esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 SLR Methodology
### Conducting the SLR
#### 4.2.2 Data Collection  
In this review study, the data collection was conducted in two parallel scenarios. Several features were extracted by reading the published article. Simultaneously, a questionnaire survey was distributed among the corresponding or first authors of the selected articles to gather their subjective remarks on the article and some features that were not clear from reading the articles. Each of the phases is elaborately described in the following paragraphs.  
Feature Extraction  
All the selected articles on the methodologies and evaluation of explainability were divided among the authors for thorough scanning to extract several features. The features were extracted from several viewpoints, namely _metadata_, _primary task_, _explainability_, _explanation_, and _evaluation_. The features extracted as metadata contained information regarding the dissemination of the selected study. Features from the viewpoint of the primary task were extracted to assess a general idea of the variety of AI/ML models that were deliberately used to perform classification or regression tasks prior to adding explanations to the models. The last three sets of features were extracted related to the concept of explainability, the explored or proposed method of making AI/ML models explainable and the evaluation of the methods and generated explanations, respectively. After extracting the features, a feature matrix was built to concentrate all the information for further analysis. The principal features from the feature matrix are concisely presented in Table 3.  
Figure 5: Flow diagram of the research article selection process adapted from the PRISMA flow chart by [PERSON] et al. [40]. The number of articles obtained/included/excluded at different stages is presented in parentheses.  
In parallel to the process of feature extraction through reading the articles, a questionnaire survey was conducted among the corresponding or first authors of the selected articles. The questionnaire was developed using Google Forms and distributed through separate emails to authors. The prime motivation behind the survey was to complement the feature extraction process by collecting authors' subjective remarks on their studies, curating the extracted features, and gathering specific information that was not present or unclear in the articles. The survey questionnaire contained queries on some of the features described in the previous section. In addition to that, queries on the experts' involvement, the use of third-party tools, potential stakeholders of this study etc., were also present in the questionnaire. In response to the invitation to the survey, approximately half of the invited authors submitted their remarks voluntarily, and these responses add value to the findings of this review.
## 1 Introduction  
Electrochemical lithium intercalation into graphite negative electrodes is a significant issue when considering the diversity of electrolyte solutions applicable to lithium-ion batteries (LIBs) [1; 2; 3; 4]. Lithium intercalation occurs when the electrode (graphite) is covered with a surface film called the solid electrolyte interphase (SEI), whose formation is strongly dependent on the kind of electrolyte species. Ethylene carbonate (EC)-based electrolyte solutions allow for the formation of an effective SEI during the initial charge process. Hence, EC is most widely used as the main solvent in LIBs. In contrast, electrolyte solutions based on propylene carbonate (PC) and ether (e.g., tetrahydrofuran, glyme-based solvents) lead to continuous solvent co-intercalation and electrolyte decomposition instead of SEI formation and lithium intercalation [5; 6; 7].  
Interestingly, the electrolyte solutions with high salt concentration are conducive to SEI formation, and thus facilitate lithium intercalation into graphite negative electrodes [8; 9; 10; 11]. In this case, the solvation structure of the lithium ion drastically changes because of the decrease in the solvation number as compared with that in low-concentration electrolyte solutions. Since solvation structure is a critical factor influencing SEI formation on graphite negative electrodes, various strategies for controlling the solvation structure have been proposed (e.g., increasing the salt concentration and the addition of a Lewis acid or base) [8; 9; 10; 11; 12; 13; 14]. Among these, increasing the salt concentration is a simple method for forming an effective SEI at graphite negative electrodes. However, salt addition largely increases the viscosity of the electrolyte solution resulting from forming ion pairs and aggregates, consequently decreasing the ionic conductivity, and thereby degrading the electrochemical performance of LIBs [15]. Therefore, a method that improves the ionic conductivity is needed to enhance the electrochemical performance of LIBs in concentrated electrolyte solutions.  
In this study, we focused on concentrated PC-based solutions. This was because PC has a lower melting point (-49 \({}^{\circ}\)C) compared to EC (34 \({}^{\circ}\)C) and thus has excellent ion conductivity at low temperatures. We investigated the effect of the addition of 1,2-dichloroethane (DCE) on the electrochemical performance of graphite in concentrated PC-based electrolyte solutions. Because of the low electron-donating ability of DCE (donor number: 0), the solvation structure that is instrumental for effective SEI formation was expected to be maintained along with a decrease in the electrolyte viscosity and an increase in the ionic conductivity [16]. To the best of our knowledge, this is the first report on a method for improving ionic conductivity while maintaining the solvation structure of lithium ions in concentrated electrolyte solutions. The electrochemical properties were investigated in the concentrated PC-based electrolyte solutions containing various amounts of DCE. The structures of the electrolyte solutions were clarified by Raman spectroscopy.
# Enhanced Precision in Chinese Medical Text Mining Using the ALBERT+Bi-LSTM+CRF Model
## 5 Conclusions and Future Prospects  
### Discussion  
Huge and varied datasets in the medical field bring several challenges and opportunities. The information, data, and links in medical texts can be effectively integrated by constructing a base of medical semantic knowledge. Then, a transformation process can be conducted to generate structured knowledge that is easy to derive insights, patterns, and relations from, so a tool that helps support better assessments and comprehension can be attained. Not only are unstructured data in medical texts then organized but also smart medical technology can be better harnessed [31].  
In this study, we implemented the proposed ALBERT+Bi-LSTM+CRF algorithm to conduct comprehensive learning tasks on medical texts, successfully attaining high-precision methods for entity and relation extraction. A graphical database for medical text knowledge was constructed based on the triplet representation of information. Thus, structured data support for auxiliary guidance in the decision-making around medical treatments was produced. The implementation of ALBERT ensures precision while substantially decreasing the complexity and memory requirements during training, enabling flexible training and updating of models within hospitals. The addition of BiLSTM+CRF further advances precision, with the utmost significance in the medical field, which pursues rigorous and precise outcomes.  
After entities and relations are successfully extracted, a detailed screening and reorganization of the data are conducted. First, duplicate nodes are removed to ensure data consistency. Then, synonyms, which are labeled entity pairs, are detected and merged to decrease redundancy.  
When entities are extracted, the constructed algorithm achieves 91.8%, 92.5%, and 94.3% for the F-score, precision, and recall, respectively. The proposed algorithm also achieves remarkable outcomes in extracting relations, with 88.3%, 88.1%, and 88.4% for the F-score, precision, and recall, respectively.
# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 7 Results
Wireless energy transfer processes can power 6G IoT devices [36] by energy beamforming, resource scheduling, distributed antenna systems, and distributed ledger and blockchain technologies. Sixth-generation IoT networks necessitate constant uninterrupted operations by ambient energy sources through scalable wireless powering. Machine learning and clustering techniques can increase transmission power and connectivity in IoT portable and mobile device communication services [35], reducing energy consumption with regard to edge and vehicular devices. Sustainable energy infrastructures can cut down transmission delays, computational overhead, and energy constraints by nearest neighbor, mobile edge-computing-based distributed federated, and cluster head selection algorithms in 6G network aerial base stations. Green and smart networks enable 6G IoT sustainable development [59], enhancing resource efficiency and increasing network lifetime by confidence information coverage reinforcement learning node sleep scheduling algorithms. Q-learning collaborative intelligence can satisfy the least active node coverage rate. Analytic hierarchy processes can be used to evaluate energy consumption, management, and monitoring [40] by neural network and system identification operations, IoT devices, deep and machine learning algorithms, and 6G communication technologies. Sixth-generation IoT services, artificial-intelligence-based smart algorithms, and communication, computation, and robust radio-over-fiber system architectures [39] integrate sensors, actuators, microcontrollers, high-density heterogeneous devices, and transceivers. Three-dimensional network-based green ubiquitous IoT wireless spectrum sharing [58] can lead to energy efficiency by blockchain, cloud, smart contract, and 6G mobile communication technologies. Smart mobile networks, Internet of Everything, and 6G spectrum technology [50] require broadening frequency bands, lower latency, and remote device connectivity. Context-aware energy efficiency management, blockchain and environmental pollutant reduction technologies, and green autonomous power consumption [64] are associated with 6G IoT smart harvesting environments. IoT industrial and smart harvesting environment develops on context-aware autonomous power efficiency and blockchain-based transitive energy management, intelligent power metering, and energy consumption reduction.
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 3 Preliminary Preparation for Data and Causal Analysis
### Data Preprocessing  
#### 3.2.1 Data Cleaning  
As the parameters in QAR data are collected at different frequencies, there are many gaps without values when integrating them into one dataset. To stabilize the data, the first step of data cleaning is to fill up the gaps with the adjacent values. Then, the related parameters will be extracted from the approach phase. Finally, in order to eliminate redundant data and make data changes smoother, uniformly reducing the data to 1 Hz, we perform a down-sampling on the extracted data and make the maximum value of the sampling interval as the value for that second after reducing the sampling rate. The function of step is shown in Figure 1. Define a parameter \(x^{(m)}_{i}=(x^{j}_{i},x^{j+1}_{i},\ldots,x^{j+m}_{i})\), where \(i\in\{1,2,\ldots,L\}\) represents an attribute column, and \(m\) is the time step. The down-sampling conversion process is as follows:  
\[(x^{j}_{i},x^{j+1}_{i},\ldots,x^{j+m}_{i})=\max(x^{j}_{i},x^{j+1}_{i},\ldots,x ^{j+m}_{i}) \tag{1}\]
## 3 Results and Discussion
[47] showed higher radical scavenging activity than did ethanolic extracts (20.76%) but showed lower radical scavenging activity than did acetone extracts (50.81%). [PERSON] et al. [17] applied the Soxhlet method for extract preparation, while [PERSON] et al. [47] performed extraction in a shaking incubator and [PERSON] et al. [48] utilized maceration. The diverse methods of extraction could explain to a certain extent the differences in radical scavenging activity of the methanolic plant extracts. [PERSON] et al. [10] found a very high radical scavenging activity of _A. calamus_ methanolic extracts (90.29%), which is much higher than the scavenging potential of the plant extracts recorded in this experiment (24.56%). [PERSON] and [PERSON] [11] obtained data that corresponded to [PERSON] et al. [10]--over 90% radical scavenging activity of _A. calamus_ methanolic extracts, which was nearly nine times higher than the scavenging activity of _A. calamus_ hexane extracts. These great differences further emphasize the major impact of solvent type on the antioxidant activity of plant extracts. [PERSON] et al. [10] used maceration as an extraction method, whereas [PERSON] and [PERSON] [11] performed extraction at 60 \({}^{\circ}\)C in a horizontal water bath shaker. Generally, the discrepancies in the radical scavenging potential (expressed as DPPH inhibition in percentage) obtained in this work and described by the authors cited could be mainly explained by the different concentrations of DPPH solution (e.g., 0.1 mM [10], 3 mM [11], 0.2 mM [26] and 0.16 mM [48]) and by the application of extract solutions in different concentration levels (e.g., 20 \(\mu\)g/mL [17] and 200 mg/mL [11]). Additionally, there are differences in the calculation of DPPH inhibition (%) (e.g., [(A\({}_{0}-\) A)/A\({}_{0}\)] \(\times\) 100 [17; 47; 48] vs. [1 \(-\)
## 5 Guidance and Control Algorithm
### Inertial Alignment Hold
#### 5.3.1 Mode Separation in Inertial Alignment Hold  
Because DADC is performed before IAH, the relative orbit cannot perfectly maintain its periodicity by perturbation. As the two satellites move toward and away, the navigation error makes the initial states of the mission mode uncertain. In addition, an eclipse should be avoided for alignment with respect to the Sun. This uncertainty inspires the consideration of an efficient strategy rather than immediate alignment. The strategy is to conform to a 1U-2U \"string-of-pearls\" configuration of 40 m (staging mode) and align with respect to the Sun right after it gets out of the eclipse (alignment mode). The relative motion with control acceleration is represented by [44]:  
\[\begin{cases}\ddot{x}(t)=3n^{2}x+2n\dot{y}+u_{x}/m\\ \ddot{y}(t)=-2n\dot{x}+u_{y}/m\\ \ddot{z}(t)=-n^{2}x+u_{z}/m,\end{cases} \tag{21}\]  
where \(x,y,z\) are the relative states in radial, along-track, and cross-track components in the LVLH frame, \(n\) is the mean motion, and \(u=\begin{bmatrix}u_{x}&u_{y}&u_{z}\end{bmatrix}^{T}\) denotes the control input.  
To maintain the relative configuration without control inputs during the staging mode, the radial and cross-track positions and the radial and along-track velocities should be zero (\(x=z=0\) and \(\dot{x}=\dot{y}=0\)). In addition, if the cross-track velocity exists, its position will eventually increase, so \(z\) must be zero. Consequently, if only the position in the along-track direction exists it is possible to maintain a stable formation without consuming thrust. Therefore, it is recommended to maintain 40 m separation in the positive along-track direction. Because the deputy is aligned in the along-track direction during the staging mode, the closer the Sun is to the along-track direction in the alignment mode, the better the alignment will be performed. Consequently, the strategy of two-submode operation is more advantageous when the Sun's cross-track component is small.
# Novel Phenotypic Elements of Type IV Collagenopathy Revealed by the _Drosophila_ Model
## 4 Intestinal Manifestation of _col4a1_ Mutations in _Drosophila_  
The skeletal and cardiac muscles appeared 700 million years ago in a common ancestor prior to the evolutionary divergence of vertebrates and arthropods. The smooth muscle of vertebrates is likely to have evolved later, independent of the skeletal and cardiac muscle types [49]. Accordingly, the visceral muscles of _Drosophila_ are striated. We studied the larval midgut through ultrathin sectioning and electron microscopy and observed that both epithelial and visceral muscle cells were severely degenerated at the restrictive temperature in _col4a1C552D2_ mutant animals [26]. Both cell types detached from the distorted and thick BM, while epithelial cells accumulated lipid droplets, autophagic vacuoles and membrane ghosts. Membrane whorls practically filled the cell and the cytoplasm and muscle fibers were rarely observed. The space between the epithelial cell and the muscle cell was enlarged [26]. Functional damage of the alimentary tract in the _col4a1C552D2_ mutant was reflected by intestinal dysfunction, degeneration of the gut epithelial cells recorded by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) in the nuclei and the abrupt onset of leakiness of the gut (SMURF phenotype), demonstrated by blue food dye leaking out from the gut [25]. Intestinal dysfunction, a motility-associated disorder including chronic intestinal pseudo-obstruction, is a direct, life-threatening and frequent complication of muscular dystrophies, requiring immediate surgical intervention [50]. Notwithstanding, complications arising from intestinal dysfunction reported in _Drosophila col4a1_ mutants in conjunction with _COLAA1_ mutations have not yet been noted in humans and mice. We suggest that intestinal dysfunction in the _col4a1C552D2_ mutant is a consequence of disrupted visceral muscle BM junction in the gut, without apparent sarcomere structure and amorphous, uneven actin deposition (Figure 4).  
Figure 3: Compromised Malpighian tubules in mutants. Fusion and uneven distribution of mitochondria as a consequence of mutation-induced stress. Even distribution of mitochondria in wild-type control (**A1**, **A2**, **A3**, **A4**) and in the mutant under the permissive condition (**B1**, **B2**, **B3**, **B4**). The normal distribution of mitochondria remained at restrictive condition (**A5**, **A6**, **A7**, **A8**). Large aggregates of mitochondria appear in the mutant at 29 °C (**B5**, **B6**, **B7**, **B8**). Bars, lower left: 10 micrometers. Experiments were carried out as described [24; 25; 26; 28; 29].
# Chinese Character Boxes: Single Shot Detector Network for Chinese Character Detection
## 5 Conclusions  
This paper proposed a novel Chinese character boxes-single shot multibox detector (CCB-SSD) network for Chinese character detection. The CCB-SSD network has a single-scale structure which enables the network to be deep enough without any additional processes such as deconvolution-based up-sampling or feature map concatenation for better small character detection. Higher layers in the original SSD were removed because Chinese characters are small and dense. In addition, a new non-maximum suppression (NMS) method was proposed to remove duplicate detections for the same character because the baseline method (SSD) does not consider the relationships between classifiers in the multi-scale feature maps. Finally, a new evaluation index, false positives per character (FPPC) was proposed instead of false positives per image (FPPI) to remove the effect of character numbers per image. Extensive experiments have created an optimal network structure and have shown good performance as a result of testing the network for old documents written in caoshu at an entirely different time. Most characters can be detected well in old documents but there is still a problem of detecting two characters in one character or detecting two characters in one because there are no clear criteria for breaking between characters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Unrestricted Random Parameter** & \multicolumn{3}{c}{**Restricted Random Parameter**} \\ \hline
**Detection Rate (\%)** & **FPPC (\#)** & **Detection Rate (\%)** & **FPPC (\#)** \\ \hline
99.4 & 0.713 & 99.9 & 0.00238 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the effects of the augmentation method on the performance of the result.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Detection Rate (\%)** & **FPPC (\#)** \\ \hline
10 & 95.20 & 1.54 \\
**16** & **96.10** & **0.89** \\
22 & 89.14 & 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance evaluation using merged data-set and restricted augmentation.  
**Author Contributions:** Conceptualization, J.R. and S.K.; methodology, J.R.; software, J.R.; validation, J.R. and S.K.; formal analysis, J.R.; investigation, J.R.; resources, S.K.; data curation, J.R.; writing-original draft preparation, J.R.; writing-review and editing, J.R. and S.K.; visualization, J.R.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K.  
**Funding:** This research received no external funding.  
Figure 9: Detection results of conventional SSD and detection results using proposed CCB-SSD. (**(a)**: conventional SSD, **(b)**: proposed CCB-SSD).  
**Acknowledgments:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017M3C1B6071500) and This work was also supported by the 2018 Yeungnam University Research Grants.  
**Conflicts of Interest:** The authors declare no conflict of interest.