esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Human Body Tracking and Joint Recognition
### Sensor Choice for the Proposed Concept  
Due to the advantages of vision-based body tracking, we decided to use this technology in our concept. We believe that the proper HRI should not rely on \"human-dependent devices\" as data gloves or body-mounted sensors, but on the perceptive ability of the robot itself, as this, in our opinion, most accurately represents natural and ergonomic interaction.  
As for the specific sensor, the Leap Motion controller was picked as the most suitable option. The main reason is the sensor's application focus. The core of the proposed conceptcenters around the hand gesture interaction between the robot and the human worker, as most manufacturing tasks are done by hand. Furthermore, the whole gestural framework is built on pointing gestures, performed by the arrangement of individual fingers. Leap Motion provides accurate and precise human hand tracking, explicitly focusing on the position and orientation of fingers. Other commercially available sensors on the market are not yet capable of such precision and generally focus on tracking the whole human body. The controller depicted in Figure 1 can track hands within a 3D interactive zone that extends up to 60 cm (24\({}^{\prime\prime}\)) or more, extending from the device in a 140 \(\times\) 120\({}^{\circ}\) typical field of view, which is illustrated in Figure 2. The controller produces an output in the form of gray-scale images captured by the IR cameras and the skeletal representation of the human hand. The data are shown in Figure 3. Leap Motion's software can differentiate between 27 distinct hand elements, such as bones and joints, and track them even when other hand parts or objects obscure them. The positions of joints are represented in the controller's coordinate system depicted in Figure 4. The connection with the PC is facilitated via USB 2.0 or USB 3.0 connection. The manufacturer also provides a software development kit or SDK for the Leap Motion controller, allowing the developers to create custom applications.
## 4 Results
### Complex Dispersion Relation of a 2D Periodic Resonant System  
We then focus on the dispersion relation of the 2D acoustic metamaterial of finite size. Specifically, we consider the structure shown in Figure 1b, i.e., a periodic system composed of QWRs arranged in a square lattice embedded in air. Both lossless and lossy cases are considered with visco-thermal losses considered solely inside the QWR resonators [24; 25]. Figure 4 shows the complex dispersion relation results of the 2D acoustic metamaterial. The real part of the wavenumber is shown in the central panel and results obtained using SLaTCoW are compared to those obtained solving an eigenvalue problem using the FEM over an infinite periodic structure with the same geometric and physical characteristics. Please note that the dispersion relation of the host medium alone, i.e., the air in this case, is also plotted in gray dashed lines.  
Figure 3: Complex dispersion relation for a SC composed of rigid cylinders arranged in a square lattice. Numerical results obtained for a \(N\times N\) finite structure are compared to the ones obtained using EPWE for the same periodic structure having infinite size.  
Results show an excellent agreement for all the propagative bands between the results obtained using SLaTCoW and FEM for both \(\Gamma\)X and \(\Gamma\)M directions. However, differences are noticed in between the propagative bands, where the eigenvalue problem does not possess real solution. Effectively, no real solution satisfy the problem \(\omega(k)\) for a periodic system of infinite extent. Moreover, acoustic modes corresponding to the air medium are also recovered via the SLaTCoW method, because the structure under analysis is of finite size and excited by a plane incident wave initially propagating in a homogeneous medium. Their magnitude are much smaller than those of periodic structure modes and decrease rapidly with the number of unit cells of the finite structure. In the band gap frequency range, air modes become more significant, because no propagative mode in the periodic system exists. They are detected by the algorithm as for example along the \(\Gamma\)M direction around 1500 Hz. This prompts a reduction of the imaginary part of the wavenumber at a small range of frequencies within the resonant gap. A closer look to \(\Gamma\)X results for both lossless and lossy cases, indicate the typical characteristics of resonant and Bragg gaps when the air modes are not detected. The attenuation peak of the resonant band gap (around 1600 Hz) is greatly reduced when the visco-thermal losses are accounted for in the QWRs. On the other hand, the attenuation is unaltered between lossless and lossy cases for the Bragg gap (around 2200 Hz), because the losses are only considered inside the resonators and not in the host medium.
## 2 Materials and Methods
### Recombinant Protein and Enzymatic Activity Assay  
Homologous recombination vector pET-28a construction was performed using a seamless cloning kit (TransGen Biotech, Beijing, China). The cutting site was selected, and the pET-28a vector was double digested with _Hind III_ and _EcoR I_. The recombinant vectors _pET-28a-ZbFPS_ and _pET-28a-ZbDXS_ were verified to be correct after sequencing and transformed into _E. coli_ BL21 Star competent cells (Biomed, Beijing, China). The resulting positive colonies were inoculated into LB (Kana) liquid medium and cultured overnight at 37 \({}^{\circ}\)C with shaking. The obtained bacterial solution was mixed with 30% glycerol in a ratio of 1: 1 and stored at \(-80\)\({}^{\circ}\)C. The glycerol bacteria (1 \(\upmu\)L) was diluted by 1:10,000, and 20 \(\upmu\)L was pipetted onto kanamycin (Kana) LB solid medium with a final concentration of 50 \(\upmu\)g/mL and cultured at 37 \({}^{\circ}\)C overnight. 150 mL of LB liquid culture medium (Kana) was shaken at 37 \({}^{\circ}\)C until the OD600 was 0.5-0.6. Isopropyl-beta-D-thiogalactoside (IPTG) was added to a final concentration of 0.5 mmol/L to induce the recombinant protein. The bacterial solution without IPTG was used as a control and centrifuged at 8500 rpm for 7 min to collect bacteria and detect protein induction. The collected bacterial cells were resuspended in 1 \(\times\) phosphate-buffered saline (PBS). The bacterial solution was sonicated for 20 min in an ice bath. The supernatant was collected by centrifugation at 12,000 rpm for 15 min, and then filtered through a 0.22-micron filter. HisTrap FF (prepacked with precharged Ni Sepharose \({}^{\mathrm{TM}}\) 6 Fast Flow, GE, USA) was used to purify the supernatant according to the manufacturer's instructions. The protein eluate was dialyzed using a dialysis bag at 4 \({}^{\circ}\)C, and imidazole was removed. The column was washed with 5 columnvolumes of distilled water, then stored at 4 \({}^{\circ}\)C. Sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) (12%) was used for analysis. A plant _DXS/FPS_ kit (mobile, Shanghai, China) was used to measure the enzyme activity based on ELISA according to the manufacturer's instructions. Three biological assays were conducted.
## 4 Results
### Evaluation of Satellite Precipitation Products
#### 4.1.2 Daily Precipitation
The observed daily precipitation during heavy precipitation events was compared with the corresponding satellite estimates (Figure 5). Table 3 shows that both IMERG and 3B42V7 were poorly correlated with the gauge-measured heavy precipitation, having CC values of less than 0.270. As shown in Figure 5 and Table 3, these two satellite products largely underestimated precipitation during heavy rain events by over 62%. 3B42V7 presented a slightly smaller magnitude of underestimation than IMERG, except at the Monywa station. RMSE for both products generally exceeded 35 mm. According to the POD statistics in Table 3, 3B42V7 captured 20.0%-31.7% of real heavy rain events, whereas IMERG captured 13.3%-20.0%. In general, both products displayed poor performance in heavy rain identifications and estimations. 3B42V7 had a slightly higher accuracy than IMERG, except at the Monywa station. during light rain events by 22.4%-65.8% and 27.8%-116.1% (Figure 6 and Table 3). Nonetheless, more than 30% of actual light rain events were effectively detected by IMERG and 3B42V7. Overall, both products manifested similar low accuracies for light rain estimations at all the gauges.
# Nonlinear Dynamics of an Electromagnetically Actuated
## 5 OAFM for Electromagnetically Actuated Cantilever Beam
#### Numerical Examples  
We illustrate the applicability, accuracy, and effectiveness of OAFM by comparing the analytical approximate solution obtained in this research with the numerical integration results obtained using a fourth-order Runge-Kutta procedure.  
For the resonant case \(\omega=\Omega_{0}+\delta\varepsilon\), the optimal values of the convergence-control parameters for the domain \(\tau\in[0,\,90]\) for \(k=2\), \(A=0.1\), \(c=0.04\), \(f=0.001\), \(a_{0}=0.001\), \(\delta=0.02\), \(\varepsilon=0.01\) are the following:  
\[C_{1}=0.0174168,\,C_{2}=-0.001988 \tag{44}\]  
In this domain, the movement is dampened. For \(\tau>90\), the movement is undamped, and the approximate solution (33) becomes the following:  
\[\overline{z}=K_{3}\cos p(\tau-90)+\frac{K_{4}}{p}\sin p(\tau-90)+C_{3}[3\sin p (\tau-90)-\sin 3p(\tau-90)]+C_{4}[5\sin p(\tau-90)-\sin 5p(\tau-90)] \tag{45}\]  
\[\begin{array}{l}\  
It can be seen that the solutions obtained by our procedure are nearly identical to those obtained from the numerical integration method, using only two parameters.  
The behavior of the investigated model is very sensitive to the changes in some key parameters, as can be seen in the following figures.  
The results in Figure 7 show that a small increase in the parameter \(a_{0}\) related to the electromagnetic force produces a small decrease in the frequency of the vibration. The damping parameter c has an influence depicted in Figure 8, where one can see that a very small increase in its value produces a significant damping of the vibration. The influence of the parameter k can be observed in Figure 9, where it is shown that a relatively large increase in this parameter simultaneously produces a damping in the motion and an increase in the frequency of vibration.  
Figure 5: Comparison between the numerical solution and approximate solution: analytical solution (47); numerical solution.  
Figure 6: Comparison between the numerical solution and approximate solution: analytical solution (46) and (47); numerical solution.  
Figure 7: The influence of parameter \(a_{0}\): \(a_{0}=0.001\) (blue), \(a_{0}=0.07\) (red), and \(a_{0}=0.11\) (green).
# Phoneme Recognition in Korean Singing Voices Using Self-Supervised English Speech Representations
## 3 Method
### English-Korean Adaptation  
Our approach uses a self-supervised pre-trained model on an English corpus, which provides generalized prior knowledge encompassing sounds that humans can produce, regardless of the language that they speak. For the phonological systems of English and Korean, ref. [30] provides a comparative analysis that thoroughly examines the structures and processes for Korean-English interpretation training. Notably, English-Korean adaptation closely resembles the work of English-Korean interpretation. The key distinction is that we are not interpreting meaning, but are instead interpreting sounds, specifically mapping Korean to English phonological systems.  
The challenge for the 'interpreter' lies in the differences between the English and Korean phonological systems, particularly the phonemes common to Korean, but lacking in English. English Phonemic Vowels are more finely differentiated, as shown in Figure 3. There are vowels present in Korean that are absent in English, such as /u/(-), /o/(-), and /s/(t), just as there are vowels present in English but not in Korean, including /i/, /v/, /ae/, /ej/, /ow/, //, /a/, /l/, and /a/. Rather than impede our research, this provides valuable insights into approaching /u/, /o/, and /\(\forall\).  
Figure 2: The proposed approach.  
We, therefore, introduced the approach from [14], and as depicted in Figure 2, we performed our speech-singing adaptation using the English-Korean adapted model. This model tackled the phoneme recognition task by the phoneme predictor (a fully connected layer) \(n\) with CTC loss (phoneme supervision) \(L_{phoneme}\), while also including a pitch detection task using a melody predictor \(m\), where \(m\) takes the feature frames \(Z\) encoded by the HuBERT encoder \(e\) as input, and predicts a distribution over the pitch classes on each frame as \(p_{m}(\bullet|Z,t)\). Defining the note (pitch) sequence target as \(Y_{melody}=[y_{1},...,y_{t},...,y_{T_{e}}]\), the frame-level cross-entropy (CE) loss (melodic supervision) \(L_{melody}\) is computed as follows:  
\[L_{melody}=\frac{1}{T_{e}}\underset{t<T_{e}}{\sum}CE(p_{t},y_{t}) \tag{3}\]  
To enhance speech-singing adaptation, the final loss is computed as follows:  
\[L=L_{phoneme}\ +\lambda L_{melody} \tag{4}\]  
in which \(\lambda\) controls for the relative weight of the melodic supervision. In our test, \(\lambda=1.5\) was the best setting for our study.
## 4 Discussion
### Intestinal Morphometric Parameters  
_Moringa oleifera_ seed powder has nutraceuticaluls that can be utilised to improve intestinal morphometric parameters, goblet cell count, tibia morphometric parameters, and tibia mineral content in chicken diets. The current study demonstrated that oral administration of MSE enhanced duodenal and ileal lengths. In contrast, [PERSON] et al. [28] found no effect on the lengths of the duodenum or ileum when broiler diets were supplemented with grape seed extract or vitamin C for 42 days. [PERSON] et al. [29] showed a decrease in the duodenal and ileal length of broilers supplemented with grape seed extracts, while [PERSON] et al. [30] found that birds fed with diets containing 0.5% green tea high in flavonoidshad shorter intestinal lengths. The disparity in results can be ascribed to the difference in the solvents used in the extraction, concentration of the phytochemicals, and the route of administration.  
Nutrient absorption from the small intestine is facilitated by specialized structures known as the villi. The increased VH suggests an increased surface area for efficient nutrient absorption. Deep crypts suggest rapid tissue turnover and a requirement for new tissue that promotes nutrient absorption. Therefore, the increased CD implies acceleration in the replacement of enterocytes, which make up the walls of the villi. The increased duodenal VH, VW, and CD are consistent with the findings of [PERSON] et al. [31], who administered teff (_Fragrostis tef_) seed extracts to broilers and observed improved VH, VH, and CD. The increased jejun VH, VW, and CD disputed the report by [PERSON] and [PERSON] [32], that grape seed extract (0.02%) increased jejun VH and decreased CD. The increased ileal VH and VW corroborated the findings of [PERSON] et al. [33] who found that the ileal VH and VW were enhanced by cumin (_Cuminum cyninum_) seed and jir (_Artemisia scoparia_) seed extracts, respectively.  
The absorption capacity of the small intestine is determined by the VH/CD ratio, suggesting that optimum digestion and absorption are attained when the VH/CD ratio is increased. The increased ileal VH/CD ratio implies that MSE improved nutrient digestion and absorption, which is not consistent with the observations of [PERSON] et al. [34]. [PERSON] et al. [34] noted the VH/CD ratio was decreased at the duodenum, increased at the ileum, and had no effect on the jejun section. The increased VH/CD ratio induced an intestinal structure more oriented to digestion, with improved absorptive and hydrolysis potential, requiring fewer nutrients to be directed towards intestinal maintenance. Therefore, with MSE administration, the intestinal structure of the small intestine was enhanced. Furthermore, the administration of MSE elevated MMT in the three segments of the small intestines and lamina propria thickness in the jejunum. The lamina propria and muscularis mucosa are reliable in moving digested and absorbed nutrients into the bloodstream.
###### Abstract  
This study aimed to compare stroke rate (SR), stroke length (SL), and stroke index (SI) during aerobic-dominated training sets applied at speed corresponding to lactate threshold (LT) or to maximum aerobic speed (MAS) and following repeated sprits (SPR). Twelve male swimmers performed four sessions in a randomized order: (i) LT, (ii) SPR-LT, (iii) MAS, and (iv) SPR-MAS. Set LT consisted of 8 \(\times\) 200 m at a speed corresponding to the lactate threshold (30 s recovery). Set MAS included 8 \(\times\) 100 m at the maximal aerobic speed (30 s recovery). Set SPR included 8 \(\times\) 25 m at maximal intensity (2 min recovery). Blood lactate (BL), SR, SL, SI, and rate of perceived exertion (RPE) were measured. SR, SL, and SI did not differ between LT and SPR-LT sessions (_p_ > 0.05). In the SPR-MAS session, SR was higher and SL was lower compared to the MAS session (_p_ < 0.05), while SI did not differ between the sessions (_p_ > 0.05). BL at the start of LT and MAS and RPE at the end of sets were higher in the SPR-LT and SPR-MAS sessions (_p_ < 0.05). BL and RPE were correlated with SR in 8 \(\times\) 200 m during the SPR-LT session (_p_ < 0.05). Repeated sprits may change the biomechanical and physiological response during a subsequent training set performed at MAS while preserving technical efficiency.  
stroke rate; stroke length; stroke index; set sequence Article
## 4 Discussion
As for the limitations of the present study, a cross-sectional design was used, so it only allowed establishing relationships between kinanthropometric variables, physical fitness variables and biological maturation, with the analysis of the progression of these variables not being possible. Another limitation of the present study was the use of an indirect method to estimate the maturity offset, such as a regression formula based on kinanthropometric measurements [13], instead of the gold standard for the assessment of the maturity offset, which is a wrist and hand X-ray analysis. It should be noted that the X-ray method is not without limitations, such as the radiation to which the subjectsare exposed, the cost of implementation or the time invested, as well as the difficulty of implementing the method correctly, so non-expert researchers could introduce error in the estimation [68]. Due to these drawbacks, some authors advocate the use of less invasive alternative methods in the adolescent population [21,68]. Among the alternative methods to X-ray assessment that can be used in cross-sectional studies are formulas that estimate the maturity offset based on anthropometric measurements, the most widely used in the adolescent athlete population being that proposed by [PERSON] et al. [13], which has been used in both adolescent athletes participating in team sports and in individual sports [3]. Despite being widely used and having been shown to be reliable in populations similar to that in the present study [21], some limitations have been identified in the use of regression formulas for the estimation of the maturity offset. It has been observed that they tend to underestimate the value for early maturers while overestimating it for late maturers [68], that they may introduce some error in the estimation of the maturity offset (0.50-0.59 years) and that the estimation values increase relatively steadily with advancing chronological age [13,69]. Therefore, knowing the limitations of this method, this research used the estimation of the maturity offset as a categorical variable, as recommended in previous research [13,69], included participants with chronological age ranges within the recommended range, and used the chronological age of the participants to control for the effect of differences between groups.
## 3 Extracting the Frequency Domain Features of Printed Images
### Neural Network Regression  
Machine learning serves as an effective solution for addressing regression problems, with various well-established methods proving successful in image quality assessment (IQA) regression. These methods include the k-nearest neighbor (KNN) model [30], support vector machines (SVMs) [31; 32; 33], random forest [34; 35], and neural networks [36]. While linear regression and KNN calculations are relatively straightforward, they often encounter challenges in achieving optimal results. SVM metrics pose difficulties in their implementation, especially with large-scale training samples. The reliance of SVMs on quadratic programming for solving support vectors involves m-order matrix calculations (where m is the number of samples). When the number of samples is substantial, the storage and computation of this matrix can significantly consume machine memory and processing time.  
The MLP neural network possesses powerful nonlinear fitting capabilities. Through learning, the MLP neural network can automatically adjust its internal weights, gradually adapting to different tasks and environments. This enables the neural network to exhibit excellent generalization capabilities in handling regression problems, and MLP neural networks demonstrate good robustness and fault tolerance. Based on the significant advantages of the MLP mentioned above, we chose the MLP neural network to train a regression mapping model between quality description metrics and subjective image evaluation scores.  
We construct the regression function \(f(X)\), which, given the input metrics, yields the final objective quality assessment score:  
\[Q=f(S_{C},S_{G},S_{S},CC_{1},CC_{2},CC_{3},CC_{4},CC_{5},S_{L},S_{M},S_{H}), \tag{35}\]  
The architecture of the MLP neural network is depicted in Figure 11. It consists of two hidden layers. The first hidden layer processes 11-dimensional input data and produces 64 output nodes. The second hidden layer then takes these 64 nodes and reduces them to 32 output nodes. A ReLU activation function is applied between these two hidden layers to introduce nonlinearity.