esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
To calculate the risk of such decorrelation, we performed the following calculation. The wall velocity is, estimated from the interframe axial displacement, reported to be about 5 mm/s [8; 13]. Considering the 1 ms needed for data acquisition by the proposed imaging sequence for a 3D volume, the target moves 5 \(\upmu\)m in the axial direction. In this scenario, the PSF obtained with a dynamic scatterer, moving 5 \(\upmu\)m in 20 transmit-receive shots, well overlaps with a static scatterer, which is mainly because the motion is lower than 10% of the wavelength (200 \(\upmu\)m) during the time needed to acquire the data of a 3D volume [43]. Therefore, we can assume that the coherent compounding from the 20 acquisitions is not negatively affected. In addition, the global movement is slow, will not interfere with the coherent image reconstruction, and can be removed by subtraction of displacement of the tissues and that of carotid artery [8]. With 20 kHz pulse repetition frequency, reflection artifacts come from depths higher than 37.5 mm. In this study, these reflection artifacts are ignored due to tissue attenuation (\(\approx\)0.4-0.6 dB/MHz/cm) as a scatterer at a higher depth (e.g., 50 mm) is attenuated by 28 dB compared to the reflection coming from 12.5 mm depth. A more advanced beamforming algorithm might be helpful with suppression of these artifacts as well [44; 45; 46; 47], taking the change of speckle patterns into account [48]. Finally, although the reported results are linked to the specifications of the matrix transducer reported in our previous publication (see [34]), we would like to stress that the procedure taken in this study to select receive/transmit parameters can be used for other array transducers (either 1D or 2D) to obtain a higher frame/volume rate at a given number of data acquisition channels. As a future study, we will use the proposed imaging sequence to conduct clinical 3D carotid pulse wave imaging and generate 3D elasticity maps.
## 3 Results and Discussion
### Performance Characterization of Dust Suppressant
#### 3.2.4 Rain Immersion Test  
The test data on samples soaked for 1 h were discarded because the experiment had a different test date from other samples. Samples soaked in rainwater for 0.5 h, 1.5 h, and 2 h were tested for moisture retention, the variation curves of water loss rate over time for which are shown in Figure 9.  
According to those experimental results, the water loss rates of soaked samples were small at the beginning of the experiment due to the increasing amount of water inside. As time passed, the water loss rates of soaked samples increased faster than those that were not soaked. At the 40 th hour, the water loss rates of all soaked samples were higher than samples that were not soaked. Therefore, when the dust suppressant was subject to rain or soaked in water, the dust suppressant solution dissolved, resulting in a decreased moisture  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Leaf Number** & **Leaf Number** & **Growth** & **Inhibition** \\  & **on Day 1** & **on Day 7** & **Speed** & **Percentage** \\ \hline Pure water & 34 & 35 & 0.0041 & 0 \\ Dust suppressant & 34 & 38 & 0.0159 & \(-\)288 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental results on plant survival rate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Absorbance** & **Absorbance** & **Chlorophyll a** & **Chlorophyll b** & **Chlorophyll1** \\  & **at 665 nm** & **at 649 nm** & **Content (mg/L)** & **(mg/L)** & **Content (mg/g)** \\ \hline Pure water & 1.151 & 0.785 & 10.656 & 11.168 & 2.182 \\ Dust suppressant & 2.510 & 1.052 & 27.777 & 7.885 & 3.566 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Measurement results on chlorophyll contents in plants.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Wind** \\ **Speed** \\ **(m/s)** \\ \end{tabular} } & \multicolumn{2}{c}{**Sample Sprayed with Dust Suppressant**} \\ \cline{2-6}  & **Mass before** & **Mass after Wind** & **Wind Erosion** & **Mass before** & **Mass after Wind** & **Wind Erosion** \\  & **Wind Erosion (g)** & **Erosion (g)** & **Rate (\%)** & **Wind Erosion (g)** & **Rate (\%)** \\ \hline
5 & 35.0575 & 32.2407 & 9.39 & 37.6814 & 37.6880 & 0 \\
10 & 35.1908 & 31.9007 & 10.97 & 37.1857 & 37.1855 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results for wind erosion resistance test.  
content rate in the dust. However, according to the experimental data, the water loss rate of the sample sprayed with dust suppressant was obviously lower than that of the sample sprayed with pure water. Moreover, within 40 h, water loss rates of all samples did not exceed 85%. Within 77 h, the water loss rate of soaked dust was about 92%; that is to say, the dust contained more than 5% of water. Based on the investigation and statistics on fugitive road dust, when the moisture content of dust was higher than 4%, it was not easy to raise. Therefore, the dust suppressant can maintain its performance for about 3 days even if it has been subject to rainfall.
## 2 Related Work
### Automated Text Categorization using Patents  
Classification algorithms are successfully applied to patent texts. In early approaches full texts serve as basis for automated categorization using k-Nearest-Neighbor (kNN), naive Bayes, Support Vector Machine (SVM) or back-propagation neural network [10; 11]. In order to improve the classification performance, which is reduced, inter alia, by the legal and blurred language of patents, further data from distinct sources were combined into increasingly complex models. For example, [PERSON] and [PERSON] [7] suggest a hybrid classification with a weighted linear combination of content-, citation-, metadata-, and network-based predictions, which outperformed each single contribution with F1 = 86.4%. However, this performance is contrasted by an enormous complexity and effort: kNN and SVM, cosine similarity, ontology-based network analysis, more than 27,000 patents for training purposes, and the adjustment of weights for the final prediction. All this illustrates the methodological challenges and the workload, which have to be overcome.  
In contrast, with only 1600 training objects a multiclass setting can successfully be established using SVM (66.4% accuracy) leading the classifier ranking followed by random forest and kNN [12]. Furthermore, SVM served as both base and fusion classifier in an ensemble approach and achieved F1 = 77.7%, further increased by implementing user feedback as active learning (F1 = 84.2%) [13]. In order to achieve these high performances in the chosen multiclass classification, [PERSON] interactively extended his model not only to active learning, but also by reducing the text features using principal component analysis or reinforcing the training process through so-called _dynamic certainty propagation_.  
IPC or CPC have rarely been used as a knowledge source, but rather as a target for automated document categorization to relieve the examiners from manual work [7; 11; 14]. Recent deep learning approaches such as BERT as well proved their predictive power in mastering the assignment of patents at the CPC subclass level [15]. In general, this evolving number of approaches with extensive pre-trained language models has a great potential for semantic tasks such as text classification. However, a high complexity (e.g., in terms of fine-tuning) and demanding huge amount of training data limit the applicability to classify patents, especially into user-defined categories with usually very limited amount of training samples. Therefore, our approach focuses on applicability with established methods as a solid basis for a proof of concept. This could serve as a basis for future studies to evaluate whether more complex methods such as BERT are worthwhile in terms of effort and outcome.
## 5 Multi-Objective Optimization  
### Optimal Algorithm  
Commonly used optimization algorithms include ARSM (adaptive response surface method), GRSM (global response surface method), GA (genetic algorithm) and MOGA (multi-objective genetic algorithm). Among them, GRSM can generate a response surface based on very few data points for computational optimization and will only stop optimization when the solution reaches the optimal value, unlike other algorithms that stop calculation after reaching the convergence criterion. After each iteration, GRSM generates new sampling points in the unsampled intervals of the global design domain based on  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variant** & \(\mathbf{R^{2}}\) & \(\mathbf{E_{\text{raa}}}\) & \(\mathbf{E_{\text{rms}}}\) \\ \hline \(F_{\text{PC}}\) & 0.999 & 0.007 & 7.789 \\ \(F_{\text{MC}}\) & 0.991 & 0.052 & 18.986 \\ \(E_{\text{SA}}\) & 0.999 & 0.008 & 0.133 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error analysis results.  
Figure 7: Response surface model diagram: (**a**) effect on peak force; (**b**) effect on mean force; (**c**) effect of specific energy absorption.  
the distribution of existing sampling points and constructs a new DOE. In this paper, the specific energy absorption \(E_{\rm SA}\), peak force \(F_{\rm PC}\) and mean force \(F_{\rm MC}\) are taken as the optimization objectives, and the variables are designed for the cone angle \(\alpha\) and the thickness of the shrinkage tube \(T\). A series of sample points and their corresponding objective values of the sample points are generated through the design of experiments. The sample point data are then fitted using the Hyper-Study software (2019) to construct the agent model. Finally, the global response surface method (GRSM) is used to obtain the optimal solution for the structural parameters of the energy absorption device based on the agent model. The optimal solution of the structural parameter configuration of the device is obtained based on the proxy model using the GRSM. Due to the superior global search capability of the GRSM, computational optimization using response surfaces is well-suited for the optimal design of crashworthiness indexes for shrink tubes. The response surface fitted in this article has high accuracy, with errors generally less than 1%. This fitted response surface can be used for optimization analysis. Therefore, this article used the GRSM for multi-objective optimization, and the optimization design process is shown in Figure 8.
## 4 Results and Discussion
### Inverse Analysis of Spatial TDR Trace and Dynamic Moisture Profile  
Figure 9a shows the performance of optimization using one-way inverse analysis of the spatial TDR technique. By applying the optimization, the TDR traces simulated using the forward modeling telegraph equation can be well-matched with the TDR traces measured using the data logging system. Based on this acceptable fitting performance, the dynamic moisture profile can be finally calculated using [PERSON]'s model, as shown in Figure 9b. From the beginning at 12:00 pm on 4 th April, the moisture content profile was fully saturated, with a mean moisture content of around 45%. Once the one-step gravitational drainage commenced, the pore water in the upper zone drained fast in the first 8 h. After two days, by 1:56 am on 6 th April, it almost achieved the equilibrium condition. The data logging from the two following days shows no significant variation in moisture profiles, indicating the final equilibrium condition achieved.  
It should be noted that there is an overestimation of moisture in the capillary fringe zone because of the transition zone between the sensor and epoxy terminals. Thus, it should not be used for the determination of saturated moisture content. Instead, the saturated moisture content at the initial condition should be used to cut off the irresponsible partition to determine both the air entry value (AEV) and moisture profile in the saturated area. These series of dynamic moisture profiles demonstrate the success of inverse analysis of spatial TDR technique functioning in principle, while more effort might be required to enhance the optimization method and take consideration of frequency dependence for measuring more conductive porous media.
## 4 Concepts of Integrating the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)
### The Idea of Integrating PSO and ACO  
A hybrid algorithm of PSO and ACO combines the global search capability of PSO with positive feedback of ACO. In [PERSON]'s [19] work, the hybrid algorithm fuses the distance measurement in ACO and the direction measurement in PSO to generate a new probabilistic metric. The new algorithm has good convergence speed and avoids trapping into the local optimal value. In [PERSON]'s [42] proposed algorithm, the global optimum is selected by comparing the optimal value obtained from PSO and ACO, with better global capability and local search capability. [PERSON] [45] proposed a hybrid algorithm using PSO to generate new particles and updates pheromone-particle table for ACO, avoiding from falling into the local optimum.  
In this paper, the key idea of fusing PSO and ACO comes from [PERSON] [46] and [PERSON] [47] in two steps: (1) generate the initial solution of problem using the randomness and global search capability of PSO, and (2) update initial pheromones based on the solutions of PSO, then apply ACO to search until the optimal value is found according to its positive feedback mechanism and higher optimizing precision. We summarize the advantages and disadvantages of PSO and ACO to clarify the characteristics of the hybrid algorithm in Table 1. The hybrid algorithm possesses the advantages of both algorithms of PSO and ACO, and its potential for a better solution to the objective function is anticipatorily higher than using PSO and ACO alone.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Characteristics** & **PSO** & **ACO** & **The Hybrid Algorithm** \\ \hline Evolutionary strategies & Yes & Yes & Yes \\ \hline The quick convergence speed & Yes & No & Yes \\ \hline The lack of information pheromone in the & & No & Yes & No \\ initial stage & & No & Yes & Yes \\ \hline Positive feedback and distributed & & No & Yes & Yes \\ computation & & No & Yes & Yes \\ \hline Guarantee of good capability to search & & No & Yes & Yes \\ global optima or any specific approximation & & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of particle swarm optimization (PSO), ant colony optimization (ACO), and the proposed hybrid algorithms.
# Quantification of Energy-Related Parameters for Near-Fault Pulse-Like Seismic Ground Motions
## 4 Database of Horizontal Fault-Normal Pulse-like Seismic Ground Motion Records  
The dataset of horizontal fault-normal pulse-like seismic ground motions employed in the present study was prepared by collecting relevant time histories from the Engineering Strong-Motion (ESM) database, the Italian Accelerometric Archive (ITACA), the Pacific Earthquake Engineering Research Center (PEER) Ground Motion Database and the Center for Engineering Strong Motion Data (CESMD). The considered dataset is characterized by closest site-to-source distances (\(D\))\(\rho\) less than 30 km, moment magnitudes (\(M_{w}\)) greater than 5.0 and soil preferred shear-velocities (\(V_{530}\)) between 160 m/s and 1000 m/s. It is highlighted, however, that the largest part of the selected records belonged to two soil classes only, namely B and C according to the Eurocode 8 soil classification. It is also  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Tag** & \(\rho\) & \(\alpha\) & \(\beta\) & \(\gamma\) & \(\rho\) \\ \hline EPP & 0 & 100 & 0 & 1 & - \\ HYST1 & 0.1 & 100 & 0 & 1 & 0.8 \\ HYST2 & 0.05 & 2 & 0.1 & 0.5 & 0.8 \\ HYST3 & 0.05 & 2 & 0.1 & 0.5 & 0.5 \\ HYST4 & 0.1 & 2 & 0 & 1 & 0.8 \\ HYST5 & \(-\)0.01 & 1 & 0.05 & 1 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of the parameters controlling the cyclic behavior of the analyzed systems (EPP: elastic-perfectly plastic model; HYST: hysteretic model).  
pointed out that the maximum closest site-to-source distance within the final dataset was slightly larger than the limit distances commonly considered in similar studies, and it was meant to possibly take into account the pulse-like seismic ground motions induced by very large magnitude earthquakes. Moreover, only records with moment magnitudes larger than 5.0 were used in order to focus on the range of magnitude values that generally dictates the hazard in medium-to-high seismicity regions. The full set of values of the most relevant seismological parameters is provided in Figure 2. Herein, the pulse period (\(T_{p}\)) was estimated according to the methodology illustrated in [14]. As already pointed out in previous studies (see for instance [6; 9; 14]), it is especially highlighted that near-fault pulse-like earthquakes can exhibit very high values of \(T_{p}\).
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
###### Abstract  
The proposed detection algorithms are assigned for the \(hpq\)-adaptive finite element analysis of the solid mechanics problems affected by the locking phenomena. The algorithms are combined with the \(M\)- and \(hpq\)-adaptive finite element method, where \(M\) is the element model, \(h\) denotes the element size parameter, and \(p\) and \(q\) stand for the longitudinal and transverse approximation orders within an element. The applied adaptive scheme is extended with the additional step where the locking phenomena are a posteriori detected, assessed and resolved. The detection can be applied to shear, membrane, or shear-membrane locking phenomena. The removal of the undesired influence of the numerical locking on the problem solution is based on \(p\)-enrichment of the mesh. The detection algorithm is also enriched with the locking assessment algorithm which is capable of determination of the optimized value of \(p\) which is sufficient for the phenomena removal. The detection and assessment algorithms are based on a simple sensitivity analysis performed locally for the finite elements of the thin-walled domain. The sensitivity analysis lies in comparison of the element solutions corresponding to two values of the order \(p\), namely current and potentially eliminating the locking. The local solutions are obtained from the element residual method. The elaborated algorithms are original, relatively simple, extremely reliable, and highly effective.  
s _Applied_  
_Article_  
A Posteriori Detection of Numerical Locking  
in \(hpq\)-Adaptive Finite Element Analysis
## 6 Results
### Crash Severity Models  
The summary of findings in the RBLR model for crash severity is provided in Table 7. The results show that crash severity increases with an increase in the average speed, hourly visibility, and the existence of sun glare, all of which are significant at the 95% BCI. The AUC value calculated using the testing dataset is equal to 0.59.  
The performance metrics for the BLR, RF, GBM, GNB, and KNN models for the crash injury severity analysis is summarized in Table 8. It can be observed that the RF model has the highest AUC value of 0.61, and the highest sensitivity value of 0.46. It can be  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Hyperparameters** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\ \hline \begin{tabular}{c} RBLR \\ RF \\ GBM \\ GNB \\ KNN \\ \end{tabular} & 0.67 & 0.53 & 0.77 & 0.67 \\ \begin{tabular}{c} mtry = 4, split rule = gini, node size = 1, sample size = full training set \\ \end{tabular} & 0.72 & 0.65 & 0.75 & 0.70 \\ \
\begin{tabular}{c} cntre = 250, interaction.depth = 1, shrinkage = 0.1, n.minobisnnode = 10 \\ GNB \\ KNN \\ \end{tabular} & 0.64 & 0.53 & 0.75 & 0.66 \\
\begin{tabular}{c} Not applicable \\ RTN \\ \end{tabular} & 0.63 & 0.52 & 0.74 & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Crash likelihood models’ performance summary and Hyperparameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Mean** & **Std. Err** & **95\% BCI** \\ \hline Speed\_avg\_1015 & 0.2 & 0.08 & (0.05, 0.4) \\ HourlyVisibility & 0.02 & 0.009 & (0.001, 0.03) \\ Sunglare & 0.01 & 0.006 & (0.005, 0.02) \\ Constant & 0.02 & 0.001 & (0.004, 0.04) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the Bayesian logistic regression model for crash severity.  
observed that overall, the sensitivity values of all models are quite low (the lowest being 0.08 for GBM, and others hovering around 0.4), which indicates poor performance of the models in predicting fatal/injury crashes (minority class). In terms of specificity, which reflects the ability of the models to correctly predict PDO cases (majority class), GBM provides the highest values (0.96), followed by GNB and RBLR (0.73), and RF (0.72). KNN provides the lowest sensitivity value (0.66) among all investigated models. Overall, as in the crash likelihood analysis, the RF appears to demonstrate the best performance of all tested models, although it is very similar to the performance of the RBLR and GNB models, with very minor differences in the values of performance criteria.  
It should be notes that despite the high overall accuracy (0.80) and specificity (0.96) values, the GBM model cannot be recommended for predicting the severity of crashes as it exhibits very poor sensitivity (0.08).
## 2 Cooperative Navigation Scheme with Relative Visibility Analysis  
Considering that the NLOS error of swarm cooperative navigation increases significantly in complex terrain environments for air-ground swarms and that the traditional NLOS error suppression method based on the pure angle of 3D terrain modellings mistakenly deletes normal propagation signals at near distances, a cooperative navigation NLOS signal identification scheme based on the analysis of air-ground visibility was designed. The air-ground swarm LOS condition is also introduced to more accurately separate NLOS signals during co-navigation and to utilize ranging and angular observations for co-navigation through the belief propagation colocalization method, as shown in Figure 1.  
In air-ground swarming, the member to be assisted (AM) first reads the octree 3D terrain in its neighborhood through the rough position of its own inertial navigation output and calculates the range of angles and distances in which it can be seen through the ray/octree intersection detection method. Then, the reference member (RM) obtains its own accurate positioning information through its own GNSS receiver and provides relative measurement information and its own reference position information to the AM through the data link. After obtaining the position and relative measurement information of the RM, the AM will solve the relative distance and angle information with the RM. Subsequently, the relative distance and angle information of the RM is compared with the  
Figure 1: Enhanced collaborative localization scheme based on air–ground visibility analysis.  
previously calculated visible range of AM. If the visibility judgment condition is satisfied, the relative positioning data of the RM are retained; otherwise, the RM localization signal is an NLOS signal and should be separated. Finally, the belief propagation co-navigation and localization solution of the to-be-assisted members is performed using the colocalization data of the RMs after performing NLOS separation, and the colocalization results after suppressing the NLOS errors are obtained.