esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Subsequent Subgrade on Seismic Response of the High-Speed Railway Track-Bridge System
## 1 Introduction
This has led some researchers to prioritize model simplification and computational efficiency, often at the expense of a thorough examination of the longitudinal constraint effect of the SSTS on the seismic response in continuous girder bridges. Therefore, conducting a detailed study on the impact of SSTS's longitudinal constraint effect on the seismic responses of high-speed railway track-bridge system (HSRTBS), particularly in the context of continuous beam bridges, holds substantial practical engineering value. Building upon existing research, this study focuses on the high-speed railway track-bridge system (HSRTBS) with CRTSII ballastless track structure, considering the longitudinal constraint effect of the subsequent subgrade track structure (SSTS). The specific research content and chapter arrangement are as follows: The Section 1 introduces the research background of the longitudinal constraint effect of the SSTS, outlines current issues and challenges, and presents the research content of this study. The Section 2 describes the research object and its engineering background. The Section 3 details the establishment of two bridge models using the finite element software SAP2000: a bridge model considering the subsequent subgrade track structure (BMCS) and a bridge model without the subsequent subgrade track structure (BMWS). These models form the foundation for nonlinear time history analysis under varying seismic intensities. The Section 4 discusses the seismic input method and calculation conditions. The Section 5 analyzes the longitudinal constraint effect of the SSTS on each critical component of the HSRTBS by comparing the seismic responses of the two models. The Section 6 introduces the risk-transferring connecting beam device used in this study, designed to shift seismic risk from the bridge structure to the subgrade structure. The findings of this study are intended to provide valuable insights for the effective redistribution of seismic forces in bridge structures, enhancing their overall resilience and safety.
## 3 Results and Discussion
### Intensity Autocorrelation  
The IAC trace observed in this study is shown in Figure 4. The full width at half maximum (FWHM) of the trace was 44 fs, suggesting a pulse width of 36 fs [10]. This value is slightly smaller than that obtained using an FRAC system, which can be attributed to the use of the reflective optics with no dispersion in the IAC system.
# A Review of Deep Learning Based Speech Synthesis
## 3 Statistical Parametric Speech Synthesis
### Parameter Prediction
#### 3.2.1 HMM-Based Parameter Prediction  
The HMM-based parameter prediction method mainly generates the sequence of F0 and spectral parameters from the trained HMMs. It is achieved by calculating the sequence of acoustic features with the maximum likelihood estimation (MLE) algorithm given a Gaussian distribution sequence. Due to the differences between F0 and spectral parameters, different methods have been adopted to model the two kinds of feature parameters. For the continuous spectral parameters, the continuous density hidden Markov model (CD-HMM) is used and the output of each HMM state is a single Gaussian or a Gaussian mixture model (GMM) [27]. However, for the variable-dimensional F0 parameters which include voiced and unvoiced regions, it is difficult to apply discrete or continuous HMMs because the values of F0 are not defined in unvoiced regions. To address this problem, the HMM-based method adopts multi-space probability distribution to model the voiced and unvoiced regions (e.g., voiced/unvoiced (V/UV) parameters), separately. To improve the accuracy and flexibility of acoustic parameter prediction, the authors in [28] introduce the articulatory feature that is related to the speech generation mechanism and integrates it with the acoustic features.
## 4 Experimental Results
### The CNN Model Evaluation of Ponding Volume in Different Pipelines  
Based on the proposed method, the recognition of ponding volume for different pipelines was also performed. The three common evaluation metrics of Precision (P), Recall (R), and F1-Measure (F1) in the classification task were chosen to evaluate the final trained CNN model, as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Target Class**} \\ \hline  & & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{6}{*}{Predicted Class} & 0 & 29 & 0 & 0 & 0 & 0 & 0 \\  & 1 & 1 & 30 & 0 & 0 & 0 & 0 \\  & 2 & 0 & 0 & 30 & 1 & 0 & 0 \\  & 3 & 0 & 0 & 0 & 29 & 0 & 0 \\  & 4 & 0 & 0 & 0 & 0 & 30 & 1 \\  & 5 & 0 & 0 & 0 & 0 & 0 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The CNN identification results of the 1*pipeline.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**1* Pipeline**} & \multicolumn{3}{c}{**2* Pipeline**} & \multicolumn{3}{c}{**3* Pipeline**} \\ \hline  & & **R** & **P** & **F1** & **R** & **P** & **F1** & **R** & **P** & **F1** \\ \hline \multirow{6}{*}{Case} & 0 & 96.7 & 100 & 98.3 & 100 & 96.8 & 98.4 & 100 & 100 & 100 \\  & 1 & 100 & 96.8 & 98.4 & 100 & 100 & 100 & 90.5 & 95.2 \\  & 2 & 100 & 96.8 & 98.4 & 100 & 100 & 100 & 90 & 100 & 94.7 \\  & 3 & 96.7 & 100 & 98.3 & 100 & 100 & 100 & 100 & 100 & 100 \\  & 4 & 100 & 96.8 & 98.4 & 96.7 & 100 & 98.3 & 100 & 100 & 100 \\  & 5 & 96.7 & 100 & 98.3 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**4* Pipeline**} & \multicolumn{3}{c}{**5* Pipeline**} & \multicolumn{3}{c}{**6* Pipeline**} \\ \hline  & & **R** & **P** & **F1** & **R** & **P** & **F1** & **R** & **P** & **F1** \\ \hline \multirow{6}{*}{Case} & 0 & 100 & 96.8 & 98.4 & 100 & 100 & 100 & 100 & 100 & 100 \\  & 1 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\  & 2 & 96.7 & 100 & 98.3 & 100 & 100 & 100 & 100 & 100 & 100 \\  & 3 & 100 & 96.8 & 98.4 & 100 & 100 & 100 & 96.7 & 96.7 & 96.7 \\  & 4 & 100 & 100 & 100 & 96.7 & 100 & 98.3 & 96.7 & 100 & 98.3 \\  & 5 & 96.7 & 100 & 98.3 & 100 & 96.8 & 98.4 & 100 & 96.8 & 98.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Three common evaluation metrics results of six pipeline dimensions.  
Figure 7: The CNN model training process. (**a**) Accuracy (%), (**b**) Loss.  
Table 7 demonstrates that the output performance of the six pipeline CNN models is an accuracy rate of 90.9-100%, a recall rate of 90-100%, and an F1-Measure of 94.7-100%. The results show the proposed approach is effective and the evaluation results can accurately classify the ponding volume in different pipelines.
## 2 Materials and Methods
### Statistical Analyses  
All statistical analyses were completed using R and JASP. The data were evaluated for normality using the Shapiro-Wilk test (_stats_ package), whereby deviations from normality were accepted when \(p<0.05\). To appraise between-group differences as a function of time (pre vs. post intervention) and limb (injured/non-dominant vs. uninjured/dominant) for each of the kinetic and kinematic drop jump variables, a linear mixed effects model was used (_lme4_ package), which also controlled for the potential effect of gender. Within this model, the independent variables accounted for the fixed effects and participants accounted for the random effect. Post hoc analyses were conducted (_emmeans_ package) to evaluate within- and between-group differences where the Holm correction was implemented to adjust the \(p\)-values for multiple comparisons. Cohen's d (_effectsize_ package) was calculated as the standardized effect size for each between-group comparison and was interpreted as follows: trivial: \(<\)0.2; small: 0.2-0.6; moderate: 0.6-1.2; and large: \(>\)1.2 [30].  
To evaluate the diagnostic accuracy of jumping-based metrics, a binomial general linear model (_stats_ package) and a receiver-operator characteristic (ROC) curve analysis (_ROCit_ package) was conducted to evaluate the sensitivity and specificity of each metric [31]. The area under the curve (AUC) was used to compare metrics and was qualitatively interpreted as excellent \(\geq\) 0.90; considerable: 0.80-0.90; fair: 0.70-0.79; and poor: \(<\)0.70 [32]. For each metric, the optimal cutoff point that maximises the sensitivity and specificity was calculated using [PERSON]'s index [33], which allows for the determination of the optimal threshold for each metric.  
The associations between AKPS scores, jumping-based metrics and isometric strength were evaluated using a correlation analysis (_correlation_ package). The magnitude of the correlation coefficients was qualitatively interpreted as follows: negligible: 0.00-0.10; weak: 0.10-0.39; moderate: 0.40-0.69; strong: 0.70-0.89; and very strong: 0.90-1.00 [34]. Statistical significance was accepted at \(p\leq 0.05\).
## 3 Materials and Methods
### Naive Bayes Classifier  
The Naive Bayes algorithm is an effective probability-based algorithm used to solve classification problems in machine learning. Naive Bayes is a probabilistic algorithm that makes predictions about the class of a given piece of text based on Bayes' Theorem, which is frequently used in statistics [49]. It trains the dataset by considering that the probability of an event occurring is equal to the product of the prior probability of the event. The Naive Bayes algorithm is an algorithm that provides fast and effective results in classification problems depending on the size and complexity of the data set.  
Naive Bayes is widely used in natural language processing (NLP) and text-based tasks such as spam detection, sentiment analysis, and document classification [50].  
The basic idea behind Naive Bayes is to calculate the probability that a piece of text belongs to a certain class, given the words in the text. The algorithm does this by calculating the probability of each word in the text, assuming that the text belongs to a certain class. It then multiplies these probabilities together and compares them with the prior probability of the last class. The class with the highest probability is selected as the classification of the text [51].  
\[\mathrm{P(X)}=\frac{\mathrm{P(C)}\cdot\mathrm{P(C)}}{\mathrm{P(X)}} \tag{1}\]  
\(\mathrm{P(A\,I\,B)}\) is the probability that event A will occur given event B ( posterior probability). \(\mathrm{P(B\,I\,A)}\) is the probability that event B will occur given event A. \(\mathrm{P(A)}\) is the prior probability of event A. \(\mathrm{P(B)}\) is the prior probability of event B occurring (also known as the marginal probability).  
While the Naive Bayes algorithm is based on the Bayes theorem, the reason it is called \"naive\" is that the algorithm assumes independence between features. In other words, the features within a sample are independent of each other. This assumption of independence simplifies the probability calculations and allows the algorithm to detect frequent patterns in the dataset more quickly [52].
## 3 Life Cycle Assessment Methodology
### Life Cycle Inventory Analysis
In this case, functions can be analyzed in a joint or separate way as established in the functional unit that can contain all functions in a joint evaluation or where many functional units have one function each in a separate analysis, as in Figure 5. In the first case, the system expansion methodology is used: the functional unit is expanded to consider all functions and it is defined as a basket of products. The environmental impact is then evaluated for the overall considered system and products. In the second case, a product-specific methodology is used such as the avoided burden or allocation methods. Among these two specific methods, in the avoided burden, the by-product is the credit and the methodology considers the environmental impact v of another process producing the same by-product, which is in this way avoided. To obtain the specific environmental result, the avoided burden v is subtracted from the overall impact u of the main analyzed process: the main product has an environmental impact equal to u\(-\)v. The avoided burden is generally suggested for a comparison with another process. On the other hand, in the allocation methodology, the environmental impact is allocated to individual products based on particular properties (mass or volume, energy, exergy, and cost). It is important to consider that the allocation is not undertaken for emissions but only for valuable products and process flows. Figure 4: Hierarchy of eight methods used in the life cycle inventory generation of chemicals with respect to the data/time requirements and accuracy (LCI = life cycle inventory) [Modified after [PERSON] and [PERSON] [64]]. In the first analysis, ISO 14040 and ISO 14044 recommend the system expansion methodology in multifunctionality, even if it is not specific to a single product of the process. However, if function-specific results are required or it is impossible to use the system expansion methodology because system boundaries should consider a huge amount of chemicals that can lead to very large baskets of products, then communication and interpretation are difficult. Generally, the choice of one methodology depends mainly on the scope of the life cycle assessment and the analyzed process. However, ISO 14044 suggests the following points: to avoid the allocation by creating sub-processes of the main process and collecting the needed environmental burden data and/or expanding the system boundaries including co-product functions; if the allocation is required, it is better to use an allocation methodology based on physical relationships; if the allocation based on physical relationships can not be considered, other allocation methodology should be used. If an allocation method is used, it is suggested to undertake a sensitivity analysis testing different allocation methods.
## 6 Synergy with Hybrid Communication Systems
### Software-Defined Networking (SDN)  
SDN is the key enabler in orchestrating hybrid communication networks because it acts as a compressor that addresses how critical resources and routing are allocated in real time. SDN controllers can autonomously change parameters in the selected links to assure optimality and manage bandwidth or frequency bands based on the current conditions [24,45].  
Also, the integration of SDN with THz-FSO systems can optimize the resources that are available in multiband systems. For instance, applications such as HTP or autonomous vehicle networks rely on minimized delay and boosted data transfer rates that result from routing based on dynamic SDN.
# Surface Analysis of 3D (SLM) Co-Cr-W Dental Metallic Materials
## 2 Materials and Methods  
### Materials and Techniques  
Complex dental problems (determined through computed tomography CT) need new solutions that involve custom items, created via the 3D printing process, in order to obtain specific elements (identified through 3D scanners) (Figure 1). The additive manufacturing technique (i.e., SLM) was used to obtain different samples from Co-Cr-W powders. High-purity metal powders of cobalt, chromium, and tungsten were used for the production through the SLM of the samples. The powder was provided by S&S Scheftner Germany and the 3D printing process was realized using SLM50 equipment (Realizer GmbH, Borchen/Paderborn, Germany) of the Romanian brand S.C. Lamas Microtech S.R.L. Bucharest.  
The samples were made with different dimensions directly via the 3D printing process (for wettability determinations: 20 \(\times\) 20 \(\times\) 2 mm; scratch and profilometry: 30 \(\times\) 10 \(\times\) 2 mm; SEM and EDS were carried out before the main tests on the same samples).  
Powders were added to the system of the 3D printing equipment, and the thickness of the layer was maintained at a constant value: t\({}_{\rm layer}\) = 25 and 50 \(\upmu\)m. We obtained six experimental specimens by the additive manufacturing of Co-Cr-W alloy using a laser power of 70 W, a scanning rate of 1000 mm/s, and an exposure time 20 \(\upmu\)s. The 3D printed samples had the follow chemical composition (average values from five different areas, wt%): Co: 59.38 (st. dev.: \(\pm\)1.5), Cr: 25.99 (st. dev.: \(\pm\)1.1), W: 9.54 (st. dev.: \(\pm\)0.8), Mo: 3.48 (st. dev.: \(\pm\)0.5), Si: max. 1.16 (st. dev.: \(\pm\)0.1), and other elements: 0.43, determined through EDS analysis.  
The samples were subject to mechanical preparation of the surfaces using two different sandblasting media. We noted the samples in the article, connected to the solidified layer during the 3D additive manufacturing process as: 25 \(\upmu\)m--0 (non-sandblasted initial state), 25 \(\upmu\)m--1 (sandblasted with Al\({}_{2}\)O\({}_{3}\) particles), 25 \(\upmu\)m--2 (successively sandblasted with Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\) particles), which were similar for the 50 \(\upmu\)m melted layer as well.  
The surface modification of the 3D printed samples was realized by mechanical sandblasting with two different working particles. The sandblasting process was performed with specialized RenfertBasic Eco equipment (Renfert, Hilzingen, Germany), equipped with ceramic nozzles to determine the dimensions of the pulverized material. The alumina particles (Al\({}_{2}\)O\({}_{3}\)) had an F100 granulation (dimensions of 100-150 (\(\pm\)5) \(\upmu\)m) and a working pressure of \(p\) = 3.5-4 bar. The silica particles had diameters between 77-110 (\(\pm\)5) \(\upmu\)m and a working pressure of \(p\) = 2.5 bar. After they were polished, each sample was ultrasonically cleaned for 30 min in both distilled water and ethanol [15; 16].
## 6 Algorithm Details  
### Uplink Satellite Reselection  
As shown in Figure 9, the ground station is \(g\), and there is a service originating from the ground station. The path before sun outage occurs is g-1-2-3-d, and \(v_{1}\), \(v_{4}\) and \(v_{5}\) are satellites that always establish feeder link connections with the ground station during sun outage. After the sun outage failure occurs, it is clear that \(v_{1}\) is no longer the best uplink satellite for the service, and the uplink satellite needs to be reselected. The principles for reselection of uplink satellites are as follows:  
1. The reselected satellite can shorten the number of hops to the destination node, and the fewer hops, the higher the priority.
2. The uplink remaining bandwidth of the reselected satellite feeder link and the maximum remaining bandwidth of the egress link of all minimum hop paths to the destination node are both greater than the bandwidth required by the current service. \[w^{d}\leq min\bigg{\{}B_{i}-b_{i},\max_{j\in N_{i}}\big{(}L_{ij}-l_{ij}\big{)} \bigg{\}}.\] (19) where \(w^{d}\) represents the required bandwidth for the service, \(B_{i}-b_{i}\) represents the remaining uplink bandwidth of the feeder link of satellite \(v_{i}\), \(L_{ij}-l_{ij}\) represents the remaining bandwidth of the inter-satellite link \(l_{i,j}\) and \(N_{i}\) is the set of neighbor nodes passed by the minimum hop path from satellite \(v_{i}\) to destination satellite \(v_{d}\).
3. Give priority to reselecting uplink satellites for services that require larger bandwidth. After selection, the bandwidth required for the current service will be deducted from the remaining bandwidth above.  
For rule 1, nodes \(v_{5}\) and \(v_{4}\) both meet the conditions, and \(v_{5}\) has a higher priority. For rule 2, taking satellite node \(v_{5}\) as an example, the egress links in the minimum hop path to destination satellite \(v_{d}\) are \(l_{5,6}\) and \(l_{5,2}\), so the rule is reformulated as  
\[w^{d}\leq min\{B_{5}-b_{5},\max(L_{56}-l_{56},L_{52}-l_{52})\}. \tag{20}\]  
Figure 8: Algorithm flowchart.
## 2 Battery Model Settings
### The Batteries  
#### 2.1.1 Lithium-Iron-Phosphate Battery, LiFePO\({}_{4}\) (LFP)  
The operation of this type of battery is based on Li-ions and on the cathode made by LiFePO\({}_{4}\). The cells are characterized by high auto-discharge ratio, and for this reason they are suitable for stationary applications. They are widely used due to their low cost, non-toxicity, and abundance of ferrous materials. Other distinctive traits are: (i) good electrochemical properties, (ii) high thermal capacity that in turn provide intrinsic stability, (iii) operational safety, and (vi) lack of reactivity at high temperatures.  
The properties of this cell allow it to withstand more than 2000 cycles even under high loads thanks to its voltage stability.  
The most significant technical limitation is the high electrical resistance, which has been partially solved thanks to the use of cathode coatings with carbonic materials obtained by doping semiconductors; even the electrical and ionic conductivity does not present relevant values. The energy density of this battery is about 90-120 Wh kg\({}^{-1}\)[24].
## 2 Materials and Experiments
### Scanning Electron Microscope (SEM) Observation and Energy Dispersive Spectrometer (EDS) Analysis  
SEM (Zeiss sigma 300) photographs were obtained to observe the surface morphology changes of silane coupling agent pretreated GF particles compared to the unpretreated GF particles. The SEM accelerating voltage was 15 kV and the imaging probe was SE2. The sample was scattered on the conductive glue stuck on the sample stage, sprayed with a thin layer of gold, and then put into the SEM for observation. Combined with the surface morphology observation, the elements distribution on the silane coupling agent pretreated GF particle sample surface was obtained by using the scanning function of the EDS equipped on the SEM.