esa-sceva/satcom-chunk-collection huggingface dataset:
# SACT: A New Model of Covert Communication Based on SDN
## 3 Materials and Methods
### Watermark Anti Detection Analysis
#### 3.2.2 Mapping Synchronization Strategy  
The mapping relationship between watermark information and binary code (i.e., binary code table) is stored in watermark embedder and extractor. In this paper, the End Hopping [42] is used as a reference. The coding mapping relationships change dynamically according to the time, so that the distribution of watermark information is more complex to confuse attackers. The continuous transformation of coding mapping relationship improves the diversity of watermark carrier data stream distribution. Correctness of time synchronization is the key to ensure the basic function of the model system.  
At present, the popular synchronization methods include ACK (_Acknowledge Character_) response synchronization, timestamp synchronization, and strict clock synchronization [43]. Each of the three methods has characteristic advantages. The accuracy of ACK response synchronization depends on ACK acknowledgement packets, and a congested network may lead to inefficient ACK packet transmission. Timestamp synchronization is relatively secure and does not change due to the network jitter, but it is complex, expensive, and inefficient to implement. Strict clock synchronization is the simplest and most efficient method, but this method is susceptible to NTP (_Network Time Protocol_) server, which can lead to error synchronization.  
In this paper, considering the efficiency of synchronization, the strict clock synchronization method based on NTP is used to realize the synchronization of the controller code mapping relationship. It can not only be used to evaluate the clock offset between multiple terminals but also to estimate the RTT (_Round-Trip Time_) of data packets. NTP synchronization is a mature and widely used synchronization method, and there are mature solutions to the clock skew problem.  
Controller sends synchronization request to NTP server. The current encoding relationship is determined based on the timestamp returned by the NTP server. In this way, the diversity of encoding is realized and the anti detection ability of watermark is improved.
## 3 Results
### Spatiotemporal Dynamic Variation Pattern in LUEmax
#### 3.1.2 Temporal Dynamics of LUEmax Across Multiple Vegetation Species in the Same Climate Type  
The LUEmax values across different vegetation types exhibited significant spatiotemporal heterogeneity within the same climate zones (Figure 4). In tropical climates, the LUEmax values for DBF and GRA showed noticeable fluctuations. GRA generally exhibited higher LUEmax values than DBF, with significant interannual variability and distinct peaks. DBF maintained relative stability and small peaks. All vegetation types (ENF, DBF, GRA, WET, and CORN) demonstrated notable variability in their LUEmax values in temperate climates. ENF and DBF exhibited higher LUEmax values and more pronounced peaks than other vegetation types. GRA and WET maintained moderate levels of LUEmax, while CORN consistently showed lower values. ENF showed the highest LUEmax values in continental climates, with frequent and sharp peaks over time. DBF and GRA also demonstrated relatively high LUEmax values but with more minor fluctuations. WET and CORN displayed the lowest LUEmax values, with WET showing slightly more variability than CORN. WET consistently exhibited the highest LUEmax values in polar and alpine climates, characterized by substantial fluctuations and peaks. ENF and DBF showed moderate LUEmax values, while GRA and WET had lower levels, with less variability and fewer pronounced peaks.  
The results reveal that LUEmax values vary significantly across vegetation types within the same climate type, reflecting differences in vegetation's physiological and ecological responses to climatic conditions. ENF and DBF generally exhibited higher LUEmax in temperate and continental climates, while WET dominated in polar and alpine climates. GRA maintained moderate values across most climates, whereas CORN showed consistently lower LUEmax across climate zones.
# 2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation
## 3 Experiments and Results
### Multiresolution Registration Experiment  
This paper introduces the multiresolution strategy to realize the registration step by step from coarse to fine to speed up the registration algorithm's convergence speed and improve the registration efficiency. At each registration experiment level, the Powell optimization algorithm's one-dimensional search accuracy is set to 0.01. The algorithm's overall iterative accuracy is set to 0.001, and the maximum number of iterations is set to 1000. The rigid-body transformation parameters are arranged in \(\big{(}\alpha,\beta,\theta,t_{x},t_{y},t_{z}\big{)}\). The first three parameters are rotation along the X-, Y-, and Z-axes, and the last three are translation along the X-, Y-, and Z-axes. The experimental results show that when the initial value is optimized, the true value point of the rotation parameter is \((0,0,0,0,0)\) and is sampled within \(\pm 30\) degrees. Likewise, the true value point of the translation parameter is sampled within \(\pm 30\) mm.  
Figure 10 shows the difference image between CT and DRR image registration results under the multiresolution framework. The true value image is shown as \((-13,4,2,10,20,10)\). NCC's true value points are based on the Sobel operator, NCC based on LOG operator, and original NCC from left to right.  
Table 4 shows the statistical registration results of the three similarity measures under the multiresolution registration framework.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Rotation (\({}^{\circ}\))**} & \multicolumn{2}{c}{**Translation (mm)**} \\ \hline \multirow{3}{*}{NCCS} & MAE & 0.4955 & \multirow{3}{*}{0.9883} \\  & MTRE & & 1.94117 mm \\  & time & & 6012.6 s \\ \hline \multirow{3}{*}{NCCL} & MAE & 0.3189 & \multirow{3}{*}{0.7750} \\  & MTRE & & 1.4759 mm \\  & time & & 5765.6 s \\ \hline \multirow{3}{*}{NCC} & MAE & 1.2083 & \multirow{3}{*}{3.14079} \\  & MTRE & & 3.19295 mm \\ \cline{1-1}  & time & & 5129 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Single-resolution experimental results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**(\(\alpha\), \(\beta\), \(\theta\), tx, ty, tz)**} \\ \hline Truth point & (10,10,10,40,40) & (40,40,40,10,10,10) \\ \hline Initial value point & (0,0,0,0,0,0) & (0,0,0,0,0) \\ \hline \multirow{3}{*}{NCCS} & (10.325, 9.00845, 10.0975 & (39.6403, 40.2076, 39.8805, \\  & 39.6466, 36.2063, 40.0849) & 10.0196, 11.3873, 9.70042) \\ \hline \multirow{3}{*}{NCCL} & (17.3676, 17.3365, 16.7622 & (50.6946, 34.7924, 10.1197, \\  & 46.5633, 47.9499, 40.5774) & 56.3769, 74.0497, 8.35779) \\ \hline \multirow{3}{*}{NCC} & (16.1266, 7.13819, 16.4462 & (49.3488, \(-\)8.45826, 0.334837, \\  & 41.8934, 47.7294, 40.4012) & \(-\)3.30738, 139.747, 5.38556) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of single resolution with enlarged sampling space.
## 3 Simulation Modeling and Verification
### Polishing Trajectory Space Optimization and Planning
#### 3.2.1 Simulation of Contact Stress Distribution Between the SG Flexible Tool and the Marble Surface  
The simulation of the contact area between the SG flexible tool and marble surfaces of different curvatures was conducted with a specified contact length (L) using Abaqus. It was observed that the contact area was blade-like, and that the pressure in this area increased from the center of the SG flexible tool to the edge, specifically from point P to point H. The pressure was symmetrically distributed along the PH line, as shown in Figure 6a.  
The node pressure along the PH path of the contact area between the SG flexible tool and the marble surfaces of different curvatures was obtained from Abaqus and plotted into the node pressure curve, as shown in Figure 8. It can be seen that in the contact areas with different curvature, the pressure along the PH path has an increasing trend and a good linear distribution, especially when the surface curvature is small. However, as the surface curvature increases, there is a tendency for the pressure to decrease near the H point along the PH path. This decrease is mainly due to the reduced contact area between the outer edge of the SG flexible tool and the workpiece as the surface curvature increases, resulting in lower pressure near the edge position. However, the trend can still be considered approximately linear.
## 3 Results and Discussion  
The experiments performed try to analyze the effectiveness of each data augmentation type to deal with the problems of packet losses and low bit-rate speech coding. The main goal is to improve the Word Error Rate (WER) of speech recognition tested on simulated data and real data coming from call centers. The different data augmentation strategies have been evaluated on twelve data sets, each one with a different condition. Table 1 shows the Mean Opinion Score (MOS) (estimated by ITU-T P.563 [36]) for each of these twelve conditions and the original data without any degradation. The results obtained show that single packet losses get worse MOS than data with burst packet losses. This can be surprising, but it may probably be related to the fact that, when the packet loss percentage is the same, for individual packet losses, the number of interruptions is three times higher than for burst packet losses. There is also a somewhat surprising result comparing MP3 coding at 8 and 16 Kbit/s. Besides this, the variations shown by MOS are as expected. MOS decreases as the distortion effect increases (e.g., higher packet loss percentage).
## 4 Empirical Results and Discussion
### The Direct Effects of Crude Oil Price Shocks
However, based on the Granger causality test, the causal relationships from crude oil to CPI are not statistically significant, proving that the fluctuation transmission of crude oil prices is not smooth. This might be because the Chinese government formulates a pricing mechanism for refined oil products and takes a series of measures to stabilize the consumer market, decreasing the impact of crude oil price shocks on CPI. From Figure 3b, it is observed that the shocks of four crude oil prices could be transmitted to RPI. The responses of RPI are larger in the first two months, after which the responses decline sharply and fade away over time. The direct effects are significant until the sixth month. We also sum the absolute values of the direct impacts of crude oil price shocks on each price index in the first 15 months, as shown in Table 1. It can be figured out that the direct impacts on RPI are the lowest, while the direct impacts on EPI are the largest. The impact of crude oil price fluctuations on trade costs could be significantly reflected in the import and export price index. Furthermore, Chinese price indices are more sensitive to Brent and Daqing crude oil price shocks.
## 3 Simulations and Results
### Orbit Determination Simulation
#### 3.2.1 Effects of the Measurement Errors
At the selected epoch, the mean position error is 114 m and the estimated mean value of \(\alpha_{\rm T}\) is 7.095 \(\times\)\(10^{-5}\) m/s\({}^{2}\) for Case 1. For Case 2, these values are 163 m and 7.058 \(\times\)\(10^{-5}\) m/s\({}^{2}\). For Case 3, the results are 200 m and 7.065 \(\times\)\(10^{-5}\) m/s\({}^{2}\). As the accuracy of the observational data decreases from Case 1 to Case 3, more iterations are needed to reach the final result. The position errors at the selected epoch also increase. Case 1 achieves position errors below 200 m by the second iteration. Case 2 requires until the third iteration to reduce errors below 300 m, while Case 3 needs three to four iterations to reach sub-500 m precision. Orbit propagation was then performed using the OD results (position, velocity, and the along-track acceleration) from these tests and Equation (4). Figure 9 summarizes the RMS three-dimensional position errors of the predicted orbits over 24 h. Overall, the 24 h RMS position errors for all cases remain below 2000 m. The more accurate the observational data, the smaller the errors in orbit determination and prediction. For the same observational data, the accuracy of OD and OP is nonlinearly related to the measurement noise used in the OD process. Additionally, it is observed that smaller OP position errors occur when the measurement noise level used for observational data generation matches that employed in the OD process. Figure 8: _Cont._  
Figure 8: Iterative histories of position errors and \(a_{\rm T}\) estimates at the chosen epoch with varying measurement noise levels for OD: (**a**) Position errors and \(a_{\rm T}\) estimates vs. iteration number for Case 1. (**b**) Position errors and \(a_{\rm T}\) estimates vs. iteration number for Case 2. (**c**) Position errors and \(a_{\rm T}\) estimates vs. iteration number for Case 3. Figure 9: 24 h RMS three-dimensional position errors based on OD results for varying measurement noise levels.
# Application of a Novel High-Order WENO Scheme in LES Simulations
## 4 Conclusions  
This study extended the novel high-order finite volume WENO scheme to viscous flows. It was embedded into the three-dimensional unsteady viscous LES-CFD solver NUAA-Turbo. The new WENO scheme uses the same linear weights at each quadrature point on the boundary of the target cell in its spatial reconstruction process, which avoids the issue of negative linear weights. A series of classical benchmark cases were tested and validated using the LES-CFD solver. The results were compared with those obtained using the classical WENO-JS scheme and experimental data from the literature. The following conclusions are drawn:  
1. Spectral characteristic analysis and DHIT case calibration reveal that the WENO-ZQ scheme exhibits lower dissipation and dispersion errors, providing better resolution for high-frequency waves.
2. Numerical simulations of the three-dimensional viscous TGV reveal that the WENO-ZQ scheme captures more turbulence structures on the grid compared to the same-order WENO-JS scheme. The flow field predictions using the third-order WENO-ZQ scheme are analogous to those of the WENO-JS5 scheme, with the results on \(64^{3}\) cells for the WENO-ZQ scheme being comparable to those on \(128^{3}\) cells for the WENO-JS scheme. This indicates that the WENO-ZQ scheme can deliver high-precision simulations with fewer grid points, significantly reducing computational resource requirements.
3. The WENO-ZQ scheme accurately predicts complex physical phenomena such as transition, separation-induced transition, and SWTBLI. Its time-averaged results are in strong agreement with those reported in the existing literature. At the same grid resolution, the WENO-ZQ scheme captures more detailed flow field structures, proving its potential for application in LES studies of complex compressible flows.  
Conceptualization, S.Z. and D.Z.; methodology, S.Z. and D.Z.; software, D.Z.; validation, S.Z., H.W. and X.W.; formal analysis, X.W.; investigation, S.Z.; data curation, D.Z.; writing--original draft preparation, S.Z.; writing--review and editing, N.G.; supervision, H.W.  
Figure 20: The coherent structures of the suction surface.  
and N.G.; funding acquisition, N.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from the National Science and Technology Major Project of China, grant number Y2019-I-0018-0017.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Problem Description
### Benchmark Simulation Model No. 1 (BSM1)  
To effectively evaluate wastewater treatment control strategies, the European Cooperation in Science and Technology (COST) and the International Water Association (IWA) jointly developed the benchmark simulation model BSM1 for the activated sludge method [24]. Figure 1 shows the general layout of the model's two components, the biochemical reactor and the secondary clarifier. The biochemical reactor consists of two anoxic tanks and three aerobic tanks. The activated sludge model No. 1 (ASM1), which has 13 components in Table 1, 8 biochemical reaction processes, and 19 parameters, was adopted by the mechanistic model of the biochemical reaction. The secondary clarifier, which has 10 layers, is where the wastewater goes after the biochemical reaction. The primary purpose of the secondary clarifier is to use sedimentation to separate mud and water. After sedimentation, the upper layer of clear water is discharged and the lower layer of sludge is sent in two parts--partly to the biochemical reaction to take part in the reaction and partly to be drained from the system. The secondary clarifier's mechanism model uses a double-exponential sedimentation velocity model.  
Due to the significant biochemical reactions and main operating variable quantity of WWTPs occurring in ASM1, a large amount of research has focused on this. The general formula for mass conservation in ASM1 can be expressed as follows:  
\[\frac{dZ_{k}}{dt}=\left\{\begin{array}{c}\frac{Q_{int}Z_{int}+Q_{t}Z_{t}+Q_{ t}Z_{t}+r_{1}V_{1}-Q_{1}Z_{1}}{V_{1}},k=1\\ \frac{Q_{k-1}Z_{k-1}+r_{k}V_{k}-Q_{t}Z_{k}}{V_{k}},k=2\sim 5\end{array}\right. \tag{1}\]  
Figure 1: The process flow diagram of BSM1.  
where \(V_{k}\) represents the volume of unit \(k\), and \(r_{k}\), \(Q_{k}\), and \(Z_{k}\) respectively represent the reaction rate, flow rate, and concentration of each component in unit \(k\). Similarly, subscripts \(r\), \(int\), and \(i\) represent external reflux, internal reflux, and inflow, respectively.  
Studies reveal that the nitrate concentration in unit 2 (\(S_{NO,2}\)) and the DO concentration in unit 5 (\(S_{O,5}\)) are directly related to the quality of effluent and they are essential to the effectiveness of wastewater treatment. In BSM1, the control of \(S_{NO,2}\) and \(S_{O,5}\) is achieved by controlling the internal return flow rate (\(Q_{int}\)) and the oxygen transfer coefficient (\(K_{La5}\)) of unit 5. The variation of \(S_{NO,2}\) can be represented by Equation (1) and the variation of \(S_{O,5}\) can be expressed as:  
\[\frac{dS_{O,5}}{dt}=\frac{1}{V_{5}}(Q_{4}S_{O,4}+r_{5}V_{5}-Q_{5}S_{O,5}+(K_{ La5})V_{5}(S_{O,sat}-S_{O,5})) \tag{2}\]  
where \(S_{O,sat}\) is the DO saturation concentration, which is usually taken as 8 g/m\({}^{3}\).
## 2 Materials and Methods
### Analysis of Handover Decision Factors
#### 2.2.3 Data Rate  
In the power system, considering the grid user data rate is essential because the power system requires responsiveness to real-time monitoring and control needs, and the user data rate directly impacts the efficiency of information transmission. Therefore, the consideration of power data rate in the design of satellite handover algorithms is an important factor in ensuring the efficiency of power system communication and data transmission.  
Due to their distance from the ground, satellites experience significant effects from the surrounding environment during signal transmission, leading to various losses and fading phenomena, resulting in a weakened signal strength. The channel model mainly comprises free-space propagation loss, atmospheric loss, shadow fading, and other factors, based on the causes of loss and fading. Among these factors, free-space propagation loss is considered the primary source of loss during wireless signal transmission. Combined with the Star-Earth link distance given in Section 2.2.2, the free-space propagation loss \(L_{F}\) can be defined as [35]:  
\[L_{F}=20 lg\big{(}\frac{4\pi df}{\lambda}\big{)} \tag{6}\]  
where \(c\) is the speed of light; \(f\) is the carrier frequency in GHz; \(L_{a}\) denotes the signal loss generated by the atmosphere, rainfall, etc.; and \(L_{o}\) is the other losses and fading. Then, the total loss \(L_{p}\) of the signal during transmission can be expressed as:  
\[L_{p}=L_{F}+L_{a}+L_{o} \tag{7}\]The received power is defined as:  
\[P_{r}=P_{t}-L_{p}+G_{r}+G_{t} \tag{8}\]  
where \(P_{t}\) is the transmit power and \(G_{t}\) and \(G_{r}\) are the antenna gains of the transmitter and receiver, respectively. The user data rate is given by [PERSON]'s capacity theorem:  
\[R=Blog(1+\frac{P_{r}}{P_{N}}) \tag{9}\]  
where \(B\) is the channel bandwidth and \(P_{N}\) is the noise power.
# Air Quality Predictions through Mathematical Modeling for Iron Ore Mine Project
###### Abstract  
Mathematical modeling was deployed to predict air quality during the construction and operation phases of an iron ore mine project in Maharashtra, India. A survey of different models revealed that the ISCST3 model was the most applicable one to predict the air quality parameters, particularly the suspended particulate matter (SPM) and coarse particulate matter (PM\({}_{10}\)). Baseline air quality data, emission rates, local meteorology, and terrain information were used to simulate the ground-level concentrations. The simulation predicted SPM and PM\({}_{10}\) peaks of 172 \(\upmu\)g/m\({}^{3}\) and 44 \(\upmu\)g/m\({}^{3}\), respectively. The prediction was within the prescribed limits of the national standards of 200 \(\upmu\)g/m\({}^{3}\) and 100 \(\upmu\)g/m\({}^{3}\), respectively, near the source, with minor exceedances in total SPM in two nearby villages and an impact on air quality due to proposed mining. Accordingly, mitigation strategies towards such villages were recommended and implemented. Later, the monitoring in the operation phase revealed that particulate matter could be controlled effectively with mitigation strategies and ensured compliance with air quality standards. The analysis also revealed strong correlations between the particulate matter and the distance of the localities and SPM and PM\({}_{10}\). Continuous monitoring and adaptive mitigation based on real-time data were thus emphasized for long-term sustainability and responsible mining practices.  
air quality; ISCST3 modeling; iron ore mining; particulate matter; construction phase; operational phase; mitigation strategies; air quality assessment
# GIS-Based Evaluation Method for Accessibility of Social Infrastructure Facilities
## 3 Methodology
### Data
#### 3.3.2 Travel Time Calculation Using Navigation API  
The walking and vehicle travel times from the RBs to the social infrastructure facilities were calculated using the navigation API (T-map API). T-Map, which was provided by SK Telecom in South Korea, is a commercial navigation system similar to the Google navigation system. The origin and destination information are needed to calculate the travel time. The nearest destination facility from each origin RB was identified in GIS. Then, the travel time was calculated via the navigation API and added to the attribute table of the corresponding RB spatial features for further analysis.  
T-map API provides a function to calculate the distance and time traveled by walking and riding vehicles. We needed to automate the travel time calculation because many combinations of origin and destination (RBs and infrastructure facilities) are possible. Therefore, this study automated T-map API using Java8.1 and Javascript-based programming. This customized code extracted data on the origin and destination, such as origin longitude, origin latitude, destination longitude, and destination latitude, from the RB spatial features combined with the nearest facility data. The extracted data were sent to T-map API to obtain the path-search results of travel distance (unit: meter), travel time (unit: second) between the origin and destination. According to the abovementioned process, the distance and travel time between the RBs and social infrastructure facilities were obtained.