esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Computational Experiments
### Experimental Results and Discussion  
#### 6.2.1 Parameter Sensitivity  
The value of \(\mu\) in the B-DMOEA determines the importance of the marginal and conditional distributions in the knowledge transfer. In this section, we analyse the relationship between \(\mu\) and problem instances to guide the setting of the optimal value of \(\mu\). We select three problem instances of different scales and analyse the MIGD and MHV of B-NSGA-II, B-REVA-i and the B-IBEA on these three problem instances when \(\mu\) takes different values (\(\mu\in\{0,0.1,\ldots,1.0\}\)). The experimental results are shown in Figure 6.  
Figure 6 shows that when \(\mu=0\), that is, when only the marginal distributions are considered, the three algorithms cannot obtain the optimal MIGD and MHV values on the three problem instances, indicating the importance of considering both marginal and conditional distributions in our B-DMOEA. Moreover, Figure 6 shows that the optimal \(\mu\) varies on different problem instances. For example, in Figure 6b, when \(\mu=0.5\), B-NSGA-II can obtain the optimal MHV on both problem instances S2 and L6, which shows that marginal and conditional distributions contribute equally to the discrepancy. In contrast, when \(\mu=0.3\), B-NSGA-II can obtain the optimal MHV on the problem instance L21, which means the marginal distributions contribute more to the discrepancy than the conditional distributions, and the performance of transfer learning depends more on the marginal distributions. The observations indicate the importance of balancing the marginal and conditional distributions based on the characteristics of the problem instances. In the other subfigures in Figure 6, the observations are similar. Therefore, B-DMOEA is more likely to achieve a better performance than Tr-DMOEA. Note that Figure 6 indicates that the values of \(\mu\) need to be adjusted for problem instances. However, the experiments in this  
Figure 6: MIGD and MHV in relation to \(\mu\) on different problem instances. (**a**) MIGD obtained by all algorithms; (**b**) MHV obtained by all algorithms.  
paper mainly aim to prove the importance of both marginal and conditional distributions between domains in feature-based transfer learning approaches. Moreover, it can also be seen from Figure 6 that when \(\mu\) = 0.5, B-DMOEA can perform better in most cases. Therefore, in subsequent experiments, \(\mu\) is uniformly set to 0.5 without adjusting to different problem instances.  
In addition, Figure 6 shows that decomposition-based algorithms, such as B-REVA-I, are more unstable than B-NSGA-II and B-IBEA, proving that decomposition-based algorithms have difficulty solving real-world problems (e.g., D-HRDLB) in which the Pareto front geometry is often irregular. This finding is consistent with the results of our previous work [5].
# Detection and Localization of Multiple Damages through Entropy in Information Theory
## 2 Wiener Entropy Measurements for SHM Purposes
### Uses for Damage Severity Assessment  
The physical, quantitative assessment of the damage severity would require some a priori knowledge about the expected damage effects. This is not considered here as the proposed framework is based only on data from the undamaged conditions of the target system. Nevertheless, it is still possible to qualitatively estimate the severity of damage by comparing the divergence between the WE values estimated from pre-and post-damage recordings.
# Transportation and Reserve of Emergency Medical Supplies during Public Health Events
## 1 Introduction
Transportation and storage of emergency medical materials are of great significance and value in promoting the construction and improvement of the national emergency management system, improving the level of public health and safety, and protecting people's health and safety. Designing a faster, more efficient, and less labor- and material-intensive mobilization reserve program is the research problem of this study. Therefore, our goal is to respond to emergency medical supplies before the large-scale outbreak of public health issues, scientifically and efficiently predict the outbreak of public health issues, and deal with the optimal route transportation and storage of emergency medical supplies in public health issues, to minimize the harm caused by public health issues. The work process in this study goes as follows: First, a SEIRD model for modern public health issues was established and the least-squares method was used to fit the model parameters, thus completing the prediction of infection trends. Then, we defined the set of emergency medical supplies and combined the characteristics of different populations to predict the amount of emergency medical supplies needed on a single day. We then defined the urgency functions for outbreak prevention and control capacity and emergency medical supplies, outlined how the parameters are calculated, and determined the overall urgency function based on their respective weights. Then, a bi-objective vehicle path optimization problem with a time window considering demand urgency and minimum response cost was constructed and a particle swarm algorithm was used to solve the problem. Finally, a simulation test of the model is carried out to verify the feasibility and rationality of the model. This paper is organized as follows: Section 2 presents the literature review, Section 3 establishes a model and designs a solution algorithm that can solve the problem of emergency medical material mobilization and stockpiling, Section 4 explores the effectiveness of the built model, Section 5 simulates the constructed model to confirm the feasibility of the model, Section 6 provides a discussion of the article, Section 7 proposes six countermeasures and recommendations, respectively, for the constructed model to effectively deal with public health issues, and Section 8 concludes the main findings.
## 4 Results and Discussion
### System Visual Display  
In order to verify the feasibility of the system in practical application, we designed a front-end visual interface for the system. The design of the interface is shown in Figure 19. The interface displays the actual distance between the front and back of the vehicle and the distance between the surrounding objects in real time in a visual form on the corresponding position around the vehicle. If the detected distance falls below the preset safety threshold, the interface will display the value with a striking red warning sign to immediately attract the driver's attention. Additionally, the driver can observe the vehicle's surroundings in real time on the right side of the interface, enabling timely corresponding driving actions and effectively preventing potential risks. In order to ensure driving safety, an audible and visual alarm system will be developed in the future: early warning can be achieved through the linkage of in-vehicle lights and sounds, and when danger is detected, lights and voice prompts will be triggered synchronously so as to improve the driver's response speed and reduce the risk of accidents [28].
# Preparation and Modification of Biochar Materials and their Application in Soil Remediation
## 3 Removal Mechanism of Major Pollutants by Biochar
### Ion Exchange  
Ion exchange means the process that acidic oxygen-containing functional groups on the surface of biochar, such as carboxyl groups, carbonyl groups, and hydroxyl groups, can ionize H\({}^{+}\) or surface base ions such as Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\), etc., to exchange with heavy metal ions or cationic organic pollutants [80].
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 3 Materials and Methods  
In a WSN with a star network topology, communications occur between two types of devices, a coordinator node that manages those with the lower nodes, and the lower nodes that transmit the signal from the sensors to the coordinator node, as shown in Figure 1.  
To describe our industrial WSN setup, this section is divided into seven parts; the first one describes the transceiver used in the ANDON tower that works as a sensor node; the second one describes the characteristics of the computer equipment used for the programming of the MLP model; the third one describes the experimental scenario where data were collected; the fourth one addresses the neuronal network's features, i.e., the model's input variables; the fifth one is the LQE classification based on PRR, i.e., the model's output variables; the sixth describes the dataset structure; and the seventh one explains the MLP model structure.
# Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR
## 4 Application to Seismic Data
### Synthetic Data Example
The expansion effect of low-frequency information is better; that is, its anti-interference ability is stronger.
## Materials and Methods
### Performance Evaluation  
Different performance metrics such as accuracy, precision, recall, and area under the curve (AUC) having been determined for all the models constructed in the previous subsection, the efficacies of these models were evaluated based on these performance metrics. Finally, the best model for predicting depression was chosen, according to the outcomes of these performance metrics.  
The proposed model used WEKA and a confusion matrix to estimate classification algorithms: SVM, NB, RF, LR, and J48. For the model, there were three most common evaluation indexes: accuracy, precision, and recall. The calculation of these evaluation indexes is inseparable from the existence of a confusion matrix, which is often used in classification algorithms. Accuracy reflects the proportion of correct classification in the classification results of each category; that is, the accuracy of each category is judged by the model. The formula for this is provided in Equation (4). The ratio of accurately identified true positives to total positive samples is known as precision. The formula is provided in Equation (5). The recall rate reflects the sensitivity of the classification model to each category dataset. The recall percentages of correctly identified positive samples, total positive samples, and total false-negative samples are illustrated in Equation (6). The Area Under the Curve is a measure of a classifier's ability to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the model's performance at distinguishing between the positive and negative classes, as shown in Table 4.  
\[Accuracy=\frac{totalcorrectprediction}{totalprediction}, \tag{4}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{5}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{6}\]  
Our model employed classification techniques such as SVM, NB, RF, LR, and J48 for prediction. Figure 4 presents the proposed model structure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**AUC Value** & **Test Quality** \\ \hline
0.90–1.00 & Excellent \\ \hline
0.80–0.90 & Very good \\ \hline
0.70–0.80 & Good \\ \hline
0.60–0.70 & Satisfactory \\ \hline
0.50–0.60 & Unsatisfactory \\ \hline \hline \end{tabular}
\end{table}
Table 4: The expert scale for AUC values shows the quality of the test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technique** & **Parameter Setting** \\ \hline SVM & Batch size = 100, cache size = 40, kernel type = RBF, gamma = 0 \\ \hline NB & Batch size = 100, numDecimalPlaces = 2 \\ \hline LR & Batch size = 100, numDecimalPlaces = 4 \\ \hline RF & Batch size = 100, numDecimalPlaces = 2, numExecutionSlots = 1, seed = 1 \\ \hline J48 & Batch size = 100, confidence factor = 0.25, numDecimalPlaces = 2, seed = 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the data mining techniques.
# Illegal Logging Detection Based on Acoustic Surveillance of Forest
## 6 Conclusions  
In this article, a framework for automatic detection of logging activity in forests using audio recordings was presented. The framework used monitoring stations installed in the forest for audio recordings using microphones, and then acquired audio samples which were then processed and automatically classified into logging or not logging sounds. Five classification algorithms were tested, using well known and widely used audio descriptors during the feature extraction step, with the evaluation focusing on the chainsaw sound identification during logging in the forests. On the basis of the experimental study and the results, the best performance was reported by the support vector machine method. The experimental evaluation involved additive noise and the framework was evaluated using different values of sound-to-noise. The results demonstrated the robustness of the wood logging identifier in noisy environments, such as the sounds in real forests. Furthermore, postprocessing on decision level was also applied per audio frame providing an improvement in the performance of more than 1% and mainly in cases of low ratios of sound-to-noise. In addition, we evaluated a late-stage fusion method, combining the recognition results of the three top-performing classifiers, and the experimental results showed a further improvement of approximately 2%, in terms of absolute improvement, with logging sound recognition accuracy reaching 94.42% when the sound-to-noise ratio was 20 dB.  
We deem that the presented framework greatly contributes as an affordable solution in the development of systems for monitoring forests and for preserving the sustainability of the environment, to reduce illegal deforestation and protect biodiversity.  
Conceptualization, I.M. and V.K.; methodology, I.M.; software, I.M. and I.P.; validation, I.M. and M.P.; formal analysis, I.M.; investigation, I.M. and V.K.; resources, I.M. and M.P.; data curation, I.M. and V.K.; writing I.M., V.K., and I.P.; supervision, I.M. and M.P. All authors have read and agreed to the published version of the manuscript. The APC was funded by the Computer Technology Institute and Press \"Diophantus\" with project code 0822/001. This work was partially supported by the Project entitled Strengthening the Research Activities of the Directorate of the GSN and Network Technologies. The authors declare no conflict of interest.
# Tantalum Alloy Welding: Does the Thermal Cycle Influence the Microstructure?
## 2 Materials and Methods
### Microstructure  
The samples were cut on a horizontal lathe, guaranteeing abundant cooling (thus eliminating the burrs after cutting that could influence the quality of the metallographic preparation). Three samples, \(15\times 15\) mm long and \(1.5\) mm thick, were cut and encapsulated (see Figure 2). These were polished and prepared according to the recommendation of E3-11 (Guide for Preparation of Metallographic Specimens) [13] and Al\({}_{2}\)O\({}_{3}\) abrasive (1200 American National Standards Institute grit number), with rotation and etching with hydrofluoric acid (48%) for 15 s, as suggested by E407-07 Standard Practice for Microteaching Metals and Alloys [14].  
A standard sample of the tantalum alloy, extracted from the non-heat-affected zone, was studied. The area was chosen to avoid any influence from the welding thermal cycle. Figure 2 shows a micrograph example of the inner structure. It was possible to identify two-phase grain morphologies, characteristic of the tantalum alloy as supplied, with the presence of large-sized, columnar, and hexagonal grains, regularly distributed throughout the matrix area.  
The \(\beta\)-phase to \(\alpha\)-phase transformation of tantalum produces a unique microstructure with very unusual characteristics, especially for a crystalline material. The \(\beta\)-phase is also metastable and will transform to \(\alpha\) when heated, from 750 to 775 \({}^{\circ}\)C, as reported in [15].
## 3 Results
### Surface Characterization Results
#### 3.1.3 Contact Angle Analysis  
Contact angle analysis post-hydrothermal treatment showed that the specimens had significantly higher hydrophobicity than the sulfonated specimens pre-hydrothermal treatment (\(p\)-value \(\leq\) 0.0001). The average wetting angle pre-hydrothermal treatments was 63.35\({}^{\circ}\), and the average wetting angle post-hydrothermal treatment was 94.04\({}^{\circ}\), as seen in Figure 7.
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 6 The Test Cases
### Second Test Case  
The core concept of the second test case is regarding how we can use smart data distribution at the manufacturing experimentation facility, using an industrial fog node as CNC so as to achieve digital shop-floor reliable data distribution with scalable sensing performance. A fog node is a local server located in the industrial facility premises, which provides localized, cloud-like services to the factory environment. This is therefore an intermediate configuration between the fully centralized one relying on remote cloud services and the fully distributed one experimented in the first use case. With respect to the first use case, this solution will provide a reliable central point for control, at an additional cost in terms of \(\Pi\) infrastructure equipment. Our purpose is not to compare such solutions, but to showcase that smart data distribution schemes can work in a multitude of decentralized configurations. A typical scenario at the manufacturing experimentation facility involves the integrated fog node to control and communicate with various devices of the shop-floor. Reliable data exchanges between the various devices regarding their status (e.g., sensed temperature levels) at any given time should be possible, regardless the operational conditions of the shop-floor. In order to address this, we implemented the integration of distributed data management on a fog node (MFN 100) provided by TITech. More specifically, we employed the lightweight data distribution layer and implement it by using small, low-cost wireless sensor motes. The proof of this concept was demonstrated by adopting the data management layer introduced in [24] and by placing four motes in the network as shown in Figure 8: two acting as data producers and two acting as data proxies (distributed nodes caching the data) as instructed by the algorithm in [24]. Again, the motes used were IEEE 802.15.4 enabled. The existence of multiple data producers and proxies guarantees that even in the case of failures, the data can still be delivered at the fog node seamlessly. This test case is demonstrating that we can achieve high data redundancy and efficiency with a low-cost approach.
# Flow Dynamics of Sulfate-Modified Water/Polymer Flooding in Micromodels with Modified Wettability
## 1 Introduction
It provides a detailed microscopic visualization and macroscopic observations of the displacement taking place during modified water flooding at a pore-scale level, while evaluating the effect of fluid-fluid/rock-fluid interactions on oil recovery. On one hand, modified water will affect the microscopic sweep efficiency by triggering fluid-fluid and rock-fluid interactions. On the other hand, polymer flooding is expected to improve the macroscopic sweep efficiency, due to a favorable displacement mobility ratio. Hence, the hybrid process is expected to provide the combined benefits of both EOR methods. Modified/low-salinity water injection as a pre-flush is expected to change the reservoir wettability from oil-wet to water-wet and change the fluid distribution in the reservoir. Detached oil droplets are expected to move from small pores to large or medium pores. Polymer flooding after modified brine is expected to produce the redistributed oil phase easily, due to improved sweep efficiency. Low-concentration polymer solutions will be required for a combination with a pre-flush of modified water, which will decrease the cost of EOR projects. [PERSON] et al. [59] presented that mixing of surfactant solution in sulfate-modified water could lead to optimum oil recovery. This study is the extension of previous experiments performed in another stage of the research [10; 32]. In this work, microfluidics are used as an approach to justify/understand the results previously obtained while flooding modified water through sandstone cores (core flooding). In addition, micromodel observations are also expected to facilitate the description of the fluid flow dynamics taking place at the microscopic scale. The same fluids as previous publications are utilized, but experiments are performed at a room temperature of 22 \({}^{\circ}\)C. Finally, data obtained from the microfluidics flooding in this study are cross analyzed with the core floods data to draw underlying conclusions.
# Performance of Differential-Phase OFDM Systems over Selective Multipath Fading Channels
###### Abstract  
This paper investigates the performance of differential-phase Orthogonal Frequency Division Multiplexing (OFDM) over frequency-selective multipath Nakagami-\(m\) radio fading channels. A closed form for the average signal to noise/interference ratio in the presence of the selective multipath fading channel and additive white Gaussian noise (AWGN) is derived. The results reveal that the system performance is impacted by the interference between the adjacent OFDM frames in two successive signaling intervals which is called Inter-Symbol-Interference (ISI). In addition, the system will possibly be distorted when the orthogonality between the adjacent subcarriers is ceased, creating Inter-Channel-Interference (ICI). This paper also studies the bit-error-rate (BER) performance of the differential-phase OFDM system over the Nakagami-\(m\) channel in the presence of AWGN, ISI, and ICI under different conditions and parameters. Moreover, the effect of adding the guard period and the number of subchannels on the probability of error is analyzed. The IEEE 802.11a standard parameters with 64 subcarriers and a decaying exponential power delay profile with root-mean-square value of 129 ns are used in this study. The system performance is also simulated at different guard intervals, number of OFDM subcarriers, Nakagami severity parameter values, and different numbers of possible differential phases.  
O 201766603390/electronics603006
# Evaluating and Ranking SCPMS Enablers Using ISM and SWARA
## 6 Research Limitations and Future Research Directions  
This research considered 14 SCPMS enablers for evaluation and ranking using the ISM, MICMAC, and SWARA approaches. The ISM and MICMAC approaches attract increased computational effort with the increase in enablers. This process may become tedious and time-consuming when deriving the actual relationships and their respective levels in a diagraph. ISM and MICMAC are based on expert judgments; hence, the chances of judgmental bias being present in the decision-making process are high. To remove the judgmental bias, a fuzzy-based approach such as fuzzy MICMAC or Fuzzy SWARA can be used. Future research may also consider additional SCPMS enablers based on Logistics 4.0, Quality 4.0, Procurement 4.0, and SCM 4.0 toward I4.0. Future research may compare the various SCPMSs regarding their capability to deal with the SCM 4.0 requirements in the era of a cyber-physical system.