esa-sceva/satcom-chunk-collection huggingface dataset:
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
## 3 Grey-Total-Influence-Based Tolerance Rough Sets
### Determining Grey Total Influence  
#### 3.3.1 Generating a Direct Influence Matrix Using GRA  
The total influence matrix in the DEMATEL can be used to indicate causal relationships among patterns. Prior to obtaining the total influence matrix \(\mathbf{T}=[t_{ij}]_{m\times m}\), a direct influence matrix, \(\mathbf{Z}=[z_{ij}]_{m\times m}\) is constructed, where \(z_{ij}\) (\(1\leq i,j\leq m\)) represents the extent to which \(\mathbf{x}_{i}\) influences \(\mathbf{x}_{j}\). The values of zero and one represent \"no effect\" and \"very strong effect,\" respectively, when \(z_{ij}\) ranges from zero to one. The higher the value of \(z_{ij}\), the more \(\mathbf{x}_{i}\) is likely to directly influence \(\mathbf{x}_{j}\).  
In particular, as \(z_{ij}\) represents the impact of \(\mathbf{x}_{i}\) on \(\mathbf{x}_{j}\), it is reasonable to attribute such an impact to a relationship between \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\). This implies that the stronger the relationship between \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\), the greater the direct impact of \(\mathbf{x}_{i}\) on \(\mathbf{x}_{j}\). As GRA is an appropriate technique to identify relationships among patterns, this inspired us to determine the grey total influence using GRA to generate the total influence matrix. Compared with the traditional method, the distinctive feature of determining the grey total influence is that it can automatically determine the impact \(z_{1v}\), \(z_{2v}\), \(\ldots\), \(z_{uv}\) of \(\mathbf{x}_{1}\), \(\mathbf{x}_{2}\), \(\ldots\), and \(\mathbf{x}_{u}\) on \(\mathbf{x}_{v}\) (\(1\leq u\), \(v\leq m\)), respectively, at a time by means of GRA, when \(\mathbf{x}_{1}\), \(\mathbf{x}_{2}\), \(\ldots\), \(\mathbf{x}_{u}\) act as comparative sequences, and \(\mathbf{x}_{v}\) is a reference sequence such that \(z_{sv}=Y(\mathbf{x}_{s},\mathbf{x}_{v})\) (\(1\leq s\leq u\)). To obtain \(\mathbf{Z}\), \(\mathbf{x}_{1}\), \(\mathbf{x}_{2}\), \(\ldots\), and \(\mathbf{x}_{u}\) act as reference sequences in turn. Thus, this study calls \(\mathbf{Z}\) a grey direct influence matrix.
# Space Robot Sensor Noise Amelioration Using Trajectory Shaping
## 3 Results  
The various control methods were applied in MATLAB(r)/SIMULINK(r) in three different trials, where the following inputs were used to drive the system controller: (1) step response, (2) sinusoidal trajectory, and (3) whiplash compensation. Figure 4 depicts each trajectory as a function of time. The system is expected to complete its maneuver by \(t=5\) s. As seen in Figure 4, the step function has an instantaneous change in value because the step occurs at \(t=0\) s. The sinusoid and whiplash trajectories, on the other hand, are not instantaneous and have a duration. The sinusoid trajectory starts at an amplitude of 0, while the whiplash trajectory starts at an amplitude of \(-1\). The whiplash method generates motion in the opposite direction as the final desired state to prevent overshoot.  
The flexible spacecraft system is driven by each of the three trajectories in Figure 4. The resulting angle of the reaction wheel is plotted in Figure 5 for all three cases. Similarly, Figure 5 shows the reaction wheel speed error for each controller method. In each figure, the response to a step function input is shown in blue, the response to the generated sinusoid is shown in a dashed green line, and the response to the whiplash compensator is shown in a bolded red line. Figure 5 depicts these results without state and rate sensor noise included.  
The mean error value and standard deviation of the error value was calculated and is tabulated in Table 4. Surprisingly, the sinusoid trajectory response shows less error than either the step response or the whiplash response. Monte Carlo simulations were performed for each of the three input trajectory generation schemes. There were 1000 Monte  
Figure 4: Generated Trajectories.  
Figure 5: Generated trajectories (**a**) reaction wheel angle \(\theta\); (**b**) reaction wheel speed \(\dot{\theta}\); (**c**) reaction wheel rotation angle error.  
Carlo trials executed in each simulation. There are two sensor noises included in the model: rotation angle state noise and rotation angle rate noise. Three combinations of noise were included in the Monte Carlo trials: (1) state noise only, (2) rate noise only, and (3) both state and rate noise.  
Figure 6 depicts the shotgun plot analysis for each of the noise combinations for the step function input trajectory. Each dot represents one Monte Carlo trial. The one-, two-, and three-sigma ellipses are depicted in red. Table 5 details the mean and standard deviations for each of the methods and combinations of sensor noise.
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 2 Vehicle Modeling
### Sensors Models
#### 2.3.1 Global Navigation Satellite System (GNSS)  
The GNSS sensor is modeled as a random noise and a bias added to the model calculated position. Note that these systems have typical accuracy of 3 m; therefore, the random noise and bias parameters have been adjusted to satisfy that performance. Because it is not the objective of this paper to model such a sensor, the reader is referred to [2; 28] for more details on this.  
These kind of sensors provide good performance during intermediate phases of flight and are employed to determine the line of sight vector expressed in NED axes. Note that GNSS sensors also provide velocity vector in NED axes. This vector is also modeled as a random noise and a bias of 0.1 ms\({}^{-1}\), which resembles real performance of these sensors.
## 4 Materials and Methods
### Analysis of Distinguishability of OTP Extraction Function
#### 4.3.1 Distinguishable Security Model for the Proposed OTP Extraction Function
Furthermore, 4-bit data are used for assigning extraction indices in 4 randomly selected locations outside of the 16 extraction locations (i.e., 4-byte = 32-bit). The possible 4-bit positions that can be used for index purposes among the 32 bits are listed as \((0,1,2,3),(0,1,2,4),\cdots,(28,29,30,31)\), resulting in a total of \(\binom{32}{4}=35,960\) cases. With this, all elements of the function family \(J\) can be represented in a canonical manner as \(E_{0}^{0},E_{1}^{0},\cdots,E_{\binom{32}{4}-1}^{0},\cdots,E_{\binom{1}{4}}^{1},E_{1}^{1},\cdots,E_{\binom{32}{4}-1}^{\binom{20}{4}-1}\), and the total number of elements in the function family \(J\), in other words, \(|J|=\binom{20}{4}\times\binom{32}{4}=174,226,200\). Based on this, we can define the selection of a function in Func \(J\) as randomly choosing a function (with identical probability for all elements) as \(E_{j}^{j}\gets J\). Then, with regards to \(\forall i,j\ (0\leq i\leq\binom{20}{4}-1,\ 0\leq j\leq\binom{32}{4}-1)\), the probability of \(E_{j}^{i}\gets J\) is \(Pr[E_{j}^{i}\gets J]=\frac{1}{\binom{20}{16}\times\binom{20}{4}}\). In contrast, the size of the random function family \(|Func(D,R)|\) is \(10^{6\times 2^{24}}\), so the probability of selecting a random function from \(Func(D,R)\) is \(Pr[g\gets Func(D,R)]=\frac{1}{10^{6\times 25\Gamma}}\). Since the selection of an instance from each function family has been explicitly described, we can define adversary \(A\) as an algorithm that distinguishes between the random function family and a specific function family in order to analyze distinguishability. Oracle selects an instance from a group of random functions or specific functions with a probability of \(\frac{1}{2}\), and \(A\) inputs a finite number of queries (\(x\in D\)) to that chosen function by the Oracle [21,22]. \(A\) does not know which group of functions the Oracle has chosen, and while a finite number of queries are inputted, the Oracle retains the same instance (function) \(g\) without selecting again. The Oracle responds with \(g(x)\), and based on the queries and responses, \(A\) outputs a 1-bit value \(b\in\{0,1\}\), indicating whether it is \(World\ 0\) (Oracle selects \(g\) as \(g\gets Func(D,R)\)), or \(World\ 1\) (Oracle selects \(g\) as \(g\gets J\)).
# Empowering Large Language Models to Leverage Domain-Specific Knowledge in E-Learning
## Appendix A Appendix A
#### a.2.1 Generated Content from LLM ChatGPT4  
Sentiment analysis is a natural language processing technique designed to identify and evaluate sentiment or sentiment polarity in text. It is generally divided into three main categories: positive, negative, and neutral. Sentiment analysis can be achieved through different methods, one of which is to use a sentiment dictionary.  
An emotional dictionary is a vocabulary that contains a word or phrase and the emotional polarity (positive, negative, or neutral) associated with it. These words are labeled as words with emotion based on how they are used in the text. There are two main aspects to the application of emotional dictionaries:  
1. Sentiment Analysis: Sentiment dictionaries can be used for sentiment analysis to help machines understand the sentiment content in text. By matching words in the text with words in the sentiment dictionary, a sentiment score for the text can be calculated to determine the sentiment polarity of the text. For example, if a word is marked as \"positive\" in the sentiment dictionary, the presence of that word in the text may indicate that the text has positive sentiment.  
2. Affective Vocabulary Expansion: Affective dictionaries can also be used to expand affective vocabulary. When a sentiment analysis model uses a sentiment dictionary, it can identify known sentiment words but may not be able to process new, unknown sentiment words. In this case, the vocabulary can be expanded using an emotion dictionary to label new words as having sentimental polarity to improve the performance of the model. Example: Suppose we have an emotional dictionary with words and emotional polarities. Here is a sample part of a sentiment dictionary: * \"Pleasant\": Positive * \"Sad\": negative * \"Surprised\": Positive * \"Disappointment\": Negative * \"Calm\": neutral Now, let us look at an example of a practical application of sentiment analysis: Text: \"The weather today is amazing!\" In this example, our sentiment analysis system will use the sentiment dictionary to match words in the text and analyze sentiment based on the sentiment polarity entries in the sentiment dictionary. For this sentence, it will notice the words \"fantastic\" and \"pleasant\", both of which are associated with positive emotions. As a result, the text will be classified as positive sentiment. The application of sentiment dictionary also includes social media sentiment analysis, sentiment evaluation of product reviews, sentiment classification of news articles, and so on. By using sentiment dictionaries in conjunction with machine learning models, we are able to more accurately analyze the emotional content in the text, allowing us to better understand how people express their emotions in different contexts.
## 4 Numerical Modelling
### Seismic Loads Input  
Numerous research findings indicate that when seismic waves occur on the foundation, the seabed and its surroundings are considered as an unbounded, infinite space. Seismic waves penetrating through the foundation experience damping from the surrounding soils. The upper boundary of the model is in contact with the water, preventing seismic waves from propagating upward and generating reflected waves. In this study, viscoelastic boundaries were employed to simulate the penetration and reflection injury of seismic waves within the SIS system. An equivalent seismic loads program for the numerical surface of the model was developed based on the principles of three-dimensional wave theory. The seismic loads were implemented and transmitted through the input model file using _MATLAB_. The input equation for this process was derived from the work of [PERSON] et al. [54].  
\[F_{n}(t)=A_{n}\sigma^{ff}(x_{n},y_{n},z_{n},t)+Ku^{ff}(x_{n},y_{b},z_{n},t)+Cv^ {ff}(x_{n},y_{n},z_{n},t)\]  
where \(F_{n}(t)\) represents the seismic load at any given time at arbitrary point, \(n\), on the boundary of the model. \(A_{n}\) corresponds to the control area at point \(n\). \(\sigma^{ff}\) denotes the stress tensor generated by the seismic waves in the normal direction to the boundary in the free field. \(u^{ff}\) stands for the displacement vector of the incident wave. \(v^{ff}\) represents the velocity vector of the incident wave. \(x_{n}\), \(y_{n}\), and \(z_{n}\) indicate the coordinates of point \(n\). \(t\) signifies the transit time of the wave, encompassing both the incident wave, \(t_{1}\), and the reflected wave, \(t_{2}\). \(K\) and \(C\) are the stiffness and damping coefficients of the viscoelastic boundary, as illustrated in Figure 9.
## 3 Architectural Overview
### Beamforming Optimization Unit in MMS-DSR  
The Beamforming Optimization Unit is a key enhancement in MMS-DSR, designed to dynamically optimize beamforming vectors using CNN-LSTM models based on the predicted network conditions. This unit plays a critical role in adjusting the beamforming strategies to enhance signal quality and reliability over time, particularly in urban scenarios where buildings and other structures can cause significant signal reflection and diffraction. The Algorithm 1 explains main working operations performed by Beamforming Optimization unit. Main steps are synthesized as follows:  
* Dynamic Beamforming Vector Adjustment: this unit uses the output from the Machine Learning-Based Prediction Unit to adjust beamforming vectors. It optimizes these vectors to maximize signal strength and minimize interference, considering both the current and predicted CSI. This real-time adjustment is crucial for urban environments where obstacles may deflect or block signals unexpectedly.
* CNN-LSTM Based Predictions: the unit employs Convolutional Neural Networks (CNNs) combined with Long Short-Term Memory networks to analyze spatial and temporal aspects of the network. This model predicts optimal beamforming directions and power levels for each node in the network.
* Real-Time Optimization: beamforming vectors are adjusted in real-time based on predictive analytics. This ensures that each node can proactively adapt to changing network conditions, maintaining high-quality communication links, which is essential in smart cities for supporting uninterrupted service delivery.
* Feedback to Scoring Engine: adjustments made by this unit are fed back into the Multi-metric Scoring Engine, allowing it to re-evaluate route scores with updated beamforming information. This feedback loop ensures that routing decisions remain optimal as network conditions evolve, particularly under the variable urban dynamics.
* Integration with Routing: information about the optimized beamforming vectors is included in the route discovery and maintenance processes, ensuring that all nodes along a chosen route adjust their beamforming strategies cohesively for uniform signal enhancement across the urban network.  
By dynamically optimizing beamforming vectors, this unit significantly contributes to the robustness and efficiency of MMS-DSR, particularly in environments where directional communication can greatly enhance performance, such as in crowded urban areas.  
```
1:procedureOptimizeBeamformingVectors(Routes, CNNLSTMModel)
2:for each Route in Routes do
3:for each Link in Route do
4: LinkCSI \(\leftarrow\) GetCurrentCSI(Link)
5: PredictedCSI \(\leftarrow\) CNNLSTMModel.Predict(LinkCSI)
6: OptimalVectors \(\leftarrow\) CalculateOptimalBeamformingVectors(PredictedCSI)
7: AdjustBeamformingVectors(Link, OptimalVectors)
8:endfor
9:endfor
10:endprocedure
```  
**Algorithm 1** Beamforming Vector Optimization Algorithm
## 3 Results
### Sources of Sound in Dental Settings  
The dental clinic is an acoustically complex environment where various sound sources intersect, often creating high levels of auditory stimulation [2; 5]. The soundscape can be categorized into four broad typologies: mechanical, environmental, human, and ambient [78]. Mechanical sounds derive predominantly from dental instruments, including air turbines, ultrasonic scalers, suction devices, and polishing tools [4; 5]. Among these, the high-frequency and tonal noise generated by air turbines is the most prominent and has been measured at levels exceeding 85 dB [5]. These instruments contribute significantly to both patient discomfort and occupational hearing risks [5; 58]. Also, environmental sounds are often byproducts of the building's infrastructure, such as HVAC systems, water pipes, and electrical equipment [31; 32]. Although less intense than mechanical sounds, their continuous and low-frequency nature raises the general noise floor, affecting background stress and reducing the clarity of verbal communication [34; 43]. In addition, human-generated sounds, including patient speech, staff communication, and procedural dialogue, also shape the clinic's acoustic environment. These can become especially disruptive in open-plan or poorly insulated layouts [5; 58]. Excessive conversational noise has been found to interfere with staff concentration, reduce team performance, and elevate fatigue [52]. Finally, ambient sound refers to the cumulative reverberations and overlaps of mechanical, environmental, and human noise within the physical space [8; 60]. Inadequate use of sound-absorbing materials, reflective surfaces, and poor zoning can result in increased echo and suboptimal auditory ergonomics [19; 26]. This has negative consequences on both patient perception and professional effectiveness [4; 5]. Together, these sound typologies underline the multifaceted nature of dental acoustics and the pressing need for integrated architectural and technological solutions. According to the reviewed literature, studies consistently emphasize that sound in healthcare environments, including dental settings, is not incidental but central to both environmental quality and care outcomes [27; 40; 63].
# An Inference Framework of Markov Logic Network for Link Prediction in Heterogeneous Networks
## 6 Conclusions  
First, we establish a perspective by transforming multi-relational LP into a node label estimation problem within MLN, treating known links as observed nodes. This transformation provides a principled probabilistic foundation for handling the inherent complexity of heterogeneous networks. Second, our substructure construction method partitions significantly enhance computational efficiency while preserving semantic relationships. Complementing this, our VEM-based framework strikes a balance between precision and computational feasibility. Third, the proposed terminating computation ensures both accuracy and efficiency in practical applications, making our approach viable for real-world heterogeneous networks. Finally, the experimental results demonstrate our method's advantages in terms of efficiency, robustness, and accuracy. In all, our approach indeed bridges the critical gap between unknown links in heterogeneous networks and unobserved nodes in MLN, establishing a hybrid approach to link prediction that combines the strengths of both machine learning techniques and logical deduction.  
Efficient inferences heavily depend on the MLN structure size, shaped by the formulated formulas for diverse multi-relational LP tasks. In future research, we aim to explore automatic formula selection to strike a balance between efficiency and efficacy. Furthermore, the combined VEM-GCN architecture possesses robust capacity for capturing temporal dynamics, strengthening MLN-based LP performance. Thus, we aim to extend our approach to dynamic graph-based applications such as sequential node classification using MLN-based inferences.  
Conceptualization, Z.L. and K.Y.; data curation, Z.L.; formal analysis, Z.L., K.Y. and L.Y.; funding acquisition, K.Y.; investigation, Z.L.; methodology, Z.L.; project administration, K.Y.; resources, Z.L.; software, Z.L.; supervision, L.Y.; validation, Z.L., K.Y. and L.Y.; visualization, Z.L.; writing--original draft, Z.L.; writing--review and editing, Z.L. and J.W. All authors have read and agreed to the published version of this manuscript.  
This paper was supported by the Key Program of Basic Research of Yunnan Province (202401 AS070138), the Program of Yunnan Key Laboratory of Intelligent Systems and Computing (202405 AV340009) and the Open Foundation of Yunnan Key Laboratory of Intelligent Control and Application(2025 ICA01).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Case Study--Description of Variants
### Variant 2  
The second variant assumed replaced the two planned compression water chillers--CWCh1 (1000 kW cooling power) and CWCh4 (650 kW cooling power)--with a dual-stage absorption water chiller with 1650 kW cooling power. It used deionized water as a refrigerant and a lithium bromide (LiBr) water solution as the absorbent. The refrigerant, i.e., water vapor, flowed from evaporator to the absorber due to the pressure difference. The resulting decrease of lithium bromide concentration necessitated the regeneration of absorbent, which was directed to the generator. A schematic of the proposed variant of system retrofitting is shown in Figure 5. The absorption water chiller operated in synergy with the open-type cooling tower. The technical characteristics of the setup described in this variant are given in Table 2.  
The implementation of Variant 2 required: (i) the development of a steam and condensate network (or modification of the existing network) from the steam CHP plant to the absorption water chiller room adjacent to the production building; (ii) the development  
\begin{table}
\begin{tabular}{l c} \hline Cooling power & [kW] & 1650 \\ \hline Generator power & [kW] & 1330 \\ \hline Refrigerant & [-] & H\({}_{2}\)O \\ \hline Absorbent & [-] & LiBr-H\({}_{2}\)O \\ \hline Concentration of absorbent strong solution & [\%] & 62.8 \\ \hline Concentration of absorbent weak solution & [\%] & 55.3 \\ \hline EER & [-] & 1.24 \\ \hline SEER (including auxiliary energy consumption) & [-] & 1.14 \\ \hline External cooling system power & [kW] & 2980 \\ \hline Chilling water temperature & [\({}^{\circ}\)C] & 7/15 \\ \hline Pressure of refrigerant in evaporator (absolute) & [MPa] & 0.00083 \\ \hline Temperature of refrigerant in evaporator & [\({}^{\circ}\)C] & 4.2 \\ \hline Chilled water volumetric flow & [m\({}^{3}\)/h] & 203.5 \\ \hline Steam pressure in the generator (gauge) & [MPa] & 0.95 \\ \hline Steam mass flow in the generator & [Mg/h] & 2.63 \\ \hline Cooling water temperature—absorber and condenser & [\({}^{\circ}\)C] & 29.0/34.0 \\ \hline Cooling water volumetric flow—absorber and condenser & [m\({}^{3}\)/h] & 512.0 \\ \hline Pressure of refrigerant in condenser (absolute) & [MPa] & 0.0066 \\ \hline Temperature of refrigerant in condenser & [\({}^{\circ}\)C] & 38.0 \\ \hline \end{tabular}
\end{table}
Table 2: Technical data of AWCh for Variant 2–1.65 MW water chiller.  
Figure 5: Schematic of chilled water system retrofitting Variant 2.  
of a steam connection station to the absorption water chiller with an automatic regulation system; (iii) the development of a service water network connecting the water chiller with the open cooling tower; and (iv) the development of a network supplying the cooling water volume in the system, along with a treatment station.  
Other elements of the system were in line with the accepted basic variant for the modernization of the chilled water system. A schematic of the connections of the 1.65 MW dual-stage absorption water chiller is shown in Figure 6[28].