esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on SIW and Its Applications to Microwave Components
## 9 SIW Filter
Mult-band SIW filters including the dual-band bandpass filter [71] and triple-band bandpass filter shown in Figure 8b [72] have been reported. These design topologies are progressively becoming essential with the contemporary fast growth, development, and advancement in multi-band wireless communication systems. This type of filter is helpful in separating a small portion of a frequency band in a larger band. Reconfigurable SIW filters [73] are important for future multi-functional radio and radar systems, including smart and cognitive radio and radar systems that abound across commercial, civilian, and defence sectors. They are popular in the control and better use of the radiofrequency spectrum [68]. A performance comparison of published works on SIW filters is given in Table 1.
## 4 Proposed Non-Linear Diffusion Method
### Technical Requirements  
All experimental results are carefully examined using MATLAB R2014a running on an Intel Core i3-6100U CPU operating at 2.30 GHz, 4 GB RAM, and the Windows 10 64-bit OS. It is essential to consider the system configuration as it plays a crucial role in determining the computational efficiency of the proposed method compared to other despeckling filters and techniques under the same settings. A fixed window size of 3 \(\times\) 3 is utilized throughout the experiments, while the number of iterations, denoted as I, varies within the range of from 1 to 10. The results are presented based on the optimal number of iterations determined during the analysis. In order to verify the effectiveness of the suggested approach, the results obtained from the experiments were contrasted with those achieved using several well-known despeckling filters and methods such as log compression filtering, frost filtering, Kuan filtering, lee filtering, Median, and HFLF. The new despeckling technique has been tested under different levels of noise variance on different synthetic and natural images, with the experimental findings specifically showcasing the performance at a noise variance level.
## 4 Results
### Correlations between LOS and GSI and Their Trends  
#### 4.3.1 Correlations between LOS and GSI (1982-2013)  
The interannual variation of GSI is extensively positively correlated with LOS in the NH (Figure 5) (Table 3, 83.09%). Nevertheless, areas with low (non-significant) and significant negative correlation can be observed, primarily in high latitudes/arctic region of NA and EA, central NA, central Asia, Mongolia, and north China, indicating that LOS and GSI are less or negatively correlated in high latitudes/arctic and arid/semi-arid regions. However, differences in the spatial coverage of these correlations between LOS and GSI are distinct between NA and EA (Table 3). EA shows larger areas with significant positive correlation (85.31%) than those of NA (78.29%), whereas pixels with negative correlation in NA (1.73%) are larger than those in EA (0.44%).  
Pixels with significant positive correlation between LOS and GSI (Figure 6A) are mostly distributed in biomes of shrublands, grasslands, and croplands, but are rarely observed in any of the  
Figure 5: Map of r-values for correlations between length of vegetation growing season (LOS) and growing season NDVI integral (GSI) 1982–2013.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**NH**} & \multicolumn{2}{c}{**NA**} & \multicolumn{2}{c}{**EA**} \\ \cline{3-8}  & \multicolumn{2}{c}{**n**} & \multicolumn{1}{c}{**\%**} & \multicolumn{1}{c}{**n**} & \multicolumn{1}{c}{**\%**} & \multicolumn{1}{c}{**n**} & \multicolumn{1}{c}{**\%**} \\ \hline \multirow{4}{*}{**1982–2013**} & **\(\mathbf{-1}\)** & 9416 & 0.85\% & 6049 & 1.73\% & 3367 & 0.44\% \\  & **0** & 177,556 & 16.06\% & 69,718 & 19.98\% & 107,838 & 14.25\% \\ \cline{1-1}  & **1** & 918,925 & 83.09\% & 273,171 & 78.29\% & 645,754 & 85.31\% \\ \cline{1-1}  & **Total** & 1,105,897 & & 348,938 & & 756,959 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics (number of pixels and percentages) of significant correlations between LOS and GSI (1982–2013) (\(p\) = 0.1).  
forest biomes, except for mixed forest. In contrast, pixels with significant negative correlation are primarily distributed in non-forest biomes for the NH and NA (shrublands and grasslands for EA), but are rarely observed in forest biomes and croplands.  
Biomes having the lowest percentages of pixels with significant positive correlation between LOS and GSI (Figure 6B) are mainly observed for non-forest biomes for the NH and NA (shrublands and grasslands for EA), whereas forest biomes and croplands show the highest percentages for the NH, NA, and EA. On the contrary, biomes having the highest percentages of pixels with significant negative correlation between LOS and GSI are primarily found in non-forest biomes for the NH and NA, whereas forest biomes and croplands show the lowest percentages.
## 1 Introduction
### CUDA for Parallel Processing  
CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) developed by NVIDIA, which enables the use of graphics processing units (GPUs) for general-purpose computation. Using the massive parallelism inherent in modern GPUs, CUDA allows for the acceleration of complex tasks, significantly reducing the computational time required for large-scale optimization problems [15; 16].  
In the context of ego-motion estimation, CUDA is employed to parallelize the evaluation of multiple candidate solutions simultaneously. This parallelism accelerates the optimization process of genetic algorithms, enabling real-time performance even for demanding applications in autonomous navigation systems. By distributing the computational workload across thousands of threads, CUDA makes it possible to handle high-dimensional problems efficiently, providing the necessary speed and scalability for real-time systems. The key contributions of integrating CUDA into our genetic algorithm-based solution can be summarized as follows.  
* Parallelized Evaluation: CUDA facilitates the simultaneous evaluation of image similarity for multiple motion candidates, significantly reducing the computational time required for each iteration of the genetic algorithm [17; 18].  
* Optimization of Memory Hierarchies: The ability to manage memory hierarchies and streamline data transfers between the host (CPU) and device (GPU) minimizes latency, ensuring efficient utilization of computational resources.
* Real-Time Capabilities: By addressing computational bottlenecks traditionally associated with large candidate populations, our method ensures rapid iterations, making it suitable for real-time applications.
* Relevance to Autonomous Vehicles: The integration of CUDA enables immediate and precise decision making, which is critical to safe navigation in autonomous vehicle scenarios with strict real-time constraints.
* Novel Combination of Precision and Efficiency: The proposed approach effectively combines high accuracy in the estimation of ego-motion with unprecedented computational efficiency. This dual achievement addresses the challenges of speed and accuracy, making our method particularly suitable for deployment in resource-constrained environments common in autonomous systems.
## 7 Conclusions
This initiative aims to ensure precise timing distribution across distant assets while addressing challenges such as clock drift, network latency, Doppler effects, and relativistic influences. To support this experimental framework, it is essential to identify high-performance inter-platform measurement devices. The characterization of all ensemble clocks will concentrate on defining parameters for Kalman filters, which are crucial for hardware implementation to optimize computation time per iteration. Together, these efforts promise to revolutionize the capabilities of pLEO systems and enhance our preparedness for future challenges.
## 2 Theory  
EMD is a key constituent of the HHT, an adaptive time-frequency analysis method for nonlinear and non-stationary data. Previous studies have described the HHT in detail [9; 10]. Here, we briefly explain how to perform the EMD.  
First, we determine the local maxima and minima of a signal \(x(t)\) and use cubic spline interpolation to obtain its upper and lower envelopes. If the mean of these two envelopes is \(d_{1}(t)\), then the difference between the signal and \(d_{1}(t)\) is the first component \(h_{1}(t)\):  
\[h_{1}(t)=x(t)-d_{1}(t). \tag{3}\]  
This is called the sifting process. To determine whether \(h_{1}(t)\) is an IMF, it must be a single-component signal that fulfills the following conditions: (i) the number of zero crossings and extrema must be no more than one forthe entire data set; and (ii) the mean value of the upper and lower envelopes, which are defined using the local maxima and minima, respectively, is zero at any time, meaning that the two envelope curves are symmetric about the time axis. Ideally, when the cubic spline interpolation is perfect, with no gentle hump on the signal slope, \(h_{1}(t)\) must satisfy the IMF requirements. However, imperfect fitting commonly produces overshoots and undershoots that generate new extrema and shift or exaggerate existing ones. Even when the fitting is perfect, humps may become local extrema after the first round of sifting. Moreover, the envelope mean may differ from the true local mean of the signal for non-stationary data, resulting in an asymmetric waveform. Therefore, the sifting process must be repeated \(k\) times until the extracted difference is an IMF. In the second iteration, \(h_{1}(t)\) is treated as the original data in the second sifting process:  
\[h_{1}(t)-d_{11}(t)=h_{11}(t). \tag{4}\]  
The sifting process is repeated \(k\) times until \(h_{1k}(t)\) is determined, which is an IMF:  
\[h_{1(k-1)}(t)-d_{1k}(t)=h_{1k}(t). \tag{5}\]  
Then, we define  
\[C_{1}(t)=h_{1k}(t) \tag{6}\]  
as the first IMF component (i.e., component \(C_{1}\)) for the data. Overall, \(C_{1}\) contains the finest and shortest period component of the signal. Subsequently, we can subtract \(C_{1}(t)\) from the signal:  
\[x(t)-C_{1}(t)=r_{1}(t). \tag{7}\]  
Because residue \(r_{1}(t)\) still contains information about the components with longer periods, we consider it new original data and apply the same aforementioned sifting process. This procedure can be repeated for all subsequent \(r_{j}(t)\) values, yielding \[r_{1}(t)-C_{2}(t)=r_{2}(t),\,\ldots,\,r_{n-1}(t)-C_{n}(t)=r_{n}(t). \tag{8}\]  
Summing Equations (6)-(8) ultimately yields  
\[x(t)=\sum_{i=1}^{n}C_{i}(t)+r_{n}(t). \tag{9}\]  
This indicates that \(x(t)\) is decomposed by EMD into \(n\) IMFs (\(C_{1},C_{2},C_{3},\ldots,C_{i}\), from high-frequency to low-frequency components) and a residue \(r_{n}(t)\), which is the signal trend with a maximum of one extremum or a constant.
## 5 Experimental Results and Discussion
### Evaluation Metrics  
The performance of the proposed algorithm is evaluated in multiple stages. The evaluation metrics are described below.
## 4 EE Improvement Issues for Each Component
### Power Amplifier  
The PA efficiency of current BS systems is around 33%. Basically, the current BS systems use a digital pre-distorter and a peak-to-average power ratio reduction scheme to increase the efficiency of the power amplifier (PA). However, in LSASs, it is very difficult to use this kind of technique, because digital pre-distorter is an expensive device and must be applied to each antenna. Regarding the peak-to-average power ratio reduction scheme, the clipping scheme can be acceptable, since it is the most simple and effective peak-to-average power ratio reduction scheme. Due to this reason, we set the efficiency of the PA as 25% for the LSAS. Figure 5 shows the PA efficiency versus peak-to-average power ratio (dB) [21; 22; 37]. The 25% PA efficiency is equivalent to 10 dB peak-to-average power ratio or Input Back-off. Since the peak-to-average power ratio of the OFDM signal, when B = 10 MHz with 1024 subcarriers, is around 11.7 dB with complementary cumulative distribution function, \(CCDF=10^{-3}\), we can say that around 1.7 dB is successfully reduced using any kind of scheme such as clipping.  
Figure 5: PA efficiency, \(\eta\)(%) versus peak-to-average power ratio (PAPR) (dB).  
There are several studies related to the PA of LSAS [38; 39]. In [38], authors proposed a power amplifier configuration for LSAS transmitter. The proposed configuration consists of the amplifier path and the linear path which correspond to the signal cancellation loop of feed-forward configuration. The amplifier path comprises the vector regulator and the power amplifier, while the linear path comprises the delay line. Their results confirm the validity of the configuration employing the signal cancellation loop of 3.5-GHz 140-W class feed-forward power amplifier. In [39], authors proposed a more general EE model of LSAS considering PA efficiency as a variable, and investigated a power allocation algorithm based on zero-forcing precoding so that one can guarantee the TP and EE at the same time.
## 3 Efficiency Analysis of Herringbone Planetary Gear
### Power Loss Due to Gear Meshing
#### 3.2.1 Calculation of Gear Frictional Power Loss
From the perspective of the end face of the helical gear, the helical gear can be regarded as the superposition of an infinite number of thin-walled spur gears rotating a helix angle. When the front face wheel of the helical gear enters into engagement and disengages, its engagement is exactly the same as that of a pair of spur gears. To simplify the calculation, we assume that the gear teeth are subjected to equal loading during meshing. When contact ratio is \(a\leq\varepsilon_{r}\leq a+1\), the load acting on the tooth is \(F_{N}\)/\(a\). As shown in Figure 6, \(N_{1}N_{2}\) includes all meshed segments of the helical gear drive, \(B_{1}B_{2}\) is a meshed segment of the tooth, \(N_{2}B_{1}\) and \(B_{2}N_{1}\) are the \(a+1\) meshed segments, and node \(P\) is locatedinside meshed tooth segment a. For \(P_{f\pm(l)}\), friction power loss in the whole meshed segment can be obtained by taking the average integral value of the meshing segment [28]. \[P_{f}=\frac{1}{P_{bt}}\int\limits_{N_{1}}^{N_{2}}P_{f\pm}(l)dl \tag{22}\]  
Integrating Equation (17) along \(N_{1}N_{2}\), we obtain  
\[ \begin{array}{ll}P_{f}&=\frac{1}{P_{bt}}\Bigg{(}\frac{3}{4}\int\limits_{-l_{ 2}}^{-(P_{bt}-l_{1})}P_{f}dl+\int\limits_{-(P_{bt}-l_{1})}^{0}P_{f}-dl+\int \limits_{0}^{P_{bt}-l_{2}}P_{f}dl+\frac{3}{4}\int\limits_{P_{bt}-l_{2}}^{l_{2} }P_{f+}dl\Bigg{)}\\ &=\frac{1}{4P_{bt}}fF_{N}\alpha_{1}z_{1}\Big{(}\frac{1}{z_{1}}+\frac{1}{z_{2}} \Big{)}\Big{(}P_{bt}^{2}+2l_{1}^{2}-P_{bt}l_{1}+2l_{2}^{2}-P_{bt}l_{2}\Big{)} \end{array}  \tag{23}\]  
where \(P_{bt}\) is normal pitch of the end face. The transmission efficiency calculation of an internal helical gear is similar to the calculation for external meshing. The force diagram of a pair of internal helical gears is presented in Figure 7. The sliding speed \(V^{\prime}{}_{21}\) of gear 2 relative to gear 1 is [29]  
\[P^{\prime}_{f\pm(l)}=F^{\prime}_{f^{\prime}}V^{\prime}_{21}=\pm f^{\prime}F^{ \prime}_{N}\alpha^{\prime}_{2}z^{2}_{2}\Bigg{(}\frac{1}{z^{\prime}_{1}}-\frac{ 1}{z^{\prime}_{2}}\Bigg{)}l^{\prime} \tag{24}\]  
Figure 6: Schematic diagram of helical gear engagement. Figure 7: Schematic diagram of internal gear transmission. Similar to the meshing analysis for the external gear, friction power loss in the internal gear can be calculated as [29]  
\[P^{\prime}_{f}=\frac{1}{4P^{\prime}_{bt}}f^{\prime}F^{\prime}_{N}\omega^{\prime}_ {2}z^{\prime}_{2}\bigg{(}\frac{1}{z^{\prime}_{1}}-\frac{1}{z^{\prime}_{2}}\bigg{)} \bigg{(}P^{\prime 2}_{bt}+2I^{\prime 2}_{1}-P^{\prime}_{bt}I^{\prime}_{1}+2I^{ \prime 2}_{2}-P^{\prime}_{bt}I^{\prime}_{2}\bigg{)} \tag{25}\]
# Experimental Comparison of Fastener Implementation Approaches in Fused Deposition Modeling
## 2 Design of Different Fastener Implementations
### Manufacturing Process  
Printing parameters, especially layer height, nozzle temperature, and material flow, are the most important factors for ensuring the quality and strength of the part [35]. The printing speed and melted plastics flow rate was limited to 100 mm/s and 10 mm\({}^{3}\)/s to ensure that the results were affected as little as possible by other factors [36]. The negative impact of ambient temperature variation on the results when printing PLA is known [37]. To minimize this effect, the printer's closure was closed, and the internal temperature was kept at around 40 \({}^{\circ}\)C.  
Samples were manufactured with 2 wall perimeters, 3 bottom perimeters, and 4 top perimeters. For the material, the PLA+ filament produced by one of the most common brands, ESUN, was preferred [38]. Gyroid infill was preferred due to its advantages in terms of better three-dimensional properties. Gyroid infill gives good results for parts with lower infill ratios as being one of the strongest infill patterns [39; 40; 41].  
Print temperature is also one of the most important parameters in FDM [42]. Based on the preliminary experiments conducted before this study and other studies in the literature, it was decided that the initial layer temperature would be 215 \({}^{\circ}\)C and the printing temperature would be 210 \({}^{\circ}\)C [43; 44].  
Samples are manufactured on a CoreXY FDM 3D printer. The device is operated with Linux based Klipper software and has a direct extruder with gear reduction for more reliable extrusion. The printer is capable of printing at 300 mm/s speed and 16 mm\({}^{3}\)/s melted plastic flow and has a 300 mm \(\times\) 300 mm \(\times\) 300 mm print volume.  
Before manufacturing the samples for this study, a series of experiments were carried out to determine the consistency of the printing process. In the FDM process, layer printing time is an important factor for both the visual quality and strength of the printed part. Tensile strength test parts were printed in batches of 4, 9, 16, and 25 pieces at once. Batches  
Figure 3: Cross-sections of flexural and torque test samples: (**a**) type Ia, (**b**) type Ib, (**c**) type II, (**d**) type III, (**e**) blank.  
printed as 4 or 9 pieces have almost the same tensile strength, but batches with 16 or 25 pieces have a lower strength. Pieces could be printed separately, but this scenario would not be realistic as real-life parts will most likely be larger than these test samples. Based on these results, the tensile strength test samples used in this study were printed as 9 pieces per batch, and the three-point flexural and torque test samples were printed as 8 pieces per batch.  
For this study, 144 samples for the tensile strength tests, 180 samples for the three-point bending tests, and 144 samples for the torque tests were produced; for each test, 12 samples and three different infill ratios were used.
## 1 Introduction  
Vital tooth bleaching, including in-office bleaching and at-home bleaching, has been adopted as an effective and safe method for the esthetic treatment of stained teeth. Continuous advancements in bleaching products have made in-office bleaching more practical and predictable [1]. The most common side effect of in-office vital bleaching teeth is postoperative sensitivity [2]. Since bleaching occurs by the penetration of free radical ions into the enamel and dentin, it leaves behind micro-porosities that can lead to postoperative sensitivity; this is where desensitizing agents come into action. Several desensitizing agents are available with different mechanisms of action, for instance, some penetrate these micro-porosities and reduce the effects of sensitivity by occluding them, and others work by reducing the ability of nerve endings to block pain [3].  
Casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) is a biologically active material that is formed by the combination of casein phosphopeptide and calcium phosphate. CPP-ACP is derived from milk protein and contains phosphoseryl sequence, which helps in the stabilization of amorphous calcium phosphate [3]. The amorphous calcium phosphate plays a significant role in repairing tooth structure and reducing dentin hypersensitivity [4]. The combination of CPP-ACP provides desensitizing action through protein binding followed by continuous deposition of calcium and phosphate on exposed dentinal tubules [5]. CPP-ACP is reported to be effective in reducing sensitivity after teeth bleaching [6].  
Potassium nitrate and sodium monofluorophosphate (PNSMP) is also a commonly used desensitizing agent in commercially available toothpaste. It has been in use for decades, and its role in reducing sensitivity is well established in the scientific literature [7; 8]. Sodium monofluorophosphate reduces teeth sensitivity by blocking dentinal tubules through calcification. Potassium nitrate works by diffusing through the dentinal tubules to act on nerve endings and blocking the transmission of pulpal pain [8; 9].  
[PERSON] et al. compared the incidence and intensity of teeth sensitivity after in-office vital bleaching using CPP-ACP and PNSMP [10]. They reported that both desensitizing agents were equally effective in reducing teeth sensitivity. In another study by [PERSON] et al., CPP-ACP was compared with sodium fluoride for reducing postoperative sensitivity after in-office vital tooth bleaching [11]. They reported a higher incidence and duration of tooth sensitivity in patients without the use of a desensitizer, compared with those exposed to the desensitizing agents.  
In the light of the aforementioned literature, it has been observed that indecisive evidence exists regarding the efficacy of both desensitizing agents in reducing teeth sensitivity. Therefore, it was worthwhile to conduct a randomized clinical trial that compared the desensitizing efficacy of CPP-ACP and PNSMP after in-office vital bleaching.
## Study Areas and Data
### Landsat Satellite Data  
Landsat sensors have continuously imaged the land surface since the 1970s [28]. The TM onboard Landsat 4 and 5 satellites with seven spectral bands sampled the shortwave range at a spatial resolution of 30 m from 1984 to 2011. TM images are an important remote sensing data source for earth resources and environments as per their high spatial resolution, spectral resolution, and positioning accuracy. We downloaded all available Landsat data during the ground measurement period. The distribution of available high-quality TM data corresponding to each FluxNet site within one year is shown in Figure 1. Abundant cloud-free TM data can be obtained for individual pixels; TM data are prone to contamination over larger areas. As shown in Figure 1, all of the sites have more than five available cloud-free TM images within one year. This provides reliable observation data for the effective operation of the EnKF algorithm.