esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 VLC System Model
### Ambient Light Noise Model  
Ambient light refers to the natural or existing light in an environment, typically from sources like the sun or artificial lighting, such as fluorescent lamps and white LED [13; 15]. It provides general, non-directional illumination that enhances overall visibility rather than focusing on a specific object or area. However, ambient light can lead to interference, signal attenuation, and reduced coverage range in VLC systems. The use of spatial multiplexing can help mitigate the interference caused by ambient light, as will be explained later.  
Mathematical modeling of the ambient light noise generated by two different light sources (fluorescent lamp and white LED) is presented in this subsection. Let \(E_{x}(t)\) represent the light of white LED and \(E_{f}(t)\) and \(E_{d}(t)\) are the ambient lights of fluorescent lamp and white LED, respectively and written as [15]:  
\[E_{x}(t)=x(t){\sum_{l=-L_{0}}^{L_{0}}}exp[j\{2\pi(f_{0}+l\delta f_{0})t+\theta _{l}\}] \tag{3}\]  
\[E_{f}(t)=n_{1}(t){\sum_{l=-L_{1}}^{L_{1}}}exp[j\{2\pi(f_{1}+l\delta f_{1})t+ \varphi_{l}\}] \tag{4}\]  
\[E_{d}(t)=n_{2}(t){\sum_{l=-L_{2}}^{L_{2}}}exp[j\{2\pi(f_{2}+l\delta f_{2})t+ \gamma_{l}\}] \tag{5}\]  
where \(n_{1}(t)\) and \(n_{2}(t)\) are the ambient interference noises from fluorescent lamps and white LED ambient light sources, respectively. \(f_{0},f_{1}\), and \(f_{2}\) represent the central optical frequencies within the spectral range of three distinct light sources, while \(\delta f_{0}\), \(\delta f_{1}\), and \(\delta f_{2}\) denote the respective elemental frequency intervals for each light source. Additionally, \(\theta_{l}\), \(\varphi_{l}\), and \(\gamma_{l}\) correspond to random optical phases that are uncorrelated for each value of \(l\). Finally, \(L_{i}=\frac{R_{i}}{2\delta f_{i}},i=0,1,2\) and \(B_{i}\) represents the spectral region of each light source.  
At the receiver side, two types of shot noise are produced: stationary shot noise, which originates from the white LED and two ambient light sources biased by DC current, and non-stationary shot noise, which results from the time-varying photocurrent created by the  
Figure 2: MIMO-VLC channel model with \(N_{t}\) light sources and \(N_{t}\) photodiodes.  
NRZ-OOK signal and interference signals. The time-varying and non-stationary shot noise current can be modeled as [17].  
\[I_{total}(t)=\frac{R\int_{-\infty}^{\infty}qp(\tau)h_{i}^{2}(t-\tau)d\tau}{2B} \tag{6}\]  
where \(R\) is the responsivity of the photodetector, \(q\) represents the electronic charge, \(p(t)\) is the optical power at receiver, \(h_{i}(t)\) represents the optical receiver impulse response, \(B\) is the noise bandwidth of the receiver, and finally \(\tau\) is the integration variable representing time delay in the convolution operation between the received optical power and the squared impulse response of the optical receiver.
# Nonlinear Extended-state ARX-Laguerre PI Observer Fault Diagnosis of Bearings
## 4 Proposed Fault Detection and Diagnosis
### Extended-State ARX-Laguerre PI Observer
According to Equations (19) and (20), the system's output and faults are estimated using the extended-state ARX-Laguerre PI observer. This technique is more accurate and robust than the ARX-Laguerre PI observer technique. The stability and convergence are proven in the Appendix A[29]. After designing the proposed extended-state ARX-Laguerre PI observer method, the residual generator is designed. The residual generation block has two inputs: (a) the system's output and (b) the system's estimation output. The robust sliding mode technique is used to generate reliable residual signals [30]. This technique is defined as the following equation. \[\left\{ \begin{array}{l}e(k)=Y(k)-\hat{Y}(k)\\ r(k)=K_{s}sgn(e(k))\end{array} \right. \tag{21}\]Here, \(e(k),sgn(. ),K_{s}\) and \(r(k)\) are the output's error, switching function, sliding gain, and residual signal, respectively. After designing the proposed extended-state ARX-Laguerre PI observer and residual generator, in the next step, the sliding mode threshold process evaluation is proposed [29]. As shown in Figure 5, this block has two inputs: (a) the system's modeling output and (b) the system's estimation output, which is represented by the proposed method. Using the robust sliding mode method, the threshold value for different conditions (e.g., normal state and faulty conditions) is calculated by the following equation [30]:  
\[ \begin{array}{l}\Delta=K_{s}sgn(S_{e})\\ S_{e}=\lambda_{e}e+\dot{e}\\ e=Y(k)-\hat{Y}(k)\end{array}  \tag{22}\]  
where \(S_{e},\dot{e},\lambda_{e},K_{e}\) and \(\Delta\) are sliding surface, output's change of error, sliding surface slope, sliding gain, and threshold value, respectively. As shown in Figure 5, the last block for fault detection and fault diagnosis using the proposed method is residual bank and logic decision. This block is divided into two main parts: (a) fault detection and (b) fault diagnosis.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 3 Materials and Methods
### Canonical Correlation Analysis
\begin{bmatrix}0&\sum\limits_{xy}\\ \sum\limits_{yx}&0\end{bmatrix}\begin{bmatrix}w_{x}\\ w_{y}\end{bmatrix} =\lambda \begin{bmatrix}\sum\limits_{xx}&0\\ 0&\sum\limits_{yx}\end{bmatrix}\begin{bmatrix}w_{x}\\ w_{y}\end{bmatrix} \] (6)
2. Here, substitutions are made where \(\sum\)'s are Covariance matrices. \[ \begin{split}\sum\limits_{xy}&=\frac{1}{N}XY^{T},\\ \sum\limits_{yx}&=\frac{1}{N}YX^{T}.\end{split} \] (7)
3. The top '\(K\)' generalised eigen vectors are: \(\{[w_{x},k;w_{y},k]\}_{k=1}^{K}\): 4. The \(K\)th generalised eigen value is equal to the correlation \(\rho(w_{x,k}^{T}X,w_{y,k}^{T}Y)\). The second solution to calculate \(W_{x}\), \(W_{y}\) involves performing SVD. 1. SVD is formulated on a matrix \(T\). 2. The matrix \(T\) is given by the expression, \(T=\sum_{xx}^{-1/2}\sum_{xy}\sum_{yy}^{-1/2}\). 3. Let \(W_{x},W_{y}\) be the \(K\)th leading left and right singular vectors of matrix \(T\). 4. This gives us canonical matrices which will be \(\sum_{xx}^{-1/2}W_{x},\sum_{yy}^{-1/2}W_{y}\). 5. The \(K\)th leading singular vector \(T\) is equal to the correlation \(\rho(w_{x,k}^{T}X,w_{y,k}^{T}Y)\). On obtaining \(W_{x}\), \(W_{y}\), Canonical Variables i.e., the new projected features are computed as: \(Z_{x}=W_{x}^{T}X\) and \(Z_{y}=W_{y}^{T}Y\). However, CCA works for two views at a time. To utilise multiple views in a single time, MCCA can be employed. Let's denote \(XX^{T}\) as the Covariance matrix \(C_{xx}\). Similarly, \(XY^{T}\) would be \(C_{xy}\) and so on. Using the transformations \(w_{k},w_{q}\) MCCA can be computed,where \(k,q\) are the set of indices of the views varying as \(k,q=1,2,3\ldots K\) and 'K' are the total number of views. It satisfies the following:  
\[ \begin{split} arg\max_{w_{k},w_{q}}\frac{\sum_{kq}(X_{k}w_{k})^{T}( X_{q}w_{q})}{\sqrt{\sum_{k}(X_{k}w_{k})^{T}(X_{k}w_{k})}\sqrt{\sum_{q}(X_{q}w_{q})^{T}(X_{q}w _{q})}}\\ =\frac{\sum_{kq}w_{k}^{T}C_{kq}w_{q}}{\sqrt{\sum_{k}w_{k}^{T}C_{ kk}w_{k}}\sqrt{\sum_{q}w_{q}^{T}C_{qq}w_{q}}}\end{split}  \tag{8}\]  
The denominators can be fixed as \(w_{k}^{T}C_{kk}w_{k}=1\), since we are interested in the direction of \(w_{k}\) and we get :  
\[ \begin{split} arg\max_{w_{k},w_{q}}\sum_{kq}w_{k}^{T}C_{kq}w_{q} \\ s.t.\sum_{k}w_{k}^{T}C_{kk}w_{k}=1\sum_{kq}w_{k}^{T}C_{k}qw_{q} \end{split}  \tag{9}\]  
By using Lagrange multipliers \(\lambda\) and equating the partial derivatives to zero, above optimization can be solved. This sums up the MCCA as:  
\[ \begin{split}\begin{bmatrix}C_{11}&C_{12}&\ldots&C_{1K}\\ C_{21}&C_{22}&\ldots&C_{2K}\\ \cdot&\cdot&\ldots&\cdot\\ C_{K1}&C_{K2}&\ldots&C_{KK}\\ \end{bmatrix}\begin{bmatrix}w_{1}\\ w_{2}\\ \cdot\\ w_{K}\end{bmatrix}\\ =\lambda\begin{bmatrix}C_{11}&0&\ldots&0\\ 0&C_{22}&\ldots&0\\ \cdot&\cdot&\ldots&\cdot\\ 0&0&\ldots&C_{KK}\\ \end{bmatrix}\begin{bmatrix}w_{1}\\ w_{2}\\ \cdot\\ w_{K}\end{bmatrix}\end{split}  \tag{10}\]  
To obtain CCA for just two views would be a special case of the above equations.
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
## 2 Status of Research
### Camera Parameter Constraints  
Based on the camera imaging model, camera calibration establishes the correspondence between a point in a 3D scene and its 2D pixel position in the image [11]. Based on the basic principles of camera calibration, some researchers make good use of camera parameter constraints to alleviate ambiguity and find the coordinates in the 3D space.
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 4 Position Solution  
This section describes the positioning algorithm considered for the hybridisation of GNSS pseudoranges with cmWave observables and for the use of mmWave observables. The unknown parameters for both cases are the 3D mobile position and the receiver clock offset.  
Figure 7: Error bounds on estimated mmWave channel parameters.
## 2 Materials and Methods  
### Setting, Ethical Approval, Participants, Informed Consent and Sample Collection  
**Setting:** This study was conducted in the Department of Hematology of the University of Calabar Teaching Hospital (UCTH) Calabar, Cross River State, Nigeria. The UCTH is the only tertiary health institution in the state. It has a 400-bed capacity, a modern ICU and accident and emergency departments. Calabar is the capital of Cross River State and the largest city in the state. The population is about 3.8 million.  
**Ethical Approval and Informed Consent:** This study received ethical approval (UCTH/ HREC/33/498) from the University of Calabar Teaching Hospital Health Research Ethics Committee. Participants recruited for the study had the study thoroughly explained to them in a language they understand. Those with a poor understanding of English language had interpreters explain the procedure before signing consent forms.  
Contents of the form were again explained, and where the subject agreed, they were required to sign or use their thumb print, as shown in Supplementary Information (SI) I.  
**Recruitment of Participants**: Patients with sickle cell anemia (SCA) registered at the Haematology Clinic, Calabar Sickle Cell Club or the Paediatric Sickle Cell Clinic, were recruited to participate in the study, based on the inclusion criteria listed in SI III. All patients who fell within the exclusion criteria were not recruited.  
**Questionnaire and Medical History**: Structured questionnaire to capture personal data, demographic, past and present records of sickle cell crisis and complications (SI II) were administered to the participants by trained research assistants.  
**Sample Collection**: Venipuncture was used for collection, and trained phlebotomists took 5 mL of blood from the ante cubital fossa of the participants. Blood samples were deposited into an EDTA sample bottle. There was only single sampling of participants' blood.
## 2 Integration of Constrained Devices into the Internet
Figure 1: Gateways and proprietary protocols are often used to interconnect sensor networks to the Internet. Figure 2: Internet protocols are extended to the sensor networks. The Gateway translates between the two standardized protocol stacks. Similar to the previous approaches, gateways are still used to translate between the protocols used in the Internet and protocols used in the sensor networks, e.g., IPv6 to 6 LoWPAN and vice versa. However, due to the use of standardized protocols, many of the disadvantages from the previous approaches are now solved. For example it is now possible to combine sensor devices from different vendors in the same network, or to use a gateway from a different vendor than the vendor of the sensor devices. Flexibility is also improved by this approach as users are not confined to the API offered by the gateway: users can directly query the sensors without the need for the gateway that understands the query or needs to interpret the data. The application payload can now travel directly from the client to the sensor, where it is processed and acted upon. The gateway takes care of the translation between standardized protocols. This end-to-end approach makes adding and removing sensor resources transparent to the gateway and improves interoperability of devices.
## 4 Model Results
### Comparison with Previously Developed Models
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Predicted Model**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{\(\mathbf{R^{2}}\)} & \multicolumn{4}{c}{**Statistical Properties of \(V_{\text{actual}}\)/\(V_{\text{MSP}}\)**} \\ \cline{4-9}  & & & & & \(\upmu\Omega\) & **SD** & **COV\%** & **Min** & **Max** \\ \hline ACI 318-19 [6] & 181.7370 & 118.6216 & 0.8803 & 1.4486 & 0.4225 & 29.1678 & 0.5641 & 4.2303 \\ BS 8110-97 [5] & 169.9748 & 112.9098 & 0.9595 & 1.3571 & 0.3319 & 24.4572 & 0.7472 & 4.1163 \\ Eurocode 2 (EC2) [7] & 105.8192 & 71.4915 & 0.9571 & 1.2459 & 0.3368 & 27.0321 & 0.7057 & 3.9481 \\ [PERSON] et al. [3] & 122.1256 & 72.7415 & 0.9392 & 1.0526 & 0.2811 & 26.7070 & 0.4951 & 3.3695 \\ [PERSON] et al. [4] & 108.3236 & 68.2791 & 0.9503 & 1.0894 & 0.2935 & 26.9456 & 0.4587 & 3.2054 \\ [PERSON] et al. [27] & 100.7650 & 59.1147 & 0.9579 & 0.9874 & 0.2416 & 24.4647 & 0.5431 & 3.0663 \\ MSP in this study & 76.3815 & 48.1163 & 0.9700 & 1.0147 & 0.1705 & 16.8060 & 0.6161 & 1.8585 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of earlier models for predicting the punching shear strength in comparison with M5P. Figure 9: Comparison of the normalized punching shear resistance results between M5P and EC2.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 3 Signal-Processing Techniques
### Time-Domain and Frequency-Domain Analysis  
Various methods exist for analyzing accelerometer data, such as time-domain or frequency-domain analysis. The paper [53] studies the time-frequency analysis of data.  
The time-domain features considered by the authors are the following:  
* The zero-crossing rate is a simple, cost-effective way to compare patterns in two sets of time-series measurements. It is a feature that is useful in analyzing noise corruption.
* Correlation coefficients are a useful tool to check if two time-series readings are similar;
* Cross correlations functionto measure the extent of the offset between two time-series measurements;
* The signal's RMS and peak-to-peak values are used to set alarms on specific thresholds based on the application's particular needs. The characteristics in the frequency domain are as follows:
* Maxima and energy are used to compare dominant frequencies in measurements, while the spectral energy of sensor readings reflects their spectral structure.
* The correlation coefficients for both FFT and STFT are useful for examining how frequencies shift over time, serving as an important measure of correlation;
* Spectral roll-off is a feature that looks at the frequency. It only considers the Fourier transformation of acceleration vectors;
* The spectral centroid shows the average location of the \"center of gravity\" of a vibration's spectrum. It is similar to the \"first n-maxima\" and helps us to understand where the main energy of the vibration is located;* Spectral flux measures the rate of change in the power spectrum.  
Their study reveals that features extracted from the absolute values of the measurements are more robust against noise and calibration errors and that the frequency-domain features are the coefficients of the absolute values of the short-time Fourier transformation.  
The authors [54] present the analysis of vibration signals using the Fast Fourier Transform and show how the frequency spectrum of vibrating sources gives information about the vibration level present.
# The Behaviour of Half-Slabs and Hollow-Core Slab in Four-Edge Supported Conditions
###### Abstract  
In this study, qualitative tests were carried out to compare the behaviour of selected slabs exposed to short- and long-term loading. Full-scale models of the half-slab and hollow-core slab with dimensions of 6.30 m \(\times\) 6.30 m, built of four different precast panels, were tested. The first two were semi-precast lattice girder slabs, the third semi-precast prestressed ribbed panels, and the last was composed of hollow-core panels. A common feature was the lack of joint reinforcement and the same modular width of 600 mm. The short-term load was applied sequentially in the first stage, and displacement was measured using an electronic method. In the second stage of long-term testing, the load was mainly applied to one part of the slab. Testing under short-term and long-term load allows determining the change in the performance of panel slabs over time. The panels maintained the ability of load redistribution based on their interaction despite the work of the longitudinal joints being only through the concrete cross-section. The behaviour of slabs with concrete topping shows more significant lateral interactions than elements connected only by shear key. Comparative calculations were made based on four computational models. Comparative analysis showed that the current design procedures lead to a safe but conservative estimation of the slab behaviour.  
structural analysis and design; half-slab; hollow-core slab; precast slab; long term load +
Footnote †: journal: Applied Sciences
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Long-Range Communications  
In all types of telemedicine approaches, whether in store-and-forward, remote monitoring, or real time telemedicine, patient information or data are usually transmitted to a physician or caregiver in a distant or remote location. Various technologies exist for remote communication, with varying capacities and ranges. One of the most widely used communication technologies in telemedicine is satellite communication, which has the major advantage of world-wide reach [87]. Other technologies include Global System for Mobile (GSM), internet, and the Wireless Area Network (WAN) communication.
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 2 Proposed System
### Texture Analysis and Classification
#### 2.3.2 Texture Description  
The texture description is performed with a multi-scale analysis using feature vectors derived from the histograms of the distribution of LBP and the Variance of the Local Binary Patterns (LBPV) [14], in different samples and radius, as shown in the following equations:  
\[H_{P,R}^{\text{r}iu2}(k)=\sum_{x=\left[R\right]}^{N-\left[R\right]}\sum_{y= \left[R\right]}^{M-\left[R\right]}w(LBP_{P,R}^{\text{r}iu2}(x,y),k),\qquad k \in[0,K] \tag{17}\]  
\[HV_{P,R}^{\text{r}iu2}(k)=\sum_{x=-\left[R\right]}^{N-\left[R\right]}\sum_{y= -\left[R\right]}^{M-\left[R\right]}W(LBP_{P,R}^{\text{r}iu2}(x,y),k),\qquad k \in[0,K] \tag{18}\]  
where \(w(x,y)\) is given by Equation (16) and  
\[W(LBP_{P,R}(i,j),k)=\left\{\begin{array}{ll}VAR_{P,R}(i,j)&LBP_{P,R}(i,j)=k \\ 0&\text{otherwise}\end{array}\right. \tag{19}\]  
\[VAR_{P,R}=\frac{1}{P}\sum_{p=0}^{P-1}(g_{p}-u)^{2} \tag{20}\]  
with \(P=8\) samples and \(R=1\) pixel distance in the first scale. In addition, an analysis is performed on the second scale with the texture image previously filtered with a Gaussian low-pass filter. For the filter design, Equations (21)-(24) are used, where \(w_{2}=3\) is obtained by Equation (23), \(\sigma_{2}=0.30\) is obtained by (24) and \(R=2.5\) is obtained by (22). \(N\) and \(M\) are the width and height of the image block texture, respectively, and \(K\) is the number of possible patterns, nine patterns in this case and additional patterns to the box labeled \"miscellaneous\". The radius is given by:  
\[r_{e}=r_{e-1}\left(\frac{2}{1-\sin(\pi/P_{e})}-1\right),\qquad e\in\{2,\cdots,E_ {s}\} \tag{21}\]  
where \(E_{s}\) is the total number of scales and \(P_{e}\) is the number of elements at a particular scale. The filter is useful only when the radius is greater than one, thus in the calculations the first radius considered is \(r_{1}=1.5\) for \(P=8\). The radio operator on the scale LBP \(e\) when \(e\geq 2\) is between \(r_{e}\) and \(r_{e-1}\):  
\[R_{e}=\frac{r_{e}+r_{e-1}}{2} \tag{22}\]  
The width and height of each spatial Gaussian filter in the scale \(e\) are given by:  
\[w_{e}=2\left[\frac{r_{e}-r_{e-1}}{2}\right]+1 \tag{23}\]  
The standard deviation of the Gaussian filter, considering a standard normal distribution in the scale \(e\), is estimated by:  
\[\sigma=\frac{r_{e}-r_{e-1}}{2\times\sqrt{-2\ln(1-p)}},\qquad p\in[0,1] \tag{24}\]  
where \(p\) is the probability that the filter mass falls into the circle area of radius \(r\), which is given by \(p=0.95\).  
For the texture description, the histograms of \(LBP^{tri2}\) and \(LBPV^{tri2}\) are concatenated together with the intensity average \(\mu^{I}\) and its standard deviation \(\sigma^{I}\) resulting in the feature vector with 14 elements. It should also be noted that the fewer elements there are in a feature vector, the more efficient is the separation of different classes.  
\[V=\{H_{8,1}^{tri2},log(HV_{8,1}^{tri2}),H_{8,2,5}^{tri2},log(HV_{8,2,5}^{tri2} ),\mu^{I},\sigma^{I}\} \tag{25}\]
###### Abstract  
Mung bean is a well-known good source of protein. To increase its bioactivity, economic value, and nutritional content as a functional food and food additive, lactostatin (IIAEK), a cholesterol-lowering bioactive peptide, was engineered into mung bean 8S\(\alpha\) globulin, a major storage protein. The results showed that the mutated 8S\(\alpha\) globulin has a significant bile acid binding capacity (cholesterol-lowering activity) up to 47.25%. Moreover, superimposed mutant (Mut2) and wild-type (Wt) 3D protein structures showed a 93-97% identity, indicating that the mutant proteins are stable. Ultra-performance liquid chromatography(UPLC)-based assay showed similar retention time for wild-type and mutant protein samples. Both IIAEK peptide standard and Mut2 digest had comparable baseline peaks corresponding to the same molecular size based on the liquid chromatography-mass spectrometry (LC-MS) data. A 573.36-Da mass spectrum was seen in Mut2, which indicates that Mut2 8S\(\alpha\) globulin has been successfully mutated and digested to release the bioactive peptide, IIAEK. In vitro bile acid binding capacity showed that the 6-h Wt and 12-h engineered protein (Mut2) digests had the highest activity. Lastly, potential allergenicity was checked in the Allergen Database for Food Safety (ADFS) and the AllerBase database, and the IIAEK peptide matched the Bos d 5 epitopes. This study provides a strong foundation and basis for mung bean nutrition improvement, development of cholesterol-lowering food supplements, and protein engineering of other food proteins.  
**Keywords:** lactostatin; mung bean; protein engineering; bioactive peptide