esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Short Memory Principle Method for Solving Fractional Damped Vibration Equations
## 4 Examples Analysis
### Two-Degree-of-Freedom Vibration Reduction Model of Vehicle Suspension  
The suspension damping mechanism of the vehicle is simplified to a two-degree-of-freedom model, as shown in Figure 14a. The force analysis is shown in Figure 14b. The constitutive model of viscoelastic material adopts the fractional Kelvin-Voigt model:  
\[\sigma(t)=\mu\varepsilon(t)+\eta D^{\alpha}\varepsilon(t) \tag{14}\]  
Figure 12: Forced vibration response.  
The equations of motion for each mass are  
\[m_{1}\ddot{x}_{1}=(x_{0}-x_{1})k_{e}-F_{c}m_{2}\ddot{x}_{2}=F_{c} \tag{15}\]  
The stress and strain of viscoelastic material are  
\[\sigma=\frac{F_{c}}{A}\varepsilon=(x_{1}-x_{2})/l \tag{16}\]  
Figure 14: Two-degree-of-freedom model of suspension structure.  
Figure 13: Error of the two SMP methods (SMP and improved SMP).  
where \(A\) and \(l\) are the cross-sectional area and height of viscoelastic material, respectively. Substituting Equations (15) and (16) into (14), the equation of motion of the system can be sorted as follows:  
\[\left\{\begin{array}{c}m_{1}\ddot{x}_{1}+\frac{\Lambda\eta}{l}D^{\alpha}x_{1}- \frac{\Lambda\eta}{l}D^{\alpha}x_{2}+\left(k_{e}+\frac{A\mu}{l}\right)x_{1}- \frac{A\mu}{l}x_{2}=k_{e}x_{0}\\ m_{2}\ddot{x}_{2}-\frac{\Lambda\eta}{l}D^{\alpha}x_{1}+\frac{\Lambda\eta}{l}D^{ \alpha}x_{2}-\frac{A\mu}{l}x_{1}+\frac{A\mu}{l}x_{2}=0\end{array}\right. \tag{17}\]  
Rewritten into matrix form:  
\[\mathbf{M}\ddot{\mathbf{X}}+\mathbf{C}\mathbf{D}^{\alpha}\mathbf{X}+\mathbf{ K}\mathbf{X}=\mathbf{F} \tag{18}\]  
where \(\mathbf{M}=\left[\begin{array}{cc}m_{1}&0\\ 0&m_{2}\end{array}\right]\), \(\mathbf{C}=\left[\begin{array}{cc}A\eta/l&-A\eta/l\\ -A\eta/l&A\eta/l\end{array}\right]\), \(\mathbf{K}=\left[\begin{array}{cc}k_{e}+A\mu/l&-A\mu/l\\ -A\mu/l&A\mu/l\end{array}\right]\), \(\mathbf{F}=\left[\begin{array}{cc}k_{e}x_{0}\\ 0\end{array}\right]\) and \(\mathbf{X}=\left[\begin{array}{cc}x_{1}\\ x_{2}\end{array}\right]\).  
This article focuses on introducing improved algorithms, so for the convenience of calculation, all parameters except \(\alpha\) and \(x_{0}\) are taken as 1. When \(\alpha=0.5\) and \(x_{0}=\sin(\omega t)\) (\(\omega\) is \(2\pi\)), the step size \(h=0.01\) is taken under the original definition method, and the displacements of masses 1 and 2 are shown in Figures 15 and 16. The comparison of the two figures shows that the isolated mass \(m_{2}\) has a better vibration reduction effect. Then use the SMP method (\(Nt=1000\)) and the improved SMP method (\(Nt=500\), \(n=10\)) to calculate Equation (18). The displacement error of \(m_{2}\) is shown in Figure 17. Obviously, the improved SMP method effectively reduces the error, and the amount of calculation is also greatly reduced. It can be seen that the improved SMP is also applicable in complex systems.  
Figure 15: Displacement for \(m_{1}\).  
Figure 16: Displacement for \(m_{2}\).  
Figure 17: Errors of the two SMP methods (SMP and improved SMP) of \(m_{2}\).
## 5 Conclusions  
The present study comprehensively evaluated the impact of tropical Indian Ocean SST variability on the vegetation photosynthetic activity in India during the summer monsoon. We explored the varying responses of vegetation photosynthesis in India to the SST anomalies in the tropical Indian Ocean regions during each of the summer monsoon months. We used OCO-2 satellite-based SIF data product to represent the terrestrial vegetation photosynthesis. SIF measurements act as a unique proxy of actual plant photosynthesis, which aids in the diagnosis of plant stress conditions in real-time and, therefore, can effectively indicate the vegetation growth anomalies well in advance. Our results revealed the robust negative relationship between SST variations in the tropical Indian Ocean regions and SIF anomalies over the central and northern Indian mainland during the summer monsoon onset phase. Compared to the cold anomalies in the tropical Indian Ocean, the teleconnections stemming from the warm SST anomalies are profound and more significant on the observed SIF anomalies in India. The notable Indian Ocean SST link with the SIF anomalies in India deteriorates immediately after the onset of the monsoon. Therefore, vegetation activity in India is not significantly affected by the SST variations in the tropical Indian Ocean after June.
# A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency
## 4 The Proposed Hybrid Control Architecture for Path Planning  
Traffic allocation is based on dynamic traffic states and real-time OD states. In reality, network-wide traffic flow data cannot be collected and uploaded promptly, and equilibrium path planning decisions based on macro real-time traffic states are iteratively generated, both of which lead to scheduling delays. To meet the real-time requirements of path planning, on the one hand, the dynamic traffic load state can be foreseen by deep learning prediction, which combines the OD evacuation volume and background traffic flow. On the other hand, mapping SO constrains the traffic equilibrium-assigned method by the neural network approach.The path plan is given in real time based on the predicted traffic state and real-time OD data, so as to improve the effectiveness and timeliness of the traffic allocation. The hybrid control architecture for path planning named equilibrium traffic assignment based on spatio-temporal neural network (ST-ETA) can be subdivided into the dynamic traffic network load prediction module and the hybrid control path planning module, as shown in Figure 1. Among them, the feature extraction and prediction mechanism of the dynamic traffic network load prediction module corresponds to Sections 4.1 and 4.2, respectively. The hybrid control path planning module corresponds to Sections 4.3.  
Considering the strong spatio-temporal correlation of OD volume or background load of the road network under discrete periods, our model assumes that we can estimate the evacuation volume for a given period by making a prediction based on the travelers' route data. And the dynamic road impedance on each section can be pre-calculated by predicting the future traffic load. The OD evacuation volume prediction module and the background traffic load prediction module are trained in parallel to capture OD trend features and background traffic flow change features, respectively. The equilibrium mapping layer fuses the two types of data to form a complete traffic flow distribution and captures the mapping relationship between this distribution and the optimal traffic equilibrium flow distribution. Ultimately, the optimal equilibrium flow distribution graph calculated from the real-time information is used as the basis for the path plan.
## 4 Accuracy Analysis of LSTM Network Models for Predicting Buildings' Settlement
### Analysis of the Effect of Spatial Parameters on the Accuracy of Model  
During the construction of a deep foundation pit in a soft soil area, the depth of the soft soil layer and the horizontal distance between the building and the foundation pit have an important influence on the settlement of the neighboring buildings. In order to study the influence of spatial parameters on the accuracy of building settlement prediction, the spatial parameters are now used as inputs together with the settlement monitoring values of the three working conditions of the aforementioned full inversion method considering the stratum changes, and the settlement prediction values are used as outputs. The prediction results are shown in Figure 9.  
As can be seen from Figure 9, the trend of the predicted values with or without considering spatial parameters is basically the same as that of the monitored values. The average relative error of the prediction of the three working conditions with space parameters considered is 5.01%, which is 3% higher than the prediction without space parameters. Meanwhile, the average values of the evaluation indexes RMSE, MAE, and \(R^{2}\) for the three working conditions were 0.57, 0.65, and 0.91, which were improved by 16%, 11%, and 3%, respectively, compared with the evaluation indexes without considering space parameters. This indicates that the LSTM model is more accurate in predicting the settlement of build  
Figure 8: Predicted settlements with and without consideration of soil parameters.  
ings around deep foundation pit construction after considering spatial parameters. The statistical results of each prediction as well as the findings of other scholars are shown in Table 5.  
After considering geological and spatial parameters, the evaluation metrics of the model, MAE and \(R^{2}\) were 0.65 and 0.91, respectively. [PERSON]'s network evaluation metrics, MAE and \(R^{2}\), for predicting settlement using Artificial Neural Network (ANN) were 0.73 and 0.70, respectively [17], compared to which the network model prediction accuracy in this study was improved by 10.96% and 30%, respectively. It indicates that the prediction of building settlement using an LSTM neural network is more accurate than using ANN. It  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Parameters** & **RMSE (mm)** & **MAE (mm)** & \(R^{2}\) \\ \hline Condition & 0.78 & 0.85 & 0.85 \\ Geological & 0.68 & 0.73 & 0.88 \\ Spatial & 0.57 & 0.65 & 0.91 \\ Shallal & / & 0.73 & 0.70 \\ Ou & / & / & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Adjustment statistics.  
Figure 9: Settlement prediction considering spatial parameters under various working conditions.  
is worth mentioning that the \(R^{2}\) of this study is 0.91, which is not as good a fit compared to [PERSON]'s \(R^{2}\) (0.95) for predicting settlement using simplified equations [13], but there is only a difference of about 4%, which can be further optimized and improved by adjusting the rest of the hyperparameters of the model, etc.
# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 4 Experimental Scenarios
### Scenario II: Training and Evaluating Industrial Pump Vibrations  
In the case of vibration measurements, the Mafaulda dataset was used. The constructed annotated dataset for the pump's rotation contains 1,500,000 acceleration samples (six measurements per sample). The applied load on the pump axis was modeled by appropriate Relative Centrifugal Force (RFC), applied on the pump axis of 6 g for class 1, 10 g for class 2, 20 g for class 3, and 30-35 g for class 4. Class 0 indicates normal operation (no RFC appliance). The relation between RFC and rotation speed (RPMs) is given by Equation (4).  
\[V_{r}=\sqrt{\frac{F_{g}}{1.118\cdot r}\cdot 10^{5}}, \tag{4}\]  
where \(V_{r}\) is the axis rotation speed in revolutions per minute (RPMs), \(F_{g}\) is the RFC force expressed in \(g\), and \(r\) is the rotational radius. Once the dataset is annotated, it is split into a training set (80%) and a testing set (20%). Table 8 presents the number of samples per class, each with six accelerations measurements, two for each axis of reference \(X,Y,Z\) accordingly.
# Estimating Tetrachloroethene Sorption Coefficients Based on Soil Properties in Organic-Poor Soils
## 2 Materials and Methods
### Statistical Analysis and Development of \(K_{\rm d}\) Prediction Models  
Statistical analyses, including both univariate and multivariate approaches, were performed to identify the soil properties influencing PCE sorption and to develop an accurate sorption model for estimating \(K_{\rm d}\). The data analysis involved [PERSON]'s correlation to identify significant variables from the dataset, including a broad set of measured soil parameters. Principal Component Analysis (PCA) reduces the complexity by summarizing the data into a few principal components (PCs) that capture the most variance. PCA helps in visualizing and highlighting the most significant relationships between \(K_{\rm d}\) and other soil properties, making the data easier to interpret and understand. Agglomerative Hierarchical Clustering (AHC) was employed to group soil samples into homogeneous clusters based on similar soil properties. Since soil data often do not meet the normality assumptions required for parametric tests, the Kruskal-Wallis test was used to compare statistically significant differences in soil properties across multiple sample groups without assuming normality. In combination with PCA, AHC, and the Kruskal-Wallis test, Linear Discriminant Analysis (LDA) was applied to identify linear combinations of soil properties that best separate the soil samples and classify them into different categories based on their properties. PCA and LDA also address potential collinearity among soil properties, improving model robustness. Finally, \(K_{\rm d}\) prediction models were developed using linear regression. Linear regressions were performed between individual soil properties and \(K_{\rm d}\) values to identify univariate linear model with the highest coefficients of determination. Subsequently, a multivariate linear regression method was used to construct a multivariate model for the prediction of \(K_{\rm d}\) values using the significant soil properties. Model precision was evaluated using the coefficient of determination (\(R^{2}\)) interpreted as the proportion of the variability of the dependent variable explained by the model, the root mean square error (RMSE) such as prediction errors and the mean absolute percentage error (MAPE) [41]. Further details are provided in Text S5.
###### Abstract  
(1) Background: Barrett's esophagus (BE) is a complication of chronic gastroesophageal reflux disease and is a precursor to esophageal adenocarcinoma. The clinical implication of subsurface glandular structures of [PERSON]'s esophagus is not well understood. Optical coherence tomography (OCT), also known as volumetric laser endomicroscopy (VLE), can assess subsurface glandular structures, which appear as subsurface hyposcattering structures (SHSs). The aim of this study is to develop a computer-aided algorithm and apply it to investigate the characteristics of SHSs in BE using clinical VLE data; (2) Methods: SHSs were identified with an initial detection followed by machine learning. Comprehensive SHS characteristics including the number, volume, depth, size and shape were quantified. Clinical VLE datasets collected from 35 patients with a history of dysplasia undergoing BE surveillance were analyzed to study the general SHS distribution and characteristics in BE. A subset of radiofrequency ablation (RFA) patient data were further analyzed to investigate the pre-RFA SHS characteristics and post-RFA treatment response; (3) Results: SHSs in the BE region were significantly shallower, more vertical, less eccentric, and more regular, as compared with squamous SHSs. SHSs in the BE region which became neosquamous epithelium after RFA were shallower than those in the regions that remained BE. Pre-ablation squamous SHSs with higher eccentricity correlated strongly with larger reduction of post-ablation BE length for less elderly patients; (4) Conclusions: The computer algorithm is potentially a valuable tool for studying the roles of SHSs in BE.  
Barrett's esophagus; glands; optical coherence tomography +
Footnote †: journal: Applied Sciences
# Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Grider Bridge
###### Abstract  
The parameters affecting structural reliability are usually regarded as independent random variables in the current reliability analyses of bridge structures while the randomness of these structural parameters in spatial distribution is neglected. To overcome this disadvantage, the random field model should be used to describe their probability distribution. In this paper, a structural reliability analysis method considering the effect of a random field is proposed, and its validity is verified by three numerical case studies. A prestressed concrete (PSC) box-grider bridge with a main span of 150 m is selected for demonstration, and the importance sampling (IS) method is applied to estimate its failure probability, in which the influences of shrinkage and creep, stress relaxation, and shear lag on the time-dependent performance of the structure are taken into account. In addition, the random fields of the structure are discretized by using the local average method (LAM). Finally, the effects of random field parameters, such as the number of discrete elements, correlation model, and correlation length, on the reliability of the box-grider bridge are discussed.  
long-span box-grider bridge; random field; local average method; important sampling; creep and shrinkage; time-dependent reliability Article
# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
## 3 Methodology for 5G NR Link-to-System Mapping
### System Configuration/Setup  
5G NR systems have been studied in many test scenarios in 3 GPP TR 38.802 [20]. However, this paper focuses on parameter settings for L2S mapping test among many scenarios. Table 2 summarizes the system parameters used in this paper, and details are described below.  
1. _Waveform_: Although several candidate waveforms for uplink were proposed, Rel-15 recently decided to use OFDM-based waveforms with cyclic prefix for 5G NR downlink and uplink.
2. _Bandwidth and sub-carrier spacing_: The size of resource blocks (RBs) is determined by the product of the number of sub-carriers on the frequency-axis and the number of symbols on the time-axis, as shown in Figure 1[21]. Bandwidth is set to 5 MHz in this paper. The maximum number of available RBs is 25 at bandwidth of 5 MHz. When sub-carrier spacing is applied to 15 kHz, 12 sub-carriers are allocated in one RB on the frequency-axis and 14 symbols are allocated per sub-frame on the time-axis. A sub-frame consists of two slots and one slot consists of seven symbols. Assuming that the full band of 5 MHz is assigned to UE, 4200 resource elements (REs) can be allocated to UE for one sub-frame since 4200 REs are calculated by 25 RBs \(\times\) 12 sub-carriers \(\times\) 7 symbols \(\times\) 2 slots per sub-frame. In other words, one RE is equivalent to the minimum resource for a symbol time on the time-axis and a sub-carrier on the frequency-axis.
3. _Channel coding_: 5G NR replaces the previously used Turbo-code to LDPC coding. The number of the information bits and the number of the encoded bit are varied depending on channel coding schemes, the number of RBs, and the modulation scheme. TB size represents the number of information bits that can be transmitted throughout 4200 REs and it is denoted as \(A\). After encoding TB of length \(A\) by channel coding scheme, the output is to be encoded bits of length \(E\). Table 3 summarizes values of \(A\) and \(E\) according to the turbo code and LDPC when using 25 RBs per sub-frame. \(M_{order}\) denotes modulation order and its values are 2, 4, and 6 for QPSK, 16 QAM, and 64 QAM, respectively.
* _Channel model_: Ideally, transmit signal is transferred over AWGN channel after LDPC encoding and rate matching. In practice, we experimented with fading channel environments in which CDL-A and TDL-A are utilized in SISO and 2 \(\times\) 2 MIMO environments.
# Recent Advances of Exciplex-Based White Organic Light-Emitting Diodes
## 2 Fundamental Concepts of Exciplex  
### The Generation of Exciplex  
When sequentially depositing the electron-donating groups of p-type materials and the electron-accepting groups of n-type materials in single layer or co-doping these two types of materials, two different kinds of excited-state complexes can be formed, namely exciplex and electrolyte [113; 114; 115], as shown in Figure 1. However, it is deserved to point out that the combination of p-type and bipolar materials or the alliance of bipolar and n-type materials can also generate the excited-state complexes, if there is a large hole/electron barrier between the two different materials [116]. For the excited-state complex emission, it is furnished by the radiative electron transition from the lowest unoccupied molecular orbital (LUMO) level of the electron acceptor molecule to the highest occupied molecular orbital (HOMO) of the electron donor molecule. In the case of planar aromatic molecules, the donor and acceptor moieties constituting an exciplex are separated by a smaller distance (-0.3-0.4 nm) than the constituents of an electrolyte (-0.4-0.7 nm) [117]. Therefore, the generation of exciplex or electrolyte is determined by the by material structures. Since free charges are normally not formed by photoexcitation (only when dissociation of excited states is facile, e.g., in a good photoconductor), the electrolyte emission can only be observed in the EL [118; 119; 120]. On the other hand, since the exciplex is readily generated through photoexcitation, the exciplex emission can be detected from both EL and photoluminescence (PL) spectra [121]. By dint of these differences, the exciplex and electrolyte can be distinguished.  
Figure 1: The diagram of formation exciplex or electrolyte at the bilayer interface of p-type donor material and n-type acceptor material (**a**), p-type donor material and bipolar acceptor material (**b**), bipolar donor material and n-type acceptor material (**c**), co-doping donor and acceptor materials (**d**). E represents exciplex or electrolyte, the black arrow and red arrow are electron and hole transporting, respectively.