esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Joint Testing of the EFSOS System
### Long-Term Running Testing in Water Environment  
An extended-duration test was carried out in the ice well, with the aim of assessing the long-term continuous drilling stability of the EFSOS system. To address the depth limitations of the ice well, both the laboratory winch and RECAS onboard winch were employed. The sonde can be held in a stationary position by synchronizing the speeds of the two winches. The test was performed continuously over a full day of operation, and the sonde movement speed was set to 2 m/h to closely mimic the actual working conditions. The RCUPS system supplied power throughout the entire test.  
The test lasted approximately 20 days. During the downward drilling, the sonde's heating power ranged from approximately 6.2 to 6.5 kW. Over 228 h, a total of 432.6 m of cable was paid out from the sonde. When drilling upward, both the upper melting tip and lateral heaters were powered, yielding a total heating power of 7.4 kW. The cable was smoothly wound into the sonde, with 431.5 m of cable retrieved in total. The RCUPS system operated continuously for about 500 h, and the two generators could complete automatic power switching within one minute. Each diesel generator underwent an automatic oil change during testing, and more than 10 automatic fuel refills were performed. Efficiency testing of the entire system showed that when the whole RECAS system's power consumption reached 7.9 kW at 600 V, the total generator power was about 14 kW. The proportion of wasted power consumption was approximately 43.6%, with 2.1 kW for the RCUPS system, 1.6 kW for the surface transformer and power regulator, and 2.4 kW for cable loss.  
Owing to the significant self-heating of the cable coil during drilling, the cable's insulation material risks melting from overheating. To observe the temperature distribution of the fully wound cable coil with five cable layers, three PT100 temperature sensors were strategically placed: one on the outer wall of the coil section, one on the surface of the cable coil, and one inside the hollow shaft of the drum. These sensors measured the temperature during drilling. When the downhole heating power reached 8.5 kW, the results indicated that the temperature on the surface of the cable coil ranged from 26 to 27 \({}^{\circ}\)C, the temperature inside the drum's hollow shaft was approximately 18 \({}^{\circ}\)C, and the temperature of the coil section's outer wall was 5 \({}^{\circ}\)C.  
This implied that the water holes at both ends of the drum's hollow shaft enabled the microcirculation of hot water generated by the cable coil, driven by the temperature difference, which effectively dissipated heat from the inner layers of the cable. In contrast, the temperature of the drum without water holes could reach 80 \({}^{\circ}\)C within 10 min. Evidently, if the RECAS sonde was used in an air environment, the cable temperature would rise even faster. Therefore, the RECAS sonde must be operated in water-filled borehole environments.
## 4 State of Art  
Nowadays there is a great diversity of optical current sensors. Taking into account the sensing mechanism employed and the materials used, optical current sensors can be organized in four main groups [1,5]:  
* **All-fiber sensor:** The fiber itself acts as a transducer mechanism. The magneto-optical effect (or Faraday effect) is used to induce a rotation in the angle of polarization of the light propagating in the fiber, which is proportional to the magnetic field. Usually, the fiber is coiled around the electrical conductor, making it immune to external currents and magnetic fields.
* **Bulk optic sensor:** These sensors use a piece of glass or crystal with high Verdet constant as the transducer, which is placed near (or around) the electrical conductor. The magnetic field is also measured using the magneto-optical effect. These sensors are usually cheap, robust and more sensitive.
* **Magnetic force sensors:** In an analogous process to the piezoelectric elements, when a magnetic field is applied to a magnetostrictive element it induces mechanical changes in the material. These changes can be again measured by attaching a fiber Bragg grating (FBG) to the magnetostrictive element.
* **Hybrid sensors:** These sensors employ some of the standard electromagnetic technology already existent and some of optical technology. In this case, the first current transducer is done with conventional electromagnetic technology (such as, a Rogowski coil) but its interrogation and information transportation is done by an optical fiber system. The objective of these sensors is to construct an interrogation system that takes advantages of the high level of electrical isolation offered by optical fibers and avoids difficulties associated with birefringence.
## 2 Materials and Methods  
### Urban Growth  
#### 2.1.1 KH-9 Classification  
KH-9 satellite imagery was collected by United States reconnaissance programs (1973-1980) at 6-9 m ground resolution and has been used in a range of applications [39; 40; 41]. A pair of KH-9 satellite images (21 June 1979) (Table S1) were downloaded from USGS EarthExplorer [42]. To our knowledge, no studies have applied image classification techniques to semi-automatically extract built-up areas from KH-9 imagery.  
To classify built-up areas in a 21 June 1979 KH-9 image (Figure 2a), the imagery was first used to generate a digital surface model (DSM) in Agisoft Metashape v1.7.2 [43] with predefined camera parameters obtained by Dehecq [41] and 11 ground control points obtained from Google Earth on static features. The DSM was used to orthorectify and georeference the imagery, which was output at a resolution of 4 m. The image was classified into built-up and non-built-up areas using an object-based image analysis (OBIA) segmentation workflow. First, we segmented the orthorectified image using Orfeo Toolbox's large-scale mean shift segmentation algorithm [44]. A spatial radius of 5 pixels, a spectral range radius of 20, and a minimum segment size of 50 pixels [44] were used, following manual inspection to capture built-up areas and minimize segmented polygons in homogenous agricultural fields. Second, WhiteBox Tools v.1.4.0 _Patch Shape Tools_ was used to add spatial variables of polygon 'compactness ratio', 'linearity index', and'shape complexity index' [45; 46]. Third, 200 built-up and 200 non-built-up polygons were digitized as training data (Figure S1), which were used to extract segmented polygons corresponding to built-up (n = 1859), and non-built-up areas (n = 682). Fourth, SAGA's parallelepiped supervised classification was used to classify the segmented polygons into built-up or non-built-up classes [47]. Finally, the classification was resampled to 10 m to match the Sentinel-2 classification and applied a slope filter of 5 degrees to remove misclassification on steep slopes using the 30 m Copernicus DSM resampled to 100 m. Isolated patches less than 50 pixels (5000 m\({}^{2}\)) were also reclassified into the dominant surrounding classification.
## 3 FCS-MPC Algorithm for Boost Converters
### Non-Minimum Phase Issue in Boost Converters  
A significant challenge in controlling boost converters is their inherent non-minimum phase behavior, where the initial response of the system deviates in the opposite direction of the desired outcome. For instance, in a boost converter, attempting to increase the output voltage results in an initial undershoot in the output before the desired voltage level is attained. This is due to the gain sign changing from a negative to a positive value after an initial transient. This non-minimum phase behavior complicates the control process, particularly when using an MPC with a short prediction horizon of one, as it fails to consider the system's long-term behavior. In such cases, controlling the output voltage by FCS-MPC can exacerbate the problem, leading to instability and performance degradation.  
In a boost converter, when the controller aims to increase the output voltage from the input voltage to a higher desired level, the non-minimum phase behavior leads to an initial undershoot. The FCS-MPC controller, using a short prediction horizon, evaluates two states: State 1, where the switch is closed and the inductor stores energy, and State 2, where the switch is open and the inductor releases energy to the load. Due to the non-minimum phase behavior, the controller predicts that closing the switch will initially cause the output voltage to drop, while opening the switch will cause a slight immediate increase. To minimize immediate error, the controller opts for State 2 to avoid the undershoot. However, this prevents the inductor from storing sufficient energy to achieve the higher desired output voltage. Consequently, the controller keeps selecting State 2, avoiding the necessary energy buildup, leading to instability and preventing the output voltage from reaching the desired higher level.  
Despite these challenges, MPC with a horizon of one step is often preferred due to its simplicity. The short prediction horizon reduces computational complexity, making real-time implementation feasible in mid/high-frequency applications.
# Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems
## 1 Introduction  
The orthogonal frequency division multiplexing (OFDM) system has been widely used in modern wireless communication systems due to its high spectrum efficiency and strong multipath fading resistance ability [1]. In the time-domain synchronous (TDS)-OFDM system, a pseudo-random (PN) sequence can be used as the guard interval (GI) for symbol synchronization and channel estimation. The TDS-OFDM system channel estimation is always implemented in an iterative way for cyclic reconstruction and reduces the inter-symbol interference (ISI). ISI and inter-carrier interference (ICI) may affect the accuracy of channel estimation [2]. Cyclic prefix (CP)-OFDM technology is often used in modern wireless communications [3, 4]. In order to obtain diversity gain, multiple transmit and receive antennas can be used in the CP-OFDM system. The advantage of the technology is to improve the performance of wireless communication systems [5, 6]. The offset quadrature amplitude modulation (OQAM) technology can improve the accuracy of channel estimation and improve the performanceof the wireless communication system, and the modulation technology can be used as a candidate technology for 4G and 5G communication [7].  
The pilot-based channel estimation method requires transmitting a large number of pilots repeatedly, which reduces the data transmission rate and spectrum efficiency. The basis of linear interpolation is to assume that the transmission function of each sub-channel between adjacent pilots varies linearly according to a certain slope. In the use of linear interpolation filtering, the interpolation is completed within the duration of each OFDM symbol, and the interpolation among OFDM symbols is independent of each other. Therefore, the channel can be allowed to change rapidly along the time dimension.  
The remainder of this paper is organized as follows. In Sections 2 and 3, the related work and system model are presented. Section 4 introduces the block-type pilot-based least square (LS), improved minimum mean square error (IMMSE), threshold value and Chi-square distribution channel estimation methods. Section 5 illustrates the channel estimation method based on statistical frames and the confidence level. The experimental results and comparison analysis are presented in Section 6. Conclusions are presented in Section 7.
## 3 Model Definitions  
In this section, we define the time and propagation models used to develop the CHP2 estimation and filtering techniques. Users interact by transmitting and receiving an RF waveform with bandwidth \(B\) at a carrier frequency \(f_{c}\). We assume a standard line-of-sight propagation model [82], but use a non-standard, high-fidelity timing model to capture the subtleties of misaligned and imperfect clock sources [83].
## 3 Novel Broad Echo State Network
### Optimization Experiments
#### 4.4.2 Optimization Results of Air Quality Dataset  
Similar to the optimization experiment of the MSO dataset, the air quality dataset is tested. A CMBESN model is selected with 36 nodes in the mapping layer, 28 nodes in the enhancement layer, and a reservoir pool size of 600. The CMBESN-OE model is obtained by optimizing the CMBESN with the optimization algorithm mentioned in Section 3.2.2 and setting \(\alpha=0.95,\beta=1.1\) in the subset.  
The CMBESN-OE is used to optimize the regularization coefficient \(\lambda\) in Formula (19). Then the trend of RMSE and \(\lambda\) is plotted in Figure 19, which shows that the RMSE has reached a smaller value by the 34 th adjustment and \(\lambda\) shows a decreasing trend in general.  
The CMBESN-OE model reaches a better state after 34 times of optimization. The results are shown in Figure 20. It indicates that the CMBESN-OE generally fits the actual data curve better.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SMAPE** & **MAE** & **RMSE** & \(R^{2}\) \\ \hline CMBESN & 0.0349 & 0.0325 & 0.0405 & 0.9996 \\ CMBESN-OE & 0.0357 & 0.0319 & 0.0401 & 0.9996 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation metrics of CMBESN and CMBESN-OE models on MSO dataset.  
Figure 19: RMSE and \(\lambda\) variation curves of CMBESN-OE model on air quality dataset.  
Figure 20: Prediction curves of CMBESN and CMBESN-OE models on air quality dataset.  
The evaluation metrics of the two models are shown in Table 7. It indicates that CMBESN-OE performs better than CMBESN in all evaluation metrics.
# AI-Enhanced Prediction of Peak Rate of Torque Development from Accelerometer Signals
## 3 Results  
All the participants completed the two sessions of testing. Figure 2 depicts the measured and predicted signals obtained. It is possible to see a clear overlapping between the measure and AI-predicted signals. The peak RTD analysis revealed no significant differences between the measured (3506 \(\pm\) 932 Nm/s) and predicted (3476 \(\pm\) 809 Nm/s) peak RTDs (\(p\) = 0.852, Figure 3). Furthermore, the analysis revealed a strong linear fit (R\({}^{2}\) = 0.81, Figure 4) and almost perfect agreement, as indicated by an excellent ICC = 0.94 (\(p<0.001\)) and a lower mean bias (30.8 Nm/s, less than 1% of the peak RTD observed values) between the measured and predicted values (Figure 4).  
Figure 2: Measured and predicted RTD signals. The solid and dotted lines represent the measured and predicted RTD signals, respectively. The shaded areas around each line indicate the standard deviation. The overlap between the signals suggests a degree of correspondence and highlights the RTD prediction model capabilities from the ACC signals. The gray shaded area highlights the window between onset and peak RTD.
###### Abstract  
This study presents an objective scheme to construct hurricane bogus vortices based on satellite microwave scatterometer observations of sea surface wind vectors. When specifying a bogus vortex using [PERSON]'s formula, the required parameters include the center position and the radius of the maximum gradient of sea level pressure (R\({}_{0}\)). We first propose determining the tropical cyclone (TC) center position as the cyclonic circulation center obtained from sea surface wind observations and then establishing a regression model between R\({}_{0}\) and the radius of 34-kt sea surface wind of scatterometer observations. The radius of 34-kt sea surface wind (R\({}_{34}\)) is commonly used as a measure of TC size. The center positions determined from HaiYang-2B/2C/2D Scatterometers, MetOp-B/C Advanced Scatterometers, and FengYun-3E Wind Radar compared favorably with the axisymmetric centers of hurricane rain/cloud bands revealed by Advanced Himawari Imager observations of brightness temperature for the western Pacific landfalling typhoons Doksuri, Khanun, and Haikui in 2023. Furthermore, regression equations between R\({}_{0}\) and the scatterometer-determined radius of 34-kt wind are developed for tropical storms and category-1, -2, -3, and higher hurricanes over the Northwest Pacific (2022-2023). The bogus vortices thus constructed are more realistic than those built without satellite sea surface wind observations.  
tropical cyclone center positioning; construction of bogus vortex; scatterometer sea surface wind Article
## 5 Results
### Measurement Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Item** &
\begin{tabular}{c} **Factor** \\ **Loading** \\ \end{tabular} & **CR** & **AVE** \\ \hline Career Development & CD1 & 0.660 & 0.814 & 0.597 \\  & CD2 & 0.739 & & \\ Business Needs & DN1 & 0.714 & 0.797 & 0.568 \\  & BN2 & 0.765 & & \\  & BN3 & 0.780 & & \\ Self-Management & SM1 & 0.849 & 0.828 & 0.616 \\  & SM2 & 0.742 & & \\  & SM3 & 0.760 & & \\ Learning Experience & LE1 & 0.809 & 0.87 & 0.691 \\  & LE2 & 0.769 & & \\  & LE3 & 0.909 & & \\ Company Atmosphere & CA1 & 0.877 & 0.843 & 0.644 \\  & CA2 & 0.808 & & \\  & CA3 & 0.713 & & \\ External Pressure & EP1 & 0.785 & 0.840 & 0.637 \\  & EP2 & 0.780 & & \\  & EP3 & 0.828 & & \\ Company Support & CS1 & 0.886 & 0.884 & 0.717 \\  & CS2 & 0.781 & & \\  & CS3 & 0.870 & & \\ Course Quality & CQ1 & 0.837 & 0.875 & 0.701 \\  & CQ2 & 0.925 & & \\  & CQ3 & 0.739 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of validity analysis. Furthermore, this study used correlational and discriminant validity analyses to assess validity, the results of which are shown in Appendix A. Distinguishing validity, in fact, is a form of structural validity. Discriminant validity ensures that measurement items intended to be distinct from each other are not grouped under the same factor. Distinctive validity is carried out by performing correlation analyses, as well as taking the AVE root mean square, and then combining the results and processing them for analytical description. The diagonal line is the AVE open root sign value of each configuration, and the lower triangles outside the other diagonals are the standardized correlation coefficients. As evidenced by the results and the AVE open root sign values for each variable, which are greater than the correlation coefficients with the other variables [81] the individual constructs studied in this paper have differential validity. Finally, this research applied [PERSON]'s single-factor test method to assess the issue of common method variance (CMV). [PERSON] et al. [82] describe CMV as the variance that is systematically shared among variables due to the data being collected through the same source or method. Evaluating CMV is vital when gathering data, mainly when independent and dependent variables derive from the same questionnaire [83]. According to the findings detailed in Appendix B, the first factor accounted for 23.5 percent of the variance, falling below the 40 percent threshold. This outcome suggests that CMV does not pose a significant concern in this study.
## 5 Parameter Optimization
### Analysis of Interaction Effects  
According to the results of the regression analysis, using the established mathematical model, the effects of the interaction of the parameters on the response values can be objectively reflected. Thus, the optimal mechanism parameters and the interactions between the parameters can be discovered, as shown in Figure 15. Among them, the three-dimensional response surface and contour diagram can intuitively reflect the influence of independent variables on the posture adjustment mechanism volume ratio, and the shape of the contour lines can reflect the strength of the interaction effect of the parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Sum of Mean Squares** & **Dof** & **Mean Square** & **F-Value** & **\(p\)-Value** & **Significance** \\ \hline Model & 0.0048 & 9 & 0.0005 & 59.88 & \textless{}0.0001 & significant \\ A-\(X_{9}\) & 0.0008 & 1 & 0.0008 & 87.50 & \textless{}0.0001 & ** \\ B-\(X_{0}\) & 0.0003 & 1 & 0.0003 & 35.90 & \textless{}0.0001 & ** \\ C-\(X_{5}\) & 0.0019 & 1 & 0.0019 & 213.49 & \textless{}0.0001 & ** \\ D-\(X_{3}\) & 0.0001 & 1 & 0.0001 & 9.90 & 0.0033 & \\ E-\(X_{2}\) & 0.0010 & 1 & 0.0010 & 111.78 & \textless{}0.0001 & ** \\ BC & 0.0001 & 1 & 0.0001 & 13.98 & 0.0006 & \\ CD & 0.0002 & 1 & 0.0002 & 16.86 & 0.0002 & \\ CE & 0.0001 & 1 & 0.0001 & 10.16 & 0.0030 & \\ C\({}^{2}\) & 0.0004 & 1 & 0.0004 & 39.33 & \textless{}0.0001 & ** \\  & \(R^{2}\) = 0.9374, \(R^{2}\) adj = 0.9217, \(CV\) = 7.05\%, Adeq Precision = 31.7698 & & & \\ \hline \hline \end{tabular} Note: ** is extremely significant (\(p<0.0001\)).  
\end{table}
Table 6: ANOVA of BBD optimization regression model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Width of moving platform \(m\) & 877.27 mm \\ Length of moving platform \(n\) & 1400 mm \\ Width of fixed platform \(M\) & 924.974 mm \\ Length of fixed platform \(N\) & 1498.308 mm \\ Angle between \(P_{3}\) axis and moving platform length direction \(\varphi\) & \(\pi/4\) rad \\ Angle between \(P_{3}\) and \(P_{2}\) axis \(\theta\) & 1.574 rad \\ Angle between \(P_{2}\) and \(P_{1}\) axis \(\psi\) & 5\(\pi\)/9 rad \\ Minimum drive trajectory of the first driving pair of limb \(l_{\text{1 min}}\) & 700 mm \\ Maximum drive trajectory of the first driving pair of limb \(l_{\text{1 max}}\) & 1000 mm \\ Minimum drive trajectory of the second driving pair of limb \(l_{\text{2 min}}\) & \(-224.759\) mm \\ Maximum drive trajectory of the second driving pair of limb \(l_{\text{2 max}}\) & 200 mm \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters of the posture adjustment mechanism.