esa-sceva/satcom-chunk-collection huggingface dataset:
# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 4 Simulation Results
### Single-Channel Data Simulations  
Multichannel EEG processing is a time-consuming and complex process. Some researchers prefer using a single-channel EEG data for the P300 speller [47]. Single-channel classification must be performed using some of the central electrodes (such as Fz, Cz, Pz, or Oz), as it is inappropriate to consider only one hemisphere of the human brain for data acquisition in BCI speller. Fz and Cz channels are not located in the visual cortex of the brain, thus for single-channel experiments either the Pz or Oz electrode should be chosen. To examine whether a single data channel usage was efficient in our case, the Pz electrode was chosen for further simulations.  
The parietal region showed the maximum activity during the oddball paradigm, thus, the Pz electrode presented in Figure 4(c) was chosen among other active options. During the simulations, the LDA-SVM-\(k\)NN-CNN voter was excluded, as there is no meaning of CNN usage on a single-channel EEG vector.  
The results obtained during testing for four healthy subjects and five ALS patients are shown in Table 5. A single-channel classification was not as efficient as multichannel usage. The average accuracy for healthy subjects' data classification was 91.28% for the proposed voters, while it was only 78.19% average accuracy for the EEG classification of ALS patients. The weighted voter was slightly more accurate for ALS data, while there was no significant change in the results for healthy subjects. The weighted fusion of three classifiers again slightly outperformed the LDA-\(k\)NN voter in terms of performance metrics  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Recall (\%)** & **F-Score (\%)** \\ \hline \multicolumn{4}{c}{**Testing on healthy subjects**} \\ \hline Gradient Boosting & 97.65 & 96.11 & 97.03 \\ XGBoost & 99.53 & 99.05 & 99.21 \\ \hline LDA & 98.32 & 98.19 & 98.19 \\ \(k\)NN & 97.64 & 96.99 & 97.57 \\ SVM & 98.56 & 98.05 & 98.51 \\ CNN & 71.20 & 70.98 & 70.55 \\ \hline LDA-\(k\)NN & 99.33 & 98.80 & 98.71 \\ LDA-SVM-\(k\)NN & 98.52 & 97.69 & 97.99 \\ LDA-SVM-\(k\)NN-CNN & 78.04 & 71.81 & 73.77 \\ W-LDA-SVM-\(k\)NN & 98.52 & 97.69 & 97.99 \\ \hline \multicolumn{4}{c}{**Testing on ALS patients**} \\ \hline Gradient Boosting & 89.92 & 87.45 & 88.59 \\ XGBoost & 92.51 & 91.87 & 92.09 \\ \hline LDA & 84.17 & 82.47 & 81.24 \\ \(k\)NN & 87.46 & 87.92 & 85.21 \\ SVM & 87.56 & 87.01 & 87.34 \\ CNN & 60.61 & 60.08 & 60.52 \\ \hline LDA-\(k\)NN & 89.33 & 89.12 & 89.17 \\ LDA-SVM-\(k\)NN & 90.02 & 88.93 & 88.88 \\ LDA-SVM-\(k\)NN-CNN & 65.00 & 61.39 & 62.93 \\ W-LDA-SVM-\(k\)NN & 91.34 & 90.08 & 90.73 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results using 4-channel data.  
for ALS data, reaching 78.74% accuracy, while LDA-\(k\)NN reached only 77.86%. Still, the proposed ensemble models provided better results than the standalone classifiers.
# SEM and FT-MIR Analysis of Human Demineralized Dentin Matrix: An In Vitro Study
###### Abstract  
Recently, the demineralized dentin matrix has been suggested as an alternative material to autologous bone grafts and xenografts for clinical purposes. The aim of this study was to investigate the effect of different times of demineralization on the chemical composition and the surface morphology of dentinal particles. Extracted teeth were ground and divided into 5 groups based on demineralization time (T0 = 0 min, T2 = 2 min, T5 = 5 min, T10 = 10 min, and T60 = 60 min) with 12% EDTA. The analysis was performed using Fourier-Transform Mid-Infrared spectroscopy (FT-MIR) and Scanning Electron Microscopy (SEM) (\(p<0.05\)). The FT-MIR analysis showed a progressive reduction of the concentration of both PO43- and CO32- in the specimens (T0 > T2 > T5 > T10 > T60). On the contrary, the organic (protein) component did not undergo any change. The SEM examination showed that increasing the times of demineralization resulted in a smoother surface of the dentin particles and a higher number of dentinal tubules.  
demicralized dentin matrix; human demineralized dentin matrix; human; bone graft; FT-MIR; SEM +
Footnote †: journal: Applied Sciences
## 3 Steady-State Error Modeling and Analysis  
The tracking threshold of the PLL is the maximum steady-state error for the receiver to keep PLL locked. Additionally, the receiver loses lock when the steady-state tracking errors exceed a certain boundary. Because the tracking loops are nonlinear, especially near the threshold regions, only Monte Carlo simulations under the combined dynamic and signal-to-noise radio (SNR) conditions can determine the true tracking performance. However, general rules that approximate the tracking errors can be used based on the steady-state error model of tracking loops. Although numerous tracking error sources are in both normal PLLs and INS-aided PLLs, it is sufficient as a rule of thumb to track thresholds to analyze only the dominant error sources. In this section, to compare the PLL performance before and after INS aiding, a normal second-order PLL steady-state error model is deduced based on previous research [1, 20, 21]. Then, INS-aided second-order PLL steady-state error model is proposed based on its error transfer function. The INS bias errors are considered in detail when the error model of the INS-aided PLL is built. Finally, the steady-state error characteristic of PLLs before and after INS aiding is compared based on their steady-state error models, which could guide the optimal bandwidth selection in the deeply-coupled system design.
# Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks
## 3 Methodology
### Construction of an Accident Injury Severity Classification Model  
To evaluate the comprehensive importance of nodes in a network, we first constructed a comprehensive importance evaluation model based on the four characteristic indicators. Afterwards, we screened out features with higher overall importance and removed unimportant features to reduce the dimensionality of data features. Finally, a classification model of accident injury severity was constructed based on the features after dimensionality reduction, and we evaluated the model's performance using precision rate, recall rate, F1-score value and ROC (receiver operating characteristic curve) as evaluation indicators. At the same time, we conducted sensitivity testing on the classification model. Four models from the machine learning algorithm were selected to construct the traffic accident injury severity classification model. They are Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT). The use of machine learning algorithms can accurately classify the severity of traffic accident injuries, and after complex network dimensionality reduction, the training process will shorten the training time. The model construction process and evaluation index formula are shown as follows:  
\[y_{i}=F(x_{i}) \tag{16}\]  
where \(F(x)\) represents different classification models. \(x_{i}\) and \(y_{i}\) denote the reduced dimensional inputs of different models and their corresponding classification results, respectively.  
Precision:  
\[P=\frac{TP}{TP+FP} \tag{17}\]  
where \(P\) represents the precision rate, which means the probability of actually positive samples in all predicted positive samples. \(TP\) and \(FP\) denote the number of positive and negative classes predicted to be positive classes, respectively.  
Recall:  
\[R=\frac{TP}{TP+FN} \tag{18}\]  
where \(R\) represents the recall rate, which is the probability predicted as a positive sample in the actual positive sample. \(FN\) denotes False Negative Class and indicates the number of positive classes predicted as negative classes.  
\(F1\)-score:  
\[F1=\frac{2(P\times R)}{(P+R)} \tag{19}\]  
where \(F1\) represents the harmonic mean of precision and recall. \(P\) and \(R\) represent the precision and recall rate, respectively.  
ROC uses FPR (specificity) as the abscissa and TPR (sensitivity) as the ordinate to evaluate the performance of the classification model. When the ROC curves of multiple models are in the same plane, the closer the ROC curve to the upper left corner, the better the performance of the model. The AUC value represents the area of the graph surrounded by the abscissa and ordinate below the ROC curve, ranging from 0 to 1. The closer the AUC value is to 1, the better the performance of the model.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 2 Related Work
In the QoS-aware Multicast Routing Protocol algorithm (QMRP) [42], receivers deliver requests based on the underlay route to the source node, searching for a path that meets the QoS requirement hop by hop. If the current search path is deemed insufficient to meet the QoS requirement, the algorithm returns to the predecessor node and initiates a search along several alternative paths. This results in significant message processing overhead and ignores the problem of link cost. Table 1 briefly summarizes the methods in related work. (2) Multicast in Information-Centric Networking (ICN)Many ICN architectures offer their multicast solutions. Some ICN solutions, such as Content-Centric Networking/Named Data Networking (CCN/NDN) [17; 22] and Data-Oriented Network Architecture (DONA) [43], inherently support multicast without explicit computation or constructing multicast trees. They achieve this by consolidating requests for the same content into an interest table and forwarding data based on this interest table. The router aggregates the same interests originating from different downstream routers or receivers and stores them in the Pending Interest Table (PIT). Subsequently, once the pertinent content is acquired, it is transmitted to the designated machine recorded in PIT. Publish-Subscribe Internet Technologies (PURSUIT) [44] achieves multicast by computing the entire multicast tree and encoding it as a single Bloom filter. In the MobilityFirst [24] architecture, every multicast group is assigned a globally unique identifier (GUID), and the Global Name Resolution Service (GNRS) maintains the mapping between multicast GUIDs (MID) and the membership sets of multicast groups. Two multicast protocols, Named-Object Multicast (NOMA) [45] and Multicast Design for the MobilityFirst (MMF) [46], have been designed for use within MobilityFirst. NOMA sustains multicast trees through the recursive mapping of multicast group GUIDs to branch node GUIDs within the multicast tree. However, NOMA uses a centralized approach to compute multicast trees, which can be inefficient and require updates every time group membership changes. In MMF, multicast sources manage join and leave requests from each receiver. This design can lead to scalability challenges in large networks. Our proposed methodology utilizes the ICN name resolution system to acquire a viable set of tree nodes, subsequently engaging in a single-path exploration process, thereby circumventing blind searches. When the detection message arrives at a tree node, the decision to either construct a multicast route or execute a constrained tree search process is determined based on the accumulated network status. This approach helps circumvent message processing overhead caused by blind exploration. Additionally, we facilitate routing decisions by upholding an accurate repository of neighborhood state information, thereby mitigating the potential risk of path construction failure resulting from inaccurate state information.
## 3 Hypothesis Development and Research Model
### User-Perceived Factors  
#### 3.1.1 Immersion  
Immersion is often seen as the key to enjoyment through technology or media, and immersion results from a good user experience. The literature on consumer experience has consistently emphasized that two critical dimensions, active/passive \"engagement\", and the spectrum from \"absorption\" to \"immersion\", are central to defining the nature of customer experiences [7]. Research, including studies by [PERSON] et al. [22], has identified that immersion as a subjective experience, along with interactions with other participants and virtual environments, significantly contributes to the user experience. The objective of immersive AR environments is to enable users to perceive the computer-generated world as seamlessly integrated with the real world, thereby fostering a sense of presence, or \"being there\", which is deemed beneficial for consumers. The cultural heritage category usually gives consumers a sense of distance and requires extensive background knowledge to help them appreciate it. Immersive AR environments can quickly help consumers understand the value of the product and be useful to them.  
[PERSON] and [PERSON] [23] found that while flow experience and immersion are distinct, there is some correlation between them, and [PERSON] [24] conceptualized the potential of AR in developing immersive flow experience experiences. There is an overlap between flow experience and immersion, with [PERSON] et al. [25] arguing that immersion is a precursor to flow experience, as the feeling of being fully engaged, where nothing else matters, is the colloquial definition of immersion. Whether or not the immersive experience provided by AR contributes to the flow experience in the consumption of cultural heritage products needs to be explored.  
AR adds a dimension to the consumer experience that is not encompassed by ordinary cultural heritage product consumption. Given the utility of AR in the field of commercial marketing, it is important to further isolate the impact of the immersive phenomenological experience of consumer consumption of cultural heritage products. Therefore, the following hypothesis is proposed:  
**Hypothesis 1a**.: _The immersion factor positively affects consumers' perceived usefulness in cultural heritage product purchases._  
**Hypothesis 1b**.: _The immersion factor positively affects consumers' perceived ease of use in cultural heritage product purchases._  
**Hypothesis 1c.** _The immersion factor positively affects consumers' flow experience in cultural heritage product purchases._
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 2 Related Work
### Anomaly Detection of KPIs Based on Correlation Analysis  
Time series anomaly detection of KPIs based on correlation analysis can solve the problem of multi-index anomaly detection. However, at present, time series anomaly detection of KPIs based on correlation analysis ignores problems such as the volatility of KPIs itself, and the presence of insufficient factors of correlation analysis. Usually, KPIs of the same category are more correlated than those of different categories, and the trend in the original data will cause misjudgment of the final results [21]. Correlation between abnormal and normal KPIs of the same category is different due to the abnormal state, and universal correlation analysis methods cannot completely solve this problem. For example, [PERSON] et al. [11] improved the Pearson method, and integrated it into anomaly detection. [PERSON] et al. [22] used Pearson correlation analysis to classify KPIs, and here, different models were used for anomaly detection in different kinds of KPIs. In one method, more models are used, but the influence of the fluctuation trend in the original data over the correlation analysis results is not considered. The partial correlation analysis method [23] analyzes the correlation between KPIs based only on correlation value, without considering the shift value, the order of influence, and other factors. To accurately distinguish the fluctuations in abnormal KPIs from normal fluctuations with different structures, [PERSON] et al. [2] proposed the Coflux method, which can determine whether two KPIs are related to abnormal fluctuations, and identify the relevant shift value and order of influence. However, due to the large amount of data required and the complex calculation methods, this method takes a lot of time.  
At present, the anomaly detection method based on correlation analysis does not perform any processing after detecting all abnormal KPIs. However, for intelligent operation and maintenance personnel, it is still necessary to identify the causes of faults from a large number of abnormal indicators, and the efficiency is not high. The Hidden Markov Model can derive observation data from known data by analyzing the time transfer relationship. Some experts and scholars combine temporal data correlation analysis with HMM [24; 25]. Correlation analysis results can be used as HMM parameters to adaptively adjust the HMM, which helps avoid the limitations and low accuracy of the inference results.  
To solve the above problems, this paper proposes an anomaly detection method based on correlation analysis and HMM. The correlation analysis method in this paper can be used to accurately detect anomalies between multiple KPIs in a short time, and HMM can be used to infer the abnormal KPIs that affect a specific KPI from the abnormal KPIs caused by a certain fault. This method can improve the accuracy of anomaly detection.
## 3 Modular Gait Generation Principle
### Driven Module Waves with Overlapping  
#### 3.2.1 Generation Algorithm for MG-II  
As the results in references [17; 18] for the peristaltic waves, as the actuated segments transmit from head to tail in the locomotion robot, the optimal gait is obtained, which is called the phase difference gait. For driven module gait generation in this study, the phase difference is considered the modular gait. In this modular gait, the number of transmission segments, \(n_{T}\), is less than the sum of the number of body segments in the driven module (\(n_{td}\) or \(n_{p}\)). For MG-II, the number of transmission actuated segments, \(n_{T}\), satisfies  
\[1\leq n_{T}<n_{td},\text{ or }1\leq n_{T}<n_{p} \tag{9}\]  
Figure 6: A gait generation demonstration for a 12-segment earthworm-like robot with two different drive modules for MG-I. A gait generation demonstration for a 12-segment robot with U-DM is shown in (**a**). The frequency of receding peristaltic wave is 1/3. The amplitude of the receding peristaltic wave is 4. A gait generation demonstration for a 12-segment robot under B-DM (“0111”) is shown in (**b**). The frequency of receding peristaltic wave is 1/3. The amplitude of the receding peristaltic wave is 3.  
In contrast to MG-I, the parameters for MG-II, \(\{k,n_{p},n_{T},TN\}\) or \(\{k,n_{td},n_{T},TN\}\), are given initially. The state switch in the process of peristaltic waves transmission is defined as  
\[s_{i}^{t_{j}}=s_{\text{Mod}[i-1+j\cdot n_{T},TN]+1}^{t_{0}} \tag{10}\]  
A generation algorithm for MG-II is proposed to obtain the locomotion gait, according to the receding peristaltic wave and the state transmission rule, given as Equation (10). The routine of the algorithm is shown in Figure 7, which can generate all the permissible locomotion gaits.
## 2 Results and Discussion
### Magnetic Susceptibility Studies  
Variable-temperature magnetic-susceptibility measurements on radical 4 were obtained using a SQUID magnetometer in the temperature region 5-300 K in an applied field of 0.4 T. Data were collected in both warming and cooling modes with no significant differences in sample susceptibility. The temperature dependence of \(\chi\)T is shown in Figure 5.  
Figure 4: Solid-state packing of radical 4 demonstrating the formation of chains along \(c\)-axis (**top**) and along \(a\)-axis (**bottom**).  
Figure 5: Temperature dependence of \(\chi\)T upon heating from 5 to 300 K. Inset: Curie-Weiss behavior, \(C=0.377\) emu K\(\cdot\)mol\({}^{-1}\) and \(\theta=-0.61\) K.  
The inverse of molar susceptibility (1/\(\chi\)) followed the Curie-Weiss law (Figure 5, inset) with \(C\) = 0.377 emu K\(\cdot\)mol\({}^{-1}\) and \(\theta\) = \(-\)0.61 K. On cooling from 300 K down to \(ca\). 50 K, \(\chi\)T remained stable with a value of 0.375 emu K\(\cdot\)mol\({}^{-1}\) expected for an \(S\) = \(\
icefrac{{1}}{{2}}\) paramagnet. Below 50 K, \(\chi\)T gradually decreased as antiferromagnetic interactions along the a-axis chain (weak hydrogen d\({}_{\text{N6}}\)... C\({}_{\text{34}}\) contacts) became dominant.
## Discussion
### Survey Limitations  
This work suffers from several qualifying limitations that are worthwhile to acknowledge herein. As with all types of reviews, there is a probability that we missed some works predicting student learning outcomes because of our selected search keywords and phrases, or during the screening process. Moreover, it was not possible to perform a meta-analysis of the previous findings to confirm the statistical significance of the synthesized predictive models, due to the unavailability of the datasets and the diverse techniques used to forecast student outcomes. We deliberately restricted our search of the intelligent predictive models of learning outcomes to the last decade only (i.e., 2010-2020), which witnessed a significant boost in machine learning on the one hand, and outcome-based education on the other hand. Therefore, we might have missed some critical works published before 2010. It was also observed that some studies did not report all experimental and prediction details, e.g., dataset characteristics, type of predictive models, and the factors influencing student academic success. For instance, 21 studies did not specify the performance metrics of the predictive models they devised. This eventually affected the quality of our synthesis analysis. Unfortunately, many studies did not follow a detailed methodology, which made the assessment more challenging. Our survey was motivated by three research questions, which could have framed the review process, and thereby, the conclusions we reached. Other research questions might be asked and answered differently, leading to different results. Our search was restricted to peer-reviewed journals and conference articles, which could have overlooked valuable studies reported in dissertations, as well as in the unpublished literature.