esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Properties of SDPPG Signals and Problems in Measurement
### Properties of SDPPG Signals  
There are two types of PPG signal that can optically detect blood volume changes in the microvascular bed of tissues, and that are widely used for healthcare: PPG and SDPPG. While PPG directly measures only the stiffness changes in the microvascular bed of the tissue, SDPPG [16] computes the twice-differentiated PPG signal. Since SDPPG emphasizes the inflection points on the original PPG wave, SDPPG provides more information than PPG; so SDPPG is more effective in diagnosing disease.  
Figure 3 shows the PPG signal and the corresponding SDPPG signal. An SDPPG signal is characterized by the five sequential waves denoted by a, b, c, d, and e in Figure 3, and the wave  
Figure 2: Examples of PPG measurement: ear, fingers, and toes.  
information is used to detect vascular diseases. For example, an SDPPG aging index, defined as (b-c-d-e)/a, is associated with atherosclerosis in elderly patients [13]. Therefore, SDPPG is considered instead of PPG in this paper, even though the algorithm is also applicable to PPG.  
Figure 4 shows the spectrum of PPG and SDPPG signals. The spectrums of PPG and SDPPG are defined between 0 and 10 Hz, and between 0 and 14 Hz, respectively [17], while according to our spectrum analysis the 99% powers of PPG and SDPPG signals are accumulated below 5 Hz and 10 Hz, respectively. Hence, low-pass filtering with cutoff frequency of 20 Hz will not distort the shape of SDPPG signals.
# Artificial Intelligence-Enhanced Colorimetric Assessment of Self-Cleaning Road Marking Points
## 2 Methodology
### Methods
#### 2.2.4 Artificial Intelligence-Enhanced CIELAB Color Space  
The images obtained from the visual analysis technique were digitally processed CIELAB color space measurements enhanced with ChatGPT 4.0 AI. The AI was commanded to read all the pixels in each of the images (samples) within the same range of color coordinates as the spectrophotometer: L* from black (0) to white (100), a* from green (\(-\)100) to red (\(+\)100), and b* from blue (\(-\)100) to yellow (\(+\)100). For each image, the L*, a*, and b* color coordinates were calculated as the average values of these coordinates across all pixels in the sample image, as shown in Equations (1)-(3):  
\[\mathrm{L}^{*}=\frac{1}{\mathrm{N}}\mathrm{\sum_{i=1}^{N}L^{*}i} \tag{1}\]  
\[\mathrm{a}^{*}=\frac{1}{\mathrm{N}}\mathrm{\sum_{i=1}^{N}a^{*}i} \tag{2}\]  
\[\mathrm{b}^{*}=\frac{1}{\mathrm{N}}\mathrm{\sum_{i=1}^{N}b^{*}i} \tag{3}\]  
where N is the total number of pixels in the image, and L*i, a*i, and b*i are the color coordinates of the i-th pixel in each image. Once each image was digitally processed by the AI, the AI was commanded to generate a table displaying the L*, a*, and b* values for each of the processed images (samples).  
After calculating the CIELAB color coordinates using spectrophotometric and AI-enhanced colorimetry, the linear relationship between each coordinate calculated using the two methods was determined and discussed. Linear trend lines were obtained with the respective coefficient of determination (R2) for a*, b*, and L* obtained by spectrophotometry versus AI-enhanced digital image processing.
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 1 Introduction
At the same time, the ionospheric weight model sets the DD ionosphere as an empirical value or empirical model related to baseline length and sampling interval, no longer relying on external ionospheric information, and this is widely used in positioning [13]. The prior value of the DD ionosphere is generally set to zero, and reasonable constraints are made by adjusting the variance of the prior value. The variance of the prior value is the most critical issue in determining the constraint. Prior variance of the ionospheric weight model can be divided into two categories: (1) empirical value methods, where the prior variance value is usually set to 0.05-0.2 m [14; 15], and (2) an empirical model method, which sets the prior variance of ionospheric delay as an empirical model related to baseline length and sampling interval [16; 17; 18]. By utilizing the dynamic variation of the atmosphere, [PERSON] et al. showed that the constraints of ionospheric parameters can be modeled and determined by the observations of the previous day [19]. Unfortunately, the prior values and prior model methods in the abovementioned methods failed to adjust the power spectral density (PSD) in real time according to the observation conditions, so they could not reflect the ionospheric variation in time, limiting the improvement of the constraint accuracy for the ionospheric parameters [20]. Ionospheric observations obtained from the original observations contain atmosphere variation information that can reflect the atmosphere variation over time. First, the troposphere and ionosphere delay information are extracted from the observation of the reference station, but the observation's noise will affect the accuracy of the atmosphere variation information. Therefore, this paper uses random walk to represent the atmospheric variation in time, and it uses white noise to represent the observation noise. Observation noise is separated by its different stochastic characteristics. Then, the clean ionosphere and troposphere are used to obtain the PSD that represents their variation, which can provide more reasonable constraints on the atmospheric parameters. It is no longer necessary to give empirical values or empirical models artificially; by determining the PSD of the atmo sphere in real time according to the observation data, the constraints of the atmospheric parameters can be adjusted in real time according to the actual atmospheric variation, so as to avoid the divergence of results caused by improper constraints. The undifferenced NRTK positioning model is briefly introduced in the Section 2. In Section 3, the reference station ambiguity-fixing method considering constraints for atmospheric time-varying characteristics is studied. The Section 4 of the paper discusses the experimental verification of the performance improvement of the URTK of the proposed method by comparing it with the empirical PSD.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 1 Introduction
In addition, the comparison to ERA5-Land's hourly soil and air temperatures will be performed in a qualitative manner, due to (1) the uncertainties of ERA5-Land's soil and air temperatures; (2) the exceedingly rare availability of direct ground measurements; (3) the high difference between ERA5-Land's resolution (9 km), and our study sites' plot surface areas (several hundredm\({}^{2}\)), which makes a direct comparison difficult. However, as the aim of this study is to evaluate the potential of S1 SAR data product to detect in near real-time frozen/unfrozen events, comparison to ERA5-Land can identify areas where our algorithm does not perform well. The study sites and the used datasets are described in Section 2, followed by a detailed description of our algorithm for mapping frozen areas at the plot scale in Section 3. The main findings and the discussion are presented in Sections 4 and 5, respectively. Finally, Section 6 presents the main conclusions.
# An Eco-Driving Controller Based on Intelligent Connected Vehicles for Sustainable Transportation
## 2 The Cooperative Car-Following System Based on Eco-Driving
### The Hardware Structure of ICVs  
The hardware structure of the ICV was composed of sensors, controllers, and actuators, where the sensors of the perception layer include a GPS positioning module, spacing measurement module, vision module, V2X communication module, and so on, as shown in Figure 2. The V2X communication module was used to communicate information with surrounding ICVs. Besides, an industrial computer equipped with an eco-driving controller is the core of the decision layer. The actuators in the control layer mainly included the throttle control unit, the brake control unit, and the steering control unit.
## 2 Model Development and Simulation Method
### Evaluation Criterions  
For the pressure difference of cooling plates, the coolant pressure drop through channel \(\Delta p\) is defined as follow:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material Properties** & **Value** \\ \hline Dimensions & \\ Plate width (W in mm) & 102 \\ Plate length (L in mm) & 150 \\ Plate thickness (mm) & 10 \\ Channel thickness (mm) & 8 \\ Channel width (w in mm) & 16 \\ Coolant fluid & Water \\ Coolant viscosity at 360 K (Pa-s) & 0.0008623 \\ Coolant conductivity (W-m\({}^{-1}\).K\({}^{-1}\)) & 0.612 \\ Coolant specific heat at 360 K (J-kg\({}^{-1}\).K\({}^{-1}\)) & 4177 \\ Coolant density (kg\(\cdot\)m\({}^{-3}\)) & 996.5 \\ Plate material & Aluminum \\ Plate conductivity (W\(\cdot\)m\({}^{-1}\).K\({}^{-1}\)) & 202 \\ Plate specific heat (J-kg\({}^{-1}\).K\({}^{-1}\)) & 871 \\ Plate density (kg\(\cdot\)m\({}^{-3}\)) & 2719 \\ Boundary conditions & \\ Coolant inlet mass flow (kg\(\cdot\)s\({}^{-1}\)) & 0.1664 \\ Coolant inlet temperature (K) & 360 \\ Coolant outlet pressure (Pa) & 0 \\ Atmosphere temperature (K) & 298 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cooling plate analysis parameters and boundary conditions.  
Figure 1: Schematic of CFD analysis models.  
\[\Delta p=p_{in}-p_{out} \tag{1}\]  
where: \(p\) is coolant pressure, and subscript \(in\) and \(out\) indicate inlet and outlet of the plate, respectively.  
The mass uniformity of the cooling plate is determined by mass distribution of flowing channels. Here, flowing mass uniformity coefficient \(\varphi_{m}\) (mass standard deviation) is defined to reflect mass maldistribution:  
\[\varphi_{m}=\sqrt{\sum_{i=1}^{n}\bigl{(}m_{c}^{i}-m_{c}\bigr{)}^{2}/n} \tag{2}\]  
where \(m_{c}^{i}\) is averaged flowing mass of the \(i\)th channel, and \(m_{c}\) is averaged flowing mass of \(n\) channels. The larger the value of \(\varphi_{m}\), the more uneven the mass distribution in the channels is. If the value of \(\varphi_{m}\) is equal to zero, it reveals that the flowing masses in each channel are all the same and mass maldistribution is eliminated. Therefore, a small value of \(\varphi_{m}\) is the objective of this paper.
## 3 Instrument Validation
### Reference Clock Steering  
The clock steering loop takes less than 10 min to adjust the system reference frequency at its nominal value. Figure 10 shows the reference clock frequency evolution. The upper plot zooms into the first 10 min where the clock frequency converges towards its nominal value, while the lower plot  
Figure 9: **Left**: RF front-end of the SPIR Instrument. The control and recording system is implemented in a standard industrial PC. **Right**: SPIR-UAV instrument to be flown on-board a UAV for the remote sensing of the sea-surface ice of the arctic.  
shows the steady state regime. The standard deviation of the stabilized reference clock frequency is below 0.06 Hz for a period of 14 h.
# Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes
## 5 Adaptive Learned Message Passing Algorithms  
The weights of the static WBP are obtained by training the network offline using a dataset. A WBP where the weights are determined for each received word \(\mathbf{y}\) is an adaptive WBP. The weights must therefore be found by online optimization. To manage the complexity, we consider a WMS decoder with Type \(T_{a}\) parameter sharing. Thus, the decoder has one weight per \(T\) iterations, which must be determined for a received \(\mathbf{y}\).  
Let \(\mathbf{c}\in\mathbb{F}_{2}^{n}\) be a codeword and \(\mathbf{y}\in U\) be the corresponding received word, where \(U=\mathbb{R}^{n}\) for the AWGN channel and \(U=\mathbb{C}^{n_{*}}\) for the optical fiber channel. Let \(\bar{\mathbf{y}}=\mathcal{D}_{\boldsymbol{\gamma}}(\mathbf{y})\) be the word decoded by a Type \(T_{a}\) WBP decoder with weight \(\gamma^{(t)}\in\mathbb{R}^{+}\) in iteration \(t\in[T]\), where \(\boldsymbol{\gamma}=(\gamma^{(1)},\gamma^{(2)},\cdots,\gamma^{(T)})\). In the adaptive decoder, we wish to find a function \(g:U\mapsto\mathcal{X}\), \(\mathcal{X}\subseteq\mathbb{R}_{+}^{\mathsf{T}}\), \(\boldsymbol{\gamma}=g(\mathbf{y})\) that minimizes the probability that \(\mathcal{D}_{g(\mathbf{y})}(\mathbf{y})\) makes an error  
\[\min_{g\in\mathcal{H}}\Pr\big{(}\bar{\mathbf{y}}\
eq\mathbf{c}\big{)}, \tag{13}\]  
where \(\mathcal{H}\) is a functional class. The static decoder is a special case where \(g(.)\) is a constant function. Two variants of this decoder are proposed, illustrated in Figure 3.  
Figure 3: Adaptive decoders. (**a**) Parallel decoders; (**b**) the two-stage decoder. \(\text{WMS}(\boldsymbol{\gamma},L(\mathbf{y}))\) refers to \(\mathcal{D}_{\boldsymbol{\gamma}}(\mathbf{y})\).
## 4 Examples and Analysis
### Introduction of Examples  
The proposal is verified on using the IEEE33 node system which is shown in Figure 19 (Figure 19). The system voltage is 12.66 kV, total active load is 3.715 MW, total reactive load is 2.300 MW, Weibull distribution parameter k = 2.30, C = 8.92, wind power access cost 6500 yuan/kW, operation and maintenance cost 0.3 yuan/kW\(\cdot\)h, environmental protection subsidy 0.1 yuan/kW\(\cdot\)h, rated lighting intensity of photovoltaic generator is 1 kW/m\({}^{2}\), shape parameter B of beta distribution is 0.85, photovoltaic access The cost is 10,000 yuan/kW, the operation and maintenance cost is 0.2 yuan/kW\(\cdot\)h, the rated capacity of a single distributed power supply is 125 kW, the equipment life is 20 years, and the discount rate is 0.1. The repair time of N-1 fault is 4 h, and the comprehensive safety index value is set to 0.5. Wind power installation nodes are 5, 7, 11, 12. The photovoltaic installation nodes are 20 and 23.  
On the premise of considering both comprehensive security indicators and active management mode, the economic and technical comparison of DG optimal allocation under the annual time series scenarios, typical day scenarios and reduced scenarios is made. Full-time scenarios are selected as benchmarks to test the planning schemes under the other two scenarios. The annual average of each period is selected as the data of typical day scenes. A summary of the three plans is shown in Table 2.  
Figure 19: IEEE-33 node distribution system.
# Laser Spot Centering Algorithm of Double-Area Shrinking Iteration Based on Baseline Method
## 4 Results and Discussion
### Experiment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Gaussian Fitting**} & \multicolumn{2}{c}{**Traditional Centroid**} & \multicolumn{2}{c}{**Square-Weighted Centroid**} & \multicolumn{2}{c}{**Proposed**} \\ \hline
**Number** & **x (Pixels)** & **y (Pixels)** & **x (Pixels)** & **y (Pixels)** & **x (Pixels)** & **y (Pixels)** & **x (Pixels)** & **y (Pixels)** \\ \hline
1 & 800.536 & 631.156 & 800.581 & 631.218 & 800.483 & 631.241 & 800.522 & 631.199 \\
2 & 800.419 & 631.208 & 800.498 & 631.272 & 800.542 & 631.149 & 800.499 & 631.206 \\
3 & 800.466 & 631.218 & 800.685 & 631.192 & 800.547 & 631.195 & 800.488 & 631.220 \\
4 & 800.504 & 631.168 & 800.487 & 631.207 & 800.612 & 631.234 & 800.526 & 631.196 \\
5 & 800.548 & 631.210 & 800.453 & 631.118 & 800.371 & 631.318 & 800.532 & 631.208 \\
6 & 800.344 & 631.345 & 800.547 & 631.394 & 800.501 & 631.118 & 800.492 & 631.221 \\
7 & 800.514 & 631.168 & 800.582 & 631.245 & 800.484 & 631.215 & 800.514 & 631.216 \\
8 & 800.489 & 631.262 & 800.391 & 631.247 & 800.625 & 631.307 & 800.497 & 631.195 \\
9 & 800.461 & 631.233 & 800.347 & 631.195 & 800.521 & 631.165 & 800.503 & 631.215 \\
10 & 800.525 & 631.192 & 800.476 & 631.290 & 800.393 & 631.197 & 800.511 & 631.198 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Laser spot center located by different algorithms. Figure 10: Laser spot images are collected by CCD: (**a**) experimental platform; (**b**) continuous image acquisition. The experimental findings show that the fluctuation range of the center positioning result of the proposed method was small when the center of the same laser spot was detected continuously. The locating stability for laser spot using the proposed method was improved sevenfold compared with the traditional centroid method and improved five-fold compared with the Gaussian fitting algorithm. It should be noted that this study had some shortcomings. We only focused on cases where the intensity distribution of the laser beam was Gaussian, while ignoring the beam distortion, top-hat beam, etc. Theoretically, the method is not affected by the spot morphology, which we will verify in future experiments.
# Thermographic Non-Destructive Evaluation for Natural Fiber-Reinforced Composite Laminates
## 1 Introduction
Ultrasound is a conventional and effective method for composite panels such as carbon fiber-reinforced polymer plastic (CFRP). It needs wedge such as water with the aim to get rid of air, but most of the plan fiber polymer composite materials are not water-proof. Air-coupled ultrasound has been developed, but it cannot penetrate thick materials which are often used in plant fiber composite applications. X-ray computed tomography (CT) is expensive, time-consuming, harmful to human body, and its contrast is relatively low [12]. Recently infrared thermography (IRT) is becoming increasingly attractive as a NDT method due to the fact that it has faster inspection-acquisition rate, higher spatial resolution and contrast, and it is contactless. The development of infrared image processing algothrims also enhances its advantages for use. It has been used in different fields such as electrical components, medical imaging, thermal comfort, in-line industrial monitoring, buildings, artworks, composite materials [13; 14; 15; 16; 17; 18]. Although IRT \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Density** & **Elastic** & **Tensile** & **Elongation to** & **Specific E Modulus** & **Specific Tensile Strength** \\  & **(g/cm3)** & **(GPa)** & **(GPa)** & **Fracture (\%)** & **(GPa per g/cm3)** & **(GPa per g/cm3)** \\ \hline E-glass fiber & 2.56 & 76 & 1.4–2.5 & 1.8–3.2 & 30 & 0.5–1 \\ Basalt fiber & 2.8 & 89 & 2.8 & 3.15 & 31.78 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: E-glass and basalt fiber properties [8]. has been applied in many fields, quantitative analysis is becoming increasingly important aside from inspection, which need further study, e.g., depth information, etc. [19; 20]. In this paper, IRT was used to evaluate impacted basalt fiber reinforced polymer laminates (BFRP), jute/hemp hybrid fiber-reinforced polymer laminates, and homogeneous particleboards of sugarcane bagasse (SCB). Different types of defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, SCB is a new type of PB made of bagasse fibers and bio-resins derived from castor oil. Pulsed phase thermography (PPT) and principal component thermography (PCT) were used to process the raw data. Ultrasonic C-scan testing (UT) and emerging continuous wave terahertz (CW THz) imaging were used on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different thermographic techniques for natural fiber-reinforced polymer composites inspection was given.
## 3 The Stability Control Strategy of the 4 WS Vehicle
### The Design of H2/H\(\infty\) Robust Controller  
The H2/H\(\infty\) robust controller is designed to improve the robustness and performance of the 4 WS stability control system, and the control structure is shown in Figure 13. The control system inputs are the front wheel angle \(\delta_{f}\), the yaw rate sensor noise \(n\), the lateral interference force \(f\), and the yaw interference torque \(T_{f}\). The current \(I\) is the output of the H2/H\(\infty\) robust controller. The outputs of the control system are divided into three parts: \(z_{1}\), \(z_{2}\), and \(z_{3}\). \(z_{1}\) is responsible for good tracking, external disturbances attenuation, and the noise restrain ability of the control system. \(z_{2}\) reflects the energy consumption of the controller. \(z_{3}\) reflects the robust stability of the control system.  
Figure 12: The bode diagrams of the weighting functions: (**a**) \(W_{1}\); (**b**) \(W_{3}\).  
Figure 13: The stability control structure of the 4 WS vehicle.  
According to the stability control structure of the 4 WS vehicle mentioned above, the standard H2/H\(\infty\) design problem in this paper can be shown in Figure 14. The H\(\infty\) norm is responsible for improving the control system's robustness and reducing output energy. The H2 controller is used to enhance the control system's dynamic performance. The generalized system can be described as follows.  
\[\left[\begin{array}{c}z_{1}\\ z_{2}\\ z_{3}\end{array}\right]=F(P,K)\left[\begin{array}{c}\delta_{f}\\ f\\ T_{f}\\ n\end{array}\right] \tag{36}\]  
where  
\[F(P,K)=P_{22}+P_{21}K(I-P_{11}K)^{-1}P_{12}\]  
\[P=\left[\begin{array}{cc}P_{11}&P_{12}\\ P_{21}&P_{22}\end{array}\right]\text{.}\]
# A Review of AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes on Sapphire
## 18 General Conclusions and Future Outlook
Accordingly, the use of encapsulation in addition to a thick sapphire layer is considered to be a practical solution. Despite the disadvantages of using a p-GaN layer, the WPE reached a competitive level, meaning that our device can be used as an alternative to high- and ultrahigh-pressure mercury lamps, and then Sub-W/cm\({}^{2}\) irradiation modules for UV curing were demonstrated to be alternatives of high- and ultrahigh-pressure mercury lamps. Low-pressure mercury lamps have high efficiency and the market price is reasonable. The most important potential application of DUV-LEDs is the sterilization of air and water, which is currently performed by low-pressure mercury lamps. The performance of DUV-LEDs is not yet sufficient to be used an alternative to mercury lamps. However, a relatively short-term improvement in IQE at wavelengths of 265-280 nm is considered to be possible by reducing the point defect density of V\({}_{\text{Al}}\). For example, incorporating Si-doped QWs is effective to reduce the number of V\({}_{\text{Al}}\), although it is difficult to improve the CIE. From an industrial viewpoint, the authors consider that the cost-feasible production of the sterilization application is a priority; thus, the development of the larger growth apparatus and cost-feasible packaging should be tackled as priority tasks. All the authors have participated in the literature review and in the correction and revision of the manuscript. This research received no external funding. The authors declare that there are conflicts of interest between the manufacturers of AlGaN-based DUV-LEDs. Therefore, researchers of the institutes having a grant from DUV-LED manufacturers might be involved in the conflicts of interest, because business of DUV-LEDs started recently.