esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Frequency Analysis of Daily Activities for Fall Detection
## 5 Conclusions  
Many existing fall detection systems and algorithms are based upon accelerometry measures and have been investigated in previous studies. However, many of these strategies are unable to accurately differentiate between rigorous activities and falls as critical peak acceleration threshold values overlap. This work expands upon prior efforts by us and others to increase sensitivity and specificity of fall detection algorithms by proposing a hierarchical approach utilizing both time and frequency analyses of accelerometer data.  
This research effort demonstrates that the frequency domain may provide additional key features useful for improving reliability and accuracy of fall detection strategies. This work characterizes critical time-domain and frequency-domain parameters and uses these parameters to develop a reliable fall detection algorithm. Validation of this algorithm demonstrates the improved sensitivity and specificity of fall detection over prior work by others using time-domain _accelerometer-only_ strategies as well as multisensor approaches to fall detection. The inclusion of frequency-based information can reduce false positives from more rigorous activities.  
Future development will evaluate the proposed time-frequency fall detection algorithm on existing fall databases such as those identified by [PERSON] and colleagues [34, 35]. Additional efforts will investigate a system to include GPS and Internet of Things (IoT) strategies for alerting and communicating with external caregivers, both formal and informal. Additionally, effective fall detection may serve as a precursor to implementation of a fall injury mitigation system that can activate safety devices that may minimize injury to fallers.  
[PERSON] and B.Q.T. contributed to project conceptualization, data collection, algorithms development, data analysis and writing of this manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of The Catholic University of America (protocol #160938; approved 03/27/17).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to author's continuing analysis of the collected data.  
The authors declare no conflict of interest.
# EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure
## 2 Theoretical Background
### Ensemble Empirical Mode Decomposition (EEMD)  
The EEMD method is known as a noise-assisted method, introduced by [PERSON] and [PERSON] [60], for decomposing noisy time signals into their fundamental components. It is based on the following steps:  
1. Generate new time signals mixing the original time signal \(x(t)\) and different white-noise series \(n_{i}(t)\) as follows: \[x_{i}(t)=x(t)+n_{i}(t)\text{ for }i=1,2,\ldots,m\] (5) where \(m\) represents the number of trials.
2. Decompose the new time signals generated in (1) using the EMD method described in Section 2.1.
3. Compute a true IMF, indexed with \(j\), as follows \[EEMD_{cj}(t)=\frac{1}{m}\sum_{i=1}^{m}c_{ij}(t)\] (6) where the IMF indexed with \(j\) for the trial \(i\) is represented by \(c_{ij}(t)\). It is recommended that the number of trials be as large as possible in order to obtain a suitable result.
# Fourier Transform (FT) Analysis of the Stress as a Tool to Follow the Fatigue Behavior of Metals
## 6 Strain-Life Curves  
In Figure 4 the strain-life curves of the materials investigated are presented. For the three materials, strain amplitude and fatigue lifetime follow a power-law correlation with exponents around \(-0.27\) via:  
\[\varepsilon_{0}=A_{strain-life}\ N_{f}^{-0.27} \tag{12}\]  
But a high scattering of the data points around the best fit is observed; i.e., the reproducibility under the same measurement conditions is low and may vary under the same measurement conditions by up to a factor of four. So, following the linear and nonlinear mechanical parameters as a function of the cycle number should give better prediction of the fatigue lifetime and better insights into the fatigue process.  
Figure 3: Strain sweep tests for: (**a**) CRS, (**b**) AL and (**c**) brass. The plots report on the complex modulus \(\mid E^{*}\mid\) and the nonlinear parameters (\(I_{2/1}\) and \(I_{3/1}\)) as a function of the applied strain amplitude. While there is no trend for \(I_{2/1}\) (only noise), the \(I_{3/1}\) intensity scales quadratically with \(\varepsilon_{0}\) above \(\varepsilon_{0}=0.7\%\).
# Luminescence Materials for Dye-Sensitized Solar Cells: Advances and Directions
## 4 Luminescent Materials in DSSCs
### Up-Conversion  
Up-conversion materials absorb two or more lower-energy photons and emit a single higher-energy photon. The research interest of up-conversion materials in the past decade from the Web of Science is depicted in Figure 4a. The year 2022 had the most research interest, followed by 2021 and 2023, with a nearly similar research interest. In the recent decade, up-conversion materials yielded a total of 86,316 publications. However, the properties of up-conversion luminescent material are advantageous for DSSCs as it allows the conversion of near-infrared (NIR) light, which is not typically absorbed by the dye, into visible light that can be used more effectively. Lanthanide-doped nanoparticles, such as those containing Yb\({}^{3+}\) and Er\({}^{3+}\) ions, are commonly used for their efficient up-conversion capabilities due to their desirable photon modulation properties of converting photons of NIR wavelengths to visible wavelengths so that the DSSC can convert them into usable electrical energy. These materials can be synthesized most often by hydrothermal synthesis and sol-gel processes. The up-conversion process involves energy transfer between dopant ions within a host lattice, typically through sequential absorption or energy transfer up-conversion (ETU) mechanisms. In DSSCs, up-conversion materials are typically integrated into the photo-anode or incorporated into the dye-sensitizer. For instance, embedding NaYF\({}_{4}\):Yb\({}^{3+}\) and Er\({}^{3+}\) up-conversion nanoparticles into TiO\({}_{2}\) photo-anodes has been shown to improve light absorption in the NIR region, thereby enhancing photocurrent and overall cell efficiency. This mechanism extends the range of usable solar spectrum, addressing the limitations of traditional dyes, which primarily absorb in the visible range [49].
## 3 Results  
A total of 81 healthy male soccer referees participated in the study, with the aim of investigating the effects of 24-week the FIFA 11+ Referees training program on CMAS scores. The results of the independent \(t\)-test (Table 2) show that no significant difference was found among age, weight, height, BMI, and experience values between the groups (\(p>0.05\)). The ICC obtained for inter-rater reliability was 0.82 (95% CI 0.70-0.89), indicating good agreement between the raters. Similarly, the ICC obtained for intra-rater reliability was 0.78 (95% CI 0.76-0.80), indicating good agreement within raters. The results of the COD tests are presented in Table 3. The Mann-Whitney test revealed differences between the groups in all the variables (\(p<0.001\)), with the 11+ group showing a statistically significantly larger decline in COD times from pre- to post-intervention. The Wilcoxon signed-rank test was conducted within each group to examine the pre-post differences in the COD maneuvers. The results revealed significant improvements in the 11+ group's post-test scores for COD 45\({}^{\circ}\) (dominant: \(p=0.001\); non-dominant: \(p=0.001\)), COD 70\({}^{\circ}\) (dominant: \(p=0.001\); non-dominant: \(p=0.001\)), and COD 90\({}^{\circ}\) (dominant: \(p=0.001\); non-dominant: \(p=0.001\)). In the control group, no significant differences were found between the pre- and post-test measures for COD 45\({}^{\circ}\) (dominant: \(p=1.00\); non-dominant: \(p=0.439\)), COD 70\({}^{\circ}\) (dominant: \(p=0.782\); non-dominant: \(p=0.083\)), and COD 90\({}^{\circ}\) (dominant: \(p=1.00\); non-dominant: \(p=0.366\)). Additionally, a comparison was made between the dominant and non-dominant legs within each group. Significant differences were observed for both the 11+ and control groups in their pre-post differences for COD 45\({}^{\circ}\) (dominant: \(p=0.008\); non-dominant: \(p=0.001\)), COD 70\({}^{\circ}\) (dominant: \(p=0.001\); non-dominant: \(p=0.001\)), and COD 90\({}^{\circ}\) (dominant: \(p=0.005\); non-dominant: \(p=0.001\)). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Group** & **N** & **Mean \(\pm\) SD** & **T** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Age (years)} & 11+ & 40 & 36.47 \(\pm\) 3.86 & \multirow{3}{*}{1.28} & \multirow{3}{*}{0.20} \\  & Control & 41 & 35.34 \(\pm\) 4.071 & & \\ \multirow{3}{*}{Body mass (kg)} & 11+ & 40 & 76.65 \(\pm\) 3.05 & \multirow{3}{*}{1.12} & \multirow{3}{*}{0.26} \\  & Control & 41 & 75.90 \(\pm\) 2.93 & & \\ \multirow{3}{*}{Body height} & 11+ & 40 & 179.65 \(\pm\) 5.01 & \multirow{3}{*}{1.64} & \multirow{3}{*}{0.10} \\  & Control & 41 & 178.07 \(\pm\) 3.52 & & \\ \multirow{3}{*}{BMI} & 11+ & 40 & 23.80 \(\pm\) 1.70 & \multirow{3}{*}{0.45} & \multirow{3}{*}{0.64} \\  & Control & 41 & 23.96 \(\pm\) 1.33 & & \\ \multirow{3}{*}{Referee experience} & 11+ & 40 & 5.57 \(\pm\) 2.17 & \multirow{3}{*}{0.97} & \multirow{3}{*}{0.33} \\  & Control & 41 & 6.09 \(\pm\) 2.61 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Demographic data with statistical analysis for group differences.
## 4 Coupled Dynamic and Control for Thermally Induced Vibration and Attitude Motion
### Attitude Controller Design Based on Slide Mode Control Theory
For each chosen switched control structure, the control law is designed so that the derivative of the Lyapunov function is negative definite, thus guaranteeing the motion of the state trajectory to the surface. The Lyapunov function for the outer loop is chosen to be:  
\[\mathbf{V}_{1}=\frac{1}{2}\mathbf{s}_{w}^{T}\mathbf{s}_{w} \tag{32}\]  
The derivative of the Lyapunov function can be given by:  
\[\dot{\mathbf{V}}_{1} =\mathbf{s}_{w}^{T}\dot{\mathbf{s}}_{w}=\mathbf{s}_{w}^{T}\Big{(} \hat{\mathbf{\rho}}_{c}-\hat{\mathbf{\rho}}+\mathbf{K}_{1}\mathbf{\Theta}_{e} \Big{)}\] \[=\mathbf{s}_{w}^{T}\Big{\{}\hat{\mathbf{\theta}}_{c}-\mathbf{R} \Big{[}\mathbf{R}^{-1}\Big{(}\hat{\mathbf{\theta}}_{c}+\mathbf{K}_{1}\mathbf{ \Theta}_{e}\Big{)}+\mathbf{R}^{-1}]\rho_{1}\mathbf{s}_{w}\Big{]}+\mathbf{R} \mathbf{\omega}_{e}+\mathbf{K}_{1}\mathbf{\Theta}_{e}\Big{\}}\] \[=-\rho_{1}\|\mathbf{s}_{w}\|^{2}+\mathbf{s}_{w}^{T}\mathbf{R} \mathbf{\omega}_{e}\]  
It can be noticed that error \(\mathbf{\omega}_{e}\) in Equation (33) needs to be small enough to ensure the derivative of the Lyapunov function is negative \(\dot{\mathbf{V}}_{1}\leq 0\), which requires fast convergence of the inner loop. Similarly, the integral sliding mode surface is used to design the inner loop, that is:  
\[\mathbf{s}_{n}=\mathbf{\omega}_{e}+\mathbf{K}_{2}\mathbf{\int}_{0}^{t} \mathbf{\omega}_{e}dt \tag{34}\]  
where \(\mathbf{K}_{2}=\text{diag}[k_{21},k_{22},k_{23}]>0\) is gain matrices. Considering Equation (24), one can obtain:  
\[\mathbf{s}_{w}=\mathbf{\theta}_{e}+\mathbf{K}_{1}\mathbf{\int}_{0}^{t}\mathbf{ \theta}_{e}dt \tag{29}\]0  
The inner loop control law is designed as follows:  
\[\mathbf{s}_{w}=\mathbf{\theta}_{e}+\mathbf{K}_{1}\mathbf{\int}_{0}^{t}\mathbf{ \theta}_{e}dt \tag{29}\]1  
where \(\rho_{2}>\text{max}|D_{i}|\), \(\mathbf{k}=\left[ \begin{array}{cc}k&0\\ 0&k\end{array} \right]>0\). Then,  
\[\dot{\mathbf{s}}_{n}=-\mathbf{J}^{-1}(\rho_{2}\text{SIGN}(\mathbf{s}_{n})+ \mathbf{k}\mathbf{s}_{n}+\mathbf{D}) \tag{37}\]  
Consider the Lyapunov function as follows:  
\[\mathbf{V}_{2}=\frac{1}{2}\mathbf{s}_{n}^{T}\mathbf{J}_{w}\mathbf{s}_{n} \tag{38}\]  
The derivative of the Lyapunov function can be given by:  
\[\dot{\mathbf{V}}_{2}=\mathbf{s}_{n}^{T}\mathbf{J}_{w}\dot{\mathbf{s}}_{n}=- \rho_{2}\underset{i=1}{\overset{3}{\sum}}|\mathbf{s}_{n}|-\mathbf{s}_{n}^{T} \mathbf{D}-k\|\mathbf{s}_{n}\|^{2}\leq-k\|\mathbf{s}_{n}\|^{2} \tag{39}\]  
We can conclude from Equation (39) that \(\dot{\mathbf{V}}_{2}\leq\mathbf{V}_{2}\), and \(\mathbf{V}_{2}\leq e^{t}\). When \(t\)\(\rightarrow\)\(\infty\), \(\mathbf{V}_{2}\) convergences. Thus, the system is asymptotically stable.
## 5 Conclusions  
In this paper, we presented an ML-based retrieval of daily ET\({}_{0}\) over a 500 m grid for croplands in South Korea using satellite images and NWP data. In a blind test for 2013-2019, the ML-based ET\({}_{0}\) model produced highly accurate results, with an RMSE of 1.038 mm/day and the CC of 0.870. The results of the blind test were stable irrespective of location, year, and month. This outcome is presumably because the input data of the ML-based ET\({}_{0}\) model were suitably arranged spatially and temporally, and the optimization of the model was appropriate. In addition, the performance comparison with the operative MODIS PET product was conducted using 8-day composite data. Our ML model outperformed the MODIS product for croplands in South Korea because the ML model was locally optimized for South Korea, whereas MODIS PET was not. Our ET\({}_{0}\) retrieval is expected to be an official product of the National Meteorological Satellite Center (NMSC) of South Korea since 2021 for the applications of agriculture, hydrology, and meteorology. Using the daily ET\({}_{0}\) data produced over the 500 m grid, we conducted a case study to examine agrometeorological characteristics in July and August of 2016-2018. We found that low rainfall and high ET\({}_{0}\) can damage the surface water balance when combined with a heatwave, resulting in reduced vegetation greenness. Through the experiments, the feasibility of the ML-based ET\({}_{0}\) retrieval was validated, especially for local agrometeorological applications in regions with heterogeneous land surfaces, such as South Korea. However, a more detailed examination is required to test if the 500 m grids created from various spatial resolutions are statistically stable. The AI approaches using satellite images with NWP data can be a viable option for studying interactions between the land surface and atmosphere.  
Conceptualization, Y.L.; Data curation, N.K. and K.K.; Formal analysis, N.K. and K.K.; Methodology, Y.L.; Resources, S.L.; Writing--Original draft, N.K. and Y.L.; Writing--Review and editing, N.K., K.K., S.L., J.C., and Y.L. All authors have read and agreed to the published version of the manuscript. This work was funded by the Korea Meteorological Administration Research and Development Program under the Grant KMI2018-05510. The authors appreciate the National Meteorological Satellite Center for providing the in situ measurement data. The authors declare no conflict of interest.
# Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems
## 5 Conclusions  
In this study, a new set-based video face recognition framework for IoT monitoring systems, called sample reduction-based pairwise linear regression classification, was proposed, which can effectively classify large-size videos. Subsequently, three classifiers (DSRbPLRC, APSRbPLRC, and AAPSRbPLRC) were also developed by decreasing the number of images in each video. Our experimental results on some popular databases demonstrate the superiority of our DSRbPLRC, APSRbPLRC, and AAPSRbPLRC algorithms. Specifically, qualitative and quantitative comparisons between our proposed method, DLRC, and PLRC for the large-size video face-recognition task demonstrate that the DSRbPLRC, APSRbPLRC, and AAPSRbPLRC algorithms can overcome the limitations of PLRC. We also find that the AAPSRbPLRC method achieves the best classification results on all databases.  
The main limitations of the proposed framework include the fact that we did not consider the importance of feature learning; only one model feature is used in our framework, while multi-model features are very common in IoT monitoring systems. Therefore, the unrelated set construction strategy can be further improved. Hence, our future studies will combine our framework with deep neural networks, classify multi-feature videos, and design new unrelated set-construction strategies. Besides this, we will also research the accelerating deploy problem in IoT systems using these algorithms.  
Conceptualization, X.G. and W.H.; Data curation, W.H.; Formal analysis, W.H.; Funding acquisition, X.G.; Investigation, X.G.; Methodology, X.G. and S.N.; Project administration, X.G.; Resources, S.N.; Software, W.H.; Supervision, S.N.; Validation, Y.C.; Visualization, Y.C.; Writing--original draft, X.G.; Writing--review and editing, W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (grant number: 62101213), the Shandong Provincial Natural Science Foundation (grant number: ZR2020 QF107), the Development Program Project of Youth Innovation Team of Institutions of Higher Learning in Shandong Province, and the Big Data Project of University of Jinan (grant number: XKY1926).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Honda** & **Mobo** \\ \hline PLRC-I & 2.03 & 3.38 \\ PLRC-II & 6.33 & 9.38 \\ DSRbPLRC-I & 20.38 & 30.89 \\ DSRbPLRC-II & 80.80 & 100.8 \\ APSRbPLRC-I & 1.28 & 2.89 \\ APSRbPLRC-II & 4.98 & 8.83 \\ AAPSRbPLRC-I & 1.79 & 3.21 \\ AAPSRbPLRC-II & 5.67 & 8.87 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The testing times for identifying a query video, on the Honda and Mobo databases.  
Data Availability Statement: The data presented in this study are available upon request from the corresponding author.
## 3 Results
### Time Consumption Comparison with CFD Simulators  
In Sections 3.1 and 3.2, we evaluated the performance of the proposed two ROMs on temperature and outlet heat flux prediction and we obtained very satisfied results. In this subsection, we focus on analyzing the computational resources required by the ROM and numerical simulation for field and scalar prediction. All the calculations of CFD have been run with a CPU AMD Ryzen 7 5700X 8-core Processor, and a GPU NVDIA GeForce PTX 2060 has been used to accelerate the model training and predictions.  
The related data of time consumption are presented in Table 5. As can be seen, the great advantage of the CFD solver is that it does not need any training time, while about one hour of training is indispensable for each ROM. For prediction, the ROM requires converting the cross-sectional geometry of the absorber tube into SDF presentation. Similarly, the CFD model also needs some time to mesh the geometry. Furthermore, it frequently takes a long time to find a good mesh strategy. Although the proposed ROMs need a great amount of time for training, the prediction time is much less than the calculation in the CFD solver, even can be ignored. Each prediction of the ROM for the temperature field costs just 0.011 s, and the value is less--only 0.004 s--for heat prediction. Meanwhile, for the CFD solver, due to repeatedly solving a large number of partial differential equations, each temperature field calculation takes more than 400 s, which is four orders of magnitude slower than the ROM prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**(Width, Height)** & **q\({}_{\text{out}}\) (CFD)** & **q\({}_{\text{out}}\) (ROM)** & **Relative Error** \\
**(mm)** & **(J/s)** & **(J/s)** & **Relative Error** \\ \hline
5, 5 & 351,411.6 & 350,942.1 & 0.13\% \\
5, 10 & 338,082.8 & 337,573.8 & 0.15\% \\
5, 15 & 324,877.0 & 324,254.9 & 0.19\% \\
5, 20 & 311,527.1 & 311,089.1 & 0.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Predictions results of absorber tube for four fin geometries with fin adjacent angle of 45\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **CFD** & **ROM** \\ \hline \multirow{3}{*}{Temperature field prediction} & Model training & - & 1.14 h \\  & Input preparation & 1.68 s (mesh) & 0.062 s (SDF) \\  & Prediction & 451s & 0.011 s \\ \hline \multirow{3}{*}{Outlet heat flux prediction} & Model training & - & 0.8 h \\  & Input preparation & 1.68 s (mesh) & 0.062 s (SDF) \\ \cline{1-1}  & Prediction & 393 s & 0.004 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the time cost for temperature field and heat flux predictions by the proposed ROMs and the CFD simulation in OpenFOAM.
# Satellite Network Task Deployment Method Based on SDN and ICN
## 3 System Model and Problem Formulation
### Task Model
#### 3.4.3 Task Computation Delay
Exhaustive search methods and branch and bound methods can solve this problem, but their complexity is prohibitive. Therefore, a heuristic algorithm is considered to achieve an approximate solution in the allowable time. A genetic algorithm is commonly used in solving complex combinatorial optimization problems. Compared with traditional optimization methods (enumeration, heuristic, etc. ), a genetic algorithm is simple and easy to implement. It takes biological evolution as the prototype, and the time complexity is related to the number of iterations and the number of populations. The time complexity of the genetic algorithm is \(O(n^{2})\), which demonstrates quick convergence and a high degree of robustness. For a given required accuracy, a genetic algorithm can find the approximate optimal solution.
## 4 Simulation Evaluation
### Simulation Setup  
#### 4.1.1 Comparison Method  
WuR-TDMA: The sink node broadcasts a WuC to activate all sensor nodes at once. Then, the sink node broadcasts the information of each node's routing and transmission timing by the data communication module using a large amount of power to reach all sensor nodes. Each sensor node communicates by TDMA (time-division multiple access) based on the received information. More specifically, after receiving the routing/transmission timing information from the sink node, each sensor node enters the sleep mode and schedules to wake up on its own time slots to transmit/forward its packets. Unlike SC-Sched, WuR-TDMA has the overhead of notifying sensor nodes that there is information (activation timing, communication possible time, and routing) for sensor data transmission. In order to verify the effect of reducing this overhead, SC-Sched is compared with WuR-TDMA.  
ContikiMAC [9]: ContikiMAC is one of the well-known duty-cycling MAC protocols commonly used in WSNs. A node periodically wakes up to detect potential communication demands (receive packets). A sender node periodically transmits its data frame until it receives an ACK from the receiver or reaches the upper limit of the number of retransmissions. SC-Sched is compared with ContikiMAC to show the effectiveness of avoiding periodical wake-up by applying WuR.  
Table 1 shows the main differences between the SC-Sched method and the two comparison methods. In the simulation of ContikiMAC, it is assumed that the routing is already known.
## 2 Electrical Resistivity/Impedance Measurement: Is It Really that Simple?
### AC Measurement of Electrical Resistivity  
With respect to DC measurements, AC measurements of electrical resistivity give electrical impedance as result value, which includes a real part (R, electrical resistance) and an imaginary part (X, electrical reactance). It is worth noting that only electrical resistance represents the ionic movement in the pore network and is related to concrete durability [22]. Recent studies [68] reported that the real part of impedance can be correlated to degradation phenomena in cement-based materials.