esa-sceva/satcom-chunk-collection huggingface dataset:
# Suppression of Harmonic Current in Magnetic Bearing-Rotor System with Redundant Structure
## 4 Suppression of Harmonic Current in AMB with Redundant Structures
### Analysis of Magnetic Bearing System with RC
Hence, we assume that \(w\)<\(w_{c}\), and we define \(F(\mathrm{s})|_{s=j\omega}=A(\omega)e^{i\theta(\omega)}\), \(C(\mathrm{s})|_{s=j\omega}=B(\omega)e^{i\theta_{c}(\omega)}\), and \(\beta(\omega)=\theta(\omega)+\theta_{c}(\omega)+T_{2}\omega\), \(T_{2}=N_{2}T_{s}\), where \(N_{2}\) is an integer and \(T_{\mathrm{s}}\) is the sampling period of the system. Combining Equation (40) with condition 2, we obtain  
\[\left|1+K_{rc}B(\omega)A(\omega)e^{i[\theta(\omega)+\theta_{c}(\omega)+T_{2} \omega]}\right|_{s=j\omega}<1. \tag{41}\]  
Then,  
\[\left|1+K_{rc}B(\omega)A(\omega)cos\beta(\omega)+jK_{rc}B(\omega)A(\omega)\sin \beta(\omega)\right|_{s=j\omega}<1. \tag{42}\]  
Furthermore, we get  
\[2K_{rc}B(\omega)A(\omega)cos\beta(\omega)+[K_{rc}B(\omega)A(\omega)]^{2}<0. \tag{43}\]  
The stability of the system with RC can be guaranteed, if the following condition is satisfied:  
\[\left\{ \begin{array}{c}90^{\circ}<\beta(\omega)<270^{\circ}\\ K_{rc}<\frac{2\min\left|\cos\beta(\omega)\right|}{\max\left|A(\omega)B(\omega )\right|}\end{array} \right.. \tag{44}\]
## 3 Proposed Scheme
### Secret Data Extraction and Index Table Recovery Processes  
The secret data extraction rules can be designed based on the embedding rules. At the receiver end, the extraction of the second layer secret data and the SOC decoding is executed first. Then, the indices are processed sequentially to determine the embedded second layer data and the original index value. The cluster label of the marked index is retrieved as the secret bit. The side-match values of the marked index and its counterpart are evaluated and compared to determine the original index value. When a reserved index is encountered, the head index represents '0', while the rear index represents '1'. In addition, its succeeding index should be recovered with abnormal solution after extracting the secret bit. The algorithm for secret data extraction and index table recovery is summarized as Algorithm 3.  
The overall scheme of phase 1 process is illustrated in Figure 14. The three examples presented in the embedding process of the first layer are applied to demonstrate the secret data extraction and index table recovery in the second phase. The first example is shown in Figure 15. We get the marked index 'CW5'. The embedded secret bit is determined by \(b=L(cw5)=1\). Then, its counterpart 'CW12' is retrieved, and their side-match values are compared. Since \(d(cw5)<d(cw12)\), 'CW5' is recorded to the recovered index table. The second example is shown in Figure 16. The marked index is 'CW14', whose cluster label 0 is retrieved as the secret bit. The side-match values of 'CW14' and its counterpart 'CW7' are calculated and compared. Since 'CW7' best matches the current environment, it  
Figure 13: The second layer of data embedding.  
is recorded to the recovered index table. The final example is shown in Figure 17. A rear index 'CW15' is encountered. It means a secret bit '1' is embedded and the current block is abnormal. Its succeeding index 'CW11' is then retrieved. The cluster label 0 of 'CW11' is retrieved as the second secret bit. At last, the side-match values of 'CW11' and 'CW4' are compared. The mismatched index 'CW11' is recorded since the current block is indicated as an abnormal one.  
Figure 14: The extraction of the second layer secret data.  
Figure 16: Extraction example 2: mismatched case.  
Figure 17: Extraction example 3: abnormal block.  
Figure 15: Extraction example 1: matched case.
# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 3 Cost-Schedule Integration and Big Data Technology
### Feasibility of Big Data Technology on Cost-Schedule Integration  
Big data has its outstanding features, which can be summarized as Volume, Variety, Veracity, and Value (as known as 4 Vs) [24]. The feasibility of the application of big data technology to the cost-schedule data integration has been analyzed.  
* As discussed in Section 3.1, the cost-schedule data is vast and created in different forms by different stakeholders in construction projects. Data including quantity, cost, schedule, payment, material, and labor are generated in varied formats (i.e., quantity calculation, bill of quantities, schedules) for multiple purposes and changed as the project proceeds. The 4 Vs of big data clearly explain vital attributes of construction data, which is large, heterogeneous, and dynamic with value in decision making [25]. Big data technology can be regarded as a suitable method to integrate and manage the 'big' cost-schedule data in construction management.
* The data accumulated in construction firms are mostly associated with financial and schedule management. According to the survey with 89 firms in Korea in 2014, 76% and 49% of them have stored cost-related data and schedule-related data, respectively [26]. It infers that the available data for cost-schedule integration is accessible and easy to collect.
* The technical level of handling big data can be categorized according to a data structure (i.e., structured, semi-structured, unstructured), data type (i.e., text, log, sensor, image), data format (i.e., RDB, HTML, XML, Json). Most of the cost-schedule data is structured text data in document format; it facilitates data processing, transmission, storage, and evaluation. Technology for collecting, storing, analyzing, and visualizing data has been developed rapidly and published as open-source packages. The high accessibility of technology and the low difficulty in handling cost-schedule data indicate the possibility of the active application of big data technology to the integrated management of cost-schedule.
* Big data technology, which enhances the accuracy of analysis and prediction, is ideal for engineering analysis, including construction engineering [27]. From a statistical perspective, big data infer the patterns within a population using random sampling. The sampling technique is useful for analyzing limited data size, especially overcoming challenges in collecting and processing data; however, it might have the disadvantage of the overfitted prediction to sample, which leads to the failure to capture real patterns of the population.
## 3 Results
### Mutagenicity  
The mutagenicity of the SFOH and SFCONV samples after the simulated digestion was assessed as some chemical compounds may be harmful or breakdown into mutagenic compounds within the digestive system (Figure 4B).  
The results showed an increase in mutagenicity of CONV samples. In contrast, the OH samples were not mutagenic, with no increase in the number of revertants with increasing extract concentrations.  
Figure 4: Cytotoxicity (**A**) and mutagenicity (**B**) of SFOH and SFCONV samples. In figure (**B**), \(-\)\(-\)\(\mathrm{SFCONV}\)\(-\)\(\mathrm{SFOH}\) represent without metabolization and \(-\)\(-\)\(\mathrm{SFCONV}\)\(-\)\(\mathrm{SFOH}\) with metabolization. Results are the means of three determinations \(\pm\) standard deviation.  
Figure 3: Metabolomic analysis, two-way ANOVA, OPLS-DA, and fold-change analysis.  
Moreover, the mutagenicity also increased when extracts were exposed to metabolic activation (in contact with S9 mix), suggesting that conventional extract yields compounds with a more significant potential to induce DNA damage when undergoing metabolism. The differences between both extracts may be attributed to less soluble components in the ethanolic extract due to their nonpolar nature. These results are following previous studies [65]. Thus, OH heating is a safer process than the CONV method, which uses organic solvents and could be retained in the flours after extraction, with mutagenic activity.  
Regarding the cytotoxicity assessed by the XTT assay, the SFOH extraction presents lower cytotoxicity than SFCONV extracts (Figure 4A). The CONV extraction showed an IC50 of 60 \(\upmu\)g/mL. These IC50 values are similar to those measured for medicinal plants [66]. [PERSON] et al. [65] also obtained mutagenicity and cytotoxicity results of Kalanchoe laciniate n-hexane extracts (corresponding to our conventional method) showing that digested conventional bagasse flour seems to be a source of different toxic compounds against cells. After the digestion procedure, less polar components, such as carotenoids and sterols, are bioavailable as dynamic species. The mechanism of cell toxicity could be due to an arrest of cell cycle at the G0/G1 and G2/M stage and by apoptosis, which has been recently reported for a \(\upbeta\)-carotene enhanced tomato variety [67]. Some studies have reported impacts of carotenoids, such as neoxanthin and fucoxanthin, on DNA fragmentation in human prostate cancer cells [66; 67; 68].
## 5 Conclusions  
The effects of saturation and impact loading on the dynamic mechanical behavior of the frozen red sandstone in the Urumqi area were investigated using a LT-SHPB system. Moreover, LT-NMR and SEM methods were employed to analyze the microstructural evolution of the frozen sandstone with different saturations. The main conclusions are as follows:  
Figure 18: Expansion behavior and influence mechanism of pore ice during sandstone freezing: (**a**) SEM image; (**b**) abstracted pore structure model; (**c**) freezing process; (**d**) pore ice effects.  
During freezing at low temperatures, when the saturation was below the critical saturation, the sample was dominated by the development of micropores, while meso- and macropores mainly propagated within the saturated sample, as evidenced by an increase in the second and third peaks in the \(T_{2}\) spectrum of 9.32% and 3.57%, respectively. Under low temperature conditions, meso- and macropores were more sensitive to saturation and more susceptible to crystallization pressure.
2. As saturation increased, the dynamic strength, elastic modulus, and brittleness of the frozen sandstone tended to increase and then decrease. At four impact loads, the strength of the samples increased by 21.2%, 27.1%, 32.5%, and 34.3% when the saturation increased from 0% to 50%, which corresponded to 1.38, 1.43, 1.51, and 1.56 times the strength of the fully saturated samples, respectively. In addition, the dynamic mechanical properties of the frozen sandstone showed a significant effect of impact strengthening with increasing impact velocity.
3. The energy dissipation capacity of the frozen red sandstone was significantly affected by its saturation at low temperatures. When the saturation increased from dry to 50%, the energy dissipation capacity during impact gradually increased, while the energy dissipation capacity gradually decreased as saturation increased from 50% to 100%. The strengthening effect reached its maximum when the saturation of the sample was 50%. In addition, the enhancement effect of saturation on energy dissipation was smaller than the weakening effect.  
Methodology, J.X. and H.P.; investigation, J.X. and Z.S.; resources, J.X.; data curation, Z.S. and J.X.; writing--original draft preparation, J.X.; writing--review and editing, J.X. and H.P.; visualization, Z.S. and J.X.; supervision, H.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51974296, No. 52061135111).  
Not applicable.  
Data available on request due to privacy restrictions.  
The authors declare no conflict of interest.
## 4 Discussion
### Discussion of MISTiC Winds Requirements and Their Relation to Other Instrument Requirements
The moisture-band channels used for MISTiC come from the shorter wavelength side of the atmospheric moisture band-as was planned for the NASA GIFTS instrument. These channels provide the same function for moisture sounding as those on the long wave side of the moisture band used by AIRS. The functionally similar temperature-sounding spectral channel range near 15.4 \(\upmu\)m (650 cm\({}^{-1}\)) in AIRS have somewhat broader vertical weighting functions, but are valuable for sounding the stratosphere, and (when used together with the 4.3 \(\upmu\)m region channels) for identification of partly cloudy footprints, and for the creation of cloud-cleared radiance products. For MISTiC, improved cloud observations, enabled by its 5x higher spatial resolution, will at least partially compensate for the lost ability to use spectral cloud-clearing methods with the VLVIR band. Moreover, by mission design--MISTiC requirements are selected to support tropospheric winds and sounding--not stratospheric sounding. Operational IR sounders will continue providing stratospheric soundings in the VLVIR band for decades to come, so MISTiC will not plan to replicate this capability. Finally, radiance forward-modeling challenges in the 4.3 \(\upmu\)m region that limited its use by earlier sounder teams have been addressed within the contemporary forward model used by the AIRS science team.
## 3 Outage Probability and Asymptotic Analysis of the RF-UWOC Dual-Hop System  
### Deduction of End-to-End SNR Cumulative Distribution Function and Its Asymptotic Value  
For the DF relay scheme, since decoding and reconstruction of the transmission signal are required at the relay node R, the instantaneous SNR of the RF link and UWOC link are independent of each other. Therefore, the equivalent instantaneous SNR of the end-to-end link can be determined by the minimum value between the instantaneous SNR of the S-R link and the R-D link, that is,  
\[\gamma^{DF}=\min(\gamma_{1},\gamma_{2}). \tag{23}\]  
Modeling the reliability of the double jump system as a series of independent link reliabilities for easier research.  
For the DF relay protocol, the CDF \(\gamma^{DF}\) can be written as follows:  
\[\begin{array}{ll}F_{\gamma^{DF}}(\gamma)&=\Pr(\min\{\gamma_{1},\gamma_{2}\} \leq\gamma)=1-\Pr(\min\{\gamma_{1},\gamma_{2}\}>\gamma)\\ &=1-\Pr(\gamma_{1}>\gamma,\gamma_{2}>\gamma)=1-\Pr(\gamma_{1}>\gamma)\Pr( \gamma_{2}>\gamma)\\ &=1-(1-F_{\gamma_{1}}(\gamma))(1-F_{\gamma_{2}}(\gamma))\\ &=F_{\gamma_{1}}(\gamma)+F_{\gamma_{2}}(\gamma)-F_{\gamma_{1}}(\gamma)F_{ \gamma_{2}}(\gamma).\end{array} \tag{24}\]  
Substituting Equations (5) and (22) into Equation (24), the CDF \(\gamma^{DF}\) can be expressed as follows:  
\[\begin{array}{ll}F_{\gamma^{DF}}(\gamma)&=\frac{1}{\Gamma(m)\Gamma(\Gamma)} G_{1,3}^{2,1}\left[\begin{array}{c}\frac{mKL_{LLR}\cdot\gamma}{\gamma_{1}} \end{array}\right]\begin{array}{c}1\\ k,m,0\end{array}\Bigg{]}+\frac{\xi^{2}}{c^{3}(a)}G_{2,3}^{2,1}\left[\begin{array} []{c}\frac{1}{(bA_{0})^{c}}\left(\frac{\gamma}{\rho}\right)^{\xi}\Bigg{|} \begin{array}{c}1,\frac{\xi^{2}}{c}+1\\ \frac{\xi^{2}}{c},a,0\end{array}\right]\\ &-\frac{\xi^{2}}{c^{3}(a)\Gamma(m)\Gamma(\Gamma)}G_{1,3}^{2,1}\left[\begin{array} []{c}\frac{mKL_{LLR}\cdot\gamma}{\gamma_{1}}\end{array}\right]\begin{array}[ ]{c}1\\ k,m,0\end{array}\Bigg{]}G_{2,3}^{2,1}\left[\begin{array}{c}\frac{1}{(bA_{0})^ {c}}\left(\frac{\gamma}{\rho}\right)^{\xi}\Bigg{|}\begin{array}{c}1,\frac{ \xi^{2}}{c}+1\\ \frac{\xi^{2}}{c},a,0\end{array}\right].\end{array} \tag{25}\]  
In the channel fading model studied by us, no one has yet derived the average outage probability of the system using the Meijer-G function.  
For the convenience of analyzing the problem, it is assumed that the average SNR of the RF link and the UWOC link are the same, that is, \(\overline{\gamma}_{1}=\rho_{r}=\overline{\gamma}\).  
When \(\rho_{r}=\overline{\gamma}_{1}=\overline{\gamma}\rightarrow\infty\), \(F_{\gamma^{DF}}(\gamma)\) it can be asymptotically represented as  
\[F_{\gamma^{DF}}(\gamma)\rightarrow\frac{\Delta_{1}\gamma^{k}}{\overline{ \gamma}^{k}}+\frac{\Delta_{2}\gamma^{m}}{\overline{\gamma}^{m}}+\frac{\Delta_{3 }\gamma^{\frac{\gamma^{2}}{r}}}{\overline{\gamma}^{\xi^{2}/r}}+\frac{\Delta 4\gamma^{\frac{\gamma^{\gamma}}{r}}}{ \overline{\gamma}^{\alpha/r}}. \tag{26}\]  
The exact derivation of (26) can be found in Appendix B.  
Similarly, currently no one has derived the asymptotic average interruption probability of the system under our novel model.
# Information-Aware Secure Routing in Wireless Sensor Networks
## 6 Simulation Results
### G2 Simulations: Comparative Studies of Two Models  
Figure 7 plots the packet delivery ratios of the two models according to the packet-forwarding schemes. The RBMSC model adopted the FPL scheme in the simulation, and it assumes that a malicious node discards all received packets. Objectively, the IASR model verifies both hypotheses that the  
Figure 6: The energy consumption of the 18 nodes near the sink node when the proportion of malicious nodes is 20%, 40%, and 60%.  
malicious node processes the packets in the simulation. In Figure 7, the blue solid line, the red dashed line, and the yellow dotted line respectively represent the packet delivery ratios of the IASR model (RPL), the IASR model (FPL), and the RBMSC model (FPL) with different proportions of malicious nodes in WSNs. As shown in Figure 7, the performance of the IASR model is superior to that of the RBMSC model for both assumptions.  
As the proportion of malicious nodes in WSNs increases, the packet delivery ratios of both models decrease. Compared with the RBMSC model, the IASR model can maintain a higher delivery ratio. This is because, in the process of path selection, the IASR model not only examines the single-hop optional node, but also evaluates the entire path according to the serial-parallel system of reliability theory to ensure global optimization.
# YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
## 3 Experiment and Results Analysis
### Analysis of Experimental Results  
#### 3.4.1 Reducing the Number of Model Parameters Using MobileNetv3 Block  
To address the constraints posed by the limited computing resources of on-site devices for sewer defect detection, modifications are made to enhance the model's lightweight characteristics. These adjustments include adopting MobileNetV3 block stacking as the backbone of the network. To assess the impact of this modification on the model's performance, horizontal comparative experiments are conducted with two other lightweight backbone networks: the Efficient Model (EMO) [34] and GhostNet [35].  
The aim of these experiments is to compare the performance of the YOLOv5-Sewer model with MobileNetV3 block stacking against models utilizing EMO and GhostNet as backbones. The experimental results, presented in Table 2, offer insights into the relative impact of these lightweight backbone networks on the detection performance of the models. This comparative analysis is pivotal in identifying the most suitable lightweight backbone, allowing the model to achieve a lightweight design with minimal accuracy loss.  
The implementation of lightweight backbones, including MobileNetV3, EMO, and GhostNet, in all three models successfully diminishes the parameter count. However, this reduction in the number of parameters has a trade-off, leading to a decrease in mean Average Precision (mAP). This is primarily attributed to the decreased number of convolutions in these lightweight backbones, leading to the weakened feature extraction capability of the backbone network.  
Among the three models, the one enhanced based on GhostNet exhibits the most significant mAP reduction, reaching 6.9%. The model improved with EMO experiences the smallest decrease in mAP, which is 2.8%. However, this improvement is achieved at the cost of increased model computation and a reduction in the detection speed. The model enhanced with MobileNetV3 has a relatively low number of floating-point operations, with minor losses in accuracy and detection speed. It achieves the optimal overall performance.  
To achieve a lightweight model design without excessively compromising the accuracy, further enhancements are applied to the YOLOv5s-MobileNetV3 model. These improvements involve the utilization of the C3-Faster module to reduce the parameters and computation. Attention modules are integrated, and the Efficient Intersection over Union (EIOU) is employed as the model's localization loss function to enhance the recognition accuracy. The iterative improvement process is geared towards optimizing the overall performance of the model, ensuring minimized accuracy loss while attaining a lightweight design. This makes it highly suitable for deployment on on-site operational devices dedicated to sewer defect detection.
# Real-Time DTM Generation with Sequential Estimation and OptD Method
###### Abstract  
Data acquisition and simultaneous generation of real-time digital terrain models (DTMs) is a demanding task, due to the vast amounts of observations collected by modern technologies and measurement instruments in a short time. Existing methods for generating DTMs with large datasets require significant time and high computing power. Furthermore, these methods often fail to consider fragmentary DTM generation to maintain model continuity by addressing overlaps. Additionally, storing the resulting datasets, generated 3D models, and backup copies consumes excessive memory on computer and server disks. In this study, a novel concept of generating DTMs based on real-time data acquisition using the principles of sequential estimation is proposed. Since DTM generation occurs simultaneously with data acquisition, the proposed algorithm also incorporates data reduction techniques to manage the large dataset. The reduction is achieved using the Optimum Dataset Method (OptD). The effect of the research is the characteristics file that stores information about the DTM. The results demonstrate that the proposed methodology enables the creation of 3D models described by mathematical functions in each sequence and allows for determining the height of any terrain point efficiently. Experimental validation was conducted using airborne LiDAR data. The results demonstrate that data reduction using OptD retains critical terrain features while reducing dataset size by up to 98%, significantly improving computational efficiency. The accuracy of the generated DTM was assessed using root mean square error (RMSE) metrics, with values ranging from 0.041 m to 0.121 m, depending on the reduction level. Additionally, statistical analysis of height differences (\(\Delta\)Z) between the proposed method and conventional interpolation techniques confirmed the reliability of the new approach. Compared to existing DTM generation methods, the proposed approach offers real-time adaptability, improved accuracy representation per model fragment, and reduced computational overhead.  
DTM; data processing; OptD; sequential estimation; reduction; visualization +
Footnote †: journal: applied sciences  
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