esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Continuous-wave near-infrared spectroscopy (CW-NIRS) is a method used to non-invasively estimate skeletal muscle oxygen consumption (mVO\({}_{2}\)). Three different signals are provided by CW-NIRS devices: (1) oxygenated hemoglobin (O\({}_{2}\)Hb); (2) deoxygenated hemoglobin (HHb); and (3) tissue saturation index (TSI). Typically, the signal's slope is interpreted with respect to high or low mVO\({}_{2}\) during a muscle action. What signal (or combination of signals) is used for slope interpretation differs according to what approach is used, and there are several published in literature. It is unclear if resulting mVO\({}_{2}\) estimates can be used interchangeably. Hence, this work aimed to compare five commonly used approaches on the same set of CW-NIRS data regarding their agreement in estimated mVO\({}_{2}\). A controlled, lab-based study setting was used for this experiment. Data are based on isometric dorsiflexion contractions of 15 subjects at 30% of voluntary maximum torque, at two different ankle angles. CW-NIRS was placed on the m. tibialis anterior and blood flow was occluded. The approaches for mVO\({}_{2}\) estimation included calculations based on (1) TSI, (2) the difference between O\({}_{2}\)Hb and HHb (Hbdiff), (3) the mean of slopes from O\({}_{2}\)Hb and HHb (Hbmean), (4) the HHb signal, and (5) the O\({}_{2}\)Hb signal. Linear regression modelling was used to calculate respective slopes (\(\mu^{2}>0.99\)). Repeated measures ANOVA identified significant differences between the approaches (\(p<0.001\), \(\omega^{2}=0.258\)). Post-hoc tests revealed that only TSI vs. Hbmean and Hbdiff vs. HHb gave comparable results (\(p>0.271\)). In addition, Bland-Altman plots showed good accuracy (mean bias \(\sim\)2%) but low precision (\(\pm 20\%\)) between the comparisons. Thus, the different approaches to estimate mVO\({}_{2}\) cannot be used interchangeably. The results from different studies using different approaches should be compared with caution.  
skeletal muscle; oxidative metabolism; oxygenated hemoglobin; muscle action; tissue saturation index +
Footnote †: journal: Applied Sciences
# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
## 4 Discussion  
The trigonometric series method is a widely used method for simulating ground motion in engineering. To compare the results of this paper with those of this method, the ground-motion synthesis is carried out using the trigonometric series method. The set of seismic information required for synthesizing ground motion and the matching of duration and response spectrum are shown in Table 5. Figure 5 shows the ground-motion time  
Figure 11: Visualization example of ground motion fields for different periods in the panel: (**a**) Sa (T = 0.01 s); (**b**) Sa (T = 0.05 s); (**c**) Sa (T = 0.1 s); (**d**) Sa (T = 0.2 s); (**e**) Sa (T = 0.3 s); (**f**) Sa (T = 0.5 s); (**g**) Sa (T = 0.7 s); (**h**) Sa (T = 1 s); (**i**) Sa (T = 2 s); (**j**) Sa (T = 6 s).  
history and duration curves, and the matching error of the response spectrum synthesized by the two methods.  
The ground motions synthesized by the software In this paper can match the spectrum and duration well, compared to the traditional methods. However, the method based on PCA and multi-objective genetic algorithm has some limitations. Therefore, it is necessary to discuss the scope of application in this software here. Firstly, for areas with real records, the ground-motion database of the software can be updated to apply to the target area, while the method has some limitations for areas lacking records. For areas lacking records, ground motions from areas with similar sources, propagation paths, and site conditions can be selected as the database of the software, which is of some significance for areas lacking data. Secondly, because the synthetic ground-motion field needs to depend on the ground-motion model, areas lacking records should first establish the ground-motion model, which is out of the scope of this study. Thirdly, due to the regional characteristic ground motions of the method in this study, only a small sample was selected for testing because of the limited number of records currently available in a specific small area.  
The synthesis results of this paper's method and the trigonometric series method can be seen in the comparison; the trigonometric series method considered by matching with the spectrum can achieve the ground-motion amplitude and spectrum constraints, but cannot be bound to the duration. In contrast, the ground-motion synthesis method of this paper can well-reflect the regional ground-motion characteristics reflected by the regional ground-motion prediction equation in the synthesis results under the control of the constraint conditions and evaluation criteria.
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 2 Methods and Mathematical Models
### Mathematical Model  
The problem with optimum satellite PRN code assignment is similar to the optimum graph coloring problem. The graph coloring problem can be depicted as follows: Given a graph \(G=(V,E)\) with vertex set \(V\) and edge set \(E\), the process of coloring the vertex set \(V\) with a set \(C=\{1,2,\ldots,k\}\) containing at least \(k\) colors can be described as a mapping \(f:V\to C\), and for any two different vertices \(v_{i},v_{j}\in V\), if there is an edge \((v_{i},v_{j})\in E\), then \(f(v_{i})\
eq f(v_{j})\). The minimum number of colors, which is denoted by \(\chi(G)\), is called the chromatic number of the graph.  
We consider the satellites to be vertices that form the set \(V\). We consider both satellites \(v_{i},v_{j}\in V\) with conflict as edges and form the set \(E\). We treat the PRN code set \(C=\{1,2,\ldots,k\}\) as a color set. The satellite PRN code assignment problem can be depicted as follows: Given a graph \(G=(V,E)\) with satellite set \(V\) and conflict relationship set \(E\), the process of assigning PRN codes to satellite set \(V\) with a set \(C=\{1,2,\ldots,k\}\) containing at least \(k\) PRN codes can be described as a mapping \(f:V\to C\), and both satellites with conflict \((v_{i},v_{j})\in E\) cannot have the same PRN code \(f(v_{i})\
eq f(v_{j})\). The minimum number of PRN codes, which is denoted by \(\chi(G)\), is needed. Figure 3 descript the consistency of the two problems in terms of input, condition and output. Therefore, the models of the satellite PRN code assignment problem and graph coloring problem are consistent, and the algorithms available for graph coloring can also be applied to this problem.  
Figure 2: The search process for navigation signal acquisition.  
According to the above definition, the mathematical model of the satellite PRN code assignment problem can be further summarized. It is assumed that there are \(N_{sat}\) satellites, \(v_{1},v_{2},\ldots,v_{N_{sat}}\), where there are \(N_{PRN}\) optional PRN codes for each satellite and the set is \(\mathsf{C}_{i}=\{1,2,\ldots,N_{PRN}\}\). The mathematical model of the satellite PRN code assignment problem is as follows:  
\[\begin{array}{l}\min\!\chi(G)=\sum_{k=1}^{N_{PRN}}\delta\!\left(\sum_{i=1}^{ N_{att}}x_{ik}\right)\\ s.t.\left\{\begin{array}{l}x_{ik}\in\{0,1\},\\ \sum_{k\in\mathsf{C}}x_{ik}=1,\\ \sum_{(v_{i},v_{i})\in\mathsf{C}}x_{ik}\cdot x_{jk}=0,\end{array}\right.&1\leq i \leq N_{sat},\ \ 1\leq k\leq N_{PRN}\\ 1\leq i,j\leq N_{sat},\ \ 1\leq k\leq N_{PRN}\end{array} \tag{3}\]  
where the function \(\delta(z)\) is a 0-1 function,  
\[\delta(z)=\left\{\begin{array}{l}1,\ \ \ \ \ (z>0)\\ 0,\ \ \ \ \text{otherwise}\end{array}\right. \tag{4}\]  
and for the variable \(x_{ik}\),  
\[x_{ik}=\left\{\begin{array}{l}1,\ \ \ \ \ \ \text{The PRN code assigned to satellite $v_{i}$ is $k$}\\ 0,\ \ \ \ \ \text{The PRN code assigned to satellite $v_{i}$ is not $k$}\end{array}\right. \tag{5}\]
# Surface Defect Detection of Preform Based on Improved YOLOv5
###### Abstract
In order to solve the problem of high reflective characteristics of the surface of preforms based on fiber prepreg, as well as the need for online and high-precision recognition of various defects during the preform process, we proposes a method for identifying defects on the preformed surface based on machine vision and improved YOLOv5 network model. Based on the established machine vision platform, the surface image of the preform during the preforming process is obtained. In actual surface defect detection of preforms, samples exhibit the characteristics of extremely small samples and imbalanced samples. Then, the idea of transfer learning is used, and the Ghost Bottleneck module is introduced to lightweight design the YOLOv5 model. In order to improve the defect recognition accuracy, the coordinate attention module is introduced to achieve a good balance between recognition accuracy and recognition speed. Compared to before the improvement, the improved model has faster recognition speed and higher recognition accuracy.
## 3 Application of MOFs as Sorbents in Selected Techniques of Preparing Liquid Samples for Analysis
### Magnetic Solid-Phase Extraction (MSPE)
To extract such compounds from rice and water samples, a material called Fe\({}_{3}\)O\({}_{4}\)@TGA@TMU-6 was used [104]. The analysis was carried out using HPLC-UV. Due to the large surface area and unique porous structure of MOF, as well as \(\pi\)-\(\pi\) and hydrophobic interactions between analytes and MOF ligands, the prepared sorbent shows a high affinity for the target analytes. Under optimized conditions, the authors achieved LOD in water samples in the range of 0.5 to 1.0 ug/L for phosalon, chlorpyrifos, and profenofos. Residues of triazole pesticides pose similar threats, one of the largest groups of pesticides, which are key tools for controlling pathogens on several vegetables and fruits. Due to their high solubility in water and stability in the environment, they can enter food and drinking water [100]. It has also been proven that they can have a negative effect on organisms not targeted by humans. In this work [100], Fe\({}_{3}\)O\({}_{4}\)@APTES-GO/ZIF-8 material was characterized by FT-IR spectra, XRD patterns, nitrogen adsorption/desorption isotherms, and magnetization curves (Figure 16). The material was then used to determine selected fungicides. Under optimized conditions, in combination with the HPLC-DAD technique, an analytical method was developed suitable for detecting test compounds with LOD in the range of 0.014 to 0.109 ug/L, in the linearity range from 1 to 1000 ug/L with a relative standard deviation of 0.3 to 6.9%, and recoveries for the tested compounds in the range from 71.2 to 110.9%. Figure 15: A simple extraction procedure showing a quick combination of MOF and magnetic particles, presented by [PERSON] et al. [99]. A summary of MOF materials used in MSPE is presented in Table 4.  
Figure 16: FT-IR spectra (**a**) and XRD patterns (**b**), the nitrogen sorption/desorption isotherms (**c**), and magnetization curves (**d**) of the prepared materials, presented by [PERSON] et al. [100].
# Reliability and Validity of the Orthotic Deformation Test
## 2 Materials and Methods
### Device Compression Test  
A compression device (EM2-200/FR, Microtest SA, Madrid, Spain) was used as reference. The device had a load cell of 1 kN, which was applied via a 20 mm-diameter indenter over the middle point of the medial edge of the medial longitudinal arch. A vertical compression force was applied at a constant displacement rate of 1 mm/min. Two heavy-duty G-clamps were used to firmly fix each custom foot orthosis in a horizontal position on a rectangular metal compression plate. The movable jaw of the screw of the G-clamps was fixed on the lateral longitudinal arch of the orthosis the same way as the researcher's non-dominant hand applied resistance to the ODT. Doing so ensured that the orthosis remained in contact with the compression plate and did not rotate while being compressed [22] (Figure 4).  
The length and maximum height of the medial longitudinal arch of each orthosis were measured by a vernier caliper. Length was considered to correspond to the distance between the proximal and distal arch in a line along the medial longitudinal arch and tangent to the medial edge of the heel; it was used to calculate the length mid-point. This point, in turn, was approximately where the ODT was applied; it was indicated on the orthosis to ensure the reproducibility of the tests. The maximum height was considered to be the distance between the floor and the highest point of the medial longitudinal arch. The maximum height was used to calculate the medial longitudinal arch collapse. The mid-point of the orthotic's length and the maximum height did not always coincide in terms of position.  
The indenter was positioned over the calculated length mid-point and the maximum height served as the reference of the zero position of medial longitudinal arch deformation. Zero position was defined as the initial contact between the intender and orthosis dorsal surfaces.  
The system continuously recorded the displacement (mm) and the corresponding force (N); force was applied until a peak value of 150 N. [PERSON] et al. [32]. determined that the human thumb can exert a maximum average of 150 N. Only one test was performed on each orthosis to avoid bias related with cyclic loads that could have caused severe damage due to fatigue of the polypropylene [33].  
Figure 3: Orthotic deformation test. The dominant thumb applies a vertical force on the medial longitudinal arch of the orthoses. Observe the point of force application on the dorsal apex of the medial longitudinal arch. The non-dominant hand can apply resistance to the lateral arch to prevent rotation of the orthosis.
# Effects of Visually Induced Self-Motion on Sound Localization Accuracy
## 1 Introduction
Results of previous studies, including that by [PERSON] [18], have revealed that head movement can influence sound localization performance, and various studies have revealed a facilitation effect based on the listeners' movement. Even if the self-motion is virtually presented in a similar manner to the experiment described by [PERSON] [7], the effect seems to be identical to the results under actual self-motion conditions. When the relative position of the sound source is changed, listeners have to process the dynamic change in the sound inputted to their ears, and we believe that this might result in increased cognitive load. Nevertheless, as mentioned above, the facilitation effect based on the listeners' movement has been observed in various previous studies [10, 11, 12, 13, 14, 15, 16, 17, 18]. Why the self-motion information facilitates sound localization performance is an interesting albeit unanswered question, considering that processing of dynamic multisensory information might increase the cognitive load. To answer this, it is important to investigate the effect of self-motion in further detail. To do this, we used a very short (30 ms) stimulus and investigated the effect of self-motion. Notably, our former studies [21, 22, 23] with 30 ms long stimuli suggest that when listeners perceive that they are moving, a kind of top-down process could decay sound localization performance. Contrastingly, the stimuli used in the studies in which the facilitation effect was observed were much longer; for example, in the study by [PERSON] [7], music was used as stimuli. To better understand the mechanism of the sound localization, further studies are required to evaluate the effects of listeners' perception of self-motion on sound localization accuracy. However, only a few studies have examined the effects of visually induced self-motion on sound localization accuracy with short sound stimuli. Hence, we investigated the effects of visually induced self-motion on the ability of listeners to identify their subjective midline orientation. This study may be helpful in designing efficient dynamic binaural displays for virtual reality (VR) applications.
# Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
###### Abstract  
User Identity Linkage (UIL) has emerged as a focal point of research in the field of network analysis and plays a critical role in the governance of cyberspace; related technologies can also be extended for applications in traffic safety and traffic management. The traditional random walk-based UIL method has achieved a balance between performance and interpretability, but it still faces several challenges, such as low discriminability of nodes, instability of feature extraction, and missing features in matching scenarios. To address these challenges, this paper presents Adap-UIL, a multi-feature UIL framework based on an Adaptive Graph Walk. First, we design and implement an Adaptive Graph Walk method based on the Restarted Affinity Coefficient (RAC), which enhances both the neighborhood and higher-order features of nodes, and then we integrate cross-network features to form Adap-UIL with a more enriched node representation, facilitating user identity linkage. Experimental results on real datasets show that the Adap-UIL model outperforms the benchmark models, especially in the P@5 and P@10 metrics by 5 percentage points, and it captures key features more efficiently and effectively.  
restarted affinity coefficient; user alignment; user identity linkage +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00  
and random walk-based methods [2]. Among these, random walk-based methods achieve a balance between performance and interpretability, and have received wide attention. In the embedding representation stage of the random walk-based method, the graph is first converted into a sequence of nodes by random walk, and the sequence of nodes is regarded as a sequence similar to text, and then text embedding methods (e.g., Word2 Vec) are applied to learn node representations, representing the nodes as low-dimensional dense vectors containing semantic information for the subsequent alignment algorithm.  
However, current approaches still encounter several limitations, such as low discriminability of nodes, instability of feature extraction, and missing features in matching scenarios. This paper is devoted to addressing these limitations. The main contributions of this paper are as follows:  
Firstly, we propose a novel Adaptive Graph Walk based on the Restarted Affinity Coefficient (RAC) that constructs a weighted graph according to the RAC and then generates node sequences to capture the higher-order features more stably.  
Secondly, we propose the Adap-UIL framework, which integrates the cross-network features by combining a cross-network walk with an Adaptive Graph Walk to enrich the node features.  
At last, experiments on two real datasets are conducted to validate the performance of Adap-UIL, and the results show significant improvement over baselines. Moreover, Adap-UIL achieves higher precision more efficiently.
## Appendix A  
The following table and figures are those omitted from the main text for brevity.  
Figure 11 illustrates the changes in in-cylinder temperature distribution at different crank angles, corresponding to various DMG substitution ratios. At 0\({}^{\circ}\) CA, a small area near the center of the cylinder head began to heat up. By 5\({}^{\circ}\) CA, the outer peripheral surface of the spray ignited, resulting in a rapid increase in temperature. As the DMG concentration in the cylinder increased with the substitution ratio, a greater number of flammable mixtures formed, leading to a broader high-temperature area. Additionally, the unburned gases surrounding the ignition region were heated, which, in turn, elevated the average in-cylinder temperature as compared with a lower substitution ratio. At 15  
CA, the fuel near the side wall ignited, causing a sharp rise in temperature. With an increase in the DMG substitution ratio, the efficiency of premixing improved, resulting in a more uniform distribution of temperatures. At a substitution ratio of 20%, temperatures above 1500 K were recorded throughout the cylinder. By 25\({}^{\circ}\) CA, the high-temperature region extended to the combustion chamber recess fillet. As the cycle progressed to 35\({}^{\circ}\) CA, residual fuel continued to burn. When a higher substitution ratio was utilized, the temperature distribution became more homogeneous, and the low-temperature region decreased in size.  
The equivalence ratio distribution for different substitution ratios is shown in Figure 11. It can be seen from the figure that the area of high equivalence ratio distributes mainly in the upper part of the combustion chamber around the spray, and the area of low equivalence ratio is found in the bottom region near the center axis. The simulation results indicate that the addition of DMG makes the equivalence ratio distribution more homogenous.  
Figure 13 illustrates the variations in in-cylinder NOx distribution with respect to changes in crank angle and DMG fraction. The observations indicate that NOx is primarily generated in regions characterized by high temperatures and rich fuel conditions, which provide sufficient conditions for generating NOx. This region expands wider with increases in the DMG substitution ratio, resulting in a higher NOx emission.  
Figure 17 exhibits the in-cylinder soot distribution concerning variations in crank angle and DMG fraction. The figure demonstrates that the total soot value increases between 5\({}^{\circ}\) CA and 30\({}^{\circ}\) CA and subsequently decreases. At 5\({}^{\circ}\) CA, first, soot emerges in the core of the spray. As the piston moves downward, soot becomes concentrated in the high equivalence ratio region. Around 30\({}^{\circ}\) CA, the soot concentration reaches its peak and is primarily distributed in the upper part of the combustion chamber. Afterward, the total soot value decreases due to the oxidation reaction. At 40\({}^{\circ}\) CA, some soot is still present near the center axis. In summary, the addition of DMG promotes the oxidation of soot.
# Improved Honey Badger Algorithm and Its Application to K-Means Clustering
## 3 Improved Honey Badger Algorithm
### Computational Complexity Analysis of IHBA  
Time complexity serves as a crucial metric for assessing the efficiency of an algorithm's solution. Therefore, an analysis of IHBA with respect to its time complexity is conducted. The computational cost of the IHBA can be categorized into four components: the generation of a chaotic initial population, the evaluation of fitness values, position updates, and the computation of inverse solutions. \(N\) represents honey badger individuals, \(t_{max}\) denotes the maximum number of iterations, and \(d\) indicates the dimension. The initial phase of the algorithm encompasses the initialization of population positions, fitness evaluation, and other related processes, resulting in an overall complexity of \(O(Nd)\). The complexity of the algorithm's operations, including position updating and the calculation of the inverse solution, is \(O(t_{max}N)\). Thus, the overall complexity of IHBA is as in Equation (15).  
\[T=O(Nd+t_{max}N) \tag{15}\]