esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 3 Proposed Methodology
### Fingerprint Image Preprocessing
#### 3.2.8 Binarization and Thinning  
The binarization process of a fingerprint image converts an 8-bit grayscale image into a 1-bit image with a value of 0 for ridges and a value of 1 for valleys. This process increases the contrast between the fingerprint ridges and valleys, making it easier to extract features from the fingerprint image later on. This method involves examining the gray value of every pixel in the enhanced image, and the pixel value is set to a binary value of one if the value is greater than the global threshold; otherwise, it is set to zero. The result is a binary image that comprises two data levels, the ridges in the foreground and the valleys in the background. The Gabor filter's useful feature is that it has a DC component with a zero value, meaning that the mean pixel value of the resulting filter image is zero. Thus, straightforward binarization of the image can be carried out by using a global threshold ofzero. The resultant binary image of the Gabor enhanced fingerprint grayscale image, \(E(i,j)\) is given by  
\[Binary(i,j)=\left\{\begin{array}{ll}1,&\text{if }E(i,j))\geq 0\\ 0,&otherwise\end{array}\right. \tag{7}\]  
Fingerprint ridge thinning attempts to minimize the redundant pixels until the ridges are just one pixel thick. To achieve desirable thinning results, an iterative parallel thinning algorithm that can be accessed in MATLAB via the 'thin' operation under the \"bwmorph\" function is applied to the binary image. The thinned image is then filtered to eliminate any H-breaks, spikes, and isolated points using the h-break, clean, and spur MATLAB functions. The algorithm marks redundant pixels in every small image window in each full fingerprint image scan (\(3\times 3\)). Eventually, all those marked pixels are excluded after multiple scans. The basic steps of the whole fingerprint image pre-processing process are shown in Figure 6.
## 6 Future Research and Work
### Adaptation to VLC-OFDM-Based Indoor Positioning Techniques  
Advancements in IoT and factory automation (FA) have created a growing demand for high-precision indoor positioning technologies [22, 23]. VLC is particularly promising due to its sharp directionality and reduced multipath attenuation compared to RF-based systems [24]. Among VLC approaches, integration with VLC-OFDM systems has been actively studied to achieve scalability and multi-user compatibility, enabling dual-purpose utilization of VLC for both data communication and positioning [25, 26]. Concretely, this technology estimates channel responses from pilot signals transmitted by multiple LEDs and calculates the time difference of arrival (TDOA) to determine user positions. However, nonlinear distortions in LED transmitters critically degrade the accuracy of channel estimation and TDOA measurements. To address this challenge, our study aims to implement a positioning system with enhanced accuracy by applying the proposed nonlinear compensation method, thereby mitigating distortion-induced errors in VLC-based positioning.
## 3 Results
### Offline Measurements  
After system integration, the functionality of the coaxially integrated OCT-system was verified in offline mode. This means that the process laser was not active, and thus effects of the laser process on the measurements could be ruled out. The testing of the functionality of the coaxially integrated OCT-system included scanning the surface topography of the substrate to determine the distance to the workpiece and check the weld bead quality. In the simplest case, in A-Scan mode, the coaxially integrated OCT-system was used to adjust the distance of the workpiece to the exact focus plane of the system. Depth calibration of the measuring system was carried out by using a stepped calibration target (Figure 8); through calibration, the absolute distance of the substrate within the measuring depth, in the positive and negative z-directions outside the focus, can be determined with particular precision. Previous work has already shown that the optimum focus distance of the process laser plays a critical role in process stability. The distance influences the size of the ring-shaped laser beam in the processing plane and thus also the amount of energy transferred to the wire. The effects on the weld are referred to as \"stubbing\" when the energy transfer is insufficient, whereas \"dripping\" occurs when the distance between the melting wire and the substrate is too large [21; 22]. The focal distance of the measuring laser corresponds to the focal distance of the process laser, since both light sources share the same focusing optics. Therefore, these effects can be avoided if the distance of the workpiece is monitored using the coaxially integrated OCT-system.  
Figure 8: Picture and OCT volume scan of a step calibration target.  
Figure 7: Analysis of the weld bead geometry (**a**) in dependence on the laser power and (**b**) in dependence on the ratio vW/vM.
# Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System
## 2 System Model
### Millimeter-Wave Channel  
Generally speaking, mmWave channels are typically sparse and dispersed. The paper [5] provided a widely used Saleh-Valenzuela (SV) channel model for mmWave communication [14]. In the actual communication system, considering the short wavelength of mmWave communication, when the signal is directly transmitted between the base station and the user, it degrades due to factors such as bad weather and building obstruction. The attenuation of the signal is not conducive to transmission, and reliable line-of-sight (LOS)  
Figure 1: Point-to-point mmWave downlink URLLC communication system with subarray structure.  
communication cannot be achieved. Therefore, we consider that the direct path from the base station to the user is non-line-of-sight (NLOS). The NLOS channel is represented as  
\[\mathbf{h}=\sqrt{\frac{N_{\mathrm{t}}}{L_{\mathrm{d}}}\sum_{l=1}^{L_{\mathrm{d}} }\alpha_{\mathrm{r}}(\phi_{\mathrm{r}})\mathbf{a}_{\mathrm{t}}(\phi_{\mathrm{t} })^{H}}, \tag{1}\]  
where \(\mathbf{h}\in\mathbb{C}^{N_{\mathrm{t}}\times 1}\), \(L_{\mathrm{d}}\) represents the total number of paths, \(\alpha_{l}\) represents the complex gain of the \(l\)th channel, \(\phi_{\mathrm{t}}\) represents the angle of departure, \(\phi_{\mathrm{r}}\) represents the angle of arrival, while \(\mathbf{a}_{\mathrm{t}}\) and \(\mathbf{a}_{\mathrm{r}}\) represent the normalized transmission and achievable array response vectors, respectively. The complex gain following Gaussian distribution \(\alpha_{l}\) can be expressed as \(\alpha_{l}\sim\mathcal{N}(0,10^{-0.1\eta})\), where \(\eta=\hat{a}+10\hat{b}\log(\hat{d})=\zeta\), \(\hat{d}\) is the distance between the base station and the user, \(\zeta\sim\mathcal{N}(0,\sigma_{\zeta}^{2})\).
###### Abstract  
Fenton reaction is a powerful technology for pollutants' removal from water. However, the cost of H\({}_{2}\)O\({}_{2}\) becomes one of the major stumbling blocks in its application. H\({}_{2}\)O\({}_{2}\) has a relatively high price and is easily decomposed during transportation and use; therefore, in situ synthesis of H\({}_{2}\)O\({}_{2}\) could improve economic benefits effectively. In this study, a Fe/Ni/Pd ternary metal-doped graphitic carbon nitride (FeNi-Pd@CN) is prepared, and in situ H\({}_{2}\)O\({}_{2}\) generation using formic acid as hydrogen sources for organics removal was proved. The catalyst is advantageous, as H\({}_{2}\)O\({}_{2}\) could accumulate to 1.69 mmol/L in 150 min when pumping air rather than oxygen gases in other studies. Furthermore, 92.0% of Acid Red 73 (200 mg/L) and 93.2% of tetracycline hydrochloride (10 mg/L) could be removed in 150 min without any pH adjustment. Characterization results show that the catalyst has good stability in metal leaching and reuse tests. It is proved that \(\bullet\)OH and \(\bullet\)O\({}_{2}\)\({}^{-}\) are the main reactive oxygen species, and a synergistic effect between Fe and Ni exists that enhances ROS generation for organics degradation. This work offers a promising method to remove refectory organic contaminants from industrial wastewater.  
in situ generation; hydrogen peroxide; pollutant removal; graphitic carbon nitride +
Footnote †: journal: Applied Sciences
## 4 Methodology  
### Data Collection  
The data were collected using subjective and objective methods to ensure they were void of prejudice. The objective was to gather data regarding footpath characteristics, including length, slope, number of junctions, and absence of footpath, using Google Earth Pro 7.3 and ArcGIS Pro 3.2.0. The digital elevation model calculated the slope with 12.5 m resolution using ArcGIS Pro and used the average for each route. Google Earth Pro calculated other criteria as shown in Table 1. The survey was employed as a subjective way to inquire about participants' preferences concerning these criteria. The survey played a crucial role in this research because, while literature reviews aid in identifying and gathering general methods and criteria, a survey can assist in evaluating and weighing these criteria. The survey also facilitated the collection of unique perspectives from stakeholders addressing accessibility-related concerns in the areas under analysis. Gaining a more comprehensive understanding of these perspectives and their congruence and divergence can provide critical insights for communities to efficiently address the lack of data and limited planning for improvements in footpath accessibility. This combination helps create a complete and accurate understanding of the challenges they encounter. This study employed Google Forms, which included a total of 20 questions. These questions encompassed demographic information, open questions, and multiple-choice questions. The criteria in the survey were assessed on a scale of 1 to 4, with 1 indicating high importance and 4 indicating low importance, to obtain the weights of the criteria using the fuzzy rank sum method. The survey was distributed to many disability support services centers and sports clubs in these three cities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Route** & **Length (m)** & \
\begin{tabular}{c} **Number of** \\ **Junctions** \\ \end{tabular} &
\begin{tabular}{c} **Absence of** \\ **Footpath (\%)** \\ \end{tabular} & **Slope (\%)** \\ \hline R1 & 1850 & 1 & 10.9 & 6.22 \\ R2 & 816.5 & 2 & 6 & 7.46 \\ R3 & 568 & 2 & 20.4 & 8.56 \\ R4 & 658.2 & 1 & 13 & 6.63 \\ R5 & 1083.5 & 0 & 10 & 3.95 \\ R6 & 1295 & 2 & 14.5 & 3.79 \\ R7 & 737 & 2 & 61.53 & 7 \\ R8 & 1060 & 2 & 25.48 & 8.16 \\ R9 & 1479 & 4 & 41.10 & 5.21 \\ R10 & 398 & 2 & 33.32 & 3.88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Salient features of the routes in the study area.
## 3 Results
### Ablation Study  
To systematically evaluate the independent contributions of each optimization strategy to the overall network performance, this study further conducted an ablation study by sequentially combining different optimization modules, and analyzing in detail their effects on detection accuracy, recall, and the comprehensive evaluation metric [26]. As shown in Table 2, when only the Wavelet Transform Conv optimization was applied, although the precision decreased by 1.65%, the recall and mAP increased by 3.72% and 3.96%, respectively, with noticeable improvements, and the parameters and gradients increased by only 0.26 M. When solely applying the Histogram Transformer optimization, the precision decreased by 1.49%, while the recall and mAP increased by 4.44% and 4.7%, respectively, with the parameters and gradients increasing by 0.89 M. In the case of loss optimization, with parameters and gradients remaining unchanged, the precision decreased by 1.11%, while the recall and mAP increased by 3.32% and 4.24%, respectively. When all three optimization strategies were applied simultaneously, the improved YOLOv10 network achieved increases in precision, recall, and mAP of 3.4%, 10.05%, and 11.95%, respectively, with the number of layers reduced by 15.67% and the parameters and gradients increasing by only 0.31 M. Through the synergistic integration of multiple strategies, the improved YOLOv10 network not only significantly enhanced detection accuracy and recall but also achieved a degree of structural simplification.
## 6 Experiments and Discussion
### System Description  
The cameras commonly used for SLAM can be divided into monocular, multiocular, RGB-D, and dynamic vision sensors (event cameras). RGB-D cameras such as Kinect are inexpensive and provide depth information quickly and efficiently. We, therefore, adopted an RGB-D camera as our main instrument for capturing images. Figure 11 presents the framework of the system proposed in this study. Kinect for Windows v1 was used to obtain color and depth images, which were then input into a personal computer equipped with an Intel Core i7-7700 CPU 3.60 GHz processor for program development and algorithm execution. The results were then output to a display.
## 3 Proposed Solution
### Computational Complexity  
The proposed framework consists of three stages, and the computational complexity of each stage is as follows:  
* For stage 1, the worst-case computational complexity is \(\mathcal{O}(t\times i)\), where \(i\) is the number of iterations for K-means clustering and \(t\) is the expected number of iterations of the main loop.
* For stage 2, the worst-case computational complexity is \(\mathcal{O}(N\times C\times i)\), where \(i\) is the number of iterations for K-means clustering, \(N\) is the number of vehicles, and \(C\) is the number of clusters.
* Similarly, stage 3 is also dominated by k-means clustering, with the worst-case computational complexity of \(\mathcal{O}(N\times C\times i)\). In stage 3, the value of \(N\) is larger than that of stage 2.  
The above three-stage framework provides a near-optimal solution. In order to find the optimal solution, we have to adopt the exhaustive-search-based method. In solving the above algorithm using exhaustive search, the worst-case computational complexity is \(\mathcal{O}(C^{N})\), which is exponentially more expensive than K-means clustering, making it infeasible for large datasets.
# Sensor Micro and Nanoparticles for Microfluidic Application
## 4 Microfluidic Systems for Chemosensing
### Sensor Particles for Detection of Environmental Pollution  
Gold nanoparticle-based sensors for the fast determination of environmental contaminants were presented by [PERSON] et al. [17]. Fluorescent gold nanoclusters functionalized with bovine serum albumin (BSA-AuNCs) and gold nanoparticle embedded with Rhodamine 6G (RB-AuNPs) were applied for the mercury and pesticide ziram detection. Measurements were performed with a limit of detection of 0.6 \(\upmu\)g/L for mercury and 16 \(\upmu\)g/L for ziram concentration. Figure 13 illustrates two detection schemes. In the \"turn-off\" method, highly fluorescent gold nanoclusters (BSA-AuNCs) were used. By adding Hg\({}^{2+}\) ions, fluorescence of BSA-AuNC was quenched due to Hg\({}^{2+}\)-Au\({}^{+}\) interactions or through Hg\({}^{2+}\) interactions with BSA carboxyl and amino groups. In the \"turn-on\" method, fluorescence of rhodamine 6G was quenched regarding adsorption of dye molecules on the surface of gold nanoparticle by fluorescence energy transfer. In the presence of Hg\({}^{2+}\), dye molecules were displaced and released from the particle surface resulting in the recovery of fluorescence intensity in the bulk solution. The results showed that the \"turn-on\" method was more sensitive for Hg\({}^{2+}\) ion detection. Additionally, it was demonstrated that by applying RB-AuNPs, indirect detection of ziram pesticide was performed by the \"turn-on\" method. When ziram was added, rhodamine molecules were set free, and their fluorescence intensity was recreated.  
For Hg (II) ion detection, [PERSON] et al. [5] developed a valve-based microfluidic micromixer system, in which droplets were generated, manipulated and effectively mixed. By applying a valve-based micromixer, it was possible to encapsulate a tiny volume of Hg (II) ion or AuNPs-RB into individual nanoliter or picoliter size droplets, merge different droplets together and, finally, perform active mixing. Measured changes in fluorescence intensity provided information about the presence of Hg (II) ions. For preparation of the detection probe, gold nanoparticles were synthesized when trisodium citrate solution was added to boiling chloroauric acid solution and stirred for 15 min. After cooling down to room temperature, AuNPs were diluted to borate saline buffer with pH 9. Additionally, gold nanoparticles were functionalized with Rhodamine B (RB) dye, whose fluorescence was quenched by gold nanoparticles. However, when Hg (II) ion and AuNPs-RB droplets merged and mixed together, the fluorescence intensity increased due to fluorescence dye realesement from AuNPs-RB composite. However, this study showed the ability to detect Hg (II) ions without more detailed concentration determination.  
Figure 13: Representation of the two detections methods (“Turn-off” and “Turn-on”) applied for Hg sensing. Adapted from [17]. Copyright © 2012 The Royal Society of Chemistry.
## 4 Numerical Analyzes
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ceiling** & \begin{tabular}{c} **Teriva** \\ **Panel 160** \\ \end{tabular} & \begin{tabular}{c} **Teriva** \\ **Panel 180** \\ \end{tabular} & \begin{tabular}{c} **Teriva** \\ **Panel 200** \\ \end{tabular} & \begin{tabular}{c} **Smart** \\ **Teriva Base** \\ \end{tabular} & \begin{tabular}{c} **Teriva Plus** \\ \end{tabular} & \begin{tabular}{c} **Teriva Termo** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Flow \\ upwards \\ Flow \\ downwards \\ \end{tabular} & 14.9 & 13.5 & 12.0 & 20.4 & 19.1 & 22.3 & 30.9 \\
\begin{tabular}{c} Flow \\ downwards \\ \end{tabular} & 28.2 & 27.2 & 25.8 & 39.4 & 31.1 & 32.7 & 38.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Percentage differences between the results of calculations carried out in accordance with EN ISO 6946:2017 [28], and numerical calculations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Unit** & **Smart** & **Teriva Base** & **Teriva Plus** & **Teriva Termo** \\ \hline \(R_{T}\) flow upwards & (m\({}^{2}\):K)/W & 0.119 & 0.263 & 0.326 & 0.326 \\ \(R_{T}\) flow downwards & (m\({}^{2}\):K)/W & 0.119 & 0.263 & 0.325 & 0.325 \\ \(R_{U}\) flow upwards & (m\({}^{2}\):K)/W & 0.259 & 0.403 & 0.466 & 0.561 \\ \(R_{U}\) flow downwards & (m\({}^{2}\):K)/W & 0.329 & 0.473 & 0.535 & 0.630 \\ \(U\) flow upwards & W/(m\({}^{2}\):K) & 3.859 & 2.480 & 2.146 & 1.783 \\ \(U\) flow downwards & W/(m\({}^{2}\):K) & 3.038 & 2.114 & 1.868 & 1.588 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Values of thermal parameters of Smart and Teriva ceilings: Base, Plus and Termo. upward flow, the differences are 19.1%-30.9%, and 31.1%-39.4% with a downward flow. As mentioned above, these differences result from the geometries of the airspace in which convective heat exchange occurs, and from how convection phenomena are taken into account in the calculations.
## 2 Literature Review
### Systems Engineering  
Systems engineering is often construed as a convergence of artistic and scientific dimensions. This portrayal is fitting, as it demands both the imaginative capacity and technical proficiency of engineers, coupled with the systematic application of management principles [21]. Systems engineering focuses on identifying customer needs and the required functionality early in the development cycle, documenting requirements, and subsequently advancing with design synthesis and system validation, all while considering the complete problem [22]. In systems engineering, budgeting is the process followed during the design process of the product. Budgeting essentially involves the collection and organization of information regarding a significant resource and subsequently allocating this resource optimally across various components of the system under consideration. A technical budget can encompass a variety of topics, such as standby power, memory, processor usage, load, accuracy, and other product-specific aspects [23]. Following this systematic approach, in space systems engineering, after analyzing the requirements, initial performance budgets are estimated [24]. Mass budget is one of the budgets among many other technical budgets estimated in the preliminary stage of space mission design [25]. The determination of the mass budget for satellites primarily relies on two distinct approaches. The first method involves the construction of an average mass budget table derived from historical data, consolidating information from past satellite missions. This approach leverages the collective experience and outcomes of previously launched satellites to establish a baseline for estimating mass parameters. The second method employs a parametric method, wherein mass budget calculations are based on a set of predefined parameters and formulas. This method involves a more detailed and specific analysis, incorporating factors such as satellite function, design specifications, and mission requirements. The subsequent sections will delve into a comprehensive discussion of these two approaches.
## 2 Materials and Methods
### Circuit Design
#### 2.3.3 PCB Design and Software Development  
Through PCB design and integrated electronic components, we can develop module circuits that are more suitable for use in fabrics. The hardware electronic circuit typically consists of several components, including a system power supply, voltage-stabilizing circuit, reset circuit, USB interface circuit, SWD debugging circuit, download circuit, sensor system circuit, microcontroller circuit, and WS2812B driver circuit. The PCB design may resemble the one shown in Figure 7, with the microcontroller model used in the circuit  
Figure 5: Fabric’s circuit module.  
Figure 6: Operational amplifier-based electrical signal conversion module.  
being STM32. It is programmed in C language for control and is characterized by its compact size, allowing it to control the entire circuit board.  
The software control logic is shown in Figure 8. After the microcontroller is powered on, it performs initialization operations. The ADC peripheral detects changes in external signals and converts the voltage signal output by the electrical signal conversion module into a digital signal using the following formula:  
\[VOLtage\_AO=4096\times\frac{U_{0}}{3.3}\, \tag{2}\]  
where \(VOLtage\_AO\) is the digital signal value and \(U_{0}\) is the output voltage value of the module. Subsequently, the microcontroller performs a judgment program on the digital signal value and delivers the judgment result to the WS2812B module through PWM pulses. The RGB-LED connected to this module expresses the digital signal value. In this study, the PWM pulses are encoded into data frames using a bipolar zero code, as shown in Table 2. The WS2812B module can extract the required 24-bit data for light emission from each data frame, and the connected RGB-LED expresses the data in red, green, and blue color values.  
Figure 8: Software control logic diagram.  
Figure 7: Printed circuit board (PCB) design.  
As shown in Figure 8, when the fabric sensing area is not pressed, the fabric's resistance remains unchanged. When the digital signal value \(VOLTAGE\_AO\) < 2500, the LED light source emits a fixed color light. When the fabric sensing area is pressed, the fabric resistance decreases. When the digital signal value \(VOLTAGE\_AO\) > 2700, the LED light source changes its emitting color. If the fabric is pressed for more than 2 s, the LED light source alternately emits different colored lights. When the fabric sensing area is no longer pressed, the digital signal value \(VOLTAGE\_AO\) will return, and the LED light source will emit a fixed color light again.