esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 DTLI Control Features
### Coupled Modulation Control  
In the case of coupled modulation, a DTLI can be treated as a single multilevel converter. To minimize switching activity, an alternating sub-hexagonal center (SHC) modulation strategy can be implemented, whereby one inverter operates while the other remains in a steady state during a switching period [104; 109; 110].  
Figure 14 illustrates the operation of this alternating SHC modulation technique. The entire space vector diagram is segmented into six regions, each associated with a central SHC point marked by green circles labeled A through F. For each SHC, one inverter performs the switching while the other remains fixed, and then their roles alternate for the subsequent SHC.  
For instance, point A corresponds to the SHC of the light-green OSGH quadrilateral in Figure 14. Within this region, the switching states can either involve keeping the master inverter fixed at \(V_{11}\) while switching the slave inverter across \(V_{11}V_{8}\), \(V_{11}V_{71}\), \(V_{11}V_{2}\), \(V_{11}V_{3}V_{1}V_{4}\), \(V_{11}V_{5}V_{1}V_{6}\) and \(V_{11}V_{77}\), or vice versa, holding the Slave inverter constant at \(V_{jk}\) and switching the master inverter among states such as \(V_{8}V_{jk}\), \(V_{11}V_{4}\), \(V_{2}V_{4}\), \(V_{2}V_{4}\), \(V_{2}V_{4}V_{4}V_{4}V_{4}\), \(V_{5}V_{4}\), and \(V_{7}V_{4}\).  
The authors in [109] conducted a thermal loss analysis of this alternating SHC scheme, demonstrating its impact on system efficiency. Additionally, the authors in [110] showed that this modulation strategy leads to an equal distribution of power losses between the two inverters.  
Figure 14: Sub-hexagonal center alternative modulation scheme principle for DTLI.  
A power-sharing strategy is also defined in [111], taking into account unbalanced power distribution and modulation index values. Moreover, several studies [68; 99; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128] have focused on capacitor voltage regulation for TI-DCB configuration and DCB-FC configuration of DTLIs, respectively.
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 7 Conclusions  
Cloud computing has revolutionized device interactions on the internet, which ushered in the Internet-of-Things and implemented a plethora of connected gadgets, with the potential to continually sense and respond to user requirements. The proliferation of networked IoT devices and ensuing big data and the rigorous demands of emerging IoT applications, such as low latency, location awareness, and mobility support in a geo-distributed scenario, have challenged the conventional cloud computing architecture. Hence, various computing paradigms such as edge and fog have emerged to address these limitations by deploying resources at the network's edge. The computing at edge and fog implies collecting, processing, and analyzing data close to the data source and transmitting refined results to the centralized cloud, favoring time-sensitive applications that require increased accuracy, low latency, high-speed analytics, faster response time, improved reliability, and availability. Combining fog/edge with cloud computing has the prospect of aiding IoT in multiple ways. Because the fog and edge computing paradigms are up-and-coming, exhaustive research on this new technology is imperative. The evolving computing paradigms, as well as the challenges and opportunities, were explored in this study. Budding researchers can largely benefit from this extensive survey to comprehend recent advances in evolving computing paradigms.  
**Author Contributions:** Conceptualization, N.A.A., D.R. and K.S.; methodology, N.A.A., D.R. and K.S.; software, P.M.D.R.V.; validation, P.M.D.R.V., K.S. and Y.-C.H.; formal analysis, N.A.A.; investigation, N.A.A.; resources, K.S. and Y.-C.H.; data curation, P.M.D.R.V.; writing--original draft preparation, N.A.A., D.R. and K.S.; writing--review and editing, N.A.A., D.R., P.M.D.R.V., K.S. and Y.-C.H.; visualization, N.A.A.; supervision, D.R.; project administration, Y.-C.H.; funding acquisition, Y.-C.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the MINISTRY OF SCIENCE AND TECHNOLOGY, TAIWAN, grant number MOST 110-2622-E-197-009.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. 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.
## 4 Experiment Results and Discussion
### Multiple Coherences of the Primary Paths  
Multiple coherence of the multiple primary paths was generated in which the 12 reference signals were the input and the acoustic disturbance signal at the error microphone in  
Figure 6: Comparison of the normalized impulse responses of the measured secondary paths when the ESG–cowl pair (**a**) or a typical built-in door loudspeaker (**b**) is used.  
the cabin was the output. The multiple coherence of the primary paths allows the prediction of the achievable attenuation after control, which can be given by [5]:  
\[Att\ (\mathrm{dB})=10\log_{10}\left(1-\gamma^{2}\right), \tag{8}\]  
where \(\gamma^{2}\) is the multiple coherence function.  
Figure 7 shows the multiple coherence of the measured signals in the primary paths in the frequency range below 500 Hz after attaching the reference accelerometers at the chosen locations, as described in Section 3.3. It was observed that the multiple coherence was about 0.95 at the frequencies of the three most dominant resonances, as can be seen from the secondary path plot in Figure 5a. This means the disturbance signal was strongly correlated with the reference signals in the frequency range of interest between 100 and 250 Hz.  
From Figure 7, the achievable attenuations, which were predicted by using Equation (8), of the three resonances can be about 15 dB at maximum. In practice, however, the reductions obtained through the ARNC system in an actual car may be less than the calculated values above due to the effect of the secondary path variation and others.  
During the measurement of the primary paths, the effect due to engine vibration to the reference and error sensors in the primary paths was analyzed because the car was driven at a constant speed of 60 kph with an engine rotation speed of 1500 rev/min. As the frequency of the second harmonic component of the engine speed, C2, was at 50 Hz, which was the most dominant in engine vibration of the four-cylinder car and was not overlapped with the frequency range of 100-250 Hz for the ARNC, the effect was negligible in this study. In addition, the physical locations of the reference sensors contributed to minimize the engine vibration effect in sensing pure reference signals, as discussed in Section 3.2. Thus, the multiple coherence of the primary paths can be improved by locating the reference sensors properly to reject various misleading noises from the ambient sources.
## 2 Dataset Establishment and Preprocessing
### Dataset Division  
In this study, the surrounding rock was classified according to the surrounding rock conditions revealed by excavation. While maintaining one-to-one correspondence between the depth-sequence MWD data and surrounding rock classes, and eventually establishing the MWD dataset for the prediction of the surrounding rock class, the dataset was divided into a training set and a test set.  
For machine learning, the data determines the upper limit of the model [29; 30]. Datasets obtained in practice are usually small and unbalanced, thereby resulting in overfitting; therefore, training the optimal model with limited data while avoiding overfitting is key. Here, the stratified K-fold cross-validation method was used to divide the training set into K parts, of which \(\text{K}-1\) parts were used for model training and one part was used as a validation set. The validation set was not involved in training and was used for cross-cycle validation in the model, i.e., cycling K times to adjust the parameters and evaluate the model. The workflow is shown in Figure 2. Regarding the unbalanced distribution of samples of different rock classes in the dataset, random division may aggravate the unbalance; hence, the stratification method was used to pass the labels of the rock classes in the division and ensure that the proportions of samples of different rock classes in the training and validation sets are the same as that of the original data, thereby reducing adverse effects.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(p\)-Value & **Coincidence Probability** & **Null Hypothesis** & **Statistical Significance** \\ \hline \(p>0.05\) & Coincidence probability greater than 5\% & Cannot refuse & No significant difference \\ \(p\leq 0.05\) & Coincidence probability less than 5\% & Refuse & Significant difference \\ \(p\leq 0.01\) & Coincidence probability less than 1\% & Refuse & Very significant difference \\ \hline \hline \end{tabular}
\end{table}
Table 2: The \(p\)-value approach.
## 3 Flow Velocity Measurement Method
### The Principle of Main Steps and Analysis
#### 3.2.4 Sweep Velocity Correction  
As the optical image is obtained by a snapshot of the CCD device for the optical PIV method, while the ultrasonic image is constructed by series of scanning lines for the ultrasound PIV system, the image forming time is the main difference. This means that different parts in the image are recorded and formed at different times. As is known, the  
Figure 5: The basic principle of the iterative multi-grid interrogation process.  
velocity displacement in the optical image is formed by a time interval between snapshot frames. The time interval is also a displacement function. When particles move with a lateral velocity that becomes close to the ultrasound probe's sweep velocity, the sweep velocity approaches the next scan line. Therefore, this sweep velocity can be corrected according to the frame rate and the lateral size of the view field according to the Doppler effect of flow velocity.  
According to the ultrasound imaging principle and researchers' discussion, the ultrasound beam sweeping effect can make some measuring errors [37] and can affect the particle's velocity in silt carrying flow. When the sweeping direction of the ultrasound beam is the same as the direction of the water flow, the measured velocity may be overestimated. Otherwise, the measured velocity may be underestimated. The ratio between sweep speed and flow speed also has some influence on the measuring results. If the ratio increases, the measuring error will decrease. Therefore, the imaging particle velocity obtained by this ultrasound PIV method can be re-corrected from the beam sweep effect by using the following formulas [20]:  
\[V_{x} = \frac{V_{s}V^{\prime}{}_{x}}{V_{s}+V^{\prime}{}_{x}} \tag{5}\] \[V_{y} = \frac{V_{s}V^{\prime}{}_{y}}{V_{s}+V^{\prime}{}_{x}} \tag{6}\]  
where \(V_{x}\) and \(V_{y}\) are the re-corrected velocity components, \(V_{x}\)' and \(V^{\prime}{}_{y}\) are the uncorrected velocity components, and \(V_{s}\) is the sweep velocity of the ultrasound beam. Lastly, we can obtain a real particle velocity after this correction.
## 4 Discussion
### Growth Performance and Nutrient Digestibility  
In the current study, growth performance and nutrient digestibility were not influenced by VBP supplementation throughout the experiment. Previous research demonstrated that _B. subtilis_-fermented soybean reduced the activity of anti-nutrients (e.g., raffinose, and stachyose), which is associated with indigestion and flatulence in monogastric animals. Additionally, this process demonstrated a reduction in the undesirable beany odor associated with soybeans, making it more palatable for animals. Moreover, it led to increased digestibility of soybeans or their components that are typically considered indigestible. [33; 34; 35]. _Bacillus subtilis_ was used to remove the bitter taste of soybean [36]. This was accomplished by raising the concentration of volatile substances during the Bacillus subtilis fermentation, which is connected to extracellular hydrolase enzymes, such as lipases, amylase, and sucrose. ADG and ADFI rose when growing and finishing pigs' diets were gradually supplemented with more vitamin B, as shown by [PERSON] et al. [37]. In rat [38] and neonatal pig [39] studies, the inclusion of fermented soybeans in diets improved ADG and feed efficiency. Moreover, the antioxidant activity of soybean isoflav from fermented soybeans improved growth performance in broilers [40] and piglets [41]. However, our findings showed that ADG, ADFI, and feed efficiency were unaffected for pigs who were fed VBP-containing isoflav, at the growing and finishing phases. The addition of a combination of B vitamins in the diet has been documented to enhance the growth performance of young pigs [42; 43]. However, there is variation in opinions regarding the optimal levels of vitamin B required for achieving maximum growth performance.  
Fermentation could bring substantial changes in the nutrient properties of soybeans. [PERSON] et al. [33] and [PERSON] et al. [35] reported that _B. subtilis_-fermented soybean, during fermentation, releases proteinases that degrade soybean protein into non-protein and soluble nitrogen and regulates the enzymatic hydrolysis of soybean (e.g., fibrinolysis and caseinolysis), while increasing the synthesis of bacterial protein. They also showed that while total sugar and crude fiber were reduced, the ash content and overall energy from fermented soybeans remained unchanged. The carbohydrate concentration was reduced by microbes for energy supply. In the current study, the apparent total-tract digestibility of dry matter and nitrogen in pigs fed VBP diets, remained unaffected. This may result from insufficient VBP supplementation or the differences between fermented soybean and vitamin B\({}_{2}\) extracted fermented soybean. Thus, further investigation is needed to clarify the adaptable amount of VBP for increasing growth performance and nutrient digestibility in growing-finishing pigs.
## 6 Issues and Future Directions
### Difficulty in Retrieving Land Cover Change  
Currently, the most difficult case of spatiotemporal data fusion is the abrupt land cover change happening during the fusion period. The abrupt change is recorded in two coarse images at \(t_{1}\) and \(t_{2}\), but the fine image at \(t_{1}\) is prior to the change. A successful fused result of the fine image at \(t_{2}\) should reflect the magnitude and extent of the change at fine resolution. Figure 7 shows such a case with flood event happening during fusion period. This event was recorded in the coarse images (c) and (d), but the boundary of flooded area is blurring due to the coarse resolution. Quite a few spatiotemporal data fusion methods were developed to improve the fusion results for this challenging case. For example, FSDAF employs the thin plate spine (TPS) to downscale the coarse image to the fine resolution and then uses the result to guide the residual distribution for pixels experiencing land cover change. This strategy works well for land cover change visible in coarse image but it may not be able to recover tiny land cover change in coarse subpixels [26]. In addition, the flooded area in Figure 7 has quite a smooth and regular boundary. FSDAF can capture the boundary for this case well but it is unknown if it can capture the boundary of land cover change with irregular shapes. The machine learning-based methods may be able to capture land cover changes. For example, the one-pair learning method using sparse representation can capture land cover change in the fused image although the boundary is a little bit blurred. The problem with this method is that it also blurs the land cover boundaries without change (see Figure 3 in the paper [35]). Now, deep learning has been successfully applied to image classification, object recognition, and information mining [102; 103]. It is possible to model complex relationships between input coarse and fine images and the trained model could be used to predict the fine image. In the future, we could see studies using deep learning to fuse images with different resolutions.  
Figure 7: An example of flood event happening during spatiotemporal data fusion: using (**a**), (**c**), and (**d**) to predict (**b**).
# Data Interpolation by Near-Optimal Splines with Free Knots Using Linear Programming
###### Abstract  
The problem of obtaining an optimal spline with free knots is tantamount to minimizing derivatives of a nonlinear differentiable function over a Banach space on a compact set. While the problem of data interpolation by quadratic splines has been accomplished, interpolation by splines of higher orders is far more challenging. In this paper, to overcome difficulties associated with the complexity of the interpolation problem, the interval over which data points are defined is discretized and continuous derivatives are replaced by their discrete counterparts. The \(l_{\infty}\)-norm used for maximum \(r\)th order curvature (a derivative of order \(r\)) is then linearized, and the problem to obtain a near-optimal spline becomes a linear programming (LP) problem, which is solved in polynomial time by using LP methods, e.g., by using the Simplex method implemented in modern software such as CPLEX. It is shown that, as the mesh of the discretization approaches zero, a resulting near-optimal spline approaches an optimal spline. Splines with the desired accuracy can be obtained by choosing an appropriately fine mesh of the discretization. By using cubic splines as an example, numerical results demonstrate that the linear programming (LP) formulation, resulting from the discretization of the interpolation problem, can be solved by linear solvers with high computational efficiency and the resulting spline provides a good approximation to the sought-for optimal spline.  
Keywords:optimal splines; linear programming; data interpolation; splines with free knots +
Footnote †: journal: Journal of Computational Physics
## 5 Conclusions  
In this paper, the aerodynamic performance of a rotor with rectangular blades under ultra-low density Martian air conditions is studied numerically based on experiment results from a Mars Air Simulator (MAS). A simulation scheme using a large eddy simulation solver is established and the influence of test bench and chamber wall of the MAS is investigated. From this, the following conclusions can be made.  
1. The established method can be used to accurately predict the lift performance of the helicopter rotor with about 1% error; however, the torque prediction is not so promising, possibly due to a friction-induced error in the experimental results;  
2. The Mars Air Simulator can be used to simulate Martian air conditions in an accurate manner, the fixture and chamber wall has little influence on the lift performance prediction, while the test bench has about 5% influence. Topology design can be made to reduce blade weight by removing some of the blade area close to the root;  
3. The rotor, with a maximum Mach number of 0.42, creates a maximum Mach number up to nearly 1 in the airflow; therefore, compressibility must be taken into account in CFD simulations of low-density Martian air conditions to improve accuracy, and airfoil optimization needs to be performed to mitigate this phenomenon.  
This paper may provide some guidance for future experimental and CFD studies on Martian helicopters. For example, the influence of the test bench can be further studied to eliminate its influence with a new design set. A new blade topology can be designed after analyzing the flow field and pressure distribution to improve efficiency.  
Conceptualization, J.H., D.H., T.C. and H.L.; methodology, J.H.; software, H.L. and T.C.; resources, J.H.; writing--original draft preparation, H.L. and T.C.; writing--review and editing, D.H., T.C. and J.H.; visualization, J.H.; supervision, J.H. and D.H.; project administration, H.L.; funding acquisition, J.H. and D.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology Innovation Method Work Special Project: The Construction and Demonstration of Multi-level and Multi-mode College Innovation Method Talent Cultivation System (2017 IM030100), the Education Reform in Jiangsu Province: Exploration and practice of the \"three-step\" training model for top-notch talents through double innovation in higher vocational colleges (2017 JSIG475); the Aeronautical Science Foundation of China (Grant No. 2020Z006052001), the Rotor Aerodynamics Key Laboratory Fund Project (RAL20190101) and the Fundamental Research Funds for the Central Universities (Grant No. 1011-YAH21001).  
Data Availability Statement: Data sharing not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 2 Materials and Method
### ERA5 Data  
ERA5 wind data matched with the imaging time and irradiation area of SAR data are also used for sea surface wind field retrieval and verification. ERA5 is the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) global climate reanalysis data, which provides hourly estimates of numerous atmospheric, wave, and surface quantities. The wind data for reanalysis has been re-divided into a regular latitude and longitude grid of 0.25 degrees.  
Figure 1: The map of collected GF3-02 SAR ocean imagery location.  
Figure 2: The map of matched GF3-02 SAR ocean scene (blue box) in NDBC’s (**a**) Station 46002, (**b**) Station 46011, (**c**) Station 46026, (**d**) Station 51000, (**e**) Station 51002, (**f**) Station 51004. The black dots represent the locations of NDBC buoys.