esa-sceva/satcom-chunk-collection huggingface dataset:
# Photoacoustic Imaging with a Finite-Size Circular Integrating Detector
## 4 Test and Discussion
### Algorithm Performance Testing in a Noisy Environment
To address this, we will further explore the field of inverse problems in the future. Figure 10: (**a**) The relative size of 531 eigenvalues with noise; (**b**) enlarged end of figure (**a**) (omitting the last two points). Figure 12 further compares the processing effects under noisy conditions through images: (a) the original pressure distribution image affected by noise and blurring kernels and (b) the reconstruction results after applying the improved algorithms. The test results show that the improved algorithm significantly suppresses the interference of noise and blurring kernels and substantially improves the clarity of the vascular contours. Table 3 presents a quantitative evaluation of image quality under noisy conditions. The PSNR of the blurred image under noisy conditions is 8.80 dB, and the SSIM is 0.2615. After applying the proposed blurring kernel extraction method under noisy conditions, the processed image shows improved metrics: the PSNR increases to 14.98 dB while the SSIM rises to 0.7113. Although the PSNR remains relatively low, the substantial improvement in SSIM demonstrates the effectiveness of our method in enhancing the overall image structure and visual coherence. Therefore, the proposed blurring kernel extraction method proves successful in addressing noise-related challenges in deblurring tasks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Condition** & **PSNR (dB)** & **SSIM** \\ \hline Blurred image & 8.80 & 0.2615 \\ Processed image & 14.98 & 0.7113 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparison of image quality under noisy conditions. Figure 11: (a) The pressure distribution curve with noise; (b) the pressure distribution curve obtained using the optimization algorithms. Figure 12: (a) Image with added noise; (b) optimized image after the algorithm was improved.
## 1 Introduction and Initial Considerations  
### State-of-the Art  
Real-world systems are often very complex in nature--inherently nonlinear, highly dimensional, time-varying, and, thus, difficult to model or control within the bounds of classical control theory. The solution can often be found in advanced control, i.e., intelligent control techniques [1], which have flourished in the last few decades, especially after breakthroughs in formal mathematical tools related to the optimality, stability, and uncertainty of these systems. These techniques found their applications in various areas such as industry [2], energy [3], or robotic systems [4]. We consider the control method \"intelligent\" if it employs some artificial intelligence computing approach such as neural networks, fuzzy logic, machine learning, evolutionary computation, or genetic algorithms. Intelligent control may often imply combining two or more artificial intelligence tools into hybrid intelligent systems, such as neuro-fuzzy control, which merges human-like reasoning of fuzzy logic with the learning capabilities and connectionist architecture of neural networks. The most valuable approach to intelligent control proved to be the one based on the application of neural networks, due to a combination of their features such as parallel processing, adaptability and power of learning. It has been proven that neural networks with nonlinear differentiable activation functions can approximate arbitrary functions, so that recurrent networks were applied in system identification when a set of input-output data pairs was given. Consequently, neural networks were successfully employed in many control algorithms where an accurate model of the controlled system was needed. Paper [5] provided a systematic classification of neural-network-based control. The main thesis was that all approaches could be classified into two categories: a neural network which was auxiliary to the main control logic, and a neural network which was the main controller. In [6], different aspects of stable adaptive neural network control were discussed. Concrete interesting examples of neural network-based control can be found in [7], where a neural network was applied in the control of the four-rotor helicopter, and [8], in the case of the autonomous underwater vehicle.  
To overcome major drawbacks in using neural networks for modelling and control of dynamical systems (such as setting the optimal neural network structure, initial values, and training method), the authors of this paper experimented intensively with structures called orthogonal polynomial neural networks [9]: single-layer neural networks based on orthogonal functions (Gegenbauer orthogonal functions [10] are used in this paper). These structures proved superior to classical neural networks in terms of accuracy/complexity ratio due to their natural in-built optimality [11]. Designed orthogonal polynomial neural networks were successfully applied in modeling [12] and control [13] of dynamical systems.
## 2 Wake Effect and Lifetime Estimation in DFIG Wind Farm
### Lifetime Estimation of the Power Converter  
As aforementioned, the lifetime of the power converter determines the lifetime of wind turbine system. A flow chart to estimate the lifetime of the power converter in a DFIG wind turbine system is as shown in Figure 4 [32].  
At a constant wind speed \(v\), the wind turbine mechanical power \(P_{m}\) and rotor speed \(\omega_{r}\) are determined by the pitch angle and tip speed ratio according to (1) and (2). In the case of the RSC the current stress \(I_{r}\) and voltage stress \(V_{r}\) of the power converter can be calculated by the DFIG model and power converter model with information regarding the generator speed and the active power [32; 33]. Then, the power loss dissipation of each IGBT \(P_{T}\) and diode \(P_{D}\), both of which consist of the conduction losses and the switching losses, can be calculated with the loss model of the power electronics components [34]. Afterwards, the thermal profile of the power semiconductors can be calculated in terms of the mean junction temperature \(T_{jm}\) and the junction temperature fluctuation \(dT_{j}\), by the thermal model of the power module [34]. They are closely related to the thermal resistance and thermal capacitance of the power module as well as its applied cooling solution. Afterwards, the \(B_{10}\) lifetime data, which is the number of cycles during which 10% of the total number of modules fails [11],  
Figure 4: Flow chart to estimate the lifetime of rotor-side converter from wind speed \(v\) to accumulated annual damage (AD) in the wind turbine.  
Figure 3: **(a)** Speed relationship between the upstream (\(v_{1}\)) and downstream wind turbine (\(v_{2}\)) in Katic wake model; **(b)** thrust coefficient of the NREL 5 MW wind turbine.  
can be obtained from the manufacturer at constant thermal stress, and it can be further extended to the mean junction temperature and the junction temperature fluctuation at certain levels by using the Coffin-Manson model [34; 35]. Finally, the annual damage \(AD\), which is defined as the annual cycles over the cycle-to-failure, can be calculated. If the mission profile is annually repeated, the \(B_{10}\) lifetime expectancy of the power converter-reciprocal of the annual damage can be estimated. Above all, it is evident that the lifetime of the power converter is determined by the pitch angle and tip speed ratio.  
The constant wind speed and active power across the entire year are assumed, and no downtime exists during the operational year. In the case of a real wind profile, the wind speed distribution can be considered by using Miner's rule [36], where the various thermal stresses have the same effect on the wear-out degradation. As a result, the actual annual damage can be calculated considering the weighting factor of the individual wind speed. Meanwhile, due to the wake effect, the wind direction in the wind farm affects the wind speed and active power distribution for each wind turbine. With the wake model and the implemented active power control method, the wind direction distribution factor can also be taken into account.
## 2 Materials and Methods
### Methods
#### 2.3.1 Hydraulic Conductivity Test  
The falling head test was conducted to measure the hydraulic conductivity of the soil specimens after improvement (Figure 4). This method is particularly suitable for fine-grained soils with low hydraulic conductivity and is based on evaluating the rate at which water flows through the soil by observing the decreasing water level over time. A peristaltic pump device was used to perform the falling head test. In this setup, the soil specimen within the mold was sealed to ensure it was watertight and then fully saturated. A transparent vertical tube (1 m in length) was filled with water to the point of overflowing to avoid the presence of air bubbles. Once the tube was filled, the peristaltic pump was stopped and the valve was closed. Then, the valve was reopened, and the change in water level over time was recorded. Using this setup, the change in hydraulic head over time across the soil sample was measured, and the coefficient of hydraulic conductivity (k) was calculated based on [PERSON]'s law. If the water height is h\({}_{1}\) at time t\({}_{1}\) and h\({}_{2}\) at time t\({}_{2}\), then the hydraulic conductivity coefficient is calculated as follows:  
\[\mathrm{k}=\frac{\mathrm{a.L}}{\mathrm{A.(t_{2}-t_{1})}}\ln\frac{\mathrm{h_{1} }}{\mathrm{h_{2}}} \tag{1}\]  
In this equation, k represents the hydraulic conductivity coefficient (cm/s); a is the cross-sectional area of the transparent tube (cm\({}^{2}\)); L is the height of the soil specimen (cm); A is the cross-sectional area of the specimen (cm\({}^{2}\)); h\({}_{1}\) is the initial water head at time t\({}_{1}\) (cm); and h\({}_{2}\) is the final water head at time t\({}_{2}\) (cm).
# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## 1 Introduction
Numerous research studies on MPPT for solar PV systems have been proposed in last few decades and the methods developed so far can be broadly classified into conventional versus soft computing techniques [13]. Conventional MPPT algorithms are based on search algorithms designed to find the maximum output in different external conditions [14], but only under uniform irradiation. These methods generally fail when partial shading occurs on the PV generator, showing poor convergence, slow tracking speed, and often high steady state oscillations [15; 16]. Among the conventional MPPT methods, the most popular are Perturb and Observe (P&O) [17] and Incremental Conductance (IC) [18]. In order to overcome the drawbacks of a specific conventional MPPT technique and to gain better performance, various methods are combined to set up MPPT hybrid algorithms [19; 20]. Soft computing and evolutionary algorithms can offer some advantages, such as their ability to handle non linearity, a wide exploration in search space, and coherent skill to reach global optimal regions [15]. Moreover, they can be combined with conventional MPPT techniques [21; 22] in order to further improve their efficiency. In literature, other comparative studies can be found among different MPPT algorithms on PV modules under dynamic shading conditions [23; 24]. Since partial shading on PV modules may result in power curves with multiple peaks and multiple local minima and maxima [25], the main advantage of using evolutionary computation techniques results in their acting as an effective global optimizer. In this context, the PSO-based MPPT algorithm here proposed appears to be a good tradeoff between simplicity in implementation and accuracy in the tracking of the Global Maximum Power Point (GMPP). This work aims also to compare this novel algorithm based on a bio-inspired approach with traditional MPPT methods in order to evaluate efficiency under partial and dynamic shading conditions. The paper is structured as follows: Section 2 describes critical issues of MPPT algorithms under partial shading conditions and describes, step by step, the proposed MPPT algorithm based on the PSO evolutionary approach. Section 3 reports the simulated test case scenario with related applied shading conditions. Section 4 shows the numerical results and the preliminary experimental tests of the proposed algorithm in comparison with other classical MPPT algorithms with respect to different shading objects and various dynamics. Section 5 draws conclusions based on the results.
## 2 Materials and Methods
### Cost-Effective Data Acquisition System  
The present research is aimed at developing a robust, cost-effective acquisition system for thermoacoustic experimentation. The effectiveness of data acquisition systems in thermoacoustics is limited by the fact that the engines' dynamics cannot be fully analyzed without a reliable data acquisition system.  
This section describes the proposed data acquisition system to calculate the acoustic power of the TA-SLiCE. The system is designed to be both precise and cost-effective, following the approach of previous research [15]. The Arduino-based system, developed and validated in earlier work, was adapted and improved to meet new requirements. The Arduino board captures analog input each firmware cycle and transmits it to the microcomputer via USB as a byte sequence, following the serial communication protocol detailed in [19]. The performance of the proposed system is compared with a high-cost Bruel and Kjaer (B&K) system, using the same type of pressure sensor. The B&K Photon+ system, designed for real-time measurements of vibrations and acoustics, features a high-performance digital signal processor (DSP) and is considered an exact reference for this investigation. All data were stored in comma-separated value (CSV) files for subsequent processing on a Raspberry Pi 3B+, which served as a PC.  
The following describes and justifies the type of sensors used and also the rest of the laboratory equipment to measure and record the raw data obtained by the sensors for further processing and interpretation. Thus, the acquisition system consists of a pressure sensor array, an electronic board that reads the values provided by the sensors, and the algorithms, based on the two-pressure sensor method, that allow for interpreting the sensors' data and calculating the acoustic power.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Common Proposed Key Recovery System Model  
In this section, we present the two key recovery system models proposed in this study. Before describing each proposed model, we present the common elements of the proposed models.
# Implementation and Evaluation of HTTP/3 Connectivity Check Using Happy Eyeballs Algorithm
## 4 Implementation and Experimental Results
### Results of External Delay Setting  
The HTTP/3 adoption rate and connectivity establishment time when external delays were intentionally generated are presented in Table 1, and the measured connectivity establishment time for Alt-Svc is displayed in Table 2. Herein, \(\sigma\) represents the standard deviation.
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
###### Abstract
[17] constructed an occlusion-aware cost volume via dilated convolutions and dynamically modulated pixels from different views. They also developed a disentangling mechanism, which incorporates prior knowledge of the LF structure for depth estimation, as well as for spatial and angular super-resolution, using macro-pixel representation as input [18]. [PERSON] et al. [19] proposed a post-refinement network for bad-pixel correction by taking an initial disparity map as input, introducing a phase-shift-based warping and data augmentation strategy. Most existing algorithms focus on how to explore the depth cues within the spatial-angular information of 4D light fields. However, the extremely narrow baseline and the trade-off between spatial-angular resolution make depth estimation from LF imaging a challenging task, often resulting in poor robustness and high computational costs. In this paper, we focus on applying a hierarchical baseline stereo-matching framework to recover depth maps with local geometric details from a pair of light field stereo images. The light field raw images are captured by a newly designed camera, and this work aims to test the imaging performance of this camera. Without a large dataset and ground-truth depth maps, the proposed framework relies on a traditional stereo-matching strategy for 4D light field data, supplemented by a post-processing step. Furthermore, we evaluate both the accuracy and the processing speed for real-world scenes and rendered synthetic samples. The contributions of this paper are as follows:  
* The imaging process of a micro-lens array-based light field camera is derived. Firstly, a macro-pixel map is built by setting each micro-lens as one macro-pixel in the light field raw image. Then, a feature vector for each macro-pixel is represented by exploiting texture and gradient cues over the surrounding k-ring neighboring macro-pixels. To develop the imaging and reconstruction platform based on a lab-designed light field camera, macro-pixel-wise coarse matching and pixel-wise fine matching are performed as hierarchical baseline stereo-matching. This approach can effectively handle complex locally textured objects and eliminate matching ambiguities. Additionally, a post-processing step with a pre-trained network is applied to improve accuracy. * The imaging performance of the light field camera is tested in terms of accuracy and processing speed by capturing real-world scenes under a studio lighting environment. Furthermore, a quantitative evaluation is carried out on rendered synthetic samples. The experimental results demonstrate that the developed platform can recover depth maps with almost real-time online processing.
# Wedge Design and Dynamic Characteristics Analysis of Unequal-Length Wedge Slant Support Clutch
## 2 Unequal-Length Wedge Diagonal Brace Clutch Design  
### Working Principle of Unequal-Length Wedge Diagonal Brace Clutch  
\"Non-isometric\" wedge diagonal clutch design refers to a nonstandard design method of two or more sets of wedges with different geometric dimensions in one clutch. By modifying the parameters, such as the length of the middle segment of the wedge and the number of wedges, the diagonal brace clutch wedge presents a \"segmented engagement\" effect. This reduces the overshoot of the diagonal clutch-output shaft speed and extends the diagonal clutch engagement time. Owing to its engagement characteristics, the new clutch is called a \"slow coupling\"-type diagonal clutch.  
Figure 2a shows a 3D view of the spray clutch. When a centrifugal-disengagement diagonal brace clutch is in the outer-ring overrun state, the wedge is subjected to the centrifugal moment generated by the centrifugal force under the non-inertial reference frame, and the inner ring and wedge tend to separate. As the rotational speed of the outer ring increases, the wedge completely detaches from the inner ring. When the differential engagement of the slow coupling clutch is made, the theoretical engagement process starts from the clutch part of the wedge contacting the inner and outer rings at the same time and ends with the complete torque transmission of the clutch; the entire differential engagement process is displayed in Figure 2b. Stage A: the clutch starts differential engagement; Stage B: the inner ring speed increases, the outer ring speed decreases, and the difference between the inner and outer ring speeds causes the clutch part of the wedge to experience engagement conditions; Stage C: part of the wedge is engaged, which can transmit a small torque between the inner and outer rings, and the speed of the outer ring continues to decrease such that other wedges contact the inner ring and produce joint conditions; and Stage D: the diagonal clutch is fully engaged.  
Figure 1: Total research flowchart of slow coupling clutch.
## 3 The Proposed Algorithm and Its Applications  
In this section, we propose a novel greedy algorithm FRSVssMP, which can be capable of signal reconstruction without requiring prior knowledge on the signal sparsity. With the proposed initial estimation approach for signal sparsity level, this algorithm integrates strategies of regularization and variable step-size adaptive, and further filters reconstruction results. Since the initial step size, which affects recovery performance obviously, is closely related to signal sparsity, this section presents a new approach to estimate signal sparsity level initially. In the following parts, regularization [16], variable step-size adaptive [19; 20; 21], and filtering mechanism are introduced, followed by the description of steps in details.  
**Regularization**: As illustrated in [16], regularization means to select a set of atoms with maximum energy, of which the maximum absolute value obtained by multiplying column vectors of the sensing matrix and residual is less than twice of the minimum one.  
**Sparsity variable step-size adaptive**: As illustrated in [20], adaptive variable step-size is a powerful step to improve reconstruction accuracy and efficiency. In this paper, we elaborate a new variable step-size pattern with comparison of thresholds and relative error. The idea of adaptive step-size utilized in [20; 21] only adopts one stage of variant step sizes. By contrast, in our algorithm, two stages of variant step sizes are leveraged to further approximate the true signal sparsity level. In the initial phase, a large step size is adopted for rapid approximation. Until the precision reaches a certain high value, a small step size is utilized for accurate approximation. Adaptation is reflected in the setting of a small step size, which can be self-tuning according to characteristics of signals.  
**Filtering mechanism:** Further, the filtering mechanism is proposed to exclude the incorrect supports. After aforementioned steps, the sparse signal can be obtained based upon the confirmed support set by least square method. However, without any prior information of signal sparsity, we are still unable to identify whether the estimated sparsity level is correct enough since the algorithm cannot traverse each possible sparsity. After numerous experiments, we find that for many applications, there is a high probability that the estimated sparsity level is larger than the actual level. In this case, we consider to further filter results for the sake of higher recovery accuracy. Filtering mechanism removes an atom that exerts minimal influence on reconstruction performance in each iteration until the residual cannot be further diminished.  
Since the initial sparsity level estimation exerts an important influence on the subsequent steps, an initial estimation approach for the sparsity level is introduced. Then, the complete steps of the proposed algorithm are illustrated in detail.