esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Materials Characterization  
The textural characteristics of the investigated samples were assessed by N\({}_{2}\) adsorption-desorption isotherms at -196 \({}^{\circ}\)C (Nova 2200e Quantachrome flow apparatus, Boynton Beach, FL, USA). The structural properties were determined by X-ray diffraction (XRD) in a Rigaku diffractometer (model RINT 2000, Tokyo, Japan). Morphological/surface analysis was performed by scanning electron microscopy (SEM, JEOL JSM-6390 LV, JEOL Ltd., Akishima, Tokyo, Japan) operating at 20 keV, equipped with an energy dispersive X-ray spectrometry (EDS) system and transmission electron microscopy (TEM) on a JEM-2100 instrument (JEOL, Tokyo, Japan). Redox properties were determined by means of H\({}_{2}\) temperature-programmed reduction (H\({}_{2}\)-TPR). More details about the aforementioned characterization studies and the corresponding apparatus can be found elsewhere [2; 35].
# Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
## 1 Introduction
For the purpose of achieving tradeoff between surveillance mission and connectivity maintenance, a novel multiple relay UAVs selection scheme is developed based on fuzzy evaluation and fuzzy optimization, and UAVs are appropriately allocated different roles over time while detection scope extending. With corresponding constraints, each UAV plans its optimal motion plan based on decentralized receding horizon control, and the elite mechanism [25] is integrated into particle swarm optimization (PSO) to calculate the optimal control command sequence. The main contributions of this paper are described as follows. Firstly, a multiple UAVs cooperative surveillance framework with ground station is constructed according to history detection information and surveillance payoff estimation. Secondly, four attributes are proposed to represent the differences among UAV alternatives in communication network containing ground station, and a multiple relay UAVs selection scheme based on fuzzy evaluation and fuzzy optimization is developed for dynamically adjusting communication links. Finally, decentralized receding horizon control is employed to optimize each UAV's control command sequence according to local information, which is solved by PSO with elite mechanism. The structure of this paper is organized as follows: Section 2 mainly introduces sensor model, and a multiple UAVs cooperative surveillance framework is constructed. In Section 3, relevant attributes representing the differences among UAV alternatives are discussed, and multiple relay UAVs selection scheme is implemented by fuzzy optimum selection. UAVs optimal motion plan based on decentralized receding horizon control is developed, and PSO with elite mechanism is employed to solve the optimal control sequence in Section 4. Subsequently, Section 5 provides some simulation results analysis, and some conclusions and future directions are described in Section 6.
## 3 Results and Discussion
Not only practical to society, the tsunami hazard map produced by three-dimensional modeling is also critical and influential for guiding the government and the Disaster Management Agency in their decision-making about evacuation structures. This is because it will allow them to assess the impact of a tsunami's magnitude on a structure. This modeling technique can also be used to assess tsunami risk reduction. Additionally, three-dimensional modeling enables the identification of risk factors, the assessment of the economic impact of disaster damage, and can assist with spatial planning and disaster management.
# Examining the Quasi-Steady Airflow Assumption in Irregular Vocal Fold Vibration
The third simulation type, quasi-steady simulation, computed the steady solution of the Navier-Stokes equations for each glottal wall shape without incorporating the glottal wall velocity. This simulation type considered neither unsteady flow nor glottal wall motion effects. Two different vibration frequencies, 100 and 500 Hz, were simulated to test the dependence of QSFA on frequency. The effect of air inertia in the supraglottic vocal tract was tested by implementing simulations with and without a vocal tract. To address the effect of glottal shapes on QSFA, two sets of asymmetric glottal shapes that incorporate different vertical and longitudinal wave patterns on the right and left vocal folds were used. The validity of QSFA was evaluated by calculating the differences in glottal flow rate and glottal wall pressure among the dynamic, pseudo-static, and quasi-steady simulations. Additionally, a momentum budget analysis was performed to determine the magnitude of unsteady acceleration in glottal airflow and its impact on the quantified flow and wall pressure. The influence of flow unsteadiness on sound generation and perception was also investigated in this study. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **VF Geometry** &
\begin{tabular}{c} **Frequency** \\ **Investigated** \\ \end{tabular} & **Vocal Tract** & **Approach** \\ \hline
[9] & Convergent & 10–120 Hz & No & Experimental \\ \hline
[11] & Uniform, circular, and divergent & 142 Hz & Yes &
\begin{tabular}{c} Theoretical and \\ experimental \\ \end{tabular} \\ \hline
[10] & Convergent, straight, and divergent & 70–120 Hz & Yes & Experimental \\ \hline
[19] & Straight, rounded, and Gaussian & 45–315 Hz & No &
\begin{tabular}{c} Theoretical and \\ experimental \\ \end{tabular} \\ \hline
[12] & Straight and rounded & 38–540 Hz & No &
\begin{tabular}{c} Theoretical and \\ experimental \\ \end{tabular} \\ \hline
[18] & Time-dependent & 28.1–112.5 Hz & Yes &
\begin{tabular}{c} Experimental and \\ numerical \\ \end{tabular} \\ \hline
[13] & Not applicable & 100 Hz & Yes & Theoretical \\ \hline
[14] & Half cylinder & 30–126 Hz & Yes &
\begin{tabular}{c} Theoretical and \\ experimental \\ \end{tabular} \\ \hline
[20] & Reproduced from the coronal section & 160 Hz & Yes & Experimental \\  & and stained slices of the VFs & 160 Hz & Yes &
\begin{tabular}{c} Theoretical and \\ experimental \\ \end{tabular} \\ \hline
[21,22] & Half cylinder & 52.5–97.5 Hz & Yes &
\begin{tabular}{c} Theoretical and \\ experimental \\ \end{tabular} \\ \hline
[23] & M5 & 112.5 Hz & No &
\begin{tabular}{c} Theoretical and \\ experimental \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the previous studies on QSFA.
# Simulation-Based Shading Loss Analysis of a Shingled String for High-Density Photovoltaic Modules
## 4 Conclusions  
In this paper, the shading characteristics of shingled PV modules and the shading loss of conventional PV modules were compared and analyzed. As a result of the measurement, the shingled PV module showed better characteristics than the conventional PV module in the horizontal shading pattern. The reason for this is that the shingled string has a higher voltage characteristic than a typical solar cell, so it is possible to manufacture a PV module with a parallel connection. A shingled PV module fabricated in parallel is less affected by current mismatch due to horizontal shading compared to a conventional PV module. Additionally, simulation was conducted to predict the exact loss of output of a shingled solar PV module affected by shading and to design a diode circuit. The divided cell was modeled using a double diode model; a five-cell shingled string was produced to assess the accuracy of the simulation, and then artificial shading was generated, and the output was measured. The difference between the measured and simulated output values was within 1%. In addition, by generating horizontal and vertical shading patterns in the shingled string, it was possible to confirm the output loss of the shingled string according to the shading pattern. These shading patterns were expanded to analyze the shading characteristics of shingled PV modules and conventional PV modules, and the simulation result of the shingled PV module was also accurate in that it differed from the actual measured value by less than 1%. Based on this study, designing a circuit considering the shading loss of a shingled PV module can compensate for the disadvantages of the shingled PV module.  
Conceptualization, [PERSON], [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript. This research was supported by the Korea Electric Power Corporation (Grant number: R17 XA05-1) and the Korean Institute of Energy Technology Evaluation and Planning (KETEP) by the Ministry of Trade, Industry, & Energy (MOTIE) of the Republic of Korea. (No. 20203030010200).  
Figure 16: Shading characteristic graph of shingled PV module (measurement and simulation value).  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.
## 3 Results
### CS Estimation for Daylight Light Sources
Compared to the training data, similar findings can be reported on the spectral ratios between the original measurements of daylight spectra used for validation and their reconstructed estimations illustrated in Figure 12c. A good agreement (\(\equiv\) spectral ratio close to unity) can again be confirmed in the wavelength range from 440 nm to 630 nm, while considerably larger deviations were obtained for shorter or longer wavelengths. As can be seen from Figure 12d, the maximal chromaticity difference \(\Delta u^{\prime}\
u^{\prime}_{\text{max}}\) was of the same order as observed for the training set, where again the greatest differences occurred in the early morning and late evening hours. However, in general, the resulting chromaticity differences for the validation data were well below the acceptance threshold defined by [PERSON], which again emphasizes the suitability of the proposed approach. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
Finally, Table 7 summarizes the different model predictions when applying the daylight reconstruction approach in comparison to [PERSON] et al. 's approximation method and the direct calculations performed on the set of measured validation data using [PERSON] et al. 's original CS definition. As can be seen, similar results were obtained as reported for the set of training data: despite showing again slightly larger chromaticity deviations, the daylight reconstruction approach still outperformed [PERSON] et al. 's approximation method in terms of CS prediction accuracy, which allowed for a proper estimation of the circadian effectiveness from color sensor responses even for \"unknown\" daylight conditions that were not included in the initial training database. To support reproducibility, the flowchart of Figure 13 eventually summarizes the proposed daylight reconstruction strategy and gives an overview of the required computational steps for a subsequent CS estimation. Figure 12: Validation set of measured daylight spectra and visualization of the CIE model performance. (**a**) Daylight spectra as measured in Darmstadt, Hessen, Germany (GPS coordinates 49°51′20.772′′ N, 8°39′12.528′′ E) on 23 September 2020 from sunrise (7:14 a.m.) to sunset (7:21 p.m.). (**b**) Reconstructed daylight spectra from color sensor readouts by applying the CIE daylight model. (**c**) Spectral ratios between the original measurements and the reconstructed spectra. (**d**) Color differences \(\Delta u^{\prime}\
u^{\prime}\) between measured and reconstructed spectra. Figure 13: CS estimation from \(RGB\) sensor responses for daylight light sources. A daylight-specific 3 \(\times\) 3 transformation matrix is used to map \(RGB\) sensor responses to \(XYZ\) tristimulus values. [PERSON]’s CCT approximation (Equation (16)) gives a starting point CCT\({}_{0}\) for [PERSON] et al.’s CCT estimation method. The resulting CCT is used as the input for the CIE daylight model (Equations (9)–(14)) to estimate the daylight spectrum. Finally, [PERSON] et al.’s original CS computation model is applied to the estimated daylight spectrum.
# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
## 3 Validating TL for Bridge Damage Detection Using Simulated Experiments
### POMs  
Strains from each model scenario in Table 1 were stored in a snapshot matrix and used to extract POMs. Healthy POMs were calculated from the snapshot matrix for each simulation using SVD, and the first POM was then extracted. The first POMs for \(M_{0}\) were trained for damage identification using ANN [14]. In this study, simulated experiments \(M_{1}\) to \(M_{5}\) from Table 1 were trained for damage scenarios using \(\Delta\varphi\) as feature, and the estimated first POMs were calculated at each location shown in Figure 1 using the developed framework from [PERSON] et al. [22]. Figure 2 plots the average of the first POMs for the different models, each without imposed damage. These plots indicate that the presence of MU at different levels in the FE model affect the first POM variations and necessitate the use of a TL approach for transfer knowledge across different simulated experiments. These POMs were used as features in the TL algorithm, as discussed in the following section, using the JDA-kernel method. The JDA-kernel's ability to map trained knowledge from \(M_{0}\) to other targeted models was then evaluated.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Assignment**} & **Normalized Rotational** \\  & & **Spring Coefficient** \\ \hline \(M_{0}\) & Uniform increase +80\% in \(M\) & 1.80 \\ \(M_{1}\) & Uniform increase +80\% in \(M\), & 1.80 \\  & estimated \(\varphi\) with \(\Delta\varphi\) & 1.80 \\ \(M_{2}\) & Uniform decrease \(-50\%\) in \(M\) & 0.5 \\ \(M_{3}\) & Random, \(\pm 50\%\) in \(M\) & Between 0.53 and 1.45 \\ \(M_{4}\) & Random, \(\pm 25\%\) in \(M\) & Between 0.76 and 1.25 \\ \(M_{5}\) & Random, \(\pm 100\%\) in \(M\) & Between 0.23 and 1.92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulated experiments.
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 4 Emerging Technologies in Human Mobility
### The Impact of Human Mobility and IoT on Mobile Networks  
It is undeniable that the IoT and mobile networks in human mobility research is an emerging research area that encouraging numerous exciting solutions to many problems encountered in numerous domains. However, we should be aware of the several human mobility and IoT challenges and solutions with emerging technologies, devices and applications.  
First, the steady increase in mobile device usage means greater demand for bandwidth on existing networks. The challenge here is to design mobile-ready networks that ensure any device in the indoor and outdoor environments has adequate network resources even when connected wirelessly. With hackers becoming even more savvy and the number of vulnerable endpoints growing, device management and securing those endpoints has become a very real challenge. Establishing a security policy that accounts for all devices on a network for every user can be complicated and difficult to enforce. For human mobility research preparing their networks for the future, mobile security should be at the top of the priority list.  
Then, IoT has already proven itself a disruptive advancement in technology, transforming the ways people live and work by connecting almost anything from microwaves to doorbells to the internet, gathering data for surveys and making user's lives easier. The problem for human mobility researchers, however, is that the end user's IoT services are created with little regard for enterprise networks, especially when it comes to cyber security. Additionally, to capitalize on the benefits of IoT while maintaining secure and efficient operations, the infrastructures networks should be updated.  
Naturally, network performance becomes a main concern in light of all this. Although, typically, data are collected from endpoints and sent to the home server or data center for processing, in IoT this does not work when dealing with vast amounts of data. The volume of human mobility data can be overwhelming and put a great deal of pressure on an network infrastructure. Therefore, the mobile network should be rethought to its foundation in order to improve performance and security while supporting disruptive technologies like IoT.
## 4 DT Use Cases and Application in Healthcare
### Precision Public Health  
Public health primarily focuses on interpersonal interactions, the chronic medical conditions of community members, and determinants of health at a population level. This is due to its overarching goal of promoting community wellness while also monitoring, controlling, and mitigating disease outbreaks and epidemics. Digital twins offer a vital complementary tool and open up significant research avenues in the realm of precision public health, a need highlighted by the experiences of the COVID-19 pandemic. One essential research opportunity lies in creating a virtual framework for monitoring and handling disease outbreaks, thereby equipping the public with enhanced preparedness and response capabilities [47].  
A recent study introduced such a system, conceived as an integral part of a smart city, drawing lessons from China's handling of the COVID-19 pandemic [64]. Their multifaceted model incorporates a patient database sourced from diverse inputs, cloud computing infrastructures, and artificial-intelligence-based location technologies. These elements collaborate to enable an effective response to outbreaks. The system offers traceability of diseases through analyzing proximity data in the patient database while also swiftly pinpointing individuals who have been in close contact with affected persons. Additionally, the system utilizes geolocation data to assess and categorize the risk levels across different regions. High-risk zones for disease transmission are identified and mapped, facilitating real-time alerts for visitors to these areas [64].
## 5 EMD-Resilient Reed-Solomon Codes  
Based upon the observations in Section 4, it can be concluded that the main vulnerability of RS Codes to CTP and CFN is due to the occurrence of one-symbol-value code words. Mitigating these specific type of vulnerability would significantly improve the EM-resiliency of RS Codes. Although one-symbol-value CTP and CFN cannot be detected by RS Codes, which have potentially detrimental impacts on safety critical applications, it is possible to detect this specific category at a lower layer [21]. In what follows, an effective layer was proposed to address this specific vulnerability.
# Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification
## 3 Results
### Normalized Data with Z-Score  
The last data normalization approach that was evaluated was the Z-Score normalizer. Table 7 presents the confusion matrix related to the experiments performed with the accelerometer sensor included in the dataset used. It was verified that the most correctly identified activities are walking upstairs and walking.  
Next, the classification results of the data acquired from the accelerometer sensor after the application of the Z-Score normalizer were analyzed, verifying that the DNN method implemented reported an accuracy of 52.71%, a precision of 59.04%, a recall value of 48.07%, and an F1-Score of 52.99%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Class**} \\ \cline{3-6}  & & **Walking** & **Walking** & **Standing** & **Walking** \\ \cline{2-6}  & **walking upstairs** & 123 & 12 & 1 & 8 \\ \cline{2-6}
**Actual Class** & **walking downstairs** & 97 & 40 & 0 & 7 \\ \cline{2-6}  & **standing** & 15 & 1 & 5 & 7 \\ \cline{2-6}  & **walking** & 15 & 1 & 5 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for normalized gyroscope data with RobustScaler.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Class**} \\ \cline{3-6}  & & **Walking** & **Walking** & **Standing** & **Walking** \\ \hline \multirow{4}{*}{**Actual Class**} & **walking upstairs** & 115 & 76 & 9 & 1 \\ \cline{2-6}  & **walking downstairs** & 104 & 77 & 20 & 1 \\ \cline{2-6}  & **standing** & 4 & 0 & 28 & 6 \\ \cline{2-6}  & **walking** & 0 & 0 & 20 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix for normalized accelerometer data with RobustScaler.  
In continuation, the Z-Score normalizer was used to normalize the data acquired from the gyroscope data related to the different human activities. Table 8 presents the confusion matrix related to the experiments performed with the gyroscope sensor included in the dataset used. It was also verified that the most correctly identified activities are walking upstairs, stalking, and walking.  
Besides, the classification results of the data acquired from the gyroscope sensor after the application of the Z-Score normalizer were analyzed, verifying that the DNN method implemented reported an accuracy of 56.81%, a precision of 63.63%, a recall value of 53.08%, and an F1-Score of 57.88%.
## 3 Integrated Adaptive Security Framework for IoT (IASF-IoT)
### Privacy-Preserving Data Processing  
To ensure data privacy while allowing for meaningful analysis, a differential privacy mechanism is implemented. Although other privacy models that require explicit user consent or permission could have been chosen, the automated nature of IoT systems and the potential for AI-driven evolution in privacy management make differential privacy a more suitable approach. This method provides strong privacy guarantees without relying on user input, which is particularly beneficial in large-scale IoT. The privacy budget \(\epsilon\) is dynamically adjusted based on the sensitivity of the data and the current threat level, using a threshold-triggered rule-based function. In low-risk conditions, \(\epsilon\) is kept small to prioritize privacy. When a threat is detected, \(\epsilon\) is increased slightly to improve utility while still preserving differential guarantees. This balancing strategy avoids overexposure while supporting adaptive threat response.  
\[\epsilon=f(\text{data\_sensitivity},\text{threat\_level}), \tag{6}\]  
The noisy version of a query function \(f\) is then computed as  
\[f^{\prime}(x)=f(x)+\text{Lap}(\Delta f/\epsilon), \tag{7}\]  
where \(\text{Lap}(\Delta f/\epsilon)\) is Laplace noise with scale \(\Delta f/\epsilon\). This approach allows for automated, context-aware privacy protection that can adapt to changing conditions without requiring constant user intervention.