esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Processing and Definition of the EAR Index  
As mentioned in the introduction, the process of calculating the EAR index for a generic asphalt mixture involves two main steps:  
* Normalization and weighting of the EPD parameters (\(EAR\) calculation). The first stage involves the normalization of the values of each individual EPD parameter. Moreover, since the different environmental parameters do not have the same impact in relation to the specific application context under study, it was decided to assign different weights to each parameter. Subsequently, the EAR index value is obtained through a linear combination of the different impact categories (emissions, use of resources, and output flows and waste categories).
* Correction according to the expected performance of the asphalt mixture (\(EAR_{\text{c}}\) calculation).  
Details of the calculation process of both indicators, \(EAR\) and \(EAR_{\text{c}}\), have been given in Sections 5.1 and 5.2.
# Personalized Visualization of the Gestures of Parkinson's Disease Patients with Virtual Reality
## 2 Background and Related Work
### Current State-of-the-Art: Conclusions
In the findings of this topic, research was identified, such as how new technologies can affect the processing and visualization of medical data, with significant effects on correct diagnosis and treatment of various diseases [27], new perspectives on the medical management of chronic diseases, focusing in particular on physical activity and exercise as central to the management of neurodegenerative disorders affecting the mobility and mental status of the elderly using wearable technologies [28], effectiveness of virtual reality-based rehabilitation compared to conventional rehabilitation in the recovery of motor function in three groups of patients [29], a literature review on the use of virtual reality in the treatment of anxiety disorder in Parkinson's disease [30], examination of the evolving field of virtual reality in rehabilitation, with a special focus on augmented virtual reality [31], the capabilities and needs of people with Parkinson's disease in relation to the use of smart glasses [32], the effectiveness of virtual reality telemedical applications (Tele-VR) in improving cognitive performance and social skills in patients with Parkinson's disease [33]. The third thematic section is general cases of addressing medical problems using immersive technologies. In the studies identified, the findings were as follows. Augmented reality can be integrated into the physical environment of the user, allowing interaction with virtual elements without the use of traditional interfaces such as screens and keyboards, including medical rehabilitation [34], exploiting augmented reality (AR), using free motion and body motion detection technologies, in order to develop interactive games for the uniform, cost-effective, and objective assessment of upper extremity motor dysfunction in various patient groups [35], augmented virtual reality for manipulation and evaluation of different motion learning mechanisms, with emphasis on error- and reward-based motion adaptation [36]. Despite the innovative and interesting approaches identified in the literature review, there is a clear lack of applications for data visualization of patients with neuro cephalic diseases such as Parkinson's disease, as well as methods for remote and direct assessment of patients by physicians using immersive technologies that can offer analysis and natural realism. The aim of this paper is to visualize data from patients with Parkinson's disease in a virtual reality environment, resulting in the remote assessment of patients by physicians.
# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 2 Materials and Methods
### Mining and Clustering of Users' Driving Habits
#### 2.1.2 Acquisition of Individualized Driving Styles
As shown in Figure 4c, the corresponding plane projection is shown in Figure 4d, where \(d_{io}\) and \(d_{ij}\) represent the Euclidean distance of sample \(i\) to the cluster center \(O\) and the high-density point sample \(j\), respectively; \(d_{c}\) is the truncated distance, which is related to the average percentage of the total sample size and number of neighbor sample points; \(\rho_{i}\) and \(\rho_{i}\) denote the local sample density of \(i\) and \(j\), respectively; \(\delta_{i}\) is the shortest Euclidean distance between \(i\) and the high-density point sample \(j\); and \(\tau\) and \(\varepsilon\) are both related boundary parameters which are often substitution of empirical value. \[\left\{ \begin{array}{l}\rho_{i}=\sum\limits_{j=1}^{n}\exp\left(-\frac{d_{ ij}}{d_{c}^{2}}\right)\geq\tau\\ \delta_{i}=\min_{j\rho_{i}>\rho_{i}}(d_{ij})\leq\varepsilon\end{array} \right. \tag{10}\]  
Step 3: The invalid samples of time, economic, and comfort cluster centers are deleted, as shown in Figure 4e. The corresponding plane projection is shown in Figure 4f. The number of samples within the effective radius \(r\) of each cluster center is counted as \(n_{\text{t}},\ n_{\text{m}},\ n_{\text{c}}\). Step 4: The distribution weight of time, economy, and comfort indexes is obtained through the normalization process shown in Formula (11). \[\left\{ \begin{array}{l}\omega_{\text{T}}=\frac{n_{\text{t}}}{n_{\text{t}}+n _{\text{e}}+n_{\text{c}}}\\ \omega_{\text{M}}=\frac{n_{\text{e}}}{n_{\text{t}}+n_{\text{e}}+n_{\text{c}}} \\ \omega_{\text{C}}=\frac{n_{\text{c}}}{n_{\text{t}}+n_{\text{e}}+n_{\text{c}}} \end{array} \right. \tag{11}\]  
The user's driving style feature vector is expressed as \(W=[\omega_{\text{T}},\ \omega_{\text{M}},\ \omega_{\text{C}}]\). Figure 4: \(Cont.\)
## 3 Materials and Methods  
### The Comprehensive Framework of LSTM-Based Risk Estimation Methodology  
The comprehensive framework of LSTM-based risk estimation methodology is shown in Figure 1, which can be essentially divided into two major stages: (a) create a LSTM-based deep learning network and train the network utilizing the historical hazard event dataset, as shown by the light blue node in the figure; (b) input a new hazard event, use the trained network to estimate its risk, and obtain the risk index of the new hazard event, as shown by the purple node in Figure 1. The detailed procedures are as follows:  
**Stage (a)** Create and train the LSTM-based deep learning network:  
1. Import the historical hazard event dataset into the network. Each hazard event is the combination of a hazardous situation description and the corresponding risk index. The dataset is partitioned into two parts, with a certain proportion of the data being used as the training set and a held-out proportion for validation. Since each hazardous situation description is associated with a corresponding risk index, the risk estimation task can be considered a classification of hazardous situation descriptions based on risk indices.
2. Preprocess the hazardous situation description text to facilitate further processing, including tokenizing the text, converting the text to lowercase, and erasing punctuation.
3. Convert the hazardous situation descriptions text into sequences. The textual descriptions of hazardous situations cannot be processed by the computer, which must be digitalized to generate vector data that the computer can process. In this step, a vocabulary dictionary is constructed upon the training data, with the serial number of each word in the dictionary being assigned.  
Figure 1: Flow chart of LSTM-based risk estimation methodology.  
Create the LSTM-based deep learning network with six layers, including _sequencelnputLayer_, _wordEmbeddingLayer_, _lstmLayer_, _fullyConnectedLayer_, _softmaxLayer_, and _classificationLayer_. The function of each layer in the network is described in Section 3.3.2.
5. Training the network by historical risk events dataset, and the weight parameters of the network are updated to obtain the trained network.  
**Stage (b)** Estimate the risk of new hazard events by the trained network:
6. For new hazard events, repeat 2 and 3, then input the processed data into the trained network to obtain the risk indices for the hazard events.
7. Output risk indices and complete the risk estimation of new hazard events.
###### Abstract  
This paper presents a numerical investigation of delaminated composite plates using a 3D degenerated plate element, with a focus on geometric nonlinearity. An 8-noded degenerated element is used to model the composite plate, ensuring higher accuracy and computational efficiency. Delamination is modeled using Heaviside step functions, providing a clear representation of discontinuities. The Green-Lagrangian formulation is employed to incorporate geometric nonlinearity and to evaluate its effects on the deflection behavior of delaminated plates under load. The developed model is validated against existing literature, demonstrating its reliability. Subsequently, a comprehensive parametric study is conducted to evaluate the influence of various factors including lamina sequences, boundary conditions, load intensities, and the size and location of the delamination (considered at the center of the laminate). A uniformly distributed pressure load is applied across the entire plate to simulate realistic loading conditions. Additionally, the effects of multiple delaminations through the thickness of the laminate are explored, with key observations on their impact reported. The findings provide valuable insights into the structural performance of delaminated composite plates under nonlinear conditions.  
delaminated plates; geometric nonlinearity; multiple delamination; composite plate +
Footnote †: journal: Applied Sciences
## 5 Applications of Proposed Solution in Geotechnical Problems
### Estimation of Stress Variations Caused by the Casing Installation  
Yi et al. [43] conducted a centrifuge model test to investigate the installation of a sand compaction pile. Before the 'compacted' sand column is formed, a cylindrical casing is jacked into the reconstituted Malaysian Kaolin clay, in which the water level is 0.25-m (prototype scale) higher than the ground. In centrifuge modelling, the total stress and pore pressure transducers were inserted at 4.6 times the casing radius and at prototype depths of 1.5 m, 2.75 m, and 4 m. The measured total radial stress and pore pressure at the instant of the casing tip's arrival at these specific depths was extracted.  
The present solution was used to predict the variations of total radial stress and pore pressure caused by the casing installation. The parameters of Malaysian Kaolin clay are taken from Purwana et al. [44] and Yi et al. [43]. The basic Modified Cam Clay parameters are \(M=0.9\), \(\lambda=0.244\), \(\kappa=0.053\), \(\dot{e}_{\text{ }IC}=2.35\), and \(\upsilon=0.33\). The lateral stress coefficient \(K_{0}\) and effective unit weight \(\gamma^{\prime}\) are 0.6 and 6 \(\text{kN}/\text{m}^{3}\), respectively. The initial shear modulus \(G_{0}\) is taken as \(20p^{\prime}0\), as in Yi et al. [43]. The Malaysian Kaolin clay used in the centrifuge model test is reconstituted; therefore, it is reasonable to take the structural parameters (\(b\), \(\gamma\), \(c\), \(\omega\)) as sufficiently small. Because there was no cavity existing before the casing was jacked, the value of \(a/a_{0}\) should be set large enough (larger than 5, see Figure 8) so that the cavity pressure is stable.  
Taking the above parameters as input data, the distribution of effective radial stress and pore pressure can be obtained. According to the location of the transducers (at the radial distance \(r/a=4.6\)), the predicted radial effective stress and excess pore pressure was extracted from the stress distribution. The predicted stresses were compared with the corresponding measured values, as shown in Figure 16. It can be seen that the stress variations caused by the casing installation can be well predicted by the present solution.
## 2 Architecture of the Proposed SDAMs  
### Hardware Architecture  
The proposed hardware platform is composed of a coreboard JN-SOM5728 [12], an interface expansion board JN-OPEN57x [12], a signal-receiving board, and a signal-transmitting board.  
The coreboard JN-SOM5728 includes a TI AM5728 CPU and storage resources. JN-OPEN57x is the interface board for the AM57 series, which can be used with coreboard JN-SOM5728. It supports a variety of functional module interfaces, such as dual gigabit Ethernet ports, HDMI, PCIE, SATA, RS232/RS485/CAN, USB2.0/USB3.0, WIFI, GPMC, CSI, and VIN/VOUT. The interface board and the corresponding extension interface modules of the underwater acoustic receiver/transmitter are shown in Figure 1.  
The receiving board is composed of an operational amplifier circuit and an ADC sampling circuit. The transmitting board is composed of a DAC output circuit and a power amplifier. The hardware design block diagram and some key parameters are shown in Figure 2. The core chips for the receiving/transmitting module are DAC7741 and AD8588S. The connections between the receiving board, the transmitting board, the coreboard, and the interface board are as shown in Figure 3. The receiving board can be connected to one to four hydrophones, and the transmitting board can be connected to one transducer. The drivers of the receiving board and the transmitting board are implemented in the DSP.  
Figure 1: The interface board and the extension modules of an underwater acoustic receiver/transmitter.  
Figure 2: The hardware design block diagram of the receiving/transmitting board for the proposed software-defined acoustic modem (SDAM).
# Structured Light Patterns Work Like a Hologram
## 6 Discussion of Results
Therefore, the method of formation of the 3D object hologram chosen by us previously may be simplified substantially, which is extremely important for the acceleration of transmission [27; 28; 29] and computer processing of the series of frames of 3D video and augmented reality. The use of the above-obtained result is proposed in order to reduce the amount of transmitted information even in comparison with that proposed in [7], however, remaining sufficient for forming the holographic video sequence at the receiving end of the radio communication channel. For this purpose, it is proposed that one should transmit, instead of two 2D frames (depth and texture maps), only one 2D frame but have it enlarged somewhat by twenty-five columns. For instance, for the Full HD standard, this will be not the 1920 \(\times\) 1080 frame but a 1945 \(\times\) 1080 frame, in which the additional 25 columns will carry the information on the structured light pattern in the form of vertical fringes that were illuminating the holographed object (Figure 2c). At the receiving end of the communication channel, these 25 columns will be used to create a diffraction structure similar to that shown in Figure 4a, restoring the 3D image of the depth map (Figure 9b). Superposition of the texture frame on this diffraction structure allows restoration of the 3D image of the object as a total, as shown in Figure 10. It should be stressed that the latter will work efficiently only in the case of a rather short Rayleigh distance, while diffraction has not yet blurred up the texture image. This condition is known to be fulfilled if the number of Fresnel zones fitting into the hologram field is much larger than unity. This condition is actually in the holograms of focused images when the 3D image is brought ahead of the hologram at the space depth not more than several diagonals of the holographic monitor or the hologram.
## 2 Applications in Chemistry
### Decarboxylation of Cyclic Carbonates
In a closely related work, [PERSON] and co-workers [33] developed an efficient protocol to construct densely functionalized quaternary carbons with the concomitant formation of a new _Csp\({}^{3}\)-Csp\({}^{3}\)_ bond through decarboxylation of carbonate **6**. The reaction was carried out in DMF at room temperature for 12 h, using palladium catalysis with the **12** ligand and resulted in the formation of branched aldehydes **10** (32-91%) and substituted acroleins **11** (26-84%).
## 5 Results and Discussion
### Summarized Discussion  
In this section, we summarize various findings regarding the hybrid WDPSs of San Cristobal Island. As mentioned above, naturally, a DPP has the capability to provide inertial support in hybrid generation because WTs do not contribute, due to the presence of fast-acting power electronics converters. This same scenario (a WT without the proposed control) is discussed in Section 4.1 (base case), and it is clearly observed that the frequency deviation in this case is 2.5435 Hz against a 0.5 pu variation in load demand. It is important to mention that the DPP is optimally tuned in this case. However, to highlight the effects of the proposed controls, we introduced such a control in a VSWT and optimally tuned it, using the SPBA (case A). The results show that the NADIR is 2.54124 Hz against the same 0.5 p.u load demand variations. This means that the VSWT contribution is minor, and the FD reduction is just 0.00222 Hz. One of the main reasons for this negligible contribution is the optimally tuned diesel governor, which mainly assists in providing inertia during transients in hybrid power systems. We finally conclude that the VSWT does not provide significant contributions for releasing synthetic inertia in hybrid mode when the diesel governor is optimally tuned. In other words, to benefit from the VSWT for FR, the hybrid model should be simultaneously tuned. With this aim, both the VSWT and DG were simultaneously tuned (case B), and the results showed a significant reduction in FD, i.e., 1.8% (from 2.5435 to 1.48776 Hz) against 0.5 pu, compared to the base case. Moreover, several other parameters, like the ISE and IAE, were also improved during this global optimization of hybrid WDPSs. In general, it is concluded that the VSWT can make significant contributions by releasing synthetic inertia during contingencies when both power plants are optimally tuned. It is important to mention that the real parameters of the San Cristobal WDPS are used in this study as a case study; however, this topology of the proposed control and SPBA can be generally implemented in any power system.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 3 Preliminaries
### Gaussian Process Regression
The posterior distribution over \(\mathbf{f}_{\text{s}}\) can be obtained by conditioning the joint Gaussian prior distribution on the observations, as follows:  
\[\mathbf{p}(\mathbf{f}_{\text{s}}\,|\mathbf{X},\,\mathbf{t},\,\mathbf{X}_{*}) \ \sim\ \mathcal{N}\Big{(}\bar{\mathbf{f}}_{\text{s}},\ cov(\mathbf{f}_{\text{s}}) \Big{)},\ \text{where} \tag{8}\]  
\[\bar{\mathbf{f}}_{\text{s}}\triangleq\ \mathbb{E}[\mathbf{f}_{\text{s}}\,| \mathbf{X},\mathbf{t},\mathbf{X}_{*}]=K(\mathbf{X}_{*},\mathbf{X})[K(\mathbf{ X},\mathbf{X})+\beta^{-1}\mathbf{I}]^{-1}\mathbf{t}, \tag{9}\]  
\[\text{cov}(\mathbf{f}_{\text{s}})=K(\mathbf{X}_{*},\mathbf{X}_{*})-K(\mathbf{ X}_{*},\mathbf{X})\ [K(\mathbf{X},\mathbf{X})+\beta^{-1}\mathbf{I}]^{-1}K(\mathbf{X},\mathbf{X}_{*}). \tag{10}\]  
The above conditional distribution is the main predictive equation for Gaussian process regression. The conditional distribution mean \(\bar{\mathbf{f}}_{\text{s}}\) can be treated as the model prediction, and the variance \(\text{cov}(\mathbf{f}_{\text{s}})\) is determined by both the initial test data variance and information from the training data [8]. To further support the explanation provided, the Bayesian model training for regression algorithm is presented as follows (Algorithm 1). ```
Input: Dataset \((\mathbf{X},\mathbf{Y})=\{(x_{i},\,y_{i})\}_{i=1}^{N}\), prior distribution \(p(w)\) over the model parameter \(w\), likelihood function \(p(y|\mathbf{x},w)\), maximum iterations \(T\), convergence threshold \(\varepsilon\). Output: Posterior distribution \(p(w\,|\mathbf{X},\mathbf{Y})\), predicted values \(\overline{y}\) for new input \(x_{*}\). 1. Initialize\(w_{0}\), \(T\), and \(\varepsilon\). 2.for each iteration \(t=0\) to \(T\)
3.for each data point \((x_{i},\,y_{i})\)
4. Draw \(w_{t}\) from \(p(w)\). 5. Compute the likelihood of the observation \(p(y_{i}|x_{i},w_{t})\). 6.endfor
7. Update\(p(w\,|\mathbf{X},\mathbf{Y})\) using [PERSON] theorem \(p(w\,|\mathbf{X},\mathbf{Y})\propto p(\,\mathbf{Y}|\mathbf{X},w)p(w)\). 8. Update\(w_{t+1}\) using posterior. 9.if\(|w_{t+1}-w_{t}|<\varepsilon\), terminate and return \(w_{t+1}\). 10.else\(t\gets t+1\)
11.endfor
12.Return\(p(w\,|\mathbf{X},\mathbf{Y})\) and \(\overline{y}=\mathbb{E}[p(w\,|\mathbf{X},\mathbf{Y})][f(x_{*},\,w)]\). ```  
**Algorithm 1**Bayesian Model Training for Regression
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 2 Materials and Methods
### Traffic State Perception  
Traffic state is an unevenly distributed and complex random variable related to time variation. At the micro-level, the traffic state perception can be regarded as perceiving the behavior state of each vehicle, and at the macro-level, as the flow, speed, density, etc. in traffic scenarios. In this section, a traffic state perception system based on V2X communication will be introduced, and the perceptual information of traffic state will be analyzed.
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## Materials and Methods
### Software  
The ENVI 4.6 version (ITT, Visual Information Solutions, Boulder, CO, USA) was applied to preprocess and analyze the hyperspectral images. The MATLAB R2017a version (The MathWorks,Natick, MA, USA) was applied to extract spectral features of all the samples and preprocess these spectral data. Besides, the selection of feature wavelengths, the establishment of discrimination models and visualization of different varieties of wheat seeds were conducted on MATLAB R2017a. During the whole process, some common toolboxes, including statistics and machine learning toolbox, wavelet toolbox were employed. Especially, two third-party toolboxes, such as libpls and libsvm, were introduced for specific analysis in Matlab.