esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Effect of Current on Wave Height
### Data Analysis of Current-Affected Wave Height  
Wave heights were measured with different current velocities, and Figure 5 shows representative time series of wave elevation for \(H_{0}\) = 8.9 cm and 13.0 cm, \(T\) = 1.35 s, and \(U\) = 0 m/s, 0.1 m/s, and 0.4 m/s. Compared to that under wave-only action, the wave amplitude decreases obviously under wave-current action, while the wave period remains nearly constant.  
Figure 6 shows the current-affected wave height decreasing gradually as the current velocity increases. Compared with the wave height under wave-only conditions (\(H_{0}=8.9\) cm and 13.0 cm), the current-affected wave height \(H\) decreases by about 8% and 9% for \(U=0.1\) m/s and by about 31% and 30% for \(U=0.4\) m/s. In addition, Equations (1)-(5) are used to compare the measured wave height variation with the theoretical calculation results; the error between the two is about 5% at most, which occurs for \(H=8.9\) cm, \(T=1.35\) s, and \(U=0.4\) m/s. This indicates that Equation (1) offers an accurate estimation of the design parameters.  
Figure 5: Time series of wave elevation: (a) \(H_{0}=8.9\) cm, \(U=0\) or \(0.1\) m/s; (b) \(H_{0}=8.9\) cm, \(U=0\) or \(0.4\) m/s; (c) \(H_{0}=13.0\) cm, \(U=0\) or \(0.1\) m/s; (d) \(H_{0}=13.0\) cm, \(U=0\) or \(0.4\) m/s.  
wave height in actual engineering environments under the combination of deep water, strong waves, and rapid current.
## 3 Results
### Multivariate Analysis  
Using the five established emotional temperature variables and applying LDA classifiers, logistic regression, KNN (for n = 1, n = 3, and n = 5), and MANOVA to the database, two types of classifications were carried out, as follows: consideration of the presence or absence of AD and consideration of the different degrees of the disease (mild and moderate AD).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Wilcoxon Test**} & \multicolumn{2}{c}{**Kruskal–Wallis Test**} & \multicolumn{2}{c}{**Median Test**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Prob \(\left|z\right|\)**} & \multicolumn{2}{c}{\(\chi^{2}\)} & \multicolumn{2}{c}{**Pearson \(\chi^{2}\)**} \\ \hline \multicolumn{6}{l}{**Variable/**} \\
**Interviewer** & **Human** & **Automatic** & **Human** & **Automatic** & **Human** & **Automatic** \\ \hline \multicolumn{6}{l}{HC * vs. AD} \\ \hline \(et_{d}\) & 0.34 & 0.05 & 0.342 & 0.05 & 0.56 & 0.12 \\ \(\overline{et}_{c}\) & 0.61 & 0.19 & 0.634 & 0.21 & 0.68 & 0.18 \\ \(\sigma_{et_{c}}^{2}\) & 0.81 & 0.08 & 0.824 & 0.09 & 0.93 & 0.28 \\ \(\tilde{\mu}_{et_{3}}\) & 0.79 & 0.06 & 0.791 & 0.06 & 0.93 & 0.05 \\ \([PERSON]_{et_{c}}\) & 0.13 & 0.55 & 0.129 & 0.55 & 0.37 & 0.96 \\ \hline \multicolumn{6}{l}{HC vs. AD1 *} \\ \hline \(et_{d}\) & 0.17 & 0.05 & 0.17 & 0.05 & 0.26 & 0.13 \\ \(\overline{et}_{c}\) & 0.47 & 0.28 & 0.50 & 0.30 & 0.66 & 0.23 \\ \(\sigma_{et_{c}}^{2}\) & 0.74 & 0.25 & 0.76 & 0.28 & 0.66 & 0.36 \\ \(\widetilde{\mu}_{et_{3}}\) & 0.60 & 0.02 & 0.60 & 0.02 & 0.93 & 0.03 \\ \([PERSON]_{et_{c}}\) & 0.34 & 0.93 & 0.34 & 0.93 & 0.66 & 0.36 \\ \hline \multicolumn{6}{l}{HC vs. AD2 *} \\ \hline \(et_{d}\) & 0.41 & 0.27 & 0.41 & 0.27 & 0.60 & 0.46 \\ \(\overline{et}_{c}\) & 0.69 & 0.30 & 0.71 & 0.32 & 0.60 & 0.57 \\ \(\sigma_{et_{c}}^{2}\) & 0.12 & 0.06 & 0.14 & 0.07 & 0.60 & 0.35 \\ \(\tilde{\mu}_{et_{3}}\) & 0.54 & 0.85 & 0.54 & 0.8483 & 0.60 & 0.85 \\ \([PERSON]_{et_{c}}\) & 0.05 & 0.16 & 0.05 & 0.16 & 0.12 & 0.35 \\ \hline \multicolumn{6}{l}{AD1 vs. AD2} \\ \hline \(et_{d}\) & 0.08 & 0.75 & 0.08 & 0.75 & 0.12 & 0.59 \\ \(\overline{et}_{c}\) & 0.52 & 0.87 & 0.55 & 0.87 & 0.53 & 0.89 \\ \(\sigma_{et_{c}}^{2}\) & 0.11 & 0.32 & 0.14 & 0.34 & 0.53 & 0.79 \\ \(\tilde{\mu}_{et_{3}}\) & 0.18 & 0.20 & 0.18 & 0.20 & 0.65 & 0.28 \\ \(Kurt_{et_{c}}\) & 0.24 & 0.14 & 0.24 & 0.14 & 0.12 & 0.08 \\ \hline \hline \end{tabular}  
* HC: healthy control, AD1: mild-grade Alzheimer *, AD2: moderate-grade Alzheimer *, Values where Prob \(\left|z\right|\) exceeds 0.05 (Prob \(\left|z\right|>0.05\)) are shaded in grey, indicating no significant differences when comparing the populations’ measures.  
\end{table}
Table 2: Results of the univariate nonparametric analysis: discriminant capacity of the different populations and interviewers in relation to the emotional temperature variables.
## 1 Introduction
investigated the RCS reduction analysis by proposing a metamaterial absorber (MA) structure that works in the microwave frequency band [11]. The work was validated by measuring conducting-plane and dihedral-angle reflectors coated with both metamaterial and FR-4 layers in an open test site. Moreover, [PERSON] et al. investigated an RCS reduction in a patch antenna by adopting an MA [12]. The introduced unit-cell MA has a height and thickness of 5.5 mm and 0.3 mm, respectively. A comparison of the patch antenna patterns and reflection coefficients for loading and unloading the metamaterial was presented. Although metamaterial research has a long tradition, limited coding metamaterial studies for terahertz frequencies have been conducted in recent years. There has been an increased recognition that more attention has been paid to this area of the field since coding metamaterials can manipulate EM waves by adopting various coding sequences with different bits. In 2014, [PERSON] et al. proposed using digital metamaterials in two steps; for instance, they proposed the construction of coding metamaterials by adopting one-bit and introducing unique metamaterial particles that have either '0' or '1' responses controlled by a biased diode [13]. Two-bit coding metamaterials were analyzed in the present work. In 2016, [PERSON] et al. proposed and experimentally demonstrated a transmission-type coding metasurface to bend normally incident terahertz beams in anomalous directions and generate non-diffractive Bessel beams in normal and oblique directions [14]. To overcome the larger reflection and strong Fabry-Perot resonance that usually originate from a thick silicon substrate, a free-standing design was presented for the coding particle that was formed by stacking three metallic layers with four polyimide spacers alternately. Meanwhile, a similar study in 2017 proposed a tensor-coding metasurface at the terahertz frequency that could take full-state control of an electromagnetic wave in terms of its polarization state, phase and amplitude distributions, and wave-vector mode [15]. Due to the off-diagonal elements that are dominant in the reflection matrix, each coding particle could reflect a normally incident wave to its cross-polarization with controllable phases, resulting in different coding digits. This overall literature review indicates that coding metamaterials have had great impacts on the current technology and require extensive research to explore all the unique properties in many research areas. Since coding metamaterials can manipulate EM waves through just simple coding sequences of unit-cell metamaterial arrangements, one may save time by avoiding and developing many metamaterial structures for an optimization process. The coding metamaterials for C- and Ku-band applications are more suitable to apply and will relatively manifest a better performance than conventional metamaterials.
## 3 Configuration of the System and Conditions
### Statistics on Computation Results  
For the purpose of result analysis, two types of statistics were utilized in this study.  
Mean relative error (MRE): To evaluate the overall performance of estimators under all conditions, it was useful to take the average of all the results by relative error format. For a measured parameter with a standard value \(x_{i}\), if its reconciled values under \(N\) conditions were denoted as a vector \(\hat{\mathbf{x}}_{i}\):  
\[\hat{\mathbf{x}}_{i}=(\hat{x}_{i,1},\hat{x}_{i,2},\dots,\hat{x}_{i,N}) \tag{13}\]  
then the MRE of the parameter \(x_{i}\) under all conditions can be computed as:  
\[\text{MRE}(x_{i})=\frac{1}{N}\sum_{k=1}^{N}\left|\frac{\hat{x}_{i,k}-x_{i}}{x _{i}}\right| \tag{14}\]  
Figure 5: Computation flowchart of the robust data reconciliation framework.  
Similarly, the concept of MRE can be extended to the overall results. One can acquire the MRE of all \(n\) measured parameters through the following equation:  
\[\text{MRE}(\mathbf{x})=\frac{1}{n}{\sum\limits_{i=1}^{n}}\text{MRE}(x_{i}) \tag{15}\]  
Root mean-square error (RMSE): The disadvantage of MRE is that it may ignore the extreme values hidden in individual conditions or parameters. To reveal such problems for estimator comparison, it was necessary to compute the RMSE of the results as well. For a measured parameter with a standard value \(x_{i}\) and with its reconciled values' vector under \(N\) conditions \(\hat{\mathbf{x}}_{i}\), their RMSE was defined as:  
\[\text{RMSE}(x_{i})=\sqrt{\frac{1}{N}\frac{N}{k}\bigg{(}\frac{\hat{x}_{i,k}-x_ {i}}{x_{i}}\bigg{)}^{2}} \tag{16}\]  
Compared with the direct average, the RMSE can magnify the influence of extreme data, which is useful for detecting fault in robust data reconciliation. As for overall results, it was also possible to define the overall RMSE of all measurements like MRE, simply through the average among all the measurements' RMSE, which can be represented by the following equation:  
\[\text{RMSE}(\mathbf{x})=\frac{1}{n}{\sum\limits_{i=1}^{n}}\text{RMSE}(x_{i}) \tag{17}\]
# P-Wave Reflection Approximation of a Thin Bed and Its Application
## 4 Discussion  
Compared with the reflection coefficient equations of a single interface, the thin-bed reflectivity approximation gives closer descriptions of thin-bed seismic responses. However, the approximate formula is also based on the fundamental assumptions of conventional AVO methods, such as horizontal interface, plane-wave incidence, etc. Therefore, the approximate formula is more applicable for the thin-bed reservoirs with relatively simple structures. For the inversion problems of complicated structure reservoirs, the inversion methods based on numerical solutions of wave equation are being studied and applied in depth. For example, recently, [PERSON] et al. [35] successfully applied elastic full-waveform inversion to a field dataset for reservoir characterization. However, at present, the published papers on the thin-bed inversion based on numerical solutions of wave equation are very rare. Therefore, the approximate formula and the corresponding inversion method proposed in this paper still has potentially exploitable superiority for thin-bed AVO inversion.  
This paper concentrates on the P-wave reflectivity approximation of a single thin bed. The situation becomes more complex if the target thin layer is embedded in a finely layered background. Research on finely layered reservoirs shows that the interlayer structure and lithologies can be generally determined through long-wavelength approximation [36; 37; 38; 39; 40; 41]. Therefore, we will introduce long-wavelength approximation in our further AVA inversion of finely layered reservoirs.  
The proposed thin-bed inversion strategy inverts for ten parameters of a thin bed, including P-wave and S-wave velocities and densities of the target thin bed and its upper and lower half spaces and thin-bed thickness. Thin-bed AVA inversion is prone to obtaining local minimum solutions due to many unknown parameters to invert. In this paper, we adopt logging information to constrain the inverted thin-bed model for reasonable inversion results. Studies on a single interface show that the parameterization method reduces the number of unknown parameters and improves the stability of single-interface inversion [42; 43]. Therefore, we will introduce the parameterization method in our further AVA inversion of a thin bed for a more stable inversion.  
In the tests of synthetic seismic data, we just made a preliminary attempt to do the inversion based on our formula. The noise tests show that the inversion method has a certain anti-noise ability. Moreover, we have not yet developed a mature process and program for the large-scale industrial applications of our inversion method. At present, we can only build the initial model manually and invert the AVA gathers one by one. In a future study, we will try to enhance the anti-noise capabilities of the inversion method, and develop matching data processing techniques.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis
### Wireless Antenna Attitude Test  
According to the principle of electromagnetic wave emission, the antenna has a certain angle when transmitting and receiving electromagnetic waves, as shown in Figure 19, where the signal is strongest in the main lobe range. When the antenna is arranged, if the antenna attitude can be adjusted to the right position, the intensity of the received signal will be enhanced.  
A spectrum analyzer was used as a wireless signal detection tool to analyze the size of the power received under different poses. Because the attitudes of the transmitting antenna and the receiving antenna were relative, the transmitting antenna was fixed horizontally during the experiment, and the attitude of the receiving antenna was changed.  
As shown in Figure 20a, when the receiving antenna was parallel to the transmitting antenna, the maximum signal intensity was \(-23.63\) db. As shown in Figure 20b, when the receiving antenna was vertical to the transmitting antenna, the signal strength was minimum, \(-35.68\) db. Therefore, when using the instrument, the two antennas should be as parallel as possible to enhance the intensity of the received signal.
## 7 Conclusions  
In conclusion, this study introduced and addressed the challenge of fair resource allocation in a diverse IoT device environment within the SAGIN framework. The problem was formulated as a multi-objective optimization problem, specifically aiming to maximize the minimum channel capacity for each category of IoT devices. The proposed MOEA/D-MFAR algorithm, incorporating a decay rate mechanism, showcased superior performance and convergence over three traditional algorithms. The decay rate mechanism significantly increased the number of feasible solutions, facilitating convergence, as verified through testing. Through extensive evaluation in various scenarios, MOEA/D-MFAR demonstrated its ability to generate a greater number of superior feasible solutions, thereby enhancing the minimum channel capacity for each type of IoT device while ensuring equitable resource distribution. The abundance of pre-provided feasible solutions enhances the adaptability to scenarios characterized by dynamic and evolving requirements. This research adeptly resolves resource allocation equity in static ground equipment scenarios for applications like fixed-point oceanic exploration and wilderness monitoring. However, its applicability to highly mobile equipment scenarios is not addressed. In the future, we will explore more complex scenarios that include more resource types and mobile terminals, and seek efficient solutions.  
Conceptualization, Y.X. and Q.N.; methodology, Y.X., Q.N. and L.H.; validation, Y.X.; investigation, Y.X. and X.T.; data curation, Y.X. and X.T.; writing--original draft preparation, Y.X.; writing--review and editing, Y.X., Q.N., L.H. and H.U.; visualization, Y.X. and X.T.; supervision, Q.N.; project administration, Y.X. and Q.N.; funding acquisition, Q.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 61801318) and the Key Research and Development Program of Sichuan Province (Grant 2021 YFQ0011).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Iterations** & **10** & **20** & **30** & **40** & **50** \\ \hline ADSR(kb/s) & 13.81754 & 7.44094 & 5.24779 & 4.38385 & 2.54977 \\ Number of solutions & 915 & 1811 & 2876 & 4021 & 5044 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The variation in ADSR with the number of iterations in a scenario with 10 types of IoT devices.
# Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations
## 5 Conclusions  
Modern MLaaS clusters utilize GPU-sharing technology to manage ever-growing machine learning workloads across heterogeneous GPU resources. However, this approach can cause resource fragmentation, which in turn decreases overall GPU utilization. In this paper, we addressed the challenge of suboptimal GPU utilization within large-scale clusters equipped with heterogeneous GPUs. We introduced a novel scheduling method that employs genetic optimization techniques and compared it with existing scheduling algorithms. Our genetic optimization process consists of machine and task instance pair encoding, single-point crossover, adaptive mutation, and replacement. Our scheduling is performed periodically based on newly submitted job information during the current period, as well as the remaining jobs in the queue from the previous period that have not yet been dispatched. We ensured that our scheduling can be applied to a variety of ML workloads including CNNs, RNNs, transformer-based language models, and reinforcement learning tasks. This encompasses over 1200 tasks, comprising 7500 task instances, on large-scale heterogeneous GPU clusters with over 1800 nodes and 6000 GPUs. Our findings demonstrated that the proposed method enhances GPU utilization in the cluster by 12.8% compared to the widely used Round-Robin algorithm, without incurring performance trade-offs, when compared to conventional scheduling strategies.  
Figure 12: Average slowdown as scheduling algorithm is varied.  
**Author Contributions:** S.K. implemented the architecture and algorithm and performed the experiments. H.B. designed the work and provided expertise. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partly supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (RS-2021-II212068, Artificial Intelligence Innovation Hub) and (RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha University)).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in Alibaba Cluster Trace at [[https://github.com/alibaba/clusterdata/tree/master/cluster-trace-gpu-v2020](https://github.com/alibaba/clusterdata/tree/master/cluster-trace-gpu-v2020)]([https://github.com/alibaba/clusterdata/tree/master/cluster-trace-gpu-v2020](https://github.com/alibaba/clusterdata/tree/master/cluster-trace-gpu-v2020)) (accessed on 6 May 2024), reference number [20].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
###### Abstract  
Urban infrastructure construction poses significant risks to surrounding the infrastructure due to ground settlement, structural disturbances, and underground utility disruptions. Traditional risk assessment methods often rely on static models and experience-based decision-making, limiting their ability to adapt to dynamic construction conditions. This study proposes an integrated framework combining digital twin and knowledge graph technologies to enhance real-time risk assessment and emergency response in tunnel construction. The digital twin continuously integrates real-time monitoring data, including settlement measurements, TBM operational parameters, and structural responses, creating a virtual representation of the tunneling environment. Meanwhile, the knowledge graph structures domain knowledge and applies rule-based reasoning to infer potential hazards, detect abnormal conditions, and suggest mitigation strategies. The proposed approach has been successfully applied to a practical tunnel project in China, where it played a crucial role in emergency response and risk mitigation. By integrating real-time monitoring data with the knowledge-driven reasoning system, the developed framework enabled the early identification of anomalies, rapid risk assessment, and the formulation of effective mitigation strategies, preventing further structural impact. This bidirectional interaction between the digital twin and the knowledge graph ensured that the real-world data informed the automated reasoning, while the inference results were visualized within the digital twin for intuitive decision support. The proposed framework not only enhances current risk management practices but also serves as a foundation for future innovations in smart infrastructure and automated emergency response systems.  
urban infrastructure construction; emergency response; digital twin; knowledge graph +
Footnote †: journal: Applied sciences  
0000-0001-523-2281][PERSON]  
1,2,3,*, [PERSON]  
1 Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, 1239 Stiping Road, Shanghai 200992, China; [PERSON]L_ADDRESS] (C.C.); [EMAIL_ADDRESS] (B.W.)  
2 State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, 1239 Stiping Road, Shanghai 200992, China  
3 Engineering Research Center of Civil-Informatics, Ministry of Education, Tongji University, Shanghai 200992, China  
4 Jinan Transportation Development Investment Co., Ltd., Jinan 250000, China; [EMAIL_ADDRESS]  
*Correspondence: [EMAIL_ADDRESS]; Tel.: +86-139-2145-0140