esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Muscle fibers that make up skeletal muscle are composed of slow muscle fibers, which are rich in mitochondria and predominate in aerobic energy production pathways, and fast muscle fibers (or intermediate types), which predominate in anaerobic energy production pathways [1]. Therefore, energy metabolism and muscle fiber type are closely related. It is also well known that skeletal muscle changes its metabolic properties in response to changes in the systemic environment [2,3]. However, the mechanism that coordinates metabolic changes with muscle status remains unclear.  
Glucose is one of the essential nutrients for living organisms, and it plays a particularly important role in energy production. Glucose is metabolized via glycolytic pathway to ATP and pyruvate, a substrate of the TCA cycle for more efficient ATP production. Although skeletal muscle is a major organ that metabolizes glucose, reduction in muscle mass and function cause disruption of glucose metabolism, result in the risk of developing type-2 diabetes [4,5]. Therefore, maintenance of muscle mass and function may be important for maintaining glucose metabolism.  
In cultured cell lines, some research reported that glucose concentration in culture media affects muscle metabolism and morphology. Recently, [PERSON] et al. reported that C2C12 myotubes themselves have a mechanism that senses a decrease in extracellular glucose concentration via the Baf60c-Deptor-AKT signaling pathway and increases glucose uptake from extracellular sources [6]. Furthermore, in C2C12 cells cultured in DMEM with Low glucose (1.0 g/L) or High glucose (4.5 g/L), which are commonly used for culture, mitochondria-mediated energy production was activated in cells cultured in Low glucose media compared to High glucose media [7]. In another study, the expression of GLUT1, which is responsible for glucose uptake into cells, and hexokinase II, a rate-limiting enzyme in the glycolytic system, was upregulated in C2C12 cells cultured in DMEM with Low glucose media [8]. In addition, the proliferative activity of muscle satellite cells, which plays a central role in muscle regeneration, was higher when cultured in glucose-free media than in High glucose media, suggesting that the High concentration of glucose in the culture media has a negative effect on muscle [9]. In other cell line, HT-29 cells cultured with Low glucose media showed increased mitochondrial respiratory activity and decreased lipid levels compared to those cultured with High glucose media, suggesting that Low glucose media enhances mitochondrial respiratory capacity [10].  
Although these findings suggest the importance of glucose concentration in muscle, it remains unclear how glucose concentration affects muscle metabolism. To clarify this relationship, we tested C2C12 cells cultured in DMEM with High (4.5 g/L), Medium (2.0 g/L), and Low (1.0 g/L) concentrations.
# Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology
## 3 Model Design  
### Overlimit Warning Model  
Based on transfer learning, this paper builds an overlimit early warning model to realize the overlimit prediction of operating equipment in the mine filling system. The model is mainly composed of two parts: pre-training and parameter migration, as shown in Figure 2. The pre-training process uses the simulation data generated by the simulation system to train the neural network model, and the parameter migration process first freezes the convolutional pooling layer parameters of the pre-training model before using a small amount of actual data to fine-tune the fully connected layer parameters of the pre-training model. Due to the factors that affect the collected data in the actual environment, such as the stability of the sensor, the impact resistance, and the use time of the acquisition equipment, the overlimit warning ability of the pre-trained model may decrease, and the accuracy of the overlimit prediction needs to be improved through the transfer learning of a small amount of data.
# Revisiting Label Smoothing Regularization with Knowledge Distillation
## 5 Experiments  
In this section, we conducted experiments to evaluate LSR-OS-TC on four datasets for image and audio classification: CIFAR100 [41], CIFAR10 [41], CINIC10 [31], and GTZAN [34]. We focused our experiments on the CIFAR-100 dataset with the most networks and training details. CIFAR-10 and CINIC-10 are ten-class tasks that are easier; thus, the performance differences among different network results are small. Thus, we picked four networks that have a larger performance gap. The GTZAN dataset is a music genre classification task that is different from traditional image recognition. Due to the difference between the STFT spectrogram and a regular image, the frequently used CNN networks like ResNet cannot obtain satisfactory results. The networks nnet1 and nnet2 from [PERSON] et al. [42] were used as the baseline to evaluate the methods.  
For a fair comparison, all results on the same dataset were obtained with the same setting. We implemented the networks and training procedures in PyTorch and conducted all experiments on a single NVIDIA TITAN RTX GPU.  
The baseline results are the corresponding networks trained with the regular cross-entropy loss. Besides LSR-OS-TC and the baseline, we also provide the results of LSR [19] and the Tf-KD method. Tf-KD was proposed by [PERSON] et al. [27], which first manually-designed a teacher distribution and then applied a high temperature (\(\tau\geq\) 20) on KD-loss. Note that the hyperparameters in Tf-KD are model dependent and hard to tune. We proceeded with several runs to determine the temperature \(\tau\), \(\alpha\), and \(\gamma\) for Tf-KD to guarantee the performance. Our LSR-OS-TC method emphasizes that a proper soft temperature is more critical than a hand-crafted teacher. LSR-OS-TC was tuned with grid search on one model and applied uniformly on all models.
## 5 Results and Discussion
### Random Forest Model Results  
Table 5 displays a confusion matrix that compares the soil types predicted by the RF model with the actual soil types. The confusion matrix shows that the model made some correct and incorrect predictions for each class. For example, the model correctly predicted 835 samples as class 2. The model has high diagonal values, signifying a high number of correct predictions, and low off-diagonal values, implying a low number of misclassifications. The model's overall accuracy is very high, indicating the model's performance. It achieved a 99.23% accuracy, indicating that it effectively predicts soil types.  
The statistics by class (Table 6) show that the RF model has a high sensitivity and specificity values for all soil types. Overall, it performed well in the classification task, achieving high scores for multiple performance metrics such as PPV, NPV, and balanced accuracy.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Future Directions
#### Advanced Control Technologies  
For effective management of water supply system facilities, an advanced control system must be integrated to in order to respond rapidly to several events in the system. Advance systems for pressure control to reduce the effect of pipe burst as well as active control valves [158] to reduce the spread of contaminant in the water distribution networks are required. Such a system must be equipped with a good response mechanism to service disaster and human error without loss of functions [162; 163; 164; 165]. Therefore, more research studies must be conducted to develop an advanced control technology that makes CPSs respond quickly to a critical events, such as contaminant spread, leaks, the valve shut down, pipe failure, etc.. in water distribution systems.
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 2 Materials and Methods
### Statistical Analysis of Pile Bearing Capacity
Probability corresponding to 20% reliability level means that the expected pile bearing capacity lies within an interval of \(0.8Q_{m}\leq Q_{p}\leq 1.2Q_{m}\). The reliability is in agreement with other authors [24; 63]. A similar reliability interval was observed by the authors of this paper in case of the determination of soil parameters from CPT outputs verified by laboratory testing. The ratio of \(Q_{p}/Q_{m}\) and cumulative probability can be utilized as another evaluation criterion for prediction methods [51; 62]. Values of \(Q_{p}/Q_{m}\) are ordered in ascending order \((1,2,3,\ldots,i,\ldots,n)\) and the cumulative probability \(P\) is calculated as follows (Equation (15)):  
\[P= \frac{i}{(n+1)}, \tag{15}\]  
where \(i\) is the serial number for the given \(Q_{p}/Q_{m}\) ratio and \(n\) is the number of piles. For quantification of capability of prediction methods, 50% and 90% cumulative probability, \(P_{50}\) and \(P_{90}\), are determined. Value of \(P_{50}\) represents the median while \(P_{90}\) corresponds to the 90 th percentile of values of \(Q_{p}/Q_{m}\). The value of \(P_{50}\) is used as a measure of tendency to underestimate or overestimate measured pile bearing capacity. The closer the value of \(P_{50}\) for the ratio of \(Q_{p}/Q_{m}\) is to 1.0, with a smaller range of \(P_{90}\)-\(P_{50}\) at the same time, the more suitable the method is.
## 5 Calculus Analysis
### Calculation Results and Analyses
#### 5.2.2 Impact of Communication Delay and Access DG Capacity on Reliability  
Micro power plays a vital role in active distribution networks and can effectively reduce load losses and increase power supply reliability. When an information system is not completely reliable, changes in the capacity of DG will have more complex impacts on the reliability of the distribution network power supply system. Considering this situation, this study gradually increased the access DG capacity from 0 to 80 kilowatts while the communication delay increased from 0 to 100 milliseconds and calculated the system's reliability index SAIDI. The results are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Failures** & **ASAI/\%** & \begin{tabular}{c} **SAIDI** \\ **(h**-**(Household** \(\cdot\)**a)**–**1)** \\ \end{tabular} & \begin{tabular}{c} **SAIFI** \\ **(Times**-**(Household** \(\cdot\)**a)**–**1)** \\ \end{tabular} & \
\begin{tabular}{c} **EENS** \\ **(MW**-**h**) \(\cdot\)**a**–**1)** \\ \end{tabular} &
\begin{tabular}{c} **CAIDI** \\ **(h**-**Times**–**1)** \\ \end{tabular} \\ \hline Physical System Failures & 99.986 & 16.42 & 1.82 & 596.12 & 9.02 \\ Dual System Failures & 99.983 & 17.73 & 1.93 & 612.43 & 9.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation results of system reliability.  
Figure 4: Trend of system reliability index under different failure rates.  
As can be seen in Figure 5, as the communication delay increased, the SAIDI index gradually increased. This indicated that in the CPDS composed of wireless communication, transmission delay had a huge impact on the reliability of the CPDS. Compared with wired communication, in the CPDS composed of wired communication, information was mainly transmitted through optical fibers. Its transmission channel was simple and it did not require complex forwarding and transmission. In addition, the risk of information loss was low. Therefore, the impact of information transmission delay on wireless communication was much greater than that on wired communication.  
However, the increase in DG access capacity delayed the increase in SAIDI and resulted in a minimum value. In this example, the reliability of the system was highest when the access capacity of DG was 68 kW. The results also indicated that the access of DG could indeed improve the reliability of the CPDS, but when the communication delay continued to increase, the further increase in the access capacity of DG led to a faster increase in the value of SAIDI. This was because at this point, the communication system had completely failed, and physical faults could only be handled manually.The research results of this case study can play an important role in the planning of reasonable DG capacity in CPDSs.
# DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain
## 3 System Preview
### Definitions  
#### 3.2.1 Attribute-Weighted Access Tree  
Data users have different identities, and their attribute sets are complex. The attributes of data users may have containment or hierarchical relationships. For example, in the traffic management department, Constable, Superintendent, Inspector, and Superintendent are one class of attributes representing different levels of police officers. There is a clear hierarchical relationship between these attributes. The access range of the high-level attributes  
Figure 1: System model.  
includes the access range of the low-level attributes, i.e., constable \(\subset\) superintendent \(\subset\) inspector \(\subset\) superintendent. This makes the access tree have a lot of redundant attributes. The attribute-weighted access tree can solve this problem.  
As shown in Figure 2, the attribute-weighted access tree has three levels. The root node is a logical \"or\" the 2 nd level non-leaf node is a logical \"and\", and the 3 rd level leaf node is an attribute expression. The construction method of the access tree for attribute assignment: The system attribute set \(U\) will be divided into categories \(L=\{L_{1},L_{2},L_{3},\ldots,L_{N}\}\) based on entities and departments. Then, weights will be assigned to attributes in the same class where there is a continuous containment relationship for access rights. For example, if there is \(L_{i,1}\subset L_{i,2}\subset L_{i,3}\cdots\subset L_{i,n}\), then the attribute \(L_{i,1}\) will be assigned weight \(w\) of 1, \(L_{i,2}\) weight \(w\) of 2, and so on for the rest. Finally, all attributes will be replaced with the corresponding category weight pairs.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 3 Public-Key Cryptography with an Authentication Table Key Management Scheme (PCAT)
### Notation and Assumption  
The notations used in the analysis of PCAT include:  
* total number of nodes (\(n\));
* quantity of bits indexed by the mask (\(m\));
* length of the public key (\(l_{pu}\));
* length of the private key (\(l_{pr}\));
* length of the hash codes (\(l_{h}\)).
* quantity of nodes within the communication range of node \(i\) (\(v_{i}\)).  
It is assumed that the WSN is composed of homogeneous mobile nodes and that there is no deployment knowledge. It is also assumed that an opponent is able to inject packets, replay older messages, eavesdrop on all the traffic and compromise a node with all the information it holds.
###### Abstract  
Traditional coal quality assessment methods rely exclusively on the laboratory testing of physical samples, which impedes detailed stratigraphic evaluation and limits the integration of intelligent precision mining technologies. To resolve this challenge, this study investigates geophysical logging as an innovative method for coal quality prediction. By integrating scanning electron microscopy (SEM), X-ray analysis, and optical microscopy with interdisciplinary methodologies spanning mathematics, mineralogy, and applied geophysics, this research analyzes the coal quality and mineral composition of the Shanxi Formation coal seams in northern Jiangsu, China. A predictive model linking geophysical logging responses to coal quality parameters was established to delineate relationships between subsurface geophysical data and material properties. The results demonstrate that the Shanxi Formation coals are gas coal (a medium-metamorphic bituminous subclass) characterized by low sulfur content, low ash yield, low fixed carbon, high volatile matter, and high calorific value. Mineralogical analysis identifies calcite, pyrite, and clay minerals as the dominant constituents. Pyrite occurs in diverse microscopic forms, including euhedral and semi-euhedral fine grains, fissure-filling aggregates, irregular blocky structures, framboidal clusters, and disseminated particles. Systematic relationships were observed between logging parameters and coal quality: moisture, ash content, and volatile matter exhibit an initial decrease, followed by an increase with rising apparent resistivity (LLD) and bulk density (DEN). Conversely, fixed carbon and calorific value display an inverse trend, peaking at intermediate LLD/DEN values before declining. Total sulfur increases with density up to a threshold before decreasing, while showing a concave upward relationship with resistivity. Negative correlations exist between moisture, fixed carbon, calorific value lateral resistivity (LLS), natural gamma (GR), short-spaced gamma-gamma (SSGG), and acoustic transit time (AC). In contrast, ash yield, volatile matter, and total sulfur correlate positively with these logging parameters. These trends are governed by coalification processes, lithotype composition, reservoir physical properties, and the types and mass fractions of minerals. Validation through independent two-sample \(t\)-tests confirms the feasibility of the neural network model for predicting coal quality parameters from geophysical logging data. The predictive model provides technical and theoretical support for advancing intelligent coal mining practices and optimizing efficiency in coal chemical industries, enabling real-time subsurface characterization to facilitate precision resource extraction.  
**Keywords:** Northern Jiangsu; coal quality; geophysical logging; predictive model; calorific value; Shanxi Formation
## 3 Proposed System Model
### Selection and Application Methods of Machine Learning Algorithms
#### 3.3.6 Support Vector Machine (SVM) Algorithm  
Support Vector Machine (SVM) is a powerful machine learning algorithm used for classifying both linear and nonlinear data [70]. It works by identifying the optimal decision boundary (hyperplane) that maximizes the margin between classes using support vectors, which are the data points closest to the boundary [71]. SVM is widely applied in various fields, such as image processing, text classification, and biological applications, and is also integral to technologies like self-driving cars and chatbots. For linear classification, SVM uses a \"Hard Margin\" for perfectly separable data and a \"Soft Margin\" for data with some errors [72]. Nonlinear data are handled using kernel functions, such as linear, polynomial, RBF, and sigmoid [73].
## 3 Methodology
### Improved Grid Search  
This paper proposes an improved grid search algorithm (IGSA) that builds on the traditional grid search algorithm and addresses the problem of the grid search computing time.  
The principle of the grid search algorithm is shown in Figure 3. First, the Cartesian product is applied to the value set of each hyperparameter to obtain the hyperparameter configuration space, which contains all possible hyperparameter combinations (the box on the left side of the figure). The grid search algorithm then trains a model for every hyperparameter combination in configuration space. The experiment that yields the best validation set error is then chosen as having found the best hyperparameters, as shown in the box on the right side of the figure [24].  
The problem is that grid search has a significant disadvantage. Grid search computation time grows exponentially \(O(n^{m})\) with n hyperparameters, each of which can take m values. This wastes a lot of computational resources, and the processing time is excrucating. To solve this problem, this research provides a IGSA method.  
Figure 4 depicts the principle of IGSA. The significance of hyperparameters is first determined via functional ANOVA, and the results are recorded as \(\Theta_{1},\Theta_{2},\ldots,\Theta_{n}\). Secondly, a single-parameter optimization algorithm is employed to perform grid search optimization on particular hyperparameters in order of importance, then the hyperparameter value of the base classifier is replaced with the best single-hyperparameter value obtained through optimization. The optimal hyperparameter combination is generated by acquiring n ideal hyperparameter values after n times of the grid search. This experiment's base classifier is the RF classifier defaulted in sklearn.  
The single-parameter optimization algorithm is shown in Algorithm 1, the input is a single hyperparameter \(\Theta_{i}\) with m values. The output is a single optimal hyperparameter.  
The performance of the classifier is validated using k-fold cross-validation in Algorithm 1. For k-fold cross-validation, all datasets are divided into k parts, with one serving as the test set without repetition and the remaining k-1 as the training set for training. Then computation of the model's score on the test set scores are then averaged k times to obtain the final score. We use 5-fold cross-validation in this experiment.  
Figure 3: Principle of the grid search algorithm.  
For n hyperparameters with m values, the calculation speed of the optimal hyperparameter search is reduced to \(O(n\times m)\) through improvement.
## 2 Common Failure Mechanisms in Laminated Composites
Composite materials, due to their layered nature, are susceptible to various types of damage such as delamination, matrix cracking, and fiber-matrix debonding, which can be successfully identified using GUWs [41]. Lamb waves, a type of GUW, are commonly used in this application, employing both symmetric and antisymmetric modes to investigate the material [42]. Changes in wave speed, attenuation, or reflection can be harnessed to detect faults by injecting a controlled ultrasonic wave into the structure and measuring its propagation characteristics [43]. Discontinuities in the composite, such as delamination or cracks, cause the waves to reflect and scatter, which can be monitored by strategically placed sensors. This enables real-time monitoring and damage localization without the need for extensive disassembly or direct visual inspection, making GUWs an effective and non-invasive method for ensuring the integrity of composite structures [41; 44]. Figure 2a illustrates the defects in composite laminates, while (b) depicts damage types occurring during the manufacturing and service stages. Table 1 illustrates the types of defects, the common detection modes, and the challenges associated with detecting them in composite laminates. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Defect Type** & **Description** & **Detection Method** & **Challenges** & **References** \\ \hline Matrix Cracking & Fracturing of the resin matrix due to thermal or mechanical stresses. & Conventional ultrasonic testing. & Difficult to detect because of its small size and signal attenuation. & [38] \\ \hline Fiber Breakage & Rupture of reinforcing fibers within the composite material. & Ultrasonic testing, utilizing changes in wave propagation (attenuation or phase velocity). & Challenging to detect as it often coincides with other failure modes. & [30; 45] \\ \hline Delamination & Separation of composite layers, typically initiated by interlaminar stresses or impact damage. & Ultrasonic testing is hindered by a high reflection due to an air gap between layers. & Mode conversion complicates signal detection and can significantly affect structural integrity. & [34; 35; 37; 45; 46; 47] \\ \hline Debonding & Loss of adhesion between fiber and matrix is often caused by mechanical stresses or environmental effects. & Ultrasonic testing involves complex interactions between ultrasonic waves and fiber–matrix interface. & [48] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Common defects and their detection using ultrasonic guided waves in composite laminates.