esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### 6-DoF Motion Tracking for Smartphones  
The proposed 6-DoF SMC component, addressing the lack of 6-DoF motion tracking in smartphones, is related to the work [16] in visual-inertial fusion mechanism. The experimental platform, introduced in [16], contains a smartphone utilizing external monocular camera and IMU (Inertial Measurement Unit). The motion detection is based on two concurrent data streams: visual frames captured from camera and IMU linear acceleration data with angular velocity. In comparison to [PERSON] et al. [16], the 6-DoF SMC component relies on a built-in smartphone camera. While the contribution of [PERSON] et al. [16] is presented as a standard for smartphone AR/VR applications, our solution offers a component that can be deployed on any platform implementing WebXR.  
Smartphone-based headsets have the potential to be closer to the standard VR, with 6-DoF motion tracking and environmental understanding [17]. This idea was implemented by [PERSON] [18], in a smartphone VR application to track the user's motion and transform it into a movement inside VR. The application can process data obtained from a camera and IMU sensors, while the user interacts through a smartphone-based headset. The motion tracking is based on the Google ARCore ([[https://developers.google.com/ar/](https://developers.google.com/ar/)]([https://developers.google.com/ar/](https://developers.google.com/ar/)), accessed on 22 April 2021) services and inside-out tracking. The 6-DoF SMC component also utilizes the same ARCore technology as Mendez [18]. Our solution is implemented in HTML and JavaScript under the WebXR Device API. Our interface is fully accessible through a web-browser on a variety of platforms while the application of [PERSON] (2018) [18] is limited by the underlying platform.
# Tribological Analysis of Steels in Fuel Environments: Impact of Alloy Content and Hardness
## 3 Results and Discussion
### Friction and Wear Performance
The scale bar is 200 \(\upmu\)m.  
Figure 7: Summary of steady state friction values of soft and hard 52100, soft CF2, and soft and hard D2 steel (**a**) alumina counterbody and (**d**) 52100 counterbody under high-frequency reciprocating at a frequency of 20 Hz under 10 N load in ethanol and decane environment. Counter-body (ball) wear rate ((**b**) alumina counterbody & (**e**) 52100 counterbody) after sliding the same cycles against soft and hard 52100, soft CF2, and soft and hard D2 steel under ethanol and decane. The flat wear rate of soft and hard 52100, soft CF2, and soft and hard D2 steel against (**c**) alumina counterbody (**f**) 52100 counterbody under ethanol and decane environment. Figure 7a shows how the steel samples behaved in terms of friction when they were paired with alumina as the counterbody material. Notably, in the ethanol environment, soft 52100, soft CF2, and hard D2 displayed lower coefficients of friction. These three steels also had lower coefficients of friction in the decane environment, with hard D2 exhibiting the lowest compared to the other steel alloys. The ball wear rates of the alumina counterbody are given in Figure 7b. In ethanol, it is evident that hard 52100 showed the lowest wear rate, followed by hard D2. In the decane environment, hard D2 stood out with the lowest wear rate of the ball, followed by soft D2. Figure 7c plots the wear rate of flat steels when paired with an alumina counterbody. Remarkably, hard D2 exhibited the lowest flat-sample wear rate in both ethanol and decane environments, which can also be confirmed by the profilometry results shown in Figure 8a,d. When we investigate how these materials performed in terms of friction against the 52100 counterbody material, as shown in Figure 7d, in both the ethanol or decane environment, hard D2 exhibited a lower coefficient of friction than the other steel samples. The ball wear rate (Figure 7e) and flat wear rate (Figure 7f) results demonstrated that hard D2 consistently displayed the lowest wear rate of both the steel itself and the counterbody. This trend observed for the ball and flat wear rates is consistent through the tests, even though the COF shows greater variability in values. The profilometry results  
Figure 8: Surface profiles of soft and hard 52100, soft CF2 steel, soft and hard D2 ((**a,b**) alumina counterbody after sliding in ethanol and decane environment. Surface profiles of soft and hard 52100, soft CF2 steel, soft and hard D2 ((**c,d**) 52100 counterbody), after sliding in the ethanol and decane environment. (Figure 8b,d) also revealed that hard D2 exhibits less material removal, compared to the other steel samples.
# Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions
## 5 Results
### Results of the Maritime Jamming Campaign
#### 5.2.1 GPS Single Point Positioning Results
This also confirms the observation of the lab experiment in the first part of this section, where we have demonstrated that the impact of the applied PPD jammer on the employed geodetic receiver is mainly caused by the reduction of the number of tracked satellites. A reduced number of satellites leads to a worse satellite geometry (larger GDOP), and this correspondingly causes larger positioning errors even in the case of the same baseline noise level on each link. Although the noise level on the range measurements is also affected by the presence of the jammer, this effect can be considered minor compared to that caused by a worsened GDOP. Figure 9: An overview of GPS positioning results of the three antennas/receivers onboard the vessel BALTIC TAUCHER II during the measurement campaign in the Baltic Sea, 2 October 2015 at UTC 6:28–8:18: (**a**) distance of the BALTIC TAUCHER II to the jammer on board the vessel AARON; (**b**) maximum receiver reported CN0 of the tracked satellites; (**c**) total number of tracked satellites; (**d**) geometric dilution of precision (GDOP); (**e**) horizontal positioning error of GPS SPP (when GPS solution available, i.e., \(N>4\)) (HPE); (**f**) reference trajectory of BALTIC TAUCHER II (black line) and GPS SPP results of the three antennas + receivers in the local navigation frame of vessel AARON.
# Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning
## 4 Results and Discussion
### Experiment Results  
The classification results of all the above-mentioned machine learning methods are shown in Table 2. BL, SVM, RBFNN and BPNN performed better than the other traditional classification  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Times** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Accuracy & 1 & 0.917 & 0.958 & 0.958 & 1 & 1 & 1 & 0.913 & 1 & 0.957 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the PSO-BL model on the test set.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Index**} & \multicolumn{5}{c}{**Geographical Origin**} & \multirow{2}{*}{**Overall Accuracy**} \\ \cline{3-4} \cline{6-7}  & & **AUS** & **RUS** & **CAN** & **IND** & **CHN** \\ \hline \multirow{2}{*}{SVM} & misclassification & 6 & 4 & 5 & 2 & 2 \\  & accuracy & 87.0\% & 88.2\% & 85.3\% & 97.6\% & 95.1\% & 92.83\% \\ \hline \multirow{2}{*}{BPNN} & misclassification & 5 & 3 & 5 & 5 & 0 \\  & accuracy & 89.1\% & 91.2\% & 85.3\% & 93.9\% & 100\% & 92.40\% \\ \hline \multirow{2}{*}{RBFNN} & misclassification & 4 & 5 & 6 & 2 & 1 \\  & accuracy & 91.2\% & 85.3\% & 82.4\% & 97.6\% & 97.6\% & 92.40\% \\ \hline \multirow{2}{*}{K-NN} & misclassification & 6 & 6 & 8 & 18 & 12 \\  & accuracy & 87.0\% & 82.4\% & 76.5\% & 78.0\% & 70.7\% & 78.90\% \\ \hline \multirow{2}{*}{RF} & misclassification & 7 & 7 & 9 & 13 & 9 \\  & accuracy & 84.8\% & 79.4\% & 73.5\% & 84.1\% & 78.0\% & 81.01\% \\ \hline \multirow{2}{*}{BL} & misclassification & 2 & 1 & 4 & 3 & 0 \\  & accuracy & 95.7\% & 97.1\% & 88.2\% & 96.3\% & 100\% & 95.78\% \\ \hline \multirow{2}{*}{PSO-BL} & misclassification & 1 & 1 & 1 & 3 & 1 \\  & accuracy & 97.8\% & 97.1\% & 97.1\% & 96.3\% & 97.6\% & 97.05\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification results of different methods.  
Figure 7: The global optimal fitness value over the validation dataset in the process of PSO optimization. Here, these 10 subfigures correspond to 10 different validation sets in the cross-validation.  
methods, and the proposed PSO-BL achieved the highest accuracy of 97.05%. The comparison results demonstrate the effectiveness of the proposed PSO-BL algorithm, strengthen its contribution and further consolidate the necessity of the optimal parameters.  
In addition, as can be seen from Table 2, the models had better performance on the samples of Indonesia and China. We suspect the potential reason was in two-fold. First, the samples set was imbalanced, resulting in a better classification with the bigger sample set. Moreover, the coal samples from Australia, Russia and Canada were mostly lignite, and the coal samples from China and Indonesia were mostly gas coal. Therefore, it can be inferred that the NIR spectroscopy had a better classification performance of coal geographical origin for the gas coal.
###### Abstract  
Optimization of energy consumption and related energy efficiency can be realized in various ways, both through measures to reduce heat losses through building partitions and the introduction of modern systems of regulation and management of heat distribution. In order to achieve the best possible results, these actions should be interlinked, especially in older buildings that have undergone thermomodernization. Therefore, the aim of the study was to evaluate actions aimed at improving energy efficiency of buildings made in prefabricated technology. These buildings were thermomodernized and then the weather-controlled central heating system was installed. The study assessed whether the application of the change of the method of central heating regulation from the traditional one, taking into account only the change of external temperature to the weather-controlled one, will contribute to the increase of energy efficiency of buildings. The research was carried out in the existing residential buildings, for which data on the actual energy consumption was collected and elaborated and includes periods before modernization, after thermomodernization and the period after the introduction of the central heating system with weather control. The collected data cover an eighteen-year period of buildings' use. The obtained results indicate that in Polish conditions the introduction of weather-controlled regulation system in buildings made in prefabricated technology (made of large slab) allows to achieve energy savings in the range of 16-23%, it may be related to their high thermal capacity resulting from the use of concrete elements in the building envelope.  
weather controlled central system; energy saving; energy consumption; thermal improved of buildings; new energy technologies; sustainable buildings +
Footnote †: journal: Applied Sciences
# Special Issue \"Advances in Control of Power Electronic Converters\"
## 2 Control of DC-DC Converters  
The paper of [PERSON] et al. [1] uses Takagi-Sugeno (T-S) fuzzy controllers to improve disturbance rejection and to optimize the control effort, guaranteeing the large-signal stability of power converters in a broad operation domain. The paper develops T-S models of boost and buck-boost converters, which were selected because of their non-minimum phase type dynamic behavior. Design of the fuzzy controllers is performed by using the Parallel Distributed Compensation technique (PDC). The approach is validated by means of simulation results for both converters, and experimental results are presented for the boost converter using a laboratory prototype of 60 W. For this work, it is worth highlighting that proposed control is entirely implemented using analogue electronics, i.e., passive components, operational amplifiers and multipliers.  
[PERSON] and [PERSON] propose in [2] a novel framework for application of robust linear quadratic regulator (LQR)-based control in DC-DC power converters. In the same vein of the previous work, the controller design is performed using Linear Matrix Inequalities (LMIs) and Lyapunov stability theory, leading to a control ensuring robust stability. In this context, the converter is described by a linear parameter-varying polytopic model, integrating both uncertainties and rate of change of the stated variables. The successful operation of this method has been validated by means of simulated results using a conventional boost converter, providing comparison with a controller obtained using the method previously published in [3]. The main contributions of the work are the possibility to enlarge the region of uncertain parameters in which stability is ensured, and the improved regulation performance and robustness.  
The paper by [PERSON] et al. analyzes the undesired impacts of quantization (limit cycle oscillation) in the coupled inductor buck-boost converter when a two-loop digital current control is used to ensure output voltage regulation [4]. The selected control architecture involves an inner loop of multi-sampled average current control and an outer loop of voltage regulation. This work integrates design constraints for control gains and signal quantization to avoid these effects when the outer voltage loop is added. A laboratory prototype of 400 V and 1.6 kW is used to illustrate the presence of the studied phenomenon and verify the correctness of the proposed design conditions when the control system is implemented into a DSP. The contribution of this paper is highly useful to dealing with practical issues of both design and implementation of digital controllers for DC-DC power converters.
## 7 Conclusions  
An insulation breakdown test was performed on a degraded PP insulating material to determine an optimized statistical distribution for analyzing the insulation breakdown strength data. The determination of the optimized statistical distribution in the breakdown strength test suggests that the most suitable statistical distribution can be selected according to the degradation time, unlike conventional studies which only use the Weibull function. The statistical distribution identified for analyzing dielectric breakdown data before degradation was log-normal, while the Weibull distribution was found to be suitable for data after degradation. The selection of the statistical distribution was based on the coefficient of determination and mean difference. The following conclusions can be drawn:  
1. The optimized statistical distribution may vary before and after degradation, emphasizing the importance of interpreting data based on this distinction.
2. A statistical distribution suitable for degradation conditions was selected using the coefficient of determination. Before degradation, the log-normal distribution proved suitable with a coefficient of determination of 0.955. After 960 h of degradation at 130 \({}^{\circ}\)C, the Weibull distribution exhibited the most suitable coefficient of determination at 0.929. Consequently, interpreting the breakdown data after degradation by using the Weibull distribution is reasonable.  
Further research is warranted not only on the materials applied in this study but also on insulating materials manufactured under various conditions.  
Conceptualization, S.-W.L. and K.-H.P.; methodology, S.-W.L. and K.-H.P.; software, K.-H.P.; validation, S.-W.L.; formal analysis, K.-H.P.; investigation, J.-S.L.; resources, J.-S.L.; data curation, K.-H.P. and J.-S.L.; writing--original draft preparation, S.-W.L. and K.-H.P.; writing--review and editing, K.-H.P. and S.-W.L.; visualization, K.-H.P.; supervision, H.-J.K.; project administration, S.-W.L.; funding acquisition, H.-J.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Electrotechnology Research Institute (KERI) Primary research program through the National Research Council of Science and Technology (NST), funded by the Ministry of Science and ICT (MSIT) (No. 23A01026).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to R&D with companies.  
The authors declare no conflict of interest.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 8 Modulation Techniques
### Two New MCSs in Wi-Fi 6  
The new modulation, 1024-QAM (10 bits per symbol), increases data rates by up to 25% compared with Wi-Fi 5. Wi-Fi 6 supports BPSK, QPSK, 16-QAM, 64-QAM, 256-QAM, and 1024-QAM modulations [5]. The new MCS 10 and 11 with 1024-QAM modulation operate with 3/4 and 5/6 coding rates, respectively. The selection of the right MCS heavily depends on the channel quality that can be measured by the packet loss ratio, SNR, bit error rate, transmission time, throughput, transmission statistics, or a combination of these metrics. The higher the SNR, the higher the MCS index that could be applied, which results in data rates of up to 9.6 Gb/s [5]. The nominal data rate can be formulated formally as follows:  
\[DataRate=\frac{N_{subcarrier}\cdot N_{SS}\cdot N_{bits}\cdot CR}{T_{P}+T_{GI}} \tag{2}\]  
where \(N_{subcarrier}\) is the number of data subcarriers, \(N_{SS}\) is the number of spatial streams, \(N_{bits}\) is the number of bits per OFDM symbol, \(CR\) is the coding rate, \(T_{P}\) is the OFDM payload symbol duration, and \(T_{GI}\) is the guard interval duration. Table 4 lists all available MCS and their data rates for a single spatial stream.
###### Abstract  
In the field of inter-satellite laser communication, achieving high-quality communication and compensating for the Doppler frequency shift caused by relative motion necessitate lasers with narrow linewidths, low phase noise, and the ability to achieve mode-hop-free tuning within a specific range. To this end, this paper investigates a novel external cavity diode laser (ECDL) with a frequency-selective F-P etalon structure, leveraging the external cavity F-P etalon structure in conjunction with an auxiliary filter to achieve single longitudinal mode selection. The laser undergoes linewidth testing using a delayed self-heterodyne beating method, followed by the testing of its phase noise and frequency noise characteristics using a noise analyzer, yielding beat spectra and noise power spectral density profiles. Furthermore, the paper introduces an innovative bidirectional temperature-scanning laser method to achieve optimal laser-operating point selection and mode-hop-free tuning. The experimental results showcase that the single longitudinal mode spectral side-mode suppression ratio (SMSR) is around 70 dB, and the output power exceeds 10 mW. Enhancing the precision of the F-P etalon leads to a more pronounced suppression of low-frequency phase noise, reducing the Lorentzian linewidth from the initial 10 kHz level to a remarkable 5 kHz level. The bidirectional temperature-scanning laser method not only allows for the selection of the optimal operating point but also enables mode-hop-free tuning within 160 pm.  
inter-satellite laser communication; external cavity diode laser; linewidth; phase noise; F-P etalon; optimal operating point
# Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
## 4 Case Analysis of Static Wind Instability and Parameter Influence of Xiangshan Harbor Bridge
### Influence of Initial Wind Attack Angle on Static Wind Stability  
The aerostatic instability wind speed when the initial wind angle of attack \(\alpha=0^{\circ}\) is given above, but in engineering practice, the angle of attack between the incoming wind and the bridge is not necessarily \(0^{\circ}\). And the wind angle of attack is an important factor affecting the critical instability wind speed because the three-component force coefficient for calculating wind load is closely related to the wind angle of attack from Figure 2 where it can be seen that the three-component force coefficient is very different under different wind attack angles. In summary, it is necessary to study the influence of the initial wind angle of attack on static wind stability. There are some studies about the range of initial wind attack angles. He [29] pointed out that the wind angle of attack in the atmospheric boundary layer may vary slightly at the range of \(-3^{\circ}\) to \(3^{\circ}\). [PERSON] [23] selected the initial wind attack angles as \(-2^{\circ}\), \(-1^{\circ}\), \(0^{\circ}\), \(1^{\circ}\), \(2^{\circ}\), and \(3^{\circ}\) to calculate and compare the critical wind speed for static wind instability of the Second Yangtze River Bridge in Fengdu. To discuss the impact of the initial wind angle of attack more deeply, in this research the initial angle of attack as \(-6^{\circ}\), \(-4^{\circ}\), \(-2^{\circ}\), \(0^{\circ}\), \(2^{\circ}\), \(4^{\circ}\), and \(6^{\circ}\) are chosen to calculate the aerostatic instability wind speed, respectively.  
Then the displacement response curves of the bridge can be plotted considering different wind attack angles and wind speeds in Figure 10. From the analysis results of static wind instability, it can be seen that the static wind instability form of Xiangshan Harbor Bridge is a flexure-torsion coupling type. And the critical instability wind speed under different initial wind attack angles can be inferred from the displacement response curves, plotted in Figure 11. While the initial wind angle of attack varies from \(-6^{\circ}\) to \(6^{\circ}\), the critical instability wind speed first increases nonlinearly and reaches the maximum  
Figure 9: Variation curves of the maximum displacement of the main beam with wind speed. (**a**) Transverse displacement; (**b**) Vertical displacement; (**c**) Torsion angle.  
value at 0\({}^{\circ}\); then it decreases nonlinearly. The maximum and minimum critical instability wind speeds are 298 m/s and 201 m/s, respectively.
## 3 Results and Discussion
### Excitation Source Contribution Analysis and Critical Transfer Path Identification
#### 3.3.2 Analysis of Vibration Transmission to a Single Target Point on the Chassis Frame
When the threshing cylinder and vibrating screen are engaged (Condition 2), the peak at 29.29 Hz remains, and a new significant peak appears at 87.89 Hz. This new peak has a larger amplitude in the Y-direction (longitudinal), followed by the X-direction, and is smallest in the Z-direction. This indicates that the engagement of the threshing and cleaning system introduces new excitation components and generates a new resonance response with the frame system. In Condition 3 (all components operating simultaneously), a vibration response similar to that of Condition 2 is observed. The PSD peak frequency of 29.29 Hz observed in all three conditions corresponds to the engine's excitation frequency under idle conditions, and the direction of the maximum PSD aligns with the direction of the engine's excitation. Figure 15: _Cont._  
To more intuitively analyze the influence of each excitation source on the vibration at chassis frame measurement point 1, this study utilized the improved OTPA method to create contribution contour plots for the lateral, longitudinal, and vertical directions at this point. These plots, in conjunction with the corresponding spectrum diagrams for the three directions, were used as a reference to identify the primary excitation components and their main transfer paths, thereby analyzing the sources of excitation for measurement point 1 in the X, Y, and Z directions.