esa-sceva/satcom-chunk-collection huggingface dataset:
# Team-Communication Modeling Framework for Multi-Unit Nuclear Power Plant Accident Analysis
## 2 Methods and Data
### Research Data  
We incorporated different types of data for the development and evaluation of the team-communication models. For the investigation model, the team-communication influencing factors were collected from a vast literature repository using a combined method of systematic literature review and text mining, as reported in our previous study [29]. Those influencing factors are considered together with the resilience engineering principles, reflected in the six aspects of the function in the functional resonance analysis method (FRAM) [43], to develop the performance-factor model. Lastly, the process model is mainly an application of FRAM for team communication during an NPP multi-unit accident. Thus, we refined the multi-unit accident scenarios from subject-matter experts and created a case study of the process model to evaluate its feasibility.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 3 Experiments and Results
### Results  
Training vectors from the training set were used to train both the IPSO-SVM model and the PSO-SVM model. During the training process, the fitness values were recorded, and the fitness curve was plotted as shown in Figure 4. This demonstrates that, for the specific scenario of high-impact data, the IPSO algorithm outperforms the traditional PSO algorithm in terms of performance.  
Use the trained model to make predictions on the test set and record the number of correct predictions, as shown in Table 8.  
For SVM models, common evaluation metrics include accuracy, recall, F1 score, and precision. Based on the prediction results, these metrics were calculated for both models, as shown in Table 9.  
The experimental results indicate that the IPSO-SVM model outperforms the traditional PSO-SVM model in terms of precision, accuracy, and recall, demonstrating superior performance in high-impact experiments. These findings further validate the potential of the IPSO-SVM model under small-sample conditions, proving that it can effectively enhance the accuracy and stability of fault prediction for high-impact equipment. Therefore, the IPSO-SVM model provides a valuable technical foundation for the reliability assessment of high-impact equipment and offers theoretical and practical guidance for implementing health monitoring and maintenance decisions in real-world applications.  
Figure 4: Fitness Curves.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Number of Test Samples** & **Actual Label** & **Number of Correct Predictions** \\ \hline \multirow{2}{*}{IPSO-SVM} & 100 & 1 & 85 \\  & 100 & 0 & 96 \\ \multirow{2}{*}{PSO-SVM} & 100 & 1 & 81 \\  & 100 & 0 & 89 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prediction Results of the Test Set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline IPSO-SVM & 90.5\% & 95.5\% & 85.0\% & 89.9\% \\ PSO-SVM & 85.0\% & 88\% & 81.0\% & 84.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Model Validation Metrics.
## 3 Results and Discussion
### Strength Prediction and Relationship Analysis
#### 3.4.2 Prediction Profiler  
One of the best features of ANN produced during the analysis is the prediction profiler (see Figure 8). A clear trend profile shows the impact of each variable on the compression, tensile and flexural strength. Replacement of additive (in %) shows up to 25% replacement produced higher strength while the type of additive shows Marble powder is the best among the three additives. The behavior of concrete from 27 \({}^{\circ}\)C to 400 \({}^{\circ}\)C is satisfactory, which shows that it can survive against fire up to that level. Curing conditions are also satisfactory, as it shows the highest strength at 28 days.
## 3 Forecasting Approach
### Performance Indexes
#### 3.2.2 Performance Indexes of Prediction Stages  
In order to compare the forecasting performance of multiple models, the index of agreement (\(d\)), the autocorrelation coefficient (\(R\)), the mean square error (\(MSE\)), the mean absolute percentage error (\(MAPE\)), and the mean absolute error (\(MAE\)) are the performance indices that were considered to calculate the estimation of the generalization error in the prediction stages (steps I and III). Equations (15)-(19) shows these performance criteria and their calculation, where \(m\) is the sample size, \(y_{t}\) is the real value of the observation and \(\hat{y}_{t}\) is its corresponding predicted value. \(R\) values are comprised between \(-1\) and \(1\) whereas \(d\) values are between \(0\) and \(1\). These indices measure the correlation between predicted and real values. By contrast, \(MAE\), \(MAPE\) and \(MSE\) measure the deviation between predicted and real value. \(d\) and \(R\) values close to \(0\) indicate a worse performance whereas lower \(MAE\), \(MAPE\) and \(MSE\) values indicate better performance:  
\[d=1-\sum\
olimits_{i=1}^{m}{(\hat{y}_{i}-y_{i})^{2}}\Big{/}{\sum\
olimits_{i= 1}^{m}{(\big{|}\hat{y}_{i}-\overline{y}\big{|}+\big{|}y_{i}-\overline{y} \big{|})^{2}}} \tag{15}\]  
\[R=\sum\
olimits_{i=1}^{m}{(y_{i}-\overline{y})(\hat{y}_{i}-\overline{y})} \Big{/}{\sqrt{\sum\
olimits_{i=1}^{m}{(y_{i}-\overline{y})^{2}}\cdot\sum \
olimits_{i=1}^{m}{(\hat{y}_{i}-\overline{y})^{2}}}} \tag{16}\]  
\[MSE=1/m\cdot\sum\
olimits_{i=1}^{m}{(y_{i}-\hat{y}_{i})^{2}}, \tag{17}\]  
\[MAE=1/m\cdot\sum\
olimits_{i=1}^{m}{\big{|}\hat{y}_{i}-y_{i}\big{|}}, \tag{18}\]  
\[MAPE=1/m\cdot\sum\
olimits_{i=1}^{m}{\big{|}(y_{i}-\hat{y}_{i})}/{y_{i}}\big{|}. \tag{19}\]
# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 2 Mask and Region-Based Convolutional Neural Network (Mask R-CNN)  
Mask R-CNN [33] is an extension of Faster R-CNN [31] for instance segmentation. Instance segmentation detects individual objects of interest in an image and segments the shapes of the detected objects. The structure of Mask R-CNN is a combination of solvers for both object detection and segmentation, namely Faster R-CNN and a mask branch. Faster R-CNN first detects objects of interest in an image and mask branch and subsequently finds the shapes of the detected objects. Faster R-CNN is divided into two stages: region proposal (which determines the region in which an object is located) and classification (which classifies the region into predefined labels of objects). For each region determined by Faster R-CNN, the mask branch finds the shape of the object in the region. Thus, the structure of Mask R-CNN is composed of three stages: region proposal, classification,and segmentation. This is depicted in Figure 1. The following paragraph explains each step of Mask R-CNN in more detail.
## 2 Optimization Methods and Design Procedure
### Principle of Particle Swarm Optimization Algorithm  
Particle swarm optimization proposed by [PERSON] et al. (2017) is a kind of intelligent optimization algorithm formed by simulating the intelligent behavior of bird foraging and so on. The initial displacement and velocity of everyone are randomly generated within a certain range. At the same time, in the process of searching for the optimal solution, each particle should not only carry on the next search according to the existing information, but also pay attention to its own experience and draw on the information of its peers to find the optimal solution. Figure 2 shows the optimal search process for population particles in the search space.  
In Figure 2, \(O\) represents the global optimal position, \(c_{1}\) and \(c_{2}\) denote the acceleration constants, \(r_{1}\) and \(r_{2}\) are two random numbers within the range of \([0,1]\), and \(X_{i}^{t}\) and \(V_{i}^{t}\) (multidimensional vectors), respectively, represent the spatial position and flight velocity of the particle swarm after \(t\)th iterations. \(P_{ibest}^{t}\) and \(G_{best}^{t}\) represent the individual optimal position and the global historical optimal position mastered by the \(i\)th particle, and the subscript \(i\) represents the \(i\)th particle. The speed and position update formula for the next optimization based on the individual historical optimal value and global optimal value is:  
\[V_{i}^{i+1}=V_{i}^{i}+c_{1}r_{1}(P_{ibestj}^{t}-X_{i}^{t})+c_{2}r_{2}(G_{best}^ {t}-X_{i}^{t}) \tag{4}\]  
\[X_{i}^{i+1}=X_{i}^{i}+V_{i}^{t+1} \tag{5}\]
## 2 Overall Design of the Integrated System
### Working Modes
#### 2.3.2. Interrupt Service Mode  
The natural conditions of underground coal mines are so rough that there may be a sudden incident, so it is very important for the safe production in underground coal mines to accurately monitor and provide timely alarms for sudden incidents [7]. Therefore, as the supplement of the periodic inspection mode, the interrupt service mode is designed for the WSN to warn of any abnormal situation. When the sensor node is asleep, the processor and the communication module will be shut down to save power, thus it can neither send nor receive messages, but the sensors will stay active and keep collecting environmental parameters. Once there is an overrun in a certain parameter, the sensor node will be woken up by an interrupt generated by the voltage comparator of the processor. Then, the sensor node marks the parameter as an emergency, and sends it to the GMDC immediately. When GMDC receives the emergency data, it will alarm the corresponding areas about the abnormal situation so as to predict the danger in time.
## 3 Proposed Method
### Residual Encoder  
The residual structure [22], denoted as ResBlock and introduced as a solution to the gradient vanishing problem, is illustrated in Figure 3. In the encoder path, the repetitive downsampling operation often leads to information loss. Therefore, this study employed a sequence of consecutive residual blocks in lieu of the initial convolutional layer. This approach enhanced the network's capacity to preserve and extract input features effectively. Furthermore, the integration of residual blocks served to mitigate to some degree the gradient vanishing challenge arising from the network's increased depth.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 9 Challenges and Future Directions
### Open Problems  
#### 9.1.1 Interference Management  
BackCom tags communicate with weak information signals in the presence of strong carrier signals and noise in the wireless networks. Moreover, the simultaneous transmission by tags also creates interference among them. The interference management schemes such as interference cancellation and interference alignment can be used to address interference problems for certain channels [182]. To address deep fading in BackCom channels [218], transmit diversity was proposed for BackCom with single-tag antennas [219]. [PERSON] et al. proposed a multi-tag selection combining scheme to combat the double fading channel [220]. Moreover, they also correlated a BackCom wireless channel with Nakagami-fading channels. A parallel approach to resolving single-source fading issues is wideband Amb-BackCom, which enables a transmitter to backscatter energy across an extremely wide bandwidth, allowing any signal in the complete band to be a host for BackCom [202]. More work needs to be done to resolve interference problems in 5G with different networking scenarios, as well as in various fading channels.
## 4 Encrypted SFC Architecture
### The Infrastructure  
The nature of an SFC accommodates Segment Routing (SR) [18; 29; 30], which implies that the sender of an IP packet specifies the packet path. Specifically, SR implies that the packet header contains SFC state information of how to route a specific packet for a selection of intermediate routers. We use NSH as a data plane enabler for SR in order to steer the traffic in such SR paths between the SFs and the EFs (Section 4.1.1). Two layers of NSH headers constitute two overlay networks. One layer  
Figure 3: Top-level architecture.  
addressing the communication between the SFs and one additional layer addressing the point to point communication between the EFs (Figure 4).  
Currently, SR by NSH is not widely supported by routers and neither is the new NSH encryption header extension that we have suggested. Therefore, in order to ensure packet forwarding through legacy network devices, we define that the NSH packet must be encapsulated by an outer transport network between the NSH-aware routers. Figure 4 shows that we use VXLAN-GPE for this underlying network. Each of these network layers accommodates the different communication layers in the architecture. For example, an NSH header is only valid between two SFs, while the new additional NSH header is only valid between two EFs. Hence, the structured packet header (Figure 4) is also reflected in a structured design of the networking components (Figure 3), where each data plane component is responsible for each layer.
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 6 Conclusions and Future Work
Motivated by this fact, this is the first study (to the best of our knowledge) in which training time has been considered for finding the effectiveness of the deep learning method. Our study also establishes a framework for validating the performance of new and alternate methods in ALSC for future research in this area. It is worthwhile to note that Hidden Markov Model (HMM) and genetic algorithm hybrids have proven to perform better than baseline models in the coarse-grained sentiment analysis [57], but researchers have not explored HMM models in ALSC literature. Thus, it would be interesting to perform the statistical comparison of HMM hybrids with deep learning hybrid methods for ALSC as a future work. This study has, however, certain limitations. One such limitation is the small number and size of datasets used in this study to evaluate various deep learning methods. Most of the existing research has used maximum of five datasets. Although we have considered eight datasets, still for better evaluation, more significant number of datasets of different domains is desired. Therefore, in the future it would be interesting to propose datasets that are large and belong to different domains. Another limitation is related to the hyper-parameter tuning of the discussed methods. The various values of hyper-parameters in our study have been taken from the original work. However, for better evaluation of such methods, hyper-parameter tuning is desired, which is not a trivial task. The hyperparameter tuning of deep learning architectures requires huge computational cost and is time-consuming. Thus, this limitation could be investigated in future work. While deep learning-based methods have transformed the research in ALSC, the surge of such deep learning methods is stained by the opaque tendency of their architecture and high computational cost for more complex architecture. Globally, researchers have started emphasizing the interpretability and model complexity of the model as well. However, the ALSC literature lacks such contribution. Our study has shown that the performance difference between most of the methods is insignificant. Thus, rather than improving accuracy or F1 score by an insignificant number, the researchers should focus on building less complex and high interpretable solutions for ALSC. Conceptualization, T.S. and K.K; methodology, T.S. and K.K. ; software, T.S. ; validation, T.S. and K.K; formal analysis, T.S. ; investigation, T.S. and K.K. ; resources, T.S. and K.K. ; data curation, T.S. ; writing--original draft preparation, T.S. ; writing--review and editing, T.S. and K.K. ; visualization, T.S. ; supervision, K.K. ; project administration, T.S. and K.K. ; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are openly available in [13; 31; 46; 47; 48; 49; 50]. The authors declare no conflict of interest.
# Numerical Analysis of a Spiral Tube Damping Busbar to Suppress VFTO in 1000 kV GIS
## 1 Introduction
The main challenge in the TCV approach is the optimum design of the disconnector which should be considered to achieve significant VFTO reduction with an acceptable sparking time, as explained clearly by [PERSON], [PERSON] et al. [24]. Moreover, surge arresters can suppress the amplitude of the VFTO, but they have no effect on the steepness. The difficulty in using surge arresters is implementing the optimum number of arresters discs for a noticeable damping effect and at a convenient location. The appropriate location of surge arresters in order to eliminate VFTO was investigated by [PERSON] [25]. It could be well seen from the literature [15; 16] that the main disadvantage of the cavity resonator method is the absorption of VFTO energy in only a narrow band of its broad frequency spectrum. Consequently, there is no observed damping effect when the resonant frequency of the resonator does not fit the dominant harmonic component of the VFTO. However, compared with the ferrite ring method, the magnetic rings become saturated in high current under high frequency and lose their suppressing effect, as confirmed by [PERSON], [PERSON] et al. [26]. Furthermore, ferrite magnetic rings are still in the experimental stage, and they may have no obvious suppressing effect on VFTO. As a consequence, in order to ensure the reliability of substations, it is essential to carry out research on suppressing VFTO. This paper presents a design of a damping busbar to suppress VFTO. We hollow out the conventional busbar to a spiral tube, and then the busbar conductor is changed into a series circuit with a multiturn hollow inductance coil and multiturn gap; based on this, paralleling the damping resistance with the newly designed spiral tube inductance circuit, the spiral tube damping busbar is formed. The mechanism of VFTO suppression by the proposed damping busbar is analyzed, and the distributed equivalent circuit is established. Furthermore, an illustrative structure of the damping busbar is introduced. VFTO with the proposed damping busbar is simulated, and the suppression effects before and after installing the damping busbar are compared. An improved design of the damping busbar is proposed, and a higher damping effect is verified. The remainder of this paper is structured as follows. In Section 2, the suppressing mechanism of the new method is analyzed. The simulation results and the damping effect of the damping busbar are discussed in Section 3. In Section 4, the influence of the inductance value of the damping busbar on suppressing VFTO is investigated. An improved design of the damping busbar in order to increase the suppression effect is proposed in Section 5. This section presents an investigation on a spiral coil made of litz wire, used to damp VFTO. Such winding meets the requirement of sufficient resistance to damp VFTO. Section 6 summarizes the damping effects of the damping busbar and the improved design. Finally, conclusions are drawn in Section 7.
## 3 Implementation and Results
### Implementation
#### 3.1.1 Order Selection  
We performed PCA on each dataset separately and calculated the cumulative explained variance (shown in Figure 2). From Figure 2, we can see that the variance retained for the FA and GM datasets follows a similar pattern, which differs from that of the fALFF dataset. For this reason, the criteria for order selection were similar for the FA and GM datasets. We used 98% as the threshold for the variance retained for the FA and GM datasets in order to balance the retention of the most signal while minimizing the effects of noise. As a result, the orders used for FA and GM datasets were 20 and 35, respectively.  
Using a similar threshold for the variance retained with the fALFF dataset would result in an order of 145. Model order that is greater than 100 has been found to decrease the stability of ICA [59]. Therefore, using a variance retained threshold is not an appropriate way to determine the order for the fALFF dataset as was done for the FA and GM datasets. Instead, we determined the order for the fALFF dataset by investigating the effect of different orders on the estimated ICs [60; 61; 12; 62]. We performed ICA to estimate ICs starting at an order of 20 and increasing the order with increments of 5 until the order reached 70. These limits were selected to cover a large range of potential, yet still reasonable, values for the order. As we increased the order from 20 to 40, we observed increasing numbers of meaningful neuroanatomical and functional components. However, when increasing the order beyond 40, we found that components of interest became split into multiple spatial maps and the number of the noise components increased. For this reason, we selected an order of 40 for the fALFF dataset, resulting in over 70% retained variance.
###### Abstract  
The use of micro-computed tomography (micro-CT) has become widespread in the examination of dental tissue due to its great precision in small-scale work. Its usefulness in measuring tissue mineral density has been demonstrated; however, it is necessary to develop image acquisition protocols that ensure the reproducibility of observations and offer a detailed step-by-step process. This study proposes a standardised protocol to quantify mineral density using volumetric measurements from micro-CT images, evaluating the reproducibility of density measurements at different points of enamel and dentine. The sample comprised 30 bovine incisors that were scanned using a micro-CT system. Using the MIMICS software v.26, seven reference points in enamel and dentine (crown, cervix, and root) were identified, and mineral density was calculated by using Hounsfield units. The reproducibility of the measurements among the three observers was assessed by calculating [PERSON]'s concordance correlation coefficient (CCC). There was substantial to almost perfect correlation for the enamel (CCC = 0.986-0.995) and dentine (CCC = 0.965-0.997), with the latter showing better results for the cervix (CCC = 0.987-0.997) and crown (CCC = 0.987-0.995) compared with the root (CCC = 0.965-0.985). In the lingual area, the concordance results were broader (CCC = 0.965-0.997) compared with the buccal area (CCC = 0.979-0.995). Despite this difference, the proposed volumetric measurements reached a high level of agreement, which demonstrates the replicability of the present protocol.  
Keywords:dental volume; image acquisition protocol; Hounsfield units; interobserver error; MIMICS software +
Footnote †: journal: Applied Sciences