esa-sceva/satcom-chunk-collection huggingface dataset:
# Type Hierarchy Enhanced Event Detection without Triggers
## 4 Experiments and Evaluation
### Case Study  
To verify whether the word-level representation modular components work as we designed, we conduct a case study. As Figure 6 shows, we visualize the attention score \(\alpha^{j}\) of our framework on a random sentence: \"the army's 3 rd infantry division 1 st marine division, leading the assault against Baghdad\", and its event type is Attack. From the experimental results, we can see that the word \"assault\" has the highest score, and according to the ranking, we obtain the top K words as follows: _army infantry 1 st division the assault against Baghdad_. In this way, we successfully captured the word-level representation associated with the event type while filtering out irrelevant words.  
Furthermore, to verify the effect of the type hierarchy concept modular components, we also conduct a case study. As Figure 7 shows, we visualize the final scores of the sentence: \"the army's 3 rd infantry division 1 st marine division, leading the assault against Baghdad\" on word-level representation, context-level representation, superordinate concepts and subordinate concepts, where T denotes the correct classification and F denotes incorrect. Due to the small granularity of scores, the difference between heatmaps at the same level is not apparent. However, the scores between correct and incorrect event types are still obvious.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline prototype triggers & 77.8 & 70.2 & 73.8 \\
**THEED (ours)** & 78.1 & 71.6 & 74.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of the framework replacing hierarchy concept with prototype triggers.  
Figure 6: The attention scores for the hidden embeddings of words, which our model learns from a sample instance.
## 4 Valorization Technologies of Avocado-Derived Biomass through Obtaining Bioenergy and Biofuels
### Fermentable Sugars and Bioethanol  
For obtaining fermentable sugars, a pretreatment is necessary to break down the lignocellulosic matrix and to facilitate the access to the hydrolyzing enzymes. Several strategies have been developed to fractionate agri-food biomasses, which can be classified according to the function of the main driver forces: chemical treatment with diluted acids, alkalis, metal salt solutions, and organosolv treatments with organic solvents; physical/physico-chemical treatments, such as liquid hot water, steam-explosion, and microwave; and biological treatments and combined treatments [41; 67]. Thanks to the high content of hydrolysate sugars of the avocado biomasses, in particular, the peels, stones, and pomace (Table 1), they are a valuable and a profitable alternative for the production of sugars. Our previous work has shown that the peel contain mainly glucose and xylose, while the stone is highly rich in glucose (93.2% of the polymeric sugar fraction) [12]. In this line, to promote the enzymatic hydrolysis of the polymeric sugars, an appropriate combination of different enzyme activities is required to complete the hydrolysis [67]. Once these sugars are released, they can serve to obtain biofuels like bioethanol and other valuable commodity chemicals, such as xylitol and furfural (from xylose), hydroxymethylfurfural (from glucose), organic acids, etc. [41; 68]. For example, [PERSON] et al. (2017) proposed the use of dilute acid hydrolysis, using sulfuric acid (2% _v/v_) at 121 \({}^{\circ}\)C for 90 min, to recover a liquid fraction rich in xylose from avocado waste and then to produce xylitol using _Candida guillermondii_ at 30 \({}^{\circ}\)C. Alternatively, the residual solid fraction, rich in cellulose and lignin, could be enzymatically hydrolyzed to recover glucose [31]. New trends include the use of microwaves as the heating source, as commented on before, and hydrothermal treatments to avoid the use of chemicals [69; 70]. An example of the application of microwaves to avocado waste is the study by [PERSON] et al. [52]. Alternatively, in another study, microwave heating was applied to dry avocado seed (without the outer layers) and then an hydrothermal treatment was performed at 60 \({}^{\circ}\)C for 60 min to recover the starch sugars (14.9%), as a first step to produce bioethanol [28].
## 1 Introduction
The Japan Road Association (JRA) developed a widely used equation for predicting kinematic force [14]. The equation assumes a triangular-shaped distribution of soil pressure that depends on soil density, pile length, and diameter. The JRA code recommends that designers check for bending failure due to kinematic force and inertial force separately. [PERSON] et al. [15] conducted further research on kinematic force by undertaking six centrifuge model tests on single-pile foundations and suggested a soil pressure of 10.3 kPa on piles. [PERSON] et al. [5] suggested that the soil pressure on piles was equivalent to the gross overburden pressure based on shaking table test results. [PERSON] and [PERSON] [16] measured a homogeneous soil pressure of 16 kPa on piles in a liquefiable stratum. [PERSON] et al. [17] proposed a uniform pressure of 19.5 kPa based on data from a single-pile shaking table test. Although existing lateral spreading soil pressure models have been proposed based on test results, significant differences have been observed among the soil pressures proposed by the studies described above. Therefore, selecting the correct kinematic force for pile design practice is challenging [18; 19; 20]. In addition, the suggested soil pressure models were obtained primarily from the results of single-pile tests. According to the FEA results of PSW pile groups on a gentle soil slope, the soil pressure decreases with increasing distance from the top of the slope [6]. Therefore, existing soil pressure models based on single-piletesting do not apply to pile group scenarios--a method of predicting the kinematic forces acting against pile groups on a soil slope needs to be developed. In light of the lack of research on the effects of kinematic force and the damping coefficient on the seismic performance of a wide PSW on a gentle slope, this study aimed to develop a method to predict these variables. PSW seismic design using FA often neglects the effect of kinematic force on a gentle soil slope; generally, only inertial force has been considered. Furthermore, the change in the damping coefficient due to a change in PSW width has often been neglected in design practice, which could lead to overdesign. The damping coefficient that should be used to calculate SA is assumed to vary depending on the PSW width. For this reason, we propose a method for setting the optimum damping coefficient for calculating SA according to the width of the PSW by creating cross sections of the PSW with various widths and by performing FEA. Furthermore, we propose a method for evaluating the kinematic force to be considered in FA using the FEA results.
## 2 Theoretical Background
### Double Coordinate Free-Interface Mode Synthesis Method
\Bigg{\}} \tag{8}\]  
\[\Big{(}\Big{[}\phi_{js}{}^{(z)}\Big{]}\big{[}\lambda_{s}{}^{(z)}\big{]}^{-1} \Big{[}\phi_{js}{}^{(z)}\Big{]}^{T}\Big{)}\Big{(}\Big{\{}p_{s}{}^{(z)}\Big{\}}- \Big{\{}f_{j}{}^{(z)}\Big{\}}\Big{)}=\{O\}. \tag{9}\]  
Therefore,  
\[\Big{\{}p_{s}{}^{(z)}\Big{\}}=\Big{\{}f_{j}{}^{(z)}\Big{\}}. \tag{10}\]  
The interface compatible conditions are obtained as follows. \[\left\{ \begin{array}{c}\Big{\{}u_{j}{}^{(A)}\Big{\}}=\Big{\{}u_{j}{}^{(B)} \Big{\}}\\ \Big{\{}p_{s}{}^{(A)}\Big{\}}=-\Big{\{}p_{s}{}^{(B)}\Big{\}}\end{array} \right. \tag{11}\]  
The constraint equation of the modal coordinates is  
\[[C]\{\overline{p}\}=\{0\}. \tag{12}\]  
Additionally,  
\[C=\left[ \begin{array}{ccc}\phi_{jl}{}^{(A)}&\psi_{js}{}^{(A)}&-\phi_{jl}{}^ {(B)}&-\psi_{js}{}^{(B)}\\ O&I&O&I\end{array} \right]=\left[ \begin{array}{cc}C_{ss}&C_{sl}\end{array} \right] \tag{13}\]  
where \([C_{ss}]\) and \([C_{sl}]\) are constraint sub-matrices corresponding to the dependent and independent modal coordinates, respectively. The second transformation is carried out by eliminating the dependent modal coordinates. \[\{\overline{p}\}=\left\{ \begin{array}{c}-[C_{ss}]^{-1}[C_{sl}]\\ I\end{array} \right\}\{p_{l}\}=[S]\{p_{l}\} \tag{14}\]  
where \([S]\) is the transformation matrix of independent coordinates. The presumed generalized coordinates are \(\{q\}=\{p_{l}
# Scalability Analyses of Premium Connections Characterized by a Surface Fractal Function
## 2 Analysis of Fractal Surface Contact Behavior
### The W-M Fractal Function  
The W-M fractal function, which was defined by [18], can be used to characterize the profile curve of a 3D fractal surface:  
\[z(x,y)=L\bigg{(}\frac{G}{L}\bigg{)}^{(D-2)}\bigg{(}\frac{In\gamma}{M}\bigg{)}^{ \frac{1}{2}}\sum_{m=1}^{M}\sum_{n=n_{1}}^{\max}\gamma^{(D-3)}\Bigg{\{}\cos\phi _{m,n}-\cos\Bigg{[}\frac{2\pi\gamma^{\prime}(x^{2}+y^{2})^{\frac{1}{2}}}{L} \cos\Big{(}\arctan\frac{y}{x}-\frac{\pi m}{M}\Big{)}+\phi_{m,n}\Bigg{]}\Bigg{\}} \tag{1}\]  
where \(z(x,y)\) is the height of the rough surface's profile, \(D\) is the fractal dimension of a fractal surface (2D fractal function: \(1<D<2\), 3D fractal function: \(2<D<3\)), and \(\gamma\) is a parameter related to the spectral density of the surface profiles (\(\gamma>1\)). \(G\) is the scale coefficient or the fractal roughness (in m), \(L\) is the sampling length (in m), \(M\) is the number of overlaps of the surface folds, \(L_{s}\) the is cutoff length (in m), and \(m\) and \(n\) are the random phase (the value range is \([0,2\pi]\)). The lowest frequency order is 0, and the highest frequency order is \(n_{\max}\), \(n_{\max}=\text{int}[\log(L/L_{s})/Log\gamma]\).  
According to Equation (1), 3D fractal rough surfaces with fractal dimensions \(D\) of 2.1, 2.5 and 2.9 are shown in Figure 3, and the fractal parameters are shown in Table 1. In Figure 3, the x and y coordinate axes represent the sampling length L, while the z coordinate axis represents the height of the rough surface's profile \(Z\) (\(x,y\)) (i.e., the height of asperities). As shown in Figure 3, the profile of the fractal rough surface is disordered, and the distribution of the asperities is random and continuous. As the fractal dimension \(D\) increases, the height of asperities decreases exponentially, the details of the fractal surface profile become more complex, and the surface becomes smoother [19]. As shown in Figure 3a-c, every 0.4 increase in the fractal dimension \(D\) will reduce the order of magnitude of the z coordinate axis (the height of asperities) by \(10^{3}\).
## 4 Conclusions  
Dynamic stall is very common on HAWTs, resulting from many aerodynamic sources. Dynamic stall plays an important role in unsteady air loads. The accurate prediction of dynamic stall is extremely important for structural analysis. This study provides a comparative analysis of dynamic stall under pitch oscillation (PO) and oscillating freestream (OF) on wind turbine airfoil and blade, using the quasi-steady thin-airfoil theory, CFD method, and Beddoes-Leishman dynamic stall engineering model.  
From the quasi-steady thin-airfoil theory, PO and OF have an opposite effect on the effective airfoil camber and the associated lift coefficient. The CFD results show that the difference between PO and OF mainly exists in the linear part of the aerodynamic loads. The deviation between these two motions is greatly influenced by the reduced frequency and the AOA amplitude. For the yawed response predictions, the Beddoes-Leishman dynamic stall model coupled with a rotational stall delay model also show a different effect between PO and OF. In addition, the effect of the rotational augmentation on the unsteady air loads is assessed. At a low yaw angle, the rotational augmentation apparently elevated the lift and drag coefficients along the whole cycle, as under steady conditions. At a high yaw angle, however, the rotational augmentation apparently flattens the hysteresis loop and its effect during which the AOA dynamically decreasing is more obvious than when the AOA is dynamically increasing.  
It may be concluded from this work that dynamic stall behaviors under PO and OF are different at certain conditions and should separately be modeled in engineering.  
Figure 18: Hysteresis loops of the lift, drag, and pitching moment coefficients at 30\({}^{\circ}\) yaw angle. The wind speed is 10 m/s, and the spanwise location is 30%. **(a)** The lift coefficient; **(b)** The drag coefficient; **(c)** The pitching moment coefficient.  
**Author Contributions:** [PERSON] and [PERSON] conceived of and designed the research; [PERSON] conducted the data collection and wrote the paper; [PERSON] revised the paper.  
**Funding:** This work is supported by the National Basic Research Program of China (973 Program) under grant No. 2014 CB046200, the National Natural Science Foundation of China under grant No. 51506088, CAS Key Laboratory of Wind Energy Utilization under grant No. KLWEU-2016-0102, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
However, the auxiliary information for the motor-state is simply designed as a one-hot vector, so the performance gain turns out to be limited. In this paper, we propose to employ the latent features that improve the speech recognition performance by utilizing the internal knowledge about whether the motor is running or not. The first DNN is carefully designed to create the motor-state dependent latent features, and the input is determined by concatenating the motor-state data in addition to the acoustic features contaminated by the ego-noise. After the preliminary training is accomplished at the first DNN to result in the latent features, these are fed into the second DNN designed for acoustic modeling under the ego-noise environments. Subsequently, to fully represent the complex relationship between the audio signal and senones, the second DNN is trained with the inputs containing both the latent features and acoustic features. In order to verify the performance of our approach against the existing methods, experiments are extensively conducted on the LibriSpeech corpus. The experimental results showed better performance in terms of the WER reduction than the baseline models including the mapping-based speech enhancement, the method using one-hot encoded input [14], and the method using only acoustic features. The rest of the paper is organized as follows: In Section 2, we describe the related works. Details about the model architecture are explained in Section 3. The experimental results are shown in Section 4. We conclude with a discussion in Section 5.
## 4 TRSA: Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation Model
### Trust-Aware Evidence Reasoning
#### 4.1.2 Credible Reasoning of Evidence Based on a Random Walk  
Based on the information dispersion network, random walkers can walk randomly in the network in two ways: jumping randomly according to the weights of network nodes (i.e., considering the authority of users who publish posts) or walking randomly along to the edges in the network (i.e., considering the information interactions in the dispersion process).  
The probability \(p_{ij}\) of random walkers jumping from node \(i\) to node \(j\) according to the node weight is defined as follows:  
\[p_{ij}=\frac{\exp(u_{j})}{\sum_{i}\exp(u_{i})}, \tag{2}\]  
where \(\sum_{n}\exp(u_{n})\) represents the summation of weights of all nodes. According to Equation (2), the probability of random jump is only correlated with the goal node weight and its value is in \((0,1)\). If \(\mathbf{P}\) denotes a jump matrix, the elements in each column are the same and the sum of elements in each row is 1.  
Walking according to edge weights means that random walkers select a node to reach an adjacent node directly with a certain probability along the edges of the network. If the probabilities of moving along edges are expressed by an edge transfer matrix \(\mathbf{S}\), then the  
Figure 4: Information dispersion network (node color represents node credibility and edge color represents the degree of recognition of content between nodes).  
probability \(s_{ij}\) of a random walker moving from node \(i\) to node \(j\) along an edge is expressed as follows:  
\[s_{ij}=\left\{\begin{array}{l}0,\ e_{ij}\
otin E\\ w_{ij},\ e_{ij}\in E\end{array}\right. \tag{3}\]  
We let \(\alpha\) represent the probability of random walkers walking along an edge, which is called the damping coefficient, and \(1-\alpha\) is the probability of random walkers jumping according to node weight. The walking process of random walkers in the information dispersion network is described as follows:  
\[\mathbf{r}^{(t+1)}=\mathbf{r}^{(t)}(\alpha\mathbf{S}+(1-\alpha)\mathbf{P}) \tag{4}\]  
Here, \(\mathbf{r}^{(t)}\) and \(\mathbf{r}^{(t+1)}\in R^{\ \pi}\) are n-dimensional vectors denoting the visiting probability distribution of random walkers to all nodes in the information dispersion network before and after the update, respectively. Their elements are in \([0,1]\). Initially, \(\mathbf{r}^{(0)}=(1,0,\ \dots,\ 0)\). [PERSON] et al. [42] pointed out that this type of random walk algorithm converges to a unique vector when the transition matrix satisfies the irreducible and periodic properties. We prove that the transfer matrix constructed in this paper satisfies this property in Appendix B. Therefore, Equation (4) eventually converges to a stable vector after multiple iterations, which can be considered as the credibility of comments.
# Leveling Control of Hillside Tractor Body Based on Fuzzy Sliding Mode Variable Structure
## 4 Simulation Analysis
### Simulink Controller Model Construction  
A MATLAB/Simulink control simulation model is constructed to reflect the leveling control strategy, as shown in Figure 8.  
The model contains four controllers that provide different control signals to the respective hydraulic proportional valves following model computation. The controllers incorporate control strategies and a sealed fuzzy sliding mode variable structure control algorithm module (Figure 9a).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline
1 & Front axle hydraulic cylinder stroke & 286 mm \\
2 & Rear axle hydraulic cylinder stroke & 281 mm \\
3 & Pump & 14.6 mL/r 2000 r/min \\
4 & The minimum used mass of the vehicle body & 1260 kg \\
5 & Mass distribution ratio (front: rear) & 4:6 \\
6 & Piston diameter & 63 mm \\
7 & Oil density & 880 kg/m\({}^{3}\) (40\({}^{\circ}\)) \\
8 & Electro-hydraulic proportional directional valve & 25 MPa 43 L/min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of hydraulic component parameters.  
Figure 8: Control system simulation model of hillside tractor.  
The specific structure of the fuzzy sliding mode variable structure control encapsulation module, which is shown in Figure 9b, comprises two parts: a sliding mode variable structure controller and a fuzzy regulator.
## 5 Conclusions  
To realize low intermediate capacitor voltage, high PF, and high efficiency simultaneously, an improved DCM-CRM Cuk PFC converter utilizing an input inductor that varies in response to transient rectified input voltage has been proposed in this paper. The operational principles of the proposed converter, along with its control strategies, have been thoroughly analyzed, with a focus on the intermediate capacitor voltage, input current, input inductor current, and switching frequency. To validate the proposed method, a 108 W experimental prototype was built and tested. The test data demonstrate that the proposed converter, operating in DCM-CRM mode in a wide input voltage range, significantly enhances the PF, reduces the THD, decreases the intermediate capacitor voltage, and improves efficiency.  
In the future, to improve the power density of the proposed converter, the authors will study how to combine the variable inductor control strategy and coupled inductor technology, which would replace both the input and output inductors of the Cuk converter with a coupled inductor at a magnetic core. Due to the verification of the feasibility of the variable inductor in this paper, inductance has become a circuit parameter that can be adjusted simultaneously with the duty cycle. The authors will also study variable inductor control strategies for Boost PFC and Flyback PFC converters to improve their performance.  
Conceptualization, T.Y. and R.L.; methodology, T.Y., R.L. and H.W.; formal analysis, T.Y., R.L., H.W. and G.Z.; validation, T.Y., R.L. and H.W.; investigation, T.Y., R.L., H.W. and G.Z.; writing--original draft preparation, T.Y., R.L. and H.W.; writing--review and editing, T.Y., R.L., H.W. and G.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Chengdu Science and Technology Bureau under Grant No. 2024-YF08-00082-GX and the National Natural Science Foundation of China under Grant No. 62271417.  
Not applicable.  
Data Availability Statement:Data from this study are contained within the article. For further inquiries, please contact the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(V_{\text{C1}}\) (V)} & \multicolumn{2}{c}{_PF_} & \multicolumn{2}{c}{**THD (\%)**} & \multicolumn{2}{c}{**Efficiency (\%)**} \\ \cline{2-9}  & **110 V** & **220 V** & **110 V** & **220 V** & **110 V** & **220 V** & **110 V** & **220 V** \\ \hline Traditional DCM-DCM Cuk PFC converter & 280 & 520 & 0.982 & 0.975 & 15.54 & 15.53 & 82.9 & 77.4 \\ \hline Traditional DCM-CRM Cuk PFC converter & 270 & 500 & 0.986 & 0.981 & 15.69 & 17.41 & 84.3 & 82.3 \\ \hline Proposed DCM-CRM Cuk PFC converter with variable inductor control & 230 & 400 & 0.998 & 0.992 & 3.91 & 8.9 & 88.9 & 87.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison.
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
## 2 Clock Prediction Methods  
In this section, we analyze the short-term stability of IGS final clock product, discuss the shortcomings of the traditional model using FFT and propose improved algorithms for short-term clock prediction.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 4 Malware Detection
### Dynamic Analysis  
Dynamic analysis, sometimes referred to as behavioral analysis, involves the running of malware to observe its behaviour, gain an understanding of its operations, and generate technological indicators for signature recognition. Dynamic analysis yields technological hints such as domain names, IP addresses, file system locations, registry entries, and other documents present in the computer or network [20; 44; 45].