esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### Lateral Scanning  
Analysis of the final holograms is accomplished in a rotation-scan setup with collimated probe beam. The transmitted signal of a 543 nm HeNe laser is detected while the hologram under test is rotated. From the angular resolved transmission information, the grating constant \(\Lambda\) by use of Equation (1) is derived, while the grating slant \(\Phi\) is determined by the bisecting line of the angular positions of the first diffraction orders.  
The probe beam features a diameter of 0.2 mm. Probing only a tenth of the exposed area is primarily for the purpose of measuring precision [7,27]. However, it also enables the scanning of the grating by moving the sample perpendicular to the optical axis. A sequence of rotation scans through the grating diameter constitutes a lateral scan. This analytical method allows the determination of the hologram characteristics along the sample surface. Thereupon, it is possible to compare and track respective properties from the centre of the grating to the edges, corresponding to the areas of highest and lowest recording intensity. As a consequence, spatial sequences of the grating parameters are derived, providing insight into the local material characteristics.  
In order to approach spatially resolved information on the Bragg selectivity, the location-related output of the individual rotation scans must be correlated and compared with respect to the lateral position. Analysing the grating constant \(\Lambda\) and the grating slant \(\Phi\) to such an extent, yields lateral progressions, _i.e._, \(\Lambda(x)\) and \(\Phi(x)\) curves, respectively, to be interpreted in terms of material response and optical shrinkage effects.  
Furthermore, every single lateral position is assigned to a certain local exposure dose, determined by the Gaussian intensity distribution of the recording beams. As a consequence, a lateral scan contains energetically resolved information as well. The respective allocation of the abscissa to the local energy density allows one to draw conclusions on the influence of the recording dose.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 1 Introduction
To make the extracted fault characteristic clear, they proposed an iterative selection process for the first and M-shift to maximize the CK. Combining CK and Redundant Second-Generation Wavelet Package Transform (RSGWPT), [PERSON] proposed an improved kurtogram in [19]. In addition to these high-frequency resonance techniques, a non-resonance-based approach is desirable in an industrial environment, such as the Auto-regression moving average [20] and higher-order energy operator fusion methods [21]. The effectiveness of these methods has been verified when a shaft rotates at constant speeds. However, bearings usually operate at variable speed conditions in practice, which leads the fault features to no longer be discrete frequency lines, but rather, frequency bands related to the shaft rotating frequency [22; 23; 24]. During speed up and speed down processes, impulses induced by the faults are non-periodic in the time domain, which means that the method based on the indexes derived from the kurtosis index will be weakened in non-stationary feature extraction. Therefore, the question of how to recover the fault impulses from the signal collected in the varying speed conditions must be solved. In this paper, a new index, Order Spectrum Correlation Kurtosis (OSCK) is proposed. By replacing the OSCK with the kurtosis in the FK, an improved kurtogram, Fast Order Spectrum Correlation Kurtogram (FOSCK) is constructed. In this method, the original non-stationary vibration signal is filtered by a 1/3 binary tree strategy, the envelope of each filtered signal is calculated by using the Hilbert transform, and the trend term of each envelope is eliminated by a low-pass filter whose cutoff frequency is lower than the minimum of the rotating frequency. Then each envelope signal is resampled into a stationary one in the angular domain using the Computed Order Tracking (COT) technique to remove the effects of speed fluctuation. The OSCK of each resampling envelope signal is calculated and utilized to generate a diagram in which the frequency band corresponding to the highest value can then be considered for further analysis. A band-pass filter is set to maintain the desired band, and is used to extract the optimal frequency band signal. After that, the envelope of the filtered signal is resampled into angular domain by using the COT. The order structure of the angular domain envelope signal can be used to determine the type of fault by identifying its characteristic order. Compared with the FK, the WPTK and the Prottrugram, the proposed method can extract bearing fault characteristic information more exactly under relatively large speed fluctuations and heavy interference environments.
## 4 Optimization Scheduling Model
### Solution Method  
CPLEX, a high-performance mathematical optimization engine, integrated with MATLAB 2019b software, is widely utilized to solve complex mathematical optimization problems such as mixed-integer linear programming. In this study, given the mixed-integerlinear programming nature of the model, the CPLEX solver within MATLAB is employed to solve the proposed optimization scheduling model.
## 2 Materials and Methods
### Hardware and Physical Architecture
#### 2.1.2 Lower Package  
The lower package consists of an albedometer and diffuse radiometer (Figure 1, Table 1). The albedometer (Schenk Dual-Pyranometer 8104; Grass Valley, CA, USA) measures net broadband radiation and thereby determines how much incoming radiation is absorbed and reflected. Radiation between 300 and 700 nm is mostly absorbed by plants, and these measurements provide the total canopy reflectance across all wavelengths. Downwelling solar radiation can also be compared to top-of-the-atmosphere solar radiation to determine the opacity of the atmosphere, which could provide insights for the amount of diffuse solar radiation.  
The diffuse radiometer (Irradiance Rotating Shadowband Radiometer (RSR2); Cambridge, MA, USA) measures direct and diffused light separately and allows for quantifying the incoming radiation components. At least every 30 s, a narrow arm passes over the sensor to cast a shadow. This movement briefly blocks direct sunlight, and the sensor only measures diffused light during that time. The radiometer measures diffused light more frequently under partly cloudy conditions than sunny conditions in order to track rapidly changing irradiance.  
The lower package also includes the control box. The control box houses a single-board computer, data logger, small battery, and cellular modem (Figure 1, Table 1). The single-board computer (Raspberry Pi) controls activities of the sensors and components in a synchronized manner, monitors functionality of the upper package, temporally stores measurements, and wirelessly transmits the temporally stored data to the remote server through cellular connection. The data logger (CR1000) logs and records measurements collected using the thermal IR sensors, albedometer, and diffuse radiometer as an average over two time periods: every 1 min and 30 min.
# PAR: Towards a Reference Architecture for Accessible Platforms in Respiratory Therapies
## 4 Discussion
### Sustainability of the PAR Platform  
User sustainability on our platform, PAR, is ensured through several key strategies:  
User-centered design: PAR has been developed with a user-centered design approach, where users' needs, preferences, and capabilities are taken into account at every stage of development. This case ensures that the platform is intuitive, easy to use, and aligns with the expectations of those who interact with it.  
Education and Training: PAR focuses on providing therapeutic education to patients and emphasizes the education and training of healthcare professionals involved in respiratory therapies. This dual approach contributes to the platform's sustainability by empowering patients and professionals, ensuring continued and effective use of the platform.  
Telemedicine Integration: Integrating telemedicine technologies improves the efficiency and accessibility of respiratory therapies. By enabling remote interaction and monitoring, PAR reduces the need for frequent in-person visits, making it more sustainable for users, especially those facing challenges traveling to healthcare facilities.  
Technological robustness: PAR is designed to be robust and adaptable to evolving technologies. Regular updates and improvements are part of the platform's sustainability strategy, ensuring that it remains compatible with the latest devices, operating systems, and technological advances. This adaptability improves the longevity and relevance of PAR users.  
Cost-effectiveness: The integration of telemedicine technologies not only improves accessibility but also contributes to cost reduction. Users can reduce associated costs such as transportation and time off work by minimizing the need for physical visits. This cost-effectiveness improves the sustainability of PAR, making it an economically viable option for both healthcare providers and patients.
# An Adaptive Reversible Data Hiding Scheme Using AMBTC and Quantization Level Difference
## 2 Related Work
### AMBTC Scheme of [PERSON] et al.'s Method  
In 2015, [PERSON] et al. presented a reversible data hiding scheme for AMBTC-compressed images by considering the mean value and the standard deviation to achieve a high payload and high-quality modified images. Following are details about the major steps of the embedding processing.  
Step 1: Define three parameters, i.e., the bitmap \(bm\), mean pixel value \(u\) and standard deviation \(\alpha\) using Equations (1), (2) and (5), respectively.  
Step 2: Define four different scenarios for each pixel of a given \(k\times k\) block. As shown in Table 1, if the secret bit is \"1\", and the bit in bitmap is \"0\", then the corresponding scenario is \"10\" by combining secret bits with bits of the bitmap.  
Step 3: Determine the cover image block is embeddable or non-embeddable. If there are only one or two different scenarios, this cover block is a non-embeddable block. If the types of scenarios are equal to three or four, the cover is an embeddable block.  
Step 4: Determine the hiding strategy. The detailed Algorithm 1 for hiding strategies is shown below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Case00** & **Case01** & **Case10** & **Case11** \\ \hline secret bit & 0 & 0 & 1 & 1 \\ Bit in bitmap & 0 & 1 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Four scenarios for each pixel of a given block.  
Figure 1: Example of absolute block truncation coding (AMBTC) encoding procedures.  
```
Input: current scenarios Output: corresponding pixel value \(cpv\) corresponding pixel value Switch(current scenarios){  case00 : \(\texttt{cpv}=u-\alpha;\textit{break}\);  case01 : \(\texttt{cpv}=u+\alpha;\textit{break}\);  case10 : \(\texttt{cpv}=u-\alpha-1;\textit{break}\);  case11 : \(\texttt{cpv}=u-\alpha+1;\textit{break}\); } return\(cpv\);
```  
**Algorithm 1**Hiding strategy of [PERSON]'s method.  
Figure 2 shows an example of the strategy. Figure 1(a) shows the secret bits and the corresponding bitmap. Figure 1(b) shows a combination. There are four types of in this example. Figure 1(c) shows the result according to Algorithm 1.
Therefore, a scalable solution with data-centric QoS-aware routing that can provide a clear differentiation in route selection between data packets with multiobjective QoS requirements, is greatly required for BSNs. In the literature, several mechanisms (outlined in Section 2) have been proposed to mitigate the problems of multiobjective QoS provisioning in wireless sensor networks. However, to the best of our knowledge, no effective solution to this problem has yet been proposed so far particularly for BSNs. The key contribution of this paper is the first complete design and evaluation of data-centric multiobjective QoS-aware routing for BSNs that has clear differentiation in route selection between multiple traffic types with respect to their QoS requirements. It also trades off the energy cost and protocol operation overheads while improving the network performance. The proposed data-centric multiobjective QoS-aware routing protocol, DMQoS, uses modular architecture and it exploits geographic locations to implement localized routing. An important property of the proposed protocol is the end-to-end QoS-aware routing with local decisions at each intermediate node without end-to-end path discovery and maintenance. This property is important for scalability to large-scale sensor networks, self-adaptability to network dynamics, and appropriateness to multiple classes of traffic flows. The routings of delay-critical and reliability-critical packets are handled separately by employing independent modules for each, whereas for the most critical packets having both stringent delay and reliability constraints, the corresponding modules operate in coordination to guarantee the required service. While the delay control module chooses the next-hop router node offering higher velocity of data packets, the reliability control module injects minimal redundant information by exploiting high reliability links. A Lexicographic Optimization (LO) [4] based approach is used to tune trade-off between the geographic progress and the residual energy levels. Therefore, our model considers not only the QoS requirements, but also the energy cost optimality of the routing path to improve the overall network performance. We evaluated our routing protocol in terms of end-to-end packet delays, packet delivery ratio, average energy consumption per packet, and routing overhead for variable source traffic loads and wireless link bit error rates. The results show that DMQoS demonstrates a substantial improvement in required data delivery services over several state-of-the-art approaches and we provide insights into the sources of the improvement. The rest of the paper is organized as follows. In Section 2, we describe the key limitations of some existing QoS-aware routing protocols. Subsequently, we present a body area sensor network model and assumptions we have considered in Section 3. Section 4 presents the proposed DMQoS architecture in detail, followed by the performance evaluations using Network Simulator-2 [5] in Section 5. Finally, we conclude the paper in Section 6.
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 4 Discussion
However, it outperforms other comparison methods significantly in terms of the _PSNR_ and information entropy indicators, which suggests that the algorithm in this paper has outstanding comprehensive enhancement effect and can retain more image detail. This paper evaluates the proposed algorithm's effectiveness by conducting experimental comparisons with several classic algorithms. The images from Figure 10 are utilized for this verification process, and the average values of the indicators after applying each algorithm's enhancement are recorded in Table 2. The analysis reveals that the algorithm proposed in this paper surpasses other algorithms in terms of comprehensive enhancement capabilities. The method proposed in this paper is not perfect and has some shortcomings. As can be seen from Figure 10, where there is very low light, the saturation of the image after enhancement still needs to be improved, although it is considered a better choice. These deficiencies are still the next task to be studied, and with the development of image enhancement algorithms, creating more perfect methods to solve existing enhancement problems is the way forward.
# Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems
## 3 Conventional Detection Schemes  
In this section, conventional detection schemes are represented, i.e., ZF, DFE, and QRD-\(M\), which are basic schemes for explanation of the proposed detection.
## Proposed System
### Hardware Configuration
The operation of the system is controlled by a programmable interface controller (PIC) equipped on a microprocessor board MA350. Due to the usage of a wide variety of rechargeable batteries, the hardware simulator proposed in this paper has to manage a wide voltage range. The challenge to design an optimal storage system, which brings out the full potential of each battery, is necessary. In order to examine the characteristic, the proposed MTC has to test any typical secondary batteries, such as lithium-ion (Li-Ion), nickel-metal hydride (Ni-MH), and lead-acid (Pb) batteries. As long as the specification of the battery pack is within the range from 2 to 12.6 V. Table 1 shows the specifications of the proposed MTC hardware scaled simulator. The number of parallel batteries (i.e., the current amplitude) can be increased by changing the final power MOS-FET. Another important and innovative feature of this method is the ability not to rely on initial values of SoC or other parameters, and it can be applied to any battery pack composition. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Min** & **Typ.** & **Max** \\ \hline Supply voltage (ACV) & 100 & - & 240 \\ Operating voltage (V) & - & 12 & - \\ Operating current (A) & - & - & 2 \\ Battery voltage (V) & 2 & - & 12.6 \\ Battery current (A) & \(-4\) & - & 4 \\ Frequency range (Hz) & 0.01 & - & 100 \\ External DAC (V) & 0 & - & 2 \\ Temperature range (\({}^{\circ}\)C) & 0 & - & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specification of the MTC (multi type chemistry charging/discharging). Figure 1: Block diagram of the charging/discharging circuit. The battery control of the developed simulator is based on a current control method. In addition, the system can control the battery according to the battery power by the current control with the measured battery terminal voltage. The appearance of the system is shown in Figure 2.  
Regarding the protection system, it is based on the use of an emergency stop switch. The temperature level drastically improved from last model [10], which is maintained cooled using a fan based on forced air method, with noise level from 12.8 dB to 28 dB. The overall size of the MTC device is optimal for mobile applications to be carried out for test outside the conventional laboratory environment, with device size of \(208W\times 282D\times 72H\) (mm).
# On Transient Stability Problems in DC Microgrids
## 2 Transient Stability Problems in AC and DC Microgrids
### Transient Stability Problems in AC Microgrids  
Figure 1(a) illustrates a simplified model of the SG or grid-forming power converter in AC microgrids, where the two AC voltage sources model the power converter (or the SG) and grid, respectively. Specifically, \(v_{\mathrm{c}}\) and \(v_{\mathrm{g}}\) represent the root-mean-square (rms) converter and grid voltages, respectively. \(\delta\) and \(\phi\) denote the converter voltage and current phase angles (with respect to the grid voltage phase angle), respectively.  
\(I_{\mathrm{c}}\) and \(X_{c}\) are the converter current and line reactance, respectively. \(j\) is the notation of imaginary numbers.  
By extracting the real part of the product of the converter voltage and conjugate current phasors, we obtain (1)  
\[P_{\mathrm{c}}=\frac{V_{\mathrm{c}}V_{\mathrm{g}}}{X_{\mathrm{c}}}\sin\delta, \tag{1}\]  
where \(P_{\mathrm{c}}\) denotes the power injected from the converter or SG to the AC grid.  
In AC microgrids, the fundamental idea of active power regulation is via power angle or frequency control. In this regard, a positive relationship between the active power \(P_{\rm c}\) and the power angle \(\delta\) is essential for stable operation. Otherwise, an increase of \(\delta\) will lead to a drop of \(P_{\rm c}\), which further causes the controller to increase \(\delta\) and leads to positive feedback and instability. Referring to Figure 3a and (1), we find that the requirement of transient stability limits the allowable power angle to \((\delta_{min}\delta_{max})\) or \(\pm\pi/2\). Accordingly, the maximum (or minimum) active power takes the form of \(P_{\rm{cmax}}=V_{\rm c}V_{\rm g}/X_{\rm c}\) ((or \(P_{\rm{cmin}}=-V_{\rm c}V_{\rm g}/X_{\rm c}\))).
# SNR Analysis for Quantitative Comparison of Line Detection Methods
###### Abstract  
The need for line detection in images is growing rapidly due to its importance in many image processing applications. The selection of an appropriate line detection method is essential for accurate detection of line pixels, but few studies provide an analytical basis for selecting a specific line detection method. In this study, to solve the problem, a method to analytically determine the signal-to-noise ratio (SNR) of line detection methods is proposed. Three line detection methods were selected for comparison: edge-detection (ED)-based, second derivative (SD)-based, and the sum of gradient angle differences (SGAD)-based line detection methods. Then, this study quantifies the SNR of the three line detectors through error propagation and signal noise coupling. In addition, the derived SNRs are graphically visualized to explicitly compare the performance of line detectors. Then, the quantified SNRs were validated by showing that they are highly correlated with the completeness and correctness observed in the experiment with a set of natural images. The experimental results show that the proposed SNR analysis can be used to select or design a suitable line detector.  
error propagation; line detection; quantitative comparison; signal-to-noise ratio +
Footnote †: journal: Applied Sciences  
**Keywords:** error propagation; line detection; quantitative comparison; signal-to-noise ratio
# Denoising of Wrapped Phase in Digital Speckle Shearography Based on Convolutional Neural Network
## 3 Experiments  
In this study, we conducted simulation experiments using an NVIDIA RTX 1080 Ti GPU (NVIDIA, Santa Clara, CA, USA) with 11 GB of memory and implemented the Python 3.6 programming language. The neural network was trained and tested using PyTorch 0.4.1. Our training dataset consists of 21,847 speckle-wrapped phase images with different shear amounts and different 3D deformations, each with a size of 480 \(\times\) 480 pixels. During SDCNN training, we employ a chunking strategy to divide the images into 32 \(\times\) 32 pixels. Finally, the training effect is verified using 100 random images generated in the same way. Furthermore, we utilize both PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural SIMilarity) and RMSE (Root Mean Square Error), to holistically evaluate the denoising effectiveness.