esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Domain Access Control Model in Industrial IoT Environment
## 2 Related Work
### Access Control Methods  
In the field of IIoT, with more data being generated by IIoT devices, access control has become increasingly frequent and important. Therefore, [PERSON] et al. [17] proposed a secure and reliable access control framework based on the maintenance of an access control list via smart contracts. With this framework, each time access control is performed, a new smart contract will be created. With the dramatic increase in the number of accesses, it requires a greater cost to deploy smart contracts and higher requirements for system storage. [PERSON] et al. [18] proposed a role-based access control approach, where each device only needs to maintain its access control list. Access control lists record the resources that devices can access. However, when the number of resources increases, the access control list becomes complex and challenging to maintain. [PERSON] et al. [19] proposed an attribute-based access control (ABAC) scheme in which the Uniform Resource Locator (URL) link of the ABAC strategy is stored on the blockchain, and a smart contract is deployed to receive access requests from the subject and then perform access control. However, the program does not provide a method of implementation. [PERSON] et al. [20] improved on this basis and proposed a framework based on smart contracts. This framework consists of multiple  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Environment** & **Architecture** & **Cross-Domain** & **Access Control** & **Scalability** \\ \hline [PERSON] et al. [13] & IoT & Centralized & No & Yes & No \\ [PERSON] et al. [14] & IIoT & Centralized & No & Yes & No \\ [PERSON] et al. [15] & IoT & Single-chain & Yes & Yes & No \\ [PERSON] et al. [16] & IIoT & Single-chain & Yes & Yes & No \\ [PERSON] et al. [17] & IoT & Single-chain & No & Smart & No \\ [PERSON] et al. [18] & IIoT & Single-chain & No & Role-based & No \\ [PERSON] et al. [19] & IIoT & Single-chain & No & Attribute-based & No \\ [PERSON] et al. [20] & Smart Cities & Single-chain & No & Attribute-based & No \\ [PERSON] et al. [21] & IoT & Master-slave & Yes & Attribute-based & Yes \\  & & chain & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of the existing work.  
access control contracts, including a registration contract and a judgment contract, and is used to implement distributed access control for IoT systems, with the Ethernet platform being used as a case study to demonstrate its application. As more and more attributes are added, the strategies for accessing resources become increasingly complex, leading to longer periods of time to match attributes with strategies. This, in turn, increases the latency of the system and reduces its stability.  
In summary, access control for the IIoT domain still needs to be enhanced regarding access control efficiency and dynamic fine-grained retrieval. Therefore, this paper proposes a combined attribute and role access control approach and presents a grouping strategy retrieval algorithm to shorten the retrieval strategy time.
## 3 Results and Discussion
### Experimental Results
Figure 12: Three-dimensional microscope images for SiC sample top surfaces printed using different scanning strategies. Figure 14: _Cont._  
Figure 13: The peak occurring in the temperature history of the concentric in–out strategy when the laser source was changing its direction. The X-ray diffraction spectrum of the SiC sample (Figure 15) produced by D-PBSLP using the zigzag strategy showed that SiC phases of silicon and carbon exist. This means that partial decomposition occurred, and this was expected due to the scanning strategies that generated high-temperature peaks during scanning when starting a new path. Figure 16 shows the temperature history (maximum temperature at the laser spot center) for the zigzag scanning strategy, where temperature peaks are clearly shown. These peaks can be eliminated by developing a new scanning strategy. After investigating the scanning strategies' effects and seeing that the zigzag strategy shows a promising result for the direct SLS of SiC, SiC samples (10 \(\times\) 10 \(\times\) 4 mm\({}^{3}\)) were printed with different sintering speeds and laser powers (as predicted from the numerical model), a 35 \(\upmu\)m hatching distance and the zigzag strategy are shown in Figure 17. The samples were successfully printed using these parameters. However, it can be noted that the sample base has some deterioration, which is mainly due to its removal from the base plate. Additionally, the samples printed using 500 mm/s and 250 mm/s show layer degradation along the sample thickness. This is due to the low interaction time between the laser and the powder when using high scanning speeds. Relative density was used to evaluate the printed samples. A relative density of 84% was achieved for sample A, while 81% was achieved for samples B and C.  
Figure 11.0 SEM images for SiC samples printed using different scanning strategies. Figure 11.1 XRD spectra of the SiC powder and SiC manufactured part by D-PBSLP. (**a**) XRD spectra of the SiC powder; (**b**) XRD spectra of the SiC powder. After studying all the process parameter effects, trials for the printing of complex shapes were investigated. The triple periodic minimum surface (gyroid) was successfully printed using a laser power of 32 W, 100 mm/s scanning speed, 35 \(\upmu\)m hatching distance, and the zigzag scanning strategy, as can be seen in Figure 11.2 The dimensions of the gyroid are (10 \(\times\) 10 \(\times\) 8 mm\({}^{3}\)), which is a challenge to be printed using D-PBSLP, and this proves the power of the developed numerical model to obtain the proper process parameters. Figure 11.3 Focused view for two scanning paths using the inclined zigzag strategy. Figure 11.4 SiC samples printed using laser power and sintering speed, as predicted from the numerical model, and zigzag scanning strategy.
## 8 Technical Challenges and Potential Solutions
### Industrial Modular Units Transportation Issues  
The transport limits the size and increases the number of IMUs, leading to an un-economical PFISB construction project [183]. Since the road transport is the primary transportation source, to avoid any unforeseeable problem [184], the external dimensions of IMUs are often not greater than 13 m in length and 4.5 m in width [185]. Therefore, the authors recommend adapting the Hickory building system to avoid IMU size problems, which involves the onsite assembling of 2D systems to the 3D IMU [31]. In the La Trobe tower, the modules of 17 \(\times\) 4.5 \(\times\) 3 m were achieved without any damage to the structural and nonstructural components through this technique.  
Furthermore, the newly proposed logistics plan is the best approach for planning, optimizing, and visualizing the trailer route to address the size and safety of IMUs [101]. Moreover, the increase in the road roughness or response acceleration during the road transport causes severe damage to the nonstructural and partial damage to the structural components [186]. As a result, it is advised that additional follow-up work be conducted to address these concerns.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 3 Methodology
### Distortion Correction
#### 3.1.1 Perspective Distortion Correction  
Due to the assembly error of the image acquisition system, the camera optical axis was not absolutely perpendicular to the receiving screen, thus introducing perspective distortion, which needs to be corrected by perspective transformation. The schematic diagram is shown in Figure 6.  
Suppose that \((x,y,1)\) is the coordinate point of the original image plane, \((X,Y,Z)\) is the corresponding coordinate point after transformation, then:  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]=A\left[\begin{array}{c}x\\ y\\ 1\end{array}\right],A=\left[\begin{array}{ccc}\text{a}_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ a_{31}&a_{32}&a_{33}\end{array}\right] \tag{1}\]  
where \([X,Y,Z]^{T}\) and \([x,y,1]^{T}\) are the target point matrix and the source point matrix respectively, A is the perspective transformation matrix. This is a transformation from two-dimensional space to three-dimensional space, as the target image point is still a two-dimensional image, suppose \((X^{\prime},Y^{\prime},Z^{\prime})\) is the point on the target image plane, then:  
\[\left\{\begin{array}{c}X^{\prime}=\frac{X}{Z}\\ Y^{\prime}=\frac{Y}{Z}\\ Z^{\prime}=\frac{Z}{Z}\end{array}\right.,\quad\left\{\begin{array}{c}X^{ \prime}=\frac{a_{11}X+a_{12}y+a_{13}}{2x_{1}X+a_{23}y+a_{23}}\\ Y^{\prime}=\frac{a_{23}+a_{23}y+a_{23}}{2x_{1}X+a_{23}y+a_{33}}\\ Z^{\prime}=1\end{array}\right. \tag{2}\]  
where \(a_{33}=1\). From the above formula, the following equations can be obtained:  
\[\left\{\begin{array}{c}a_{11}x+a_{12}y+a_{13}-a_{31}xX^{\prime}-a_{32}X^{ \prime}y=X^{\prime}\\ a_{21}x+a_{22}y+a_{23}-a_{31}xY^{\prime}-a_{32}yY^{\prime}=Y^{\prime}\end{array}\right. \tag{3}\]  
There are eight unknowns in the equations. The correction of distorted image by perspective transformation requires obtaining the coordinates of a group of four points of distorted image and a group of four points of target image. The transformation matrix of perspective transformation can be calculated by two sets of coordinate points A, and then the transformation matrix of the whole original image can be implemented, realizing perspective distortion correction.  
Since the position relationship between the laser receiving screen and the camera was fixed in this paper, once calibrated, the perspective transformation matrix was also constant. Therefore, the perspective transformation matrix can be obtained by prior calibration, and the perspective distortion can be corrected in real time by using the same matrix.
## 5 Decision-Making and Other Practical Cases
Let us  
Figure 16: Noisy image Fr02 (**a**) with noise variance equal to 196 and the same image compressed using \(Q_{\text{COOP}}=37\) (**b**), the noise-free image (**c**). demonstrate that the OOP is also possible for images corrupted by Poisson noise compressed by BPG. Note that two approaches to such a compression are possible. The first one is to apply BPG directly. The second one is to apply a proper VST (Anscombe transform in the considered case [53]) and some pre-normalization before the compression with inverse operations at the decompression stage. In this case, the signal-dependent noise converts to an almost pure additive and we arrive at the image/noise model studied above; thus, let us concentrate on the first approach. The analysis has been carried out for six RS test images. The obtained rate-distortion curves \(PSNR-HVS-M^{tc}(Q)\) and \(MS-SSIM^{tc}(Q)\) are given in Figure 17. As one can see, the OOP exists for the metric MS-SSIM for the images, Frisco and Fr04, and for the metric PSNR-HVS-M for the image, Frisco (similar situations have been earlier observed for the AWGN case, see the plots for the test image Fr02 in Figure 7). In all three cases, the OOP is observed for \(Q=37\). According to \(PSNR^{tc}\), the OOP exists as well and this happens for \(Q=37\), while \(\Delta PSNR\) can reach 3 dB; thus, we can expect that automatic procedures for lossy compression can be designed for BPG for images corrupted by signal-dependent noise, including the speckle typical for synthetic aperture radar images. It might also be true that two, three or more image components are corrupted by the noise and these images have to be compressed in a lossy manner. Certainly, a component-wise compression for which all the results obtained above are valid is possible; however, the joint compression of several component noisy images is possible as well. For example, available BPG software allows for compressing color, i.e., three-channel images; thus, it is easily possible to test this practical situation. As an initial case, let us assume that all the component images are corrupted by AWGN with the same noise variance. Since the results can be of interest for both three-channel and color images, four test images have been considered: the RS three-channel images, Frisco and Diego, and the widely used color test images, Lena and Baboon. The original images of size 512 \(\times\) 512 pixels were presented in RGB, an AWGN with variance equal to 100 was independently added to each component image, a 4:2:2 version of BPG was applied, and the metrics were calculated independently for each component image. The obtained dependences are presented in Figure 18.  
It follows from the preliminary analysis that the OOP can also exist and it takes place practically for the same \(Q\) for all component images. Meanwhile, there are also some specific results, such as more obvious OOPs for the G component; thus, additional studies are needed. Figure 17: Dependences \(PSNR-HVS-M^{tc}(Q)\) (**a**) and \(MS-SSIM^{tc}(Q)\) (**b**) for six test images corrupted by Poisson noise.
## 3 Results
### Analysis of Meteorological Factors  
#### 3.2.1 Spatial Distribution of Precipitable Water Vapour over the Loess Plateau  
Figure 3 presents the average PWV time series with a 5-year sliding average and its spatial distribution from 1982 to 2015. Figure 3 shows that the PWV did not undergo evident variations in the Loess Plateau over the past 33 years, and values ranged from 12 mm to 14 mm. However, the spatial distribution of PWV in Figure 3 reveals that the atmospheric water decreased from southeast to northwest with values varying from 6 mm to 23 mm, which is partially caused by the topography of Loess Plateau (Figure 4). In addition, an increase in the continental effect toward the northwest increased the dryness of the climate. Figure 4 shows that the variation of elevation is consistent with that of PWV, especially for the eastern portion of Qinghai Province (marked by a red rectangle in Figure 3), which features an elevation of approximately 2250 m above sea level. However, the average PWV was only approximately 6 mm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Significance Level** & **Period** & \multicolumn{2}{c}{**Increase**} & \multicolumn{2}{c}{**Decrease**} \\ \cline{2-7}
**Spificance Level** & **S** & **S1** & **S2** & **S** & **S1** & **S2** \\ \hline P \(<0.01\) & 72.23 & 12.28 & 56.76 & 1.96 & 0.14 & 0.90 \\
0.01 \(<\) P \(<0.05\) & 6.38 & 9.64 & 13.68 & 1.05 & 0.53 & 0.72 \\
0.05 \(<\) P \(<0.1\) & 2.67 & 6.78 & 16.66 & 0.75 & 0.75 & 0.44 \\
0.1 \(<\) P & 9.42 & 50.5 & 5.42 & 5.54 & 19.63 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The proportion of pixels that showed increases and decreases in vegetation growth for S, S1, and S2, for each significance level according to the F-test (Unit: %).  
Figure 3: Annual average time series (**a**) and the spatial distribution of the average precipitable water vapour of multiple years (1982–2015) over the Loess Plateau region (**b**).
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 2 Materials and Methods
### Model Definition
Both models are compact, making them suitable for training on small datasets without overfitting, while still being expressive enough to model complex temporal patterns. In this architecture, both the generator and discriminator are designed to handle one-dimensional time-series data representing 24 h daily load profiles. Each sample is a fixed-length vector of 24 values, where each value corresponds to the load at a given hour. Although the data are temporal, the model treats it similarly to how an image is processed in convolutional neural networks--each \"pixel\" (hourly load value) is contextually related to its neighbors. This perspective justifies the use of Conv1D layers, which are particularly effective in capturing local dependencies and patterns along sequential data.
## 3 Proposed Method
### Noise-Reduction Processing
#### 3.1.1 Discrimination between Day and Night  
The input image is divided into two regions, the upper area and the lower area, known as the ROI, illustrated in Figure 5. The upper area is utilized to determine the time period of the input image, while the lower area is dedicated to lane detection.  
Lane detection can be challenging due to variations in lighting conditions particular to day and to night. At night, factors such as dark surroundings, vehicle headlights, and backlights of cars ahead can hinder lane detection. During the day, lanes are generally easier to detect, but excessive sunlight can lead to blurred lane markings. Figure 6a,c demonstrate the disappearance of lanes due to inappropriate parameters, while Figure 6b,d show the benefits of using appropriate parameters for lane detection. Increasing contrast is advantageous for lane detection, especially in bright daytime conditions. However, in low-light conditions at night, the presence of oversaturated areas caused by vehicle headlights can obscure lane markings. Therefore, increasing contrast can sometimes eliminate lanes in these oversaturated regions. The parameters used in the system are specifically designed to preserve detailed edge information, which is crucial for accurate lane detection.  
To distinguish between daytime and nighttime in the upper area of the image, a criterion was established based on experimentation. A total of 1800 daytime images and 1740 nighttime images were used in this experiment. The goal was to determine an appropriate threshold that can effectively differentiate between the two conditions. In order to measure brightness in the area on the road, there are numerous variables that can affect the measurement. To mitigate this, we calculated the average pixel value in the sky area of the image, as this region tends to have fewer variables. Figure 7 depicts a histogram showing the average brightness values in the sky area of each grayscale image. By cropping the sky area of the input image and calculating the average brightness, this paper determined that a value over 80 indicates a daytime condition, while a value below 80 suggests a nighttime condition. This threshold value serves as the criterion for distinguishing between day and night in the upper area of the image.  
Figure 5: Result of ROI: (**a**) input image; and (**b**) upper area used for distinguishing between day and night, lower area used for lane detection.
## 2 Methods
### Microbial Community Structure  
Bacterial community structure was evaluated using the Illumina 16S amplicon sequencing protocol [84; 85]. Genomic DNA was extracted from soil samples using the DNeasy Powersoil Kit (Qiagen; Germantown, MD, USA) following the manufacturer's protocol. Two \(\upmu\)L genomic DNA from each sample was assessed for DNA concentration using a DNA High Sensitivity Kit on a Qubit 3.0 fluorometer (Thermo Fisher Scientific; Waltham, MA, USA) diluted to a concentration of 5 ng/\(\upmu\)L in molecular grade nuclease free water, and 2.5 \(\upmu\)L DNA from each extraction was used to amplify the variable 3 and 4 regions of the 16S rDNA using a GoTaq Green PCR (Promega Corporation; Madison, WI, USA) kit with the V3/V4 primers. Samples were then run on two 1% agarose gels to check for the presence of 16S rDNA V3/V4 amplicon at 550 bp. Then, 2.5 \(\upmu\)L DNA at 5 ng/\(\upmu\)L from each library was amplified with a KapHiFi PCR kit (Qiagen; Germantown, MD,USA), cleaned with AMPureXP (Beckman Coulter; Brea, CA, USA) beads, indexed using Illumina Nextera XT index adapters (Illumina; San Diego, CA, USA), and cleaned again with AMPureXP beads before checking for fragment size distribution using DNA 1000 chips on the Agilent 2100 bioanalyzer (Agilent Technologies; Santa Clara, CA, USA). Fragment size distribution and DNA concentrations from a final Qubit 3.0 check were combined to determine the molarity of sequencing-ready libraries, and libraries were diluted to a concentration of 4 nM. Samples were pooled, denatured, and diluted to a final loading concentration of 6 pM with a 10% PhiX spike-in. The pooled libraries were then run on an Illumina V3 (Illumina; San Diego, CA, USA). 600 cycle cartridge for 2 \(\times\) 250 bp [84; 85].  
QIIME2-2022.8 software was used to demultiplex the 16S rRNA sequences in 29 samples (three samples were eliminated in the final analyses due to quality) [86], remove chimeras, and the final 8,140,642 sequences were denoised using the DADA2 1.22.0 plugin for QIIME2-2022.8 software [87]. The SILVA SSU 138.1 database was used to assign taxonomy and formatted by using RESCRIP 2022.8.0 software [88; 89]. Artifacts were converted into a phyloseq object using R-Studio 2020.03.01 and R version 4.2.3 [90; 91; 92] and then transformed into a'microtable' for the Microeoc 1.3.0 package [93]. All ASVs (amplicon sequence variants) not assigned to Archaea or Bacteria, and those assigned to \"mitochondria\" or \"chloroplast\" were removed. The 'tidy dataset' function was used to trim the dataset to eliminate samples with 0 artifacts, and the'microtable' was rarefied to a sample size of 100,000 sequences per sample [94].
## 4 Results and Discussion
### Comparison with Previous Studies  
In order to assess the effectiveness of our proposed approach, we conducted a comparative analysis with prior research. Previous studies employing various machine learning methods for predicting student academic performance [9; 10; 11; 12; 13; 14] have reported accuracies reaching up to 92%. Research utilizing deep learning approaches [15; 16; 17] has achieved accuracies of up to 95.23% in similar prediction tasks. Further advancements involving the fusion of deep learning and ensemble learning techniques [29; 30; 31; 32] have yielded accuracies as high as 98.99%. While the application of image transformation techniques to behavioral data in conjunction with deep learning for academic performance prediction remains relatively unexplored, a study similar to ours [18] employed clickstream data transformed into images, combined with deep learning, achieving an accuracy exceeding 95%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Feature**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1 Score**} \\  & **Transformation** & & & & & \\ \hline DL Models & & & & & \\
1D CNN & / & 0.8491 & 0.8491 & 0.8491 & 0.8491 \\ MobileNet & / & 0.8208 & 0.8255 & 0.8208 & 0.8213 \\ EfficientNetB4 & / & 0.8302 & 0.8335 & 0.8302 & 0.8307 \\
2D CNN & PR & 0.8208 & 0.8271 & 0.8208 & 0.8176 \\
2D FCN & PR & 0.8396 & 0.8410 & 0.8396 & 0.8384 \\
2D EnCF & PR & 0.8396 & 0.8517 & 0.8396 & 0.8358 \\
2D CNN & GAF & 0.8679 & 0.8689 & 0.8679 & 0.8672 \\
2D FCN & GAF & 0.7925 & 0.8034 & 0.7925 & 0.7868 \\
2D EnCF & GAF & 0.8585 & 0.8606 & 0.8585 & 0.8588 \\
2D CNN & SWT & 0.8868 & 0.8869 & 0.8868 & 0.8865 \\
2D FCN & SWT & 0.8679 & 0.8679 & 0.8679 & 0.8676 \\
2D EnCF & SWT & 0.9057 & 0.9072 & 0.9057 & 0.9051 \\
2D CNN & RP & 0.9340 & 0.9340 & 0.9340 & 0.9339 \\
2D FCN & RP & 0.8774 & 0.8820 & 0.8774 & 0.8777 \\
2D EnCF & RP & 0.9528 & 0.9529 & 0.9528 & 0.9528 \\ \hline \end{tabular}
\end{table}
Table 9: _Cont._  
Figure 7: Performance comparison of all models on 1D and 2D data.  
In theory, converting 1D data into 2D images allows for capturing complex temporal and spatial relationships, which a DL model, particularly a CNN or FCN, is especially proficient at processing. DL models automatically extract hierarchical features, overcoming the limitations of conventional ML methods that depend on manual feature selection. Practically, this approach improves the performance by employing rich behavioral data that reflects students' learning patterns, which are not fully captured by traditional numerical features. The use of image-based representations allows for improved generalization and robust predictions. Additionally, this method facilitates personalized educational interventions and supports the development of intelligent, data-driven educational systems that can proactively address students' needs.