esa-sceva/satcom-chunk-collection huggingface dataset:
### Heat Pipe Exucuated Tube Solar Collectors  
Heat pipe evacuated tube solar collectors (HPETCs) consist of a heat pipe within a vacuum-sealed tube, with multiple tubes connected to a single manifold. The vacuum minimizes convection and conduction losses, allowing higher operating temperatures than flat plate solar collectors. Each collector has a heat pipe inside a vacuum-sealed tube, typically a sealed copper pipe attached to a black copper fin that acts as the absorber. At the top, a metal tip connects to the condenser. The heat pipe fluid evaporates with solar heat, travels to the heat sink, condenses, and releases heat, repeating the cycle [11].  
A range of small-scale industrial and domestic solar applications include water heating systems, solar desalination, space heating, simple power plants, solar water desalination, drying systems, and electrical power production sectors [16]. The works presented by [16; 17] reviewed the latest advancements, types, sizing, working fluids, incorporation techniques, thermal performance analysis methods, and applications of heat pipes across various engineering fields.  
Concerning works that deal with the operation of the HPETC with different heat transfer fluids, an experimental study was developed by [18] for an evacuated tube heat pipe solar collector using various fluid, including water and ethylene glycol. They found that pure water gives higher values of collector efficiency, and as the concentration of ethylene glycol increases, the collector efficiency decreases. Previous theoretical and experimental works explored the use of long-chain alcohol mixtures at appropriate concentrations to obtain a better performance in heat pipes [19], as well as a comparison of diverse fluid types (water, ammonia, acetone, methanol, and pentane) [20]. Six working fluids were investigated in [21], the results suggesting that chloroform and acetone are the best choices in terms of energy and exergy performance.  
Regarding the comparison of the types of solar collectors involved in this study, in [22], an experimental assessment of the thermal performance of a one-ended evacuated tube solar air collector and a flat plate air collector installed in India was presented. The results obtained indicated that the evacuated tube solar air collector exhibits superior thermal performance compared to the flat plate solar collector across various flow rates. Comparing two major solar installations [23] revealed that evacuated tube solar collectorsoutperform flat plate collectors in solar energy productivity for the absorber area. The authors summarized well the previous works on comparisons of these systems ([24; 25] among others). The study by [PERSON] et al. [26] compared the performance of ETCs, FPCs, compound parabolic concentrators, and thermosiphon-driven systems in Australia and Pakistan. The results showed that evacuated tube collectors achieved the highest solar fraction and collector efficiency. Another study [27] compared the performance of flat plate collectors and evacuated tube collectors in different climates using TRNSYS 16 software, indicating the suitability of each type under various conditions.
## 2 Materials and Methods
### Calculation of Process Parameters  
To calculate the process parameters, different variables need to be taken into consideration. The changes in these variables occur due to the desired characteristics of the product, which have different combinations of width (W) and thickness (e). Within these combinations, the neck height (NH) was also varied. These variables are seen in Figure 2.  
The process parameters that where calculated were the following:  
* The blow-up ratio (BUR), which is calculated according to Equation (1), where \(D_{f}\) is the ratio of the blown diameter, which is also \(2/\pi\) (around 0.637) times the width of the collapsed film, and \(D_{o}\) is the initial diameter [8]. \[\text{BUR}=\frac{D_{f}}{D_{o}}\] (1) \[\text{BUR}=\frac{2W}{\pi D_{o}}\] (2)
* The take-up ratio (TUR), defined in Equation (3), is expressed as a ratio between the speed of the film above the height of the freezing line (given with the pulling rollers) and the melting speed at the exit of the die [9]. \[\text{TUR}=\frac{V_{f}}{V_{o}}\] (3)  
Figure 1: Differences between two configurations: (**a**) in the pocket and (**b**) high stalk.  
In the current study, the melting speed was measured by calculating the amount of time in which a dot that is just leaving the die exit reaches a given distance (in this particular case, 24 inches), thus calculating the initial linear speed of the material leaving the die. The final speed was calculated using the angular speed of the pulling rollers, turned into linear speed.  
Nevertheless, using the variables from this study shown in Figure 3, the TUR could also be calculated using the mass conservation principle [13]:  
\[\rho_{f}A_{f}v_{f}=\rho_{o}A_{o}v_{o} \tag{4}\]  
\[\text{TUR}=\frac{\rho_{o}\big{(}R_{o}^{2}-  
\[\text{TR}=\frac{e_{f}}{e_{o}} \tag{7}\]
* The neck height (\(NH_{DD}\)) is calculated according to Equation (8), where the value, in millimeters, is divided by \(D_{o}\). The ratio between the neck height and the die diameter used in the process is due to the different types of dies that are available [15]. \[NH_{DD}=\frac{NH_{mm}}{D_{o}}\] (8)
* Finally, it is also important to consider the forming ratio (FR). While it is not an independent variable, it relates the TUR with the BUR to determine the grade of symmetry between these two parameters, because the BUR mainly impacts the transverse-direction properties and the TUR mainly impacts the machine-direction properties [13]. \[\text{FR}=\frac{\text{TUR}}{\text{BUR}}\] (9)  
The experimental design can be described as the following: the initial diameter, the initial thickness (or die gap), and the initial velocity are kept constant; the blown diameter, the final thickness, and the final velocity are changed with the purpose of obtaining different product sample groups with different BURs, TURs, and TRs; and finally, within those product sample groups (which have the same width and thickness) three variations in the neck height are made: tall (between 140 and 160 cm), medium (between 110 and 125 cm), and small (between 70 and 100 cm).
# FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
## 3 Experimental Results and Analysis
### Performance Analysis  
Figure 18 shows the Sobel edge map using different magnitude calculation equations (Equations (3) and (5)). It can be seen from Figure 18 that different calculation equations will make the intensity of edge pixels different. The intensity of edge pixels calculated by the Equation (5) is generally greater than that of Equation (3). However, this does not affect subsequent NMS operations.  
A comparison of the quality of the NMS map due to the use of quantized tanh is shown in Figure 19. As shown in Figure 19, the quality of the NMS image is not affected by the use of quantified tanh, which means that the gradient direction can be found correctly using this technique during the NMS process.  
Figure 19: NMS map: Left: original image; Middle: NMS map using Equation (4); Right: NMS map using quantized tanh.  
Figure 18: Sobel edge map: Left: original image; Middle: Sobel edge map using Equation (3); Right: Sobel edge map using Equation (5).  
Since we introduced logarithmic approximation into the architecture, a comparison was made between using and not using logarithmic approximation. As shown in Figure 20, the edge map using logarithmic approximation is basically the same as that without logarithmic approximation, which verifies the feasibility of using this technique.  
The performance of traditional Canny edge detection with a fixed threshold is compared with the performance of the proposed Canny edge detection algorithm. Illumination variation is a challenging task to detect the edges since the edges present in an image are sensitive to variation in light intensity that might occur because of various local and global lighting conditions. Therefore, the performance is evaluated at various illumination. Figure 21 shows the comparison results of traditional and proposed Canny edge detection algorithms. Figure 21a shows the grayscale of an image under different illumination. Figure 21b shows the edge map detected by the Canny edge detection algorithm with a fixed threshold value, which is implemented in MATLAB. The Gaussian kernel used is 9 in size and 1.5 in standard deviation, and the fixed threshold value is 70. Figure 21c shows the edge map detected by the proposed Canny edge detection algorithm.  
It is observed from Figure 21 that the proposed algorithm shows better performance under various illumination conditions than traditional Canny edge detection with a fixed threshold. This is due to the fact that the proposed edge detection algorithm in this research utilizes adaptive threshold technology to reduce the impact of light variations on the detection effect.
# Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System
## 6 Conclusions  
The changes in formation configuration have been confirmed to be feasible. However, the challenge of maneuvering the formation while avoiding obstacles and converging to desired values has not been adequately addressed to enhance practical application. Moreover, the concept of a rigid body may not suffice in diverse environmental situations. Although the affine technique has somewhat resolved this issue by continuously providing multiple geometric patterns for configuration changes, it requires an adjustable environment and the maintenance of a rigid body at all times. As a result, this method lacks some generality for practical application, particularly in complex environments.  
Therefore, this paper introduces a new consensus protocol based on a hybrid control model to enhance the effectiveness of formation maneuver control and obstacle avoidance in complex environments. The protocol employs distributed control laws with a virtual leader for double-integrator control and establishes the system's stability. The presented algorithm showcases the capability to autonomously adjust each local trajectory of a formation based on potential forces when encountering obstacle colliding zones. Crucially, the control law is confined to each agent's local reference frame, thereby improving the adaptability and scalability of the formation in realistic environments. Furthermore, the results show that the formation, steered by the proposed control algorithm, can still reach the predefined configuration even in the presence of violent trajectory changes. Additionally, the algorithm effectively reduces communication consumption.  
In the future, there are several important topics that require attention. These include investigating more intricate graph structures, dynamic leader reassignment, and transitioning between distributed and decentralized control laws to enhance autonomy. Moreover, it is crucial to develop more autonomous control laws for adapting to environmental changes, improve deep learning techniques to establish rule-based response mechanisms, explore the interconnections between agents in complex networks, utilize the MPC method for local environment prediction, and delve into other related areas.  
Conceptualization, C.K. and J.X.; methodology and writing--original draft, C.K.; writing--review and editing, Y.B.; visualization C.K.; supervision J.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 10: Simulation results of contrast test: (**a**) experimental scenario; (**b**) trajectory error; (**c**) velocity of the classic method in X axis; (**d**) velocity of the classic method in Y axis; (**e**) velocity of the proposed method in X axis; (**f**) velocity of the proposed method in Y axis.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 3 Results
### Gel Permeation Chromatography (GPC) Analysis of FRB  
In a previous study, the fermentation and extraction process were performed to optimize the solubility of polysaccharides and \(\beta\)-glucan, which are reported to have high immunomodulatory functions [23], and the extract was prepared by performing process standardization using \(\beta\)-glucan and solubility, which has high stability and detectability, as a biomarker.  
The GPC profiles of the FRB tested for the investigation of the improving effect of immunity and solubility were determined. In Figure 1 we can see the different \(\beta\)-glucan profiles between FRB and RB. As a result of measuring the change in the molecular weight profile due to bioconversion, we observed that \(\beta\)-glucans were decomposed and converted into \(\beta\)-glucans of various molecular weights (M.W. 393, 543, 752, 1128, and 1574) through the bioconversion process (Figure 1a,b) (Table 4). More specifically, the initial molecular weight distribution (M.W. 451, 858, and 1538) was changed to a more diverse low molecular weight profile (M.W. 393, 543, 752, 1128, and 1574) after fermentation and bioconversion. These results indicate that insoluble molecules present in rice bran tissue are converted into low-molecular-weight bioactive substances through the improved fermentation and bioconversion process.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Peaks (a)** & **1** & **2** & **3** & **Peaks (b)** & **1** & **2** & **3** & **4** & **5** \\ \hline Peak Start & 2107 & 1183 & 594 & Peak Start & 1818 & 1368 & 928 & 595 & 491 \\ \hline Peak Top & 1450 & 898 & 453 & Peak Top & 1511 & 1132 & 782 & 549 & 446 \\ \hline Peak End & 1183 & 594 & 192 & Peak End & 1368 & 928 & 595 & 491 & 256 \\ \hline Mn & 1504 & 825 & 440 & Mn & 1564 & 1146 & 744 & 542 & 379 \\ \hline Mw & 1538 & 858 & 451 & Mw & 1574 & 1128 & 752 & 543 & 393 \\ \hline \hline \end{tabular}  
Mn: Number-average molecular weight, Mw: Weight-average molecular weight.  
\end{table}
Table 4: Average molecular weights of peaks (Processed Rice Bran (RB; **a**) and Fermented Rice Bran (FRB; **b**)  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Dry Weight of** & **Dry Weight of** & **\% of Insoluble** \\  & **Precipitate (g)** & **Supernatant (g)** & **Component** \\ \hline FRB & 0.62 \(\pm\) 0.03 & 2.13 \(\pm\) 0.21 & 22.5 \(\pm\) 1.1 \\ \hline RB & 1.32 \(\pm\) 0.12 & 1.43 \(\pm\) 0.06 & 47.9 \(\pm\) 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Changes in insolubility of FRB and RB by fermentation.
## 4 Multiple Services Set Identifier (MSSIDs) Technique
### Mathematical Model of MSSID  
This research aims to reduce the calculation cost of the MSSID-based indoor positioning system and increase the performance of IPS as well. Accordingly, N RMs (\(\Psi_{1}\), \(\Psi_{2}\),..., and \(\Psi_{\text{N}}\)) are generated as in Equations (4)-(6):  
\[\Psi_{1}=\left[\begin{array}{cccc}\text{SSID}_{1,1,1}&\text{SSID}_{1,2,1}& \ldots&\text{SSID}_{1,\text{M},1}\\ \text{SSID}_{2,1,1}&\text{SSID}_{2,2,1}&\text{SSID}_{2,\text{M},1}\\ \vdots&&\ddots&\vdots\\ \text{SSID}_{1,1,1}&\text{SSID}_{2,1}&\cdots&\text{SSID}_{\text{M},1}\\ \end{array}\right] \tag{4}\]  
Figure 2: Multiple Services Set Identifiers (MSSIDs) technique.  
\[\Psi_{2}=\left[\begin{array}{ccccc}\text{SSID}_{1,1,2}&\text{SSID}_{1,2,2}& \ldots&\text{SSID}_{1,M,2}\\ \text{SSID}_{2,1,2}&\text{SSID}_{2,2,2}&&\text{SSID}_{2,M,2}\\ \vdots&&\ddots&\vdots\\ \text{SSID}_{\text{L},1,2}&\text{SSID}_{1,2,2}&\cdots&\text{SSID}_{\text{L}, M,2}\end{array}\right] \tag{5}\]  
\[\Psi_{\text{N}}=\left[\begin{array}{ccccc}\text{SSID}_{1,1,N}&\text{SSID}_{1,2,N}&\ldots&\text{SSID}_{1,M,N}\\ \text{SSID}_{2,1,N}&\text{SSID}_{2,2,N}&&\text{SSID}_{2,M,N}\\ \vdots&&\ddots&\vdots\\ \text{SSID}_{\text{L},1,N}&\text{SSID}_{\text{L},2,N}&\cdots&\text{SSID}_{ \text{L},M,N}\end{array}\right] \tag{6}\]  
where, L and M represent a number of deployed APs, and a number of RPs in the AOI, respectively.
# On Evaluating IoT Data Trust via Machine Learning
## 4 Features
### Correlation Features  
We hypothesize that trustworthy and untrustworthy data can be accurately distinguished using features that capture data autocorrelation and cross-correlation. For the time-series data of any given time window, the autocorrelation features that we calculate relate to the representation of the data in the frequency domain and our cross-correlation features are Pearson's correlation coefficients of the data and those of the neighboring sensors.  
Figure 3: An example actual trustworthy data instance (time-series data collected by a sensor in a 24-h period) and a corresponding untrustworthy data instance synthesized via Drift.
## 3 Results
### Nanostructured Hydroxapapatte  
Nanotechnology has revolutionized bone regeneration by offering innovative approaches to enhance the mechanical, biological, and functional properties of biomaterials. Key applications include the following:  
1. Engineering nanocomposites with improved mechanical strength and biocompatibility;
2. Modifying material surfaces at the nanoscale to enhance cell adhesion and function;
3. Developing degradable nanoceramics for better integration with natural tissues;
4. Employing nanotopography to stimulate osteoblast activity;
5. Enabling targeted drug delivery systems to facilitate healing and recovery [2].  
Nano-hydroxapatite crystals, due to their excellent biocompatibility and bioactivity, have emerged as a promising material in bone regeneration, offering superior osseointegration and reduced inflammatory responses compared to traditional porous HA [20]. Nano-HA mimics the dimensions of natural calcified tissues such as bone and teeth, with optimal sizes ranging from 10 to 100 nm for biomedical applications [13; 20]. These nanostructured materials enhance osteoblast adhesion, proliferation, and differentiation, while promoting calcium deposition, thus supporting effective bone remodeling [13].  
The distinctive properties of nanomaterials stem from two primary factors: quantum effects and an enlarged surface-to-volume ratio. Nanomaterials exhibit a 30 to 50% higher surface area compared to their micrometer-scale counterparts, significantly improving protein adsorption and osteoblast attachment [20; 21]. The altered surface characteristics of nanostructured materials further facilitate cell interactions, leading to enhanced bioactivity.  
Despite its advantages, nano-HA alone often lacks sufficient mechanical properties and resorbability for broader clinical applications. Customized biomaterials that integrate nano-HA with other components are essential for hard tissue replacements. These hybrid materials must be tailored to meet specific requirements, including density, porosity, thermal stability, bioactivity, and mechanical strength [13].
## 4 Quadrature Chaos Shift Keying Communication System  
This section presents the architecture, design particularities, and noise immunity analysis of the QCSK data transmission system across various configurations and different chaos oscillators. The objective is to evaluate how the choice of chaotic oscillator--specifically, the Vilnius and Colpitts chaos oscillators--and the selection of the synchronization signal influence the overall performance of the data transmission system.
## 2 Platform Architecture  
In onboard applications, real-time processing of particle identification not only focuses on identification accuracy, but also takes into account the unique requirements of in-orbit operation, such as the limitation of FPGA resources and the dynamic change in space particle flux. Firstly, considering the limited onboard resources and the prevalence of space FPGA applications, it is especially critical to develop a lightweight CNN network architecture for particle identification that is adapted to FPGA operation platforms. Secondly, the wide range of changes in cosmic space particle fluxes, especially during solar activity, necessitates the design of extremely low-latency network architecture to improve the system's responsiveness and dynamic range. Moreover, the network model is expected to train more accurate architectures as the data are updated; therefore, developing a modular and reconfigurable FPGA operation architecture is also crucial.  
In order to meet the above requirements, this paper builds a spaceborne convolutional neural network particle identification FPGA real-time classification platform. The platform is mainly composed of four parts, namely, the particle waveform data software preprocessing module, convolutional neural network software training module, particle waveform data FPGA forward inference module, and convolutional neural network training set extension module; moreover, its platform architecture is shown in Figure 1.  
Figure 1: Structural diagram of the convolutional neural network particle identification platform.
# Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images
## 2 Materials and Methods
### Gaussian Filter  
The architecture for the Gaussian filter is shown in Figure 3, which mainly consists of a coefficient table, an image register unit, an image window and a convolution module [3]. The coefficients of the \(M\times M\) Gaussian filter are generated offline. The normalized filter coefficients (in floating-point data type) are converted into fixed-point data type. In this implementation, 16-bit fixed-point representation (with fraction length of 14) has been used (without any significant loss of accuracy). The filter coefficients are stored in ROM IP core on the FPGA board.  
Figure 3: Architecture of 2-D Gaussian Filter.  
Figure 2: Schematic of the proposed nuclei detection technique.  
To enable the process of shifting the window of 2-D filter coefficients for a raster scan of the entire image, \(M\) shift register IP cores of length equal to the image width (see Figure 3) are used for generating a Serial-In-Parallel-Output (SIPO) image register unit [3]. Each shift register stores one row of image data. Input to this register unit is an 8-bit image pixel data comes at a rate of one pixel per clock. The \(M\) pixels from each shift register are transferred to the image window to access randomly in convolution.  
The architecture of the convolution module is shown in Figure 4. The module uses the image data \(r\) (stored in the image window) and filter coefficients \(f\) (stored in the coefficient table) to calculate the output \(h\). The entire convolution process with \(M\times M\) size filter is divided into \(M\) cycles. In each cycle, one column (e.g., \(i\)th column) of the image window data \(r\) and coefficient table data \(f\) pass through the multiplier array and first stage adder tree, and is then stored in the register queue. In each subsequent cycle, the register data shifts right by one unit, and the next columns of image data and filter coefficients go through the module and the output is stored in the register queue. This process continues for \(M\) times. After \(M\) cycles, the outputs of every column (stored in register queue) are added by the second stage adder to calculate the convolution output \(h\) (pixel value in the Gaussian filter output image \(H\)). In this work, \(h\) is truncated into 8-bit precision and the output image \(H\) is stored in the FPGA block RAM.