esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress of Capillary Flow-Related Hydrovoltaic Technology: Mechanisms and Device Applications
## 2 Current Understandings of Capillary Flow-Related Electricity Generation Mechanisms
### Streaming Potential
For example, in a typical device based on a porous film, the electrodes are placed at the two ends of the film, and the ends are sealed to prevent electrode corrosion. However, in the previously introduced narrow channel model, this sealing blocks the channel ends, preventing continuous liquid flow and thus not meeting the conditions necessary for streaming potential. We also used natural wood equipped with a charged micro-channel array that meets the conditions for streaming potential [40]. Although decent voltage and current were obtained, we found that wind and humidity had little to no impact on device performance. This was unexpected, as we assumed that the evaporation rate could be affected by wind and humidity, which in turn should influence device performance according to streaming potential theory. Additionally, using tap water and ethanol as working solutions resulted in device performance that deviated from predictions based on streaming potential theory, which was difficult to explain. In general, while the streaming potential mechanism provides some preliminary insights, this simplified model may not fully grasp the complexity of evaporation-induced electricity. Other factors need to be considered for a comprehensive understanding.
# Hyperspectral Python: HypPy
## 2 Design and Implementation
### Image Formats
#### 2.2.5 Sorting Wavelengths  
Often, sensors consist of a number of detector banks, each covering a specific wavelength range. The data from these detectors may have overlapping wavelength ranges or may even appear in an unexpected order. For instance, the raw data from the OMEGA instrument of the European Mars Express have the data from the SWIRI (0.93 um to 2.69 um) and SWIR2 (2.53 um to 5.09 um) detector banks before the data from the VNIR detector (0.36 um to 1.07 um) [13]. Another well-known hyperspectral instrument, AVIRIS, consists of four spectrometers with overlapping ranges [14]. Note that redundant bands in the spectral overlap of the AVIRIS spectrometers can also be removed or put in the bad band list; see, for instance, [15].  
Obviously, the switched order and overlapping ranges may lead to problems when plotting or processing such data. Therefore, one of the design criteria of HypPy was to be able to sort the wavelengths of a hyperspectral data cube on-the-fly. In a trade-off between memory use and CPU use, HypPy does not convert the input image into memory; therefore,it must convert the image on-the-fly. Many HypPy tools offer the'sort_wavelengths' option when opening an image.  
HypPy sorts the wavelengths by keeping an index-to-index table as an attribute of the images objects. This table translates the band numbers of an input image into bands that are sorted by wavelength. The advantage is that, internally, the data look like they have their bands sorted by wavelength. The obvious disadvantage is that the band numbers of the input image may not correspond to the band numbers of the original image object.  
However, if bands are addressed by wavelengths rather than by band numbers, then this last point is irrelevant. Wavelengths have physical meaning and should be the preferred way of addressing image bands. Band numbers may change and even have a different meaning from one remote sensor to another. Unfortunately, not all images have wavelengths, either because the wavelengths are missing or the images do not contain spectral data, which is why band numbers can still be used in HypPy.
## 2 Literature Review
### Hand-Engineered Techniques  
Different feature extraction methods and classification algorithms have recently been used to detect schizophrenia. [PERSON] [17] extras statistical moment features in the frequency domain using Tunable Q-Factor Wavelet Transform (TQWT) and selects discriminative features using the ReliefF method. For classification, K-Nearest Neighbor (KNN) is employed. [PERSON] et al. [18] select the AF7 channel using the Fisher score method. Then, it is decomposed into sub-bands using Flexible-TQWT (F-TQWT), statistical features are extracted, and the Kruskal Wallis test is used to select the discriminative features. The flexible least square support vector machine (F-LSSVM) is employed for the classification.  
Some researchers have studied brain connectivity maps for feature extraction. [PERSON] et al. [19] extracted features using the symbolic transfer entropy (STE) method and select using the Relief method. Various methods are used for classification; the best performance was achieved with KNN. [PERSON] et al. [20] extract brain network connectivity features using minimum norm estimation (MNE) and phase-locking value (PLV). The sequential forward selection (SFS) method is used for feature selection, and the linear discriminant analysis (LDA) is used for classification. [PERSON] et al. [21] perform feature extraction using a histogram of local variance (HLV) and symmetrically weighted-local binary patterns (SLBP), which are then reduced by a correlation-based feature selection algorithm (CBFS).  
The hand-engineered methods require several preprocessing stages, including feature extraction, feature selection, and classification, making them laborious. The traditional methods are time-consuming and subject to human mistakes. Important information could be lost when using unsuitable feature extraction measures, and experts with extensive knowledge in this area are required. Though [PERSON]'s method [17] gives high accuracy, it seems to suffer from overfitting because the number of subjects used in the study was relatively small (i.e., 24 participants), and trials of long temporal length (i.e., 25 s) were used. [PERSON] et al. [18] use only one out of 64 channels; the leftover channels can contain sensitive and important information. There is uncertainty about the generalizability of their findings. These methods give good performance only with trials of long temporal length.
# Experimental Verification of the CFD Model of the Squeeze Film Lifting Effect
## 4 Conclusions  
The model developed and used by the authors allows for the observation of not only the height of the levitation but also allows observing the motion of the sample itself.  
The model was verified by an experiment with a very good agreement of the levitation height and worse prediction of the object oscillations during levitation.  
The use of the Navier-Stokes equations, as opposed to the Reynolds equations, provides more reliable results. It allows for detailed observations of the fluid film behavior  
Figure 16: Height of levitation of samples: (**a**) single; (**b**) double stack; (**c**) triple stack.  
Figure 17: Levitation height [\(\mu\)m] as a function of mean pressure in the gas film [Pa] for various amplitudes of the membrane [\(\mu\)m].  
and, in the future, may be used for creating a full FSI model with a flexible sample and multiple actuators.  
There is a clear influence of the mass on the lifting height--the higher load causes a thinner film. The influence of the frequency is shown, and the increase of the frequency causes an asymptotic decrease in height.  
The amplitude of motion of the sample was observed. It was determined that the amplitude of the motion of the sample in stationary conditions is smaller than the amplitude of the excitation plate (membrane). Moreover, the motion of the sample and the membrane is in counter-phase. This is important in case of small levitation heights, as there is a possibility of contact between the sample and membrane, with a risk of destruction in case of vulnerable elements.  
Conceptualization, B.B., R.G. and M.W. ([PERSON]); methodology, B.B., R.G.; software, M.W. ([PERSON]) and B.B.; validation, B.B., R.G. and M.W. ([PERSON]); formal analysis, B.B.; investigation, B.B., R.G.; resources, M.W. ([PERSON]); data curation, B.B.; writing--original draft preparation, B.B.; writing--review and editing, M.W. ([PERSON]) and R.G.; visualization, B.B. and R.G.; supervision, M.W. ([PERSON]); project administration, M.W. ([PERSON]); funding acquisition, M.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by Polish National Science Centre, grant number 2016/23/B/STS/00210. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available from the authors upon reasonable request. Acknowledgments: CFD computations were carried out using the computers of the Centre of Informatics Trichy Academic Supercomputer and Network (TASK) in Gdansk. Conflicts of Interest: The authors declare no conflict of interest. References
## Discussion and Limitations
### The Contribution of Proposed SWEDA Application on SGBINs  
A campus-level SWEDA-SHM was designed. The inputs from two physically deployed SHM systems were used to test SWEDA, visible in Figure 14. The step-wise operations of SWEDA are given below:  
1. Step 1: All the SGBIN data SP1 needed normalization so that the algorithm can be fed with maximum zeros to reduce the floating-point operations, as demonstrated in Figures 16 and 17.
2. Step 2: All the SGBIN SP1s outputs were filtered by SNF (Figure 21) using a MicroPython embedded filter signal. The savgol_filter eliminated unwanted frequencies by only allowing a range of 1\(\sim\)24 Hz. Furthermore, the ranges of more than \(\pm\)5\({}^{\circ}\) amplitudes were eliminated, which were increasing the computation costs as well as directing towards false detection.
3. Step 3: All the SP2s were checked for unsafe thresholds, i.e., in the case of limestone in Qatar it was \(\pm\)0.57\({}^{\circ}\). No emergency value was observed by SWEDA from SGBINs in SP1 and SP2 as exhibited in Figures 18-23.
4. Step 4: The unique peak detection operation generated SCAGS and triggering amplitudes for STA/LTA in SLAWS for a stationary signal, as no seismic conditions were observed by PDS in Figure 22.
5. Step 5: The runtime similarities estimation using ratio computation with SCAGS and SLAWS generated an EPS for an expected earthquake or seismic signal needed magnitude ratios as well as the signal clustering, presented in Figure 23 as the green gradient.
6. Step 6: The EPS was divided by SP2 to compute the maximum similarities for a possible earthquake for a computed EPS from stationary data. No earthquake was found in Figures 24 and 25 with EPS = 0.00-0.09.  
The proposed SWEDA had two limitations that were observed during the implementation phase, which are given below:  
1. The limitation of this work in the implementation phase is a dependency on proposed SGBINs.
2. The sequence of operations has to be observed in the same pattern.
## 5 Discussion and Conclusions
We note in this context that, with only one MPA-type instrument aboard a spacecraft, one would only be able to measure VDFs from one single look direction (notwithstanding the use of spacecraft spin to measure in multiple directions). Depending on the local field geometry at the time of the measurement, MPA samples cuts along varying directions relative to the local magnetic field. This leads to a lack of information on, for example, the instantaneous temperature anisotropy at the time of the measurement. However, the combination of multiple MPA units covering different look directions can resolve this shortcoming at the expense of mass and cost. Alternatively, we recommend the combination of MPA with a traditional ESA, so that MPA provides the high-resolution and high-cadence measurements in one look direction, while the ESA provides the 3D distribution at a lower resolution and cadence.
# A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare
## 4 Implementation Details and Results
### Transformation Profile Generation Module Implementation
#### 4.2.2 CTP Generation  
The Color Transformation Profile (CTP) is used to reduce color differences between the original image and the geometrically transformed captured image. These differences are mainly due to the color distortion during image projection and capture with the camera. In the captured image, the edges of the screen are generally darker than the center and may have some glares. In our study, to determine the color distortion, a polynomial linear regression was performed, and LASSO regularization was applied to eliminate the computation with little relevance [32; 33]. For the training data set, 500 images have been generated with random colors. Each pixel has been sampled from random coordinates in those images. Figure 10 shows the polynomial linear regression training results with LASSO regularization. In LASSO regularization, the alpha value represents the strength of the regularization. Hence, a low strength indicates that the regression model will include many coefficients that are less effective, increasing the amount of computation in the system. Based on the RMSE and R-square calculated by training, we used the correlation coefficient of alpha value \(-4.0\) for this study.  
The correlation coefficients inferred from the polynomial linear regression are used as a filter to correct the color distortion based on the location. To make similar effects with the glare on the edge, the Gaussian blur method is applied [34]. Figure 11 shows the raw image, camera image, and affected image by filter and blur.  
Figure 10: Result of the polynomial linear regression with LASSO regularization: (**a**) RMSE; (**b**) R-square score by alpha value.  
Figure 9: Process of GTP Generation: (**a**) histogram of the K-means clustering result, red bars are low brightness pixels, blue bars are high brightness pixels; (**b**) quantized image by threshold. (**c**) camera image with detected intersections of straight lines; (**d**) transformed image using a GTP calculation based on intersections.  
Table 3 summarizes the means and standard deviations of the SSIM values improved by those color filtering and blurring effects. As shown in the table, the blur effect can improve the similarity of two images (the original and captured ones) more than color filtering.
## 3 Results
### Physical Test Verification
#### 3.5.2 Analysis of Test Results  
Test results and discussion: the verification test results of the optimal parameter combination for the combined fertilization device of the deep fertilizing machine for orchard manure are shown in Table 8.  
The physical test results are compared with the optimal results of the simulation test, as shown in Figure 9.  
Figure 8: Physical verification test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test No.**} & **Average Fertilizer Mass** & **Fertilization** & **Qualified Rate of** & **Comprehensive Qualified** \\  & **per Segment (kg)** & **Uniformity** & **Fertilization Amount (\%)** & **Rate of Fertilization (\%)** \\ \hline
1 & 16.89 & 0.966 & 92.3 & 93.59 \\
2 & 20.42 & 0.958 & 88.4 & 90.62 \\
3 & 20.47 & 0.960 & 88.1 & 90.47 \\
4 & 19.67 & 0.962 & 92.5 & 93.61 \\
5 & 17.92 & 0.960 & 97.9 & 97.33 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Physical test results.  
Figure 9: Comparative analysis of physical experiment and simulation experiment.  
As can be seen from Figure 9, the maximum error between the physical test value and the numerical simulation optimum value of the fertilizer application rate was 8.67% and the minimum error was 1.75%. The test results showed that the average comprehensive qualified rate of fertilization for the combined fertilization device of the deep fertilizing machine for orchard manure was 93.13%, and the average error between the physical test value and the optimal value of numerical simulation for the comprehensive qualified rate of fertilization was 5.99%. The combined fertilization device ran well, with stable performance during its operation process.
## 1 Introduction
[17] investigated the impact of the lifted load subjected to wind on the tower crane. The load under wind pressure is modeled as a pendulum. [PERSON] [18] designed a special spring-damper device that can reduce the payload vibration caused by wind. If the wind or gust of wind is sufficiently strong, the whole tower crane can be overturned. This kind of accident is the most dangerous because of the potential deaths and relatively significant material losses. However, works devoted strictly to the problem of tipping over cranes of different kinds or other similar structures caused by a strong wind are very rare. Here can be quoted the following works concerning the overturning of the gantry container crane with payload [19], gantry cranes [20; 21; 22], or scissor lift [23]. Finally, it is worth noting that the surroundings (for example, buildings and other cranes) of the operating crane-like structures can have a significant impact on the wind load acting on the investigated structure. This phenomenon is known as an interference effect. As before in the case of the overturning of the crane-like structures due to wind load, the works devoted to this problem are also very rare. Here can be quoted the work by [PERSON] et al. [24], where the system of three container cranes is studied. Each other's impact as well as the dynamic response of the cranes are studied. Furthermore, the antenna system and its mutual impact are investigated by [PERSON] et al. [25], [PERSON] et al. [26], and [PERSON] et al. [27]. It has been found that the interference factor of microwave antenna dishes is greater than one for specific wind directions. It is worth noting that the lattice structure of the antenna is very similar to that of most crane structures. This phenomenon is also studied in the case of scaffolding [28], low-rise buildings [29; 30; 31; 32], tall buildings [33], and cooling towers [34; 35]. The currently presented work is a continuation of the problem discussed in the previous one [36]. In that work, we tried to determine the magnitude of the aerodynamic forces induced by the wind acting on the sectional model of the tower lattice and the horizontal jib lattice. The experimental tests in the aerodynamic tunnel and the CFD simulationswere carried out. The obtained results were compared with appropriate standards and codes. The main aim of the current work is to investigate the impact of the wind on the fast-erecting crane, namely determining the most dangerous geometric configuration of the structure against the wind direction. We are also going to take into account the potential external load that is carried by the crane. To deal with this problem, we will exploit the results reported in the previous work as well as the CFD simulations of the whole structure on a real scale with the various wind profiles considered.
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 5 Discussion  
Here, we have presented a novel anti-jamming technique for integration based on BBVI. First, KLD was used to prove that compared with Inv-Gamma or Inv-Wishart distributions, the Gaussian distribution with time-varying mean value is closer to the time-varying noise. Accordingly, the prior distribution of the MNCM was assumed to be Gaussian. Second, to solve the problem that the VBAKF cannot be applied when the prior distribution and likelihood distribution are non-conjugate, we proposed the use of the BBVI method, which is based on stochastic optimization, instead of the VI method to estimate the time-varying MNCM. Finally, the validity and engineering practicality of the proposed method were verified by simulations and trial data. This novel anti-jamming algorithm, which deals with time-varying measurement noise provides more accurate estimation and stronger anti-jamming performance than previous methods. Significantly, compared with VBAKF, the algorithm in this paper improves the accuracy of estimation. However, the algorithm is more complex than VBAKF because it needs to calculate the gradient of ELBO and then update the parameters by stochastic optimization. This is where we need to improve in future work.  
Methodology, P.D.; writing--review and editing, J.C.; investigation, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was jointly funded by the National Natural Science Foundation of China (Nos. 61633008, 61773132, 61803115), the 7 th Generation Ultra Deep Water Drilling Unit Innovation Project sponsored by Chinese Ministry of Industry and Information Technology, the Heilongjiang Province Science Fund for Distinguished Young Scholars (No. JC2018019), and the Fundamental Research Funds for Central Universities (No. HEUCFP201768).  
Not applicable.  
The authors declare no conflict of interest.
# The Impact of Karate and Yoga on Children's Physical Fitness: A 10-Week Intervention Study
###### Abstract  
This study investigated the effects of a 10-week intervention incorporating karate and yoga on the physical fitness of children aged 10-13. Conducted on a sample of 67 boys, the participants were divided into three groups: karate, yoga, and a control group. Each group underwent physical assessments before and after the intervention, focusing on various fitness components measured by the International Physical Fitness Test (MTSF). The intervention sessions were held twice a week and included warm-up exercises, karate or yoga elements, and cool-down. The results demonstrated significant improvements (\(p>0.05\)) in most physical fitness parameters among the karate and yoga groups in trials: run 50 m, jump, run, endurance, hanging, run, agility, sit-ups, and forward bend. However, there was no significant improvement in hand strength, indicating that the intervention might not have adequately targeted this aspect of fitness. The overall MTSF values changed as follows. For the karate group, the results improved significantly, with the mean increasing by 6.95 \(\pm\) 0.71. In the yoga group, the results also improved significantly, with the mean increasing by 8.74, while the standard deviation changed by \(-0.19\). In contrast, the control group, which did not participate in additional activities, showed a decline in performance in some areas, notably hand strength and jumping ability. Total MTSF value decreased by \(-1.05\), and the standard deviation changed by 0.71. The findings suggest that both karate and yoga can effectively enhance children's physical fitness, bringing their performance closer to age-adjusted norms. The study highlights the value of structured physical activity programs in promoting the physical development of children. Although no significant differences were observed between the karate and yoga groups, both interventions contributed similarly to overall fitness improvements. Future research should explore additional factors, such as diet and other physical activities, to provide a more comprehensive understanding of the intervention's effects.  
m artial arts; physical activity; children; MTSF; training 2025  
1,*(c)2025