esa-sceva/satcom-chunk-collection huggingface dataset:
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 3 Methodology
### Proposed Credibility-Based Imbalance Boosting Method  
CIB includes two key processes: (1) calculation of credibility factors of synthetic samples and (2) the weight update scheme for synthetic samples in boosting iterations. Algorithm 1 shows the pseudo-code of our proposed credibility-based imbalance boosting (CIB) method.  
```
1:Input: Training dataset \(S_{tr}\); Amount of SMOTE \(P\%\); Number of the nearest neighbors \(k\) (default 5); Maximum number of iterations \(T\); Base classifier learner.
2:Output:\(h_{f}(x)\) % Final hypothesis, i.e., the learned classifier.
3:\(S_{tr}^{pos}\): the positive class samples in \(S_{tr}\).
4:\(r=\frac{|S_{tr}^{pos}|}{|S_{tr}|-|S_{tr}^{pos}|}\).
5:\(S_{syn}=SMOTE(S_{tr},P,k)\) % Generate the synthetic minority class samples with WEKA toolkit.
6:\(cf_{j}=1-exp\left(-\beta*r^{2}\right)\) % Calculate the credit factor of each sample in \(S_{syn}^{pos}\).
7:\(S_{com}=S_{tr}\cup S_{syn}\).
8:\(N=|S_{com}|\) % Number of samples in \(S_{com}\).
9: Initialize the distribution weight of \(S_{com}\) as: \[D_{t}(x_{j})=\left\{\begin{array}{ll}\frac{1}{Z_{1}*N}&if\ x_{j}\in S_{tr}^{ pos},\ i.e.,\ j\leq n\\ \\ \frac{cf_{j-n}}{Z_{1}*N}&if\ x_{j}\in S_{syn},\ i.e.,\ n+1\leq j\leq N\end{array}\right.\]
```  
**Algorithm 1**\(CIB(S_{tr},P,k,T,learner)\)where \(Z_{1}\) is a normalization factor, \(Z_{1}=\sum_{j=1}^{n}\frac{1}{N}+\sum_{j=n+1}^{N}\frac{cf_{j-n}}{N}\).  
```
1:for\(t=1\to T\)do
2: Provide \(S_{com}\) with distribution weights \(D_{i}\) to the learner, then get back a hypothesis: \(h_{t}:x\to y\).
3: Calculate the weighted error of \(h_{t}\) on \(S_{com}\) as: \(\varepsilon_{t}=\sum_{j:h_{t}\left(x_{j}\right)\
eq y_{j}}D_{i}\left(x_{j}\right)\).
4:if\(\varepsilon_{t}\geq 0.5\)then
5: End this iteration.
6:endif
7: Set \(\alpha_{t}=\frac{1}{2}\ln\left(\frac{\varepsilon_{t}}{1-\varepsilon_{t}}\right)\).
8: Update the distribution weight as: \(D_{t+1}\left(x_{i}\right)=\left\{\begin{array}{cc}\frac{D_{i}\left(x_{i} \right)*e^{-\alpha_{i}}}{Z_{t}}&if\ h_{t}\left(x_{i}\right)=y_{i},\ x_{i}\in S _{tr}\\ \frac{D_{t}\left(x_{i}\right)*e^{\alpha_{i}}}{Z_{t}}&if\ h_{t}\left(x_{i} \right)\
eq y_{i},\ x_{i}\in S_{tr}\\ \frac{D_{t}\left(x_{i}\right)*cf_{i}}{Z_{t}}&if\ x_{i}\in S_{syn}\end{array}\right.\)
9: where \(Z_{t}\) is a normalization factor, \(Z_{t}=\sum_{x_{i}\in S_{syn}}D_{t}\left(x_{i}\right)*cf_{i}+\sum_{h_{t}\left(x _{i}\right)=y_{i},\ x_{i}\in S_{tr}}D_{t}\left(x_{i}\right)*e^{-\alpha_{i}}+ \sum_{h_{t}\left(x_{i}\right)\
eq y_{i},\ x_{i}\in S_{tr}}D_{t}\left(x_{i} \right)*e^{\alpha_{i}}\).
10:endfor
11: Output the final hypothesis: \(h_{f}\left(x\right)=\underset{y}{argmax}\sum_{t=1}^{T}\alpha_{t}\sigma\left( h_{t}\left(x\right)\right)\).
```
## 6 Experimental Results and Analysis
### Online Time and Net Flow Profile Based on Clustering  
To some extent, the online time and duration of users reflect their dependence on the network. Clustering the user's online time and duration can divide the user group's online behavior preferences into different levels, thus tagging the user profile.  
Figure 3: Comparison of time preference between individuals and groups.  
Based on the experiment in Section 6.1, we further calculated the online time length of each time interval in a month and used the K-MEDIODS algorithm to cluster the online time and the duration. Due to a large amount of data, the clustering results of high-density points affected the observation. In order to make the clustering results clearer, we also performed further processing of the data. We used the steps in Section 4.2 to calculate the online time of users in a month. After repeated tests, the clustering effect was most obvious when the final K value was 4, as shown in Figure 4.  
In Figure 4, the X-axis represents the online time interval, the Y-axis represents the total time used in the time interval, each point represents a user, four different colors are different clusters, and their online preference levels are low, normal, high, and extreme. For example, most users liked to be online after 19:00, which is a break without classes. Such students were labeled as \"self-disciplined\". Some users were online at 0:00, they preferred to stay up late, and they used a lot of net flow during this time interval, thus, such students were labeled as \"night owls\".
# Architectural Design for Digital Twin Networks
## 5 Design and Implementation Strategies
### Interfaces
#### 5.4.1 Network-Facing Interfaces  
These interfaces connect the real network with its twin, enabling the exchange of critical network data. Typically, interfaces as indicated above (e.g., SNMP, NETCONF, and IPFIX), are utilized for real-time data collection. Due to the absence of internet protocol and the limitations of both in-band telemetry and out-of-band methods, it might be required to develop a custom model to gather monitoring data.  
For the network-facing interface from the DTN to the network for configuration/reconfiguration, it is important to note that when the frequency of updates (up to real-time granularity) becomes too high, a central (single) DTN may not be feasible. In such cases, shifting to a different system is necessary. This system would involve an intermediate managing separate entity handling the southbound Application Programming Interface (API) and traffic, enabling the operation of multiple DTNs for a single network. These DTNs can then focus on algorithms, models, and AI/ML. By creating an overarching entity to manage this process, the concept of a DTN is maintained, but with enhanced scalability and robustness.
## 3 Results and Discussion
### Model Development  
Two types of HEC-HMS models were constructed with different hydrological modeling approaches as presented in Figure 8. The first model, which applies [PERSON]'s method, can simulate areal-averaged precipitation based on rain gauge observations, and it consisted of a total of 25 subbasins. The ModClark model, designed to utilize grid-based RAR and IMERG precipitation, includes 1337 and 76 analysis units of 1 and 10 km grid cells, respectively. In the ModClark model, the calculated flow lengths and the gridded Curve Number for each grid were incorporated as essential information for model development. In addition, discharge data from the gauging points of six upstream rivers and watershed outlet were also inputted to assess and verify the flood analysis results against the model simulated outputs.
## 1 Introduction  
Intelligent image and optical information processing have paved the way for the recent epoch of new intelligence and information era. Certainly, information acquired by various imaging techniques is of tremendous value, thus, an intelligent analysis of them is necessary to make the best use of it.  
The objectives of intelligent processing range from the refinement of raw data to the symbolic representation and visualization of the real world. The extraction and manipulation of the descriptive features are essential for such a task [1]. It comes through unsupervised or supervised learning based on statistical and mathematical models or computational algorithms. With recent advances in computing power and learning algorithms, many applications have become more practical and further development is expected.  
This Special Issue focuses on the intelligent processing of images and optical information acquired by various imaging methods. Images are commonly formed via visible light; there are other imaging sources that represent scenes and objects in a multi-dimensional frame [2]. For example, radiography can be used to see internal objects for medical diagnosis and non-destructive inspection. Infrared imaging detects thermal variations in a non-visible environment. Spectral imaging is useful for collecting information at high spectral resolution or over a wide spectrum. Microscopic imaging reproduces the microscopic world, while satellite imaging probes the Earth from space. Since the statistical distribution depends on the nature of the source, suitable intelligent processing should be contrived accordingly. A broad range of research fields is included in the Special Issue. Many studies focus on object classification and detection. Registration, segmentation, and fusion are performed between a series of images. Many valuable and up-to-most recent technologies are provided to solve the real problems in selected papers.  
A total of 61 manuscripts were submitted and only 18 research, and one review, papers were verified through a thorough review process. The first volume of the Special Issue on the topic is closed; more in-depth research of the same topic is expected in the second volume of the Special Issue. It is anticipated that the scope of the intelligent processing would be even broader in the future.
## 3 The Case Study
### DSF Energy Modelling  
A Double-Skin Facade with a 1.00 m cavity depth is installed on the northwest and southeast building elevations, and external blinds with low reflectivity slots are installed and designed to be operable only during the hottest months, thus preventing the risk of overheating the inner spaces. The DSF is designed to be naturally ventilated. External and internal grills are located, respectively, on the outer skin and the inner layer, represented by the existing building envelope. External grills are active, allowing the air to enter the Double Facade during the hottest months, whereas they are closed for the winter period, creating a buffer area. The internal grills, instead, are designed to be adjustable according to the inner temperature distribution. The natural ventilation of the building is controlled by conducting an outdoor maximum temperature check. This prevents overheated air inside the cavity from entering the building and having adverse effects on the cooled side. The outer skin of the DSF is made of a steel structure and high-performance windows composed of triple glazing (a 13 mm cavity filled with Argon gas sealed by two layers of 3 mm single clear glass) with a low U value and solar heat gain coefficient equal to, respectively, 0.78 W/m\({}^{2}\)K and 0.47. The DSF inner partitions, both horizontal and vertical, present steel frame and single-pane windows made of 6 mm clear glass with a transmittance and solar heat gain coefficient of 3.78 W/m\({}^{2}\)K and 0.72.  
The energy modelling of the DSF was achieved by setting the cavity as an unoccupied zone with no HVAC or lighting template data. Moreover, the internal convection mechanism was activated to model the cavity air space correctly, and a complete interior and exterior solar distribution algorithm was switched on, allowing solar radiation to be accurately transmitted through the interior glazing in the partition.  
Various configurations were modelled and tested to evaluate the effectiveness of DSF systems. Multi-Storey, Shaft-Box, Corridor and Box-Window DSFs were investigated in terms of energy consumption and thermal comfort conditions. Two different cases were studied for the Corridor type: the outer grills were inserted on the principal elevation of the facade in one case and on the lateral envelope in the other. The inner partitions, which were eventually inserted inside the cavity according to the selected category, present single-glazed windows enclosed in a steel structure. Figure 4 schematises the chosen options, identifying the air fluxes which enter/exit each DSF typology.
## 5 Use Case: Collaborative Issues Detection in an Intelligent Vehicle Surveillance System  
This use case illustrates the uses of the proposed HMPS in a practical and scalable manner, ensuring secure and trustworthy collaboration in distributed systems. The use case objectives are as follows: (1) ensuring data integrity and commands sent to vehicles have not been tampered with; (2) ensuring that commands are hierarchically authenticated; and (3) trust enhancement by building a tamper-proof system through the digital signature of each layer of the transmission chain.
## 4 Case Studies
### Coordinated Emergency Dispatch of Thermal Units and ESSs
To test the robustness of the proposed robust emergency dispatch strategy under multiple sets of emergency scenarios, the following uncertainties are introduced:  
* The maximal wind speed \(v_{\text{max,i}}\) at period \(i\) is added with a fluctuation term \(\Delta v_{\text{max,i}}\) which follows a Gaussian distribution \(\Delta v_{\text{max,i}}\in\mathcal{N}(\mu_{\text{v}},\sigma_{\text{v}}^{2})\). * The radius of maximum wind speed is also added with a fluctuation term \(\Delta R_{\text{max,i}}\) which follows a Gaussian distribution \(\Delta v_{\text{max,i}}\in\mathcal{N}(\mu_{\text{r}},\sigma_{\text{r}}^{2})\). In order to study the influence of wind speed's fluctuation on the robustness, the standard deviations of wind speed fluctuation are set to \(\sigma_{\text{v}}=0.2\) m/s, while the means of wind speed fluctuation increase from 0 m/s to 10 m/s in increments of 1 m/s. The average curtailment costs under different \(\mu_{\text{v}}\) are shown in Figure 6a. As can be seen, larger \(\mu_{\text{v}}\) would cause larger \(v_{\text{max,i}}\), as well as line fault probability \(Pr^{c}(B)_{k,j,q}\), which results in early line disconnection. Initially, the proposed strategy can tolerate a certain degree of early line disconnection. However, when \(v_{\text{max,i}}\) increases significantly (\(\mu_{\text{v}}=7\)), early line disconnection will result in additional curtailment cost. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \hline non-robust strategy & 0\$ & 0\$ & 2.31e55 & 2.31e55 \\ proposed strategy & 0\$ & 0\$ & 0\$ & 0\$ \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Load and wind power curtailment costs under different emergency scenarios. Figure 6: Curtailment cost under (**a**) different \(\mu_{\text{v}}\) (**b**) different \(\mu_{\text{r}}\). Similarly, the standard deviations of wind speed's fluctuation are set to \(\sigma_{\rm r}=0.2\) m, while the means of radius fluctuation increase from 0 m to 20 m in increments of 2 m. The average curtailment costs under different \(\mu_{\rm r}\) are shown in Figure 6b. As with Figure 6a, when \(\upsilon_{\rm max,i}\) increases significantly (\(\mu_{\rm r}=17\)), the short distance between the wind circle and the line leads to increased wind speed and fault probability; consequently, early line disconnection may result in additional curtailment cost.
## 3 Results and Discussion
### Fiber Distribution
The correlations of properties of UHPFRCs investigated in this study are listed in Table 3. Although the data in this study are limited, the strong correlations were observed between fresh properties, i.e., flow, yield stress, and plastic viscosity, and the flexural strength has a strong correlation ([PERSON]'s linear correlation coefficient over 0.76) with all other properties under controlled fabrication of the material. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multirow{2}{*}{**WRA**} & \multirow{2}{*}{**Flow**} & **Plastic** & **Yield** & **Compressive** & **Flexural** & \multirow{2}{*}{\(\alpha_{f}\)} & \multirow{2}{*}{\(\eta_{\theta}\)} \\  & & & **Viscosity** & **Stress** & **Strength** & & & \\ \hline WRA & 1 & - & - & - & - & - & - & - \\ Flow & 0.75 & 1 & - & - & - & - & - & - \\ Plastic viscosity & \(-\)0.79 & \(-\)1.00 & 1 & - & - & - & - & - \\ Yield stress & \(-\)0.72 & \(-\)1.00 & 0.99 & 1 & - & - & - & - \\ Compressive strength & \(-\)0.88 & \(-\)0.39 & 0.43 & 0.34 & 1 & - & - & - \\ Flexural strength & \(-\)0.98 & \(-\)0.79 & 0.82 & 0.76 & 0.87 & 1 & - & - \\ \(\alpha_{f}\) & \(-\)0.87 & \(-\)0.95 & 0.97 & 0.95 & 0.52 & 0.86 & 1 & - \\ \(\eta_{\theta}\) & \(-\)0.96 & \(-\)0.80 & 0.82 & 0.76 & 0.86 & 0.99 & 0.83 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation of properties of UHPFRCs. Figure 11: Fiber orientation coefficients and fiber dispersion coefficients of UHPFRC. From this analysis of fiber dispersion and orientation distribution, it can be said that the flexural behaviors of four different mixtures depended in part on the compressive strengths of the matrices and in part on the fiber orientation distributions due to the rheological properties.
## 1 Introduction
Taking into consideration that SAE deep learning algorithms are recent tools in the SHM area, studies focused on evaluating them to solve novelty detection problems are still welcomed, as seen in a significant number of recent works that address this topic. However, most studies already published still focus on numerical models or simplified laboratory structures. Although some applications have been found in practical and unsupervised applications of SHM systems, as reported by [PERSON] (2021) [5], the SHM solution is not universal for different types of structures. A set of techniques that might work for one structure might not work for another. Moreover, most analyses already conducted with SAE are applied to the detection of machine failure components, problems that are much more particular than civil engineering structures. For these reasons, the authors understand that there is a lack of studies to consolidate the application of the SAE in distinct types of civil engineering structures. From this perspective, and understanding that the success of an unsupervised SHM approach initially requires an evaluation within the framework of supervised techniques, the objective of the present work is to evaluate the SAE algorithm to extract parameters from vibration signals, allowing the identification of structural alterations. In so doing, the SAE avoids the need for additional processing steps and the constant demand for modal parameter estimation and tracking. The proposed approach is initially assessed through numerical simulations performed on a simply supported beam model. Several analyses are then carried out considering the acceleration time histories obtained from a highway viaduct in Brazil. Such a structure was subjected to a strengthening procedure after being damaged. The structural condition assessment is performed using SAE to \"learn\" important data features; that is, to characterize the vibration signals, and SVM to classify the corresponding damage classes (i.e., before and after the strengthening), according to SAE's extracted attributes. It should be mentioned that other classification algorithms could be applied to run the classification task, such as linear and non-linear regressions, Artificial Neural Networks, and Clustering. References [PERSON] and [PERSON] (2012) [34], [PERSON] et al. (2016) [35], and [PERSON] et al. (2019b) [36] are dedicated to this kind of analysis. In general, SVM has competitive results.
## 4 Numerical Results
### Sensitivity Analysis
#### 4.4.2 Psychological Behavior Coefficients  
The advantage of the proposed method is the reinforcement of regret theory in the prioritization process. Therefore, the coefficients reflecting the psychological behavior of decision makers should also be analyzed for sensitivity. As shown in Figure 8, the risk aversion coefficient is believed to have an influence on the prioritization outcome. However, the effect of this coefficient mainly swaps the rank of adjacent alternatives. For example, when the risk-aversion coefficient is greater than or equal to 0.88, the rankings for India and Thailand are interchangeable. Similarly, the rankings of Indonesia and Singapore swap with very low or very high values of the risk-aversion coefficient.  
Figure 7: Weighting scenario analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Criteria**} & \multirow{2}{*}{**Group**} & \multicolumn{5}{c}{**Scenarios**} \\ \cline{3-6}  & & **Base** & **1** & **2** & **3** & **4** \\ \hline Construction, installation cost & Cost-related & 0.094 & 0.1 & \(+\)30\% & \(-\)10\% & \(-\)20\% \\ Diversity of transportation services & Production-condition-related & 0.099 & 0.1 & \(-\)20\% & \(-\)10\% & \(+\)30\% \\ Labor cost & Cost-related & 0.096 & 0.1 & \(+\)30\% & \(-\)10\% & \(-\)20\% \\ Human resources availability & Production-condition-related & 0.109 & 0.1 & \(-\)20\% & \(-\)10\% & \(+\)30\% \\ Political stability & Policy-related & 0.120 & 0.1 & \(-\)15\% & \(+\)30\% & \(-\)15\% \\ Environmental management system & Production-condition-related & 0.073 & 0.1 & \(-\)20\% & \(-\)10\% & \(+\)30\% \\ Logistics cost & Cost-related & 0.095 & 0.1 & \(+\)30\% & \(-\)10\% & \(-\)20\% \\ Land cost & Cost-related & 0.095 & 0.1 & \(+\)30\% & \(-\)10\% & \(-\)20\% \\ Government policy & Policy-related & 0.128 & 0.1 & \(-\)15\% & \(+\)30\% & \(-\)15\% \\ Climate & Production-condition-related & 0.090 & 0.1 & \(-\)20\% & \(-\)10\% & \(+\)30\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Weighting scenarios.  
For the regret-aversion coefficient, the sensitivity analysis shows that this coefficient can create a large disturbance in the results of the proposed method. As shown in Figure 9, the ranking disturbance becomes larger with a larger regret-aversion coefficient, especially from the threshold of 0.7. Accordingly, it can be concluded that the proposed method can effectively reflect the psychological behavior of the decision maker in the evaluation process. As a result, it is possible to determine the solution that best suits the context. In other words, while the efficient-evaluation process, based on objective data, is optimized by the DEA model, the effectiveness of subjective judgments is maximally tailored to the decision maker.
## 2 Materials and Methods
### Characterization  
Fourier transform infrared spectroscopy (FTIR) spectra were performed on an Avatar 370 spectrometer (Nicolet, USA). The KBr pellet within an appropriate amount of MWCNTs was prepared. Raman spectra of the MWCNTs were gauged using a DXR Raman spectrometer (Thermo Scientific, USA) with the excitation wavelength of the laser at 532 nm. A laser intensity of 7.0 mW, an exposure time of 3 s, and the exposure rate of 20 times were applied in each measurement. An HP-6890 gas chromatograph (GC, Agilent, USA) measured the monomer conversion. Waters 515 gel permeation chromatography (GPC) was equipped with three columns (average pore sizes of 104, 105, and 106 nm, monodisperse polystyrene was used for the calibration standard sample). A Waters RI detector at 35 \({}^{\circ}\)C measured the molecular weight and molecular polydispersity of azide-terminated PMMA. Thermogravimetric analysis (TGA) was performed on a 209 F3 thermogravimetric (TG) analyzer (Netzsch Inc., Germany) under N\({}_{2}\) protection with a flow rate of 50 mL/min. The sample was heated from 50 \({}^{\circ}\)C to 700 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min. The grafting percentage (GP) for MWCNTs-\(g\)-PMMA can be calculated as shown in our previous work [24]. Transmission electron microscopy (TEM) was performed on a JEM-1200 EX/S transmission electron microscope (JEOL, Japan). Before observation, the dried MWCNTs were pretreated in THF under ultrasonic vibration for 20 min and then deposited on a covered copper grid.