esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Application of BRBS in the Detection of Diabetic Nephropathy Biomarkers
### Homocysteine (Hcy)
The sensor exhibited excellent signal detection, selectivity, and reproducibility, with a good linear relationship between Hcy concentration and \(\Delta\)I in the linear range of 1-100 \(\upmu\)M, a wide linear range, and a detection limit of 1 \(\upmu\)M (Table 2). APT can effectively recognize biomarkers by non-specifically adsorbing on the surface of gold electrodes or AuNPs. Small molecule-assisted technology (SMAT) refers to the formation of an auxiliary interface by small molecules that enable the linear chains of APT to be vertically oriented on the interface. The specificity of APT is enhanced by restricting the non-specific binding sites at the sensor interface through 6-mercaptoethanol, dithiothreitol, and 3-mercaptoethanol-propropionic acid. Existing research reports that the binary self-assembled monolayer composed of thiol APT and 6-mercaptoethanol can significantly reduce the surface molecular density of APT, confirming that SMAT can reduce the non-specific adsorption of APT [59]. Hcy is a type of redox molecule [60]. Using SMAT [PERSON] et al. [61] developed an unlabeled electrochemical sensor for detecting Hcy (Figure 4). Firstly, the aptamer solution was cast onto the surface of a glassy carbon electrode modified with AuNPs, and stood upright in a humid room to complete the self-assembly of the thiol aptamer. The modified glassy carbon electrode was then immersed in a 6-mercaptoethanol  
Figure 3: The fabrication procedures and detection mechanism of the aptaseneor. (**a**) The stepwise modifications of the GCE surface. (**b**) The electrochemical response of the aptasensor with/without Hcy. Ip represents the peak current [56]. solution and washed with 0.1 M PBS at a pH of 7.4. Small molecule interface technology was used to form a linearly coordinated aptamer chain and limit non-specific binding sites. The Hcy binds through interaction with the aptamer and undergoes an electrochemical reaction at the modified electrode surface, resulting in a significant enhancement of the peak current. This sensor, under optimal conditions, identifies Hcy using the differential pulse voltammetry (DPV) method. The detection limit is 0.01 uM, and the linear range is 0.05-20.0 uM (Table 2).
###### Abstract  
Foreign objects such as kites, nests and balloons, etc., suspended on transmission lines may shorten the insulation distance and cause short-circuits between phases. A detection method for foreign objects on transmission lines is proposed, which combines multi-network feature fusion and random forest. Firstly, the foreign object image dataset of balloons, kites, nests and plastic was established. Then, the Otus binarization threshold segmentation and morphology processing were applied to extract the target region of the foreign object. The features of the target region were extracted by five types of convolutional neural networks (CNN): GoogLeNet, DenseNet-201, EfficientNet-B0, ResNet-101, AlexNet and then fused by concatenation fusion strategy. Furthermore, the fused features in different schemes were used to train and test random forest, meanwhile, the gradient-weighted class activation mapping (Grad-CAM) was used to visualize the decision region of each network, which can verify the effectiveness of the optimal feature fusion scheme. Simulation results indicate that the detection accuracy of the proposed method can reach 95.88%, whose performance is better than the model of a single network. This study provides references for detection of foreign objects suspended on transmission lines.  
transmission lines; foreign object detection; feature fusion; random forest; convolutional neural network +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
###### Abstract  
In the past few years, one of the largest industries in the world, the agriculture sector, has faced many challenges, such as climate change and the depletion of limited natural resources. Smart Agriculture, based on IoT, is considered a transformative force that will play a crucial role in the further advancement of the agri-food sector. Furthermore, in IoT-based Smart Agriculture systems, radio wave propagation faces unique challenges (such as attenuation in vegetation and soil and multiple reflections) because of sensor nodes deployed in agriculture fields at or slightly above the ground level. In our study, we present, for the first time, several models (Multi-slope, Weissberger, and COST-235) suitable for planning radio coverage in a cornfield for Smart Agriculture applications. We received signal level measurements as a function of distance in a corn field (R3 corn stage) at 0.9 GHz and 2.4 GHz using two transmitting and two receiving antenna heights, with both horizontal and vertical polarization. The results indicate that radio wave propagation in a corn field is influenced not only by the surrounding environment (i.e., corn), but also by the antenna polarization and the positions of the transmitting and receiving antennas relative to the ground.  
IoT; radio wave propagation; precision agriculture; smart agriculture; attenuation in vegetation and soil; near-ground propagation; above-ground propagation; wireless communications 2020 IEEEexample:BSTcontrol  
1
## 2 The Design of Clustered Detection System
### The Design of Fuzzy Logic System  
In the distributed detection system, CHs not only need to fuse multiple-bit soft information in clusters but also need to send one-bit hard information to the FC. Therefore, CHs must have sufficient energy and be close to the FC. Therefore, two antecedents, including every local sensor's remaining energy and distance to the FC, are considered to design the FLS. Therefore, two antecedents of a local sensor are considered in our designed FLS:* Antecedent 1: every local sensor's remaining energy.
* Antecedent 2: every local sensor's distance to the FC.  
The linguistic variables used to represent antecedent 1 are divided into three levels: low, moderate, and high; and those to represent antecedent 2 are also divided into three levels: near, moderate, and far. Two antecedents use the same kind of membership functions (MFs), and in this paper, MFs for normalized antecedents are shown in Figure 2. Two antecedents as the input of MFs have been normalized to [0, 10]. The linguistic variables, which are used to represent the consequent and denote the possibility that a local sensor will be selected as a CH, were divided into five levels: very small, small, medium, large, and very large. In this paper, MFs for normalized consequents are shown in Figure 3.  
Based on the fact that an ideal CH should have sufficient energies and be close to the FC, we design our FLS using rules for every input (\(x_{1}\), \(x_{2}\)) like:  
R\({}^{1}\): IF the remaining energies of a local sensor (\(x_{1}\)) is F\({}_{1}^{i}\), and its distance to the FC (\(x_{2}\)) is F\({}_{2}^{i}\), THEN the possibility that this local sensor will be selected as a CH(\(y\)) is G\({}^{1}\), where l = 1,..., 9. Nine rules are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rule** & **Antecedent 1** & **Antecedent 2** & **Consequent** \\ \hline
1 & low & near & medium \\
2 & low & moderate & small \\
3 & low & far & very small \\
4 & moderate & near & large \\
5 & moderate & moderate & medium \\
6 & moderate & far & small \\
7 & high & near & very large \\
8 & high & moderate & large \\
9 & high & far & medium \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rules in FLS.  
Figure 3: MFs for normalized consequents.  
Figure 2: MFs for normalized antecedents.  
For every input \((x_{1},x_{2})\) of each local sensor, the output is computed using:  
\[\mathrm{Y}(x_{1},\ x_{2})=\frac{\sum\limits_{l=1}^{9}\mu_{F_{1}^{l}\ (x_{1})}\ \mu_{F_{2}^{l}\ (x_{2})}c^{l}}{\sum \limits_{l=1}^{9}\mu_{F_{1}^{l}\ (x_{1})}\ \mu_{F_{2}^{l}\ (x_{2})}} \tag{1}\]  
According to the output, which is the probability of a local sensor selected as a CH, we can select M local sensors with the highest probabilities as CHs. M is the number of CHs, and it can be obtained using:  
\[\mathrm{M}=floor(\mathrm{p}\times\mathrm{N}) \tag{2}\]  
where \(\mathrm{p}\) is a constant ratio decided by people and represents the proportion of CHs in all sensors, and N is the total number of local sensors in the distributed detection system.
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
###### Abstract  
Point clouds acquired by mobile laser scanners (MLS) are widely used for generating as-built building information models (BIM), particularly in indoor construction environments and existing buildings. While MLS offers fast and efficient scanning through SLAM technology, its accuracy and precision remains lower than that of terrestrial laser scanners (TLS). This study investigates the potential to improve MLS-based as-built BIM accuracy by analyzing and utilizing error distribution patterns inherent in MLS point clouds. Based on the assumption that each MLS device exhibits consistent and unique error distribution patterns, an experiment was conducted using three MLS devices and TLS-derived reference data. The analysis employed iterative closest point (ICP) registration and cloud-to-mesh (C2M) distance measurements on mock-ups with closed shapes. The results revealed that error patterns were stable across scans and could be leveraged as correction factors. In other words, the results indicate that when using MLS for as-built BIM generation, robust fitting methods have limitations in obtaining realistic object dimensions, as they do not account for the unique error patterns present in MLS point clouds. The proposed method provides a simple and repeatable approach for enhancing MLS accuracy, contributing to improved dimensional reliability in MLS-driven BIM applications.  
point cloud; As-built BIM; Scan-to-BIM; mobile laser scanner; error distribution pattern 2020 IEEEexample: 2015
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Pollution Sampling with Adaptive Path Planning
#### 4.6.1 Smog Profiling over Zywie
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**\#** & **CT [s]** & **FT [s]** & **CFEs** & **COST** & **LEN [m]** & **SMOO** & **EEe [Wh]** & **NCOL** \\ \hline
**1** & 30.554 & 2151.837 & 15,350 & 3020 & 47,257 & 0.868 & 62.116 & 0 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
**4** & 95.526 & 2993.788 & 35,750 & 2742 & 65,447 & 0.801 & 86.473 & 0 \\
**5** & 87.318 & 3021.026 & 40,850 & 2844 & 66,236 & 0.850 & 87.191 & 0 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
**10** & 34.790 & 3024.412 & 17,450 & 3071 & 66,391 & 0.825 & 87.320 & 0 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
**15** & 23.032 & 2135.013 & 12,050 & 3232 & 46,867 & 0.883 & 61.633 & 0 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
**22** & 44.314 & 2982.508 & 22,850 & 2847 & 65,386 & 0.855 & 86.106 & 0 \\
**23** & 18.425 & 2135.032 & 10,250 & 3233 & 46,867 & 0.884 & 61.633 & 0 \\
**24** & 47.061 & 3236.482 & 22,850 & 2862 & 71,025 & 0.841 & 93.491 & 0 \\
**25** & 38.915 & 2202.007 & 19,550 & 3023 & 48,372 & 0.887 & 63.551 & 0 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
**30** & 39.131 & 2272.361 & 20,150 & 2805 & 49,857 & 0.871 & 65.579 & 0 \\ \hline
**Ref** & N/A & 2488.966 & N/A & N/A & 54,596 & 0.826 & 71.857 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Metrics calculated for the simplified APPLowEmissionZywiec scenario. Figure 6: The path designed by a human expert (**top**) and the best path generated by the GPP (**bottom**). Interestingly, Path 4, while being the shortest and most energy-efficient, has the highest \(\text{COST}=3233\). This is due to the energy criterion not fully accounting for wind effects, leading to a difference between the optimized EEE and the simulated EEE. This indicates potential for further refinement of the energy criterion to better model wind impact. Path 28 has the highest SMOO value, while Path 1 is the least smooth. As expected, SMOO is highly negatively correlated with EEE (\(-0.705\)), indicating that smoother paths generally consume less energy. FT shows the strongest correlation with EEE (0.999), reflecting a very close positive relationship. The shortest local CT of 0.061 s occurs in the 20 th path, while the longest local computation time, 1.030 s, is noted in the 8 th path. For global paths, Path 25 has the shortest CT at 17.181 s, while the longest is observed in Path 2. Consequently, no path exceeds the CT threshold. Additionally, all paths remain free of collisions. In total, 28 out of 30 paths fulfill the success criteria, resulting in a 93.33% success rate for this scenario.
## 3 Results & Discussion
### Detection of Incomplete Cuts
Figure 12 shows the detected signals using the example of complete and incomplete cuts for stainless steel, galvanized steel, and aluminum. It is shown that the selection of one, two, and four occurs in the example of an incomplete cut in 3 mm zinc-coated steel. This indicates that the area of the melt is particularly large, the blue portion exceeds the green  
Figure 11: Flowchart of the algorithm to detect incomplete cuts. portion, and the center of the melt shifts significantly. In the case of an incomplete cut in 1 mm zinc-coated steel, selection three occurs, i.e., the melt pool area is neither too large nor is the blue portion larger than the green portion. However, the melt has an elliptical shape, and the blue melt pool area approaches the width and length of the green melt pool area. In the example of the incomplete cut in stainless steel, a combination of all four selections occurs. For aluminum, as described, only selection one is used to detect incomplete cuts. The example for a complete cut in 3 mm galvanized steel shows an incorrect detection. As can be seen, selection two is triggered occasionally during the cut. Figure 12: Signals for the green and blue channels and the signals of the interruption detection of complete cuts (**a**–**c**) and incomplete cuts (**d**–**f**) for stainless steel with a sheet thickness of 1.5 mm and zinc-coated steel with a sheet thick-ness of 1.0 and 3.0 mm and of complete cuts (**g**–**i**) and incomplete cuts (**j**–**l**) for aluminum for sheet thicknesses of 1.0, 3.0, and 5.0 mm. The algorithm described here provides the best detection probability. We have evaluated this algorithm for 220 cutting tests in stainless steel with a sheet thickness of 1.5 mm, zinc-coated steel with a sheet thickness of 1.0 and 3.0 mm, and aluminum with a sheet thickness of 1.0, 3.0, and 5.0 mm. We varied the laser power between 2.0 and 4.0 kW and the feed rate between 30 and 300 mm/s to enforce incomplete cuts. From this series of tests, all 74 incomplete cuts are reliably detected. For five complete cuts out of a total of 146, however, an incomplete cut was detected by the algorithm, which leads to an error of 2.3% for this test series, showing a detection probability that is comparable to other quality monitoring systems for laser cutting [40; 41].
## 1 Introduction  
Due to the modern problems in wireless communication system, the Wireless Ad-hoc Visible Light Communication Network (WAVLCN) has become a popular technology among industrial and academic communities [1, 2, 3]. The Visible Light Communication (VLC) is applied in an extensive range of applications: underwater communication [4], localization networks [5], vehicular networks [6], and indoor communication networks [7]. The VLC system consists of two major elements, like receiver and transmitter, where a Light Emitting Diode (LED) is used as a transmitter that modulates the light intensity for transmitting the data [8]. On the other hand, the photo-sensors are utilized to capture light at the receiver side, and then convert light into data streams [9]. In the VLC network, the LEDs transmit light by using its field of view that reduces the angle of emission, so it works on the basis of sight line between transmitter and receiver [10]. However, the VLC system has several issues during data transmission, like shadowing and obstacles [11; 12]. To address these problems, a new dynamic routing protocol is proposed in this research paper.  
A dynamic routing protocol named the Modified Grasshopper Optimization Algorithm (MGOA) is proposed as a cross-layer routing protocol for WAVLCN. In this research, the proposed routing protocol is inspired from the mobile ad hoc network routing protocols [13]. On the basis of mobile ad hoc network routing protocols, the proposed MGOA routing protocol is created using VLC by adapting to network change [14; 15]. In this research paper, the main contribution of MGOA is its integration with the Medium Access Control (MAC) protocol, named as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), which is applied for routing maintenance. The CSMA/CA protocol makes visible light highly sensible to shadows, and obstacles. The performance analysis of the proposed model is done by using several performance metrics such as Packet Delivery Ratio (PDR), end-to-end delay, throughput, and routing overhead. From the simulation results, the proposed routing protocol almost reduced 0.3 s of end-to-end delay in data transmission and showed 3% to 8% improvement in PDR compared to the existing protocol.  
This article is pre-arranged as follows. Some recent research works on the topic \"VLC\" are surveyed in Section 2. The proposed MGOA-WAVLCN is briefly explained in Section 3. The experimental evaluation of the proposed MGOA-WAVLCN is given in Section 4. The conclusion of the research work is represented in Section 5.
###### 1 Introduction
Both fluorescent and image analysis have opened the door to fast and non-destructive methods to determine pesticide residue coverage, attaining the spectral and spatial data from the sample [28; 39]. Some of the previously reported work harnessed hyperspectral imagery to analyze pesticide residues using Vis-NIR and NIR apparatus on grape berries. The data were randomly divided into training, test, and validation sets, with a combination of three machine learning algorithms and two deep learning neural networks [28]. Similarly, fluorescent hyperspectral imagery was recently reported for pesticide residue identification on black tea samples assisted by machine learning [40]. However, these recent reports fail to provide simplicity to the data collection process, still requiring NIR and UV hyperspectral apparatus. While these apparatuses are less expensive, they are not transferable to field conditions for in situ assessment, in particular in citrus groves. More importantly, these methods will require scientific knowledge, limiting the accessibility of the technique for use by growers. For these reasons, the present work aims to provide to citrus growers an accessible, inexpensive, and portable imaging chamber to assess the pesticide residue on leaves. Herein, an imaging analysis software is developed and assisted with a lightweight machine learning system for pesticide residue detection and coverage approximation on the leaf surface from the captured image. This platform will reduce analysis costs and guide the decision-making process regarding pesticide applications, preventing the excessive overuse of pesticides. In this study, Kocide 3000 and Oxytetracycline (OTC) were selected as model pesticides due to their high relevance to the citrus industry and their widespread use as pest management strategies for different plant diseases. The effective implementation of this developed technological tool has the potential to serve as a grower friendly alternative for qualitative pesticide residue analysis to provide more sustainable and equitable agricultural practices in the citrus industry.
## 3 Experimental Test-Rig and Data Acquisition  
A RC valve leakage fault test rig was designed to study the quantitative diagnosis method of RC valve failure based on system characteristics diagnosis technology. Additionally, three different severities of exhaust valve leakage faults were simulated for comparative experiments.
## 3 Results
### Rat Simulations  
Figure 8 shows the \(\mid\)B\({}_{1}\mid\)-field, \(\mid\)E\(\mid\)-field and temperature map under the same conditions (S\({}_{11}\)) as the phantom simulation.  
The proposed patch antenna configuration can produce a uniform \(\mid\)B\({}_{1}\mid\)-field in the target area; furthermore, it shows that it can also be used for RF heating applications. The \(\mid\)B\({}_{1}\mid\)-field in the target area had a mean value of 0.27 \(\upmu\)T and a standard deviation of 0.0082 \(\upmu\)T. The electric field had a maximum value of 400 V/m, and the temperature had an average value of 37.8 \({}^{\circ}\)C and maximum value of 38.2 \({}^{\circ}\)C in the cancer tissue after 200 s. For the background tissues the average temperature was 37.35 \({}^{\circ}\)C with a standard deviation of 0.15 \({}^{\circ}\)C and maximum of 37.9 \({}^{\circ}\)C. The safety of the surrounding tissues must be maintained; thus, we compared the temperature rise between the cancer tissue and healthy tissue. Figure 9 shows the SAR average 10 g maps for the case of the 300 MHz and 1.06 GHz, for the z-y and x-y plane, top and bottom, respectively. From Figure 9 it can be seen that the SAR is larger in the location of the cancer-mimicking tissue. The maximum SAR value was 0.25 and 6.32 W/kg for 300 MHz and 1.06 GHz, respectively.  
Figure 7: The computed (**a**) \(\mid\)B\({}_{1}\mid\)-field, (**b**) \(\mid\)E\(\mid\)-field and (**c**) temperature map for the cylindrical phantom.  
Figure 10 compares the evolution of temperature in time for a point in a nearby healthy large intestine tissue (R2) and a point at the cancer tissue (R1), at the regions marked by a circle in the top of Figure 8c. In addition, we included the volume histogram to show the evolution of the temperature in the cancer tissue Figure 10b and also on the healthy tissue Figure 10c. The histogram shows the normalized volume at different temperature ranges. The bars in each bin represent the time in seconds. It can be seen that the temperature on the cancer tissue has move volume counts at the highest temperature range than in the healthy tissue.  
Figure 8: The computed (**a**) \(\mid\)B\({}_{1}\) \(\mid\)-field, (**b**) \(\mid\)E \(\mid\)-field and (**c**) temperature map, for (**d**) voxel of rat model.  
Figure 9: The SAR average 10 g acquired at (**a**) 300 MHz and (**b**) 1.06 GHz, for the z-y plane (**top**) and x-y (**bottom**).