esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Modeling  
The model considers 11 components: the sun, planet, carrier, crankshaft, pin gear, and beveloid gear in Figure 2. The coordinate system \(O-xyz\) is established on the internal gear with variable tooth thickness (fixed during operation) with its geometric center as the origin. We established a dynamic co-ordinate system on the remaining components with their respective geometric centers as the origins: \(O_{s}-x_{s}y_{s}\), \(O_{pi}-x_{pi}y_{pi}(i=1,\,2,\,3)\), \(O_{c}-x_{c}y_{c}z_{c}\), \(O_{shi}-x_{shi}y_{shi}z_{shj}(j=1,\,2,\,3)\), \(O_{r}-x_{r}y_{r}z_{r}\) and \(O_{gk}-x_{gk}y_{gk}z_{gk}(k=1,\,2)\). The first-stage planetary transmission has no axial force, so the vibration displacement of the sun along the \(x_{s}\) axis, \(y_{s}\) axis and \(u_{s}\) direction, and the vibration of the planet gear along the \(x_{pi}\) axis, \(y_{pi}\) axis and \(u_{pi}\) direction is considered. The beveloid gear is subjected to radial force, axial force, and torque during meshing, and the beveloid gear and the carrier are adjusted by the elastic deformation of the disc spring axial displacement. Therefore, the vibration displacement (\(k=1,\,2\)) of the beveloid gear is considered along the \(x_{gk}\) axis, \(y_{gk}\) axis, \(z_{gk}\) axis and u\({}_{gk}\) direction (\(k=1,\,2\)), where 1 and 2 show carrier and crank, respectively. The shafts are subjected to radial force, axial force, and torque, respectively. Hence, the vibration displacement of the carrier is considered along the \(x_{c}\) axis, \(y_{c}\) axis, \(z_{c}\) axis and \(u_{c}\) direction. Having a look at crankshaft along the \(x_{shi}\) axis, \(y_{shi}\) axis, it is obvious that the vibration displacements are along the \(z_{shij}\) axis and \(u_{shij}\) direction (\(j=1,\,2,\,3\)). For multi DOF gear dynamic systems, it is more convenient to use Lagrangian equations to establish dynamic models [22; 23]. The system kinetic energy, potential energy, and dissipation energy of the PRVT are shown in Equations (2)-(4). Figure 1: Transmission principle and three-dimensional structure diagram of beveloid gear rotary vector reducer. System kinetic energy:  
\[T=\frac{1}{2}\Big{(}\sum\limits_{j}i\dot{u}_{j}^{2}+\sum\limits_{m}j_{j}\dot{x }_{j}^{2}+\sum\limits_{m}j\dot{y}_{j}^{2}+\sum\limits_{m}\dot{x}_{k}\dot{z}_{k} ^{2}\Big{)} \tag{2}\]  
where  
\(I,\ m\): the moment of inertia and mass of each component;  
\(\dot{u},\ \dot{z},\ \dot{y},\ \dot{z}\): the vibration speed of each component;  
\(j=s,\ r,\ c,\ p,\ sh,\ g\);  
\(k=r,\ c,\ sh,\ g\);  
\(s,\ r,\ c,\ p,\ sh,\ g\): respectively represent the sun, planet gear, carrier, crankshaft and beveloid gear and pin gear.
## 3 Results and Discussion
#### 3.1.10. Saponins  
Previously, cucumber was reported for the presence of triterpen saponins [41]. In this study, five saponins in peaks 78-82 were detected in cucumber fruit (Table 1). Peak 82 detected in positive ionization mode at [M+H]\({}^{+}\) 943.5275, C\({}_{48}\)H\({}_{79}\)O\({}_{18}\)\({}^{+}\) was identified as soyasaponin I based on fragments at \(m/z\) 797, [M+H-rhm], \(m/z\) 635, C\({}_{36}\)H\({}_{89}\)O\({}_{9}\) [(M+H)-rhm-hex], 617, C\({}_{36}\)H\({}_{57}\)O\({}_{8}\)\({}^{+}\), \(m/z\) 599, C\({}_{36}\)H\({}_{55}\)O\({}^{+}\), \(m/z\) 581, C\({}_{36}\)H\({}_{53}\)O\({}_{6}\)\({}^{+}\) and \(m/z\) 459, C\({}_{30}\)H\({}_{51}\)O\({}_{3}\)\({}^{+}\) for soyasapogend B post loss of glucuronide moiety (C\({}_{6}\)H\({}_{8}\)O\({}_{6}\),176 amu) followed by dehydration, demethylation and RDA fragmentation of sapogenin at \(m/z\) 441, C\({}_{30}\)H\({}_{49}\)O\({}_{2}\)\({}^{+}\), \(m/z\) 423, C\({}_{30}\)H\({}_{47}\)O\({}^{+}\), \(m/z\) 405, C\({}_{30}\)H\({}_{45}\)\({}^{+}\), \(m/z\) 383, C\({}_{27}\)H\({}_{43}\)O\({}^{+}\) and other fragments (Figure S12) matching references and databases [42]. On the other hand, all saponins were detected in negative ionization mode analysis as formate adducts (+46 amu, HCOOH), in which soyasaponin I showed few fragments indicative for the loss of hydroxymethyl, hydroxyl and ring A cleavage at \(m/z\) 397 and \(m/z\) 341, as predicted in HMDB spectra. In negative MN (Figure 2), cluster E consists of saponins correlated with soyasaponin I for their soyasapogend B/E MSn ions at \(m/z\) 397 and \(m/z\) 341, in addition to other ions indicating terminal sugar loss or a fragment of hexose at \(m/z\) 113 (Table 1, Figure S13). From the aforementioned data, peaks 79 and 81 were annotated as soyasapogend E-O-dihexosyl-O-glucuron (soyasaponin Bd) and soyasapogend B-O-rhammosyl-O-pentosyl-O-hexosyl-O-glucuron (mellidus saponin O1), respectively. The mixture of these saponins is reported in legumes [43], but this was the first time it was reported in cucumber. Other saponins maybe present in _L. siceraria_ and _C. sativus_ fruits that could be revealed upon using an extraction-targeting method instead of crude methanol extracts.
## 3 Results and Discussion
### Determining the Optimum Extraction Conditions
#### 3.3.1 Extraction pH  
For type A gelatin, significantly thicker \(\alpha\)-, \(\beta\)-, and \(\gamma\)-bands were obtained when extraction was conducted at pH 7 (Figure 2a and Figure S2a). The yield was the highest at pH 12 (7.74%), followed by pH 7 (6.99%, Table 2). The gelatin bands of type A were more extensively destroyed under both highly acidic and basic conditions than under neutral pH (Figure 2a and Figure S2a). The low molecular-weight bands were visible at pH 12 where \(\beta\)- and \(\gamma\)-bands were completely hydrolyzed (Figure S2a). This finding suggested that strong alkaline conditions more easily destroyed higher gelatin bands than acidic conditions. Thus, pH 7 was identified as the optimum pH for extracting type A gelatin. Previous studies on the effects of pH for the suitable extraction of fish type A gelatin have not been conducted, except for that by [PERSON] et al. (2017). The authors found that gelatin extracted at pH 5 had a higher protein content and molecular weight than that extracted at pH 3. Most other studies ([PERSON] et al., 2018; [PERSON] et al., 2017) used neutral pH to extract type A gelatin, but comparisons with other pH levels were not shown. In contrast, the strongest intensities of gelatin bands were recorded at pH 8 for type B gelatin (Figure 2b and Figure S2b). The yield was the highest at pH 12 (10.60%, Table 2). Similar to type A gelatin, type B gelatin also showed weak \(\alpha\)-, \(\beta\)-, and \(\gamma\)-bands under both highly acidic and basic conditions (Figure 2b and Figure S2b). The low molecular-weight bands were stronger at pH 2 and 12 (Figure S2b). Similar to type A gelatin, \(\beta\)- and \(\gamma\)-bands were also completely hydrolyzed at pH 12. Thus, pH 8 was considered as the optimum condition to extract type B gelatin. The highest yield at pH 4, but the highest quality at pH 6-7 was reported in type B gelatin from skate skin ([PERSON] et al., 2017). This previous study also documented that highly acidic or basic extraction conditions destroy the gelatin bands. In comparison, the band intensities of gelatin, especially those of \(\beta\)-bands, were higher in type A than type B (Figure 2a,b and Figure S2a,b). A tendency of higher \(\alpha\)-band intensities than \(\beta\)-band intensities was clear in type B gelatin. This finding suggested that \(\beta\)-band was more strongly affected by alkali-pretreatment than acid-pretreatment. Moreover, the intensities of the low molecular-weight
# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
## 2 Methodology
### Model Optimization
#### 2.4.1 Hyperparameter Tuning  
One of the key steps during the model training or model optimization, is the process known as hyper-parameter tuning. The hyper-parameter are values used to control the learning of the model, for example the model architecture, the number of training epochs (Training iterations), the learning rate \(\alpha\), among others. This process is performed with the objective of obtaining an optimal performance of the model [71; 72; 73], and correct issues like high bias and high variance.  
The fundamental problem of hyper-parameter tuning consists in finding the sets of hyper-parameter \(\lambda\) that minimize the generalization error \(\mathbb{E}_{(D_{train},D_{val})\sim D}V(\mathcal{L},\mathcal{A}_{\lambda},D_{ train},D_{val})\) of a machine learning algorithm \(\mathcal{A}\) given a data set \(D\) (Divided in training \(D_{train}\) and validation \(D_{val}\)). Where the hyper-parameters \(\lambda\) belong to the configuration space \(\wedge\) and \(V\) is the loss function of the algorithm \(\mathcal{A}\) parametrized by \(\lambda\) (\(\mathcal{A}_{\lambda}\)). Equation (13) shows the mathematical expression of this optimization problem [71].  
\[\lambda^{*}=\underset{\lambda\ \in\ \wedge}{argmin}\mathbb{E}_{(D_{train},D_{val}) \sim D}V(\mathcal{L},\mathcal{A}_{\lambda},D_{train},D_{val}) \tag{13}\]  
The literature has proposed many techniques to address the problem of hyper-para meter tuning. The most widely used are: manual search, in which the hyper-parameters are tuned by hand with an iterative process; grid search, in which a finite set of values for the defined hyper-parameters are specified and then the combination of these values is evaluated; and random search [74], which follows the same principle of grid search but the set of values for the hyper-parameters is set randomly [71; 73; 74; 75].  
In this research, manual hyper-parameter tuning was performed due to its simplicity and low computational cost. This optimization was done by changing the following hyper-parameters: the learning rate \(\alpha\), the batch size, the number of training epochs and the model architecture. With the future work in mind, in which the model might be embedded in a micro-controller to make the system portable; the architecture of the model started as simple as possible and the it was increased with more layers and hidden units in order to achieve the desired performance. The results of this process can be seen in greater detail in Section 3.1.
## 3 Results and Discussion
### Characterization of Dense Media
#### 3.1.1 Stability of Dense Media  
The stability of the suspensions was measured from the settling velocity using the method described in Section 2.3.2. Figure 3A shows how the height of the suspension changes as a function of time. The stability value is calculated from the slope of these curves. When comparing the plots for the different ferrosilicon suspensions (CG, F, C40), one can see how the stability of the media decreases (slope increases in absolute value) as the particle size increases. On the other hand, the suspension containing magnetite (FM; 33% magnetite) has a much slower settling rate than that of pure ferrosilicon (F, CG, C40). This indicates that the addition of magnetite (with more irregularly shaped particles) to the ferrosilicon (with more spherical particles) suspension increases its stability. Therefore, to increase or maintain the stability of the suspension, fine particle minerals (e.g., bentonite) should be added to the coarser heavy mineral to reduce the settling velocity of the media [33]. These results are consistent with those of other authors [33; 34; 35]. Thus, the FM mixture performs better than suspensions with only ferrosilicon. Additionally, the use of dispersants and/or magnetic dopants could be alternative ways to improve the stability of the suspension [33; 36; 37].  
Figure 2: Hydrocyclone system and the type of samples recovered in the two output streams (under (U), over (O). The mineral was then separated by sieving into two fractions with different particle sizes (higher (H) or lower (L) than 250 \(\upmu\)m).  
Figure 3: Plots to determine the heavy mineral suspension stability and viscosity. (**A**) Distance of suspension versus time; (**B**) shear stress versus shear rate. CG (Course Grade), C40 (Cyclone 40), FM (mixture of ferrosilicon and magnetite), Fine (F).
## 2 Testbed Setup and Measurement Methodology
### Experimental Setup for Available Bandwidth Estimation  
Experiments described in this section were gained to test previously obtained results of timing accuracy in a virtual environment on the application example--AvB measurement tools.
## 3 Methodology
### Spatial Location of Sugarcane Nodes Based on Binocular Vision
#### 3.3.2 Solution of Homography Matrix \(H\)  
In order to obtain the three-dimensional coordinates of sugarcane nodes, it is necessary to know the parallax of the nodes in the left and right camera images, which is the difference in values between the \(X\) coordinates of the nodes in the left and right camera image coordinate systems. The homography matrix \(H\)[21] reflects the mapping relationship of corresponding points between the left camera image and the right camera image in the binocular camera. It can be obtained through the optimised stereo matching feature points. Assuming a pair of optimised matching feature points, the pixel coordinate in the left camera image is \(p_{1}(x_{1},y_{1})\), its corresponding point in the right camera image is \(p_{2}(x_{2},y_{2})\), and the homography matrix is \(H\), then:  
\[\left[\begin{array}{c}x_{1}\\ y_{1}\\ 1\end{array}\right]=sH\left[\begin{array}{c}x_{2}\\ y_{2}\\ 1\end{array}\right]=s\left[\begin{array}{ccc}h_{11}&h_{12}&h_{13}\\ h_{21}&h_{22}&h_{23}\\ h_{31}&h_{32}&h_{33}\end{array}\right]\left[\begin{array}{c}x_{2}\\ y_{2}\\ 1\end{array}\right] \tag{4}\]  
The above formula is expanded as follows:  
\[\left\{\begin{array}{c}x_{1}=s(h_{11}x_{2}+h_{12}y_{2}+h_{13})\\ y_{1}=s(h_{21}x_{2}+h_{22}y_{2}+h_{23})\\ 1=s(h_{31}x_{2}+h_{32}y_{2}+h_{33})\end{array}\right. \tag{5}\]  
Substituting the third equation in Equation (5) into the first two equations, there are eight unknown parameters in matrix \(H\), in which \(s\) is the scale factor (\(s\
eq 0\)). Because each pair of matching points can provide two equations, the homography matrix \(H\) can be obtained by only four pairs of matching points. Since the optimised pairs of matching points in our stereo matching experiment above are far more than four pairs, this is enough to solve the homography matrix \(H\). In order to improve the robustness, the RANSAC algorithm is introduced into the process of solving the homography matrix, so as to achieve the optimal correspondence matrix \(H\).
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 5 Applications of Microencapsulated Polypphenols in Food
### Dietary Supplements and Functional Foods  
Dietary supplements and functional foods represent another growing sector for the application of microencapsulated polyphenols. These products aim to deliver concentrated doses of bioactive compounds, including polyphenols, to support health and well-being. However, as with beverages, the stability and bioavailability of polyphenols in supplements can be compromised due to environmental conditions and interactions with other ingredients [2]. Microencapsulation significantly improves the effectiveness of polyphenol-based supplements. For instance, polyphenols encapsulated in biopolymers like chitosan or alginate not only protect them from degradation but also provide targeted release in specific areas of the gastrointestinal tract. This targeted delivery enhances the bioactive effect of the polyphenols, allowing them to reach the bloodstream more effectively [256]. Additionally, encapsulation helps in developing sustained-release formulations. In supplements such as capsules or tablets, encapsulated polyphenols can be engineered to release gradually, providing long-lasting health benefits. This is particularly advantageous for polyphenols like curcumin, which has a short half-life in the body. Through encapsulation, curcumin can maintain its therapeutic levels in the bloodstream over a longer period [257]. Moreover, the sensory properties of functional foods are also improved through encapsulation. Polyphenols, when incorporated into food products like protein bars or fortified snacks, may impart undesirable flavours or cause textural issues. Microencapsulation prevents these negative sensory impacts by masking the taste or altering the release mechanism, ensuring that the polyphenols are only released once consumed and digested [7]. On the market, a variety of functional foods and dietary supplements incorporate encapsulated polyphenols. For example, supplements containing encapsulated grape seed extract or green tea polyphenols are popular for their antioxidant properties. Similarly, functional food products like breakfast cereals and protein bars are often enriched with encapsulated resveratrol or quercetin to enhance their health-promoting qualities [193; 197].
## 2 Materials and Methods
### Proposed Solution
These transformations between coordinate systems are presented on Figure 3.  
The point cloud acquired by the depth camera is converted to HMD world coordinates by multiplying the points by the inversed extrinsics matrix [38]:  
\[T_{Depth}^{World}=\left[R\bigm{|}t\right]_{Depth}^{-1} \tag{2}\]  
where \(R\) denotes 3D rotation and \(t\) denotes 3D translation. To obtain the related pixel coordinates in an image from the PV camera for a given point in the world coordinates, it is multiplied by the camera matrix, resulting in its position in the image coordinate system [38]:  
\[T_{World}^{PV} =K_{PV}\left[R\bigm{|}t\right]_{PV} \tag{3}\] \[K =\left[ \begin{matrix}f_{x}&0&c_{x}&0\\ 0&f_{y}&c_{y}&0\\ 0&0&1&0\end{matrix} \right] \tag{4}\]  
where \(K\) denotes the camera's intrinsic matrix and \(\left[R\bigm{|}t\right]\) denotes the camera's extrinsic matrix. The intrinsic matrix parameters \(f_{x}\) and \(f_{y}\) are the focal length of the camera in the \(x\) and \(y\) dimensions, respectively, while \(c_{x}\) and \(c_{y}\) denote the principal point of the camera expressed in pixels. The position of the projected point in pixel coordinates can then be calculated as follows:  
\[x_{i}=\left|\frac{x_{i}}{z_{i}}\right|,y_{i}=\left|\frac{y_{i}}{z_{i}}\right| \tag{5}\]  
where \(x_{i},y_{i}\), and \(z_{i}\) are point coordinates in the image coordinate system. The needle itself is invisible to the depth camera; consequently, it is estimated based on the grabber part of the tool. Due to high distortion of the point cloud, the needle's pose is determined by selecting a set of points that most accurately align with the needle line, filtering out outliers, and conducting a linear regression. To enhance the stability of needle estimation, the Kalman filtering and exponential moving average  
Figure 3: Transformations between coordinate systems. To obtain needle's pose relative to the data, the coordinate systems of applications running on the HMD and workstation need to be synchronized. Thus, the line segment forming the needle is successively transformed into model coordinates:  
\[T_{HMD}^{Model}= \begin{bmatrix}sR_{11}&sR_{12}&sR_{13}&t_{x}\\ sR_{21}&sR_{22}&sR_{23}&t_{y}\\ sR_{31}&sR_{32}&sR_{33}&t_{z}\\ 0&0&0&1\end{bmatrix} _{M} \begin{bmatrix}R_{11}&R_{12}&R_{13}&t_{x}\\ R_{21}&R_{22}&R_{23}&t_{y}\\ R_{31}&R_{32}&R_{33}&t_{z}\\ 0&0&0&1\end{bmatrix} _{C} \tag{6}\]  
where matrix the with subscript \(C\) denotes transformation between the coordinate systems of the devices, the matrix with subscript \(M\) denotes transformation to the model coordinates, \(R\) denotes 3D rotation, \(t\) denotes 3D translation, and \(s\) is a scale factor.
## 2 Materials and Methods
### Getting Road Condition Data via the Internet  
Since one of the main vehicles used in delivery is the truck, the traffic density on the roads along the route is an important factor that will affect the delivery time. Calculations made independent of traffic density values remain only in theory. Even the most optimal route to be calculated may take longer to complete than routes that were not previously considered optimal due to traffic on the road along the route. In order to prevent this, road condition data is taken into consideration while finding the optimum route.  
The traffic data for the relevant roads on the graph are obtained via the Internet, and the traffic density is numbered 1-4. Afterward, an updated distance matrix is created using traffic data. While calculating the route cost, the updated cost matrix is used instead of the standard distance matrix for deliveries made by the truck.  
With this method, the proposed GA will have the ability to choose the traffic-free route, which has the shortest distance in terms of distance but is slightly longer and less costly than the route with heavy traffic.
# Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks
## 4 Experimental Results  
In this section, we present a series of experiments in order to evaluate the performance of the WCRNN training algorithm on three famous economic classification problems acquired by the UCI Repository of machine learning databases [21]: the bank marketing problem, the German credit approval problem, and the banknote authentication problem.  
The implementation code was written in MATLAB 7.6, and the simulations have been carried out on a PC (2.66-GHz Quad-Core processor, 4-Gbyte RAM) running the Linux operating system, while the results have been averaged over 100 simulations. We have chosen the RNN architecture of the nonlinear autoregressive network with exogenous inputs, which have been reported to have very good performance in the literature [22; 23]. All networks received the same sequence of input patterns; the weights were initiated using the Nguyen-Widrow method [24], and all nodes had logistic activation functions. Moreover, the categorical variables in all datasets were handled utilizing the label-encoding process.  
The classification performance was evaluated utilizing the standard procedure called _stratified 10-fold cross-validation_ and the following two performance metrics: \(F_{1}\)-_score_ and _accuracy_. It is worth noting that the \(F_{1}\)-score consists of a harmonic mean of precision and recall, while accuracy is the ratio of correct predictions of a classification model [13].  
Our experimental analysis was obtained by conducting a two-phase procedure: in the first phase, the classification performance of the WCRNN algorithm was evaluated against the state-of-the-art neural network training algorithms; while in the second phase, we compared the performance of the RNNs, which were trained with the proposed algorithm against the most popular and frequently-utilized supervised classification algorithms.
## 3 Results and Discussion
### Molecular and Structural Modifications Identified by FTIR Spectroscopy
#### 3.3.3 Lipid Degradation and C-H Stretching (3000 and 2850 cm\({}^{-1}\) Region)
EDS results for these samples revealed more balanced carbon and oxygen levels and only minor reductions in sulfur, in line with a more controlled oxidative action that maintained the general chemical integrity of the fiber surface. 3.3.7. Comparative Summary of Effects Across Hair Types  
The FTIR and EDS results confirm that the chemical response to PAW treatment is highly dependent on hair type:  
* Coily/kinky hair (Sample 4), with its higher porosity, exhibited the most profound changes after PAW-N, including marked lipid oxidation (FTIR) and increased carbon content with sulfur loss (EDS). * Wavy hair (Sample 1) also showed significant degradation from PAW-N, with strong dehydration signals (FTIR) and reduced oxygen levels (EDS), while straight hair (Samples 2 and 3) proved more resilient. * PAW-P induced milder FTIR changes across all types, with EDS confirming minimal elemental alterations, reaffirming its potential as a safer oxidative treatment, especially for porous and sensitive hair.