esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 BILSTM-Based Deep Neural Network Rock-Mass Classification Method
### Evaluation Metrics  
Accuracy is the most intuitive and commonly used metric for assessing model performance; however, it tends to ignore the effects of unbalanced datasets and skews toward  
Figure 7: Structure of the deep neural network.  
the majority class of the sample, thereby resulting in incorrect evaluations [33]. Therefore, to accurately evaluate the model's predictive performance for unbalanced samples from different perspectives, we used the accuracy, precision, recall, F1 values, and area under curve (AUC) of the receiver operating characteristic curve (ROC) to evaluate the model. These evaluation metrics are based on the confusion matrix shown in Figure 8 and classify the prediction results into four cases.  
All of the above evaluation metrics are used for evaluation in the binary classification, and the first four are calculated as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{12}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{13}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{14}\]  
\[\text{F}_{1}=\frac{2\times\text{Recall}\times\text{Precision}}{\text{Precision }+\text{Recall}} \tag{15}\]  
Rock-mass classification is a multiclassification problem that can be viewed as a multiple binary classification problem. Because of the unbalance rock-mass percentages in each class, we used the weight-average method to evaluate the overall performance of the model as follows:  
\[\text{Accuracy}_{\text{overall}}=s_{c}/s \tag{16}\]  
\[\text{Precision}_{\text{overall}}=\sum_{i=1}^{O}\text{Precision}_{i}w_{i} \tag{17}\]  
\[\text{Recall}_{\text{overall}}=\sum_{i=1}^{O}\text{Recall}_{i}w_{i} \tag{18}\]  
\[\text{F}_{1-\text{overall}}=\sum_{i=1}^{O}\text{F}_{1i}w_{i} \tag{19}\]  
Parameter \(s\) represents the total number of samples, \(s_{c}\) represents the number of correctly predicted samples, \(w\) is the ratio of samples of class \(i\) to the total number of samples, \(\text{Precision}_{i}\), \(\text{Recall}_{i}\), and \(\text{F}_{1i}\) represent the precision, recall, and \(\text{F}_{1}\) values of the rock in class \(i\), respectively.  
We used the ROC curve and its corresponding AUC for the binary classification problem. To solve multiclassification problems and evaluate the performance of the model, we used the macro- and micro-average ROCs improved on the basis of ROCs and their corresponding macro- and micro-average AUC values, where the AUC represents the area under the ROC. The closer the value was to 1, the higher the model performance; the closer the value was to 0, the lower the model performance.  
Figure 8: Confusion matrix.
# Review of Recent Type-2 Fuzzy Controller Applications
## 3 Review of IT2 FLSs
### Robotic Control
The position of the bucket with respect to the boom, arm and bucket axes was controlled. A Delta Parallel Robot was also studied using a T1 FLC and an IT2 FLC [56]. The performance of the two controllers was evaluated in the presence of sensory noise and other uncertain system parameters. The parameter being evaluated was the position control of the robot. Experiments were done using both controllers and it was determined that the IT2 FLC provided improvements in both performance and robustness. It was also found that increasing the fuzziness also improves the uncertainty robustness to a certain level. Once this level is hit, increasing the fuzziness can lead to massive performance degradations.
## 1 Introduction  
The adhesive bonding of composites to hard tooth tissues has become the predominant restorative technique. The adaptation (adhesive bond) of restorations to the teeth determines their clinical durability. In clinical evaluations of composite restorations, according to FDI criteria, marginal adaptation is one of the 16 evaluation criteria, belonging to the category of functional parameters [1]. However, this criterion does not necessarily indicate the degree of internal adaptation of the composite restoration to the tooth. Experimental studies therefore evaluate the internal adaptation of restorations to the enamel and dentin [2]. In this context, the extent of the interfacial adhesive defects (interfacial gaps) is considered a criterion for assessing the quality of the tooth-composite bond, or vice versa, for the failure of the bond. For this purpose, the restored teeth are sectioned and the extent (length) of the interfacial adhesive defects, or the microleakage, is determined on several tooth sections. A problem arises from the fact that for several sections no normal distribution of the values for interfacial gap length (or the fraction for the interfacial gap length) can be assumed. The smaller the number (Ni) of the sections, the more the mean values determined per restoration can deviate from the true values (N \(\rightarrow\infty\)), an error that results from the inherent statistical sampling error due to the geometrical properties of B-scans and interface defects. In other words, the quality of the mean value of the object depends on the number of object sections (sample size), or the larger the sample size, the more its amount should approach the true object mean value (N \(\rightarrow\infty\)).  
In experimental studies, the sectioning of restorations is limited by their small size (cervical composite restorations (Class V), [2]). For tomographically obtained 3D image stacks with hundreds of cross-sectional images, the question arises at which sample size the calculated mean fraction of interfacial gap length or microleakage approaches the true value (N \(\rightarrow\infty\)) within an acceptable (statistical) range. Based on invasive preparation techniques for light and electron microscopic object imaging, the assessment of tooth-composite bond failure using the extent of interfacial gaps or microleakage was performed on two [3] to seven sections per object [2; 4; 5; 6; 7; 8; 9]. In contrast, based on nondestructive tomographic imaging, up to twenty-five, thirty-five [10; 11; 12; 13] or several hundred cross-sectional images per tooth have been used [14; 15]. Optical coherence tomography can generate 3D image stacks of teeth with hundreds of B-scans with high spatial resolution without invasive sample preparation. The aim of the study was to determine for Class V composite restorations (enamel, dentin) the dependence of the calculated mean fraction of interfacial gap length on the number of underlying B-scans in order to experimentally deduce N-values for defined errors compared to N \(\rightarrow\infty\).
## 5 Results and Discussion
### The Validation Case
#### 5.1.2 Comparison between E-Gas and E-STR  
The result, shown in Table 7, indicates that similar syngas compositions and outlet temperature are observed between E-Gas and E-STR without char recycling. This implies that the lengths of both  
Figure 3: Comparison of mid-plane temperature distributions for (**a**) E-STR model and (**b**) E-Gas model.  
gasifiers are sufficiently long to achieve almost equilibrium status at the exit of the gasification process. When the E-STR is operated at higher pressure (70 atm), the difference between these two cases becomes obvious.  
Comparisons of particle resident times are shown in Figure 2. The fuel feeding configuration is different in the E-Gas and E-STR gasifiers. In the former, the fuel is separately fed at the both injection ports, particles with 20% mass flow rate at the 2 nd stage are push upward by the flow coming from 1 st stage without reaching the opposite wall, as shown in Figure 2b. Longer particle resident time is observed in E-STR due to concentrated injection at 2 nd stage (Figure 2a), while it is separately injected in E-Gas. Therefore, the particles in the E-STR reactor require a longer time to be fully consumed.  
The comparison of temperature contours is presented in Figure 3. It is found that the benchmark case E-STR results in higher interior temperatures near the 2 nd stage injector than those of E-Gas, as shown in Figure 3b. In the E-Gas gasifier, the main combustion reactions of the injected fuel take place in the horizontal cylinder, where 80% of coal/slurry and all oxidant are fed on both side of the 1 st stage injection ports; while further downstream to the 2 nd stage injector, gasification mechanisms dominate the reaction regimes, so its temperature is lower. In contrast, all the fuel is fed at the 2 nd stage in E-STR and intensive combustion reactions occur downstream the throat, where the vessel diameter expands to accommodate the gasification region, so its temperature in the vicinity is higher.  
In the present results, no unreacted char is seen leaving the benchmark E-STR reactor. Since in a real operation, a certain percentage of char is unreacted at the gasifier exit, modification should be considered to reduce the char reaction rate to match the operation data.
###### Abstract  
Long-distance oil and gas pipelines are inevitably impacted by rockfalls during geologic hazards such as mud-rock flow and landslides, which have a serious effect on the safe operation of pipelines. In view of this, an experimental and numerical study on the strain behavior of buried pipelines under the impact load of rockfall was developed. The impact load exerted on the soil, and the strains of buried pipeline caused by the impact load were theoretically derived. A scale model experiment was conducted using a self-designed soil-box to simulate the complex geological conditions of the buried pipeline. The simulation model of hammer-soil-pipeline was established to investigate the dynamic response of the buried pipeline. Based on the theoretical, experimental, and finite element analysis (FEA) results, the overall strain behavior of the buried pipeline was obtained and the effects of parameters on the strain developments of the pipelines were analyzed. Research results show that the theoretical calculation results of the impact load and the peak strain were in good agreement with the experimental and FEA results, which indicates that the mathematical formula and the finite element models are accurate for the prediction of pipeline response under the impact load. In addition, decreasing the diameter, as well as increasing the wall thickness of the pipeline and the buried depth above the pipeline, could improve the ability of the pipeline to resist the impact load. These results could provide a reference for seismic design of pipelines in engineering.  
impact load; buried pipeline; finite element analysis; theoretical calculation; strain behavior +
Footnote †: journal: Applied Mechanics and Physics Letters
# Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
## 5 Determination of Local Stress and Strain  
### Neuber Equation  
The Neuber equation, a concise method for calculating local stress-strain of a notch [27; 28], is as follows:  
\[K_{t}=(K_{e}K_{\sigma})^{\frac{1}{2}} \tag{5}\]  
\[K_{e}=\frac{\varepsilon}{e} \tag{6}\]  
\[K_{\sigma}=\frac{\sigma}{s} \tag{7}\]  
where: \(K_{t}\)--theoretical stress concentration factor of notch, \(K_{e}\)--strain concentration factors, \(K_{\sigma}\)--factor of stress concentration, \(e\)--nominal strain, \(S\)--nominal stress.  
\(K_{e}\) and \(K_{\sigma}\) were placed in Equation (5) and converted into an incremental form, and the Neuber hyperbolic equation was then obtained:  
\[\Delta\sigma\Delta\varepsilon=K_{t}^{2}\Delta S\Delta e \tag{8}\]  
If the nominal stress is in the elastic range, then \(\Delta e=\frac{\Delta S}{E}\) can be obtained, and the equation is as follows:  
\[\Delta\sigma\Delta\varepsilon=(K_{t}\Delta S)^{2}/E \tag{9}\]  
The Neuber equation is derived from shear stress analysis of a prism. In order to be widely applied in the design and development of HMAC [29; 30], the Neuber equation is modified by replacing the theoretical stress concentration factor \(K_{t}\) with the fatigue notch factor \(K_{f}\). The general method for determining \(K_{f}\) is to fit the regression equation through fatigue test data of the specimen notch, and \(K_{f}\) is typically treated as a constant for most influencing factors. \(K_{t}\) is usually used when there is a lack of \(K_{f}\) data, which is a conservative method for estimating fatigue life. Values of \(K_{t}\) at different strain levels are shown in Table 3.  
Figure 7: Stress-time history under control of strain.
# A Visual Analytics Framework for Inter-Hospital Transfer Network of Stroke Patients
## 3 Analysis of Data and User Task
### Data Preprocessing
The collaborating researchers used our tool to analyze 93 clusters from Louvain clustering and found that 86 groups had a node located in the cluster's center with a degree centrality higher than 0.5. Three groups only had one node, and the remaining four had a central node with a centrality between 0.3 and 0.5. As a result, the collaborators determined the threshold as 0.5 in this research. Measures calculated for each cluster include group degree centralization, intra-cluster density, and inter-cluster density, which are used for community analysis of networks in previous studies [25; 26]. Regarding group-degree-centralization, the previous study calculated the value for the undirected network. Thus, we adjusted the denominator from \([(\mathsf{g}-1)(\mathsf{g}-2)]\) to \([(\mathsf{g}-1)(\mathsf{g}-1)]\) to accommodate the characteristics of the directed network (i.e., minimum in-degree is 0). Furthermore, three types of patient transfer rates were calculated for each cluster based on feedback from medical researchers. The proportion of patients transferred within each cluster, the proportion of patients transferred out to other clusters, and the proportion of patients transferred in from other clusters are calculated using the number of patients who visited the hospital in each cluster at least once (i.e., excluding stay patients) as the denominator. Indices calculated for the entire network included modularity [14], which is calculated for community detection, and three densities (i.e., global, mean intra-cluster, and mean inter-cluster), which are used to evaluate community detection results in a previous study [26]. In this study, the adequacy of community detection is examined by determining if three density values satisfied \(\overline{K}_{inter}<\overline{K}<\overline{K}_{intra}\).
## 3 Results and Discussion
### 3.3. Bread Characterization
#### 3.3.3. Bread Moisture and Acidity  
A higher moisture retention in bread is economical and is also required to lengthen shelf life [88].  
A higher moisture content was found in bread prepared from WF-Rp mixtures compared to the control (41.81 \(\pm\) 0.40%). The differences were significant (\(p\) < 0.05). The increase in bread moisture was probably determined by the increase in the water absorption of WF-Rp mixtures due to the higher fibre content of Rp. However, the bread moisture of samples with an addition of 2.5% Rp and AA as an improver did not follow the increasing tendency, as they were situated between the moistures of the control and the sample with the addition of 0.5% Rp. This might be expected, as several factors not determined in this study could influence the moisture content of bread.  
Similar results are reported in the literature. Toasted bread with a high fibre content was obtained by adding 10%, 20%, and 30% of fine or coarse bran that was dark or light in colour, and wheat germ. The bread had a higher moisture content than the bread obtained from unbleached wheat flour (39.29 \(\pm\) 0.34%) for all types of bran. The increase in moisture was explained by the higher amounts of water used to prepare the dough for samples with bran fractions [88]. [PERSON] et al. observed that moisture content was slightly higher for bread with increasing levels of bran addition (10-30%) [89]. Another study reported an increase in moisture with increased levels of polyols [88]. They found a higher moisture content in bread prepared after the incorporation of 4% sorbitol as compared to the control.  
Bread acidity (Table 4) values increased for all breeds compared to the control (2.0 acidity degrees), reaching 2.25 \(\pm\) 0.07 acidity degrees for bread prepared with WF-Rp with the 2.5% Rp addition. The differences were not significant (\(p\) > 0.05) according to the ANOVA one-way statistical analysis. Ascorbic acid is consumed during kneading, and if it somehow remains in excess in the dough, it will be distorted when baked. Other components of Rp, such as the organic acids of rosehip composition, may be responsible for the values obtained for acidity. This issue will be followed in future research.
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 3 Results
### Correlation between the Salinity Levels and the River Discharges  
In order to determine the quantitative relationship between the river discharges and the salinity levels, the linear function (y = Ax + B) was used, as detailed in Figure 8 and Table 5.  
Figure 8: Correlation between the river discharge and sea level in the YRE and offshore area.  
The correlations between the river discharge and the mean salinity levels in the YRE and offshore were determined. It was found that the river discharge and the mean salinity levels had a notable positive correlation, with a correlation coefficient in the range of 0.971 to 1, which revealed the high precision of the fitting curves. The slope (coefficient A) of the curve could show the influence of the river discharge on the mean salinity levels. The slope was observed to generally decrease along the channel from station SB1 to station SC1. Meanwhile, the coefficient B increased. This indicated that the greater the distance from the offshore, the larger the impact of the river discharge; the lower the salinity value; and the weaker the change in salinity. In the upper reaches of the NB, the slope of the NB1 was the greatest with a value of \(-1.382\), which indicated that the impacts of the runoff on the mean salinity levels of the NB1 were the largest in the NB and SB. Due to the generally high salinity in the NB, the slopes of the stations in the SB were lower than those in the NB. The salinity values of the T1, T3, and T5 were lower than those of the T2, T4 and T6, respectively, while the slopes were larger. This is due to the fact that the T2, T4, and T6 stations were located away from the estuary. Therefore, the runoff was weak, and the salinity values were high and stable.
## 4 Proposed System Model
### Workflow of System Model  
The logical flow of the proposed system is presented, which is shown in Figure 3. The data sharing system works as follows:  
* at fist, a subject sends request for any service (i.e., data, file, storage unit) in the IoT networks,
* further, communication and access management for subject is managed by the smart contracts in blockchain,
* when the request of subject is generated, ACC (main smart contract) is executed to control the overall access management,
* after that, authentication of the users is done by the RC, which registers the users' information and maintains records of users via a registration table,
* then, trustfulness of the system is maintained by JC, which checks the behavior of a subject,
* if any type of misbehavior is conducted by the subject, JC determines penalty for the subject and halt its state in the system,
* in another case, if no misbehavior is done, then permission level for the subject is checked,
* then ACC forwards the request of subjects to corresponding objects,
* after that, request of the subject is fulfilled by the object, and
* at last, transaction is stored into the blockchain.  
Figure 3: Flow Diagram of Proposed System Model.
## 6 Training and Simulation Results  
We trained each model on a simulated urban environment that incorporates real-time risk from ST-GCN and dynamic AV sensor states. The models are evaluated based on cumulative reward, which reflects the balance between crime coverage, fuel efficiency, and travel constraints.  
As shown in Figure 6, DDQN consistently earns higher cumulative rewards than DQN, demonstrating better policy learning and fewer fluctuations. PPO, while slower to converge, exhibits strong generalization and is resilient to variations in AV input states such as traffic or incomplete data.  
To establish statistical robustness, we performed a paired t-test over 10 evaluation runs using cumulative reward scores from DDQN and PPO models. The test yielded a test statistic of \(t(9)=-18.2735\) with a \(p\)-value of \(
## 4 Experimental Results  
In order to verify the performance of the proposed PLC methods, the proposed PLC method based on time domain phase correction and DNN (denoted as DNN+WSOLA-PLC) and the PLC method based on frequency domain phase correction and DNN (denoted as DNN+GLA-PLC) are compared with the existing PLC method based on DNN [21] (denoted as DNN-PLC) and the PLC method based on WSOLA [13] (denoted as WSOLA-PLC). In order to further explain the inaccuracy of the DNN-based phase estimation, the PLC method of combining DNN-based amplitude spectrum estimation and real phase spectrum in the experiment is denoted as DNN+PHA-PLC. The experiments are conducted and analyzed under the packet loss rates of 5%, 10%, 15%, 20%, and 30%.  
To evaluate speech quality and intelligibility of the proposed methods, perceptual evaluation of speech quality (PESQ) [32], short-time objective intelligibility (STOI) [33], and log-spectral distortion (LSD) [34] are used to test the speech processed by the PLC, respectively. To demonstrate the superiority of the proposed methods, the most advanced DNN-based PLC method [21] and conventional WSOLA-based PLC method [13; 15] are used as the references for comparison.  
Figure 8: Illustration of the frequency domain PLC method.
## 3 Integrated Radio Coverage and Device Capacity Analysis Framework
### Theoretical Approach
Considering also \(T_{p}\) as the packet transmission period per device, the total number of devices that can be covered is the following:  
\[N_{D}=N_{D}^{0}\frac{T_{p}}{T_{RACH}} \tag{11}\]