esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overall System
### Visual Recognition System  
The tasks of the visual recognition system are to identify and localize rebar intersection points in a complex, interference-prone environment using RGB-D images captured by a camera and to output the accurate pixel coordinates of these intersection points. To achieve this objective, several key aspects need to be considered:  
* Extracting rebar layers from complex grid backgrounds remains a significant challenge;
* Ambient light interference must be minimized to ensure reliable TOF camera imaging and system recognition accuracy;
* The sensor precision must be sufficiently high to guarantee the accurate localization of intersection points.  
Therefore, we divide the visual recognition system into several components, including an image preprocessing system, a robust depth data augmentation system, and an intersection point recognition system, each addressing the respective issues. Figure 2 illustrates the overall workflow of the visual recognition system.  
Figure 2: Overall workflow of the visual recognition system for rebar intersection detection.
## 3 Results  
Besides emission from the OH, N\({}_{2}\) and N\({}_{2}^{+}\) molecular bands and from atomic nitrogen and atomic oxygen in the spectra, traces of iron and tungsten were identified. While the metallic species are due to erosion of the electrode, the other species originated from the feed gas and the surrounding air.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{\lambda}\)/**nm** & **Transition** & \(\mathbf{A_{\text{ki}}(10^{6}s^{-1})}\) & \(\mathbf{E_{\text{k}}/eV}\) & \(\mathbf{g_{\text{k}}}\) \\ \hline
818.49 & \(3p\,^{4}\text{P}^{0}-3s\,^{4}\text{P}\) & 8.21 & 11.844 & 6 \\
818.80 & \(3p\,^{4}\text{P}^{0}-3s\,^{4}\text{P}\) & 12.5 & 11.840 & 4 \\
859.40 & \(3p\,^{2}\text{P}^{0}-3s\,^{2}\text{P}\) & 20.9 & 12.122 & 2 \\
862.92 & \(3p\,^{2}\text{P}^{0}-3s\,^{2}\text{P}\) & 26.7 & 12.126 & 4 \\
868.03 & \(3p\,^{4}\text{P}^{0}-3s\,^{4}\text{P}\) & 25.3 & 11.764 & 8 \\
868.34 & \(3p\,^{4}\text{D}^{0}-3s\,^{4}\text{P}\) & 18.8 & 11.758 & 6 \\
868.62 & \(3p\,^{4}\text{D}^{0}-3s\,^{4}\text{P}\) & 11.5 & 11.753 & 4 \\
871.17 & \(3p\,^{4}\text{D}^{0}-3s\,^{4}\text{P}\) & 12.9 & 11.753 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected spectral lines for determination of the excitation temperature.  
For both spectral ranges, the emission intensity decreased exponentially. To compare results from both spectral ranges, all spectra were normalized to the intensity of N\({}_{2}\) (\(C-B\)) at 337.1 nm, which was observed in both.  
Close to the nozzle in the UV-region from 200 to 355 nm the second positive system (SPS) of molecular N\({}_{2}\) (\(C^{3}\Pi_{i}^{+}\to B^{3}\Pi_{S}^{+}\)) was the dominant species, and OH (\(A-X\)), NO (\(A-X\)) and NO (\(B-X\)) were present. With increasing distance to the nozzle N\({}_{2}\) emission intensity decreased while NO and OH intensities increased with a maximum intensity relative to N\({}_{2}\) at a distance of 10 mm from the nozzle as depicted in Figures 3 and 4.  
In the UV-VIS region from 330 to 960 nm different nitrogen species were present. The first negative system (FNS) of N\({}_{2}^{+}\) (\(B^{2}\Sigma_{g}^{+}\to X^{2}\Sigma_{g}^{+}\)) was clearly visible and of higher intensity than the N\({}_{2}\) (\(C-B\)) transition. For distances of 2 and 14 mm the 0-0 transition of the FNS showed no spectral overlap with other species and was thus suitable for rotational temperature determination.  
The first positive system of N\({}_{2}\) (\(B^{3}\Pi_{g}^{+}\to A^{3}\Sigma_{u}^{+}\)) led to characteristic spectral features for wavelengths above 500 nm. Intensities of atomic nitrogen species were very high for all distances. Atomic oxygen (\(3p^{5}\mathrm{P}\to 3s^{5}\mathrm{S}\)) at 777 nm was clearly visible, but no other oxygen transitions were detected. With increasing distance to the nozzle an increase in FNS, FPS, atomic nitrogen and atomic oxygen relative to SPS was detected. No significant differences were found for gas flows of 35 L min\({}^{-1}\) and 45 L min\({}^{-1}\).  
Figure 3: Spectra in the wavelength range of 333.1–394.5 nm at a gas flow of 35 L min\({}^{-1}\). For (**a**) 2 mm, (**b**) 6 mm, (**c**) 10 mm and (**d**) 14 mm distance to the nozzle.
# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 3 Results
### Results with Sensory Perception Systems Using Test Data
#### 3.5.2 Strawberry  
Figure 18 illustrates the multivariate analyses for the strawberry with the sensory devices. It was determined that similar behavior was maintained concerning the plum. In this way, it was possible to compare the data from the training stage and those acquired from the cultivated fruits.  
The results with the E-nose, as seen in Figure 18A,B, represent a separation between the organic strawberry and the cultivated strawberry regarding the strawberry contaminated with the Bricol and Across pesticides to the concentration of 3 mg/kg. In the same way, proximity is noted between the sample contaminated with Bricol and the cultured sample, which is observed more clearly using the E-tongue, as depicted in Figure 18C,D. Therefore, the overlap presented between the samples is assumed to be that the cultivated strawberry is found at a higher concentration of the pesticide; furthermore, it is essential to highlight  
Figure 19: Validation of data obtained with E-nose (**A**,**B**) and E-tongue (**C**,**D**) in apple.  
Figure 18: Validation of data obtained with E-nose (**A**,**B**) and E-tongue (**C**,**D**) in strawberry.  
that this same behavior was not obtained with the Across sample since it is located in a more distant region concerning the cultivated strawberry.  
The above confirms that with the E-tongue, it is possible to detect the predominant incidence of Bricol on the fruit. The responses are represented through LDA and PCA analysis, which indicates that the contribution of non-volatile substances is more significant than that of volatile substances.
## 3 Materials and Methods
### Definition of Assessment Indicators  
For the purpose of comparison, the following terms will be defined and introduced for the discussion of the results:  
* The normalized average concentration (\(NAC\)) is a dimensionless number that gives the average concentration of aerosol droplets received on average by all students normalized by the initial concentration ejected from the infected instructor's mouth. \[NAC=\frac{C_{ave}}{C_{o}}\] (1) where \(C_{ave}\) is the average concentration for the whole students in the exam room, and \(C_{o}\) is the concentration ejected from the mouth of the infected instructor.
* The specific normalized average concentration (\(SNAC\)) is also a dimensionless number that gives the average concentration of aerosol droplets received by exposed students only divided by the concentration ejected from the infected instructor's mouth. \(SNAC\) can be calculated as per the following formula: \[SNAC=NAC\times\frac{\text{Total No of students in class}}{\text{No of exposed students}}\] (2)
* The ventilation rate ratio (\(VRR\)) is the ratio of the actual total ventilation rate divided by a reference ventilation rate. In this study, the reference ventilation rate was selected to be the minimum ventilation rate recommended by ASHRAE standards [44] (the reference case, R2): \[VRR=\frac{AVR}{VR_{min}}\] (3) where \(AVR\) is the actual total ventilation rate supplied to the lecture room; \(VR_{min}\) is the minimum ventilation rate recommended by ASHRAE standards (equals 3.8 L/s/person for this study).
* The exceedance in exposure ratio (\(EER\)) is a dimensionless number that can be used to identify the location and degree of a student's risk of exposure to harmful droplets. In other words, it describes the exceedance or decadence of a student's exposure to the droplets for a given ventilation rate compared to the corresponding average exposure that is expected in case of a minimum ventilation rate: \[EER=\frac{C_{i}-C_{ave}^{*}}{C_{ave}^{*}}\] (4) where \(C_{i}\) is the actual concentration received by a student \(i\) and \(C_{ave}^{*}\) is the average class concentration in applying minimum ventilation rate (the reference case, R2).
* Based on the definition mentioned earlier, the value of \(EER\) could be positive, zero, or negative. A positive value means the student exposure exceeds the average conditions, whereas a negative value implies vice versa. Therefore, \(EER\) could represent the spatial risk of exposure to the droplets among the students in the class based on their location and the applied ventilation rate.
# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 3 Materials and Methods
### EBLSCP & EBMCLP
The expression in Equation (6) explains the case when there is an emergency bell at each candidate location and the distance from the new emergency bell to the crime occurrence location falls within the 50 m buffer; The sum of such cases should be greater than or equal to \(yi\). This statement indicates that if \(y_{i}\) is 1, then unmonitored crime occurrence coordinates \(i\) must be covered by at least one new emergency bell. When \(y_{i}\) is 0, in which none of the new emergency bells cover any unmonitored crimes \(i\), the formula can still be maintained. The difference between constraints (3) and (6) arises from a characteristic of EBLSCP and EBMCLP. While EBLSCP seeks to minimize the number of emergency bells needed to cover all unmonitored crimes, EBMCLP aims to maximize the weighted number (frequency) of unmonitored crimes with a fixed number (\(F\)) of new emergency bells for cost-effectiveness. In other words, for EBMCLP, not all unmonitored crimes need to be covered by new emergency bells, while EBLSCP must cover all unmonitored crimes. Equation (7) describes the number of new emergency bells as \(F\). Equation (8) serves the same purpose as Equation (4), and Equation (9) defines the role of variable \(y_{i}\) as a binary parameter. In this case, \(y_{i}\) takes the value of 1 if the distance from new emergency bell \(j\) to the location of crime occurrence \(i\) is equal to or less than the coverage of the 50 m buffer and 0 otherwise. EBMCLP and EBLSCP do not significantly differ from traditional MCLP and LSCP models. However, in order to take into account the critical coverage area of emergency bells in this study, we added \(d_{ij}\) as constraints (3) and (6). Moreover, in contrast to the conventional MCLP model, which primarily considers population as weights, EBMCLP selected the number of crimes as its weights. In comparison to other papers that applied LSCP and MCLP, we have included additional detailed explanations for each variable and formula.
## 2 Experimental Investigation
### Signal Analysis
#### 2.4.1 Coherence  
The coherence between two signals \(x(t)\) and \(y(t)\) is defined by Equation (3) [36], where \(S_{xy}(f)\) is the cross-spectral density of the signal and \(S_{xx}(f)\) and \(S_{yy}(f)\) are the power spectral density functions of \(x(t)\) and \(y(t)\), respectively. \(f\) is frequency and \(t\) is time:  
\[Coherence_{xy}\ =\ \frac{\left|S_{xy}(f)\right|^{2}}{S_{xx}(f)\cdot S_{yy}(f)} \tag{3}\]  
In this case, \(x(t)\) is any of the sensor signals and \(y(t)\) the excitation signal sent to the PZT. If this value is close to 1, both signals are coherent, which means that the signals acquired by the sensors are caused by the excitation of the PZT. A good coherence between the sensor and the PZT indicates that the transmission path is good. However, if the coherence is close to 0, it means that there is no linear causality effect between the sensor and the excitation signal and, therefore, in this case, it is not useful for the detection of erosive cavitation.
# High-Pressure Technologies for the Recovery of Bioactive Molecules from Agro-Industrial Waste
## 2 Presurized Liquid Extraction Processes
### PLE of Bioactive Compounds from Waste  
Food industry by-products and waste have been proven to be a cheap raw material for the recovery of compounds with commercial value. For example, molecules of interest for the industry are caffeine from spent coffee grounds [33; 48], lycopene and carotenoids from tomato waste [49], astaxanthin from crustacean and shrimp waste [50; 51], polyphenols from fruit waste and rice [52], and lipids and protein from the waste of olive or microalgae [53; 54; 55]. These active substances have potential health benefits and find applications as ingredients or additives in the medical field, as well as in the food and cosmetic industries. By enabling the use of green and food-grade solvents, pressure-assisted extraction has been successfully applied to extract active substances (Table 1) such as proteins, polysaccharides (such as lignans), lipids, polyphenic compounds (carotenoids and tocopherols), and essential oils from food waste [18]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Compounds** & **Source** & **Extraction** & **Operating Conditions** & **Sample Weight** & **Extraction Method** & **Ref** \\ \hline Proteins & _Sambucus nigra_ L. & water & 50 \({}^{\circ}\)C and 15 MPa & 4 g & PHWE & [56] \\ \hline Proteins & Wheat bran & water & 80 \({}^{\circ}\)C, pH 9.3, and 30 bar & 15 g & SWE & [57] \\ \hline Proteins & Spirulina & water & 40 C, pH 4, and 103.4 bar & 500 mg & PLE & [58] \\ \hline Proteins & Sea & & 1500 psi; & & & \\  & Bream (_Sparus_ & & 20 \({}^{\circ}\)C, pH 7, 5 min for muscle; & & \\  & _aurata, Salmon salar_ & water & 60 \({}^{\circ}\)C, pH 4, 15 min for heads; & & \\  & and _Dicentrachus_ & & 50 \({}^{\circ}\)C, pH 7, 15 min for viscera; & & \\  & _labrax_) Side Streams & & 55 \({}^{\circ}\)C, pH 7, 5 min for stin; & & \\  & & 60 \({}^{\circ}\)C, pH 7, 15 min for tailfins & & & \\ \hline Proteins & Pomegranate peel & 70\% (\(\
u/\
u\)) & 120 \({}^{\circ}\)C, 1500 psi static & & \\  & (_Punica granatum_ & L.) & EtOH & extraction time, 3 min; & \\  & & & & extraction time, 12 min & & \\ \hline Proteins & _Saccharina japonica_ & water & 140 \({}^{\circ}\)C and 50 bar for 5 min & 10 g & PLE & [63] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example for extraction of biocompounds with pressurized extraction. EtOH = ethanol; PWE = Pressurized water extraction; PHWE = Pressurized hot water extraction; SWE = Subcritical water extraction; AS = Agitation speed; S/L = solid to liquid ratio; HPTE = high- pressure high temperature extraction.
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 3 Experiments and Results
### Data Description
#### 3.1.2 DeepGlobe Road Dataset  
Originating from the CVPR DeepGlobe 2018 Road Extraction Challenge, the DeepGlobe Road Dataset consists of 8570 high-resolution (0.5 m per pixel) images, each measuring 1024 \(\times\) 1024 pixels. This collection is segmented into 6626 training images, 1243 validation images, and 1101 test images. In particular, only the binary labels for the training set were publicly accessible subsequent to the conclusion of the challenge; therefore, this study specifically opted for the 6626 training images and their corresponding labels for experimental implementation purposes. These images were also partitioned randomly in a 7:2:1 ratio.
## 3 Methodology  
This work aimed to predict the depth of hydrocarbon in the seabed logging (SBL) application by using a novel methodology, namely Gaussian process (GP)-based inverse modeling. Here, four subsections are discussed: 3.1. Preprocessing computer outputs, 3.2. Evaluating electromagnetic (EM) profile using the GP, 3.3. Predicting depth of hydrocarbon, and 3.4. Estimate and GP model validation.
## 5 Limitations  
The carpal bones move during flexion and extension of the wrist, which changes the geometry of the carpal canal and consequently affects the movement of tendons and the median nerve; this was not taken into account in this study. However, consideration of the carpal bone interaction is a separate complex task considered in many other studies [11; 12; 13; 14; 15]. Adding the effect of carpal bone motion would greatly complicate calculations and increase calculation time to an unacceptable level.  
Most of the tendon surface area in this study is in the void. The annular ligaments are surrounded by tissue only where tendons pass through. In fact, the tendons are surrounded by connective tissue throughout. Nevertheless, there was a small amount of movement of the tendons in the direction normal to the phalanges compared to movements in the carpal tunnel. This suggests that the boundary conditions used for the calculations were correct.  
This study did not take into account friction between the tendons and other modeled objects. [PERSON] et al. 2003 [34] developed a method to indirectly measure friction between the flexor tendons and pulleys of the middle and ring finger in vivo. The coefficient of friction according to the calculation was \(\mu=0.075\) (\(\pm 0.021\)). [PERSON] et al. 2009 [35] determined friction between the flexor tendons and pulleys of eight human cadaver fingers using an isokinetic movement device. The coefficient of friction according to measurement was \(\mu=0.045\) (\(\pm 0.0028\)). The values of friction coefficients in these studies will have a slight impact on the results of this study. However, taking into account the friction force between tendons and other objects may be a further research issue.  
The modeling of finger and wrist flexion in this study did not take into account the wrist muscles and finger extension tendons. These tissues provide resistance to wrist and finger flexion. Taking these tissues into account will result in increased pressure in the carpal tunnel. However, the wrist muscles and finger extensor tendons do not pass through the carpal tunnel, and therefore do not contact the median nerve.  
In this study, the median nerve was considered only in the carpal tunnel region, because the aim of the study was to determine the effect of tendon movement on median nerve compression. However, the median nerve innervates fingers 1-3 of the wrist and is anatomically attached to the tissues of the fingers. This influences the results of the solution; however, determination of the places of median nerve attachment distal to the carpal tunnel by MRI is very difficult due to its size.
# Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images
## 2 Methods  
Let \(\mathcal{F}\) be a color image. Represent by \(W\), the filtering window with the center at pixel \(\mathbf{x}\), and with size \(n\times n\) (\(n\) is an odd natural, \(n\geq 3\)). The image pixels in \(\mathcal{F}\) are represented as vectors \(\mathbf{x}_{i}=(\mathbf{x}_{i}(1),\mathbf{x}_{i}(2),\mathbf{x}_{i}(3))\), as is common for RGB color images, where the vector components \(\mathbf{x}_{i}(\ell),~{}\ell=1,2,3\) are the values corresponding to the RGB color channels. We propose a two-stage hybrid denoising technique called FRF-FPGA. The hybrid filter structure is formed as a superposition of an impulse removal filter and a Gaussian filter. In the first stage, a variation of the FRF filter [34] is used to reduce the impulses. At the second stage, a filter based on the FPGA filter [10] is applied to attenuate the Gaussian noise. In [10], authors use the idea of fuzzy peer group in order to implement a denoising method structured in two steps. In the first step, authors use a fuzzy rule switching impulsive denoising method, and in the second stage a fuzzy averaging is computed over the pixels of the peer group. At the present study we utilize the concept of optimal number of elements in a peer group introduced in [10]. This notion is used in the Gaussian noise smoothing, but not in the impulse detection and reduction process, where a two steps procedure based on the fuzzy ROD (FROD) statistic [34] is employed. In Section 3 we show that this new hybrid approach outperforms the results obtained in [10]. In the next lines we explain the two stages of the hybrid filter separately. Algorithm 1 shows the pseudocode of the hybrid filter applied over each image pixel.  
```
Require: Noisy image \(\mathcal{F}\), Parameters \(\alpha,\alpha^{\prime},th_{1},th_{2},th_{3}\), and \(F_{\sigma}\).  Ensure: Filtered image. Impulse noise detection: Step 1 for\(x\) pixel in \(\mathcal{F}\)do  Calculate: \(d=FROD_{\alpha}(x)\); if(\(d>th_{1}\))then  pixel \(x\) is labeled as impulse-free; else if(\(d<th_{2}\))then \(x\) is labeled as impulsive; else \(x\) is labeled as non-diagnosed; endif endif endfor Impulse noise detection: Step 2
```  
**Algorithm 1**Hybrid filter.
# A Low-Cost Evaluation Tool for Synchronization Methods in Three-Phase Power Systems
###### Abstract  
The use of renewable energy sources (RESs) together with energy storage systems (ESSs) allows for smoothing power variations, thus improving power backup capabilities and power quality in the electric power grid. These applications require power converters to transfer energy between the renewable generator or energy storage and the power grid. In any case, the control algorithm of the power converter requires the synchronization method to provide a correct estimation of the instantaneous voltage of the power grid. This work provides engineers and researchers with an accessible platform at a low cost (less than USD 100) and a methodology for the experimental validation of digital synchronization algorithms as a step before their implementation in grid-connected equipment. The methodology evaluates the performance of the digital algorithms when there are variations in amplitude, frequency, phase, and harmonic content in the emulated three-phase power grid, as well as the execution times (\(t_{ex}\)), while a digital platform emulates the electrical signals and generates reference signals for the evaluation. To illustrate this proposal, two synchronization algorithms--SRF-PLL and DSOGI-PLL with a low-pass filter--are implemented in a digital controller and tested. The evaluation tool confirms the algorithms' performance and shows that the execution time of DSOGI-PLL is 91% longer than that of SRF-PLL, which is well known in the literature.  
digital algorithms; digital control; evaluation tool; phase-locked loop; synchronism method; synchronism techniques 2025  
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