esa-sceva/satcom-chunk-collection huggingface dataset:
# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 3 Methods
### Feature Selection  
Three major feature selection methods for machine learning are examined: filter, wrapper, and embedded methods [55; 56; 57]. The filter method is the most computationally efficient but is unable to handle redundant features. Furthermore, important features that are less informative on their own but are informative when combined with others may be discarded. The wrapper method tends to produce better classification accuracy, but it is the most computationally complex method and does not generalize well to other datasets. The embedded approach tries to alleviate the computation time required by the wrapper method by incorporating the feature selection process with the overall training process. While the embedded approach is computationally less expensive than the wrapper method, it still has the issue of generalizability.  
The filter method works by selecting the best features based on univariate statistical tests. The selection of features is independent of any machine learning algorithm. Features are ranked based on statistical scores which determine the correlation of features with the outcome variable. In our case, we use ANOVA F-values since our groups are categorical. F-values are the ratios of two Chi-distributions divided by its degrees of freedom, as shown in Equation (10).  
\[\mathrm{F}\;=\;\left(\chi_{1}^{2}/\;n1-1\right)/\left(\chi_{2}^{2}/n2-1\right) \tag{10}\]  
Next, we test two embedded feature selection methods, an L1-based (lasso) feature selection and a tree-based feature importance method. Tree-based estimators are used to compute impurity-based feature importance values, which in turn are used to discard irrelevant features. The lasso method is a regularization approach where a penalty is applied over the coefficients of a linear model. This is accomplished by modifying the mean squared error cost function to contain the L1-regularization (\(\lambda\)), as shown in Equation (11). Features with non-zero coefficients are selected for model training.  
\[\mathrm{J}(\theta)\;=\;\frac{1}{m}\sum_{i=1}^{m}Cost\left(h_{0}\left(x^{i} \right)y^{i}\right)+\;\frac{\lambda}{m}\sum_{j=1}^{n}\left|\theta_{j}\right| \tag{11}\]  
The wrapper method of feature selection used is Recursive Feature Elimination (RFE). RFE performs a greedy search to find the best performing feature subset. It iteratively creates models and determines the best or the worst performing feature at each iteration, then constructs the subsequent models with the leftover features until all the features are explored. The current study uses a linear SVM as the model evaluator. RFE then ranks the features based on the order of their elimination.
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations
### Hydrogen Diffusion at Temperatures between 900 and 1400 \({}^{\circ}\)C
#### 3.1.3 Discussion  
The temperature dependence of the determined diffusivity is given in an Arrhenius plot in Figure 8, together with temperature correlations calculated from correlations given in the literature [15; 16; 17]. The length of the lines gives the temperature range used for the  
Figure 7: **(a)** Dependence of the total neutron cross section on the distance to the surface where the hydrogen penetrates at 1200 \({}^{\circ}\)C for annealing times between 28 and 68 s and (**b**) fit of the measured diffusion hydrogen distribution after 15 min annealing at 1100 \({}^{\circ}\)C with Equation (1).  
determination of the correlations. The values determined here agree at temperatures of 1100 and 1200 \({}^{\circ}\)C with the correlation given by [PERSON] [16], but at 900 and 1000 \({}^{\circ}\)C with the one published by [PERSON] [17]. It looks like the different limited temperature ranges of the two investigations are the reason for the difference in the temperature correlation.  
A detailed description of the experiments and the results is given in [11].  
Hydrogen Uptake during Steam Oxidation in the Temperature Range between 900 and 1400 \({}^{\circ}\)C  
The hydrogen uptake of nuclear fuel claddings made of zirconium alloys under accident conditions is of high safety importance. The oxidation of the hot fuel cladding tubes by steam can be described very simply by:  
\[\text{Zr}+2\text{H}_{2}\text{O}=\text{ZrO}_{2}+4\text{H}\]  
The produced free hydrogen can recombine with molecular hydrogen H\({}_{2}\) or can be absorbed by the remaining metallic zirconium.  
\[4\text{H}=2(1-\text{f})\,\text{H}_{2}+4\text{f}\,\text{H}^{\text{absorbed}}\]  
f is the hydrogen pickup fraction. This so-called secondary hydrogenation during oxidation in steam (it is the hydrogen uptake during the accident, the primary hydrogenation is the uptake during operation) can degrade the toughness of the material if a certain hydrogen concentration is exceeded. This embrittlement increases the risk of fuel rod fragmentation during emergency cooling to terminate the accident. Fuel and fission product release and redistribution would be the consequence.
# Simulation Study on Ground Vibration Reduction Measures of the Elevated Subway Line
## 1 Introduction
[29] proposed a new periodic composite rubberized concrete barrier (PCRCB) with precast assembly characteristics. The numerical study results show that the PCRCB has a better vibration damping effect on the ground under the subway train load. [PERSON] et al. [30] designed rubber supports to reduce the effect of vertical vibration on buildings from subway operation. The implementation of vibration isolators can reduce the high-frequency response by making the natural frequency of vibration modes with large effective mass significantly lower than the dominant frequency of train-induced ground vibration. [PERSON] et al. [31], in order to discuss the vibration control effect of vibration isolation support, established a finite element model of the track-foundation-vehicle segment structure, and the calculation results were verified with measured data. A good vibration isolation effect was achieved near the main frequency of the floor. Along with the construction of urban rail transit, viaducts have become an important type of urban rail transit structure. Most of the above studies are about the ground vibration induced by the subway when running on underground lines and the vibration of adjacent buildings. For the viaduct line waiting to be built, the research of ground vibration damping measures is carried out by means of simulation. According to the simulation results, the speed is reasonably controlled, and an effective vibration damping scheme is adopted, in which the damping effect of installing continuous support on ground vibration is proposed in the vibration damping measures. The results of the study can not only provide a reasonable reference scheme for the line to be built to meet the environmental vibration requirements, but they are also conducive to the control of line costs. It has important engineering significance for the elevated line to be built. This paper analyzes the ground vibration near buildings induced by metro vehicles running on viaduct lines; investigates the vibration reduction measures of controlling the speed, adjusting the stiffness of fasteners, arranging the track damping pads, and installing the track continuous support; and analyzes the vibration reduction effect of different vibration reduction measures. Therefore, this paper establishes the train-track-viaductrigid-flexible coupling dynamics model and the piers-ground-buildings finite element model in Section 2. In Section 3, the ground vibration near the building is simulated and analyzed, and the effects of train running speed and fastener stiffness on the ground vibration are investigated. The vibration reduction effects of the track damping pads and continuous support damping structure vibration reduction measures are analyzed.
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 4 Experimental Results and Discussions
### Experiemts including Bayesian Network
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Figure** & **Humidity** & **Time** & **Ratio of Voting** & **Top-1 Place** & **Smoke** & **Bayesian** & **Area of** & **Area of** \\  & & & **Classification** & **Detection** & **Network Result** & **Flame** & **Smoke** \\ \hline \multirow{4}{*}{(a)} & \multirow{2}{*}{Normal} & 1 min & fire: 0.93, non-fire: 0.07 & building: 0.84 & yes & fire: 0.968, non-fire: 0.032 & 43.2 & 834.8 \\  & 2 min & fire: 0.95, non-fire: 0.05 & building: 0.89 & yes & fire: 0.977, non-fire: 0.023 & 13.9 & 841.7 \\  & 3 min & fire: 0.92, non-fire: 0.08 & building: 0.87 & yes & fire: 0.964, non-fire: 0.036 & 48.7 & 947.5 \\  & 4 min & fire: 0.94, non-fire: 0.06 & building: 0.76 & yes & fire: 0.973, non-fire: 0.027 & 237.8 & 1238.5 \\ \hline \multirow{4}{*}{(b)} & \multirow{2}{*}{Dry} & 1 min & fire: 0.87, non-fire: 0.13 & building: 0.71 & yes & fire: 0.975, non-fire: 0.025 & 53.4 & 449.2 \\  & 2 min & fire: 0.92, non-fire: 0.08 & building: 0.75 & yes & fire: 0.985, non-fire: 0.015 & 67.9 & 372.8 \\  & 3 min & fire: 0.95, non-fire: 0.05 & building: 0.79 & yes & fire: 0.991, non-fire: 0.009 & 294.7 & 691.3 \\  & 4 min & fire: 0.94, non-fire: 0.06 & building: 0.78 & yes & fire: 0.989, non-fire: 0.011 & 319.6 & 538.1 \\ \hline \multirow{4}{*}{(c)} & \multirow{2}{*}{Wet} & 1 min & fire: 0.1 non-fire: 0.9 & forest or mountain: 0.84 & yes & fire: 0.075, non-fire: 0.925 & 0 & 682.9 \\  & 2 min & fire: 0.08, non-fire: 0.92 & forest or mountain: 0.87 & yes & fire: 0.059, non-fire: 0.941 & 0 & 685.7 \\  & 4 min & fire: 0.07, non-fire: 0.93 & forest or mountain: 0.86 & yes & fire: 0.051, non-fire: 0.949 & 0 & 694.6 \\ \hline \multirow{4}{*}{(d)} & \multirow{2}{*}{Normal} & 1 min & fire: 0.13, non-fire: 0.87 & irrelevant place: 0.83 & yes & fire: 0.079, non-fire: 0.921 & 78.4 & 218.9 \\  & 2 min & fire: 0.08, non-fire: 0.92 & irrelevant place: 0.85 & no & fire: 0.047, non-fire: 0.953 & 23.7 & 0 \\  & 3 min & fire: 0.1, non-fire: 0.9 & irrelevant place: 0.87 & no & fire: 0.039, non-fire: 0.961 & 68.6 & 0 \\  & 4 min & fire: 0.1, non-fire: 0.9 & irrelevant place: 0.94 & no & fire: 0.039, non-fire: 0.961 & 87.4 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Experimental results for Figure \(11\)[26].
# Balancing Staff Finishing Times vs. Minimizing Total Travel Distance in Home Healthcare Scheduling
## 4 Solution Method
#### Simulated Annealing (SA)  
Our proposed algorithm is based on SA, as we include both our balancing and insertion algorithms. The algorithm also employs general neighborhood operators. At each iteration, one of the neighborhood operators will be chosen based on their performance records (probability). At the start, the probability for each operator is identical. The variables \(pc_{1},pc_{2}\) and \(pc_{3}\) represent the number of improvements that arise from applying the moving, swapping and path exchanging operators, respectively. Using the roulette wheel selection method frequently used in Genetic algorithm [30], the probability of choosing the operator \(i\) can be calculated by \(pc_{i}/(pc_{1}+pc_{2}+pc_{3})\). At each iteration, if applying the operator \(i\) can improve the solution, the number \(pc_{i}\) will be increased by 1; otherwise, the number will be decreased by 1. The pseudocode is presented in Algorithm 1 below.  
```
1.Input: Initial temperature as \(T\), terminating temperature as \(T_{m}\), decreasing rate as \(\alpha\), and maximum iteration as \(I_{m}\).
2.Initial step: Generate an initial solution using Algorithm 2, set as \(Sol\). Set the counter iteration variable as \(i=1\). The number of improved \(pc_{1}=pc_{2}=pc_{3}=1\).
3.While the termination criteria do not meet (\(T>T_{m}\) and \(i<I_{m}\))do
4. Choose the Local search procedure based on the probability.
5.Apply Local search procedures to the solution, set as \(Sol_{new}\).
6.If the objective value of \(Sol_{new}<\) objective value of \(Sol\)do
7.Update new solution, \(Sol=Sol_{new}\).
8.If the objective value of \(Sol<\) objective value of \(Sol_{best}\)do
9.\(Sol_{best}=Sol\) and increase the number of improvements of the operator by \(1\).
10.End If
11.Else
12.Randomize a value uniformly, set to \(s\).
13.If\(s<\exp(t)\)do
14.Accept new solution, \(Sol=Sol_{new}\).
15.End If
16.End If
17.Apply ruin-and-recreate procedure.
18.Increase the number of iterations, \(i=i+1\).
19.End While
20.Output:\(Sol_{best}\)
```  
**Algorithm 1** The framework of SA
## 2 Offline Handwritten Text Recognition
### Optical Model  
The main component of the HTR system, the optical model, is represented by the steps following preprocessing. It is responsible for observing an image, understanding the context, and decoding it in text [21]. Currently, using Convolutional Recurrent Neural Networks (CRNN) with Connectionist Temporal Classification (CTC), the state-of-the-art optical model is subdivided into three steps [2,49].  
In the first step, the input image, already preprocessed, feeds the CNN layers to extract features.This process can be done through the traditional CNN layers [2,15] or using the Gated mechanism [40,50], in which it aims to extract even more relevant information.  
The second step uses the sequence of features extracted within RNN layers to map and generate a matrix containing character probabilities for each time-step (positions in the sequence). In this process, Bidirectional Long Short-Term Memory (BLSTM) and Bidirectional Gated Recurrent Unit (BGRU) layers are generally used to better handle large text sentences and their contexts [15].  
Finally, in the third step, the probability matrix has two processing ways through the CTC: (i) calculate the loss function with the loss algorithm CTC in order to adjust the weights for learning the model (training phase); and text decoding (ii) using an CTC decoding algorithm (classification phase) [43,49].
## 4 Results
### Model Testing on DS2 (COVID-19 Dataset)  
Table 4 shows the results obtained by testing the selected machine learning models on the DS2 data set by analyzing LIWC, n-grams, and combining those two approaches. The logistic regression model achieved the highest accuracy of 74.62% in the analysis using LIWC while the multinomial Naive Bayes gave slightly worse results with 74.58%. SVM using the same approach achieved an accuracy of 73.32% while the random forest model tested using LIWC analysis gave the worst accuracy of 69.98%. Examining the model through combinations of n-grams, Naive Bayes tested on a combination of unigrams and bigrams leads with an accuracy of 73.86%. It is followed by SVM tested on bigrams with 72.05% and logistic regression tested on a set of bigrams and trigrams with the accuracy of 71.67%. All models tested with the LIWC analysis achieved higher accuracy than the maximum model performance obtained by the n-gram analysis. Using the combined analysis of n-grams and LIWC, the maximum accuracy of 76.39% on the analysis of unigrams and bigrams was achieved with the multinomial Bayes, followed by the SVM tested on bigrams with an accuracy of 76.34%. The logistic regression and random forest also gave satisfactory results of 75.05% on unigrams and 72.97% on bigrams.
# Redundancy-Reduction-Based Hierarchical Design in Synchronization of Multi-Agent Systems
## 5 Conclusions  
In this paper, we present a general network-partitioning method for the synchronization of multi-agent systems. While using global exponential synchronization criteria conditions, we decompose the computational burden by slightly trivial connection weights that do not affect the global synchronization target. Under the projection, some unnecessary edges and information are ignored in the networks, such that the amount of calculation is reduced; the efficiency of synchronization has also been improved, and the main structure of the network has also been highlighted and decomposed into multiple sub-network chains, offering convenient conditions for further analysis. Moreover, after applying this method, the information transfer and synchronization characteristics between nodes are not affected. Both a mathematical proof and numerical examples demonstrate the effectiveness of the method. Future results will be achieved in the form of coordinated missions in swarms of UAVs.  
Conceptualization, H.Q.; methodology, H.Q.; software, Z.X.; validation, H.Q. and Z.X.; formal analysis, H.Q.; investigation, H.Q.; data curation, H.S.; writing--original draft preparation, H.Q.; writing--review and editing, H.Q. and Z.X.; supervision, H.S.; project administration, H.S.; and funding acquisition, H.Q., Z.X. and H.S. All authors have read and agreed to the published version of the manuscript.  
Figure 4: The comparison results of the trajectory graph of synchronization methods in [14].  
**Funding:** This work is supported by the National Natural Science Foundation of China (61903260, 62003219) and the Stable Supporting Programme for the Universities of Shenzhen (GXWD20201231165806004, 20201019173610001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to express their sincere gratitude to [PERSON] and [PERSON] for helping to finish this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
In a variety of applications, e.g., psychoacoustic experiments, virtual sound propagation demonstration, or synthesized noise production, noise samples are played back in laboratories. To simulate realistic scenes or to mask unwanted background sounds, it is sometimes preferable to add background ambient sounds to the noise. However, this can influence noise perception. It should be ensured that either background sounds do not affect, e.g., annoyance from foreground noise or that possible effects can be quantified. Two laboratory experiments are reported, in which effects of mixing background sounds to foreground helicopter samples were investigated. By means of partially balanced incomplete block designs, possible effects of three independent variables, i.e., helicopter's sound exposure level, background type, and background sound pressure level were tested on the dependent variable annoyance, rated on the ICBEN 11-point numerical scale. The main predictor of annoyance was helicopter's sound exposure level. Stimuli with eventful background sounds were found to be more annoying than those with less eventful background sounds. Furthermore, background type and level interacted significantly. For the major part of the background sound level range, increasing the background level was associated with increased or decreased annoyance for stimuli with eventful and less eventful background sounds, respectively.  
Keywords:noise annoyance; psychoacoustics; laboratory experiments; ambient sounds; virtual audio applications; virtual acoustics; background sounds +
Footnote †: journal: Applied Sciences
# Airborne Radar Staggered PRF Coherent Processing Method for Down-Looking Target Detection
## 3 The Flow Design of Multilayer Detection and Blind Zone Optimization
### Multilayer Detection and Coherent Processing of Pulse Train Signals
The methodology employed in this study has certain limitations. DFC signals are utilized as the transmission signals. However, in the presence of a significant amount of  
Figure 5: Signal processing flowchart. irregular clutter, the orthogonality of DFC signals with respect to clutter is insufficient. The matched filtering of different DFC signals results in residual clutter energy. After performing the DFT, the residual clutter energy is distributed across the entire range-Doppler cell, which has a certain impact on target detection. It is suggested to consider using orthogonal waveforms with better clutter suppression properties as radar transmission pulses to address this issue.
# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
## 4 Methodology
### Attention-Based Structure Embedding
#### 4.3.1 Relation Extension  
Some actual relations in a social network may not be observed because they are not explicitly built or obtained by the crawlers. The lack of a sufficient number of social relations on UA issues can lead to an inefficient model. Typically, two related social networks have a small number of distinctly aligned nodes that are called pre-aligned users. The existing pre-aligned user pairs have contributed to the relation extension. This section describes the extension of the user relations between networks through pre-aligned user pairs.  
Suppose that there are a couple of pre-aligned users from target networks \(G^{X}\) and \(G^{Y}\). If a user has a connection edge in network \(G^{Y}\), the corresponding user in network \(G^{X}\) should also have this edge. Based on such an observation, we add a relation to a user in network \(G^{X}\) if their counterpart in network \(G^{Y}\) has this relation. Formally, given networks\(G^{X}\) and \(G^{Y}\) with a set of pre-aligned pairs \(P\), the extended relation set \(\tilde{R}^{X}\) of \(G^{X}\) can be estimated by:  
\[\tilde{R}^{X}=R^{X}\cup\{(u_{i}^{X},u_{j}^{X})|(u_{i}^{Y},u_{j}^{Y})\in R^{Y},(u_ {i}^{X},u_{j}^{X})\
otin R^{X},(u_{i}^{X},u_{i}^{Y})\in P,(u_{j}^{X},u_{j}^{Y}) \in P\} \tag{7}\]  
Similarly, the relation set of the target network \(G^{Y}\) is extended into \(\tilde{R}^{Y}\).
## 4 Multihoming and Interworking
### Traffic Engineering Support
#### 4.5.2 Gateway Selection for Incoming Traffic  
Gateway selection for incoming traffic requires support from the DNS and the global mapping system. A node may offer different services: one requires best-effort transport and another requires premium transport. The DNS name for the best-effort service should resolve, e.g., to E(g).1 and the name for the premium service should resolve, e.g., to F(g).10. Nodes without upgraded networking stacks use this information to contact the server. Nodes with upgraded networking stacks use just the destination ID 1 or 10 and query the local or global mapping system for an appropriate local or global address. Therefore, the global mapping system should be configured to return E.1 and F.10 as the default and F.1 and E.10 as the alternative to be used when the default values do not work. This ensures that GLI-nodes with upgraded networking stacks usually contact the best effort service through ID 1, and gateway E and the premium service through ID 10 and gateway F, as desired.