esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Implementation, and Field Testing
## 4 System Implementation
### User Registration Mechanism  
To protect the privacy of patients, a participating nursing assistant is required to register with the system before she or he could be tracked and receive feedback. Before the lease mechanism was introduced, the only way to register with the system is through a manual request by pressing the select button of the Pebble smartwatch while making a predefined gesture. Here we assume that each participating nursing assistant is given a smartwatch exclusively (i.e., not shared with other nursing assistant). The WatchApp generates a unique identifier to represent the user. This identifier is used in all components of the system for the user, including the activity log for the user.  
The flow control for user registration at the KinectServer is shown in Figure 4. The registration request arriving at the KinectServer is handled by the Kinect Coordinator. First, the corresponding User object is searched. If one is found (meaning the user has registered with the KinectServer previously), the registration request is passed to the User object for further processing. The User object then invokes the Activity Recognizer to check if the user is making the registration gesture based on what is defined in the Configuration file using the most recent copy of the skeleton frame. If the registration request is for a new user, the request is dispatched to the KinectDriver object, and the KinectDriver subsequently invokes the Activity Recognizer to detect if any of the skeleton contained in the current frame is making the predefined registration gesture. If successful, a User object is created for the user. Finally, a response message is composed indicating whether or not the registration is successful, and is sent back to the PhoneApp and eventually the WatchApp. The registration result is then displayed on the screen of the smartwatch.  
The registration mechanism not only protects the privacy of the patient, it enables the system to know who it is tracking and enables continuous tracking of a nursing assistant even when she or he goes to different rooms for bedside (as long as all such rooms are equipped with the KinectServer. In essence, this person-specific tracking is made possible by synchronizing two sensing modalities:  
Figure 4: The flow control for user registration at the KinectServer.  
(1) computer vision via the Kinect sensor; and (2) wearable sensing via the smartwatch worn by a user via the predefined registration gesture.  
In the current study, we choose to use a registration gesture where the left or right forearm of the nursing assistant pointing straight upward because this gesture allows the her/him to do so using one arm to make the gesture while pressing the select button of the smartwatch using the remaining hand.  
When the lease mechanism is enabled, a user is automatically registered before the expiration of the lease provided that the corresponding User object is found in KinectServer and there is one and only one skeleton in the current frame.
## 5 Discussion  
Through the above comparison and analysis, it was found that distributed generation and electric vehicle access have a great influence on improving the network voltage stability, and the characteristic of electric vehicle charging also has a positive effect on the network to absorb distributed generation.  
In addition, to further illustrate the effectiveness of the distributed generation and electric vehicle capacity selection results given by the kernel extreme learning machine, the results are compared with those obtained by the support vector machine, particle swarm optimization algorithm and genetic algorithm method, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Method** & **DG Access Number** & **DG Capacity (MW)** & **Active Power Loss (kW)** \\ \hline Basic load & - & - & - & 202.45 \\ \hline Reference [23] & PSO & 1 & 3.62 & 131.85 \\ \hline - & SVM & 2 & 3.37 & 200.72 \\ \hline \multirow{2}{*}{Reference [24]} & \multirow{2}{*}{GA} & \multirow{2}{*}{3} & 1.5 & \multirow{2}{*}{106.3} \\  & & & 1.07 & \\ \hline \multirow{4}{*}{Proposed} & \multirow{4}{*}{KELM} & 1 & 3.87 & 108.6 \\ \cline{3-5}  & & 2 & 3.26 & 107.2 \\ \cline{3-5}  & & & 2.97 & \\ \cline{3-5}  & & 3 & 0.55 & 105.6 \\ \cline{3-5}  & & & 0.54 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of kernel extreme learning machine with other optimization methods. (PSO: Particle swarm optimization; GA: Genetic Algorithm).  
Figure 7: Voltage distribution curves by comparing KELM with SVM under different DG&EV configuration scheme; (**a**) DG access in case1 and with or without EV; (**b**) DG access in case2 and with or without EV; (**c**) DG access in case3 and with or without EV; (**d**) voltage profile of KELM with different DG access modes and with or without EV.(DG: Distributed generation; EV: Electric vehicle)In order to make the results are comparable, focusing on the distributed generation sizing problems without electric vehicles, and with the same objective function, that is the active power loss is minimum, and the same access location, to compare the results of the kernel extreme learning machine with the particle swarm optimization algorithm, genetic algorithm and support vector machine.  
From Table 5, we can see, the capacity of distributed generation obtained by kernel extreme learning machine is larger than particle swarm optimization algorithm and support vector machine, while the active power loss is smaller. So through this comparison, we believe that the results obtained by the kernel extreme learning machine are better.
# Electrocatalysis for the Oxygen Evolution Reaction in Acidic Media: Progress and Challenges
## 2 Mechanisms for the OER in Acidic Media
Incorporating suitable foreign metal atoms, such as Cr, Ni, Zn, and Cu atoms, can surprisingly improve the conductivity and alter the electronic structures of the original catalysts, thus enhancing their intrinsic activity [64; 65]. It was reported that a low Ru content oxide material (Cr\({}_{0.6}\)Ru\({}_{0.4}\)O\({}_{2}\)) derived from a Cr-based metal-organic framework showed remarkable OER performance in acidic media [66]. The superior catalytic activity and stability can be assigned to the lower occupation at the Fermi level and the altered electronic structures by incorporating Cr. Regarding Ni-modified oxides, it is suggested that Ni serves as the sacrificial component, as its leaching generally leads to enhanced OER activity due to the formation of active OH-containing surface structures [67]. For instance, Ni leaching was observed during the OER process of bulk Ir-Ni mixed oxides with increased Ni contents (21 atomic%, 39 atomic%, and 89 atomic%), and the remaining Ni concentrations were similar in all systems, which turned out to be ~12 atomic% relative to the total amount of Ir and Ni in the oxides [68]. However, differences in their OER performance suggested variations in the resulting active sites caused by the sacrifice of Ni. This means that the OER performance of the catalysts is directly related to the transport kinetics of the electrons involved, and the reaction rate is determined also by the number of active sites. Therefore, it is important to prepare electrocatalysts with a sufficient reaction surface area in order to enable facile mass/electron transport and alter the interaction between metals and supports [69]. The most effective way is to minimize the size of catalyst nanoparticles to within several nanometers to make full use of each active site [36]. In addition, composition modification may also increase the number of catalytic sites. Doping Zn and Cu can confer moderate binding strength on oxygen intermediates, provide more defects or vacancies to enhance the  
Figure 2: Diagrammatic sketch of (**a**) the AEM mechanism proposed by [PERSON] et al. and (**b**) the LOER mechanism proposed by [PERSON] et al. [24]. intrinsic activity, and significantly increase the surface area to expose more active reaction sites [70]. However, difficult issues such as well-controlled monodispersity, stabilization of active sites, targeted synthesis, and macro-scale configuration for OER electrocatalysts still remain, especially in acidic media, both experimentally and theoretically [23, 71].
# Single-Stage Fractionation of Vine Shoots Using Microwave Heating
## 1 Introduction
OS find a number of applications in the food and pharmaceutical sectors, related to their bioactivity (for example, as prebiotics). The International Scientific Association for Probiotics and Prebiotics (ISAPP) defined \"prebiotic\" as \"a substrate that is selectively utilized by host microorganisms, conferring a health benefit\" [26]. The literature on the manufacture of OS with prebiotic properties from vine shoots indicates that OS are constituted by backbones of pentoses with different substitution patterns by hexoses and acetyl groups [6]. Besides OS, the aqueous phases contain undesired compounds for prebiotic manufacture, such as monosaccharides and non-saccharide compounds, which limit the purity of the final product [12; 13]. Diverse methods have been reported on the manufacture and refining of hemicellulose OS, including the enzymatic depolymerization into short chain OS, which are preferred for applications as food additives or nutraceuticals [27]. Additionally, liquid-liquid extraction, ion exchange or membrane filtration have been employed as purification treatments [28; 29]. This work handles a single stage fractionation method based on water/1-butanol reaction media, which was able to separate the structural constituents of vine shoots (intact or as derived products) in separate streams, allowing their individual utilization. Specifically, the experimental duties considered in this study include: (i) compositional study of the substrate; (ii) fractionation under diverse experimental conditions and distribution of products among the process streams; (iii) selection of optimal reaction conditions based on the amount of oligosaccharides generated from hemicelluloses; (iv) in-depth evaluation of the composition hemicellulose-derived products; (v) DP tailoring of oligosaccharides by xylanases and purity assessment; and (vi) evaluation of the cellulose-enriched solid from processing as a substrate for glucose manufacture by enzymatic hydrolysis.
# Noise Reduction in CWRU Data Using DAE and Classification with ViT
## 3 Application Example
### Classification Result Analysis
#### 3.4.2 Comparison with CNN-Based Classification Techniques  
Figure 21 presents the results reported by [PERSON], who proposed and utilized S-AlexNet, an edge intelligence diagnosis method based on the parameter transplantation CNN [3]. While the failure type was classified with 100% accuracy in outer-race and normal cases, for different classes, the accuracies ranged from 70% to 90%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Majority** & **Extra** & **Ada** & **S-AlexNet** & **1D-CNN** & **2D-CNN** & **3D-CNN** & **ViT** &
\begin{tabular}{c} **DAE + ViT** \\ **(Ours)** \\ \end{tabular} \\ \hline Classification accuracy (\%) & 97.91 & 94.76 & 93.75 & 91.3 & 95.27 & 98.21 & 98.5 & 99.46 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification accuracy by classification algorithm.  
Figure 21: Classification result based on CNN—1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Fold1** & **Fold2** & **Fold3** & **Fold4** & **Fold5** & **Average** & **Standard Deviation** \\ \hline ViT & 99.82 & 100 & 99.82 & 100 & 99.82 & 99.86 & 0.0048 \\ \hline DAE + ViT & 100 & 100 & 99.64 & 100 & 99.82 & 99.89 & 0.0674 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for each fold.  
Figure 22 summarizes the results reported by [PERSON] [2]. Figure 22a shows the classification accuracy on the CWRU dataset achieved using one-channel, two-channel, and three-channel CNNs. All three models achieved accuracies exceeding 95%, with the three-channel CNN exhibiting the highest accuracy (98.5%). Figure 22b presents the classification results in a confusion matrix.  
Based on the foregoing comparisons, the ViT achieved the highest accuracy in classifying the raw data as well as the preprocessed data. This can be attributed to the unique characteristics of the ViT in processing time series data, such as the use of a natural language-type temporal sequence.
# Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder
## 5 Conclusions  
As an essential technology in quantum computing and communication, quantum error-correcting codes have significant research value and practical applications. However, the reliability of codes is severely challenged in complex quantum environments. Meanwhile, the development and application of emerging quantum error-correcting codes also face many difficulties and challenges. In recent years, heavy hexagonal codes have been proposed as a new type of quantum error-correcting codes and gradually attracted the attention of researchers. Compared with other quantum error-correcting codes (such as surface codes and concatenated codes), heavy hexagonal codes are more accessible to implement in the laboratory and can effectively resist conventional errors.  
However, the decoding process of the heavy hexagonal code still faces a series of problems. Due to the complexity of coding methods, the distances for different heavy hexagonal codes vary significantly, which poses a great challenge for the design and use of decoders. For example, the traditional MWPM can be used for low-to-moderate-distance heavy hexagonal codes but shows a considerable drop in performance at higher distances. Therefore, it is necessary to explore new algorithms and technologies to improve the error-correcting performance of the heavy hexagonal code. Therefore, this paper proposed a machine-learning-based decoder for the heavy hexagonal code, which achieved better quantum error correction performance under ideal conditions. We empirically tested the decoder using 7-layer and 14-layer residual networks for heavy hexagonal codes with distances three, five, seven, and nine. We also demonstrated that the CNN-based training of heavy hexagonal codes achieved a threshold of 0.65%, outperforming the existing MWPM decoder. However, machine-learning-based decoding algorithms still face many challenges, such as how to train and test with complex noise models, and how to use machine learning decoders in real quantum environments. In the future, we will continue to explore more efficient and accurate prediction models for quantum error correction.  
Writing--original draft, A.L.; data curation, A.L.; formal analysis, F.L. and Q.G.; methodology, A.L. and H.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Shandong Province, China (grant no. ZR2021 MF049), and Joint Fund of Natural Science Foundation of Shandong Province (grant no. ZR2022 LLZ012), and Joint Fund of Natural Science Foundation of Shandong Province (grant no. ZR2021 LLZ001).  
Not applicable.  
The data are contained within the article.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Formulation of the Problem  
The main elements of the PDM are the power and transmission sections (Figure 3), and the smaller their diametrical and axial dimensions are, the smaller the curvature radius that can be obtained in the constructed channel [19].  
When using a discontinued PDM, because of the complexity of manufacturing a D-42.9/10 type PDM, a higher rate of penetration (by 50-70%) was observed compared to when using the 2D-43.5/6.42 PDM. The reason for the higher rate of penetration while drilling using the D-42.9/10 PDM was the high rotation moment developed by the power section using the 9:10 lobe configuration [20]  
Figure 3: Photo of a two-section PDM with TS Perfobore centralizers.  
The use of a high-torque drilling motor makes it possible to use it both for drilling and for intermediate casing milling (currently, the Perfobore company (Houston, USA) uses a single-section D-55 motor for milling, and a two-section 2D-43.5/6.42 PDM for drilling), which reduces the number of modules of the Perfobore technical system, significantly reducing the technological deadline for the preparation and mobilization of equipment, which increases both the technical and economic efficiencies of the process as a whole.  
The algorithm for choosing the optimal version of the power section (PS) of a small-sized PDM, taking the given rotation moment and restrictions on its diametrical and axial dimensions into account, as well as the main calculated dependences of the geometric and operational parameters, is presented in Reference [21]. Initial data for calculating PDM variants are presented in Table 1.
# Photonic Crystal-Based Sensors for Detecting Alcohol Concentration
## 4 Conclusions  
In conclusion, the three-dimensional nanoporous inverse opal films display perfect optical sensing properties. The peak positions of the reflectance spectra can be used for detecting the ethanol concentration with different refractive indices. The sensitivity is enhanced by choosing titanium dioxide inverse opal film as the sensor. This TiO\({}_{2}\) inverse opal could be used for quantitative detection of the ethanol concentrations in the corresponding range. From the results, the 600 \({}^{\circ}\)C sintered TiO\({}_{2}\) film not only retains highest sensitivity from the studied samples for identifying the ethanol concentration. The 600 \({}^{\circ}\)C sintered TiO\({}_{2}\) inverse opal structure possesses the highest sensitivity for ethanol concentration because the majority of the anatase phase was transformed into the rutile phase at the higher sintering temperature, resulting in an increase in the refractive index and the amount of the red shift. Despite this, the SPR approach is a more exact system than the present one. However, compared to the SPR approach, the chemical sensor based on TiO\({}_{2}\) inverse opal film has several advantages. First, the low-cost fabrication is rapid. Second, the high-temperature (600 \({}^{\circ}\)C) sintered TiO\({}_{2}\) inverse opal film has a higher refractive index and good hydrophilic surface, which provides a high-sensitivity optical sensor. A chemical sensor based on inverse opal films has the potential to be a powerful tool for the fast screening pesticide residues in fruits and vegetables or to be a next generation senor for identifying gas concentrations in the future.  
The authors gratefully acknowledge research funding from the Ministry of Science and Technology of Taiwan (MOST 103-2221-E-150-021).  
[PERSON] conceived and designed the experiments and the whole concept; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON], [PERSON] and [PERSON] drafted the manuscript; [PERSON], [PERSON] and [PERSON] revised the whole manuscript.  
The authors declare no conflict of interest.
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 4 Results and Discussion
### Result Analysis with XAI Techniques
#### 4.3.2. LIME (Local Interpretable Model-Agnostic Explanations  
A single prediction from a machine learning model that categorizes student adaptability levels is shown in Figure 6, along with LIME study. The adaptation levels that the model predicts are 'Moderate' (probability = 0.62) and 'Low' (probability = 0.38). The prediction for 'High' adaptability is highly unlikely based on the given feature values.  
The middle section illustrates the weighted impact of each feature on the prediction decision, with positive contributions towards a 'Not Low' adaptability prediction. For  
Figure 5: SHAP force plot for an individual prediction of student adaptability. (**a**) Instance for an explanation; (**b**) SHAP value of the instance; (**c**) instance local explanation.  
Figure 6: (Local) Interpretation of a model prediction using LIME.  
instance, 'Class Duration' and 'Institution Type' exhibit the most significant positive influence, suggesting that longer class durations and certain institutional characteristics may be associated with higher adaptability. Conversely, features such as 'Device' and 'IT Student' status have a notable negative weight, implying that the absence of certain technological factors may lead the model to predict a lower adaptability level.  
The rightmost section lists the values of the features for the specific instance being explained, with orange representing positive contributions and blue for negative ones. This visualization aids in understanding the local behavior of the machine learning model, providing transparency into how feature values are aggregated into a predictive outcome. The LIME interpretation offers valuable insights for educators and policymakers by pinpointing specific areas that could be addressed to potentially improve a student's adaptability. It also emphasizes the intricate nature of the factors that impact educational outcomes and highlights the requirement for sophisticated approaches in educational interventions.
## 4 Microbial Adhesion
### Theoretical Models  
#### 4.2.1 DLVO Theory  
One of the theories that were used to describe the reciprocal influences between bacteria and the surface is the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. This theory is widely applied to delineate interactions between the colloidal particles or a colloidal particle with a surface. As such, the size of bacteria (0.5-2.0 \(\upmu\)m) corresponds to the size of colloidal particles; this theory has been used for the interactions between bacteria and the material surface [39].  
The balance between the attractive van der Waals forces \(\Delta G^{\alpha dN}\) and the electrostatic repulsion forces \(\Delta G^{\textit{all}}\) from the overlap between the electrical double layer of the cell and the substratum have determined the total interactions \(\Delta G^{\textit{all}}\) of the bacteria with the material surface. The classical DLVO is expressed as:  
\[\Delta G^{\textit{all}}(d)=\Delta G^{\textit{all}}(d)+\Delta G^{\textit{all}}(d) \tag{5}\]This formula shows that both forces are dependent on the distance between the cell and the substratum. The van der Waals forces become more dominant and the cell will irreversibly adhere to the surface if the bacterium is close to the surface. On the other hand, if the distance between the cell and substratum becomes higher the van der Waals forces drastically decrease and the Coulomb interactions become more dominant, resulting in the cell repelling from the surface [39]. In addition, the ionicity of the cell is a crucial factor for the bacterial adhesion. If the ionic strength is high, the positively and negatively charged particles will attract naturally towards each other. The particles will naturally repel if the ionic strength is low by having the same charge.  
However, reversible adherence may occur when the bacterial cell approaches the surface due to its motility or Brownian motion. In this case, the bacterial cell needs to overcome a certain energy barrier. Once the bacterial cell comes close to the surface it can use its appendages, like pili and flagella, to attach to the surface. These appendages can pierce the energy barrier and bacterial adhesion is facilitated [39]. For clarity sake, the DLVO theory describes the probability of an organism to overcome any electrostatic barrier. Current research showed that there is a relation between the ionic strength and bacterial adhesion, which is consistent with the DLVO theory [40].
## 4 Experimental Design and Results Analysis
### Experimental Results and Analysis
#### 4.4.2 Pruning Results and Analysis
The larger the area enclosed by the curve and the coordinate axis, the better the surface model performance. As shown in Figure 14, the area enclosed by the PR curve and the coordinate axis of the model before and after pruning is basically unchanged, and the accuracy of each category of obstacles fluctuates slightly, indicating the superiority of the LAMP method with a Speed-up of 3.