esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A General Framework for Grid Partitioning Techniques and Grid Renumbering Methods
### Grid Renumbering Framework  
The neighbor relationship between adjacent cells reflects the memory access order of the numerical simulation. The system of discrete equations is obtained by discretizing on each interval of the control volume of the entire domain to be solved. Discrete systems of equations are presented as sparse matrices in a computer [13], where the non-zero elements of each row denote the coefficients of the unknown quantities of the current set of equations. The matrix usually contains a few nonzero elements, and the number of non-zero elements is exactly equal to the number of elements associated with this row.  
For different grid types, the coefficient matrix of a structured grid is banded, while the coefficient matrix of an unstructured grid is relatively irregular and only has the characteristics of a diagonal matrix. Therefore, based on the above characteristics, many solutions have been proposed. The methods for solving large-scale equations can be divided into direct and iteration approaches. Since the real case grid size of thermal hydraulics is usually large, the solution process of direct method requires a large amount of computational resources and the cost of solving the inverse is very high. Therefore,  
Figure 5: Example of packaging of process data. (**a**) unpacked transfer of data; (**b**) packet transmission of data.  
iteration approaches are usually used to addressing massive thermal-hydraulic problems. The iterative method means that the algorithm needs to be used several times to reach a convergence state. For example, ILU decomposition method, and the conjugate gradient method can be used as representatives of the iterative method for solving.  
The key to improving the speed of solving is that, by writing part of the memory data to the cache, the solver can access the required solved data with higher efficiency. In other words, by rearranging the mesh without altering the program structure [26], more valid data can be written to the cache to prevent frequent memory reads for a higher cache hit rate. Therefore, this section integrates part of the grid renumbering framework in YHACT, including but not limited to the Greedy algorithm and CQ algorithm implementation, which can be found in [27], and the RCM algorithm is elaborated below.
# Research on the Reliability of Bridge Structure Construction Process System Based on Copula Theory
## 3 Engineering Applications
### Calculation of System Reliability Indicators
#### 3.2.5 Calculation of the System Failure Probability
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(g_{i}\delta_{j}\)} & \multicolumn{2}{c}{**Copula Function \(\theta\)**} & \multirow{2}{*}{**[PERSON]**} \\  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline \(g_{33}\xi_{4}\) & \(0.99610\) & \(33.6443\) & \(1.3755\) & \(17.8221\) \\ \(g_{33}\xi_{5}\) & \(0.99920\) & \(75.5795\) & \(1.4054\) & \(38.7898\) \\ \(g_{33}\xi_{6}\) & \(0.99970\) & \(126.3697\) & \(1.4163\) & \(64.1849\) \\ \(g_{33}\xi^{7}\) & \(0.99940\) & \(90.5497\) & \(1.4098\) & \(46.2749\) \\ \(g_{48}\xi_{5}\) & \(0.99640\) & \(35.1471\) & \(1.3776\) & \(18.5736\) \\ \(g_{48}\xi_{6}\) & \(0.99690\) & \(37.5804\) & \(1.3808\) & \(19.7902\) \\ \(g_{48}\xi_{7}\) & \(0.99700\) & \(38.8247\) & \(1.3823\) & \(20.4123\) \\ \(g_{58}\xi_{6}\) & \(0.99980\) & \(157.4896\) & \(1.4196\) & \(79.7448\) \\ \(g_{58}\xi_{7}\) & \(0.99989\) & \(213.0538\) & \(1.4231\) & \(107.5269\) \\ \(g_{68}\xi\) & \(0.99986\) & \(189.3876\) & \(1.4218\) & \(95.6938\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Relevant parameter values of the alternative Copula function. modes were similar, which were all around \(10^{-6}\) and \(10^{-7}\). It can be seen that these four failure modes are highly correlated. Therefore, the influence of failure mode correlation on the reliability of the structural system should not be ignored in practical engineering. Bringing the individual two-dimensional joint failure probabilities into Equation (25), the probability of failure of the bridge structure system during the construction period can be calculated as \(1.8869\times 10^{-6}\), and the system reliability index is \(4.6235\). According to Section 5.5 of reference [34], the reliability index range calculated by the traditional wide bound method was (4.376, 4.52), and the reliability index range calculated by the narrow limit method was (4.4177, 4.4455), and the reliability index calculated by PENT method was 4.52. Compared with the reliability index 4.6235 calculated in this paper, it can be seen that the reliability index of the bridge structure system calculated in this paper is relatively high. Based on the additional calculation and analysis of other reference examples, it is concluded that the reliability of the bridge construction period system based on Copula theory is safer.
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 2 Underwater Acoustic Channel Model
### Attenuation  
The attenuation in underwater acoustic communications is characterized by a path loss that depends not only on the distance between the transmitter and the receiver, but also on the signal frequency. In particular, the path loss or the attenuation that occurs in an underwater acoustic channel over a distance \(d\) for a signal of frequency \(f\) is given by [37]:  
\[A(d,f)=d^{k}\alpha(f)^{d} \tag{1}\]  
where \(k\) is the spreading factor (ranging from 1 to 2) and \(\alpha(f)\) is the absorption coefficient of signal frequency \(f\). Note that the different values of \(k\) describe the different scenarios of geometry of propagation: \(k=1\) for cylindrical spreading; \(k=2\) for spherical spreading.  
Usually, Equation (1) can be expressed in dB as follows:  
\[10\log A(d,f)=k\times 10\log d+d\times 10\log\alpha(f) \tag{2}\]  
Generally, if the frequency \(f\) is above a few hundred Hz, the absorption coefficient \(10\log\alpha(f)\) in dB/km for \(f\) in kHz can be expressed as follows:  
\[\begin{split} 10\log\alpha(f)=0.11\times\frac{f^{2}}{1+f^{2}}+44 \times\frac{f^{2}}{4100+f^{2}}\\ +2.75\times 10^{-4}f^{2}+0.003\end{split} \tag{3}\]For the lower frequency \(f\), we have the absorption coefficient \(10\log\alpha(f)\) as follows [38]:  
\[10\log\alpha(f)=0.002+0.11\times\frac{f^{2}}{1+f^{2}}+0.011f^{2} \tag{4}\]
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 3 DF Indoor Location Estimation
### Experiment Scenario
#### 3.1.2 Conference Room  
The room model of the conference room and the position of each antenna are shown in Figure 10a,b. The receiving antenna was a 3-element linear array placed at a height of 2.0 m. One transmitting antenna (Tx1) was placed at a height of 0.9 m inside the room, and one antenna (Tx2) was placed at a height of 1.6 m outside the room. In this environment, the CSI was captured at each position when a person moved along a predetermined route. Here, the person moved at 0.5 m intervals with a random orientation. One-hundred fifty-one CSIs were captured in total.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Room Type** & **Small Office** & **Conference Room** \\ \hline Size & 4.0\(\times\)4.6 (m\({}^{2}\)) & 7.8\(\times\)8.8 (m\({}^{2}\)) \\ Bandwidth & 67.813 MHz (6 channel bonding) \\ \hline Transmitting antennas & 1 & 2 \\ \hline Receiving antennas & 3-element linear array \\ \hline Element pattern & Omni-directional \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurement specifications.  
Figure 9: Small office condition. (**a**) Room model. (**b**) Antenna position. (**c**) Defined area.
## 8 Simulations
### Simulation Results
First, we validate the experimental effect of pairing optimization by comparing it with the following edge-client pairing methods on the MNIST dataset, ensuring that all other conditions remain the same:  
(1) Random pairing;  
(2) Pairing based on similarity only;  
(3) Pairing based on computing capability only, where all clients are sorted by computing capability from highest to lowest, and each edge only considers clients with the same or similar computing capabilities. Figure 5 shows the impact of different pairing methods on global accuracy. It can be observed that although the differences among the four methods are not significant, there are still subtle variations: pairing based solely on computing capability and random pairing yield the worst results, further confirming the importance of similarity-based pairing within this framework. It also indicates that there is no need to pair clients based solely on computing capability similarity, as the dropout method can approximate this effect.
## 3 Results and Analysis  
Currently, most researchers focus on the aerodynamic sensitivity of a single-position or a single-modal deviation, and the influence of other coexisting deviations have not been considered enough. The following analyses focus on analyzing the difference between the performance influence of two deviations coexisting and the influence sum of two deviations existing separately. Through analyzing the fluid mechanism, the coupled effect model was created to estimate the blade performance when two deviations coexist. For the convenience of description, the coexisting condition is defined as the condition where two deviations in different positions exist on the blade surface simultaneously, and the separated condition is defined as the condition where only single deviation exist on the blade surface. In addition, the sum of the separated condition means adding the aerodynamic influences of two deviations when they exist separately.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 3 The Knowledge of Underwater Acoustic Sensor Networks
### The Characteristics of Underwater Acoustic Communication
#### 3.1.4 High noise and interference  
Due to the water currents, the machines, and the ships in the water, the noise under water is much more serious than in the terrestrial environment. The interference under the water is also higher than in the terrestrial environment, which is mainly due to the reflections caused by the surface, the bottom, or animals and impurities in the water. In addition, the underwater refraction is serious, too, so the multipath interference in underwater acoustic sensor networks is more serious than in terrestrial wireless sensor networks.
## 4 Channel Modeling
### Indoor Physical Channel Model
#### 4.1.2 RIS-UE Channel  
In this model for indoor communications, we can simplify the analyses by assuming that the RIS and the receiver are in close proximity, allowing for a direct LoS connection without any significant NLoS components present between them. The LoS-dominated RIS-UE channel \(\mathbf{g}\) is obtained by recalculating array response vectors for corresponding azimuth and elevation angles \(\phi_{UE}^{RIS-UE},\theta_{UE}^{RIS-UE}\), with the steps discussed in [25; 26]. \(L_{\text{LoS}}^{RIS-UE}\) can be measured by replacing \(d_{cs}\) in (17) with \(d_{RIS-UE}\).  
The gain associated with RIS elements that reflect the signals to UE is calculated like in (13) by considering \(\theta_{UE}^{RIS-UE}\) instead of \(\ \theta_{c,s}^{BS-RIS}\). Finally, the vector \(\mathbf{g}\) can be formulated as follows:  
\[\mathbf{g}=\sqrt{G_{e}\left(\theta_{UE}^{RIS-UE}\right)L_{\text{LoS}}^{RIS-UE}}\ e^{i\eta}\textbf{a}\left(\phi_{UE}^{RIS-UE},\theta_{UE}^{ RIS-UE}\right) \tag{20}\]
# Effect of Water Content on Light Nonaqueous Phase Fluid Migration in Sandy Soil
## 5 Conclusions  
This study investigated the effect of water content on the occurrence and migration of LNAPL based on indoor one-dimensional soil column experiments. The main conclusions are as follows:  
1. In sandy media, the migration rate of LNAPL presented an L-shaped trend during subsurface imbibition and a nonlinear relationship with migration time, while the migration rate during surface infiltration increased linearly with time;
2. In the homogeneous unsaturated state, the LNAPL residual rate was negatively correlated with water content and positively correlated with oil content. With the increase in the amount of leaked oil, 40% of the leaked LNAPL is sorbed within the soil;
3. When the water content in the medium was below 14%, and the oil content was below 11%, LNAPLs mainly occurred as solid phases in the soil unsaturated zone. As the water content increased, the residual rate of LNAPLs gradually decreased and eventually realized the oil saturation point;
4. After the water content of the medium exceeded 8%, the LNAPLs were subjected to oil-water interfacial tension, with the flow rate migrating to the saturated zone first decreasing and then increasing, displaying a nonlinear growth relationship.  
G.Z.: Conceptualization, formulation, or evolution of overarching research goals and aims. J.C.: Data curation, Writing--Original draft preparation. L.L.: Writing--Reviewing and Editing. H.Z. ([PERSON]): Data curation, Management activities to annotate. H.L.: Supervision, Oversight, and leadership responsibility for the research activity planning and execution. H.Z. ([PERSON]): Validation, Verification. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (41972261), The 15 th Graduate Innovation Ability Improvement Project of North China University of Water Resources and Electric Power (NCWUYC-202315072), and the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2021490511).  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** Throughout the writing of this dissertation, I have received a great deal of support and assistance. I would particularly like to acknowledge my team members, [PERSON] and [PERSON], for their wonderful collaboration and patient support. I would also like to thank my tutor, [PERSON], for their valuable guidance throughout my studies. You provided me with the tools that I needed to choose the right direction and successfully complete my dissertation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
## 4 Discussion
### Implications for Industry  
The importance and the impact of this research for manufacturing enterprises that are striving to support sustainability cannot be overemphasized. Evidence clearly indicates that adopting MES, IIoT, and AM can result in the most significant environmental and productive changes, making them an important part of any manufacturing strategy that aims to be sustainable. To give another example, real-time data provided by MES help manufacturers make educated decisions about production processes so they can efficiently reduce waste. Furthermore, IIoT enables meaningful maintenance procedures, as well as better resource distribution, and ultimately diminishes downtime and operating expenses. Once this is achieved, AM adds to the offer of precision manufacturing capability, which helps in the reduction of material waste as well as the localization of production, a critical aspect of climate change mitigation strategies. Manufacturers should adopt the technologies methodologically and in a phased manner to exploit their combined advantages. It is best if manufacturers start with MES to standardize and improve efficiency within the facility, then proceed with IIoT to unify all operations that depend on data and information, and finally integrate AM to introduce greener methods of production. Apart from this, the fact that AR, MR, and other similar systems are useful in improving job safety and in training workers points to incorporating these technologies into the broader bulk of corporate strategies, including those hatched to boost workplace productivity and create a friendly working environment (Table 8, Figure 15).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Sustainability** & **MES Adoption** & **IIoT Adoption** & **AM Adoption** & **Carbon Emissions** & **Energy Savings** \\  & **Score** & **(\%)** & **(\%)** & **(\%)** & **Reduction (\%)** & **(\%)** \\ \hline Year 1 & 70 & 40\% & 45\% & 30\% & 10\% & 15\% \\ \hline Year 2 & 78 & 60\% & 65\% & 50\% & 20\% & 22\% \\ \hline Year 3 & 85 & 75\% & 80\% & 70\% & 30\% & 30\% \\ \hline \hline \end{tabular} Note: sustainability scores and adoption percentages are based on progressive integration of Industry 4.0 technologies.  
\end{table}
Table 8: Longitudinal analysis of sustainability performance over three years.  
Description: a bi-axial diagram showing the impact of Industry 4.0 technologies on productivity (left Y-axis) and societal benefits (e.g., workplace safety and employee training efficiency, right Y-axis). The X-axis represents the different Industry 4.0 technologies (MES, IIoT, AM, AR, MR, and ICT), and the biases show the change in the two types of metrics, respectively.
## 5 Conclusions and Discussion  
In the context of space gravitational wave detection, this paper analyzes the main lobe and sidelobe phase noise in a ranging communication system based on BPSK modulation. The study reveals that, due to the influence of the DC component, the main lobe phase noise under the current design specifications is approximately 158 \(\upmu\)rad\(/\sqrt{\mathrm{Hz}}\), which is two orders of magnitude higher than the phase-tracking requirement of 5.9 \(\upmu\)rad\(/\sqrt{\mathrm{Hz}}\). To eliminate the impact of main lobe noise, this paper proposes an optimized design method for BBC sequences and establishes a corresponding performance optimization system. The experimental results demonstrate that the optimized BBC sequences significantly outperform the original Weil sequences in four key metrics: even autocorrelation, odd autocorrelation, maximum spectrum amplitude, and even cross-correlation. The respective improvements are 7.17, 2.83, 1.22, and 7.16 on average. Furthermore, the experiments confirm that BBC sequences are immune to random data, achieve dynamic bit balancing, and effectively suppress DC components. In future research, the differences in actual inter-satellite communication environments can be further considered by incorporating complex channel conditions into experimental designs to comprehensively verify their robustness. Moreover, an in-depth study of the sidelobe noise in BPSK modulation is also of great significance, as it would provide broader technical support for space gravitational wave detection and other high-precision ranging communication applications.  
Methodology, H.L. and T.Y.; software, H.L.; validation, H.L., T.Y. and K.X.; writing--original draft preparation, H.L., T.Y., K.X. and Z.W.; writing--review and editing, H.L., T.Y.,  
Figure 6: Comparison of BBC sequences before and after encoding the data.  
K.X. and Z.W.; visualization, H.L.; project administration, T.Y. and Z.W.; funding acquisition, T.Y. and Z.W. All authors have read and agreed to the published version of the manuscript.
* [1]**Funding:** This work was supported by the National Key R&D Program of China (2020 YFC2200602), the National Key R&D Program of China (2020 YFC2200604) and the National Key R&D Program of China (2020 YFC2200600).
* [2]**Data Availability Statement:** The data presented in this study are not publicly available due to privacy, and access can be requested from [[EMAIL_ADDRESS]] upon reasonable request.
* [3]**Conflicts of Interest:** The author of the current study has no conflicts of interest to declare.
## 3 Materials and Methods  
### Material Preparation  
Synthesis of PANI-GO: To prepare PANI-GO, 80 mg of GO (purchased from GaoxTiech Company, Hangzhou, China) was dispersed in 40 mL of deionized water and sonicated for 2 h to form a uniform GO dispersion. Separately, 1.2 g of aniline (C\({}_{6}\)H\({}_{7}\)N, 99.5%, Shanghai Macklin Biochemical Co., Ltd., Shanghai, China) was dissolved in 40 mL of dilute hydrochloric acid (1 mol L\({}^{-1}\), AR, Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) and then added to the GO dispersion, followed by stirring for 30 min. Subsequently, 0.8 mL of 30% hydrogen peroxide solution (H\({}_{2}\)O\({}_{2}\), AR, Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) solution was added dropwise to the mixture, which was stirred at room temperature for 24 h, resulting in a dark green dispersion. The product was centrifuged washed with deionized water until neutral, and then redispersed in 80 mL of deionized water to obtain a PANI-GO dispersion with a concentration of 1 mg mL\({}^{-1}\).  
The synthesis of MoS\({}_{2}\)-NG, MoS\({}_{2}\)-G, and MoS\({}_{2}\) was carried out through a hydrothermal method followed by calcination. For MoS\({}_{2}\)-NG, 800 mg of ammonium molybdate tetrahydrate ((NH\({}_{4}\))\({}_{6}\)Mo\({}_{7}\)O\({}_{24}\) 4H\({}_{2}\)O, 99%, Shanghai Macklin Biochemical Co., Ltd., Shanghai, China), 2 g of thiourea (CH\({}_{4}\)N\({}_{2}\)S, AR, Sinopharm Chemical Reagent Co., Ltd., Shanghai, China), and 200 mg of polyvinylpyrrolidone (PVP, K30, Shanghai Dibai Biotechnology Co., Ltd., Shanghai, China) were added to 80 mL of PANI-GO dispersion, stirred for 2 h at room temperature, and then transferred to a hydrothermal reactor at 200 \({}^{\circ}\)C for 20 h. The product was centrifuged, washed with deionized water and ethanol, freeze-dried, and finally calcined at 800 \({}^{\circ}\)C under a nitrogen atmosphere at a heating rate of 10 \({}^{\circ}\)C per minute to obtain MoS\({}_{2}\)-NG. The MoS\({}_{2}\) content in the composite was approximately 40.7% (Figure S6). MoS\({}_{2}\)-G was synthesized similarly, using a GO dispersion instead of PANI-GO, while MoS\({}_{2}\) was prepared without any GO or PANI-GO, following the same procedure.