esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 3 Dynamic Model of the Transmission System
### Derivation of the Motion Differential Equations  
In the dynamic model, a load torque, \(T_{out1}\), is applied to the output shaft of the ring gear, \(rR\); a load torque, \(T_{out2}\), is applied to the output shaft of the carrier, \(h\); and an input torque, \(T_{in}\), is applied to the sun gear, \(a\). By analyzing the forces acting on each component and employing the Lagrange equations, the motion differential equations for each component, considering the translational motion along the \(x_{j}\), \(y_{j}\), and \(z_{j}\) axes and the torsional motion around the \(z_{j}\) axis (totaling four degrees of freedom), can be established.
# Aerodynamic Window Sealing of a Large-Aperture Channel for High-Power Laser Transmission
## 3 Numerical Simulation
### Numerical Simulation Modeling of Three Blowing Schemes  
The last mirror in the large-aperture channel is positioned 700 mm from the exit section. It is necessary to ensure that the sealing requirements are met at a monitoring distance of \(L\geq 100\) mm to avoid the influence of the external environment on the optical components. Therefore, multiple monitoring surfaces are established at the position of \(L<100\) mm with \(v_{\rm z}\) and \(c\) as indicators, where \(v_{\rm z}\) indicates the minimum non-zero z-directional wind speed of the monitoring surface and \(c\) indicates the maximum particle mass concentration of the monitoring surface. If \(v_{\rm z}<0\) m/s or \(c>0\) kg/m\({}^{3}\), it is considered that the position cannot be sealed for the large-aperture channel. It should be mentioned that different values of \(l\) and \(v_{\rm a}\) will impact the sealing performance of the large-aperture channel in the axial-radial blowing scheme.  
For example, consider testing the sealing performance of the large-aperture channel at the \(L=20\) mm position. First, the schemes of axial blowing, radial blowing, and axial-radial blowing (\(l=0\) mm) are established for the large-aperture channel. We set the turbulent intensity at the inlet-\(v_{\rm out}\) to 0.032 and the hydraulic diameter to 1000 mm. In the axial blowing scheme, we set \(v_{\rm a}=1.6\) m/s, \(v_{\rm r}=0\) m/s, \(v_{\rm out}=6\) m/s, and the turbulent intensity at the inlet-\(v_{\rm a}\) to 0.047 and the hydraulic diameter to 154 mm. In the radial blowing scheme, we set \(v_{\rm a}=0\) m/s, \(v_{\rm r}=8.3\) m/s, \(v_{\rm out}=6\) m/s, and the turbulent intensity at the inlet-\(v_{\rm r}\) to 0.046 and the hydraulic diameter to 36 mm. In the axial-radial blowing scheme, we set \(v_{\rm a}=1\) m/s, \(v_{\rm r}=3.2\) m/s, \(v_{\rm out}=6\) m/s, the turbulent intensity  
Figure 2: Fluid simulation model of the large-aperture channel. (**a**) The schematic diagram of the model boundary conditions; (**b**) the monitoring distance and the radial wind distance location diagram.  
at the inlet-\(v_{\rm a}\) to 0.05 and the hydraulic diameter to 154 mm, and the turbulent intensity at the inlet-\(v_{\rm r}\) to 0.052 and the hydraulic diameter to 36 mm. Following the numerical simulation, the velocity vector of \(x=0\) mm section and the z-directional wind speed and particle mass concentration of the \(L=20\) mm monitoring surface are obtained, as shown in Figure 3. It can be observed that the wind speed in the large-aperture channel is different for different blowing schemes. At \(L=20\) mm, the \(v_{\rm z}>0\) m/s of the axial blowing and axial-radial blowing scheme prove it can resist the ambient gas blowing in; however, with the \(v_{\rm z}<0\) m/s of the radial blowing scheme, it is obvious that there is ambient gas blowing into the large-aperture channel. The \(c\
eq 0\) kg/m\({}^{3}\) for the three blowing schemes illustrates that none of the three blowing schemes can achieve a seal on the dust particles at this monitoring distance.
# Changes in Collagen across Pork Tenderloin during Marination with Rosehip Nanocapsules
## 3 Results
### pH Changes in Meat  
Acid marinades improve tenderness, make muscle structures more flexible, and increase the water-holding capacity. This is achieved by reducing cooking losses; improving color, juiciness, and flavor; and mitigating off-flavors [32]. Figure 4 shows the changes in meat pH from the beginning until 48 h later. First, the acid marinades decreased the meat pH after marination and before 48 h in the treatment with NC-RH at pH 4.0. It is worth noting that the acid marinade favors pork meat preservation, decreases the vulnerability to microbial growth, and facilitates the action of collagenases alongside enzymes associated with meat tenderization [33].  
Figure 4: Changes in meat pH (initial, after 24 h, and after 48 h). Different letters indicate statistically significant differences (\(p<0.05\)).  
The pH also determines the protein net charge, dissociation or aggregation, and the electrostatic repulsive forces between protein molecules [34]. The preference to use an acid pH in the marinade is due to protein denaturation, which makes the meat soft, decreases moisture release and hardness, and improves chewiness [35]. In addition, the tenderness provided by an acidic marinade occurs in the first few days of marinating, and this continuous increment in tenderness is attributed to a more significant release of enzymes. In this case, the greater amount of rosehip oil released was at around 7 h, after which the concentration was constant. On the other hand, the temperature also affects the structure of myofibrillar proteins during the processing and storage of meat and meat products. For example, at 30 \({}^{\circ}\)C, myosin tails start to unspin and unfold, enhancing protein interactions. The hydrogen bonds within the myofibrillar protein expose more active sites to the surface, forming a continuous cross-linked three-dimensional gel matrix [34].  
Therefore, pH and temperature can modify the structure of the myofibrillar proteins. This can be demonstrated in the amount of rosehip in the pork tenderion. The advantage of the amount released at 4 \({}^{\circ}\)C is that the meat is stored at a refrigeration temperature, and the rosehip oil can be released slowly, favoring its action in the meat and increasing its shelf life. This results in significant improvements in the meat characteristics.
## 2 Material and Methods
### Cell Culture and Clonogenic Assay  
The ARPE19 human retinal pigment epithelial cell line was purchased from American Type Culture Collections (ATCC, Manassas, VA, USA) and cultured according to the manufacturer's instructions. Cell culture maintenance was performed with the same methods previously described [31]. The 92.1 human uveal melanoma cell lines were kindly provided by Prof. [PERSON] and cultured according to the procedure described in the work by [PERSON] et collaborators [32]. A clonogenic survival assay was adopted to evaluate cell survival after proton and gamma-ray irradiation. After irradiation, cells were seeded in 6-well plates in triplicate and at an increasing density, depending on the dose delivered; they were incubated for 12-14 days to allow for colony formation. Colonies were stained with 0.05% crystal violet, diluted in 20% ethanol for 30 min at room temperature. Colony-forming ability was determined, both for unirradiated (controls) and irradiated samples, by dividing the counted colony number by the number of cells plated after irradiation  
Figure 1: The relative dose measured with the Markus chamber (black crosses), simulated dose distribution (black line), primary LET-dose computed for only primary protons (red line) and LET-Total-dose considering also the contribution of generated secondary particles (red circles) are reported.The vertical blue lines show the positions at which measurements were performed. Simulated and measured values for the absorbed dose are in good agreement when a 2% uncertainty is considered in both sets of data.  
in order to obtain the plating efficiency (PE) and survival fraction (SF) of the samples respectively. For each dose, the SF was then derived by normalizing the above-mentioned results by the PE. For each experimental session, the effect due to the different doses was evaluated on three cell samples. Each point of the survival curves represent the average of three independent experiments. The survival curve was fitted by the linear-quadratic (LQ) equation [33]:  
\[S=e^{-\alpha D-\beta D^{2}} \tag{1}\]  
The fit was performed by using the MATLAB-R2019b software, where D is the physical dose deposited by protons and photons, S the surviving fraction, and \(\alpha\) and \(\beta\) the radiosensitivity parameters. The fit provided the linear and quadratic parameters with their standard error and the \(R^{2}\) value. These parameters was used to calculate the enhancement ratios at 10% survival.  
Irradations with \(\gamma\)-rays issued by \({}^{60}Co\) source (CIRUS-Cis Biointernational, Gif-sur-Yvette, France) were performed at the Vinca Institute of Nuclear Sciences, Belgrade, Serbia. Cells were irradiated with doses ranging from 1 to 6 Gy at the rate of \(\simeq\)1 Gy/min, in the air at room temperature, following the same experimental procedures as defined for irradiations with protons.
# Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition
## 2 Theory and Methodology
### Time-Frequency Analysis Method of GPR  
The radargram is the synthesis of the time domain signal. The instantaneous frequency spectrum method cannot analyze the radargram very well. The instantaneous frequency analysis method of GPR needs extending from single time domain signal. During the computation process of the GPR time-frequency analysis, every trace data was decomposed by VMD into the IMF. The analytic signal was constructed using the Hilbert transformation. Subsequently, the instantaneous frequency and amplitude was obtained. Finally, we obtained the Hilbert spectrum. Recording the data of two-dimensional GPR along a measuring line can be expressed as \(f(x,t)\). The reflected pulse spectrum of GPR by Hilbert transform can be expressed as:  
\[H(x,t,\omega)=\text{Re}\left(\sum_{k=1}^{n}a_{k}(x,t)e^{\iint a_{k}(x,t)dt}\right) \tag{13}\]  
The GPR input data of the time-frequency analysis is profile data. According to Formula (13), the GPR data of the time-frequency analysis is three-dimensional (3D) data. Thus, 3D GPR data can be divided into three frequency attributes. Figure 2 shows the analysis process.  
When 3D GPR data frequency \(\omega\) is fixed, \(H(x,t)\) is the division frequency section:  
\[H(x,t)=H(x,t,\omega)|\omega=const \tag{14}\]  
The division frequency section is a filter cross-sectional view, as different frequency bands for the filtered scan and the reflected signal can be investigated in each frequency band. The division frequency section is the most common method for frequency analysis of GPR.  
Figure 2: Ground penetrating radar (GPR) time-frequency analysis solving process.  
When the 3D GPR data location x is fixed, \(H(t,\omega)\) is the time frequency section:  
\[H(t,\omega)=H(x,t,\omega)|x=const \tag{15}\]  
The time frequency analysis section records the frequency characteristics and the reflected wave distributed in the frequency axis.  
When the 3D GPR data time \(t\) is fixed, \(H(x,\omega)\) is the space frequency section:  
\[H(x,\omega)=H(x,t,\omega)|t=const \tag{16}\]  
The space frequency section shows the spatial frequency analysis of GPR data. The frequency components correspond to the changes in the reflection signal of certain depth along the survey profile.  
In the three frequency attributes, the transverse profile is the division frequency section, the longitudinal profile is the time frequency section, and the horizontal profile is the space frequency section. The time-frequency analysis method of GPR is based on the three frequency attributes.
# A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions
## 4 Insole Sensor Technology
### Pressure Sensors
#### 4.1.3 Piezoelectric  
Piezoelectricity is the phenomenon where electric charge builds up on the surfaces of materials that lack a center of symmetry when they are subjected to mechanical force. Insole piezoelectric sensors typically comprise sandwich structures of electrodes and a piezoelectric film [5]. These sensors possess benefits such as minimal energy usage, uncomplicated structure, and exceptional sensitivity [18]. Nevertheless, these devices cannot detect static forces caused by the leakage current flowing through the amplifying circuit that follows, hence limiting their use in commercial products.  
Piezoelectric pressure sensors can be classified into three categories according to the materials utilized: inorganic, organic, and composite. Combining piezoelectric materials with transistors makes it possible to reduce the interference caused by noise along the transmission line and improve the functionality of the piezoelectric pressure sensor. Piezoelectric film-based technology offers a practical option for measuring shear stresses or forces in many directions on the insole of the foot [5].
## 2 Research Methodology
### Data Preprocessing  
Given the unstructured nature of employee reviews, preprocessing was conducted to optimize analysis efficiency and accuracy. The preprocessing sequence consisted of three main steps: tokenization for noun extraction, bi-gram and tri-gram extraction, and stopword removal. Morpheme analysis was performed on the collected text data. Despite the importance of sentimental analysis, this study utilized only nouns for DMR topic modeling, as nouns serve as primary semantic indicators in natural language processing applications, including information retrieval, document classification, text summarization, and information extraction [24].  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Before Filtering**} & \multicolumn{2}{c}{**After Filtering**} \\ \cline{2-5}  & **Number of Reviews** & **Ratio (\%)** & **Number of Reviews** & **Ratio (\%)** \\ \hline May 2019 & 3655 & 6.95 & 2184 & 7.31 \\ \hline Jun 2019 & 3234 & 6.15 & 1893 & 6.34 \\ \hline Jul 2019 & 3617 & 6.88 & 2100 & 7.03 \\ \hline Aug 2019 & 3266 & 6.21 & 1885 & 6.31 \\ \hline Sep 2019 & 2971 & 5.65 & 1786 & 5.98 \\ \hline Oct 2019 & 2979 & 5.67 & 1777 & 5.95 \\ \hline Nov 2019 & 3140 & 5.97 & 1775 & 5.94 \\ \hline Dec 2019 & 3175 & 6.04 & 1758 & 5.89 \\ \hline Jan 2020 & 3929 & 7.48 & 2088 & 6.99 \\ \hline Feb 2020 & 3610 & 6.87 & 1915 & 6.41 \\ \hline Mar 2020 & 3225 & 6.14 & 1775 & 5.94 \\ \hline Apr 2020 & 2807 & 5.34 & 1558 & 5.22 \\ \hline May 2020 & 2818 & 5.36 & 1575 & 5.27 \\ \hline Jun 2020 & 3158 & 6.01 & 1752 & 5.87 \\ \hline Jul 2020 & 3610 & 6.87 & 2079 & 6.96 \\ \hline Aug 2020 & 3367 & 6.41 & 1964 & 6.58 \\ \hline Total & 52,561 & 100 & 29,864 & 100 \\ \hline \end{tabular}
\end{table}
Table 1: Number of Reviews Before and After Filtering.  
Figure 3: Number of reviews after filtering.  
This approach required Part-Of-Speech (POS) tagging, which was implemented using the KoNLPy library with the MeCab-ko package in Python. MeCab [25], an open-source morphological analyzer based on Conditional Random Fields (CRF), was originally developed for Japanese language processing but has been adapted for broader applications. MeCab-Ko, the Korean extension of MeCab, leverages the morphological and syntactic similarities between Japanese and Korean. Since its introduction in 2013, it has been extensively employed in Korean Natural Language Processing (NLP) tasks due to its high accuracy and usability [26]. Compound nouns and neologisms were extracted using the NLTK package's N-gram collocation feature. N-gram represents a sequence of n consecutive tokens (in this study, syllables), where a token is defined as a syllable unit within a sentence. Table 2 illustrates N-gram decomposition using the example sentence, \"This is not a pipe\".  
To extract meaningful topics and reduce the number of words processed during preprocessing, special characters, numbers, English words, and emoticons were excluded from the analysis, and stopwords were removed. As a result, a total of 29,848 reviews containing 10,663 unique nouns were used for DMR topic modeling.
## 2 Methods
### Indicators to Evaluate Node Importance
#### 2.3.1 Connectivity Strength  
The connectivity strength (CS) of a node is defined as the product of its in-degree and out-degree. This measure considers the number of edges associated with a node to evaluate its topological connectivity strength, reflecting the extent of its connections with other nodes. The metric focuses on the relationships between nodes, with higher values indicating stronger connections and greater importance. Its expression is given as follows:  
\[CS_{i}^{(m)}=\sum_{j=1}^{N^{(m)}}AM_{ij}^{(m)}.\sum_{j=1}^{N^{(m)}}AM_{ji}^{(m)}, \tag{9}\]  
where  
* \(AM_{ij}^{(m)}\): Adjacency matrix of \(RSCN^{(m)}\);
* \(N^{(m)}\): The number of nodes in the \(RSCN^{(m)}\).
## 2 Material and Methods
### Quality Control and Assurance  
The accuracy and precision employed in determining the concentrations of Co, Cr, Cu, Fe, Ni, Se, and Zn were confirmed through recovery experiments. Certified Reference Materials (Mussel tissue ERM-CE278k) from IRMM in Geel, Belgium were utilized for this validation process. Certified Reference Materials were treated and analyzed in the same way as the samples. Recovery percentages for Co, Cr, Cu, Fe, Ni, Se, and Zn were near to 100%. The limits of detection (LOD) and quantification (LOQ) were established following standard procedures that are used in the determination of chemical elements and species in food products [20]. The samples of all the elements analyzed were above the LOQ, except for Co (13% > LOQ) (Table 2).
# Analysis of Earth Pressure Variation for Partial Displacement of Retaining Wall
## 3 Results and Discussion
### Load Sharing Ratio  
It should be mentioned from the above data that when the trap-door produced movement, the load sharing effect generated between the movable plate and the model box wall. In this section, the study of load sharing was established based on the data of the horizontal trap-door test. The load sharing ratio is defined as \(n\), as expressed in Equation (3).  
\[n=\frac{P_{door}}{P_{wall}} \tag{3}\]  
where \(P_{\text{door}}\) is calculated by summing the earth pressure acting on the trap-door in each test condition, and \(P_{wall}\) is the resultant earth pressure on the fixed wall within the same width of the movable plates.  
Analysis of the load sharing ratio \(n\) in Figure 21 showed that the maximal \(n\) of all displacement modes occurred at the 10 cm wide trap-door. The load sharing ratio had a minimum value at \(w/H\) = 0.4 in T mode while \(w/H\) = 0.8 in RT and RB modes. The minimal load sharing ratio usually tends to be sought because the lower load sharing ratio showed that the moveable wall undertook less load, which is favorable to the stability of retaining wall. It is clear from the figure that \(n\) reached the minimum at a specific displacement and did not decrease continuously as displacement increased. The minimum was about 0.1 in the three modes.  
Figure 20: Distribution of active earth pressure coefficient with displacement of the trap-door.
###### Abstract  
This paper presents a model predictive control (MPC) scheme for the stabilization of high-speed autonomous ground vehicles (AGVs) considering the effect of road topography. Accounting for the road curvature and bank angle, a single-track dynamic model with roll dynamics is derived. Variable time steps are utilized for vehicle model discretization, enabling collision avoidance in the long-term without compromising the prediction accuracy in the near-term. Accordingly, safe driving constraints including the sideslip envelope, zero-moment-point and lateral safety corridor are developed to handle stability and obstacle avoidance. Taking these constraints into account, an MPC problem is formulated and solved at each step to determine the optimal steering control commands. Moreover, feedback corrections are integrated into the MPC to compensate the unmodeled dynamics and parameter uncertainties. Comparative simulations validate the capability and real-time ability of the proposed control scheme.  
Keywords:autonomous ground vehicles; model predictive control; obstacle avoidance and stabilization; road topography +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Classification of Stalk Lodging Resistance
The orange ellipse represented those with medium lodging properties such as YD606 (60,000), XY335 (60,000), ZD958 (75,000), and XY335 (75,000) while blue ellipse represented those corn varieties with low lodging properties of corn stalks such as XD20 (60,000), YD606 (75,000), and XD20 (75,000). The lodging rate was calculated by dividing the number of lodged stalks of corn with the total number of corns in the planting area. The average lodging rates were recorded before harvest in 2016 and 2017 (see Table 2). The one-way analysis of variance (ANOVA) test was applied to verify the significant differences among the average lodging rates. The statistical results show that the average lodging rates of DH605 (60,000), DH605 (75,000), and ZD958 (60,000) were relatively lower than other corn varieties, and the most serious lodging stalks happened in the corn variety of XD20 (75,000), XD20 (60,000), and YD606 (75,000). Obviously, the results of stalk lodging resistance obtained from the SVD algorithm are consistent with the actual results of stalk lodging rate. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples (plant/ha)** & \begin{tabular}{c} **Lodging Rates** \\ **Recorded in 2016 (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Lodging Rates** \\ **Recorded in 2017 (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Average Lodging** \\ **Rates (\%)** \\ \end{tabular} \\ \hline DH605 (60,000) & 0.00 g & 0.00 f & 0.00 f \\ DH605 (75,000) & 3.18 f & 2.96 e & 3.07 e \\ ZD958 (60,000) & 7.33 e & 8.01 d & 7.67 d \\ YD606 (60,000) & 8.24 de & 8.10 d & 8.17 d \\ XY335 (60,000) & 8.23 d & 8.77 d & 8.50 d \\ ZD958 (75,000) & 9.11 d & 9.55 cd & 9.33 cd \\ XY335 (75,000) & 9.74 cd & 10.00 c & 9.87 cd \\ YD606 (75,000) & 10.11 c & 10.43 c & 10.27 c \\ XD20 (60,000) & 21.21 b & 22.13 b & 21.67 b \\ XD20 (75,000) & 31.22 a & 29.30 a & 30.26 a \\ \hline \hline \end{tabular} Note: Values within columns followed by different letters are statistically different at \(p=0.05\), and different lowercase letters denote differences between each average lodging rate.  
\end{table} 
Table 2: The lodging rates recorded from the experimental samples in 2016 and 2017. Figure 2: The principal components (PCs) with individual variances. The accumulated contributions of each PCs are represented with black dots, while the solid line depicts the eigenvalue.