esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 5 Discussion  
### Spatial Hashing Storage Structure and Its Influence on Power Line Extraction  
The gridded massive point cloud was stored in the designed 3D hashing matrix, which subdivided the space more effectively compared to simple hierarchical storing structures, and this structure was able to manage the 3D point cloud data more efficiently and practically. The upper surface of a hashed grid point cloud is roughly sketched in Figure 20. Local point cloud 3D gridding helped to compute the geometric features of PL for local line segmentation by ensuring that other objects were separated from the voxel block containing PLs. Thus, when setting the grid and voxel sizes, the cross section of bundle conductors should be considered as an important reference.  
Figure 19: Reconstruction of the transmission corridor: (**a**) reconstruction of PLs; (**b**) center points of extracted towers; (**c**) modeling of the 3D scene in view of the modeling center of pylons.  
The 2D plane gridding was to facilitate power transmission object extraction. There was a strong correlation between grid size, planar projection size of pylons, and the distribution range of overhead PLs. In order to improve the efficiency and guarantee the accuracy of object extraction, these relevant factors were taken into account in grid size selection. On the premise that the accuracy of HV tower recognition was not affected, grids were set to \(5\times 5\) m empirically.  
The proposed method was proven to be efficient in terms of speed, quality, and scalability, thanks to the mentioned data structure and the unnecessity of accurate ground point filtering. Original data after removing noisy points were directly fed into the processing pipeline. Moreover, for PL extraction, the data processing unit was the 3D grid cluster, which was able to substitute the usage of Kd-tree or Octree to simplify the computation in this scenario. It also improved the clustering efficiency to a certain extent.  
The parameter sensitivity of the PL extraction test is listed in Table 8, which shows that the result of line fitting was related to the sampling interval and grid size. When the grid size was \(5\times 5\) m and the sampling interval was 0.132 m, the PL extraction accuracy reached 100%. However, as the grid size increased, or as the sampling interval decreased, the rate of correct results decreased and the overall accuracy became worse. However, when the sampling interval increased further, the accuracy decreased too. This is because that if the sampling interval was too small, the point density and the number of detected straight lines were large, and the impact of noise was also large, which resulted in a high error rate for the straight-line clustering. However, if the sampling interval is too large, the sampled point density will be too low, and some key points can be mistakenly removed, affecting the subsequent line detection and fitting.
## 2 ARD Framework
### Processing Pipelines
#### 2.4.2 Sentinel-2 Product Processing  
The processing pipeline for Sentinel-2 products is shown in Figure 5 and follows the procedure recommended by ESA [58]. Sentinel-2 products contain different bands at different spatial resolutions of 10 m, 20 m and 60 m and consequently re-sampling is required to harmonise all bands to the same spatial resolution of 10 m. Masks are obtained from the original product using the BandMaths operator and merged with the re-sampled product using the BandMerge operator. Similarly to the Sentinel-1 processing pipeline, a subset operation in SNAP performs the cropping. The configurable bands are passed as parameters to the band extractor operator to only select the bands and masks required by the user.
# Recent Research Progress in Surface Ligand Exchange of PbS Quantum Dots for Solar Cell Application
## 3 Solution-Phase Ligand Exchange
### A Facet-Specific Quantum Dot Passivation Strategy for Infrared Quantum Dots  
PbS CQDs show size- and facet-dependent properties, and the diameter of PbS CQDs must become larger to harvest the IR region solar spectrum. However, these PbS CQDs exhibit a large amount of non-polar (100) facets on their surface, which is less detectable in small-diameter PbS CQDs. In the solution-phase ligand exchange methods optimized for small-diameter PbS CQDs, the ligand exchange of native oleic acid ligands to lead halide anions occur mainly on the polar (111) facets of Pb-abundant sites with the assistance of ammonium NH\({}_{4}\)\({}^{+}\) cations [64]. However, this approach is not applicable for large-diameter PbS CQDs showing a large fraction of (100) facets [46; 61]. These large-diameter PbS CQDs are easily aggregated and even fuse during the ligand exchange step because on (100) facets, the oleic acid ligands are easily removed by polar solvents.  
Figure 8: Film geometry and charge transfer characteristics of infrared (IR) PbS CQD films treated with hydroiodic acid (HI) during solution-phase ligand exchange process. (**a**) Interdot spacing of PbS CQD film according to HI treatment. (**b**) Mobility of PbS CQD films characterized by field-effect transistor (FET) measurements. Reprinted from reference [63]. Copyright 2018 American Chemical Society.  
Very recently, a new solution-phase ligand exchange process to passivate (100) facet has been suggested by the careful selection of metal cations and anions. By replacing the ammonium acetate with sodium acetate in a ligand exchange process, (100) facet passivation can be achieved by Na owing to the proper cation size and dissociation constant of Na (Figure 9a) [65,66]. This approach protects IR-CQDs from aggregation during the solution-phase ligand exchange, leading to improved stability and enhanced photophysical properties of the resultant PbS CQDs. The Na-passivated IR CQD solids prepared via this approach demonstrated the improvement of PCE values under the full solar spectrum (33% of enhancement) and silicon-filtered solar spectrum (48% of enhancement) as shown in Figure 9b,c [66].
# Intelligent Scheduling with Reinforcement Learning
## 1 Introduction  
The usage of information technology has transformed modern organizations [1]. An information system, as a set of interrelated components that manages the available information, exists to support decision making and control in organizations that have to deal with complex problems. Scheduling, one of these problems, is a decision-making process that is regularly used in service and manufacturing industries, and where proper information systems have a major impact.  
Considering the importance that the scheduling process has in many industries and its high complexity, it has been, historically, an area that has attracted many researchers with different backgrounds--from computer science to mathematics or operational research. The goal is common: to discover the best method to solve a scheduling problem, optimally, as efficient as possible. However, due to its complexity, it is impractical to solve these problems optimally. The most common methods usually apply classic optimization approaches [2] that require some compromises: either obtain a solution quickly with acceptable quality, or have a more optimized solution that might require considerable time to be calculated.  
The main contribution of this paper is the proposal of a novel method that takes inspiration from the latest developments in the machine learning field to solve scheduling problems, by using reinforcement learning techniques to train intelligent agents. With an innovative approach based on a unique architecture, this method is capable of generating solutions almost instantly while maintaining a good level of quality. It achieves the proposed goal of generating optimized plans as soon as possible that can be put inproduction in real industry environments. To further reinforce the quality of the achieved results, a comprehensive computational study with a detailed statistical inference analysis is presented.  
The remaining sections of this paper are organized as follows: Section 2 presents a review on the relevant topics required to understand this work, namely scheduling problems (including a detailed description of the job shop problem), machine learning and reinforcement learning; Section 3 characterizes the formulation of the job shop scheduling problem as a reinforcement learning problem; Section 4 presents the complete scheduling system that was developed in the context of this work, including the chosen methodology for its construction and each one of its components; Section 5 contains the computational study and statistical inference analysis that was conducted to validate the results of the system; and Section 6 discusses the overall conclusions of the work that is presented.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 6 Results and Discussion  
In this section, the results of applying the evaluation methodology described in Section 5 to our proposed MPResiSDN scheme are presented and discussed. The MPResiSDN scheme was evaluated using different \(k\) values; (\(k=1\)), (\(k=2\)), and (\(k=3\)). We refer to the results as MPResiSDN(\(k=1\)), MPResiSDN(\(k=2\)), and MPResiSDN(\(k=3\)). For execution time, the algorithm performance is evaluated with different network sizes and numbers of paths. Our execution time evaluation is shown in Figure 11 and the results show that execution time is not significantly high, which is approximately 0.003 ms for a network with 100 nodes with MPResiSDN(\(k=1\)) and 0.008 ms for a network with 100 nodes with MPResiSDN(\(k=2\)) and MPResiSDN(\(k=3\)). For comparison, the same evaluation methodology was applied to the same evaluation network topology, using the traditional Spanning Tree scheme that is integrated in the RYU controller. For all four schemes, the evaluation was made under three cases: normal operation, fire challenges, and storm challenges. The results for each case are presented and discussed individually in the following subsections.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Operating System & Ubuntu 18.4 \\ Memory & 2 GB of RAM \\ CPU & 1.90 GHZ \\ Emulator & Mininet 2.2.2 \\ SDN Framework & RYU Controller \\ Programming Language & Python 2.7.17 \\ Traffic Generator & iPerf3 \\ Link Bandwidth & 10 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Emulation Parameter Values.  
Figure 11: Evaluation of execution time using different \(k\) values and network sizes.
## 4 Case Study and Results  
To evaluate the proposed methodology a real-world earthmoving site was selected. This was a highway extension project. The earthmoving operation was mainly carried out using five dump trucks and two excavators. There were two loading sites, each having one excavator, and one dumping site. Thus, the trucks need to decide upon which excavator to go to after dumping their load. Figure 5 shows a schematic layout of the site.
## 3 Proposed Methods
### Procedure of the Method  
In order to diminish the overall complexity, the receiver uses least squares (LS) estimation to obtain the CFR based on the OFDM signal at the pilot position. The LS estimation fails to consider the influence of noise without using prior information, leading to a certain level of discrepancy with the actual CFR. Since the data is unknown except for the pilot position, the upsampling operation is required when the CFR matrix is sent to the neural network. Specifically, the nearest neighbor interpolation is used to initially estimate the frequency domain response at the data position with the help of the pilot position.  
There is one more thing that needs to be dealt with before model training: the matrices processed by the VDSR network are real values, whereas the CFR matrix is a complex matrix. Inspired by the processing of multi-channel color images by VDSR, the complex matrix is converted into a two-channel real matrix, as shown in Figure 7. After training, the output CFR matrix completes high-precision interpolation and noise reduction at the data positions, which is the prediction of the real CFR matrix.  
Figure 5: Architecture of VDSR for channel estimation.  
Figure 6: ReLU function (**left**) and PReLU function (**right**).  
The super resolution-based channel estimation method is illustrated in Figure 8.
# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 4 Sensors
Satellites and RPAs are two prominent platforms of RS technology and therefore, it is important to consider their use in the 5 th industrial revolution, mainly focusing on the agricultural sector. Various studies have highlighted the limitations of the one or the other, but both are beneficial in general. In this regard, their current and future roles in on-board sensors and cameras are reported here.
## 3 Experimental Results
### Frequency Domain Analysis  
A comparison of simulated and measured DM insertion loss (S\({}_{\rm dd21}\)) results is shown in Figure 8. The DM insertion loss in the range from DC to 21 GHz range was kept below \(-\)2.183 dB in the simulated results. The measured results used the AFR process to achieve more accurate values, and the DM insertion loss in the range from DC to 21 GHz range was kept below \(-\)2.306 dB, which proves that the MDCRP structure filter has good signal integrity in DM.  
In addition, a comparison of simulated and measured CM insertion loss (S\({}_{\rm cc21}\)) results are shown in Figure 8. In the simulated results, the CM noise can be reduced by over 10 dB from 4.69 GHz to 19.82 GHz, the fractional bandwidth was 123.5%, and the center frequency was 12.25 GHz. To obtain more accurate measurement values through AFR technology, in the measured experiment, the CM noise was reduced by over 10 dB from 5.09 GHz to 20.62 GHz, the fractional bandwidth was 120.8%, and the center frequency was 12.85 GHz. The measurement of the suppression bandwidth was somewhat different in the simulated results, where it shifted to high-frequency areas, resulting in poorer fractional bandwidth. It can be observed that there is a difference in the CM insertion loss (S\({}_{\rm cc21}\)) for 10 dB between the simulation results and the measurement results because of PCB manufacturing errors and the inductance characteristics of the via, which are very difficult to control in the manufacturing process. This results in resonant frequency changes, so the measurement results do not match the simulation results unanimously. The fact that the different parts in both can be kept below \(-\)10 dB proves that the filter has a good CM noise rejection effect. The CM characteristics indicate that the actual measured results show 3 resonance frequencies, which are 6.20 GHz, 11.24 GHz, and 17.07 GHz, respectively. The MDCRP structure filter shows wideband CM noise suppression and good signal integrity.
## 3 Results and Discussion
### RVA Profiles
This phenomenon was easily observed in our study within the FxF flour and CKP powder blends because the pasting profile does not show a rapid onset of increasing viscosity between 0 and 5 min, which is typically observed in flour or meal samples high in starch and low in fat [15]. The same trend was observed for both BKD and STB and, specifically, viscosity decreased with an increasing level of cricket in the blend [1]. The lower STB viscosity due to the addition of cricket flour indicates lower gel stability and lower sensitivity to retrogradation, probably caused by the more difficult re-association of the starch molecules because of the higher protein and lipid content, as reported by other authors [1,22]. In a study by [PERSON] and collaborators (2014) [23], they suggested that the pasting temperature of wheat flour increased with the addition of chickpea flour. Furthermore, the chickpea flour has a higher amount of lipids, which may be explained the observed decrease in the PV of the blend. The same authors reported that both FV and STB significantly increased with the increasing level of mealworm powder [23]. The high FV and STB viscosity indicated a high level of retrogradation of the sample, which occurs at lower temperatures. Retrogradation of starch, followed by syneresis, is the main cause of the increase in hardness of bread products, which influences shelf-life and consumer acceptance [24]. The pasting properties of the FxF and CPF flour mixtures were also investigated to understand their techno-functional properties compared with the CKP powder (Figure 1C). It was observed that as the concentration of CPF powder increased in the FxF flour blends, there was an increase in PV and FV. However, blends higher in FxF flour had an instant increase in viscosity after heating, which is a result of a higher concentration of free soluble fibre in the FxF flour samples [1,22].
## 5 Results and Discussion  
This section presents the results of the application of the proposed approach described in Section 3 on the data set described in Section 4. This section is structured as follows: Section 5.1 focuses on the detection of interference, which is implemented as a binary classification problem. Section 5.2 focuses on the identification (i.e., multi-class classification) of the different types of interference considered in this study: wideband, Gaussian, and narrowband interference to the GNSS signal. Finally, Section 5.3 discusses the limitations of the proposed approach.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 4 Analysis of Simulation Result
### Hiding Performance
#### 4.2.2 PSNR and SSIM Analysis  
The PSNR (peak signal-to-noise ratio) is an important index to measure image quality. It is often used to compare the difference between two images or to measure the effect of an algorithm on an image. The PSNR is calculated as follows:  
\[PSNR=10\times lg(\frac{I^{2}_{max}}{MSE}) \tag{9}\]  
\[MSE=\frac{1}{M\times N}{\sum_{i=1}^{M}{\sum_{j=1}^{N}(P(i,j)-C(i,j))^{2}}} \tag{10}\]  
where \(M\times N\) represents the image's size, \(C(i,j)\) is the recovered image's gray value of the pixel, \(P(i,j)\) is the original image's gray value of the pixel, and \(I_{max}\) is the maximum pixel value of the original image.  
SSIMs (structural similarity index metrics) are often used to assess the degree of similarity between two images. SSIMs can be calculated by Equation (11):  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+\sigma _{y}^{2}+C_{2}\right)} \tag{11}\]  
where \(x\) and \(y\) denote the two images, \(\mu_{x}\) and \(\mu_{y}\) denote their means, \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) represent their variances, \(\sigma_{xy}\) represents their covariances, and \(C_{1}\) and \(C_{2}\) are two constants used to ensure calculation stability. The value range of the SSIM is \([-1,1]\). When two images are identical, the SSIM is 1.  
Table 2 shows that the PSNR and SSIM values of the restored image are obtained after data of different sizes are embedded into four images of different sizes through the proposed scheme. The experimental results show that the SSIM value of the original image and the restored image in the proposed scheme is 1, further demonstrating the reversibility of the scheme.
# Porosity Elimination in Modified Direct Laser Joining of Ti6 Al4V and Thermoplastics Composites
## Materials and Methods  
Commercial Ti6 Al4V and PA66-GF30 were used as joining partners. PA66-GF30 is a polyamide resin (PA66) matrix reinforced by 30% chopped glass fibers. Ti6 Al4V and PA66-GF30 specimens have a thickness of 3 mm and 5 mm, respectively. Both Ti6 Al4V and PA66-GF30 specimens have dimensions of 20 mm in width, and 70 mm in length. Before laser joining PA66-GF30 to metal, surface texturing was performed on Ti6 Al4V to improve the adhesion between PA66-GF30 and Ti6 Al4V. Laser surface texturing was carried out with a continuous-wave (CW) fiber laser, and laser joining of PA66-GF30 and Ti6 Al4V sheets were conducted with a nanosecond laser with central wavelength of 1064 nm. The modified laser processing parameters used in the experiments are shown in Table 1.  
A total surface area of 5 mm \(\times\) 20 mm was textured on Ti6 Al4V and overlapped with PA66-GF30 sheets. Figure 1a illustrates the schematic diagram of the commonly-used LAMP method. The Ti6 Al4V specimen was placed on a PA66-GF30 sheet, and two preset pads were used to set the level of two connected parts. Two preset clamping Ti6 Al4V plates with thickness of 1.5 mm were used to provide suitable pressure for the overlapping area, as shown in Figure 1a. Laser irradiates the Ti6 Al4V plate surface and heat conducts from the plate surface to the Ti6 Al4V-PA66-GF30 interface. The plastic near the interface melts and flows into the created grooves on Ti6 Al4V surface under the clamping pressure. To improve the joint strength, a modified laser joining process, as schematically illustrated in Figure 1b, was proposed to eliminate the micro-pores generated in LAMP produced joints. The preset pads were removed, and a preset clamping force was exerted on each side of the overlap area during the laser joining process. Structure density is defined as the ratio of the structured area to the overall overlapped area, and can be varied by controlling the pitch of laser scans.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Process** & **Laser Power (W)** & **Frequency (kHz)** & **Pulse Duration (ns)** & **Scan Speed (mm s\({}^{-1}\))** \\ \hline Texturing & 75 & CW mode & - & 100 \\ LAMP & 55 & 100 & 90 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Laser parameters used in microstructure fabrication and laser joining process.  
After the laser joining process, tensile shear tests were carried out to evaluate the fracture strength of the produced joints. The schematic representation of the shear tests is exhibited in Figure 2. Two pads of the same thickness as the PA66-GF30 and Ti6 Al4V parts were used for alignment during the tests. The tests were conducted at a travelling speed of 2 mm/min by using Instron universal testing machine (Instron 5982) with the maximum load of 30 kN. Cross-sections of the produced joints were characterized using optical microscopy and scanning electronic microscopy (SEM, JSM-6701F) equipped with energy dispersive X-ray spectroscopy (EDS).