esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 4 Experimental Results and Discussion
### Experimental Results and Discussion
#### 4.2.2 Dynamic Test
The results of the tracking performance in the dynamic test show that the proposed IF-DDC method can optimize the tracking band, sharing tracking information to aid the weak signal channels with strong signal channels to reduce the noise and scintillation interference, and utilize SINS information to reduce the dynamic requirement of the tracking loop. Finally, the position and velocity errors for different methods in the dynamic test are shown in Figures 13 and 14. Similar to Figures 9 and 10, the time axis of Figures 13 and 14 starts from 432,018 s which is the start epoch of the system outputting the navigation results after about 34 s of tracking. In order to assess the effectiveness of the ionosphere-free combination of L1 C/A and L2 CM signals, the non-ionosphere-free dual-frequency deep-coupled navigation (i.e., the L1 C/A and L2 CM signals used as independent information in the system, denoted T-DDC) is also compared in the navigation results. Table 7 shows the RMS position error statistics of the dynamic test. \begin{table}
\begin{tabular}{l c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**RMS Position Errors (m)**} \\ \cline{2-4}  & **X** & **Y** & **Z** \\ \hline T-GNSS & 7.0663 & 10.2338 & 7.0663 \\ T-DDC & 3.6790 & 2.5797 & 2.2351 \\ IF-DDC & 0.6486 & 1.2961 & 1.2655 \\ \hline \end{tabular}
\end{table} 
Table 7: RMS position error statistics of the dynamic test. Figure 12: Carrier frequency tracking performance of SV #25: (**a**) carrier Doppler; (**b**) Doppler error. Figure 13: The position errors in the dynamic test using different methods in polar areas. From Figure 13 and Table 7 we can see that both the T-DDC and IF-DDC methods can obtain more accurate position results than the T-GNSS method. Meanwhile, IF-DDC has better position performance than the T-DDC, which indicates that the ionosphere-free combination of L1 C/A and L2 CM signals can further reduce the position error caused by ionospheric delay. Table 8 shows the RMS velocity error statistics of the dynamic test. As depicted in Figure 14 and Table 8, we also see that both the T-DDC and IF-DDC methods can obtain more accurate velocity results than the T-GNSS method. Moreover, the velocity of T-GNSS is still affected by the effects of jerk, while the velocities of T-DDC and IF-DDC are much smoother than that of T-GNSS during the jerk periods. As a whole, the proposed IF-DDC method can obtain more stable and accurate navigation performance compared with the traditional T-GNSS method.
## 5 Experiment and Result Analysis
### Result Analysis
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Weather Type**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Metrics**} \\ \cline{3-5}  & & **MAPE/\%** & **MAE/MW** & **RMSE/MW** \\ \hline \multirow{8}{*}{Sunny} & TCN & 11.3537 & 1.9486 & 0.7029 \\  & GRU & 10.7660 & 1.6261 & 0.5929 \\  & SVM & 9.8237 & 1.6269 & 0.6050 \\  & WLSSVM & 7.4394 & 1.3569 & 0.5363 \\  & SAO-WLSSVM & 5.8246 & 1.0328 & 0.4139 \\  & MISAO-WLSSVM & 4.4907 & 0.8707 & 0.3643 \\  & MISAO-ICEEMDAN-WLSSVM & 3.3567 & 0.5309 & 0.2287 \\ \hline \multirow{8}{*}{Cloudy} & TCN & 28.5691 & 3.0543 & 1.9109 \\  & GRU & 29.1185 & 2.9264 & 1.9895 \\  & SVM & 24.7850 & 2.4711 & 1.6014 \\  & WLSSVM & 26.7472 & 2.3479 & 1.4691 \\  & SAO-WLSSVM & 17.8016 & 1.9377 & 1.2764 \\  & MISAO-WLSSVM & 11.3291 & 1.4569 & 0.9562 \\  & MISAO-ICEEMDAN-WLSSVM & 8.0349 & 1.4569 & 0.8033 \\ \hline \multirow{8}{*}{Rainy} & TCN & 40.1888 & 5.0142 & 2.9073 \\  & GRU & 34.2816 & 3.9762 & 2.3077 \\ \cline{1-1}  & SVM & 30.7951 & 3.5970 & 2.0810 \\ \cline{1-1} \cline{2-5}  & WLSSVM & 26.0090 & 3.1009 & 1.8872 \\ \cline{1-1}  & SAO-WLSSVM & 18.0956 & 2.3070 & 1.4174 \\ \cline{1-1}  & MISAO-WLSSVM & 14.3216 & 1.9603 & 1.2211 \\ \cline{1-1}  & MISAO-ICEEMDAN-WLSSVM & 9.6233 & 1.5711 & 1.0589 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of evaluation indexes of prediction results. racy of the model, which verifies the superiority and validity of the MISAO-ICEEMDAN modal decomposition. In order to make a strong argument for the necessity of classifying PV forecasts according to different weather conditions, we selected the same models as in the previous experiment, i.e., TCN, GRU, SVM, WLSSVM, SAO-WLSSVM, MISAO-WLSSVM, and MISAO-ICEEMDAN-WLSSVM, and we started this experiment based on the data from 1 July to 15 July 2018. The experimental metrics are recorded in Table 8, and the prediction results are displayed in Figure 11.  
The data in Table 8 show that the key metrics such as MAE, MAPE, and RMSE are significantly higher when not categorized based on weather conditions compared to the previous experiments categorized based on weather. Figure 11 visually presents the prediction result curves of each model in this unclassified experiment. It can be observed that the prediction curves in the unclassified case are more volatile and deviate from the actual values to a more significant extent than the relatively smooth and accurate prediction curves in the previous classification case. It is especially noteworthy that even under such unfavorable experimental conditions, the MISAO-ICEEMDAN-WLSSVM model proposed in this paper still shows relatively optimal performance. This result strongly supports the critical role of weather condition-based classification in improving the accuracy of PV forecasts. It demonstrates the robustness of the proposed model under different experimental settings.
## 7 Conclusions  
In this paper, a novel technique called Hybrid Frequency Shift-Pulse Width Modulation (HFSPDM) was proposed and implemented on an in-house Li-Fi testbed. The other two modulation techniques, OOK and BFSOOK, were implemented on the same testbed to compare their performances. The best results for each technique were compared. It was observed that BFSOOK provided consistent BER/bps of close to 0 for a frequency 200 Hz while our proposed HFSPDM technique has shown BER/bps closer to 0.04. It was also observed that in HFSPDM, the number of frames required to transmit the same amount of data as that in BFSOOK was considerably lesser but more than OOK; this is due to the fact that HFSPDM represents, on average, 2 bits per 5 frames, whereas BFSOOK represents 1 bit per 3 frames. The bit-to-frame ratio is higher in terms of OOK; however, HFSPDM gives better BER for longer distances than OOK as OOK is more sensitive to noise. Thus, it can be concluded that HFSPDM can be used in OCC applications that require low computation and high accuracy for comparatively longer distances.  
Conceptualization; methodology, S.S.; validation, G.V.; formal analysis, S.S.; investigation, S.S.; resources, S.S.; writing--original draft preparation, S.S.; writing--review and editing, S.S. and G.V.; supervision, G.V.; project administration, G.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The code and relevant data is available at the following link [[https://github.com/SanketSalviNITK/LiFiPrototype.git](https://github.com/SanketSalviNITK/LiFiPrototype.git)]([https://github.com/SanketSalviNITK/LiFiPrototype.git](https://github.com/SanketSalviNITK/LiFiPrototype.git)) (accessed on 27 June 2022).  
**Conflicts of Interest**: The authors declare no conflict of interest.  
Figure 19: Accuracy Comparison of OOK, BFSOOK and HFSPDM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modulation Technique** & **BER/bps** & **No. of Frames per Bit** & **Distance (cm)** \\ \hline OOK & 0.06 & 0.05 & \textless{}20 \\ BFSOOK & 0 & 3 & \textgreater{}20 \\ HFSPDM & 0.04 & 2.5 & \textgreater{}20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: HFSPDM vs. OOK vs. BFSOOK.
## 5 Performance Analysis
### Performance Analysis on Commercial Asset Tracking Application  
The results of the existing new asset tracking architecture [59], optimization decomposition framework [60], and optimization framework [61] are discussed with the CATS (Consolidated Asset Tracking System) dataset collected from Kaggle. The URL of the dataset is given as [73]. In total, 500 sensor nodes are deployed to conduct the simulation in commercial asset tracking.  
Figure 17 illustrates the Error Rate (\(ER\)) in Equation (7) performance of three different methods evaluated across various asset data points ranging from 100 to 1000. At 600 asset data points, the \(ER\) for these methods are recorded at 27%, 34%, and 44%, respectively. As the number of data points increases, the \(ER\) correspondingly rises. Notably, the New Asset Tracking Architecture exhibits a significantly lower \(ER\) compared to the other two frameworks. This improvement can be attributed to its use of a hybrid RFID thermal sensor in distributed networks, which enhances asset tracking accuracy and reliability. The architecture facilitates seamless asset tracking and searching through the integration of  
Figure 15: Measurement analysis of execution time.  
radio frequency recognition, Bluetooth low-energy tags, and smartphones. Additionally, it optimizes battery life and distance estimation accuracy, resulting in a minimal \(ER\). Specifically, the New Asset Tracking Architecture reduces the \(ER\) by 21% and 42% compared to the other frameworks, underscoring its superior performance.  
Figure 18 presents the \(ER\) performance of three different methods. The recorded \(ER\)s are 26%, 33%, and 44%, respectively. These results demonstrate that the New Asset Tracking Architecture achieves the lowest error rate, significantly reducing errors compared to the other two methods. This indicates its effectiveness in minimizing inaccuracies and improving overall performance in asset tracking compared to existing frameworks.  
Figure 16: Overall performance analysis of execution time.  
Figure 17: Measurement analysis of error rate.
# Aerodynamic Efficiency Improvement on a NACA-8412 Airfoil via Active Flow Control Implementation
## 2 Numerical Method
### Drag coefficient  
\[C_{d}=\frac{2F_{d}}{\rho CU_{\infty}^{2}} \tag{13}\]  
where \(F_{d}\) is the dimensional drag force. Note that the lift and drag coefficients are a function of the angle of attack (AoA) and Reynolds number.  
**Aerodynamic efficiency**  
\[E=\frac{F_{l}}{F_{d}}=\frac{C_{l}}{C_{d}} \tag{14}\]  
**Pressure coefficient**  
\[C_{P}=\frac{P-P_{\infty}}{\frac{1}{2}\rho U_{\infty}^{2}} \tag{15}\]  
The parameter \(P\) is the generic static pressure and \(P_{\infty}\) is the free-stream one.  
**Friction coefficient**  
\[C_{F}=\frac{\tau_{w}}{\frac{1}{2}\rho U_{\infty}^{2}} \tag{16}\]  
where \(\tau_{w}\) are the wall shear stresses.  
**Dimensionless wall distance**  
\[y^{+}=\frac{u_{\tau}y}{v} \tag{17}\]  
With:  
\[u_{\tau}=\sqrt{\frac{\tau_{w}}{\rho}} \tag{18}\]  
\(u_{\tau}\) characterizes the friction velocity and \(y\) is the dimensional distance with respect to the wall.  
**Jet momentum coefficient**  
\[C_{\mu}=\frac{h(\rho U_{max}^{2})\text{sin}\theta_{j}}{C(\rho U_{\infty}^{2})} \tag{19}\]  
where \(h\) characterizes the jet width, \(U_{max}\) is the maximum jet velocity and \(\theta_{j}\) stands for the jet inclination angle measured versus the airfoil surface (see Figure 2).  
**Non-dimensional forcing frequency, (Strouhal number)**  
\[Strouhal=F^{+}=\frac{fC}{U_{\infty}} \tag{20}\]  
The non-dimensional forcing frequency is often given as:  
\[F_{*}^{+}=\frac{f}{f_{0}} \tag{21}\]  
The variable \(f\) is a generic frequency and \(f_{0}\) is the vortex shedding frequency obtained from the baseline case. As in the preset study \(C=1\) and \(U_{\infty}=1\), the relation between \(F^{+}\) and \(F_{*}^{+}\) becomes, \(F^{+}=F_{*}^{+}f_{0}\).  
**Courant-Friedrichs-Levy number**  
\[CFL=\frac{u\Delta t}{\Delta x} \tag{22}\]  
where \(u\) is the generic fluid velocity, \(\Delta t\) is the time step and \(\Delta x\) the generic mesh cell length.
# Design of Indoor Visible Light Communication PAM4 System
## 4 Modulation Characteristic Analysis
### Mixed Noise  
There will be both additive and multiplicative noise in the optical communication system, so we use the measured low-frequency multiplicative noise and the additive noise data of Xi'an, Beijing, and Changchun for research and analysis. The obtained rainy days, snowy days, and foggy days relationship of BER with SNR is shown in Figure 14a-c, Figures 15a-c and 16a-c. It can be seen from Figure 14 that for the BER of \(10^{-5}\), light rain, moderate rain, and heavy rain need 39.4 dB, 35.3 dB, and 37.5 dB, respectively. Dry snow and wet snow need 37.2 dB and 39.8 dB, respectively; mist needs 38.6 dB, and thick fog is greater than 40 dB, comparing Figure 14 with Figure 13. It can be seen that there is almost no difference between the BER under the condition that the multiplicative noise and the additive noise exist at the same time and the BER where only the multiplicative noise exists. The reason is that the influence of the multiplicative noise is much greater than the influence of the additive noise. The additive noise generally refers to the background noise of the system, mainly thermal noise and shot noise. The multiplicative noise refers to the physical characteristics of the signal through some medium during propagation so that the amplitude of the signal itself changes randomly, and the information contained in the signal is lost. The multiplicative noise appears with the appearance of useful signals. When the signal disappears, the multiplicative noise will also disappear. The randomness of the multiplicative noise is caused by the time-varying and nonlinearity of the system, reflecting the fading of the channel, so the influence of the multiplicative noise is greater than that of the additive noise. When considering the multiplicative noise, the influence of the additive noise is generally negligible.  
Comparing the BER curves in Xi'an, Beijing, and Changchun under the same weather, it can be seen that whether it is rainy days, snowy days, or foggy days, the BER performance is almost indistinguishable, which proves that the additive noise in different cities has a very small impact on the BER performance, and also verifies that the effect of the multiplicative noise on the BER performance is much greater than that of the additive noise.  
Figure 16: The relationship of BER with SNR in Changchun: (**a**) rainy days, (**b**) snowy days, and (**c**) foggy days.  
Figure 14: The relationship of BER with SNR in Xi’an: (**a**) rainy days, (**b**) snowy days, and (**c**) foggy days.  
Figure 15: The relationship of BER with SNR in Beijing: (**a**) rainy days, (**b**) snowy days, and (**c**) foggy days.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 3 Architecture of the Network Emulation Platform
### Architecture
#### 3.1.4 Measurement Plane  
The measurement plane mainly has two functions, namely real-time network information collection and full packet capture. The capture container is the basic unit of the measurement plane. Each capture container covers a Python-based Daemon which realizes network information collection and supports full packet capture according to the configuration of researchers. Measurements are stored in the database and real-time network information is displayed by the Web UI.
## 1 Introduction
[PERSON] et al. proposed a review that shows the wide usage of the topology method utilized on aircraft designs. The problems of stiffness ribs design, multi-component design, and multi-fasteners design could be solved using the topology method [21]. [PERSON] et al. presented a 2D topology code using the Optimality Criteria (OC) method [22]. [PERSON] et al. extended the topology research into the 3D situation and provided relevant codes [23]. At present, piezoelectric topology focuses on improving the electromechanical conversion coefficient and reducing material flexibility, however there is little multi-objective research exploring binding power output and natural frequency. This is because of the low manufacturability after topology design, and the emergence of 3D printing solves this problem. In recent years, the study of topology optimization has been developed rapidly. In this work, the three-dimensional topological optimization based on piezoelectric materials has been discussed. Using the Optimality Criteria, topology optimization is applied to the cantilever piezoelectric transducer. The structure optimization was realized with the voltage and stiffness as the objective function. The corresponding codes are given to show the process of optimization. This bi-objective optimization shows an obvious increase in both stiffness and voltage output. As the iteration process shows, the results of bi-objective optimization prove the value of additive mass at the bottom of the cantilever. This lays the foundation for future piezoelectric transducer structural optimization. Using only stiffness as the objective, the objective increases inconspicuously. Bi-objective optimization shows its superiority. As far as the author knows, relevant research that focuses on stiffness and voltage is limited, and even fewer papers extend it to the 3D situation. Existing papers [24; 25] have conducted research based on the 2D situation for simplicity, and some of them used a truss structure. Their research results focus on amplifying displacement and enhancing electromechanical conversion efficiency. Therefore, this research, which focuses on enhancing the voltage and stiffness of the piezoelectric energy harvester based on the 3D situation, is innovative. Additionally, this is also the first time a piezoelectric topology code has been shared.
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
## 3 Experimental Results
### Influencing Factor
#### 3.2.4 Cuttings Particle Shape  
Figure 12 illustrates the relationship between cuttings return velocity and maximum bed height for different particle shapes. The return velocity of Type 1 cuttings is 77.0% of that of Type 3 cuttings, with a corresponding 49.2% increase in cuttings bed height. Similarly, when comparing Type 1 and Type 4 cuttings, the return velocity of Type 1 is 85.5% of that of Type 4, with a 22.2% increase in bed height. These comparisons indicate that flaky cuttings (Type 1) are more prone to forming cuttings beds than spherical particles (Types 3 and 4).  
This tendency becomes more pronounced with larger particle sizes, as the gravitational forces acting on the particles increase, making it easier for them to settle and form a bed. Shale cuttings, in particular, due to their irregular shape and flaky structure, experience greater resistance during transport and require higher critical velocities to be effectively carried by the drilling fluid compared to spherical particles.  
Figure 11: Cuttings bed height and cuttings return speed at different eccentricity.  
Figure 12: Cuttings bed height and cuttings return speed of different types.
## 3 Results and Discussion
### CO Diffusion Law
#### 3.2.2 Effect of \(u_{0}\) on the CO Diffusion  
Figure 13 demonstrates a significant effect of the \(u_{0}\) range from 2 m/s to 10 m/s on CO concentration. Under the condition of small \(u_{0}\) values, the CO concentration increases rapidly to a fairly high peak concentration in a short time, and then starts to decrease at a rate lower than the concentration increase. Additionally, the higher the \(u_{0}\) value, the lower the peak concentration that can be measured at each test point, and the shorter the \(t\) required for all concentration profiles to fall to 0. This means that as \(u_{0}\) increases, the emission rate of CO also increases, and the pollutant transport function in the model strengthens.  
Figure 12: Variation in CO concentration at different \(L_{0}\) values.  
Figure 13: Variation in CO concentration at different \(u_{0}\) values.
###### Abstract
Though some studies have revealed that the inner scale has a more significant impact on the scintillation index than the outer scale when an optical wave propagates though the moderate-to-strong turbulence [35], this conclusion has not been certified under a weak turbulence, to the best of our knowledge. According to the above analysis, the individual influences of the inner and outer scales on UWOC system were studied, while their comprehensive impacts on this system under anisotropic weak oceanic turbulence have not been simultaneously studied yet. To fill this gap, we focus on revealing the effect of inner and outer scales on a spherical wave's scintillation index under weak anisotropic oceanic turbulence. The major contributions of this study are summarized as follows. The aperture averaging technique is used to alleviate the fluctuation of the intensity. Specifically, the channel and system parameters are considered, such as inner and outer scales, anisotropic factors, structure parameters, aperture diameters and wavelengths. Moreover, the influence of the system and channel parameters on the scintillation index is scrutinized in detail. The rest of this study is structured as follows. In Section 2, we introduce a modified anisotropic spatial power spectrum model that takes the inner and outer scales into account. Considering the effects of both the inner and outer scales, the scintillation index model of a spherical wave propagating in the weak oceanic turbulence with the aperture average effect is derived in Section 3. The simulation results are presented and evaluated in Section 4. Finally, concluding remarks are given in Section 5.
## 4 Discussion: Comprehensive Analysis of the Deep-Learning-Oriented Framework
### Contribution to Seaport Operations' Efficiency and Sustainability
#### 4.1.4 Environmental Sustainability  
The sustainability of seaport operations is of paramount importance in the contemporary era. Our framework contributes significantly to environmental sustainability by reducing the carbon footprint associated with transportation activities within seaports. By precisely mapping routes and optimizing drayage movements, our system minimizes unnecessary vehicle movements, thereby decreasing fuel consumption and emissions [49]. This eco-friendly approach aligns with global sustainability goals, marking a significant stride towards greener seaport practices.  
These real-life examples, supported by credible studies, demonstrate the practical implications of our framework in seaport operations. While these examples provide a glimpse of its potential, it is essential to acknowledge that these are theoretical advancements awaiting practical validation through further research and implementation. Our framework serves as a pioneering step toward a future where seaport operations are not only efficient and productive, but also environmentally conscious and sustainable.