)}. \tag{8}\]  
Then, the conductivity of the individual links is updated based on the flux. The rate of conductivity update formula is considered, based on the change in tubular thickness of Physarum due to flux, as follows  
\[\frac{d}{dt}D_{ij}=f\big{(}\big{|}Q_{ij}\big{|}\big{)}-\mu D_{ij}\, \tag{9}\]  
where \(f\big{(}\big{|}Q_{ij}\big{|}\big{)}\) is an increasing function, and \(\mu\) is a positive constant. In our case, we considered \(f\big{(}\big{|}Q_{\bar{i}j}\big{|}\big{)}=\big{|}Q_{\bar{i}j}\big{|}\) for simplicity. Thus, the conductivity change (\(\Delta D_{ij}\)) is  
\[\Delta D_{ij}=\big{|}Q_{\bar{i}j}\big{|}-\mu D_{ij} \tag{10}\]  
The equation of the conductivity update suggests that conductivity tends to increase with large flux edges. Consequently, the conductivity of individual links is updated, reflecting the above physiological mechanism as  
\[D_{ij}=D_{ij}+\Delta D_{ij}. \tag{11}\]  
After updating the conductivity using Equation (11), the system calculates flux with a new conductivity and continues the process. The iterative processes of flux calculation and conductivity are performed until termination criteria are reached, which might be the maximum number of iteration or steady-state of the conductivity matrix. Finally, the conductivity of the network is normalized with respect to its maximum value, thus \(0\leq D_{ij}\leq 1\).  
\[D_{ij}=\frac{D_{ij}}{\text{max}(D)}. \tag{12}\]
# Hyperledger Fabric Access Control for Industrial Internet of Things
## 2 Preliminary
### Access Control
#### 2.5.2. Mandatory Access Control (MAC)  
MAC is a \"stricter\" system that assigns labels to data and users according to the security levels, and the access control mechanism determines whether to grant or deny users access to resources based on the security labels [21]. Users can only access the corresponding objects according to the permissions granted by the security label as shown in Figure 3. Each security label has different security levels. These range from Unclassified (anyone can access) to Confidential to Secret and finally (we believe) to Top Secret; other countries use similar classifications. The security levels of principals and objects are compared when performing access control. It is a method to force the subject to obey the access control policy. The MAC access control method implements the one-way flow of messages through gradient security labels, which effectively prevents Trojan horse attacks. However, MAC has the drawbacks of large workload, inconvenient and inflexible management, over-emphasizing confidentiality, and lack of the consideration of continuous workability of the system and the manageability of authorization.
## 3 Results
### Density and Mechanical Properties  
Relative density (not shown in detail) reached >99.4% of theoretical for composites sintered at 1500 \({}^{\circ}\)C and reached 100% at 1550 \({}^{\circ}\)C so that the sintering conditions can be considered sufficient in all cases to fully densify the materials. The Young's modulus E (not shown in detail) was in a close range between 213-217 GPa. A slight trend to decline with progressive alumina substitution by SA6 was detected which, however, did not exceed the range of the standard deviation of measurements (\(\pm\) 4 GPa). Figure 3 shows the Vickers hardness of the composites depending on composition and sintering temperature.  
The hardness shows a declining trend with increasing SA6 content, which starts at SA6 fractions >5 vol%. 15A and 10-5 SA6 materials show a hardness HV10 of 1180 HV10. Then, the hardness declines linearly to 1120 HV10 for 15 SA6.  
Figure 3: Vickers hardness HV10 depending on composition and sintering temperature.  
Figure 2: SEM images of crack propagation in different composites for: (**a**) 10A-5 SA6-1525, (**b**) 15 SA6-1525, (**c**) 15 SA6-1525 (crack direction always from bottom to top).  
Figure 4a shows the bending strength \(\sigma_{\text{4 pt}}\) of the composites. A slight improvement with increasing sintering temperature can be observed. As far as the SA6 fraction is concerned, no clear trend is visible. 10A-5 SA6 composites show the highest bending strength of 860-970 MPa, while the lowest strength is found in 15A materials (690-750 MPa). Between 5-15 vol% SA6, an intermediate minimum at 720-810 MPa is observed. 15 SA6 materials all reach strength values of \(\sim\)860 MPa.  
The fracture resistance K\({}_{\text{ISB}}\) is shown in Figure 4b. All materials show a very high fracture resistance of \(>\)10 MPa\(\surd\)m. The maximum toughness of 12.5 MPa\(\surd\)m is reached for 5A-10 SA6-1550. This material also shows excellent strength. In case of materials with lower strength (15A or 10A-5 SA6), it must be considered that the measured residual strength is very close to the bending strength. This means that for these samples with a bending strength of \(<\)700 MPa there exists an upper limit for measurable toughness at \(\sim\)11.5 MPa\(\surd\)m, as the residual strength cannot be higher than the bending strength. The \"real\" toughness could be even higher. The general trend shows a slight decline in toughness with increase in SA6 content and a slight increase with increasing sintering temperature.
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 3 Materials and Methods
### Motor and Rudder Servo Sizing
#### 3.2.2 Rudder Servo Motor Sizing  
The vessel is oriented through the rudder action, which movement is executed by a servo motor. Concerning its sizing the forces produced over the surface of the rudder in operation were considered. The force exerted was calculated with the distribution of pressures on its surface:  
\[F_{Rudder}=\int_{0}^{L}\rho\cdot g\cdot(L_{Rudd}-z)\cdot b\ dz \tag{12}\]  
Figure 5: Forces affecting the system. On the **left**: areas of the skate and landing surface where the water and air resistance forces affect, respectively. On the **right**: frontal areas affected by air.  
where \(g\) [\(m\)\(s^{-2}\)] is gravitational acceleration, \(L_{Rudd}\) [\(m\)] is the rudder length, \(b\) [\(m\)] its width and \(z\) [\(m\)] the vertical that corresponds to the height in a Cartesian coordinate system. Through the rudder action \(r_{action}\) [\(m\)] radius, the torque \(T_{Rudder}\) exerted by the servo is expressed by:  
\[T_{Rudder}=F_{Rudder}\cdot r_{action} \tag{13}\]  
For the setup of the Strider V1.0 the values of this design parameters are \(L_{Rudd}=0.09\)\(m\) and \(r_{action}=0.039\)\(m\).
# Formal Matters on the Topic of Risk Mitigation: A Mathematical Perspective
## 2 Formalizing Risk
### Vulnerability  
As implemented for hazard and exposure, the _pointwise vulnerability_ can also be defined as a function \(v:\Omega\to[0,1]\). Vulnerability, though, does not depend only on the coordinates, but also on the resilience of the individual exposed elements, as well as on their interactions with the other exposed elements.  
Consider the example of seismic hazard: the vulnerability of a building depends on the ground properties (sand vs. rock) of the location where it was built, on the resilience of the building to the shaking, but also on the presence of other surrounding buildings that could affect it by bounding due to their oscillation during an earthquake [15]. Finally, the vulnerability may also depend on the same environmental factors that also affect hazard (e.g., a rampart may reduce the vulnerability of a building, and also divert a geophysical flow, affecting the hazard).  
If we denote by \(\mathcal{Q}(P,B)\) the family of functions that describe the resilience of the individual exposed elements described by \(P\in\mathcal{P}\) and \(B\in\mathcal{B}(P)\), then \(v\) depends both on \(P\) directly, but also through \(B\in\mathcal{B}(P)\), and through some \(Q\in\mathcal{Q}(P,B)\), making the signature of the vulnerability function  
\[v:\mathcal{P}\times\mathcal{B}(\mathcal{P})\times\mathcal{Q}(\mathcal{P}, \mathcal{B}(\mathcal{P}))\times\Omega\to[0,1].\]
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 5 Evaluating LEO Satellite Internet with SatScope: Case Studies
### Single-Shell Satellite Network Service Performance
Consequently, the fifth and sixth shells (Table 3) are prioritized for development due to their optimal coverage over these latitudes.
## 3 The 6G Technology
### Sixth-Generation Key Enabling Technologies
#### 3.4.7 Mobile Edge Computing (MEC)  
Edge Computing (EC) is a critical enabling technology for future 6G networks. Its roles and functions will include facilitating super-speed edge computing and offering caching services for the incoming 6G network to ease cloud-to-edge communication [8]. MEC can enhance closest proximity communication at the user end among other things [34]. Other features include low latency, real-time accessibility, location and context intelligence, high bandwidth capacity, etc. MEC reduces the data transfer burden on the central computing servers, thereby enhancing latency and improving real-time applications. In dynamic and heterogeneous networks with high complexity, ML-enhanced MEC techniques can provide additional improvement in its application. ML-based techniques can potentially utilize big data from the network to provide intelligent solutions such as prediction, smart decision making, and optimization in MEC-assisted networks [15; 34; 108].
## 5 Problem Solution
### Dueling DQN  
The dueling DQN network [19] makes further improvements on the DQN network structure. In DQN, the network directly outputs the state-action values \(Q(s_{t},a_{t},\theta)\) corresponding to each relay selection policy. However, in dueling DQN, the output Q-value is split into two branches: the state value \(V(s_{t})\) indicating the value of the current channel and queuing state, and the action advantage value \(A(s_{t},a_{t})\) representing the value brought by the relay selection action. Finally, the output values of the two branches are combined to make the estimation of \(Q\) more accurate. The combination of the two branches can be written as  
\[Q(s_{t},a_{t};\theta,\sigma,\theta)=V(s_{t};\theta,\theta)+A(s_{t},a_{t}; \theta,\sigma), \tag{33}\]  
where \(\theta\), \(\sigma\), and \(\theta\) are the coefficients of the neural network. In order to prevent multiple sets of state value \(V(s_{t})\) and action advantage value \(A(s_{t},a_{t})\) with the same state-action value \(Q(s_{t},a_{t};\theta,\sigma,\theta)\), and to make the algorithm more stable [38], Equation (30) is replaced by  
\[Q(s_{t},a_{t};\theta,\sigma,\theta)=V(s_{t};\theta,\theta)+\left(A(s_{t},a_{t} ;\theta,\sigma)-\frac{1}{|A|}\sum_{a_{t+1}}A(s_{t},a_{t+1};\theta,\sigma) \right). \tag{34}\]  
The proposed multiagent dueling DQN is shown in Algorithm 1.  
```
1:Initialization: Initialize the maximum buffer capacity \(M\) and packet length \(L\); Initialize the number of nodes along the rail \(N\); Initialize network memory size \(J\), batch size \(B\), greedy coefficient \(\varepsilon\), and learning rate \(\varphi\).
2:for episode in range K do:
3: Reset channel quality \(C\) and the queue length \(Q\) of each node as initial state \(S_{initial}\)
4:While\(a(t)!=Destination Node\)do
5: Choose action: with probability \(\varepsilon\) to choose next hop node in random.
6: Otherwise, choose action \(a_{t}\) with \(\text{argmax}Q_{\pi}(s_{t},a_{t},\theta)\).
7: From current state \(s_{t}\) and action \(a_{t}\) of this hop, obtain the reward \(r_{t}\) for this action \(a_{t}\) and the next state \(s_{t+1}\).
8: Store \(\langle s_{t},a_{t},r_{t},s_{t+1}\rangle\) into experience reply to memory.
9: Randomly take minibatch of \(\langle s_{t},a_{t},r_{t},s_{t+1}\rangle\) from experience reply to memory.
10: Combine two branches \(V(s_{t};\theta,\theta)\) and \(A(s_{t},a_{t};\theta,\sigma)\) into \(Q(s_{t},a_{t};\theta,\sigma,\theta)\)
11: Calculate target Q-value \[Q_{target}=\begin{cases}r_{t},&\text{if }a_{t}\text{ is the destination node}\\ r_{t}+\gamma\max\limits_{a_{t+1}}Q(s_{t+1},a_{t+1};\theta^{lat\sigma},\sigma, \theta),&\text{otherwise}.\end{cases}\]
12: Minimize loss function \(L(\theta)\) using Equation (30)
13: Update the target network after several steps using the parameters of the evaluation network
14:endwhile
15:endfor
```  
**Algorithm 1** Dueling-DQN
# Experimental Test and Prediction Model of Soil Thermal Conductivity in Pernafrost Regions
## 3 Testing Scheme
### Thermal Conductivity Probability Distribution Characteristics for Three Clusters  
The average value and probability distribution of thermal conductivity of three soil clusters is shown in Table 3 and Figure 7. It can be seen that the roughly thermal conductivity magnitude order is LWHCS, MWHCS and HWHCS. The average thermal conductivity of unfrozen clusters (\(\overline{\lambda}_{u}\)) is 1.822, 1.556 and 1.418 \(\mathrm{W}/\left(\mathrm{m}\cdot\mathrm{K}\right)\). Among the main probability range (20%-80%), the thermal conductivity value of each unfrozen cluster varies between 1.518-2.171, 1.311-1.816, 1.193-1.605 \(\mathrm{W}/\left(\mathrm{m}\cdot\mathrm{K}\right)\). Whereas the corresponding average values are 2.258, 1.999 and 1.822 \(\mathrm{W}/\left(\mathrm{m}\cdot\mathrm{K}\right)\), and the high-probability range is 1.811-2.709, 1.611-2.334 and 1.493-2.154 \(\mathrm{W}/\left(\mathrm{m}\cdot\mathrm{K}\right)\) for the frozen clusters (\(\overline{\lambda}_{f}\)), respectively (see Table S2 in Supplementary Materials). This is because thermal conductivity of soil's mineral skeleton and the particle size of LWHCS are relatively larger than those of MWHCS and HWHCS. Meanwhile, based on the average dry density and water content data in Table 3, it can be inferred theoretically that the average density (\(\rho\),\(\rho\) = \(\rho_{d}(1+w)\)) of LWHCS is still large one under natural condition. So the test results show that the average values and main probability range of thermal conductivity of LWHCS is generally larger than those of MWHCS and HWHCS.  
\begin{table}
\begin{tabular}{c c} \hline
**Cluster** & **Soil Types** \\ \hline HWHCS & clay, silty clay \\ MWHCS & silt, silty sand, weathered mudstone, weathered phyllite, weathered slate \\ LWHCS & medium fine sand, gravel sand, boulder, breccia, gravel \\ \hline \end{tabular}
\end{table}
Table 2: Classification of testing specimens.  
Figure 6: Probability distribution of thermal conductivity of 12 types of frozen soil. (**a**) Fine grained soil, (**b**) Coarse grained soil.
# Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
## 3 Methodology
### Overview of Machine Learning Algorithms and Libraries  
This section provides an overview of the machine learning algorithms and libraries used in our web framework, emphasizing their relevance to fake news detection. The source code primarily utilizes Python, leveraging well-established machine learning libraries.  
Logistic Regression(LR) serves as a fundamental classification algorithm, often employed as a benchmark for binary classification tasks. Our implementation utilizes the LogisticRegression module from the sklearn library, allowing customization of parameters such as solver, C, penalty, and max_iter to optimize model performance.  
Decision Trees (DT) offer a simplistic yet powerful method for classification tasks. Our project adopts the DecisionTreeClassifier from the sklearn library, allowing customization of parameters like criterion, min_samples_split, splitter, and max_depth for effective model tuning.  
Nearest Neighbors: The k-nearest neighbors (KNN) algorithm is a versatile instance-based learning technique. We employ the KNeighborsClassifier module from sklearn, enabling parameter adjustments such as algorithm, n_neighbors, weights, and p to optimize model performance for fake news detection.  
Gradient Boosting Classifiers (GBC) are robust ensemble learning algorithms. Our project harnesses the GradientBoostingClassifier from sklearn, facilitating customization of parameters like criterion, loss, learning_rate, max_depth, and min_samples_split for enhanced model effectiveness.  
Naive Bayes Classifiers are probabilistic models widely used for text classification tasks, including fake news detection. These classifiers are based on Bayes's Theorem, making probabilistic predictions about the class of a sample given its features. Specifically, we focus on two variants: multinomial naive Bayes (MNB) and Bernoulli naive Bayes (BNB), both available in the scikit-learn library.