esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## 5 Conclusions and Future Work  
This paper introduces an error state extended Kalman filter algorithm and segmented Rauch-Tung-Striebel (RTS) smoothing algorithm to enhance the localization accuracy and robustness of GNSS and IMU sensors. The cumulative error of INS over time are overcome when GNSS signal is disturbed. The simulation results show that the proposed method is more linear and has higher localization accuracy than the traditional EKF algorithm. It also demonstrates good robustness in achieving better accuracy with low\(-\)quality GNSS signals. The algorithm can serve as a foundation for low\(-\)cost sensor fusion processing and is a valuable reference for further research.  
Over the next few years, combined navigation systems will see increased usage and development across a range of applications. As sensor and communication technologies progress, and the demands for navigation safety and reliability continue to rise, there will be a greater need for more accurate and dependable navigation systems.  
Furthermore, the RTS smoothing algorithm\(-\)assisted combined navigation algorithm presented in this paper was simulated for offline computation. For practical applications in the future, real\(-\)world testing will be necessary to verify the hardware feasibility of the algorithm, and to consider real\(-\)time data transmission and computation to achieve unmanned driving. Overall, positioning is a crucial component of intelligent driving, and its potential impact on society and the economy is vast. Combined navigation will be more and more widely used in production and practical life.  
Conceptualization, Y.Y. and J.Z.; methodology, J.Z. and Y.Y.; software, M.G. and X.N.; validation, Y.W. and J.L.; formal analysis, X.N. and Y.W.; investigation, M.G.; resources, J.L.; data curation, M.G. and J.L.; writing--original draft preparation, J.Z.; writing--review and editing, X.N., Y.Y. and M.G.; visualization, Y.W. and J.L.; supervision, X.N.; project administration, J.L.; funding acquisition, X.N., Y.Y. and J.L. All the authors have read and agreed to the published version of the enclosed manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Provincial Natural Science Foundation of China, grant number LY22E050018; This research was funded by the Basic Public Welfare Research Program of Zhejiang Province, grant number LGG22E050019; the National Natural Science Foundation of China (NSFC), grant number 51905483.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.  
The authors declare no conflict of interest.
## 5 Microcontroller
### ZVS Detection  
The transistor losses can be greatly reduced if the switching is timed correctly. This method is known as Zero-Voltage Switching (ZVS). In ZVS, the transistor's voltage fluctuation is monitored, and the turn-on pulse is not applied until the collector-emitter voltage reaches zero. This causes the transistor to operate in a soft-switching area, and therefore, the losses due to switching are significantly reduced. As discussed earlier, in a quasi-resonant inverter, applying ZVS methods is like swinging a pendulum at the right time, as it requires precise real-time information about the system's condition. Ref. [4] proposes a novel multi-coil ZVS inverter that can be used in flexible surface induction cookers.  
This feature is implemented in the MCU's program, and \(T_{off}\) is calculated by designing an algorithm that measures the blackout time needed for \(V_{CE}\) to reach zero. Since the flux interaction between the coil and the load often changes with displacement on the coil or the use of different materials, the presumed oscillation frequency is no longer constant and must be calculated periodically. It is also noteworthy that the oscillation period of the system is slightly different from \(T_{off}\).  
Figure 10: ZVS parameters.  
The algorithm is written in such a way that after the coil is briefly energized with specific periods (\(T_{on}\) in Figure 11), the ADC_IGBT_VCE signal (IGBT voltage) is sampled regularly by the ADC2 unit; then, by applying the differential analysis, the point of ZVS (zv in Figure 11) is determined. After that, \(T_{off}\) gets calculated, and the highest \(V_{CE}\) value is recorded for the given \(\Delta t_{on}\) (which should not surpass the rated voltages).
# Experimental Study on Streamwise Vortex-Induced Vibration of a Flexible, Slender Cylinder
###### Abstract  
Laboratory tests on streamwise vortex-induced vibration (VIV) of a flexible, slender cylinder were carried out in a towing tank. A cylinder model, 20 mm in diameter and 3.91 m in length, was towed horizontally to generate a uniform profile of relative velocity in calm water. The aspect ratio (length to diameter) and mass ratio (structural mass to displaced fluid mass) of the cylinder were 195.5 and 1.39, respectively. The Reynolds number was in the range of 1000-3000. The displacement amplitudes and vibration frequencies were investigated in order to examine the dynamic characteristics of the flexible cylinder undergoing streamwise VIV. Experimental results indicated that there were two detached branches in the streamwise response curve, i.e., the first excitation region (\(1.0<Vr<2.6\)) and the second excitation region (\(2.6<Vr<4.0\)). This phenomenon is similar to the streamwise VIV of an elastically-mounted rigid cylinder, except that a larger and shifted peak was observed in the second branch in this study. The cylinder's linear orbits in the first excitation region indicated that the streamwise vibration frequency was equivalent to its transverse counterpart. However, in the second excitation region, the cylinder's motion showed a figure-of-eight pattern, and the streamwise frequency was twice that in the transverse direction. In addition, two curve-fitted formulae of the maximum displacement amplitudes in the first and second excitation regions, versus the reduced mass-damping parameter, were proposed. This was accomplished by compiling the published streamwise VIV data and those in the present research work.  
v 2018, 8, 311; doi:10.3390/app8020311
# Quality of Service Based NOMA Group D2D Communications
## 2 System Model
### Power Allocation Coefficients Policies  
The optimal power and resource allocation improve overall performance and utilise the system resources efficiently. However, the optimum power allocation strategies proposed in existing literature, for example, [25], cannot be directly applied to the current work because of significant difference in system model or underlying transmission method. Therefore, in this sub-section, we discuss two simple methods to compute power allocation coefficients \(\{\beta_{i}\}_{i=1}^{M}\) that would lead to two possible implementations of Q-NOMA group D2D communications.  
Figure 1: Example of inband non-orthogonal multiple access (NOMA) group device-to-device (D2D) communications with overlay cellular network.
## 5 Conclusions  
An experimental investigation on the influence of premixed port injection and biogas injection at the intake valve was carried out in RCCI mode. B5 and regular diesel were, directly, injected as test fuels along with the biogas in a premixed and port-injected at the valve, as a modified gaseous fuel delivery approach. The influence of B5 and biogas fuels' reactivity on the CO and UHC emissions reduction along with an improvement in other essential parameters was investigated. The results showed that the combustion of B5 and biogas injected at the intake valve was disadvantageous to the rise in the cylinder pressure due to the poor quality of the biogas used. The use of low-quality biogas significantly affected the in-cylinder pressure development resulting in very poor combustion. Besides, the burning of the fuels was found beneficial to the rate of heat released at low to medium engine loads due to higher in-cylinder temperature developed. The result also shows retarded the combustion phase as the engine capacity increased with the highest indicated thermal efficiency of 36.33% at a load of 5.5 bar IMEP. The CO\({}_{2}\) and NOx emissions increased, but the UHC emissiondecreased, significantly, as the capacity increased while the CO emission increased at a load of 4.5-5.5 bar IMEP, then reduced as the load increased. The use of these fuels together with the in-cylinder mixing, due to injection at the valve, was capable of averagely reducing the persistent challenge of the CO and UHC emissions, by about 20.33% and 10% respectively, compared to the premixed mode. The trends of the emissions were found comparable to that of the traditional diesel RCCI combustion for both approaches. Furthermore, the patterns of the emissions in response to the change in the engine speed signify that the high engine speed selected might also contribute to high emissions for both premixed and in-cylinder mixing approaches.  
I.B.D.; Conceptualization, methodology, investigation and writing, M.A.S.; Funding acquisition, supervision, review and editing, Z.A.A.K.; Resources, supervision, review and editing, S.E.M.; Project administration and review. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Malaysia's Petroleum Research Fund (PRF) Grant (Cost Centre: 0153 AB-A34).  
The authors would like to acknowledge the support given by the Universiti Teknologi Petronas (UTP) and Centre for Automotive Research and Electric Mobility (CAREM) in conducting this research.  
The authors declare no conflict of interest. And The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Methods
### Data Processing Method
#### 2.3.2 GNSS Monitoring Technology  
In this study, the real-time monitoring of three-dimensional deformation of the channel surface was conducted using the GNSS relative positioning method. GNSS relative positioning involves collecting GNSS observational data simultaneously from multiple receivers. Through differential processing of the observation values, errors such as satellite clock difference, receiver clock difference, and atmospheric delay can be eliminated or weakened, thus realizing high-precision GNSS positioning. Typically, least-squares (LS) or Kalman filtering methods are employed in GNSS relative positioning solutions. These methods necessitate the appropriate determination of the functional model and stochastic model within the solution framework. The functional model describes the mathematical relationship between the unknown parameters and the GNSS observations, while the stochastic model describes the statistical properties of the observations themselves and their interdependencies, usually represented by a suitable variance-covariance matrix. To obtain highly accurate and reliable positioning results, it is crucial to accurately and appropriately determine both the functional model and the stochastic model [33].
## 3 Experimental Results
### Reversibility, Security, and Complexity  
We further evaluated the reversibility, security, and complexity of our proposed scheme in this section. The project was run on a Mac mini device with a M2 chip using MATLAB R2023b. In Table 4, there are three Hausdorff distances [35]; the Hausdorff distances1 represent the distances between the original 3D model and the recovered 3D model. It can be observed that the values are all equal to 0, which indicates the original 3D mesh models can be totally recovered in our scheme. The Hausdorff distances2 and Hausdorff distances3 were calculated using the original 3D model with the encrypted 3D model and the embedded 3D model, respectively. These distances were too large, so the shapes of original 3D models could be well protected to ensure security. In addition, the distances under different \(\alpha\) values show no noticeable difference, which means that the security is not affected by the number of vertices in a group. The run time refers to the processing time on the content owner side. It can be seen that the run times of different models are mainly affected by the number of vertices, and the 3D models can be processed in a short time to adapt the real-time application. Additionally, as \(\alpha\) increases and more usable vertices need to be processed, it results in a linear growth in processing time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**3D Models** & \begin{tabular}{c} **Number** \\ **of Faces** \\ \end{tabular} & \begin{tabular}{c} **Number** \\ **of Vertices** \\ \end{tabular} & \begin{tabular}{c} **\(\alpha\)** \\ **Distance1** \\ \end{tabular} & \begin{tabular}{c} **Hausdorff** \\ **Distance2** \\ \end{tabular} & \
\begin{tabular}{c} **Hausdorff** \\ **Distance3** \\ \end{tabular} & **Run Time (s)** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Dog \\ Face \\ \end{tabular} } & 3230 & 1618 & 3 & 0 & 18.45 & 18.73 & 3.50 \\  & & 4 & 0 & 18.61 & 18.52 & 4.69 \\  & & 2 & 0 & 15.98 & 15.95 & 2.14 \\  & 3604 & 1884 & 3 & 0 & 15.81 & 16.03 & 3.71 \\  & & 4 & 0 & 15.92 & 15.85 & 5.26 \\  & & 2 & 0 & 36.66 & 36.90 & 5.86 \\  & 9528 & 5986 & 3 & 0 & 36.85 & 36.71 & 11.20 \\  & & 4 & 0 & 36.97 & 36.82 & 16.64 \\  & & 2 & 0 & 33.71 & 33.57 & 6.54 \\ Car & 11,230 & 5911 & 3 & 0 & 33.77 & 33.69 & 12.51 \\  & & 4 & 0 & 33.58 & 33.62 & 18.48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relevant Data of Four Test Models.  
Figure 7: Embedding rate comparison on the dataset [27; 28; 29; 30; 31; 32].
## 4 Results and Discussion
From Table 5, it can be seen that at the terminal moment, the distance between the defender and the pursuer is 0.4223 km, which is shorter than the safety distance 0.5 km, and at the terminal moment, the distance between the pursuer and the evader is 1.7823 km, which is longer than the safety distance 0.5 km. All the above show that at the terminal moment, the defender successfully intercepts the pursuer and that the evader successfully evades the capture of the pursuer. As shown in Figure 11, the defender intercepts the pursuer at the terminal moment. From Table 6, it can be seen that the distance between the pursuer and the defender in the game is 0.3959 km, which is shorter than the safety distance 0.5 km, and that the distance between the pursuer and the evader is 1.6016 km, which is longer than the safety distance 0.5 km. This shows that the defender intercepts the pursuer successfully at the terminal moment, and the evader evades the capture of the pursuer successfully. Figure 12 shows the curves of the control variable of each player changing with time in the directions of \(X\), \(Y\), \(Z\). From the figure, it can be seen that the control curves of the pursuer remain overlapped with those of the evader. Comparing Example 3 with Example 4, it can be seen that, because of the different control strategies adopted by the pursuer, the time that the defender takes to intercept the pursuer in Example 3 is longer than that in Example 4. Moreover, at the terminal moment, the distance between the pursuer and the defender in Example 4 is shorter than that in Example 3. The comparison between Example 1 and Example 2 shows that when the control variable of the pursuer is in a dominant position, the optimal control strategy proposed in this paper makes the pursuer bypass the defender and capture the evader. The comparison between Example 3 and Example 4 shows that when the control variable of the pursuer is not in a dominant position, the optimal control strategy proposed in this paper prolongs the time that the defender takes to intercept the pursuer. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Pursuer** & **Evader** & **Defender** \\ \hline \(X\)/km & 8.384 & 9.069 & 8.714 \\ \(Y\)/km & 8.956 & 9.593 & 9.132 \\ \(Z\)/km & 11.81 & 10.51 & 11.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Position of each player at the end of the game. Figure 11: The position of each player changing with time in (**a**) x-axis, (**b**) y-axis, and (**c**) z-axis. Figure 12: The control variable of each player changing over time in (**a**) x-axis, (**b**) y-axis, and (**c**) z-axis.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 4 Dataset Description and Analysis
### Preprocessing of the Rome Dataset
Let us indicate with \(y_{i}\) the \(i\)-th original value and with \(\hat{y}_{i}\) the corresponding interpolated value; furthermore, let us indicate with \(\overline{y}\) the average of the original values:  
\[\overline{y}=\frac{1}{m}\sum_{i=1}^{m}y_{i}. \tag{8}\]  
Given the above assumptions, MSE and \(R^{2}\) for the interpolation technique used to generate the \(\hat{y}_{i}\) values are defined as follows:  
\[MSE=\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}, \tag{9}\]  
\[R^{2}=1-\frac{SSE_{res}}{SSE_{tot}}, \tag{10}\]  
where \(SSE_{res}=\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}\) is the residual sum of squares, and \(SSE_{tot}=\sum_{i=1}^{m}(y_{i}-\overline{y})^{2}\) is the total sum of squares. MSE and \(R^{2}\) are correlated since \(SSE_{res}=m\cdot MSE\); however, they can provide complementary information on the performance of the interpolation technique since they  
Figure 5: RSSI RF data collected for a sample NPCI as a function of time during a measurement run for the Oslo dataset (**a**) vs. the Rome dataset (**b**). take values over different ranges [47]: \(MSE\) is defined in \([0,+\infty)\), where 0 indicates a perfect interpolation, while \(R^{2}\) is defined in \((-\infty,1]\), where 1 indicates a perfect interpolation. Note that for reasonably good interpolators, \(R^{2}\) typically takes non-negative values; a \(R^{2}=0\) corresponds, in fact, to an interpolator that replaces each missing value with the average value \(\overline{y}\), that can be considered as a baseline benchmark for the other techniques. The results of the evaluation are presented in Figures 6 and 7, showing a box plot representation of the performance in terms of MSE and \(R^{2}\), respectively; each point in the box plot represents the performance for a specific NPCI according to the considered indicator. The results indicate that all interpolators achieved an average performance better than did the baseline benchmark (\(R^{2}=0\)); nevertheless, the performance gap between the best interpolator and the worst one was not negligible, as highlighted in Figure 8. Figures 6 and 7 show that several interpolators had a good performance, with an average MSE under 10 and an average \(R^{2}>0.7\). Among these, the one that consistently provided the best performance for both indicators in terms of average value and of the variability around it was the linear interpolator, which was thus adopted to preprocess the Rome dataset. Figure 8: Comparison of best and worst interpolators vs.the original data for a sample NPCI from the Oslo dataset. \[x_{i}=\frac{(i-k)x_{l}+(l-i)x_{k}}{l-k},\]0 \(R^{2}\) obtained for the considered interpolation techniques (**a**) and zoom of the same data in the range \([0,1]\) (**b**), where MA stands for moving average and MM for moving median; these techniques were tested for different average window lengths, from 5 to 20.
## 2 Multifocused Analysis Methodology
### Groundwater Agriculture Problem: Multifocused Analysis
#### 2.1.2 Water Analysis  
As previously discussed, climate change has an important impact on pumping solutions and it must be considered for irrigation groundwater purposes. In fact, well-irrigated areas have increased rapidly over the last century thanks to a large investment in pumping technology. Thanks to advances in irrigation technology, farmers have changed their agricultural models from rainfed lands (with a low agricultural productivity ratio) to high-yield irrigated crops. Indeed, some of these crops have a very high water demand, such as maize, beets, and rice. Therefore, these advances have given farmers a remarkable opportunity to diversify their crops for greater economic value but also higher water demand value, by increasing pressure on aquifers [39].  
Concerns about groundwater sustainability became relevant when many aquifers reached overexploitation and encountered emergency situations [40]. Overexpression of aquifers also involves direct environmental impacts on discharges or sources [41; 42]. Indeed, sources and rivers dependent on aquifers have considerably reduced their flows, generating problems downstream of the aquifers, for both irrigation and human consumption purposes. At the same time, lake and fluvial aquatic ecosystems have been degraded, depending on maintenance of the phreatic level. Examples of these situations can be found in the Tables of Daimiel (Spain), the Saiss plain (Morocco), the flow losses of the Mikkes River, and other sources [43]. Overexpression and other water problems are transformed into greater energy demands. Irrigation methods present different efficiency values, with significant discrepancies among them. For example, methods based on gravity, furrows, or flooding are the most inefficient irrigation solutions (50%). Apart from water inefficiency, their use can bring serious consequences to underground aquifers [44]. To achieve suitable crop maintenance, a more efficient use of water resources should be proposed, such as localized irrigation (90% efficiency) or irrigation by spraying (70-80%) [45]. In addition to the previous problems, which are easily discernible by their immediate impact on the agricultural economy, other problems associated with the continuous depletion of aquifer resources can be identified, such as the problematic subsidence of the terrain due to different pressures of water storage inside [46].
## 4 Corpus Appraisement
### Evaluation and Comparison Using the Corpus  
Now that the corpus contribution has been obtained via the methodology shown in Figure 3 and explained in Section 3.5, this section describes how the corpus can be used when a routing algorithm's evaluation and comparison process is needed. For the sake of clarity, some in-depth details are not included in this section, such as software configurations. The reader is asked to keep in mind that this section is intended to outline the usability of this study's main proposal rather than providing a closed recipe for using the corpus of OppNet scenarios.  
When the research stage requires an evaluation of a routing algorithm, researchers interested in using a corpus will have to implement a simulation environment such as the one shown in Figure 2. The researchers should start by configuring the OppNet simulation software. After this, to use the corpus, researchers will have to download it. The corpus is available entirely free and without the necessity of login information. It is understood that at this stage, the routing algorithm that is going to be evaluated is already selected. Finally, researchers might configure nodes and messages and establish the metrics that will retrieve the information to evaluate the performance. If the researcher desires to assess a comparison, the process will have to be repeated only by changing the routing algorithm. Then, the researchers should compare the corresponding routing metrics obtained from the respective simulations.  
In this section we have evaluated the behavior of the corpus with a high replication algorithm to limit the response of the corpus when transmitting messages, and the results show that the corpus scenarios have different network behaviors between them. This result ratifies the positive assessment of the corpus. From now on, the scientific community has a collection of scenarios where their routing algorithms and features can be tested, thus avoiding scenario selection, reducing time and eliminating unintended bias. The corpus contributes towards establishing a proper benchmarking scheme for OppNet routing algorithms where the routing performance is not relative to other routing algorithms but is examined overall.