esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Resolving Uncertainty in DIKWP Model
## 3 Problem Definition
### Incompleteness of Content  
Based on the content in Table 1, we employ the DIKWP model and semantic existence calculation to analyze the uncertainty issues arising from the incompleteness of the case study's content while ensuring compliance with the law and the company's profitability purpose. Specifically, we identify the following uncertainties resulting from the content's incompleteness:  
* **Lack of descriptive details in the legislation:** Initially, the legislation lacked specific details, including the identification and management of trading activities deemed to pose risks to market stability, compliance with targeted regulatory requirements, understanding regulatory expectations, and addressing potential regulatory enforcement and penalty standards.
# Equivalent Linearization Methods for a Control System with Clutching Inerter Damper
###### Abstract  
Inerter-based dampers have gained great popularity in structural vibration control. In this paper, equivalent linearization methods (ELMs) for a single-degree-of-freedom (SDOF) system with a cludling inerter damper (CID) are studied. The comparison of a SDOF system with a CID and an inertial mass damper (IMD) shows the advantage of the CID. Considering that the system with the CID is nonlinear, which is problematic for its performance evaluation and the integrated design of the structure and control system, three equivalent linearization methods based on different principles are proposed and discussed in this paper. The CID is considered to be equal to a combination of an IMD and a viscous damper. The equivalent inertance and damping can be calculated using the obtained formulas for all methods. In addition, all methods are compared in a numerical study. Results show that the ELM based on period and energy is recommended for small inertance-mass ratios.  
Keywords:inertial mass damper; clutching inerter damper; equivalent linearization method; statistical linearization method; performance evaluation +
Footnote †: journal: Applied Sciences
# Gender Differences in Visual Information Perception Ability: A Signal Detection Theory Approach
###### Abstract  
The accurate perception of visual stimuli in human-machine systems is crucial for improving system safety, usability, and task performance. The widespread adoption of digital technology has significantly increased the importance of visual interfaces and information. Therefore, it is essential to design visual interfaces and information with user characteristics in mind to ensure accurate perception of visual information. This study employed the Cognitive Perceptual Assessment for Driving (CPAD) to evaluate and compare gender differences in the ability to perceive visual signals within complex visual stimuli. The experimental setup included a computer with CPAD installed, along with a touch monitor, mouse, joystick, and keyboard. The participants included 11 male and 20 female students, with an average age of 22 for males and 21 for females. Prior to the experiment, participants were instructed to determine whether a signal stimulus was present: if a square, presented as the signal, was included in the visual stimulus, they moved the joystick to the left; otherwise, they moved it to the right. Each participant performed a total of 40 trials. The entire experiment was recorded on video to measure overall response times. The experiment measured the number of correct detections of signal presence, response times, the number of misses (failing to detect the signal when present), and false alarms (detecting the signal when absent). The analysis of experimental data revealed no significant differences in perceptual ability or response times for visual stimuli between genders. However, males demonstrated slightly superior perceptual ability and marginally shorter response times compared to females. Analyses of sensitivity and response bias, based on signal detection theory, also indicated a slightly higher perceptual ability in males. In conclusion, although these differences were not statistically significant, males demonstrated a slightly better perception ability for visual stimuli. The findings of this study can inform the design of information, user interfaces, and visual displays in human-machine systems, particularly in light of the recent trend of increased female participation in the industrial sector. Future research will focus on diverse types of visual information to further validate these findings.  
perception capability; signal detection theory; response time; field dependence; CPAD
# Does a Greater Hamstring Muscle Thickness Mean a Greater Aponeurosis Thickness?
###### Abstract  
The aim of the present study was to assess the association between muscle thickness and the superficial and deep aponeurosis thickness of the hamstrings. Ultrasound images were captured from the semientdinosus (ST) and biceps femoris long head (BFIh) of fifty young individuals (28 males and 22 females). Measurements were taken at six sites along the thigh in prone position. Superficial and deep aponeurosis and muscle thickness were evaluated across the entire length of the ST and BFIh using a computational image segmentation approach, which generated approximately \(\sim\)360 data points per participant. There was significant interindividual variability in superficial (coefficient of variation, CV: 19.37-31.40%) and deep aponeurosis thickness (CV: 21.22-31.82%). Correlation analysis revealed a limited association between muscle and superficial aponeurosis thickness, with no significant correlations (r = \(-\)0.21 to 0.22, \(p\) > 0.05) observed across regions. However, moderate positive correlations were found for deep aponeurosis thickness in the proximal region of the BFIh (r = 0.43, \(p\) = 0.002) and in the middle (r = 0.34, \(p\) = 0.014) and distal-middle region of the ST (r = 0.35, \(p\) = 0.022). No consistent relation between muscle and aponeurosis thickness was observed, suggesting that aponeurosis morphology varies independently of muscle size. This variability may impact the mechanical strain distribution, emphasizing the need for individualized assessments in hamstring injury prediction.  
hamstrings; ultrasound; aponeurosis; biceps femoris; muscle thickness Article
# Mobility Classification of LoRaWAN Nodes Using Machine Learning at Network Level
## 2 An Overview on LoRa and LoRaWAN
### LoRa  
LoRa is a proprietary physical layer technology patented by Semtech [17], based on chirp spread spectrum (CSS) modulation techniques that enable long-distance and low-power communications. It operates in the sub-GHz ISM band, and it is an M-ary digital modulation [18; 19], whose waveforms are not perfectly orthogonal [20]. Each LoRa transmission is characterized by four parameters: spreading factor (SF), transmission power, bandwidth, and coding rate. All these variables are managed and controlled by an adaptive data rate mechanism, which is part of the MAC protocol.
## Main Design of ERF-IMCS
### Iterative Quantization  
In this work, the ITQ hash encoding method [40] is introduced to transform the CNN features obtained in coarse filtering and fine retrieval into 64-bit binary hash codes while maintaining the adjacent relationship of data in the original space, which minimizes the loss of information. Assuming that \(X=\{x_{1},\ x_{2},\ x_{n}\},\ x_{i}\in R^{d}\) is a set of feature data and are zero-centered. The goal of ITQ is to learn a binary code matrix \(B\in\{-1,\ 1\}^{n\times c}\), where \(c\) is the code length. The binary encoding function is defined as \(B=\text{sgn}(XW)\), where \(W\) is the matrix with column vector of hyperplane coefficients \(\omega_{k}\). In order to make the hash codes of different data perpendicular to each other and maximize the variance between each bit, the following objective function can be constructed:  
\[\begin{array}{l}\tilde{\Gamma}(W)=\sum\limits_{k}E(\|xw_{k}\|_{2}^{2})= \frac{1}{n}\sum\limits_{k}w_{k}^{T}X^{T}Xw_{k}\\ =\frac{1}{n}tr(W^{T}X^{T}XW),\ \ W^{T}W=I\end{array}, \tag{1}\]  
where \(x\) is original feature vector, and \(w_{k}\) is a column vector of hyperplane coefficients. The constraint \(W^{T}W=I\) requires the hashing hyperplanes to be orthogonal to each other. As can be seen, if \(W\) is an optional solution, there is \(\tilde{W}=WR\) for any orthogonal matrix \(R\). So, the projected data \(V=XW\) can be orthogonally transformed to minimize the quantization loss:  
\[Q(B,R)=\left\|B-VR\right\|_{F}^{2}. \tag{2}\]  
The process of ITQ is to search a local minimum of the quantization loss, which can be divided into two steps.  
First step: fix \(R\) and update \(B\).  
For a fixed \(R\), minimizing (2) is equivalent to maximizing  
\[\text{tr}(BR^{T}V^{T})=\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{c}B_{ij} \tilde{V}_{ij}, \tag{3}\]  
where \(\tilde{V}=VR\), updating \(B\) through \(B=\text{sgn}(VR)\) to maximize (3).  
Second step: fix \(B\) and update \(R\).  
For a fixed \(B\), minimizing (2) is equivalent to a classic Orthogonal Procrustes problem. First, decompose the matrix \(B^{T}V\) into \(S\Omega S^{T}\) via SVD algorithm, and then update the matrix \(R\) through \(R=\hat{S}S^{T}\). The learning process is completed by repeated iteration until convergence.
###### Abstract  
A method was developed for the isolation of geranyl acetate from the crude essential oil (EO) of _Melaleuca armillaris_ (Sol. ex Gaertn.) Sm. Leaves, and the purity of the isolated compound was analyzed by GC/MS spectral and NMR analysis and was found to have high purity (98.9%). In addition, the EO isolated presented 0.907 g/cm\({}^{3}\), 1.474 and \([\alpha]^{20}_{D}=-17.6\) of density, refraction index and optical rotation, respectively. The chemical composition of the EO obtained for steam distillation from _M. armillaris_ was analyzed by gas chromatographic and spectroscopic techniques (GC/MS and GC/FID). Thirty-eight compounds were identified, representing 99.92% of the total EO analyzed on a DB-5 ms (5% phenylmethylpolysiloxane) capillary column. This analysis showed that the EO consisted mainly of oxygenated monoterpenes (77.01%), followed by monoterpene hydrocarbons (21.31%) and sesquiterpene hydrocarbons (1.31%). Furthermore, the essential oil of _M. armillaris_ was rich in 1,8-cineol (67 \(\pm\) 2%), followed by limonene (10 \(\pm\) 1%), \(\alpha\)-Terpineol (9 \(\pm\) 1%) and \(\alpha\)-Pinene (5 \(\pm\) 1%). Finally, the results suggest that the geranyl acetate isolated with high purity from crude essential oil is recommended to be explored as a component in medicinal or industrial use.  
Keywords:essential oil; geranyl acetate; _Melaleuca armillaris_; isolation; chemical composition +
Footnote †: journal:
# W-Band Modular Antenna/Detector Array for the Electron Cyclotron Emission Imaging System in KSTAR
## 3 Antenna/Detector Array Design
### Unit Antenna/Detector Module
#### 3.2.1 Elliptical Mini-Lens  
A perspective view of the proposed elliptical mini-lens is shown in Figure 5a. The proposed elliptical mini-lens consists of two dielectric structures: a half-elliptical lens and a rectangular dielectric block. The half-elliptical lens is placed on the top of the rectangular dielectric block. The rectangular dielectric block with the dimensions of \(l_{x}\) and \(l_{y}\) in the \(x\)- and \(y\)-directions is placed on the bottom side of the dual-dipole antenna. The width \(l_{y}\) is selected as 27.5 mm, which is larger than twice the minor axis \(b\) to prevent interference from nearby channels. Additionally, to accommodate the screw holes, the width \(l_{x}\) is selected as 30.0 mm. In Figure 5b, a cross-sectional view of the proposed dielectric lens in the \(x\)-\(z\) plane is shown. The major axis \(a\) and minor axis \(b\) are related with the relative dielectric constant (\(\epsilon_{r}\) = 2.27) as  
\[a=b/\sqrt{1-1/\epsilon_{r}}. \tag{1}\]  
Figure 4: Unit antenna/detector module: (**a**) perspective view, (**b**) exploded view.  
With consideration of the channel spacing and the focal point size, the minor axis \(b\) is chosen as 12.6 mm, and the major axis \(a\) of the half-elliptical lens is then calculated as 16.8 mm [21]. For the collimation of the incident beam towards antenna, the focal point of the half-elliptical lens is located at the bottom of the rectangular dielectric block. Therefore, the height \(f\) of the rectangular dielectric block is designed to be equal the focal length, which is calculated as 11.8 mm from the center of the ellipse using Equation (2) [22].  
\[f=a/\sqrt{\epsilon_{r}} \tag{2}\]
## 3 Results and Discussion
### Cognitive Experience Assessment
#### 3.3.3 NASA-TLX Scale Accuracy Assessment  
In the \"physical power consumption\" score of the NASA-TLX scale filled out by the two groups of students, where the percentage of confidence interval is 95% (CI = 95%), Sig = 0.321 (Sig \(\geq\) 0.05), and the variance is homogeneous, then t (9) = 6.709, \(p\) = 0.000 (\(p\)\(\leq\) 0.05), and thus the results showed significant differences. Therefore, compared with traditional teaching, in VR teaching, students need to expend significant physical exertion. In the \"frustration\" score of the NASA-TLX scale filled out by the two groups of students, where the percentage of confidence interval is 95% (CI = 95%), Sig = 0.620 (Sig \(\geq\) 0.05), and the variance is homogeneous, then t (9) = \(-\)5.588, \(p\) = 0.000 (\(p\)\(\leq\) 0.05), and thus the results showed significant differences. Therefore, compared with traditional teaching, in VR teaching, the students' negative feelings, such as frustration, are significantly reduced. In summary, in terms of the overall learning experience, the use of VR technology will produce significant physical exertion, but negative emotions, such as frustration, can be significantly reduced.
# An Integration Strategy for Acoustic Metamaterials to Achieve Absorption by Design
## Appendix C Derivation of Self-Energy Due to Cross-Channel Coupling by Evanescent Waves
\begin{array}{c}\Lambda_{mm}=\frac{16}{L}\frac{f_{\mathbf{\alpha}}}{L}\frac{f_{ \mathbf{\alpha}}({\bf x})d{\bf x}f_{\mathbf{\alpha}}f_{\mathbf{\alpha}}({\bf x}^{\prime}) d{\bf x}^{\prime}}{|{\bf k_{\mathbf{\alpha}}}|}\\ =16\frac{\sum\limits_{\mathbf{\alpha}}\frac{\sin^{2}(x_{\mathbf{\alpha}}\pi/4)\sin^{2 }(x_{\mathbf{\alpha}}\pi/4)}{\pi^{4}a_{\mathbf{\alpha}}^{2}a_{\mathbf{\alpha}}^{2}}|{\bf k_ {\mathbf{\alpha}}}|}\exp[i{\bf k_{\mathbf{\alpha}}}\bullet({\bf x}_{m}-{\bf x}_{n})] \end{array}  \tag{105}\]  
where \(\sigma_{n}\) denotes the cross-sectional area of the \(n^{\text{th}}\) FP channel, and \(v_{m}=v({\bf x}_{m})\), \(\delta p_{n}=\delta p({\bf x}_{n})\). According to the definition of the Green function, at the mouth of the \(n^{\text{th}}\) FP channel, we have  
\[v_{n}=-i\omega g_{n}(\overline{p}+\delta p_{n}) \tag{106}\]  
Substitution of Equation (104) into Equation (106) gives  
\[ \begin{array}{c}v_{n}=-i\omega\bigg{(}g_{n}+\omega^{2}\rho_{0}\sum\limits_{ m}g_{n}\Lambda_{mm}g_{m}+\cdots\bigg{)}\overline{p}\\ =-i\omega\bigg{[}\Big{(}g_{n}+\omega^{2}\rho_{0}g_{n}^{2}\Lambda_{nm}+\omega^{4 }\rho_{0}^{2}g_{n}^{3}\Lambda_{nm}^{2}+\cdots\Big{)}+\omega^{2}\rho_{0}\sum \limits_{m\
eq n}g_{n}\Lambda_{mm}g_{m}+\cdots\bigg{]}\overline{p}\\ =-i\omega\bigg{(}\frac{g_{n}}{1-\omega^{2}\rho_{0}g_{n}\Lambda_{mm}}+\sum \limits_{m}\Pi_{mm}\bigg{)}\overline{p}\end{array}  \tag{107}\]We have rearranged the series by separating the terms involving only \(\Lambda_{nn}\), since \(\Lambda_{nn}>>\Lambda_{nm}\)\((m\
eq n)\) by orders of magnitude. Numerically, the last term in the bracket is also small and hence only constitutes small adjustment to the results. According to the Equation (8) in the main text, the renormalized impedance is given by \(Z^{\rm(e)}=16\overline{p}/\sum_{n=1}^{16}v_{n}\). Substitution of Equation (101) (with the \(\sum_{nn}\Pi_{nm}\) term neglected) into this expression for \(Z^{\rm(e)}\) gives  
\[Z^{\rm(e)}=i\Bigg{(}\frac{\omega}{16}\sum_{n=1}^{16}g_{n}^{\rm(e)}\Bigg{)}^{-1} \tag{102}\]  
where the effective Green function can be expressed in the form of the Dyson equation with a self-energy term:  
\[\Big{(}g_{n}^{\rm(e)}\Big{)}^{-1}=g_{n}^{-1}-\omega^{2}\rho_{0}\Lambda \tag{103}\]  
Here \(\Lambda\equiv\Lambda_{nn}\).
# Comparing the Quality and Speed of Sentence Classification with Modern Language Models
## 5 Conclusions  
A comparison of the results obtained for the TREC6 set and the MPD suggested that they differed strongly in the magnitude of the MCC scores. For the TREC6 set, almost all LMs achieved MCC scores greater than 0.9, whereas on the MPD, the highest values were only slightly above 0.6, and the lowest values were below 0.4. The finding that even LMs creating static embeddings achieved very good results on the TREC6 data set was due to the simplicity of the classification tasks. Consequently, there was little room for improvement of state-of-the-art LMs, thus making any conclusions regarding the quality of newer LMs difficult to draw. In contrast, the MPD was challenging because using simple static word embeddings allowed for a 0.47 \(\pm\) 0.047 MCC score at best. However, interestingly, the sophisticated context-aware LMs demonstrated their superiority on the MPD since they were able to locate the necessary information in sentences full of contextual meaning and achieve scores of up to 0.603 \(\pm\) 0.023. From these results, we postulate that for context-aware LMs to fully demonstrate their advantages, a sufficiently difficult task must be used.  
A similar phenomenon was encountered in a deep learning task from another domain, namely the detection of objects in image analysis. Owing to the progress in the quality of solutions offered by models using complex convolutional neural network architectures, the differences in performance on once broadly addressed Pascal Voc data set became very small. Many models were able to achieve up to an 80 percent mean average precision score [33]. A solution was proposed by the MS CoCo challenge [34], in which the metrics emphasized the need for more precise localization of detected objects, thus increasing the task difficulty. As a result, the state-of-the-art models achieved a less than 50 percent mean average precision score in this challenge [35], thus leaving a lot of room for further improvement and demonstration of high-quality solutions. Unfortunately, owing to the very small size of the MPD, other explanations for this phenomenon cannot be excluded: some newer deep learning models might enable the framework to learn properly even given the small amount of data, or a framework based on baseline LMs might simply need more data. To rule out these possibilities, a larger yet still challenging data set would be required.  
Figure 7: Visual comparison of the baseline and state-of-the-art LMs. Principle component analysis (PCA) of sentence embeddings computed for the whole MPD by frameworks based on GloVe (**left**) and RoBERTa large scalar mix (**right**) LMs. Class labels are represented by colors: red—performance assessment, green—subject, blue—recommendations, black—technical, and magenta—time.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 3 Methodology
### Proposed Credibility-Based Imbalance Boosting Method
#### 3.2.1 Calculate the Credibility Factors of Synthetic Samples  
Given the training data \(S_{tr}=S_{tr}^{pos}\cup S_{tr}^{neg}\), where \(S_{tr}^{pos}\) and \(S_{tr}^{neg}\) represent the positive (or minority) class samples and the negative (or majority) class samples, respectively. We first perform SMOTE [40] on the \(S_{tr}^{pos}\) and denote the synthetic minority class samples that are generated by SMOTE as \(S_{syn}\) having \(n_{syn}\) samples. SMOTE has two important parameters \(P\) and \(k\) where \(P\) denotes how many synthetic minority class samples will be generated, e.g., \(P=100\%\) means that the number of synthetic minority class samples is equal to the number of real minority class samples and \(k\) represents the number of the nearest neighbors. For each synthetic sample, we determine its \(K\) (default 10) nearest neighbors, which are the real samples and then calculate the proportion of the number of minority class samples to that of majority class samples based on above \(K\) nearest neighbors, as follows:  
\[r\left(x_{j}\right)=\frac{n_{j}^{min}}{n_{j}^{maj}},\ j=1,2,\cdots,n_{syn} \tag{1}\]  
where \(n_{j}^{min}\) and \(n_{j}^{maj}\) denote the number of minority class samples and the number of majority class samples in the \(K\) nearest neighbors of \(j\)-th synthetic minority class sample, and \(n_{syn}\) represents the number of samples in \(S_{syn}\).  
The high value of \(r\) means synthetic samples are surrounded with more minority class samples than majority class samples. Low value of \(r\) means that synthetic samples may be introduced in high density of majority class samples or sparse regions with no minority class samples around. The synthetic sample with a high value of \(r\) is more reliable and it should be assigned a higher credibility factor. We define the credibility factor of a synthetic sample, as follows:  
\[cf\left(x_{j}\right)=1-e^{-\beta*\left(r\left(x_{j}\right)\right)^{2}},\ j=1,2, \cdots,n_{syn} \tag{2}\]  
where \(\beta\) controls the steepness of the nonlinear curve.