esa-sceva/satcom-chunk-collection huggingface dataset:
# Lean Manufacturing Soft Sensors for Automotive Industries
## 2 Methodology
### Soft Sensor Modeling
#### 2.2.1 Artificial Neural Networks (ANN)  
ANN is a bio-inspired computational network model composed of input, output, and hidden layers of \"neurons\". ANN networks imitate the structure of interconnected neural connections in the human brain to learn relationships between various training dataset features. The input data features are fed to the input layer nodes of the ANN and assigned initial weights. Hidden layers are composed of nodes that hold the sums of products of each input and its corresponding weight. Thereafter, an activation function  
\begin{table}
\begin{tabular}{c c c} \hline
**LM** & **Level Labels** \\ \hline
0.50 to 0.65 & low \\
0.65 to 0.80 & medium \\
0.80 to 0.96 & high \\ \hline \end{tabular}
\end{table}
Table 2: LM levels for classification.  
Figure 1: Block Diagram of LM Soft Sensor.  
is applied to the outputs of all hidden layer nodes and multiplied with respective output layer weights to finally give the model estimation. Figure 2 shows a general representation of the ANN architectures used in this study. In this figure, '\(*\)' indicates the presence of multiple instances of the same feature.  
In the present study, a rectified linear unit (ReLU) activation function was employed for improved computation performance. The ReLU activation is given as follows:  
\[f(x)=max(0,x) \tag{1}\]  
This function returns 0 if it receives any negative input. For any positive value of \(x\), it returns the same value. Hence, it gives an output ranging from 0 to infinity and prevents the neurons from assuming very low values. Figure 3 depicts the ReLU activation function response for different values of \(x\).  
The hidden and output layer nodal weights in an ANN structure represent the underlying relationships among the different features of the training dataset. In a feedback ANN, estimation errors are propagated back through output to input layers to re-adjust nodal weights to improve prediction in the next iteration. On the other hand, this re-adjustment of  
Figure 3: ReLU Activation Function.  
Figure 2: ANN Classification. ‘\(*\)’ indicates the presence of multiple instances of the same feature.  
nodal weights takes place from the input towards output layer in a feedforward ANN. Variations of ANN structures include different numbers and sizes (number of neurons/nodes in a particular layer) of fully connected layers.  
Table 4 depicts the hyperparameters of the various ANN architectures explored for LM soft sensing--narrow, medium, wide, bilayered, and trilayered networks. Narrow, medium, and wide ANN have just one fully connected layer, whereas bilayered and trilayered ANN have two and three fully connected layers, respectively. All layers have 10 neurons each (layer size), except for 25 and 100 neurons in the medium and wide ANN, respectively. Regularization was kept at zero values to prevent underfitting of the ANN soft sensors. All data were standardized, i.e., scaled to fit a standard normal distribution. Maximum 1000 iterations were fixed for all networks.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 2 Methods  
### Study Design  
This study is a quantitative, randomized, blinded, pre-post, prospective clinical trial. Patient allocation was conducted using a computer-generated randomization process to minimize selection bias. The groups were structured as follows. _Exergame group_ (\(n=25\)): Participants engaged in interactive, gamified VR-based exercises specifically designed to enhance agility, balance, and motor coordination. _Cycling group_ (\(n=25\)): Patients performed high-intensity cycling exercises, continuously monitored with real-time motion tracking to ensure safety and optimal performance. _Agility Training group_ (\(n=25\)): Participants underwent structured agility-based exercises conducted in a clinical setting, focusing on dynamic balance and motor control. _Robot-Assisted Training group_ (\(n=25\)): This group utilized robotic-assisted treadmills (e.g., Cmill) to improve gait, stability, and functional mobility. Before the intervention, all participants had been receiving standard physiotherapy, which was temporarily suspended for the duration of the study to ensure exclusive participation in the assigned intervention programs. Written informed consent was obtained from all participants prior to enrollment, in accordance with ethical guidelines. Baseline characteristics, including sex distribution, were recorded, with 37 males and 37 females participating in the study, contributing to a total of 74 evaluable participants. The intervention groups were assessed using standardized clinical measures, including the Berg Balance Scale, Dynamic Gait Index, BESTest, and Tinetti Assessment Tool (TAT), to evaluate motor function, balance, and fall risk. The inclusion criteria were as follows: diagnosis of idiopathic Parkinson's disease (Hoehn & Yahr stage 2-4), stable pharmacological treatment, Mini-Mental State Examination (MMSE) score of \(\geq\)24, and the ability to walk independently for at least 6 m with or without assistive devices.  
The exclusion criteria included severe cognitive impairment (MMSE \(<24\)), presence of comorbid neurological or orthopedic disorders affecting mobility, unstable cardiovascular disease, recent orthopedic surgery (within 6 months), ongoing participation in other clinical trials, or any contraindications to high-intensity physical activity, as determined by the treating physician.
# Analysis of Key Storage Mechanism of Asymmetric Key-Related Functions in CNG Crypto Library
## 2 Background/Related Works
### CNG Programming Technique Analysis  
The following typical steps are required to implement the CNG API [7; 9]. To program with the CNG API, the following steps are typically needed: a BCryptOpenAlgorithm-Provider function to open the algorithm provider; a BCryptGetProperty or BCryptSetProperty function to obtain or set the property of the algorithm, respectively; a BCryptGenerateSymmetricKey or BCryptGenerateKeyPair function for key generation; a BCryptImportKey or BCryptImportKeyPair function for importing the key; a BCryptExportKey function for exporting the key; a BCryptEncryptonreport function to perform cryptographic operations; and a BCryptCloseAlgorithmProvider function to close the algorithm provider [7].  
* Open the algorithm provider with the BCryptOpenAlgorithmProvider function.
* Obtain and set the algorithm properties with the BCryptGetProperty and BCryptSetProperty functions.
* Generate and import the key with BCryptGenerateSymmetricKey and BCryptGenerateKeyPair for key generation and BCryptImportKey and BCryptImportKeyPair for importing the key.
* Perform cryptographic operations with the BCryptEncrypt and BCryptDecrypt functions.
* Close the algorithm provider with the BCryptCloseAlgorithmProvider function.  
For this paper, we analyzed the programming method of asymmetric key cryptography among the features of CNG, such as algorithm provider, random number generation, hash function, symmetric and asymmetric key cryptography, and signature and verification features, based on the above steps [10; 11]. In this paper, we describe asymmetric key cryptography.  
Asymmetric key cryptography solves the sharing problem of initialization vectors and secret information by using a public key scheme that maintains a private key and a public key, where the public key is open to anyone. The key pair is used to decrypt the cipher text, which is encrypted with the public key, using the corresponding private key. To realize this feature, there is a price. When comparing the calculation amounts, asymmetric key cryptography is known to be considerably different from symmetric cryptography. However, it is very useful for communication because it includes a structure to mutually share secret key information. Figure 2 shows the procedure for asymmetric key cryptography.  
Figure 2: The programming technique of CNG. The process of asymmetric key cryptography for encryption and decryption. * Red color font is key properties and information used for encryption and decryption.
## 2 Results
### Content of Esters along Ripening in Italia and Bronx Seedless Table Grapes  
Table 6 shows the content of esters of Italia and Bronx Seedless table grapes obtained in the six phenological stages of vine ripening. The variety factor significantly affected the contents of ethyl acetate, ethyl isobutyrate, butyl acetate and ethyl hexanoate, whereas phenological stage factor affected the content of all the esters presented in Table 6. None of the esters were affected by the interaction between variety and phenological stage factors. Italia grapes presented higher contents of ethyl acetate, ethyl isobutyrate, butyl acetate and ethyl hexanoate than the Bronx Seedless grapes. Grapes harvested at the BBCH-89 stage presented the highest content of ethyl propionate compared to the studied stages. In addition, grapes harvested at BBCH-89 showed higher contents of ethyl propionate and ethyl butyrate than the grapes harvested in the rest of the stages, except BBCH-85. BBCH-77 showed lower contents of propyl acetate, ethyl 3-methyl-butanoate, ethyl pentanoate, ethyl hexanoate and hexyl acetate, and a higher content of butyl acetate than the grapes harvested at BBCH-85 and BBCH-89.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **Hexanal** & **(Z)-3-Hexanal** & **(E)-2-Hexanal** & **Hexanol** & **(E)-3-Hexenol** & **(Z)-3-Hexanol** & **(E)-2-Hexenol** \\ \hline \multicolumn{8}{l}{**Variety (V)**} \\ Italia & 121.06 b & 68.55 a & 71.62 b & 102.20 b & 53.63 a & 41.26 a & 37.09 b \\ Bronx Seedless & 104.54 a & 70.33 a & 63.81 a & 93.89 a & 61.42 b & 41.41 a & 32.90 a \\ \multicolumn{8}{l}{**Phenological stage (S)**} \\ BBCH-77 & 82.98 a & 72.99 a & 49.56 a & 73.97 a & 42.64 a & 31.19 a & 36.79 b \\ BBCH-79 & 93.11 b & 71.53 a & 55.71 b & 82.25 b & 47.71 a & 34.68 a & 36.05 ab \\ BBCH-81 & 104.46 c & 70.10 a & 62.62 c & 91.47 c & 53.39 b & 38.56 b & 35.33 ab \\ BBCH-83 & 117.21 d & 68.70 a & 70.38 d & 101.71 d & 59.75 c & 42.88 c & 34.62 ab \\ BBCH-85 & 131.51 e & 67.33 a & 79.11 e & 113.10 e & 66.85 d & 47.68 d & 33.93 ab \\ BBCH-89 & 147.55 f & 65.98 a & 88.91 f & 125.76 f & 74.81 e & 53.03 e & 33.25 a \\ \multicolumn{8}{l}{**Significance a**} \\ V & 0.0000 & 0.4277 & 0.0000 & 0.0001 & 0.0000 & 0.8794 & 0.0001 \\ S & 0.0000 & 0.4766 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.2108 \\ V \(\times\) S & 0.0943 & 1.0000 & 0.8862 & 0.9803 & 0.9613 & 1.0000 & 1.0000 \\ \hline \end{tabular} Data are expressed as mean of the data. a Significance (\(p\)-value) of variety (V), phenological stage (S), and V \(\times\) S interactions. For a given factor and significance (\(p<0.05\)), different letters within a column represent significant differences (Duncan test, \(p<0.05\)).  
\end{table}
Table 4: C6 compounds content (ug L\({}^{-1}\)) of Italia and Bronx Seedless table grapes harvested in six different phenological stages (BBCH-77, BBCH-79, BBCH-81, BBCH-83, BBCH-85 and BBCH-89).
## 4 Grape Pomace Extracts and Their Bioactives as Ingredients in Sun Oils for Skin Care
### Impact of Phenolics on the Skin
#### 4.4.4 Cyanidin-3-glucoside
  
* An in vitro assessment was conducted on the penetration of caffe acid, chlorogenic acid, and  
oraposide through the skin of **pigs**
* All of the skin’s layers had traces of chlorogenic and caffe acids
* CSE-based formulations successfully reduced roughness, scaliness, and wrinkles, while  
increasing skin elasticity and moisture  
\begin{tabular}{c c} \hline \hline
**Cyanidin-3-glucoside** & 2018 [41] \\ \hline
**Antioxidant** & & \\
**Photoprection from UVA and UVB** & & \\
**Anti-aging** & & \\
**In vitro** & & \\
**Cyanidin-3-glucose:** The most prevalent anthocyanin in nature
* In provides photoprotection against UVA and UVB radiation **for human keratinocytes**
* Pre-tating the cells with cyanidin-3-glucose reduces some of the negative effects of UVB exposure \begin{table}
\begin{tabular}{c c c c} \hline \hline Component Structure & **Main Findings/Type of Study** & **Year of Study** & **References** \\ \hline p-Couramic Acid/Trans-Resveratrol & \(\bullet\) & **Photop protection from UVB** & 2023 & [43] \\ \hline
**In vitro** & **GFE-CHF and GPE-EAF**: Fractions recovered from the V. vinifera waste of the winemaking process, with the highest concentration of quercetin, trans-resveratrol, and syringic and p-couramic acids & \\ \hline \hline In normal conditions, both fractions were cytobiocomputable with fibroblasts & When the cells were exposed to H\({}_{2}\)O\({}_{2}\) (stressed environment), GPE-CHF acquired cytotoxicity & \\ \hline
**GFE-EAF** & 1. & Had a higher content of the assessed phytocompounds and established a protective profile in the fibroblast culture in the presence of H\({}_{2}\)O & 2. \\ \hline
**GFE-CHF** & 2. & Increased the in vitro SF and its photosolability for a minimum of one hour, suggesting that winery waste may be a viable source of active compounds and/or & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._Moreover, cyanidin-3-glucose has also been found to reduce UVB-induced chronic photodamage by inhibiting oxidative stress and inflammation in mice [44]. More specifically, the dorsal of Kunming mice was treated with moisturizing gel containing cyanidin-3-glucose (100, 200, 300 \(\upmu\)mol/L) after UVB exposure, where it was shown that chronic photodamage caused by UVB was reduced by such a treatment, while the UVB-induced epidermal barrier dysfunction was ameliorated, including an increase in the skin hydration and decrease in the transepidermal water loss. This cyanidin-3-glucose-containing cosmetic product inhibited UVB-induced epidermal hyperplasia, the destruction of collagen fibers, ROS levels, and the expression of the inflammatory enzyme COX-2 and the inflammatory cytokine IL-6.
# Deep-Learning-Based Identification of Broad-Absorption Line Quasars
## 3 Method
### Data Dimensionality Reduction
#### 3.1.1 Principal Component Analysis (PCA)  
PCA is a common dimensionality reduction algorithm that extracts the most important information from high-dimensional data [27]. It is used to reduce the number of features while preserving the maximum variance. PCA uses a linear transformation to map high-dimensional datasets to a lower-dimensional space, retaining as much of the original variance as possible. These new lower-dimensional features are called principal components. The first principal component has the highest variance in the data, the second has the second highest variance, and so on, with each subsequent component capturing progressively less variance. By selecting the top few principal components, we could reduce the dimensionality of the data while still retaining most of the dataset's key information [28]. This approach was supported by previous work (e.g., [PERSON] and [PERSON], 2019) that achieved a high level of accuracy in detecting BAL quasars using the CNN model in combination with PCA for quasar classification [29]. [PERSON] et al. (2006) used PCA to analyze the spectra of galaxies, helping to identify the characteristics of star groups and highlighting the effectiveness of PCA in reducing the size of astronomical datasets [30]. In addition, [PERSON] (2020) applied deep learning methods for emission-line predictions, demonstrating the effectiveness of deep learning in processing complex spectral data [31].  
The data were subjected to a principal component analysis. The first eight main components accounted for 8.7%, 4.6%, 3.5%, 3.4%, 2.3%, 1.9%, 1.7%, and 1.3%, respectively, of the total variance. The cumulative difference for the eight major component explanations was 27.4%, and for the first 100 major component explanations, it was 55%. This showed that the first 100 main components retained 55% of the information. The first 100 main components were, therefore, used as input features in the deep learning model.
## 2 System Model and Channel Model
### MmWave Channel Model  
According to [28], we define the BS-RIS channel and the RIS-vehicle channel in the V2X communication reflection link as  
\[\mathbf{H}_{BR}=\sqrt{\frac{N_{t}N_{RIS}}{L_{BR}}}\sqrt{A_{BR}}\sum_{l=1}^{L_{BR}} \alpha_{BR}^{(l)}\mathbf{\alpha}_{RIS}(\phi_{BR,r}^{(l)},\phi_{BR,r}^{(l)})\mathbf{a}_ {RS}^{H}(\phi_{BR,r}^{(l)},\phi_{BR,r}^{(l)})\mathbf{e}^{2\pi f_{d}\pi T_{s}} \tag{34}\]  
\[\mathbf{H}_{RUI}=\sqrt{\frac{N_{t}N_{RIS}}{L_{RUI}}}\sqrt{A_{RUI}}\sum_{l=1}^{L_{ RUI}}\beta_{RU}^{(l)}a_{UE}(\phi_{RUL}^{(l)},\phi_{RUL}^{(l)})\mathbf{a}_{RIS}^{H}( \phi_{RUL}^{(l)},\phi_{RUL}^{(l)})\mathbf{e}^{2\pi f_{d}\pi T_{s}} \tag{35}\]  
where \(L_{BR}\) and \(L_{RUI}\) are the total number of signal paths in the BS-RIS and RIS-vehicle channel, respectively. \(A_{BR}\) and \(A_{RUI}\) represent the path loss in the BS-RIS and RIS-vehicle channel, respectively. \(\alpha_{BR}^{(l)},\beta_{RU}^{(l)}\sim\mathcal{CN}(0,1)\) are the composite path gain in the BS-RIS and RIS-vehicle channel, respectively.\(\phi_{BR,l}^{(l)}\) and \(\phi_{BR,l}^{(l)}\) are the azimuth and elevation angles of the angle of departure (AOD) of the \(l\)-th path in the BS-RIS channel, \(\theta_{BR,r}^{(l)}\) and \(\phi_{BR,r}^{(l)}\) are the azimuth and elevation angles of the angle of arrival (AOA) of the \(l\)-th path in the BS-RIS channel. Similar, \(\theta_{RU,L}^{(l)}(\theta_{RU,L}^{(l)})\) and \(\phi_{RU,L}^{(l)}(\phi_{RU,L}^{(l)})\) are the azimuth and elevation angles of the AOD (AOA) of the \(l\)-th path in the RIS-vehicle channel. \(f_{d}\) is the Doppler frequency,and \(T_{s}\) is the system sampling period. \(\mathbf{a}_{o},o\in\{BS,RIS,UE\}\) is the BS, RIS or vehicle array guidance vector, respectively.  
Uniform planar array (UPA) antennas are used at BS, RIS, and vehicle. Therefore, the typical array guidance vector for UPA antennas can be expressed as  
\[\mathbf{a}_{o}(\phi,\theta)=\frac{1}{\sqrt{UW}}[1,\ldots,e^{j\frac{2\pi}{\lambda}d (u\sin\phi\sin\theta)+w\cos\theta},\ldots,e^{j\frac{2\pi}{\lambda}d((U-1)\sin \phi\sin\theta+(W-1)\cos\theta)}]^{T} \tag{36}\]  
where \(\lambda\) is the signal wavelength, \(d\) is the adjacent antenna spacing, generally taking mmWave half wavelength, and \(u\in\{0,1,\ldots,U-1\}\), \(w\in\{0,1,\ldots,W-1\}\), \(U\), and \(W\) are the number of antennas in horizontal and vertical directions, respectively.
## 4 Results and Discussion
### Correlation Analyses through Frequency-Based Technique
These values indicate a remarkable relationship between the PSD of these data for both rails. Also, there is a notable correlation in the power spectrum density, particularly within the wavelengths of 1.5 and 3 m (corresponding to 1-4 rad/m wave number range). This correlation in specific wave number ranges highlights significant patterns in the interaction between vertical ballast stiffness and longitudinal track alignment. This finding makes the relationship intelligible between ballast stiffness and longitudinal levels from the infrastructure managing perspective. This can provide a practical framework for assessing track health that is outlined based on the correlation between ballast stiffness and track longitudinal levels. Thus, the structural condition of railway superstructures and their related maintenance requirements are evoked by extracting the ballast stiffness from  
Figure 11: The PSD of vertical ballast stiffness (kb) and longitudinal levels: (**a**) LLL, (**b**) LLR. longitudinal levels. This insightful framework can promote maintenance decision-making in the planning of railway track management.
## 1 Introduction
used the automated matched layer (AML) approach to establish FEM models for the TL analysis of ABH circular plates and investigated the effect mechanism of ABH parameters on TLs [28]. To the best of our knowledge, the current research on the acoustic properties of ABH structures mainly focuses on the acoustic radiation of beams or plates embedded with ABHs in the free field, with few investigations on the vibro-acoustic coupling characteristics in the enclosed acoustic cavity. The elastic plate-rectangular acoustic cavity coupling system is a general study case of vibro-acoustic coupling in theoretical analysis and engineering applications. Different researchers have carried out many studies on the analysis of the vibro-acoustic coupling characteristics of the elastic plate-rectangular acoustic cavity [29; 30], the derivation and solution method of the vibro-acoustic coupling equation [31; 32], and the mechanism of the vibro-acoustic coupling [33; 34]. Based on the characteristics of the ABH to suppress vibration and sound radiation, embedding 2D ABHs on the elastic plate of the plate-cavity parallelepiped coupling system is expected to reduce the structural sound in the acoustic cavity. As the ABH region of the 2D ABH plate has a sharp change in the thickness, it is often difficult to obtain an accurate analytical solution to its vibration equation. In this paper, series of numerical vibro-acoustic coupling models of a 2D ABH plate-cavity system are established to investigate the effect and mechanism of the ABHs on noise suppression in the acoustic cavity, and a vibro-acoustic measurement platform for the coupling system is built to verify the accuracy of the established numerical models through experiments. This study is structured as follows, Section 1 is the introduction and Section 2 is the design of the ABH plate. Section 3 is devoted to the modeling of the ABH plate-cavity coupling system. Section 4 exposes through the analysis of influence of ABHs on coupling modes, the properties of vibro-acoustic coupling, and the mechanism of the ABHs and damping layer are revealed in Section 5. The vibro-acoustic measurement experiment of the 2D ABH-cavity coupling system is carried out in Section 6 to validate the numerical modeling and simulation. In Section 7, this paper draws to a close with conclusions.