esa-sceva/satcom-chunk-collection huggingface dataset:
# Passivity-Based Control Design for Magnetic Levitation System
## 5 Discussion  
From the signal flow diagram and the above data, it can be seen that the roles of the interconnection and damping assignment is:  
(1) \(\alpha\) exists in the feedback gains of \(z\) and \(\dot{z}\), indicating that \(\alpha\) is configured in \(J_{d}(x)\) to provide channels for \(z\) and \(\dot{z}\). With these two channels, the entire system is likely to become a passive system.  
(2) The feedback gains of \(\lambda\), \(z\) and \(\dot{z}\) are all related to \(c_{1}\), so the value of \(c_{1}\) directly determines the response speed of the system. This can be seen Equation (40), \(c_{1}\) contains the dissipation coefficients \(R_{0}\) and \(R_{\alpha}\), especially \(R_{\alpha}\) is added to the dissipative matrix \(R_{d}(x)\). The role of the dissipative matrix is to accelerate the energy dissipation, so that make it quickly converge to the equilibrium point.  
(3) It can be seen from Equation (38) that the relative relationship between the variables in the quadratic term is consistent with the relative relationship between the feedback gains of \(\lambda\), \(z\), \(\dot{z}\) in Figure 3, indicating that the gain of the feedback loop is closely related to the composition of the Lyapunov function \(H_{d}(x)\).  
The performance of the IDA-PBC system can be seen from this example that the nonlinear term in \(H_{a}(x)\) [Equation (36)] is mainly used to cancel a part of the nonlinear characteristics of the object, and the other function \(\Phi\) is a quadratic form. Therefore, \(u=\beta(x)\) obtained by using Equation (39) and the increments are linear. Therefore, although it is a nonlinear object, due to the linear feedback, the nonlinear influence of the forward link is weakened by the feedback, so that the whole system exhibits approximately linear characteristics. Figure 5 is a graph showing the sensitivity characteristics of the output of the system in this example. This set of curves is obtained by simulation of the actual nonlinear model. It is based on the curve of the ratio of the sinusoidal output to the sinusoidal input at different frequencies. The amplitude of the signal during the test was 0.1 mm, 0.5 mm, 0.8 mm, and 1 mm. As the amplitude of the signal increases, the sensitivity \(\left|S(j\omega)\right|\) peak shifts slightly to the left. This is because the squared term causes the gains in the positive and negative directions to be different, resulting in a decrease in the equivalent gain. In general, however, the sensitivity characteristics at different amplitudes does not change significantly. The time domain response of the system is shown in Figure 6.  
Figure 5: Sensitivity plots of the real nonlinear system.
## 3 Results and Analysis
### Experimental Results on the Decline Rate of Network Efficiency  
Figure 4 reflects the changes in the decline rate of network efficiency \(\mu\) when nodes are removed in descending orders according to the importance ranking by different algorithms. The larger the value of \(\mu\), the more significant the decrease in network efficiency, and the more accurate the node importance identification algorithms. It can be observed that, in the static attack mode, the value of \(\mu\) under the NBF attack strategy is larger than other strategies, which shows that the NBF algorithm is more accurate than other algorithms. Similar to the results on the network maximum connectivity coefficient, the NBF algorithm has more obvious advantages when the proportion of node removal is small, so the proposed algorithm has the best application value.  
Furthermore, we investigate the decline rate of network efficiency \(\mu\) subjects with different dynamic attack strategies, and the result is shown in Figure 5. The result is consistent with that of static attack strategies. It can be seen from the figure that the NBF algorithm designed in this paper has the highest impact on network fragmentation compared with the other five algorithms. The effectiveness of the proposed algorithm is further verified by different evaluation criteria.  
In summary, the comparison experiments on the maximum connectivity coefficient and the decline rate of network efficiency show that the proposed algorithm performs better than degree centrality, k-shell algorithm, WL algorithm, LLS algorithm and ego betweenness centrality in static  
Figure 4: The decline rate of network efficiency (\(\mu\)) subjects with different static attack strategies. The decline rate of network efficiency (\(\mu\)) on the \(y\)-axis against the rate of removed node (\(p\)) on the \(x\)-axis. (**a**) the USAir network; (**b**) the Netscience network; (**c**) the Infectious network; (**d**) the USAirport network; (**e**) the Yeast network; (**f**) the Power network; (**g**) the synthetic network WS.  
attack strategies and in dynamic attack strategies. The advantage of the proposed algorithm is more obvious in the initial stage of attack. In addition, the dynamic attack effect is better than the static attack for the same importance ranking algorithm.
# SnoreLab Application in the Assessment of Obstructive Sleep Apnea Syndrome: A Pilot Study
## 2 Materials and Methods
### Patients' Selection and Study Design  
Male and female patients between 18 and 75 years of age, seeking medical counseling for suspected OSAS, were considered potentially eligible to participate in this study.  
All patients received instructions regarding the study procedures and the functioning of SnoreLab (Premium version). Detailed medical histories, including information on comorbidities such as diabetes mellitus, dyslipidemia, high blood pressure, and obesity, as well as age and timing of OSAS diagnosis, were obtained. Each patient underwent a full-night PSG examination, with simultaneous recordings using SnoreLab. For the recordings, an iPhone SE was used in all patients. To minimize bias, the recording smartphone was positioned within one meter of the patient's head, connected to a power source, and set to flight mode. To avoid background noise, no other patients were present in the room during completion of the exam. Exclusion criteria encompassed severe psychological or neurological disorders, substance abuse, and an inability to follow instructions or maintain focus and concentration.
# Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
## 4 Experimental Results and Analysis
### Quantitative Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Mean** & **Median** & **Trimean** & **Best 25\%** & **Worst 25\%** \\ \hline \multicolumn{6}{c}{**Statistics-based methods**} \\ White patch [6] & 7.55 & 5.68 & 6.35 & 1.42 & 16.12 \\ Gray world [4] & 6.36 & 6.28 & 6.28 & 2.33 & 10.58 \\
1 st-order gray edge [8] & 5.33 & 4.52 & 4.73 & 1.86 & 10.03 \\
2 st-order gray edge [8] & 5.13 & 4.44 & 4.62 & 2.11 & 9.26 \\ Shades of gray [7] & 4.93 & 4.01 & 4.23 & 1.14 & 10.20 \\ General gray world [8] & 4.66 & 3.48 & 3.81 & 1.00 & 10.09 \\ Bright-and-dark color PCA [35] & 3.52 & 2.14 & 2.47 & 0.50 & 8.47 \\ Local surface reflectance [26] & 3.31 & 2.80 & 2.87 & 1.14 & 6.39 \\ Proposed method & 2.96 & 2.00 & 2.14 & 0.57 & 7.12 \\ CCATI [40] & **2.34** & **1.60** & **1.91** & **0.49** & **5.28** \\ \multicolumn{6}{c}{**Learning-based methods**} \\ Edge-based Gamut [45] & 6.52 & 5.04 & 5.43 & 1.90 & 13.58 \\ Bayesian [46] & 4.82 & 3.46 & 3.88 & 1.26 & 10.46 \\ CART-based combination [10] & 3.90 & 2.91 & 3.21 & 1.02 & 8.27 \\ ExemplarCC [41] & 2.89 & 2.27 & 2.42 & 0.82 & 5.97 \\ CNN-based method [42] & 2.75 & 1.99 & 2.14 & 0.74 & 6.05 \\ Simple feature regression [35] & 2.42 & 1.65 & 1.75 & 0.38 & 5.87 \\ DS-Net [47] & 2.24 & 1.46 & 1.68 & 0.48 & 6.08 \\ Squeeze-FC [12] & 2.23 & 1.57 & 1.72 & 0.47 & 5.51 \\ AlexNet-FC [12] & 2.12 & 1.53 & 1.64 & 0.48 & 4.78 \\ [PERSON] et al. [43] & **2.09** & **1.42** & **1.60** & **0.35** & **4.65** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative statistical metrics between the proposed method and conventional methods with the Gehler-Shi dataset (the lower, the better). Most results of previous methods are directly from [11; 19; 43; 44]. so, our method has a significant advantage in the fact that fuzzy models ease of encoding into imaging signal processors. We also implemented some cross dataset experiments to investigate the performances of the proposed method: training with Gehler-Shi and Cube+ datasets combined into one large set, and (1) testing on the Gehler-Shi dataset, or (2) testing on the Cube+ dataset; (3) training on the Gehler-Shi dataset, and testing on the Cube+ dataset; (4) training on the Cube+ dataset, and testing on the Gehler-Shi dataset. For experiment (1), we obtained better results compared with ours in Table 2; and for experiment (2), similarly better results compared with ours in Table 3. It is as expected; since the models in both experiments were trained with more data and then can effectively cover a wider range of features. But experiment (3) obtained slightly unsatisfied results; in contrast, experiment (4) just achieved acceptable performances. What makes this significant could be that, compared with the Gehler-Shi dataset, the Cube+ dataset has more diverse image scenes and more extensive distribution of illuminations.
# A Design for SDN-Based Identifier-Locator Separation Architecture on IoT Networks
## 3 Proposed SDN-Based Identifier-Locator Separation Architecture for IoT Networks
### EID and LOC  
We use two namespaces for the roles of identifier and locator: EID and LOC, respectively. The EID is an unrouteable IPv6 address in an IP-based network, and it is generated by using the EID generating mechanism based on the work of [5; 23; 24; 25]. According to [5; 23], the EID is generated with the public key of an asymmetric key pair of the node. The EID, which is used in protocols to represent the sensor node's identity, is a hashed encoding value of the identifier of a sensor node. In the proposed scheme, an MN's network access identifier (NAI) is used for generating an EID instead of using the public key of an MN. The LOC is a routable IPv6 address that is used as a general IP address in IP-based networks and is generated by using the current network prefix with an MN's media access control (MAC) address based on the address autoconfiguration mechanism to indicates the current location of the MN to which it is attached.
## 3 Experiments and Analysis
### Experimental Settings
#### 3.1.3 Implementation Details  
We trained and tested RCCD and mainstream methods on LEVIR-CD+, S2 Looking, and VS subsets using a single 4090 GPU. Dataset images were cropped to \(256\times 256\) pixel size, maintaining the original training and test set divisions. Models were selected from a valid set.The RCCD backbone was initialized with pre-trained parameters. Model optimization employed the AdamW [42] optimizer with a learning rate of \(5\times 10^{-5}\), a batch size of 32. Data augmentation included random photometric and geometric distortions. Our RCCD was implemented in PyTorch 2.1, while others used their official implementations.
# An Organizational Perspective on Robotic Process Automation Adoption and Usage Factors
## 4 Discussion
### Technological, Organizational, and Environmental Contexts  
In the technological context, Compatibility emerges as the most influential factor affecting the adoption and use of RPA. The higher the degree of compatibility with existing technologies in the organization, the greater the inclination for the adoption and use of RPA. Relative Advantage, although showing lower significance in both the pilot test and the complete model, does exhibit signs of statistical significance in the model.  
Low Complexity does not prove to be a significant antecedent of the propensity for the adoption and use of RPA in this study. However, organizations have resources to deal with complexity and align processes if they conclude that the innovation will bring sufficient advantages.  
Based on the obtained results, it is suggested that there is a necessity to further examine the DOI theory within an organizational context, where some factors may be less relevant.  
In the organizational context, the obtained results suggest that neither Technology Competence nor low Managerial Obstacles emerge as significant antecedents for the adoption, or lack of adoption, of RPA.  
The TOE framework allows for the expansion of factors commonly used in traditional technology adoption models as it includes environmental factors. In this particular study, Environmental Pressure was not shown to be statistically significant.  
Despite expectations that managerial obstacles and environmental pressure would influence RPA adoption, the data did not confirm this relationship. These results may be related to the fact that adoption is based on technical feasibility (ease of implementation and compatibility) rather than external pressure. The main drivers of adoption are financial and operational factors (cost reduction and increased productivity). Other potential explanations include organizational resistance to change or a lack of awareness or knowledge about RPA in organizations.  
Considering the results obtained, we can validate Hypothesis 7, meaning that the service industry shows a greater propensity for the adoption and use of RPA.  
This result can be explained by the fact that, in the manufacturing industry, processes are more physical and operational, typically requiring more complex industrial automation, which makes implementation more challenging. Unlike the manufacturing industry, where automation often demands higher investments in hardware, infrastructure, and system integration, service automation primarily focuses on digital processes, making it easier to implement. Additionally, in manufacturing, process automation requires reconfiguring production line machinery, which can further complicate the adoption of RPA.
# Common Data Model and Database System Development for the Korea Biobank Network
## 6 Discussion and Conclusions  
In the KBN, the active utilization of data is limited because the data collection method, data format, and collection items are different for each bank. The KBN CDM was built by modeling the database to enable standardized data collection, and an integrated management system was developed to utilize the collected data.  
Each of the 17 participating hospitals provide databases for different types and numbers of diseases according to the different kinds of data they collect. Conventional biobanks exist on their own. That is, each institution with biobanks has its own data format and individual management system. Naturally, this causes difficulties in the collaborative analysis of biobanks. This research hence proposed KBN: a unique biobank that integrates all the participating institutions with biobanks into one system, enabling integrated analysis, research diversity, and easily converged research.  
An important point is that the capacity of the KBN is not restricted to 27 types of disease. If any hospital or institution is willing to participate by adding data for a new type of disease, a new table can be constructed using the system configuration and will be compatible with the rest of the system. Because of this functionality, the KBN CDM can be expanded without limitations.  
One limitation of this research is that it is integrated at a domestic level and not internationally. In addition, in the current system, each biobank manages the KBN_ID that identifies the patient, so if a patient is admitted to another hospital, the data are treated as the data of two patients, causing redundancy errors.  
Therefore, research to improve work efficiency and data quality by developing an extract, transform, and load process that can automatically extract data from EMRs in KBN CDM format is needed. Future research should also include integration and should link with internationally integrated biobanks such as MIABIS, which is currently feasible because KBN follows international code and mapping standards [23, 24, 25].  
Conceptualization, S.-J.K. and I.Y.C.; methodology, S.-J.K., W.C. and K.-H.K.; software, S.-J.K., W.C. and K.-H.K.; validation, S.-J.K., W.C. and K.-H.K.; formal analysis, S.-J.K.; investigation, H.M.; resources, H.M.; data curation, S.-J.K. and H.M.; writing--original draft preparation, S.-J.L., S.-J.K. and W.C.; writing--review and editing, H.M., S.-J.L., S.-W.O. and I.Y.C.; visualization, S.-J.K., supervision, I.Y.C.; project administration, H.M. and I.Y.C.; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript.  
This study was conducted with data from Korean Biobank Network. The study was supported and funded by the Korea Disease Control and Prevention Agency (#4845-303).  
Not applicable.  
Data Availability StatementData sharing was not applicable to this study. Data supporting the findings of this study are available from the National Biobank of Korea.  
The authors declare no conflict of interest.
# Light Harvesting and Optical-Electronic Properties of Two Quercitin and Rutin Natural Dyes
## Results and Discussion  
### Geometric Structures  
The ground state structures of Q and R were optimized in both phases of gas and ethanol. Figure 1 presents the chemical structures of Q and R. The standard molecular name of Q is named after 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-3-(3,4,5-trihydroxy)-6-methyl-tetrahydro-2H-pyran-2-yloxy)-4H-chromen-4-one, and R is 2-(3,4-bis(2-hydroxy)phenyl)-5-hydroxy-7-2 (2-hydroxyethoxy)-3-((2S,3R.4S,5S,6R)-3,4,5-trihydroxy-6-((2R,3R,4R,5R,6S)-3,4,5-trihydroxy-6 methyl-tetrahydro-2H-pyran-2-yloxy)methyl)-tetrahydro-2H-pyran-2-yloxy-4H-chromen-4-one. Q and R with the same core groups of 2-(3,4-dihydroxyoxyphenyl)-3,5,7-trihydroxy-4H-chromen-4-one, as presented in the black region in Figure 1.  
For comparing the structures of R and Q in gas and solvent, Table S1 showed the main dihedrals and bond lengths of the two molecules. The bond lengths of R have no obvious change in ethanol when compared with gas. For example, the major bond lengths of C2-C3, C4-C5, and O5-C6 only changed 0.001, 0.003, and 0 A between the two phases. The main bond lengths of Q have decreased in ethanol as compared to that with in a vacuum. For instance, C4-O5 has a shorter bond length of 1.373 A in ethanol when compared with the bond length of 1.550 A in a vacuum. The stability of the molecule can be determined by the bond length. Generally, a shorter bond length means better molecular stability. It demonstrates that the ethanol solvent has an active influence on improving the molecular stability of Q. Main dihedrals (\(\angle\) C1-C2-C3-C4, \(\angle\) C3-C4-O5-C6, and \(\angle\) C4-O5-C6-O7) of R have no obvious change in ethanol when compared with that in gas, and it can be inferred that the ethanol solvent has very little influence on the structure of R. The main dihedrals (\(\angle\) C1-C2-C3-C4, \(\angle\) C3-C4-O5-C6, and \(\angle\) C4-O5-C6-C7) have obviously changed in the phase of ethanol when compared with that in a vacuum, and ethanol has big influence on the structure of Q.  
Under the condition of ethanol, the main bond lengths and dihedral angles of S-Q and S-R have been analyzed, showing that the corresponding bond lengths of S-Q and S-R are nearly at the same level. According to the bond length to judge the molecular stability [41], it can be inferred that the stability of S-Q and S-R are nearly at the same level. S-Q has a relative planar structure as compared to that of an S-R from the calculated dihedrals (Table S1) and optimized geometry structure (Figure S2), which illustrates that S-Q should be more conducive to electrons transfer than S-R.
# Diversity Maintenance for Efficient Robot Path Planning
## 2 Materials and Methods
### Diversity Maintenance
#### 2.3.1 Fitness Sharing  
This method is based on the idea that \"sharing\" of individual fitness values before the selection controls the number of individuals in each niche. In this method, every possible pairing of individuals \(i\) and \(j\) is considered, and then a distance was calculated between them. The fitness \(F\) of each individual \(i\) is then adapted according to the number of individuals falling within some pre-specified distance \(\sigma_{share}\) using a power-law distribution:  
\[F^{\prime}(i)=\frac{F(i)}{\sum_{j}sh(d(i,j))} \tag{4}\]  
\[sh(d)=\begin{cases}1-\left(\frac{d}{\sigma_{share}}\right)^{\gamma},\text{if }d \leq\sigma_{share},\\ 0,\text{otherwise}.\end{cases} \tag{5}\]  
The sharing function \(sh(d)\) is a function of the distance. Constant value \(\gamma\) determines the shape of the sharing function and it was taken as \(\gamma=1\) to make the function linear in this example. The last parameter was share radius \(\sigma_{share}\), which defines how many niches can be maintained. Common valuesare in the range of 5 to 10 [16], in this algorithm value of 10 was taken to promote more niches and consequently larger diversity.  
\[d_{ij}=\sum_{k=1}^{k=n}\sqrt{\left(a_{1}-b_{1}\right)^{2}+\ldots+\left(a_{n}-b_{ n}\right)^{2}} \tag{6}\]  
It is preferred to use phenotypic distance, but also genotypic distance can be used as a measure of diversity e.g., Hamming distance for binary presentations. Distance \(d\) is defined here as Euclidean distance, see Equation (6), calculated from one individual to every other member of the population, including with itself. With the use of fitness sharing, members with a lower fitness value have increased probability of becoming parents when compared with the basic algorithm. This way we have a larger gene pool which directly increases the diversity of the population.
###### Abstract  
Babies express their needs, such as hunger, discomfort, or sleeplessness, by crying. However, understanding these cries correctly can be challenging for parents. This can delay the baby's needs, increase parents' stress levels, and negatively affect the baby's development. In this paper, an integrated system for the classification of baby sounds is proposed. The proposed method includes data augmentation, feature extraction, hyper-parameter tuning, and model training steps. In the first step, various data augmentation techniques were applied to increase the training data's diversity and strengthen the model's generalization capacity. The MFCC (Mel-Frequency Cepstral Coefficients) method was used in the second step to extract meaningful and distinctive features from the sound data. MFCC represents sound signals based on the frequencies the human ear perceives and provides a strong basis for classification. The obtained features were classified with an artificial neural network (ANN) model with optimized hyperparameters. The hyperparameter optimization of the model was performed using the grid search algorithm, and the most appropriate parameters were determined. The training, validation, and test data sets were separated at 75%, 10%, and 15% ratios, respectively. The model's performance was tested on mixed sounds. The test results were analyzed, and the proposed method showed the highest performance, with a 90% accuracy rate. In the comparison study with an artificial neural network (ANN) on the Donate a Cry data set, the F1 score was reported as 46.99% and the test accuracy as 85.93%. In this paper, additional techniques such as data augmentation, hyperparameter tuning, and MFCC feature extraction allowed the model accuracy to reach 90%. The proposed method offers an effective solution for classifying baby sounds and brings a new approach to this field.  
baby cry classification; mel-frequency cepstral coefficients; data augmentation grid search optimization; artificial neural network +
Footnote †: journal: Applied Sciences