esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Comprehensive Vulnerability Rate Model
### Multisource Situational Awareness Analysis
#### 2.3.2 Power Supply Situation
\(P_{j,ch}^{t}\) and \(P_{j,dis}^{t}\) represent the charge and discharge power of the ESS in the period \(t\), respectively. \(\eta^{ch}\) and \(\eta^{dis}\) are charge and discharge efficiency. \(E_{j,bat,max}\) indicates the maximum capacity of the ESS. \(P_{j,ch,max}^{t}\) and \(P_{j,dis,max}^{t}\) are the maximum charge and discharge powers of the node \(j\) in the ESS. \(D\) is a 0-1 variable and \(D_{j,ch}^{t}\) and \(D_{j,dis}^{t}\) indicate the state of charge and discharge at time \(t\). \(T\) is the duration of a risk assessment. \(N_{T}\) is the total number of risk assessment time intervals. \(N_{bus}\) is the number of nodes in the ADN. Then the power supply index (PSI) of the distributed ESS for distribution line \(k\) is:  
\[\gamma_{k}\ =\ \frac{\sum_{m=1,\cdots,\mu_{3,k}}\big{|}e_{m.ess}\big{|}P_{m.ess}}{\sum_{m=1,\cdots,b_{1}}\lambda_{m.2}\ +\ \sum_{m=1,\cdots,b_{2}}P_{m.dg}\ +\ \sum_{m=1,\cdots,b_{3}}P_{m.ess}} \tag{14}\]  
In order to distinguish the influence of the different power supply equipment status on the operation of the distribution lines, the power contribution tracking coefficient of the transformer, wind power photovoltaic equipment and energy storage device to the distribution lines can be obtained by the power flow tracking, which are \(\varepsilon_{m,g}\), \(\varepsilon_{m,dg}\), \(\varepsilon_{m,es}\) respectively. The number of transformers, wind power photovoltaic equipment and distributed energy storage devices on the distribution line are \(a_{1,k}\), \(a_{2,k}\), \(a_{3,k}\) respectively. The number of transformers, wind power photovoltaic equipment, and distributed energy storage devices in the distribution network area are \(b_{1}\), \(b_{2}\), \(b_{3}\) respectively. Then the power supply equipment risk situation index of the line can be expressed as:  
\[\eta_{k,2}^{I}\;=\;1\;-\;(\tau_{1}\cdot\lambda_{k}\;+\;\tau_{2}\cdot\delta_{k} \;+\;\tau_{3}\cdot\gamma_{k}) \tag{15}\]  
where \(\tau_{1}\), \(\tau_{2}\), and \(\tau_{3}\) are the weight.
# Investigation of Failures during Commissioning and Operation in Photovoltaic Power Systems
## 3 Results and Discussion
### DC Line-Line Short-Circuit Fault  
For a line-to-line short-circuit fault to occur on the DC side, a fault condition was created by triggering the fault block selected in Figure 12. The graphs in Figures 4 and 5 were obtained. Looking at the graphs obtained from the measurements taken from the DC side, it is seen that a line-to-line short-circuit fault causes the current and voltage to decrease to zero. In the graphs created with measurements taken from the AC side, it is seen that a line-to-line short circuit occurring on the DC side does not directly affect the transformer output voltage but does affect the transformer output current.
## 3 Results
### Animal Condition and Tissue Neuroxidative Responses  
_S. senegalensis_ morphometric data (i.e., weight, W, and total length, TL) and [PERSON]'s condition index K) in each treatment at the end of the exposure period (i.e., day 28) are presented in Table 4.  
After 28 days, fish from the Non-CONT treatment exhibited significantly higher W (10.1 \(\pm\) 1.3 g: \(p\) \(<\) 0.05) and TL (9.8 \(\pm\) 0.5; \(p\) \(<\) 0.05) compared to those exposed to Hg alone, whereas these morphometric parameters were not significantly affected by climate change related stressors (in both Non-CONT and CONT treatments; \(p\) \(>\) 0.05). Despite changes elicited by Hg exposure, animal condition (K) was not significantly affected by any stressor (\(p\) \(>\) 0.05; Table 4). Correlations among morphometric variables, animal condition (K), and Hg contents, NARs, and EFs in the different tissues are shown in Table 5. While a positive correlation was observed between fish TL and W (\(r\) = 0.84, \(p\) \(<\) 0.001), K was only significantly correlated with TL (\(r\) = -0.43, \(p\) \(<\) 0.001). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multirow{2}{*}{**Hg (\(\text{\mu g}\cdot\text{g}^{-1}\) ww)**} & \multirow{2}{*}{**Reference**} & \multicolumn{4}{c}{**Hazard Quotient (HQ)**} \\ \cline{4-7}  & & & **Actual** & **W 1** & **A 2** & **W + A 3** \\ \hline \multicolumn{7}{c}{Demersal 4  \\ \hline \multicolumn{7}{c}{Pelagic 5  \\ \hline \multicolumn{7}{c}{Meagre 0.12  \\ \multicolumn{7}{c}{Seabass 0.04  \\ \multicolumn{7}{c}{Blegar 0.01  \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mercury average concentrations (Hg, \(\text{\mu g}\cdot\text{g}^{-1}\) ww) of demersal and pelagic species found in literature in the European region, their actual hazard quotient (HQ; baseline reference), and HQ estimations under climate change predicted scenarios. With regard to tissue neuro-oxidative responses, CAT and SOD activities varied according to fish tissue and treatment (Figures 2 and 3). CAT activity values ranged between 21.8 nmol-min\({}^{-1}\)-mg\({}^{-1}\) protein (in the muscle of fish from CONT + W treatment) and 99.5 nmol-min\({}^{-1}\)\(\cdot\)mg\({}^{-1}\) protein (in the liver of fish from non-CONT + A treatment). Overall, CAT activity in fish muscle was not affected by climate change-related stressors, while Hg contamination had a significant effect in all treatments, with the exception of those simulating seawater acidification (i.e., \(p>0.05\) in treatments CONT + A vs. non-CONT + A; Figure 2a). Yet, different trends were found in the liver, with acidification acting alone
###### Abstract  
Background: Periodontal disease is a multifactorial infectious pathology that affects dental supporting tissues, impacting the health and quality of life of patients. Assessing chewing efficiency before and after periodontal therapy is crucial to better understand its impact on the quality of life of periodontal patients. Thus, the aim of this case series study was to determine whether basic periodontal therapy improves masticatory ability and efficiency in patients with mild to moderate periodontitis. Methods: A prospective, longitudinal, case series study was carried out in 42 periodontal patients treated at the University Odontology Clinic. Masticatory efficiency before and after basic periodontal treatment was assessed using both objective (HueCheck Gum test) and subjective (Quality of Masticatory Function Questionnaire: QMFQ) methods. Results: Significant improvements (\(p\) = 0.0001) were observed in periodontal variables such as hygiene index (HI), probing depth (PD), and clinical attachment level (CAL), as well as in the initial and final QMFQ total score (\(p\) = 0.045). In addition, significant differences were found in post-treatment QMFQ scores related to parafunctional habits (\(p\) = 0.046) and facial patterns, especially in meat consumption (\(p\) = 0.042) fruit and vegetable consumption (\(p\) = 0.007, \(p\) = 0.044). The initial mean HueCheck Gum test values were 0.59 \(\pm\) 0.09 and the final values were 0.61 \(\pm\) 0.11. These differences were not statistically significant. Conclusions: Basic periodontal treatment does not objectively improve the masticatory efficiency of patients with mild-moderate periodontitis, evaluated by means of the test of mixing two chewing groups, but it does offer subjective improvements.  
Keywords:chewing gum; masticatory function; periodontal disease; periodontitis; quality of masticatory function +
Footnote †: journal: Applied sciences
# Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
## 4 Discussion
### Model Evaluation  
Many studies have focused on a specific disease, resulting in more specialized cohorts and features [27; 28; 29; 30; 31], and thus poorly comparable to the present study. However, another recent study has used the same setting (predicting ICU LOS using data collected within the first 24 h of admission) and achieved high accuracy, but with different features and cohort conditions which makes the comparison difficult [69]. In fact, they use additional procedure information, more demographic features such as marital status, the number of previous surgeries or smoking history, and free text diagnosis. We note that they also considered a wider range of LOSs and performed a multiclass classification (with three classes). However, their results show an AUC (macro AUC) of 0.77, which is still lower than the best AUC of the present work (0.80). This could be due to the fact that it is generally more difficult for the model to predict longer LOSs (>25) with a very limited amount of data, as well as the higher complexity due to the multiclass classification. Thus, model evaluation remains a key challenge for ML in healthcare due to the lack of common standard benchmarks.  
Figure 6: Evaluation of different classifiers (logistic regression, SVM, random forest, XGBoost) in terms of (**A**) balanced accuracy and (**B**) MCC, where temporal features are represented by different measures including mean, maximum, mean and maximum, and mean and the standard deviation. The points and error bars indicate the mean and standard deviation of the results from 10 different experiments using repeated random train/test split.
# Machine Learning-Assisted Secure Random Communication System
## 3 Allied Concepts
### CART Classifier Algorithm  
The CART was introduced by [PERSON] et al. in 1984 [59]. It uses a binary splitting mechanism to construct DTs. The splits are evaluated on the basis of the Gini index (\(GI\)). It is used to compute the impurities of a particular node. The \(GI\) is the deciding factor in selecting the splitting attribute variable. For a dataset \(\mathcal{D}\), the \(GI\) is computed as follows:  
\[GI\;(\mathcal{D})=1-\sum_{i=1}^{m}p_{i}^{2} \tag{2}\]  
where the \(i\)-\(th\) class proportion of instances is denoted by '\(p_{i}\)' among the total '\(m\)'. A value of '\(GI=0\)' reflects a perfectly pure node, whereas equal classes reflect the maximum impurity. The \(GI\) of the split '\(GIS\)' is given by  
\[GIS\;(\mathcal{D})=\frac{n_{1}}{n}\;GI\;(\mathcal{D}_{1})+\frac{n_{2}}{n}\;GI \;(\mathcal{D}_{2}) \tag{3}\]  
during the splitting of dataset \(\mathcal{D}\) into subsets \(\mathcal{D}_{1}\) and \(\mathcal{D}_{2}\). The subsets \(\mathcal{D}_{1}\) and \(\mathcal{D}_{2}\) are of sizes '\(n_{1}\)' and '\(n_{2}\)', respectively. The '\(n\)' represents a size of \(\mathcal{D}\). To classify a specific class, the CART algorithm proceeds with the split that attains the minimum \(GI\) for the subsequent subsets. The basic structure of the DT is shown in Figure 3.  
Note: The topmost node in the tree is the root node representing the entire dataset. All the nodes are split on the basis of the attribute variable, resulting in branches and internal nodes. A node that does not split further is known as a leaf node. The leaves represent the final decisions of the tree and contain the target value as the predicted outcome, which is referred to as the 'class'.  
CART Classifier Variations: Fine tree (FT), coarse tree (CT), and medium tree (MT) are variations of DTs; these differ in their stopping criteria when training a tree by recursively splitting the data. Table 1 summarizes the three variations used in this study.
## 4 Discussion
### Strength Degradation  
Repeated cyclic loading resulted in strength degradation for the anchored blind-bolted joints. The strength degradation in the specimens under the same drift level can be evaluated using the strength degradation coefficient (\(\lambda_{i}\)) recommended by [PERSON] et al. [29]. For the tested joints, a significant stiffness degradation occurred only when the drift ratio exceeded 1%. After reaching that drift ratio, there were only two loading cycles for each drift level. Hence, the strength degradation coefficient (\(\lambda_{i}\)) can be expressed as the ratio of the peak point load in the second cycle to the peak point load in the first cycle.  
Figure 18 shows the curves of the strength degradation coefficient (\(\lambda_{i}\)) at the same drift level versus the drift ratio. There was no significant stiffness degradation in the specimens at the same drift level in the elastic stage. After the specimens reached the flexural resistance, specimens SJ-1 and SJ-2 exhibited significant strength degradation at the same drift level, whereas the other specimens exhibited moderate stiffness degradation. For specimens SJ-1 and SJ-2, which exhibit failure mode I, when the specimens reached the flexural resistance, anchorage failure would occur, and the infill concrete suffered significant damage, directly resulting in significant strength degradation. However, as the loading continued, the stiffness degradation coefficient (\(\lambda_{i}\)) of the two specimens gradually increased, mainly due to the redistribution of the internal forces between the different components inside the steel tube after anchorage failure.  
To evaluate the degree of strength reduction with increasing drift ratio during the entire loading process, the strength degradation coefficient of the entire loading process (\(\lambda_{j}\)) is introduced as follows:  
\[\lambda_{j}=\frac{P_{j}}{P_{\text{max}}} \tag{6}\]  
where \(P_{j}\) is the peak load associated with the \(j\)th drift level and \(P_{\text{max}}\) is the maximum load during the entire loading process.  
Figure 19 shows the curves of the strength degradation coefficient of the entire loading process (\(\lambda_{j}\)) versus the drift ratio. After reaching the peak values, the \(\lambda_{j}\) curves of specimens SJ-3 and SJ-5 were relatively gentle with an approximately horizontal stage, indicating that the two specimens, which had thick steel tube walls and thin end-plates, did not fail immediately after reaching the flexural resistance and could continue to withstand a certain load, thereby enhancing the joint performance. When specimens SJ-1 and SJ-2 reached the flexural resistance, significant stiffness degradation occurred throughout the entire process. This phenomenon was also due to the failure mode of these two specimens.  
Figure 18: Strength degradation coefficients at the same drift level. (**a**) Specimens SJ-1 through SJ-5; (**b**) Specimens SJ-3, SJ-4, SJ-6 through SJ-8.
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
## 1 Introduction
For decompression, a dynamic state machine is utilized to further enhance system performance. The system has been tested on a Xilinx Zynq-706 evaluation board, Xilinx, Inc., San Jose, CA, USA. Experimental results show that a compression throughput of 480 Msubpixels/s and a decompression throughput of 372 Msubpixels/s are achieved. These results demonstrate that the proposed method outperforms conventional implementations in both speed and resource efficiency. The proposed solution is particularly well suited for real-time applications in areas such as medical imaging, multimedia processing, and video surveillance. These fields require high-speed and lossless image handling capabilities, which are supported by the architecture presented in this work. In summary, the main contributions of this research are as follows:  
1. A novel high-throughput, lossless compression and decompression system is proposed, which employs a tile-based approach to enhance parallel processing capabilities. By distributing images across multiple compression and decompression cores within a specially developed multi-core architecture, the system significantly improves processing efficiency while building on previously introduced algorithms. 2. A four-stage pipeline is implemented for the compression algorithm to maximize clock frequency, further increasing throughput. 3. Dynamic state machines are utilized in the decompression algorithm, significantly improving throughput. The remainder of this paper is organized as follows. Relevant studies are reviewed in Section 1. The proposed architecture is described in Section 2. In Section 3, the experimental results are presented and discussed. Finally, the conclusions of this paper are provided in Section 4.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Advanced Malware Detection Techniques
#### 3.6.4 IIoT Malware Detection Using Edge Computing and Deep Learning  
The article titled IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factoriesby [PERSON] and [PERSON], published in _Applied Sciences_, addresses malware detection in smart factories through an edge computingand deep learning-based detection system. This study focuses on implementing an efficient system to detect cyberattacks by distributing large amounts of IIoT traffic data to edge servers for deep learning processing.  
Edge computing is an emerging technology that can solve latency and bandwidth issues associated with cloud computing. In the context of cybersecurity in smart factories, resource-constrained IIoT devices cannot directly run deep learning models due to high computational and memory demands. Edge computing allows offloading intensive processing tasks to edge servers equipped with high-performance hardware, thus improving the efficiency and speed of the malware detection system.  
The proposed malware detection system consists of three layers: edge device, edge, and cloud. Each layer has specific functions for training, deploying, and inferring deep learning models, as well as transmitting training data. The edge device layer includes IIoT terminals that collect and process data. The edge layer consists of devices and servers that perform detection using deep learning models. The cloud layer handles the training of global deep learning models and optimizes local models sent from edge servers.  
In the experiments, the Malimg dataset, containing malware images from 25 different families, was used. This dataset was preprocessed and converted into RGB images to improve classification accuracy. In the experiment, the malware detection system based on a Convolutional Neural Network (CNN) achieved an accuracy of 98.93%, precision of 98.93%, recall of 98.93%, and an F1-score of 98.92%.  
Four standard metrics were used to evaluate the performance of the proposed model: accuracy, precision, recall, and F1-score. These metrics were calculated using performance evaluation parameters, including true positives, true negatives, false positives, and false negatives [64].
## 3 Proposed Method
### Decision Tree-Based Segment Clustering
Here, \(L_{old}\) is the log-likelihood of a node before splitting, and \(L_{left}\) and \(L_{right}\) are the log-likelihood of the left, and right, nodes after splitting, respectively:  
\[\mu_{p}=\frac{1}{N_{p}}\sum_{c=1}^{N_{p}}x_{c} \tag{1}\]  
\[\Sigma_{p}=\frac{1}{N_{p}}\sum_{c=1}^{N_{p}}x_{c}^{2}-\mu_{p}^{2} \tag{2}\]  
\[f_{p}(x)=\frac{1}{\sqrt{\left(2\pi\right)^{D}\left|\Sigma_{p}\right|}}\exp \left(\frac{-1}{2}(x-\mu_{p})^{\mathrm{T}}\Sigma_{p}^{-1}\!\left(x-\mu_{p} \right)\right) \tag{3}\]  
\[L_{p}=E\!\left[\log\!\left(\prod_{c=1}^{N_{p}}f(x_{c})\right)\right]=N_{p} \!\left(-0.5\!\left(\log\!\left|\Sigma_{p}\right|+D\log 2\pi\right)\right) \tag{4}\]  
\[\Delta L=L_{left}+L_{right}-L_{old}. \tag{5}\]  
Finally, a decision tree is built using the statistical table and questions, as shown in Figure 3d. To build a decision tree, we first build a decision tree using a single root node and then create leaf nodes consisting of each original phoneme. Next, we calculate the log-likelihood difference before the separation and the log-likelihood after the separation according to the context of each phoneme and find the optimal question with the highest difference. Finally, we repeat the process of splitting the leaf nodes with the highest log-likelihood difference for all leaf nodes. The leaf nodes of the decision tree have acoustically similar context phonemes, which are used as new subword units instead of the phoneme units for spontaneous speech recognition. As the parameter for building the decision tree, we used 40 questions, which equals the number of CI phonemes, following the default setting of the Kaldi toolkit [29]. The decision tree was constructed to derive 120 leaf nodes according to the left and the right neighboring phonemes. Here, the number of leaf nodes indicates the number of subword units to be extended.
## 5 Discussion  
After analyzing the results, it was observed that the method using XGBoost performed better compared to the other method, for one reason: EEG, ECG and, GSR are continuous time signals with large memory content, manual feature engineering and, the better features can be obtained by using JMI-Score algorithm. The second reason are: machine learning can remove irrelevant features from feature sets, which deep learning cannot do. In this study, machine learning has great advantages. In addition to basic interpretability, the combined use of user device and edge device resources can accelerate computing, while affecting accuracy, reduce processing time, and achieve decentralization processing methods. The time reduction is not very significant. The reason for the analysis is that, the main purpose of the new computing architecture are to reduce the load on the cloud center, and effectively use the computing resources on the edge and the terminal, while the overall computing resources have not increased significantly. Of course, the advantages of deep learning are also obvious, which can avoid complex feature extraction, extract high-dimensional features, and obtain better results. In order to seek the choice of better features, this paper extracts many features, including time domain, frequency domain, and nonlinear features. According to the Spearman correlation coefficient, it is more stable in GSR. The ECG signal  
Figure 7: Overall mean correlation coefficient of different subjects.  
has higher inter-class variability. According to the correlation coefficient, some features are low and the jump is serious, so it is very necessary for the feature selection of ECG.  
Compare with other studies, in this experiment, the amount of data collected has increased, and the uniqueness of the decision labels needs to be further verified. The proposed method and framework are used to obtain promising results, which are expected to solve the problem in the epidemic era. The majority of teachers and students encounter the problem of interaction channels in distance education.  
It is an experiment to move Affective computing based on physiological signals towards life. Of course, in this study, the research method was applied to the actual scene, and subjective factors such as subjects' different educational backgrounds, and different answering backgrounds were not considered. It is an important factor, but because it is difficult to express mathematically, it is not considered in training data and needs to be studied in the future.  
The model selection of machine learning is also very critical. This article selects several representative models. JMI-Score is an iterative version of JMI, which is relatively new. XGBoost is a widely used stacking ensemble algorithm, which can solve the limitations of a single model. Naive Bayes is a traditional basic algorithm model and the origin of machine learning, which is very representative. In this study, XGBoost performed better, indicating that it is more appropriate to use traditional optimized machine learning when the amount of data is not large.