esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Expressway PTV Detection Algorithm for Over-the-Horizon
### PTV Quantitative Evaluation Algorithm
#### 4.5.2 Determination of Membership Function  
The evaluation of PTVs is subjective and ambiguous and cannot objectively measure whether vehicles are dangerous while in transit. In this paper, a quantitative method of PTV evaluation based on a fuzzy set is designed to accurately evaluate the vehicle hazard. The core idea of the evaluation model is to use fuzzy mathematics to make an overall evaluation of things or objects constrained by multiple factors. Fuzzy set theory was put forward as a classical concept of set theory [37] as early as 1965. The fuzzy set is an abstract concept, whose elements are uncertain and can only be determined by the membership function.  
Firstly, the traffic state in the section is defined as a fuzzy set \(\mu_{A}(x)\)\(\rightarrow\)[0,1], and the representation of the degree of affiliation adopts the vector representation. Then, determine the membership function and use the assignment method to determine the membership function. There is a positive correlation between OS, VT, LD, TF, and expressway accident rate. Based on the consensus that the faster the speed, the larger the vehicle type, the longer the cumulative driving time, the greater the traffic flow, and the higher the degree of threat, an S-type membership function was selected for the four indicators. Its calculation is shown in Formula 8, and its function diagram is shown in Figure 6.  
S-type membership function:  
\[\mu_{A}(x)=\left\{\begin{array}{c}0,\ x\leq a\\ 2\left(\frac{x-a}{b-a}\right)^{2},\ a<x\leq m\\ 1-2\left(\frac{x-b}{b-a}\right)^{2},\ m<x\leq b\\ 1,\ x>b\end{array}\right. \tag{8}\]  
where x is a measurement parameter of element A, such as vehicle traffic speed, section traffic flow, and so on, and element A represents OS, VT, LD, and TF. The closer the value of \(\mu_{A}(x)\) is to 1, the higher the affiliation of x to the fuzzy set A, and the higher the vehicle danger. On the contrary, the closer the value of \(\mu_{A}(x)\) is to 0, the lower the affiliation of x tothe fuzzy set A, and the lower the vehicle danger. If \(\mu_{\rm A}(x)=1\), it means that x belongs to A completely, and if \(\mu_{\rm A}(x)=0\), it means that x does not belong to A at all.
## 1 Introduction
Silk fibroin (SF) is obtained from silkworm _Bombyx mori_ and consists of two chains (H-fibroin (heavy with 390 kDa) and L-fibroin (light with 26 kDa)) connected by a disulfide bond [18]. The SF is considered nontoxic and biocompatible and presents outstanding water binding and absorption abilities, which make this protein ideal for cosmetic applications [19]. Keratin proteins are a broad group of insoluble proteins found in the hair, making up more than 75% of his weight and having 7-20% cystine and a \(\alpha\)-helix structure [20; 21]. Due to its outstanding mechanical and protective properties, keratin is widely explored for the development of cosmetic formulations. Keratin and primarily keratin hydrolysates have been commonly used in personal and hair care products [22]. The KP-UM is a fusion protein with the ability to color and improve the mechanical properties of hair. This protein was first described by [PERSON] et al., 2019, and is constituted by an ultramarine chromogenic protein (UM) and a keratin-based peptide (KP) linked by a glycine-alanine linker (GA)\({}_{5}\). The KP-UM protein undergoes a change in color depending on the polarity and pH of the solution where it is resuspended. Independent of the \begin{table}
\begin{tabular}{c c c c} \hline
**Protein** & **Molecular Weight (kDa)** & **Isoelectric Point** & **Structure** \\ \hline BSA & 66.5 & 4.7 [11] & Globular \\ Silk Fibroin & Heavy chain 390 [12] & 2.1 [13] & Anti-parallel \\  & Light chain 26 & & \(\beta\)-sheet \\ Keratin & 44 to 66 [14] & (4.9–5.4) * (6.5–8.5) **[15] & \(\alpha\) -helix \\ KP-UM & 26.3 & 8.39 & \(\beta\)-barrel \\ KP-Cryst & 24.8 & 7.13 & Greek Key \\ \hline \end{tabular}  
* Isoelectric point of type I keratin. ** Isoelectric point of type II keratin.  
\end{table} 
Table 1: Molecular weight, isoelectric point and structure of bovine serum albumin (BSA), silk fibroin, keratin, KP-UM and KP-Cryst proteins. formulation, the KP-UM protein is able to dye overbleached hair (at the cuticle and cortex level) while improving the mechanical properties of hair [23]. The KP-Cryst fusion protein combines the thermodynamic stability of the \(\gamma\)D-crystallin protein present in the human eye lenses, with the ability of the keratin-based peptide (KP) to bind to the hair. The KP-Cryst shows a great ability to bind to the hair fibers, to improve the hair's mechanical properties and to reduce the water loss of the hair fibers after heat styling procedures [24]. In this work, we evaluated the abilities of BSA, SF, keratin, KP-UM and KP-Cryst proteins to change the shape of virgin Asian hair. The capacity of the proteins to modulate the shape of hair was determined in terms of hair permeing efficiency across six wash cycles. To validate the proteins as potential products for hair styling, the permeing efficiency determined for each protein was compared with the results obtained for a commercial chemical-based method. Moreover, the protective effect of the proteins and the relation between the hair water content after permeing and the capacity to retain the curling effect was also investigated.
## 3 Methodology
### Rice Mapping  
Firstly, SAR data were pre-processed for thermal noise removing, radiometric calibration and geometric terrain correction. Thermal noise is additional background energy caused by microscopic motions of electrons due to temperature. SAR backscatter values were adjusted by radiometric calibration, meanwhile, effects of side-looking geometry were removed by geometric terrain correction.  
Figure 3: Overall flow chart of the study methodology.  
In this study, SAR imagery pre-processing was rapidly implemented using the Google Earth Engine. After that, the processed S1A data was sampled at 10m resolution, in which each S1A image contained VV and VH bands. The feature extraction step created four time-series datasets--VV, VH, \(\frac{VV-VH}{VV+VH}\) and VVVH to train classification models and test their qualities.  
SVM is a supervised machine learning model which has been widely applied in remote sensing and land-cover mapping in particular [57]. The SVM classifier was selected because it was originally designed for binary classification [58] and also due to the good performance of rice classification of the previous studies in rice mapping [11; 12; 25; 59]. The model parameters (penalty C and gamma) were optimized with kernel function Radial Basis Function (RBF) using cross-validation to reach the best performance of classification [18; 60]. In this study, data from 75 locations were used for training classification models. In total, four classifiers were trained with corresponding features including VV, VH, \(\frac{VV-VH}{VV+VH}\) and VVVH. They were evaluated with independent testing datasets and statistical provincial data based on OA, F1-score (F1) and Kappa score (Kappa). Python programming language with Scikit-learn machine learning library was used for the implementation.
## 5 Discussion
### Open Challenges
#### 5.3.2 Flexible Irregular Beams  
Multibeam satellite systems are most commonly designed based on a regular beam pattern with the same beamwidth for all beams. This regular pattern is very convenient as it reduces the complexity of optimization. Moreover, while some of the beams cover very congested areas with extremely high demand aggregate, other beams only cover low demand. As a result, narrow beams with higher antenna gain can be preferable in high-demand areas, while a broad beam can cover large areas with low population density [57; 58]. However, such joint architectures with a reconfigurable beam pattern and multiple active antennas are challenging, especially from a hardware point of view. Uncertainties resulting from this can reduce overall system performance. A reconfigurable beam introduces new optimization parameters into the RRM problem, such as beamwidth, beam shape and beam orientation [8]. These parameters render RRM problems more complex and less tractable. Therefore, we can anticipate that ML techniques will be of great utility.
# Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer
## 5 Conclusions  
A fusion sensor architecture is introduced, comprising a homogenizer, sensor fusion stage, and voter. It has a dual purpose: generating virtual sensors comparable to the system output and providing reliable estimated signals (state and system outputs) for diagnosers, controllers, etc. To test this architecture, a case study focusing on diagnosing faults in actuators of a mobile robot was conducted, utilizing two diagnosers. In both cases, the results demonstrate that employing the proposed architecture enhances the efficiency of the diagnosis process. Specifically, false positive/negative fault detections are drastically reduced, and diagnoser convergence time is minimized.  
As mentioned above, the reported diagnoser requires the input observability property from the system. Additionally, using a Kalman filter demands fast CPUs for real-time applications. In some cases, these could be considered limitations to be overcome.  
In future endeavors, this architecture will be applied in various domains, including controller design, and particle filters will be considered.  
Conceptualization, O.B., A.L. and A.R.-T.; methodology, O.B., A.L. and A.R.-T.; software, A.L.; validation, O.B., A.L. and A.R.-T.; formal analysis, O.B., A.L. and A.R.-T.; investigation, O.B., A.L. and A.R.-T.; writing--original draft preparation, O.B., A.L. and A.R.-T.; writing--review and editing, O.B., A.L. and A.R.-T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare no conflicts of interest.  
Figure 7: Comparison of the diagnosis with \(\Sigma^{1}\) (**left**) and \(\Sigma^{2}\) (**right**).
## 4 Numerical Results
### Estimation of Parallel Computing Speed-Up  
Since DD-POD does not seem to have an improvement in computation time compared with a direct resolution, parallel computing should be introduced.  
Currently, the part of code in parallel computing has not been achieved; however, one can simulate the parallel computing time by recording the resolution time in each cell several times and calculating the average. Since there are 2500 nodes in each cell and only a few dozen nodes in contact with neighbouring cells, the exchange of information between cells is neglected in the simulation. The estimated time of parallel computing for the material divided into \(2\times 2\times 2\) cells with the cell size being 400 \(\upmu\)m \(\times\) 400 \(\upmu\)m \(\times\) 400 \(\upmu\)m is 6.35 s, which is about 1.6 times faster than a direct solution. When increasing the number of cells, the time of parallel computing does not change significantly when the total number of cells is less than the number of processors if the time needed to exchange information  
Figure 10: The comparison of sorted current and power.  
For the convergence condition based on potential, the resolution stops at the 23 rd iteration if the tolerance is defined as \(10^{-4}\); it stops at the 37 th iteration if the tolerance is \(10^{-5}\); and it stops at the 60 th iteration if the tolerance is \(10^{-6}\).  
For the convergence condition based on current, the resolution stops at the 36 th iteration if the tolerance is defined as \(10^{-4}\); it stops at the 63 rd iteration if the tolerance is \(10^{-5}\); and it cannot stop in 100 iterations if the tolerance is \(10^{-6}\).  
For the same tolerance value, the difference between the potentials obtained by two iterations is reached faster than the difference of current. In addition, for the convergence condition based on potential with tolerance defined as \(10^{-4}\), the relative error is sufficient. With \(\mathbf{B}_{y}\) or \(\mathbf{B}_{z}\) imposed, one has similar results.  
Therefore, the convergence condition based on potential with tolerance as \(10^{-4}\) will be used in the following.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 5 Use Cases  
There are various use cases in telemedicine systems, including the provision of diagnoses through video conferencing, transmission of still images, and simple phone conversation between patients and distant caregivers [119]. Communication in telemedicine can be viewed in terms of the relevant application, such as in emergency situations, or in terms of the type of information transmitted.
## 3 Results and Discussion
### Sensory Analysis of Millet Huangjiu Samples  
Sensory evaluation of millet Huangjiu was performed by well-trained panelists to assess the overall aroma profiles. Six aroma attributes, including alcoholic, caramel, sour, ester, fruit, and Qu aromas, were selected for descriptive analysis. The results highlighted distinct differences in aroma profiles across various samples of millet Huangjiu. Figure 5 shows that the Glu-2 group scored the highest for alcohol aroma with a value of 7.8, while the control group had the lowest score (4.5), with the rest of the millet Huangjiu samples falling within a similar range of 5.3-5.6. This result was consistent with the data observedfor ethanol contents in Table 1. In comparison with the control group, the protein-fortified samples did not exhibit remarkable changes in the scores for caramel (4.3-4.7) and Qu aromas (5.8-6.6), with the Glu + Alb group achieving the most balanced scores (4.5 for caramel and 6.4 for Qu aroma). In terms of ester aroma intensity, Alb-2 scored the highest (7.2), followed by Alb-1 (7), Glu + Alb (6.9), Glu-2 (5.8), Glu-2 (5.7), and the control (5.3). The finding was further corroborated by the results depicted in the heat map, which exhibited a similar trend in the fluctuations of VOC levels in esters. These findings demonstrate that the addition of albumin protein is a viable approach for augmenting the ester and fruity fragrances of millet Huangjiu. Additionally, the score for the sour attribute suggested that an excess of glutelin (Glu-2) in the fermentation substrate may lead to an unpleasant level of sour aroma (score value of 6.6). Collectively, the appropriate amount of protein fortification employing a Glu + Alb combination demonstrated favorable outcomes in enhancing the sensory attributes of millet Huangjiu, while simultaneously preserving its distinctive flavor characteristics.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 3 Dynamic Characteristics of Positioning Stage
### Vibration of Stage in Motion  
In this experiment, the vibration level generated by the stage during the movement was measured within a stroke length of 1000 mm. The variables controlled in the experiments were the preload state of the linear guide and the feed rate, thereby allowing the evaluation of the change in the vibration response on the positioning stage during the movement when the preload changed or an abnormality occurred in motion. To mimic the change in the structural rigidity of the stage, the four slide blocks on the linear rails were preloaded at different amounts, as shown in Table 4. In the first three cases, stages were assembled with the linear guide modules with different preloaded sliding blocks, which were rated in the factory at high, medium, and low preload amounts, respectively. The other two cases were mixed with a combination of high- and low-preloaded guides. The stage rigidity was rated in order from high to low: cases I, IV, II, V, and II.  
In the experiments, the stage was driven to move along the rails at different feed rates, from 2000, 4000, 6000, and 8000 mm/min. In addition, the vibrations of the stage in the X, Y, and Z directions were simultaneously measured by three single-axis accelerometers, which were attached at the mid-points of the two edges and the center of the stage, as shown in Figure 9a. In addition, the vibration responses were also measured by three accelerometers attached at the corner in three axial directions (Figure 9b).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Vibration Mode**} & \multicolumn{6}{c}{**Stage Modal Frequency (Hz)**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Low Preload**} & \multicolumn{2}{c}{**Medium Preload**} & \multicolumn{2}{c}{**High Preload**} \\ \cline{2-7}  & **FEM** & **Experiment** & **FEM** & **Experiment** & **FEM** & **Experiment** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Yawing \\ Rolling \\ Pitching \\ \end{tabular} } & 249 & 246 & 314 & 295 & 345 & 323 \\  & 279 & 266 & 354 & 323 & 393 & 352 \\  & 501 & 626 & 653 & 671 & 730 & 781 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modal frequencies of positioning stage with different preloads.
# Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
## 2 System of Remote-Controlled Robotic Arms  
This section provides a technical overview of the remote-controlled robotic arm system under investigation. It introduces the main hardware components, including the leader (Geomagic Touch) and the follower (QArm), as well as the communication protocol used between them. Additionally, the types of faults considered in this study--sensor faults, actuator faults, and communication faults--are defined. This background serves as a foundation for understanding the fault diagnosis methodology proposed in the subsequent sections.
## 3 Results and Discussion
### Pavement Performance Evaluation  
The correlation analysis between the MTD value of the reconstructed asphalt macro-texture and the results measured by the sanding method and the \(f_{8}\) index is shown in Figure 13.  
Figure 13a shows the correlation between the MTD measured by the sanding method and the MTD values of the asphalt macro-texture reconstructed using the proposed CNN.  
Figure 12: Example of 3D reconstruction results for pavement macro-texture.  
Figure 13: Evaluation of the effectiveness of reconstructed pavement macro-texture: (**a**) Correlation of MTD; (**b**) Correlation of \(f_{8}\).  
The MTD values for both methods are closely aligned along the line y = 0.8417\(x\) + 0.4719, with a correlation coefficient of \(R\) = 0.9131. Figure 13b presents the correlation between the reconstructed macro-texture's\(f_{8}\) feature and the actual measured \(f_{8}\). The figure illustrates that the reconstructed \(f_{8}\) has a strong correlation with the actual measured \(f_{8}\), with a correlation coefficient of \(R\) = 0.9531. These results demonstrate that the 3D macro-texture with a resolution of 208 \(\times\) 144 meets the requirements for pavement condition evaluation. The characteristics of the reconstructed pavement macro-texture can be used effectively to assess the corresponding pavement texture. Compared to the traditional sanding method, the proposed 3D reconstruction approach based on a monocular visual depth network is more applicable. Thus, the CNN-based asphalt pavement macro-texture reconstruction method can partially replace existing methods for asphalt macro-texture reconstruction.
## 2 Principle  
### The Principle of PMD  
As shown in Figure 1, the PMD system consists of sinusoidal fringe patterns displayed on the screen, a camera with an external diaphragm, and the surface under test. Based on the reverse Hartman theory, the light from the pinhole camera reaches the test surface through the entrance pupil center of the camera lens and is reflected off the measured surface to the LCD screen, where the corresponding pixel on the screen is illuminated. Due to the shape of the surface, the phase-shifted sinusoidal fringe patterns that are captured by the pinhole camera will be deformed, and the slope of the surface under test can be obtained by decoupling the deformed fringe. The slope \(\tan\theta_{x}\) and \(\tan\theta_{y}\) at the measured surface can be calculated using the coordinates of the'mirror pixel' M, the screen pixel S, and the camera aperture C.  
The surface slope equations are shown in Equation (1) by [PERSON] et al. [5] in 2010.  
\[\begin{array}{l}\tan\theta_{x}(x_{m},y_{m})=-\frac{r_{x}-p_{x}}{r_{z}-p_{z}} =\frac{\frac{(y_{m}-y_{z})}{d_{m2z}}+\frac{(y_{m}-y_{z})}{d_{m2z}}}{\frac{(z_{m }-y_{z})}{d_{m2z}}+\frac{(z_{m}-z)}{d_{m2z}}}\\ \tan\theta_{y}(x_{m},y_{m})=-\frac{r_{y}-p_{y}}{r_{z}-p_{z}}=\frac{\frac{(y_{m }-y_{z})}{d_{m2z}}+\frac{(y_{m}-y_{z})}{d_{m2z}}}{\frac{(y_{m}-y_{z})}{d_{m2z}}+ \frac{(y_{m}-y_{z})}{d_{m2z}}}\end{array} \tag{1}\]  
where \((x_{c},\ y_{c},\ z_{c})\) are the coordinates of the pinhole camera. \((x_{m},y_{m},z_{m})\) are the coordinates of the sampling point on the test surface. \((x_{s},y_{s},z_{s})\) are the coordinates of the illuminated pixel on the LCD screen that can be calculated by the phase shifting method. \(d_{m2z}\) is the distance between the test surface and the screen and \(d_{m2c}\) is the distance between the test surface and the camera. Then, substituting these coordinates into Equation (1) can obtain the slope data, and the surface is reconstructed with slope-based integration algorithms.
# Fast Numerical Reconstruction of Integral Imaging Based on a Determined Interval Mapping
## 2 Numerical Reconstruction of the Integral Imaging
### Position Error Compensation and Adaptive Normalization  
Figure 4 shows a process for the compensation of the position error and the use of adaptive normalization.  
Figure 3: Image formation model for the proposed numerical reconstruction.  
When each flipped EI is shifted by the determined interval \(\Delta\tau\), the shifted EIs should be mapped in non-integer pixel locations at the reconstruction plane. The position error \(\Delta x_{e}\) between the non-integer pixel locations of the shifted EI and the integer pixel location of the reconstruction plane appears. Thus, this position error \(\Delta x_{e}\) has to be corrected. Prior to the compensation of the position error, the pixels of shifted EIs are rounded down to the nearest integer pixel. These EIs are overlaid with the others to form the superimposed image, and then this image is divided by the number of the superposition. This process can be called an adaptive normalization used to remove the grid noise caused by the unequal superposition. After applying adaptive normalization, the position error in the superimposed image is corrected using interpolation because the interpolation method can estimate the value of the non-integer pixel location. As a result, the refocused image with the correction of position error and grid noise is finally reconstructed.
# The Concept regarding Vehicular Communications Based on Visible Light Communication and the IoT
## 2 Methodology and Design Parameters
### Proposal for Software Infrastructure and Architectural Components
The proposed architecture provides, using several LEDs, an authentication ID regarding the location and identification data for the vehicle, these being managed with cryptography [60; 61]. The network undertakes the distribution of the authentication ID and the lighting sources, a process that increases the degree of security through power lines. Extremely high scalability can be achieved through the efficient management of IDs, highlighting how the use of VLC in a direct approach with new technologies can be extremely interesting. Therefore, ID management guarantees the validity of the IDs and offers a control mechanism through which the necessary data can be obtained. The combination of the existing infrastructure with the IDs generated through validation within the existing nomenclature with preset IDs interchangeable between vehicles offers a new security policy for optical communications.