esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Adaptive In-Transit Co-Processing
### Block Actions  
Block actions consist of three stages: initialization, pre-processing, and post-processing. Block actions can act as tags for a wide variety of acceleration purposes, and can be seen as the action that has been taken for a specific block, based on its importance and the used filter conditions. However, in the context of this paper, we consider actions as tags for different types of compression methods. The initialization stage can be used to initialize resources and to specify the estimated compressed data size. Such information could be used to, for example, improve load balancing. The pre-processing and post-processing stages can be used to compress and decompress a block's data, respectively. Five different actions are used in our testing:  
* No Action (NONE). No reduction or compression is performed. Blocks have this action set by default. This action is mainly useful for blocks where the time overhead introduced by compression outweighs the speedup of data transfers.
* Skip. Blocks with this action are never allocated or sent to the transit nodes. This action is useful if, for example, a region of the simulated grid is not of interest. Using the Skip action can, as such, substantially reduce the data transfer and co-processing times in some scenarios.
* Run-Length Encoding (RLE). Blocks with the RLE action are compressed using RLE. Both the compression and decompression of data can be completed in one pass.
* LZ77. Block with this action are compressed using the LZ77 compression algorithm [32]. Compared to the RLE compression method, LZ77 can be used to more effectively compress repeating sequences of data. This means that its usefulness differs depending on the simulation as well as on each individual block.
* Homogeneous (HOMO). Blocks that are Homogeneous are allocated as a single value. Similarly to the Skip action, it can dramatically reduce the data transfer time. This action is useful for empty or homogeneous space, and can also be used to reduce regions that are not of interest. The main benefit that the Homogeneous action has over other types of compression and reduction algorithms is the compression time, which has a time complexity of \(\mathcal{O}(1)\).  
We note that although these compression and reduction methods are used in the context of this paper, the proposed approach can be used in combination with any existing compression and reduction methods, including lossy methods like zfp [30], SZ [33], and its extension [31].
## 4 The Running Reference Approach to Obtain Strain Values from OBR Data  
The new analysis method obtains strain measurements by using sequentially different reference records. When a record is taken, it is compared to the previous record to calculate the change in strain between both records. Strain measurement number \(n\) is calculated from record \(n\) and record \(n-1\). Record \(n-1\) acts as the reference file, while in the traditional method, the reference was always the very first record. Afterwards, measurement number \(n+1\) is taken; record \(n+1\) is the new to-be-analyzed record; and record \(n\) takes over the role of the reference record. The strain is now calculated from these two records. The process continues like this for further measurements, hence the \"running reference method\".  
This approach calculates initially only the strain difference between two subsequent records \(\Delta\varepsilon_{n-1-n}\). As logic dictates, adding up these strain differences between each measurement will yield the absolute strain \(\varepsilon_{\text{absolute, }n}\) for each measurement \(n\):  
\[\varepsilon_{\text{absolute, }n}=\sum_{1}^{n}\Delta\varepsilon_{n-1-n}\quad, \tag{1}\]  
The reference measurement taken before the experiment is \(n=1\) and defined to exhibit zero strain.  
[PERSON] et al. briefly mentioned in [51] a method to obtain positive strain data that seems to be similar to this approach. However, no additional details or comparison with data obtained by the traditional method were given.  
It is shown below that \(\varepsilon_{\text{absolute, }n}\) is the same as the traditional method. In addition the running reference method yields meaningful strain values in the areas in which the regular method fails. This is due to the fact that the differences in the subsequent references and actual measurements' amplitude records are much smaller in the running reference method. Since the diminishing in the Rayleigh backscatter signal from one measurement to the other is small enough in the new proposed method, the cross-correlation algorithm can function as intended. Thus, the running reference analysis method avoids the circumstances leading to the generation of random values when using the traditional method.  
In the following, it will be shown that the running reference method indeed works and allows strain measurements in situations where the traditional method fails. However, some things and procedures may be perceived as impractical and may prevent possible users from actually applying the new method. These challenges will be addressed, and it will be shown how to use the running reference method most efficiently.
# Analysis of Satellite Compass Error's Spectrum
###### Abstract
Parameters such as the duration of system initialization, maintaining its continuity and required accuracy in a real time in a dynamic environment, were examined at the beginning of satellite compasses development. Confirmation of the hypothesis was obtained that multi-antenna GPS receivers, in addition to the positioning ability, are able to determine reliable data regarding a ship's spatial orientation. Another example is the proposal contained in the patent of 1998 [13]. The authors proposed a compass, which determines its spatial orientation based on a construction with two antennas rotated by a stepper motor until a phase equalization of both antennas occurs. Figure 1: The idea of the use of two antennas to determine the heading of the ship [1]. The first devices available to wider users were introduced in the 1990s. In 1991, Ashtech launched the first multi-antenna GPS receiver: 3 DF. Using this system, it was possible to determine the heading, longitudinal and transverse tilt, as well as the position, using a system consisting of four antennas, one of which served as the base antenna, and three others were supporting the base. Each antenna cooperated with a separate receiver. All of them, using signals from at least four different satellites simultaneously, by measuring the difference in phase, determined the orientation of the antenna assembly in three-dimensional space. The research was continued by scientists from the University of Calgary, who in 1994 conducted tests on the Canadian research ship, \"Endeavor\". Four GPS antennas were mounted on the vessel's helipad. The purpose of the tests was to compare the indications of this system with the Sperry Mark 3 Model C gyro compass available on the vessel [14; 15]. Other analyses related to this subject, especially over the optimal configuration of antennas, have also been published in [5; 16]. In 1994, Trimble introduced a four-antenna system called TANS Vector [17]. The system performance was based on phase measurements between one of the antennas (main, base) and each of the others, which were treated as slave antennas.
# Structural Design and Parameter Optimization of Bionic Exhaust Tailpipe of Tractors
## 2 Materials and Methods  
### Establishment and Meshing of Bionic Model  
The nonsmooth surface of a shark has an effect on reducing resistance [37]. As shown in Figure 1a [38], the surface of a shark with triangular grooves has the function of drag reduction, which provides a basis for research on drag reduction and noise reduction. Inspired by the surface of shark skin, [PERSON] et al. designed a sawtooth riblet structure, as shown in Figure 1b [29], and performed many experimental studies [39; 40]. The research showed that the isosceles triangular groove has the best drag reduction performance. In this paper, the bionic exhaust tailpipe of a tractor was designed, as shown in Figure 1c. Bionic triangular convex textures were added to the inner wall surface of the tractor exhaust tailpipe. The top angle \(\theta\) of the triangular texture was selected from 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\), in sequence. The texture height h was selected from 0.5, 0.75, 1, 1.25, and 1.5 mm. The number of circumferential columns n of the texture was selected from 18, 24, 30, and 36. The length of the triangular texture along the inner wall of the tube was 384 mm. A three-dimensional diagram of the air cavity of the prototype and bionic exhaust tailpipe is shown in Figure 2.  
Figure 1: Bionic model diagram: (**a**) SEM micrographs of shark-skin (_Squulus acanthias_) replica [38], Reproduced with the permission of ref. [38], copyright © [PERSON]. [PERSON], 2010. (**b**) schematic of sawtooth riblets bioinspired by shark-skin surfaces [29], Reproduced with the permission of ref. [29], copyright © Philips. T. R. Soc. A, 2010. (**c**) tractor bionic exhaust tailpipe.  
The grid division of computational aeroacoustics is shown in Figure 3a,b. To make accurate calculations, a hexahedral structural grid was used for mesh generation. The number of total grids of the prototype tailpipe was approximately 1,831,074, and that of the bionic tailpipe was approximately 7,000,000. To calculate the transmission loss of the exhaust tailpipe, the sound field was analyzed with LMS Virtual Lab software. The condition that the side length of the grid unit should satisfy is \(L\leq c/(6f_{\text{max}})\)[41]. \(c\) is the propagation speed of sound in the fluid medium,\(f_{\text{max}}\) is the highest calculation frequency, and \(L\) is the length of the unit to be calculated. The air cavity grids of the prototype and bionic exhaust tailpipe are shown in Figure 3c,d, where the maximum unit size is 10 mm and the minimum unit size is 0.5 mm.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 9 Discussion and Comparison among Experiments
### On the Quality and Content Range of Assessment  
This analysis is focused on comparing the quality and content responses of Experiments 1, 2, and 3. To this end, we gather the quality and content range from all three Experiments. Figure 14 presents a comparison of the ranges of the MQSs gathered in Experiments 1, 2, and 3. It can be observed that the MQS ranges for Experiments 1 and 2 (2.58 and 2.4, respectively) are similar. However, there is a negative difference of 0.6 points (maximum range) and 0.42 points (minimum range) in the MQS scale from Experiment 1 to Experiment 2. More interestingly, by comparing the MQS ranges of Experiments 1-3, it can be observed that the range of Experiment 3 overlaps the ranges of Experiments 1 and 2. More specifically, the range of Experiment 3 varies from the minimum range limit of Experiment 2 (1.5) to almost the maximum range limit of Experiment 1 (4.4). This behaviour can also be observed for the MCS range in Figure 14, but at a smaller intensity. As mentioned earlier, there is a correspondence between quality and content scores that needs to be further studied. Overall, this analysis shows that audio-visual quality and content (at a certain level) ranges encompass the audio and video ranges. As stated in the previous analysis, the smaller range of the perceived quality in Experiment 2 might be related to the inclusion of only audio distortions in the sequences.
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 2 Related Works
### HSI Color Model  
At present, most of the color image enhancement algorithms process the three color channels independently, i.e., red (\(R\)), green (\(G\)) and blue (\(B\)), though the \(R\), \(G\) and \(B\) components are strongly correlated. In general, color distortion is occurred if the ratio of the three components change. HSI model uses hue (\(H\)), saturation (\(S\)) and intensity (\(I\)) to represent color images and it is compatible with human visual system as well [18]. The HSI model representation is shown in Figure 2.  
Figure 1: Retinex model.  
Hue represents the feeling of human sense to different colors. Saturation indicates the purity of the color that means the greater saturation is equivalent to the brighter color. Intensity is the brightness of the color. The angle range of the hue is \([0^{\circ},360^{\circ}]\), in which \(0^{\circ}\), \(120^{\circ}\)and \(240^{\circ}\) represent red, green and blue colors, respectively. HSI color space has two important characteristics; (1) changing \(I\) component does not have any effect on the image color information, (2) \(H\) and \(S\) components are closely related to the way people feel colors, which is more in line with the perceptual characteristics of human visual perception of the outside world. However, both HSI and RGB color spaces are just different representations of the same physical quantity. The conversion relationship from RGB to HSI are given in following:  
\[H=\left\{\begin{array}{cc}\theta&B\leq G\\ 360^{0}-\theta&B>G\end{array}\right. \tag{2}\]  
\[S=1-\frac{3\text{min}(R,G,B)}{R+G+B} \tag{3}\]  
\[I=\frac{R+G+B}{3} \tag{4}\]  
where \(\theta=\arccos(\frac{(R-G)+(R-B)}{2\left[(R-G)^{2}+(R-B)(G-B)\right]^{1/2}})\).
## 3 Results and Discussions
### Simulations of Other Crucial Elements in the Water Cycle
#### 3.2.1 Temporal Variation of the Water Cycle Elements
Taking Event 5 as an example, the S1 in the first five hours of the storm reached nearly 0.4 from around 0.3, and it was then maintained at a high level for the next five hours. The same thing happened for the other three events, and only S1 and S2 changed. The fully coupled WRF/WRF-Hydro soil moisture values all exceeded WRF-only values. For S1 and S2 in Event 4 and Event 6, there was higher S1 and S2 before and after the peak period, which might be used as an explanation for the sudden increase of \(P\) and \(Dw\) in Figure 10. In the coupling WRF-Hydro system, soil moisture, as a key feedback variable, could affect the spatial fluctuation range of precipitation. Compared with the accumulative difference of rainfall, we found that a larger fluctuation corresponded to initial drier soil conditions (Figure 7c, Figure 8 c, Figure 11). The more soil moisture the columnar soil layer lacks, the more the hydrological module could feedback to the land surface system and WRF to affect the elements spatiotemporal distribution in the fully coupled system. Figure 10: Temporal variations of the grid average values for the crucial elements in the water cycle: (a) WRF-only; (b) the fully coupled WRF/WRF-Hydro. Figure 9: Heat map of grid average values of the crucial elements in the water cycle: (a) WRF-only; (b) the fully coupled WRF/WRF-Hydro; \(P\), \(R\), \(Et\) and \(Dw\) are the statistics for the 24 h cumulative values (mm), and \(T\)s represents the hourly grid averaged values (\({}^{\circ}\)C).
# A Simple Method to Estimate Weed Control Threshold by Using RGB Images from Drones
## 2 Materials and Methods
### Determination of the Economic Threshold (ET)  
The relationship between the WGC and the Relative Yield Loss (RYL) of maize was computed according to [21] who adapted the two-parameter regression model proposed by [41] to obtain the Equations (11) and (12), which relate RYL (expressed as a ratio to the maximum yield) and WGC (expressed as a ratio to the total area):  
\[\text{RYL}=\frac{q\times\text{WGC}}{1+(q/m-1)\text{ WGC}} \tag{11}\]  
\[\text{WGC}=\frac{\text{RYL}}{q-(q/m-1)\text{ RYL}} \tag{12}\]  
where \(q\) is the damage coefficient attributed to the weed population and \(m\) represents the maximum yield loss that occurred when the weed cover reaches the highest possible value (WGC = 1).  
The values of \(q\) and \(m\) chosen for the calculation of Equation (11) were \(q=2.5\) and \(m=0.70\) (RYL = 2.5 WGC/1 + 2.57 WGC, curve 1 = C1). The values of \(q\) and \(m\) are consistent with the literature [21] and were calibrated by considering the site-specific conditions of the experimentation. The estimated values of RYL were calculated by substituting, in C1, the values of WGC obtained with the two methods (M1-WGCWP+CP and M2-WGCWP+CP).  
We calculated the other two curves from Equation (11): curve 2 (C2) with \(q=3.7\) and \(m=0.39\), obtained by fitting the two parameters by using the data from the M2-WGCCP at 10 m, and curve 3 (C3) with \(q=6.2\) and \(m=0.32\) obtained by fitting the two parameters using the data from Er-WGCCP at 10 m. These two curves were used only to make a comparison with the economic threshold values obtained by using C1.  
The costs of weeding were estimated by summing the current price of herbicides used (49.00 \(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\  
The comparison between the estimates obtained by using the two proposed methods (M1 and M2) and the results of the image processing by Erdas was reliable, even in this case, with the \(t\)-test for paired samples and the Pearson correlation coefficient (\(r\)).  
The goodness of fit used to calibrate the parameters of the two estimation methods (AM and AW for M1 and AW* for M2) was evaluated by comparing the obtained values of the MGC and WGC (predicted data) with the Er-MGCWP+CP and Er-WGCWP+CP (observed data) (\(n=8\)). In the case of RYL, we used the values obtained from Equation (12) as the predicted data, and the values of RYL were calculated by subtracting the yield of each CP as observed data (\(n=4\)) from the maximum maize yields (assumed equal to the average of yields of the WP).  
The model quality indices used are reported in Table 1, e.g., RMSE (Root Mean Square Error), RMSE/\(\tilde{\text{O}}\) (where \(\tilde{\text{O}}\) is the average of the observed data), MAE (Mean Absolute Error), EF (Modelling Efficiency), and CRM (Coefficient of Residual Mass) [43].
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
## Appendix A  
Remarkably, \(\mathbf{R}_{ir}\) has embedded the localization information of those signals that have been generated with the repetition pattern \(\mathbf{C}_{i}\). To prove it, it is useful to realize that if:  
\[\mathbb{E}\Big{\{}\mathbf{x}_{k}\mathbf{C}_{i}\mathbf{C}_{i}^{\text{H}} \mathbf{x}_{k}^{\text{H}}\Big{\}}=0, \tag{10}\]  
for \(i\
eq j\), then (20) can be simplified as follows:  
\[\mathbf{R}_{ir}=\sum_{k\in\mathcal{N}_{i}}\mathbb{E}\Big{\{}\mathbf{x}_{k} \mathbf{C}_{i}\mathbf{C}_{i}^{\text{H}}\mathbf{x}_{k}^{\text{H}}\Big{\}} \mathbf{h}_{k}\mathbf{h}_{k}^{\text{H}}. \tag{11}\]  
In notation terms, the set \(\mathcal{N}_{i}\) gathers the index of those users that satisfy \(p_{k}=i\). Interestingly, OFDM and SC-FDM waveforms fulfil (10) when all the carriers are active, as the rows of the inverse discrete Fourier transform (IDFT) matrix are orthogonal. In conclusion, when (10) holds true, the optimal beamvectors lie within the subspace spanned by \(\{\mathbf{h}_{k}|k\in\mathcal{N}_{i}\}\). Another interesting result is that \(\mathbf{h}_{i}\) and \(\mathbf{h}_{j}\) are asymptotically orthogonal when the number of antennas tends to infinity [28]. Hence, for large scale antenna array systems, any vector that belongs to \(\{\mathbf{h}_{k}|k\in\mathcal{N}_{i}\}\) solves (21). Closely analyzing (22) we can also resolve that, under the orthogonality conditions, the MV beamformer is proportional to the array vector (8) in the direction of the user of interest. The immediate consequence that results from the orthogonality is that the vector that fulfils (16) is proportional to the MV beamformer.
# FEM Based Preliminary Design Optimization in Case of Large Power Transformers
## 3 Results and Discussion
### Discussion of the Results
The FEM calculation made by Agros2D [34]. Symbol LV means the low voltage winding, symbol HV represent the modeled high voltage winding. Figure 7: Evolution of the TOC during the optimization process. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Design Parameters** & **Dimension** & **Metaheuristic** & **NSGA2+GP** \\ \hline \multicolumn{3}{l}{Core data} \\ \hline core diameter & mm & 570 & 600 \\ flux density & T & 1.64 & 1.58 \\ core mass & t & 16.65 & 21.05 \\ turn voltage & V & 83.6 & 89.3 \\ main gap & mm & 37 & 58 \\ \hline \multicolumn{3}{l}{Low voltage winding} \\ \hline inner diameter & mm & 610 & 720 \\ winding height & mm & 1003 & 1210 \\ winding width & mm & 89 & 80 \\ turn number & \# & 228 & 214 \\ current density & A/mm\({}^{2}\) & 2.35 & 2.02 \\ h* & mm & - & 3.6 \\ w* & mm & - & 2.5 \\ \hline \multicolumn{3}{l}{High voltage winding} \\ \hline inner diameter & mm & 861 & 1027 \\ winding height & mm & 973 & 1170 \\ winding width & mm & 107 & 110 \\ turn number & & 1579 & 1478 \\ h* & mm & - & 8.1 \\ w* & mm & - & 2.7 \\ current density & A/mm\({}^{2}\) & 2.01 & 1.53 \\ \hline \multicolumn{3}{l}{Regulating winding} \\ \hline inner diameter & mm & 1149 & 1220 \\ winding height & mm & 853 & 1025 \\ winding width & mm & 10 & 10 \\ current density & A/mm\({}^{2}\) & 2.7 & 2.71 \\ \hline \multicolumn{3}{l}{load loss} & kW & 114.9 & 88.3 \\ core loss & kW & 13.2 & 17.82 \\ \hline \multicolumn{3}{l}{TOC} & \multicolumn{1}{c}{\(\in\)} & 447,627 & 448,597 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of the optimization model results. The most significant difference is found in the main gap selection. The metaheuristic method was found a solution, where the length of the main gap is equals with the possible minimum (37 mm), according to the practical design rules. However, the proposed method was yielded the cheapest solution with a much larger main insulation distance (58 mm). This difference shows also the non-linearity of the task: a small difference between the optimized TOC values can lead a significant difference in the cost optimal key-design parameters and the minimisation of the insulation distances not leads automatically a cheaper design. The optimal conductor sizes in the case of the HV winding are very realistic, these results verify the applicability of the method. The optimal conductor shape values of the LV winding corresponds with the theoretical expectations, however they are not so realistic. The reason of this problem, that the thermal and the mechanical properties of the winding are still not considered during the calculation. The relatively small copper height and the cubic shape of the conductor leads to a wrong thermal behaviour and manufacturability. Therefore, the thermal and the mechanical properties should be considered in the future to get a more practical solution.