esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### EEG Recordings and Data Analysis  
The EEG was continuously recorded from 60 sites according to the extended 10-20 system. The signal was digitized with a 24-bit resolution at a sampling rate of 1000 Hz. The ground electrode was placed on the top of the head between CP and CPz. Two pairs of electrodes were used to record the vertical electrooculogram (VEOG) and horizontal electrooculogram (HEOG). The VEOG electrodes were positioned above and below the left eye, while the HEOG electrodes were placed 1 cm from the outer corner of each eye. Throughout the experiment, the impedance between the electrodes and the scalp was kept below 10 k\(\Omega\).  
Figure 4: Illustration of the stimulus paradigm applied.  
In the data processing phase, all continuous EEG recordings were filtered offline using a band-pass finite impulse response (FIR) filter with the following specifications: a critical low-pass frequency of 30 Hz and a slope of 8 dB/octave. The ocular artifact detection time window was set from \(-\)200 ms to 500 ms, with a voltage threshold ranging from \(-\)200 uV to 0 uV. An artifact model was created based on the covariance matrix of the relevant electrodes, and covariance analysis was applied to identify and remove ocular artifacts. Bad block detection was used to remove additional artifacts. The voltage thresholds for artifact detection were set at \(-\)100 uV and 100 uV, with signals exceeding these limits identified as artifacts. The time window for artifact detection was defined as \(-\)200 ms to 500 ms relative to the event. After these processing steps, ERPs were computed for each condition with a length of 1200 ms, including a 200 ms pre-stimulus baseline, separately for each individual participant. The ERP data were then averaged across trials for each condition and each participant to obtain condition-specific ERPs.  
All analyses were based on individual mean amplitudes within the specified time window at the respective electrode locations. Both the grand-average ERPs and isopotential contour plots were inspected to identify the electrodes and time windows influenced by the different experimental conditions. For each of these time windows, a repeated measures analysis of variance (ANOVA) was conducted with the following factors: context (genuine, fake), features (Class I, Class II), and 60 channels. The Greenhouse-Geisser correction was applied for nonsphericity, and multiple comparisons were corrected using the Bonferroni method where appropriate. The statistical analyses were performed using SPSS statistical software (IBM SPSS Statistics 26).
## 2 Basic Principles of RNS Arithmetic
### Base Extension Operations  
In applications involving modular computing structures, base extension operations play a fundamental role, as they serve as the foundation for synthesizing algorithms for nearly all non-modular operations, including general division, scaling, fractional multiplication, code transformations, and more. In information security systems, for instance, computationally intensive operations on large number ranges--such as Montgomery multiplication, modular exponentiation, and others--are implemented using base extension techniques [8; 9].  
The advantages of minimally redundant modular arithmetic in simplifying non-modular procedures are particularly evident in residue code extension operations.  
Let an RNS be defined by the moduli set \(\{m_{1},m_{2},\ldots,m_{k}\}\), and suppose that the residue code \((\chi_{1},\chi_{2},\ldots,\chi_{l})\) of a number \(X\) is given with respect to the basis \(\mathbb{M}_{1}=\{m_{1},m_{2},\ldots,m_{l}\}\), where \(1<l<k\). The task is to determine the residues corresponding to the basis \(\mathbb{M}_{2}=\{m_{l+1},m_{l+2},\ldots,m_{k}\}\), i.e., the residue code \((\chi_{l+1},\chi_{l+2},\ldots,\chi_{k})\). This process constitutes the essence of the residue code extension operation from the moduli \(\mathbb{M}_{1}\) to the remaining moduli in \(\mathbb{M}_{2}\). For this operation, we use the conventional notation \((\chi_{l+1},\chi_{l+2},\ldots,\chi_{k})=\text{BEX}(X;\{m_{1},m_{2},\ldots,m_{ l}\},\{m_{l+1},m_{l+2},\ldots,m_{k}\})\), or, in a simplified form, \(\text{BEX}(X;\mathbb{M}_{1},\mathbb{M}_{2})\).  
A necessary condition for the correctness of the base extension is the uniqueness of the mapping \(\mathbb{Z}_{m_{1}}\times\mathbb{Z}_{m_{2}}\times\cdots\times\mathbb{Z}_{m_{l} }\rightarrow\mathbb{Z}_{M_{l}}\). This means that the code \((\chi_{1},\chi_{2},\ldots,\chi_{l})\) must uniquely determine the integer \(X\). When this condition is satisfied, the number \(X\) can be represented in rank form with respect to the moduli \(m_{1},m_{2},\ldots,m_{l}\), by analogy with Equation (1).  
Determining the rank \(\rho_{l}(X)\) is significantly simplified when the RNS defined over the basis \(\mathbb{M}_{1}\) is minimally redundant. In comparison to non-redundant residue coding, this approach yields an approximately \(l/2\)-fold reduction in the number of required modular addition operations and the size of lookup tables [25; 27]. This advantage becomes especially important when working with large numbers.  
Therefore, for the number \(X=(\chi_{1},\chi_{2},\ldots,\chi_{l})\), the base extension operation to the moduli \(m_{l+1},m_{l+2},\ldots,m_{k}\) reduces to determining the rank \(\rho_{l}(X)\) of the number \(X\), and subsequently applying to its rank form the procedure for computing the residues with respect to the moduli in the basis \(\mathbb{M}_{2}\):  
\[\chi_{j}=\left|\sum_{i=1}^{l}\left|M_{i,l}\chi_{i,l}\right|_{m_{j}}-\left| \rho_{l}(X)M_{l}\right|_{m_{j}}\right|_{m_{j}}\ \ (j=l+1,l+2,\ldots,k).\]
## 2 Numerical Method
### Numerical Terrain Model
#### 2.4.2 Terrain Model Construction  
With reference to the existing literatures on mountainous terrain wind characteristics, as shown in Table 2, the bridge site is taken as the center, and an area with a diameter of 8.0 km was selected to establish the mountainous terrain model. The computational domain with 32.34 m long, 8.0 m wide, 2.0 m high, and the geometry scale of 1:1500 were adopted according to the value of blockage ratio of the terrain model. The lowest elevation of the terrain model was taken as the bottom of the computational domain. The left side of the computational domain is defined as velocity-inlet boundary condition. The right side of the computational domain is defined as pressure-outlet boundary condition. The front, back, and top sides of the computational domain are defined as symmetry boundary conditions. The bottom of the computational domain is defined as non-slip wall boundary condition. The boundary conditions and computational domain of the terrain model without BTS are given in Figure 4. As can be seen in Figure 4, the distance between the center of the terrain model and the front and back walls of the computational domain is 4.0 m, respectively. The center of the terrain model is 9.67 m and 22.67 m away from the left and right sides of the computational domain.  
Figure 3: Elevation of the Xiangjiang Bridge and the mountainous terrain (Unit: cm). (**a**) Elevation of the Xiangjiang Bridge; (**b**) Complex terrain at the bridge site.  
The Power Law Profile Model is currently the only model of boundary layer wind profile [46]. The wind speed at the velocity inlet boundary after scaling of the computational domain are defined as follows:  
\[\begin{array}{l}V(z)=V_{0.007}\Big{(}\frac{z}{0.007}\Big{)}^{a},0\leq z\leq 0.23 3\text{m}\\ V(z)=V_{ref},z>0.233\text{m}\end{array} \tag{18}\]  
where \(z\) is the distance between the monitoring point and the ground; \(a\) is the wind profile index and \(a=0.16\) when the ground surface belongs to class-B wind field; \(V(z)\) is the wind velocity of the monitoring point which is \(z\) away from the ground; \(V_{0.007}\) is the basic wind speed at the level of \(z_{0}=0.007\text{m}\) in the power law profile. \(V_{ref}=V_{0.233}\) is the wind velocity when \(z=0.233\text{m}\).  
In order to investigate the effects of the improved BTS on wind characteristics over mountainous terrain model, numerical simulation of the terrain model with/without BTS were conducted, respectively. With reference to the results of two-dimensional numerical simulation of the terrain model BTS, it is determined that the virtual inclination of the transition section is \(\theta=57^{\circ}\) and the length of the horizontal line is \(L=0.80\text{m}\)([PERSON] et al. [32]).
## 4 Discussion  
In this study, a random forest model was utilized to predict the ROP in the complex subsurface formations of Fukang. The following are some key discussion points:  
An analysis was conducted on algorithms such as ELM (extreme learning machine), ANN (artificial neural network), SVR (support vector regression), and LS-SVR (least squares support vector regression) to identify their limitations in predicting ROP. Furthermore, the advantages of machine learning algorithms in predicting ROP in complex deep subsurface formations were explained. These advantages include their suitability for handling large-scale datasets and their capability to capture complex nonlinear relationships between parameters.  
Through the mean square error (MSE) evaluation, we validated the performance of the random forest model developed in this study. The evaluation results indicate that the model demonstrates a high accuracy and reliability in predicting ROP.  
The importance ranking of features within the model was analyzed. This helps us to understand which subsurface properties play a critical role in predicting ROP. Feature ranking indicates that, in the deep well sections of the Fukang Depression in the eastern Junggar Basin, variables such as the equivalent density, annular pressure, inflow rate, and pump speed may significantly influence ROP. Mechanistic explanations of these important parameters were also provided.  
The model's performance is still constrained by data quality and feature selection. Additionally, geological conditions and drilling operations may vary across different locations and times; thus, the model may need customization to adapt to specific drilling scenarios.  
To enhance the accuracy of ROP predictions further, future research can explore the integration of more complex subsurface attributes, including seismic data and core analysis. Moreover, research can focus on developing tailored prediction models for different geological conditions.
# Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
## 2 Related Works
### Discriminative Approaches  
The deep features extracted from face images at different ages usually contain two types of information, which is related to the age and the face identity, respectively. Therefore, the cross-age discriminative models focus on how to separate the identity-dependent components from the extracted facial features. [PERSON] et al. [20] introduced a novel coding method called cross-age reference coding that encodes an image on a cross-age reference to obtain an age-invariant feature representation. [PERSON] et al. [34] proposed a cycle age-adversarial model that extracts age-invariant features and only uses age labels for training. [PERSON] et al. [35] proposed the Age-Puzzle FaceNet (APFN) based on an adversarial training mechanism to address the CAFR task. [PERSON] et al. [36] later updated the APFN to make it more compact and robust to age variation.  
Some recently proposed methods assume that whole-face features are composed of age-related factors and age-invariant factors. Those methods focus on decomposing the aging and identity components separately [4; 21; 30]. For example, [PERSON] et al. [21] separated identity-related factors and age-related factors using a hidden factor analysis (HFA). [PERSON] et al. [27] developed a latent identity analysis layer to separate the two components. The age-estimation-guided convolutional neural network [28] uses the age estimation task to guide the separation of age features from the identity feature layer. [PERSON] et al. [29] presented feature decomposition in an orthogonal embedding CNN (OE-CNN) and adapted SphereFace loss [37] to deal with the CAFR task. [PERSON] et al. [30] proposed the DAL algorithm to achieve feature decomposition in an adversarial manner. [PERSON] et al. [38] divided face features into groups and then recombined them to create identity-dependent feature representations that are resistant to age progression. [PERSON] et al. [31] proposed a purification unit to remove the irrelevant age information and retain the identity information only.  
In mathematics, the direct sum of two abelian groups of identity features and aging features forms another abelian group consisting of ordered pairs of the two features. Therefore, the entire set of facial features extracted from a well pre-trained general FR network can be decomposed into an identity-specific feature and an age-specific feature. We introduce an attention-based module and direct sum loss to facilitate this characteristic in our proposed method.
## 4 Conclusions  
In this work, we proposed and demonstrated an 8.07 Gbit/s red color LD based VLC system using two kinds of digital domain multiplexing schemes, OFDM and PowDM. Here, similar to OFDM, the PowDM can also enhance the total system capacity by allowing acceptable signal spectra overlapping among different power division signals to maximize the bandwidth utilization. By employing the SIC at the Rx side, the data signal can be retrieved. The modulation and demodulation of the OFDM and PowDM are discussed. In the experiment, when the power ratios are larger than 5 dB, the BER performances of Data 1 and Data 2 are \(2.41\times 10^{-3}\) and \(3.94\times 10^{-3}\). Both data can satisfy the 7% pre-forward-error-correction (pre-FEC) BER of \(3.8\times 10^{-3}\). By considering the FEC limit, Data 1 can achieve a data rate of 1.66 Gbit/s at BER of \(2.20\times 10^{-3}\) and Data 2 can achieve a data rate of 6.41 Gbit/s at BER of \(1.21\times 10^{-3}\). Hence, the maximum data rate achieved by the PowDM signal fulfilling the pre-FEC BER limit is 8.07 Gbit/s at the BER of \(1.7\times 10^{-3}\). We also measured the SNRs of Data 1 and Data 2 and their average SNRs were 10.96 dB and 14.45 dB, respectively.  
Data curation, W.-H.G.; funding acquisition, C.-W.C.; investigation, W.-H.G.; resources, Y.-H.C.; writing--original draft, W.-H.G.; writing--review & editing, Y.L., C.-W.C. and C.-H.Y. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Ministry of Science and Technology, Taiwan, under Grant MOST-110-2221-E-A49-057-MY3, MOST-109-2221-E-009-155-MY3.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The data presented in this study are available from the first author upon request.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
Figure 6: Measured SNRs of the two individual signals against all the OFDM subcarriers in the proposed PowDM VLC system.
# Intrinsic and Extrinsic Factors Affecting the Color of Fresh Beef Meat--Comprehensive Review
## 3 Results and Discussion
### Intrinsic Factors
#### 3.1.2 Age of the Animals  
Higher myoglobin and iron levels were detected in the meat of older animals and resulted in darker muscle colors, probably caused by more oxidative muscle fibers [27; 35]. Increasing the slaughter age in cattle also resulted in a higher proportion of red oxidative fibers that grew at a faster pace than the glycolytic ones in the muscles of older animals and generated an overall negative effect on meat color [36]. The meat of older animals from discontinuous growth systems (24 months of age) showed lower L*, a*, b*, and pH values in muscle than younger animals (18 months of age) from continuous growth systems [37]. It was also documented that extending the slaughter age in cattle from 14 to 18 months produced similar L* and slightly higher pH and a* values in the meat of older animals [38]. Another study reported that extending the average slaughter age for three additional months produced beef muscles with lower L* and higher a* and b* values, probably due to higher pH found in the meat of older animals [39]. Interestingly, it was observed that higher myoglobin amounts in the muscles of older animals resulted in significantly lower a* and higher C* values [40]. Significantly higher L*but almost steady a* values were reportedwhen bulls were slaughtered at 17 instead of 12 months of age, probably due to similar meat pH values [41]. It was also reported that steers slaughtered at 30 months of age produced higher pH and a* but lower L* values than animals slaughtered at 18 months [42]. Increasing the slaughter age in cows (up to 11 years) resulted in lower L*, a*, and b* values in fresh meat due to higher inherent lipid oxidation and radical production in the muscles of old cattle, even though there was higher production of antioxidant enzymes such as catalase, superoxide dismutase, and glutathione peroxidase [35; 43]. This manuscript agrees that higher quality of feed provided to animals with increased growth rates could consistently shorten the time to achieve commercial slaughter weight, thus increasing L* values in meat. Most of the references also claimed that myoglobin amounts in meat proportionally increased with higher biological age of the animals, thus showing higher a* values in the meat of older animals. Lower myoglobin contents in the muscles of younger animals might positively influence in producing brighter and less red fresh beef muscle.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 3 Selected Beamforming Techniques
### Capon Beamformer  
The _Capon_ beamformer (CAP), also known as the _minimum variance distortionless response_ (MVDR), is the most general beamforming approach. The beauty is that the array beam pattern is controlled by applying a series of constraints to the different directions to be either pointed at or nulled out. More particularly, the underlying idea of the CAP is to minimize the power at the beamforming output under the constraint that the LOSS be, conversely, unaltered. This is  
\[\min_{\mathbf{w}}P_{y}=\min_{\mathbf{w}}\mathbf{w}^{H}\mathbf{R}_{\mathbf{X}} \mathbf{w}\text{ subject to }\mathbf{a}_{0}^{H}\mathbf{w}=1 \tag{6}\]  
where \(\mathbf{R}_{\mathbf{X}}\doteq\mathbb{E}\big{[}\mathbf{X}\mathbf{X}^{H}\big{]} \in\mathbb{C}^{L\times L}\) is the auto-correlation matrix of the data in \(\mathbf{X}\), and the term \(\mathbf{a}_{0}^{H}\mathbf{w}=1\) is the so-called distortionless response constraint. After some mathematical calculations with Lagrange multipliers, the weights \(\mathbf{w}_{\text{CAP}}\) become [5]  
\[\mathbf{w}_{\text{CAP}}=\frac{\mathbf{R}_{\mathbf{X}}^{-1}\mathbf{a}_{0}}{ \mathbf{a}_{0}^{H}\mathbf{R}_{\mathbf{X}}^{-1}\mathbf{a}_{0}}. \tag{7}\]
# Data Mining Based on Chinese Traditional Calendar in the Han Dynasty Yang Mansoleum Museum
## 3 Lunar Model Based On ELM  
### Structure Of ELM  
Given \(N\) arbitrary samples \((\mathbf{x}_{i},\mathbf{t}_{i}),i=1,2,...N\), where \(\mathbf{x}_{i}=[x_{i1},x_{i2},...x_{im}]^{T}\) represents the \(i\)-th feature vector with \(n\) dimensions, and \(\mathbf{t}_{i}=[t_{i1},t_{i2},...t_{im}]^{T}\) is target data vector with \(m\) dimensions. \(g_{i}(*)\) is the activation function. It is because sigmoid function is a common activation in the neural network which is derivable infinitely and easy to implement. In addition, it can also map from \((-\infty,+\infty)\) to (0,1), which accords with probability distribution. The standard SLFNN output with \(\hat{N}\) hidden layer nodes neural networks is defined as follows:  
\[\sum_{i=1}^{\hat{N}}\beta_{i}g_{i}(\mathbf{\omega}_{i}\mathbf{x}_{j}+b_{i})= \mathbf{t}_{j},j=1,2,...N \tag{1}\]  
where \(\mathbf{\omega}_{i}=[\omega_{i1},\omega_{i2},...\omega_{in}]^{T}\) is the input weight. It is from the \(i\)-th hidden layer node to input layer. The output weight vector \(\mathbf{\beta}_{i}=[\beta_{i1},\beta_{i2},...\beta_{im}]^{T}\) is from the i-th hidden nodes to the output layer. \(b_{i}\) is the bias of the \(i\)-th hidden node. The left side of the equation is the actual output of the model. The right side is the ideal output of the model. The network structure is shown in Figure 8.  
Figure 7: Data processing.  
Which can be rewritten as:  
\[\mathbf{H}\mathbf{\beta}=\mathbf{T} \tag{2}\]  
\(\mathbf{H},\mathbf{\beta},\mathbf{T}\) can be written as:  
\[\mathbf{H}=\left(\begin{array}{ccc}g_{1}(\mathbf{\omega}_{1}\mathbf{x}_{1}+b_{1})&\ldots&g _{\hat{N}}(\mathbf{\omega}_{\hat{N}}\mathbf{x}_{1}+b_{\hat{N}})\\ \ldots&\ldots&\ldots\\ g_{1}(\mathbf{\omega}_{1}\mathbf{x}_{N}+b_{1})&\ldots&g_{\hat{N}}(\mathbf{\omega}_{\hat{N} }\mathbf{x}_{N}+b_{\hat{N}})\end{array}\right)_{N\times\hat{N}} \tag{3}\]  
\[\mathbf{\beta}=[\mathbf{\beta}_{1},\mathbf{\beta}_{2},...\mathbf{\beta}_{\hat{N}}]_{\hat{N} \times m}^{T} \tag{4}\]  
\[\mathbf{T}=[\mathbf{t}_{1},\mathbf{t}_{2},...\mathbf{t}_{N}]_{N\times m}^{T} \tag{5}\]  
To find \(\beta\) smallest norm by least-square method,  
\[\underset{\beta}{min}\parallel\mathbf{H}\mathbf{\beta}-\mathbf{T}\parallel \tag{6}\]  
Under the constraint of Equation (6), the least-square solution can be derived from the previous equation.The least-square solution:  
\[\mathbf{\beta}=\mathbf{H}^{\dagger}\mathbf{T} \tag{7}\]  
where \(\mathbf{H}^{\dagger}\) represents Moore-Penrose generalized inverse of \(\mathbf{H}\). \(\mathbf{H}^{\dagger}\) is often used to find the least-square solution of nonuniform linear equations with the smallest norm, and to simplify the form of the solution.
# Patient Mobility Support for Indoor Non-Directed Optical Body Area Networks
## 1 Introduction
This configuration is considered the most flexible in the literature [14]. Considering the variations in human body movements in an OBAN environment, it is not always possible to establish a perfect link between the transmitter and the receiver. In this view, we considered non-directed optical links. The OBAN system consists of body sensors fixed on various parts of the body for monitoring the physiological parameters. These sensors are directly connected to an optical coordinator. The optical coordinator is composed of a white light-emitting diode (LED) which collects physiological signals from the sensors and transmits the data via an optical link. The photodetector (PD) is used as the receiver installed on the ceiling. In addition, we focused on non-directed optical links and developed an analytical model for multiple reflections. In the proposed mobility support scheme, we considered three mobility scenarios. The first scenario is that the coordinator is fixed at the shoulder and the second is that the coordinator is placed at the wrist of the patient body. The third scenario involves the coordinators placed at both the wrist and the shoulder. This scenario can be viewed as the transmit diversity to exploit for the mobility-supported OBAN. The performance of the proposed mobility model is evaluated in terms of bit error rate (BER) and experiments are conducted indoors to verify the effectiveness. The contributions of the present study are listed below:  
* An effective and convenient mobility support model is designed;
* Analytical expressions are newly derived for the VLC-based mobility support model;
* Simulations and experimental validations are performed;
* Multiple LEDs placed on body are employed for both data transmission and transmit diversity without causing any interference;
* Comparative study on mobility support scheme is presented. The rest of the paper is organized as follows. In Section 2, the description of the OBAN system and the mobility model is provided. The channel model is presented in Section 3. Section 4 detailsthe simulation results. Section 5 describes the experiments and validates the results. Conclusions are drawn in Section 6.