esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Microstructure of the Filling Materials
### Filling Material Type AlMgSc  
Based on the two-phase Al-Sc equilibrium diagram defining the solubility limit of scandium in aluminium [55] and for the ternary AlMgSc alloy [56], three types of castings were made with scandium contents in the sub-eutectic, eutectic and super-eutectic ranges.  
An analysis of the chemical composition of the resulting alloys is shown in Figure 9. The scandium contents were, respectively, 0.30 wt.% (Figure 9a), 0.61 wt.%. (Figure 9b) and 0.84 wt.% (Figure 9c). As in the additional materials of the AlMgZr type, a similar magnesium content of 5 wt.% was retained here, too.  
Figure 9: X-ray microanalysis of the chemical composition of the filling materials cast: (**a**) AlMg5 Sc0.30; (b) AlMg5 Sc0.61; (c) AlMg5 Sc0.84.  
The microstructure of AlMgSc-type alloys is shown in Figure 10. In the cast AlMg5 Sc0.3 type alloy with scandium contents in the sub-eutectic range (Figure 10a), the grains were irregular with a tendency towards growth with few Al\({}_{3}\)Sc separations. Large separations of intermetallic phases are also evident.  
In the case of AlMg5 Sc0.61 alloys, where the scandium content was in the peri-eutectic range, the microstructure showed an equiaxial grain structure with phase separations at the grain boundaries. An increased amount of Al\({}_{3}\)Sc precipitates was also observed (Figure 11), which became shims for further crystallisation of the casting.  
Figure 11: Al\({}_{3}\)Sc precipitates in an AlMgSc0.63 alloy.  
Figure 10: Microstructure of the cast Al-Mg-Sc additive materials: (**a**) AlMg5 Sc0.3; (**b**) AlMg5 Sc0.61; (**c**) AlMg5 Sc0.84.  
With a further increase in the proportion of scandium in the AlMg5 Sc0.84-type casting, a tendency towards an increase in the amount of precipitates of the super-eutectic Al\({}_{3}\)Sc phase and a further, albeit minor, fragmentation of the equiaxial grains became apparent. At the same time, in the AlMg5 SC0.61 and AlMg5 Sc0.84 castings, a certain amount of small Al\({}_{3}\)Sc precipitates at the grain boundaries, which were displaced during the crystallisation process, as well as iron precipitates, was observed (Figure 12).  
Structural changes in the fabricated AlMgSc additives of 0.61 wt.% and 0.84 wt.%, where the amount of fine Al3 Sc precipitates after grain boundaries probably depended on the cooling rate of the casting mould. The amount of fine precipitates across the grain boundaries of the equiaxial grains increased with keeping the casting in the mould in the elevated temperature range and extending the annealing period. The annealing period itself did not affect the recrystallisation of the castings as might be expected, since scandium at concentrations above 0.6 wt.% added to AlMg alloys acts as a strong modifier and significantly increases the recrystallisation temperature due to the high dispersity of secondary Al3 Sc precipitates distributed throughout the casting volume [57].
# Representing Aspectual Meaning in Sentence: Computational Modeling Based on Chinese
## 1 Introduction
### Research Questions  
This paper focuses on the lexical aspect in Chinese, which is neglected in previous studies, and we intend to cover the following research questions in later sections.  
Firstly, how to classify situation types in a more reliable way, compared with the intuition method. The priority before studying the interaction between viewpoint aspects and situation types is to confirm their classification. While viewpoint aspects, which are grammatical markers, are limited in number and thus have no need to be classified, situation types are hard to be determined objectively. Previous studies determined the situation type of a structure simply based on intuition, which is subjective and inconsistent. In this paper, the co-occurrence ability with aspectual markers for a verb is annotated by ourselves and then cross-validated in other language resources. Instead of classifying verbs into different situation types by intuition from top to bottom, hierarchical clustering is applied on the co-occurrence matrix to construct the classification system from bottom to top by continuously merging verbs with similar distribution into one type.  
Secondly, how can we validate the classification system in a corpus? Although cross-validation and hierarchical clustering are applied to guarantee the reliability of the classification of the situation type, statistical validation in the corpus is still needed. Multi-nominal logistic regression is used to test the dataset of verbs' situation types based on verbs' co-occurrence frequencies with various aspectual markers extracted from SINICA corpus [PERSON] et al. (2018).  
Thirdly, is it possible to predict the situation type of a verb in a sentence according to our validated model and annotated dataset of verbs' situation types based on word embedding vectors? Previous computational studies are not able to conduct experiments based on word embedding vectors because of their rejection of verbs' situation type. In this paper, word embedding vectors are trained on a combined corpus of SINICA and Chinese Gigaword [20], which are used for the prediction of verbs' situation type in our dataset.
## 3 Results and Discussion
### Study on the Support Effect of Bolts in Soft Rock Tunnels
#### 3.4.2 Comparative Analysis of the Mechanical Characteristics of Bolt  
After the stabilization of the tunnel, the axial force distributions of the conventional and yielding bolts were recorded, as shown in Figure 22. The thickness of the black shadow in the figure represents the magnitude of the axial force. The thicker the black shadow is, the greater the axial force at this position of the bolt. Compared to other locations in the tunnel, the deformation of the surrounding rock mass was greater at the arch crown, and the stress characteristics of the bolts at different positions at the arch crown are depicted in  
Figure 21: The average displacement of the monitoring points on the surrounding rock surface of a tunnel under three support scenarios.  
Figure 20: Displacement of the monitoring points on surrounding rock surface of the tunnel.  
shorter duration, with the central position of the maximum load-bearing capacity quickly breaking under tension, resulting in an hourglass-shaped axial force distribution. In contrast, as shown in Figure 24, after reaching yield strength, the yielding bolts, due to the good extension effect of the yielding component, were able to sustain yield strength for a longer period, allowing for a greater strain, with the axial force exhibiting a spindle-shaped distribution.  
Quantitatively comparing the speed of reaching the yield strength and the strain maintained at yield validates the yielding effect. As shown in Figures 18 and 19, in the initial stages of support, yielding bolts effectively release the deformation energy of the surrounding rock mass and improve the stress distribution in the support structure, fully embodying the support concept of the New Austrian Tunneling Method [39; 40]. When the surrounding rock mass undergoes significant deformation, as shown in Figure 22, conventional bolts are prone to failure by breaking under tension, while yielding bolts accommodate larger deformations, continuing to reinforce the rock mass.
## 5 Performance Evaluation
### Performance Analysis
#### 5.3.3 Impact of Transmit Power  
With the simulation default settings (as shown in Table 2), we vary the transmit (Tx) power of a transmitting vehicle to find the impact of transmit power on V2X network performance as shown in Figure 6. With an increasing transmit power, the inter-packet reception time of a Tx-Rx pair vehicle decreases; hence, the overall PIR decreases. On the other hand, with an increasing Tx power, the signal strength increases; hence, more packets are received correctly by a receiving vehicle. As a result, with an increasing Tx power, PRR increases, and so does the throughput. Like the previous set of results, among the different numerologies, \(\mu=2\) has the best performance, \(\mu=0\) ranks second, and \(\mu=1\) is last.  
Figure 5: Impact of communication range.  
Figure 6: _Cont._
# Application of Areal Topography Parameters in Surface Characterization
## 3 Assessment of the Quality of Measurement Results Obtained via an Atomic Force Microscope
\[En=\frac{\left|\overline{h}_{i}-\overline{h}_{\text{ref}}\right|}{2\cdot\sqrt{ u_{i}^{2}+u_{\text{ref}}^{2}}}\leq 1 \tag{2}\]  
where \(\overline{h}_{i}\)--step height obtained by an individual laboratory, \(\overline{h}_{ref}\)--step height according to NIST, \(u_{\text{i}}\)--the estimated standard measurement uncertainty of an individual laboratory, and \(u_{\text{ref}}\)--standard measurement uncertainty according to NIST. The calculated \(En\) factor is 0.4, which is significantly below the acceptance threshold of 1. This indicates good agreement between the measurement result and the reference value, thereby confirming both the accuracy of the measurement and the consistency of the estimated measurement uncertainty. In order to ensure the reproducibility of measurement results, an interlaboratory comparison was conducted. An interlaboratory comparison \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Certificate of Calibration** & **Lab A** \\ \hline \(\overline{h}/\)nm & 97.6 & 98.9 \\ \(u/\)nm & 0.7 & 1.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of step height measurements with certificate value [57]. Figure 6: Probability density function of AFM reference standard step height [57]. included four laboratories and evaluated step height h, as well as areal parameters \(Sa\) and \(Sz\). The results are shown in Table 3.  
Interlaboratory comparisons showed good agreement in step height measurements (_h_) and the \(Sz\) parameter, while the \(Sa\) values differed significantly. While good agreement was observed for \(h\) and \(Sz\), significant discrepancies were noted in \(Sa\), especially in Lab B and Lab C, due to uncalibrated lateral components. The obtained results not only demonstrate the metrological properties of the selected parameters in AFM surface characterization, but also define a specific research direction focused on ensuring measurement reliability. To explore this further, the next section presents a simulation-based analysis of the influence of surface profile geometry on selected areal parameters using a Monte Carlo approach.
## 1 Introduction
### Motivations and Contributions  
This paper investigates and proposes a new high-performance tracking control method for flexible space manipulator joints with a simple model structure, moderate calculation requirements, and practical applications. This method maintains the damping and stiffness of the control system at the desired state, despite simultaneous disturbances from external load inertia and internal transmission stiffness in the joint. To achieve this, the paper designs a Luenberger state observer to estimate the internal flexible deformation of the joint reducer and observe real-time transmission stiffness and load inertia. By simplifying the model, the paper analyzes the influence of parameter changes on the control characteristics, improving on traditional three-loop feedback control algorithms that are sensitive to changes in model parameters. The paper introduces speed dampers and feed-forward gain regulators to keep the damping and stiffness of the flexible joint control system at the desired state, Henceforth, this strategy is referred to as Constant Damping and High Stiffness (CDHS) algorithm. Thereby improving the joint control system's robustness and the ability of the flexible space manipulator to transfer large-inertia scientific payloads in orbit. The paper's contributions to the control of flexible space manipulators include:  
* A comprehensive state-space dynamic modeling method for flexible joints is proposed, and the perturbation parameters of the flexible joint model are comprehensively analyzed.
* A Luenberger state observer [27] based on the joint dynamics model is constructed, which enables the accurate estimation of the internal flexible deformation of a joint that cannot be directly measured.
* The dynamic identification of joint transmission stiffness and external inertia is realized according to the estimated value of joint flexible deformation and joint physical motion characteristics.
* A control algorithm for flexible joints with constant damping and high stiffness is proposed, based on a dual-adjustment mechanism of speed damping and feed-forward gain. The algorithm is proposed and its effectiveness is verified by simulation experiments and physical experiments.  
The remainder of this paper is organized as follows: In Section 2, we present the problem formulation and the comprehensive joint dynamics model. Section 3 describes the design of the CDHS controller with a Luenberger observer. In Section 4, we compare and discuss the simulation results and physical experiment results. Finally, Section 5 contains the conclusions of this study.
# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
## 3 Research Methodology and Used Tools
### Used Tools  
For the purpose of the present study, the following tools are used:  
* Network packet analyzer: Wireshark [31] was used due to its integration with GNS3 and the functionality to analyze RTP flows.
* Network analyzer: Colasoft Capsa 11 free was used [32]. It monitors the network interface of the Asterisk FreePBX.
* hping3: This is a network tool that generates/sends modified ICMP/UDP/TCP packets. It is used to flood the attacked device--it implements UDP/TCP DoS attacks [33].
* Nmap: It is a tool for discovering network devices and for studying the network security level of a device [34].
# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 7 Experimental Result
### Comparison between Machine-Learning Methods and CNN  
For an additional performance comparison, we compared the existing machine-learning method with the proposed method. Decision trees, SVM, and MLP were compared to the late-merge dual-stream CNN. As decision trees, SVM, and MLP require their unique feature extraction methods and parameters for learning and testing, the feature extraction methods and learning details of previous machine learning studies were conducted with reference to the method used in [16].  
Table 5 shows the experimental results with the previous network. Compared to the existing machine-learning method, the late-merge dual-stream CNN method showed a higher F1-score of at least 6.1%, and up to 44.3%. In terms of precision, the proposed method showed a slightly higher performance at 3.6% than the SVM with the highest precision in the non-CNN based method. As for recall, we can see that the proposed method shows a 7.6% higher performance than SVM.
# Numerical Coupling between a FEM Code and the FVM Code OpenFOAM Using the MED Library
## 2 The Numerical Platform Environment
### FVM Code: OpenFOAM  
OpenFOAM is a well-known, open-source, object-oriented C++ library, developed primarily for computational fluid dynamics simulations, and it is maintained separately by ESI (Engineering System International) Group and the OpenFOAM Foundation [4]. Its versatility, scalability, and extensive set of solvers and libraries make it one of the most widely used codes in industry and academia, empowering researchers and engineers to simulate a wide range of phenomena. OpenFOAM provides a comprehensive and powerful modeling platform for complex fluid dynamics scenarios, such as multiphase flow, aerodynamics, turbulence, and heat transfer phenomena. Its modular architecture offers adaptability, which allows expert users to integrate new functions and models specific to innovative research fields. This library ensures continuous development and improvement, keeping the code relevant in many scientific and engineering research fields. The OpenFOAM community provides additional resources, tutorials, and user-contributed improvements.  
The OpenFOAM library is based on the finite volume model (FVM). The FVM technique discretizes the computational domain into elements, usually referred to as cells, on which the PDEs are solved. In addition, the software provides utilities for processing static and dynamic meshes, for pre-/postprocessing, and for multiple processor architectures.
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
## 1 Introduction
Our proposed algorithms consider the preferences of different users for transmission quality under the condition of satisfying the overall service quality of the network. First, we use the multiple objective particle swarm optimization (MOPSO) algorithm [10] to optimize link weight for path cost and load balance. Then, the first \(k\) shortest paths are selected from the optimized weighted matrix between the source node and the destination node. Finally, according to the preferences of users, the model takes application service as a guide [11], and the path with the largest comprehensive evaluation value is selected from the first \(k\) shortest paths. In order to obtain higher accuracy, we also propose an improved MOPSO algorithm for the optimization of link weight in the first part of the algorithm. Our proposed algorithms have better network performance than other traditional routing algorithms such as the shortest path first (SPF) [12], shortest widest path (SWP) [13], widest shortest path (WSP) [14], minimum interference routing algorithm (MIRA) [15], and the algorithm in [16], which we refer to as the Lee algorithm in our paper. Simulations show that our algorithms not only can reduce path consumption, better balance the network load, and decrease the maximum link utilization rate but also obtain a larger comprehensive evaluation value of the path compared with the above algorithms. The structure of the paper is as follows: in Section 2, we discuss related work. In Section 3, we introduce our proposed algorithms. In Section 4, the performance evaluation is demonstrated. In Section 5, we complete this paper with a conclusion.
## 4 Simulation Results and Discussion
### Electrical Insulation Simulation  
The electrical insulation performance is evaluated by the electric potential and field distribution near the CDPS mechanism in Figures 8 and 9. The supply voltage is set to 35 kV, and the surface of the insulating plate is assumed to be dry and clean.  
Figure 8 shows that the insulating plate near the upper platform sustains most of the potential and has a higher probability to discharge than other locations of the mechanism.  
The electric field distributions with different thicknesses of the insulating plate are shown in Figure 9.  
Since the integration of the electric field strength along the field line equals the same supply voltage, the maximum electric field strength is the critical factor to affect the air breakdown and discharge. The maximum values of three field distributions near the upper platform are 2.76 kV/mm, 2.13 kV/mm, and 1.67 kV/mm. The maximum electric field with a thickness of 15 mm is less than the dielectric strength of air. Since the dielectric strength of air is 2.1 kV/mm (RMS), the air corona  
Figure 8: Potential distribution along the field line.  
Figure 7: The cable forces when \(\theta_{r}\) increases from 0 to \(\pi\)/4.  
discharge and breakdown occur with 5 and 10 mm insulating plates close to the upper platform where the electric field strength exceeds the dielectric strength of air. The discharge around the live-line operation robot can be observed in the withstand voltage test [31]. Therefore, the thickness of the insulating plates is selected as 15 mm to achieve reliable insulation performance.
## 2 Materials and Methods
### Classifier Comparison
#### 2.4.2 Spectral Angle Mapping Classifier  
The spectral angle mapping (SAM) algorithm computes the spectral angle between the spectral signatures of the image pixels and the training spectral signatures. The spectral angle \(\theta\) is given by  
\[\theta(x,y)=\arccos\left(\frac{\sum_{i=1}^{n}x_{i}y_{i}}{\sqrt{\sum_{i=1}^{n} x_{i}^{2}}\sqrt{\sum_{i=1}^{n}y_{i}^{2}}}\right) \tag{6}\]  
Thus, a pixel resides in the macro-class or class that has the lowest spectral angle, as provided by  
\[xeC_{k}\Longleftrightarrow\theta(x,y_{k})<\theta(x,y_{j}) \tag{7}\]  
where \(k\
eq j\).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**LULC Type** & **Description** \\ \hline Sea & Area of salt water that surrounds landmasses \\ Water courses & Natural or artificial water courses serving as water drainage channels \\ Water bodies & Natural or artificial water bodies with presence of standing water surface \\  & during most of the year \\  & **Built-up** \\ Industry & Any factory processing of raw materials and manufacture of goods \\ Residence & Land use in which housing predominates \\ Commercial & Areas for retail sales, services, offices, etc. \\ Roads & Roads of any artificial paved surface material (asphalt or concrete) \\  & **Vegetation** \\ Mixed forest & Vegetation formation composed principally of trees, including shrub and \\  & bush understorey \\ Shrub & A shrub or clump of shrubs with stems of moderate length \\ Wetland & Areas of low-lying, uncultivated ground where water collects \\  & **Bare soil** \\ Dune & A mound of sand or other loose sediment formed by the wind \\ Bare surfaces & Soil with low vegetation density \\ Sparse vegetation & Soil with a mix of low and high vegetation densities \\ Grassland & Soil with high vegetation density, such as grasslands \\ \hline \hline \end{tabular}
\end{table}
Table 3: Land use and land cover (LULC) classes and descriptions.