esa-sceva/satcom-chunk-collection huggingface dataset:
# Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
## 4 An Application of the Framework: Use Case of Cold Rolling
### Producing a Random Forest  
For this study, we deploy a supervised RF model to predict the condition of the work rolls at a given interval. The outcome can be one of three classifications: class 0, implying the condition of the work roll is Bad, i.e., the roll requires a considerable amount of stock reduction to remove the worn surface; class 1, where the condition of the work roll is considered as Best, and thus requires minimal stock removal; or class 2, implying the condition of the work roll is Good and an average stock reduction value is necessary.  
There are many impacting factors that affect the rolls, which are collected and used for training and testing the RF model. This includes a combination of dynamic sensory and historical static data of the work rolls. The dynamic data contain live data read from sensors, which include the total tonnage and meterage rolled during a trip, speeds, temperature, as well as the coil usage data. This coil usage contains information regarding the steel grades and full chemical composition for each coil processed, e.g., its carbon or silicon values. Meanwhile the static data provide information regarding the roll historical data, such as their previous grindings, stock reductions, positioning, tons and length rolled, etc. This wide collection of data was explained by domain experts and data scientists from our industrial partners, which assisted in the data collection and aggregation aspects to build our random forest model.  
To build the RF classifier in our application, 80% of the original dataset (9781 samples) was used as the training set. The train-test split was performed randomly and in such a way that the original proportion was respected. The value of the hyperparameters n_estimators and max_depth was set using grid search and validated through the performance of the metrics. Finally, the optimized values for n_estimators and max_depth were 20 and 22, respectively.  
The RF contained a total of 20 decision trees that contained a total of 25,657 paths. The majority voting strategy technique was applied to calculate the final classification by computing the modal value of all the decision trees in the RF. The RF was exported to plain text using the export_tree method mentioned previously. The accuracy of the random forest is measured in terms of precision, recall and f1-score and their weighted values are displayed below.  
Precision: 0.78 Weighted Precision: 0.78 Recall: 0.75 Weighted Recall: 0.78 F1-score: 0.76 Weighted F1-score: 0.77  
When running Algorithm 1 for this particular RF, the 25,657 different paths produced an equal amount of SWRL-rules that were passed to the semantic reasoner for inference.
# Applications of Graphene-Based Nanomaterials in Environmental Analysis
## 2 Graphene-Based Nanomaterials and Nanocomposites
### Synthesis of Fe\({}_{3}\)O\({}_{4}\)-GO Nanocomposites  
[PERSON] et al. [50] presented two ways of preparing magnetic GO (mGO): by co-precipitation (mGOp) or by impregnation (mGOi). In the former method, graphite oxide is scattered in deionized water with the help of a sonicator for 30 min. Then, specific amounts of FeCl\({}_{3}\cdot\)6H\({}_{2}\)O and FeCl\({}_{2}\cdot\)4H\({}_{2}\)O are dissolved in deionized water at room temperatureand the mixture is added drop by drop to the GO solution, under vigorous stirring and under a N\({}_{2}\) atmosphere. After the completion of ion exchange, 28% \(v/v\) ammonia solution is added dropwise so the pH reaches the value of 10 for the synthesis of MNPs. Next, the solution is heated to 80 \({}^{\circ}\)C and after agitating for 45 min, the black solid is collected via centrifugation, is washed many times with CH\({}_{3}\)OH and is lyophilized. In the latter method, graphite oxide is again added in deionized water and put into a sonicator for 30 min to obtain GO and then an amount of Fe\({}_{3}\)O\({}_{4}\) nanoparticles are added to the mixture. After another 30 min, a homogenous suspension is obtained and the nanocomposites are collected by centrifugation and freeze-dried.  
The aforementioned material was studied for dye adsorption experiments, with Reactive Black 5 (C\({}_{26}\)H\({}_{21}\)N\({}_{5}\)Na\({}_{4}\)O\({}_{19}\)S\({}_{6}\)) being the target compound. From the X-ray diffraction measurements (XRD) presented in the paper, the average crystallite size of the MNPs were found to be around 18.4 nm. Scanning electron microscopy images were taken to identify the morphology of the mGOi nanoparticles and the iron distribution map of the material. Also, the magnetization of the two materials were studied and the mGOi adsorbent had 65 emu/g, which was a bit higher than the same value of mGOp. Lastly, from Fourier transform infrared (FT-IR)spectra, the identification of functional groups, such as carboxylic groups, epoxy groups and ammonium groups, was carried out.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 4 State-of-The-Art WSN Based Air Pollution Monitoring Systems  
Twenty state-of-the-art TNGAPMSs that significantly improve the spatio-temporal resolution of the air pollution information and the quality of services provided are presented in this section. The existing works are classified into three categories based on the carriers of the sensor nodes, and the advantages and disadvantages of each category are discussed.  
Air pollution in urban areas with ubiquitous emission sources attracts extensive attentions worldwide due to the tremendous impacts on human lives at anytime and anywhere. Networks of monitoring stations using traditional measurement instruments have been deployed to mitigate these impacts. Data acquired by these stations can be utilized for building pollution maps and models that provide authorized environmental situation information and prediction. However, limitations in spatio-temporal resolution and Quality of Services (QoS) are prevalent in these systems [93, 94, 95]. These limitations result in issues and problems of the conventional air pollution monitoring systems, like non-scalability of system, limited data availability on personal exposure, and out-of-the-fact warnings on acute exposure.  
In order to address these prevalent problems, researchers have put lots of efforts into the concept of TNGAPMS by utilizing the advance sensing techniques, MicroElectroMechanical Systems (MEMS), and Wireless Sensor Networks (WSN).  
According to the definition of participatory sensing [96, 97] and vehicular wireless sensor networks [98, 99], and our insights while reviewing the related works, the existing works are classified into three categories based on the carriers of the sensor nodes, namely Static Sensor Network (SSN. Sensor nodes are usually mounted on the streetlight or traffic light poles, or carefully selected locations.), Community Sensor Network (CSN. Sensor nodes are carried by the public communities, usually by volunteers or people who are keen on air quality.), and Vehicle Sensor Network (VSN. Sensor nodes are carried by the public transportations or specially equipped cars.).  
These existing works greatly improve the spatio-temporal resolution and QoS of the air pollution information compared with that of conventional monitoring systems. However, in TNGAPMSs, it is impossible to use the same high-end measurement instruments as the ones utilized in stationary monitors of Conventional Stationary Monitoring Network (CSMN). Hence, whenever we deal with the TNGAPMSs, we face the same interesting trade-off as shown in Figure 7. In the following subsections, the three types of sensor networks (SSN, CSN and VSN) are discussed in detail.  
Figure 7: Trade-off between tolerable sensor node cost, obtainable measurement coverage/resolution, expected data quality and achievable measurement temporal resolution for Conventional Stationary Monitoring Network (CSMN), Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) [100].
## 4 Proposal for Data Transformation to Resolve Incompatibilities between Medical Formats
### Experimental Approach to Unit Conversion  
The proposed technique takes inspiration from similarity-based recommendation algorithm and draws from the idea that the \"converted\" value for the feature in a sample could be predicted by basing it off similar data in the dataset it is being merged with. The step-by-step explanation of this idea is as follows:  
Let \(N\) and \(k\) are hyper-parameters, then:  
Figure 4: Two dependent features end up in one table, with filled values obscuring their dependency, making it a potential problem for the system.  
Choose \(N\) samples from the dataset with the column that needs converting;
2. For \(N_{i}\) in \(N\), choose \(k\) most similar samples in the dataset that is being merged with, according to features that either match or have already been transformed;
3. Take the values of the feature that needs conversion and calculate an average between them. This will be the predicted \"converted\" result;
4. Use the result from the previous step to and the value of \(N_{i}\) to calculate an estimated conversion rate;
5. Repeat steps 2-4 until all \(N\) samples have an estimated conversion rate;
6. Calculate an average of the estimates and assign it as a final conversion rate.  
Such an approach is purely experimental. The initial suspicions are that it may give not very accurate results due to people being generally diverse. However, the idea that patients with similar disease history may share similar traits, and therefore a unit conversion rate could be accurately predicted based on similarity, was considered worth putting to a test. Another problem with such an approach would also be the computation time, due to the amount of time it would take for the program to go through a large dataset \(N\) times.  
Finally, there is no guarantee that the datasets presented will contain features that share the same units. In this case, there may not be enough similar data for the method to base coefficients off, making this approach most effective when used in conjunction with the other methods. Furthermore, the units of matched columns may not always have linear dependency.
## 5 Conclusions  
Based on the basic principles of the hydrodynamic mechanism and [PERSON]'s law, a theoretical model for the grout pressure distribution was established, considering the temporal variation in grout viscosity. The following conclusions were obtained:  
1. The temporal variation in grout viscosity has little effect on the grout pressure during the circumferential filling phase. In contrast, its effect is not negligible during the longitudinal and radial diffusion phases.
2. During the circumferential filling phase, the grout pressure decreases significantly with the increase in yield shear stress. When the thickness of the shield tail void is greater than 0.1 m, its effect on the grout pressure is limited.
3. The larger the radius of the tunnel, the more uneven the distribution of grout pressure. Increasing the number of grouting holes for constructing large-radius tunnels is a better way to control the uniform distribution of grout pressure.
4. The stratum with large porosity and permeability coefficient is conducive to grout diffusion. However, it is not conducive to the maintenance of grout pressure. The increase in the grout viscosity causes the reduction of the diffusion distance, which reduces the pressure loss in the grout flow process.
5. The research and development of grout materials should be combined with the characteristics of the injected stratum, considering the grout diffusion characteristics and the retention rate to optimize the grouting effect. In addition, the relationship between the grout properties and the permeability of the soil should be further studied.  
The application of the model is conducive to properly controlling the synchronous grouting in shield tunnels. The established theoretical model has the ability to predict the distribution and dissipation of the grout pressure. However, the grout consolidation should be considered in further study to improve the model's accuracy.  
J.M.: Conceptualization, Methodology, Funding acquisition, Writing--review and editing. A.S.: Methodology, Writing--original draft, Data curation. A.J.: Supervision, Writing--review and editing. N.G.: Resources, Writing--review. X.L.: Writing--review and editing. J.S.: Investigation, Graph editing. T.L.: Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities, grant number 3132022179.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Numerical Results
### Channel Gain  
Figures 2 and 3 show the channel gain distribution for the VLC system without and with IRS units, respectively. The reflectances of the IRS units in Figure 3 are set to be its maximum \(\gamma=0.9\) to illustrate the channel gain improvement due to the IRS units. It is observed that the total channel gains increase significantly with the IRS-reflected NLoS channels. Moreover, the exact enhancement of the channel gain relies on both the locations of the LED and the PD and the placement of the IRS units, because of the changing distances and angles, e.g., \(\|R_{k}S\|_{2}\) and \(\theta_{R_{k}D}^{MS}\), within the IRS reflected channel gain Formula (4).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline The number of IRS units \(K\) & 16, 32 \\ The modulation order \(M\) & 2, 4, 8, 16 \\ The dimming coefficient \(\xi\) & 0.1\(\sim\)0.9 \\ The Lambertian index \(m\) & 1 \\ The PD area \(A_{P}\) & 1 cm\({}^{2}\) \\ The FoV of the concentrator \(\Psi_{c}\) & 70\({}^{\circ}\) \\ The optical filter gain \(g(\psi)\) & 1 \\ The refractive index \(n\) & 1.5 \\ \(\beta\) & 100 \\ The maximum reflectance \(\gamma\) & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 2: The channel gain distribution for point-to-point VLC systems.
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 4 Experiments  
### Datasets and Metrics  
Our model was evaluated on two public pedestrian trajectory datasets, ETH [29] and UCY [30], which are widely used for future trajectory prediction. The two datasets are composed of five outdoor scenes that were recorded from a top-view. ETH contains two scenes: ETH and HOTEL. UCY contains three scenes: UNIV, ZARA1, and ZARA2. Since our task does not involve activity prediction and multi future prediction, other datasets such as ActEV/VIRAT [62] and The Forking Paths dataset [63] were excluded. The number of pedestrians in each scene varied from 0 to 51 per frame. The datasets exhibited complex interactions such as nonlinear trajectories, collision avoidance, walking together, moving from different directions, etc. It was recorded at 25 frames every second, the pedestrian trajectory was sampled every 0.4 s. The datasets provided all the pedestrians' location coordinates in each frame. We evaluated our model following the same \"leave-one-out\" [64] strategy as commonly adopted by previous works. More specifically, the model was trained and validated on four sets and tested on the remaining one. In order to be consistent with the previous works, the model predicts the next 12 frames' (4.8 s) conditions on the 8 observed frames (3.2 s) ahead.  
Two conventional metrics were employed to evaluate our model, namely, the Average Displacement Error (ADE) and the Final Displacement Error (FDE). The ADE is defined as the average Euclidean distance over all estimated positions in both the predicted trajectory and ground-truth trajectory.  
\[ADE=\frac{\sum_{n\in N}\sum_{t}\big{|}\big{|}\hat{Y}_{n}^{t}-Y_{n}^{t}\big{|} \big{|}_{2}}{N\times T}\quad t=T_{obs+1},\dots,T_{pred} \tag{13}\]The FDE is the Euclidean distance between the predict position and the ground truth position at the final destination \(T_{pred}\).  
\[FDE=\frac{\sum_{n\in N}\left|\left|\hat{Y}_{n}^{t}-Y_{n}^{t}\right|\right|_{2}}{N} \quad t=T_{pred} \tag{14}\]
## 1 Introduction
[28] put forward a multiscale derivation algorithm for traditional SE, namely multiscale sample entropy (MSE). MSE accurately and comprehensively captures the dynamical characteristics of time series from the view of numerous scales. MSE has been widely used in the fields of signal processing and mechanical fault diagnosis [29]. Previous research has confirmed the efficiency and superiority of MSE [30; 31]. It should be noted that the quantitative features extracted by entropy-based theories can be used as the input of neural networks to realize intelligent fault diagnosis and structural health monitoring. Intelligent health monitoring has become a research hotspot due to machine learning technology's rapid development [32; 33]. Convolutional neural network (CNN) [34] is a kind of classical algorithm in deep learning, which has been extensively used in the fault diagnostic domains and structural health monitoring domains, due to its advantages of extracting feature information adaptively and pattern recognition [35]. CNN-integrated approaches can effectively process massive and high-dimensional datasets, while avoiding noise interference and low accuracy in condition identification. This paper presents a novel intelligent timber damage monitoring approach using PZT-enabled active sensing and intrinsic multiscale entropy analysis. The PZT-generated response signals are divided into multiple sets of BLIMFs using VMD. MSE values of certain orders of BLIMFs are calculated and adopted as quantitative features denoting timber health conditions. Thus, the intrinsic multiscale entropy analysis obtains the quantitative features of multiple BLIMFs at multiple scales, which provides the feature matrices for subsequent CNN-integrated machine learning. Section 2 reviews the methodologies involved with intrinsic multiscale entropy analysis, which consists of VMD, MSE and the typical structure of CNN. Section 3 illustrates the proposed intelligent timber damage monitoring approach by giving its detailed steps. Section 4 introduces the whole apparatus setup and experimental procedures. Section 5 verifies the proposed intelligent timber damage monitoring approach by experimental data processing and comparative experiment. Section 6 concludes the paper by presenting the major findings and essential discussion.
# Correlation of Parental and Child Dental Plaque Levels: A Clinical Study
###### Abstract  
Objective: This clinical study aimed to explore the correlation between dental plaque levels in parents and their children. Specifically, the study examined differences in plaque correlation between mother-child and father-child pairs and evaluated the role of parental education in influencing children's oral hygiene practices. Materials and Methods: A total of 196 subjects were recruited, comprising 49 mother-child and 49 father-child pairs, with children aged 6 to 15 years. Dental plaque was assessed using the O'Leary Plaque Control Record, applying a liquid plaque discloser and explorer. The plaque index was calculated as the percentage of dental surfaces with visible plaque relative to the total surfaces examined. Results: The findings revealed a significant positive correlation between the parental plaque index (IP_PARENTS) and the children's plaque index (IP_CHILD) (\(p<0.001\)), indicating a strong relationship. While the father's plaque index (ID_PARENTS(FATHER)) showed a marginally positive effect on IP_CHILD (\(p=0.0589\)), the interaction between father identification and IP_PARENTS had a significant negative impact (\(p=0.0344\)), suggesting that paternal influence on children's plaque levels may vary. The model accounted for 28% of the variability in IP_CHILD (pseudo R\({}^{2}=0.2758\)). Conclusions: The analysis demonstrated a significant association between parental and child plaque levels, with minor differences observed between maternal and paternal influence. No significant effects were found for the age or gender of the children. Overall, increased plaque levels in parents were associated with higher plaque levels in their children, with slight variations depending on the parent's role.  
oral health education; plaque index; children's dental health Article
# Optimization Strategies in Quantum Machine Learning: A Performance Analysis
## 3 Model Architecture
### General Architecture  
The model follows the principles of variational quantum algorithms (VQAs), which combine classical and quantum computing to solve optimization problems. First, we preprocess the classical data and select relevant attributes. The processed data are then encoded into quantum states, and the subsequent optimization process leverages the hybrid framework of VQAs.  
A key component of any VQA is the cost function, which maps trainable parameters (\(\theta\)) to real numbers. The cost function evaluates how well the quantum circuit approximates the desired solution. Another critical element is the ansatz, a parameterized quantum circuit that defines the structure of the trial wave function. The ansatz plays a pivotal role in determining how efficiently the parameters \(\theta\) can be optimized to minimize the cost function [11]. The success of the VQA heavily depends on the optimizer's ability to effectively adjust these parameters during training.
## 1 Introduction
[37] proposed a modeling method that combines Kriging and sliced inverse regression (SIR). The method uses SIR to construct a new projection vector to achieve dimensionality reduction. The projection vector reduces the dimensionality of the original input vector without losing the basic information of the model response. In low-dimensional space, a new Kriging correlation function is constructed using the tensor product of several correlation functions in each projection direction. The above-mentioned dimensionality reduction method reduces the modeling time while ensuring that certain model accuracy requirements are met. After all, things have two sides. The improvement of modeling efficiency will lead to the loss of accuracy to a certain extent. Therefore, how to improve the modeling efficiency while minimizing the loss of precision still needs further research. For this purpose, a new Kriging modeling method (KMDS) based on multidimensional scaling (MDS) is proposed in this work. The basic idea is as follows: firstly, high-dimensional design data are converted to low-dimensional data through MDS; then, the reduced dimensional data are used to establish a low-dimensional Kriging model. After that, new high-dimensional design points can be obtained via Latin hypercube sampling and estimated using expensive functions, and then the sampling points can be added to the high-dimensional data set. Finally, we can go back to the first step and repeat the process until the number of sample points meets the stopping criterion. The remaining arrangements for this work are as follows. The background of the Kriging model is introduced Section 2. The third states the key technology and outlines the realization process of the proposed method. The benchmark functions are tested and analyzed in Section 4. The fifth section is the study of the simulation examples, which are based on aircraft longitudinal flight control data. Conclusions are given in Section 6. The KMDS algorithm addresses the difficulties of achieving high-dimensional modeling based on the Kriging model, and greatly improves the modeling efficiency on the basis of meeting certain accuracy requirements. It guides a new direction for the modeling of high-dimensional engineering problems. Therefore, it is suitable for publication in the _Algorithms_ journal.