esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 5 Case Studies
### IEEE 118-Bus System
#### 5.2.2 Light and Heavy Load Conditions  
For testing the effectiveness of the proposed method in light and heavy load conditions of the IEEE 118-bus system, 80% and 110% of the base load situations are respectively employed. The tested controlled variables are the same as those in Section 5.2.1. The ranking results of each condition in descending order are shown in Table 14.  
From the data of Table 12 and the above table in the IEEE 118-bus system, it can be seen that the rankings of the outage lines are basically the same. Moreover, for the same outage line, the heavier loads cause a large security index value, and the conclusion can be drawn that the proposal is suitable for light, normal, and heavy load conditions in the larger scale system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **NRLF** & **MSA-Lasso** & **MLFFN [16]** & **RBFN [16]** \\ \hline Time (s) & 12.269 & 0.875 & 1.438 & 1.172 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of computation times of the 118-bus system. MLFFN: multi-layer feed forward network; RBFN: radial basis function network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Outage Line** & **NRLF** & **MSA-Lasso** & **Relative Error (\%)** \\ \hline L 133(85–86) & 45.3983 & 45.5113 & 0.2490 \\ L 7(8–9) & 5.6776 & 5.6735 & \(-\)0.0724 \\ L 97(64–65) & 3.2101 & 3.2149 & 0.1500 \\ L 60(34–43) & 2.6039 & 2.5991 & \(-\)0.1851 \\ L 121(77–78) & 1.7891 & 1.7866 & \(-\)0.1439 \\ L 104(65–68) & 1.5820 & 1.5841 & 0.1347 \\ L 128(77–82) & 1.3925 & 1.3961 & 0.2607 \\ L 74(53–54) & 1.3513 & 1.3500 & \(-\)0.0965 \\ L 40(29–31) & 1.2995 & 1.3023 & 0.2105 \\ L 147(94–95) & 1.2748 & 1.2757 & 0.0679 \\ L 155(94–100) & 1.1396 & 1.1389 & \(-\)0.0633 \\ L 160(100–101) & 0.8759 & 0.8780 & 0.2436 \\ L 156(95–96) & 0.8673 & 0.8685 & 0.1333 \\ L 68(45–49) & 0.7681 & 0.7699 & 0.2366 \\ L 103(66–67) & 0.6108 & 0.6108 & \(-\)0.0029 \\ L 151(80–97) & 0.5323 & 0.5312 & \(-\)0.2069 \\ L 169(105–106) & 0.4454 & 0.4448 & \(-\)0.1327 \\ L 35(28–29) & 0.3897 & 0.3905 & 0.2001 \\ L 131(83–85) & 0.3318 & 0.3302 & \(-\)0.4858 \\ L 157(96–97) & 0.2903 & 0.2892 & \(-\)0.3773 \\ L 135(85–88) & 0.2532 & 0.2528 & \(-\)0.1366 \\ L 123(77–80) & 0.2277 & 0.2275 & \(-\)0.0820 \\ L 106(49–69) & 0.2091 & 0.2082 & \(-\)0.4515 \\ L 114(70–74) & 0.2088 & 0.2088 & 0.0060 \\ L 108(69–70) & 0.2087 & 0.2091 & 0.1831 \\ L 166(103–105) & 0.2073 & 0.2064 & \(-\)0.4133 \\ L 164(100–104) & 0.2044 & 0.2041 & \(-\)0.1624 \\ L 142(89–92) & 0.1972 & 0.1966 & \(-\)0.2829 \\ L 139(89–90) & 0.1887 & 0.1892 & 0.2562 \\ L 141(89–92) & 0.1529 & 0.1529 & \(-\)0.0317 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Security index results of two methods in the base load condition of the IEEE 118-bus system.
# Special Issue on \"Augmented Reality, Virtual Reality & Semantic 3D Reconstruction\"
## 1 Introduction  
Augmented Reality is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs. Enter augmented reality (AR) technology, which can be used to visualize data from hundreds of sensors simultaneously, overlaying relevant and actionable information over your environment through a headset. Semantic 3D reconstruction makes AR technology much more promising, with much more semantic information. Although, there are several methods currently available as post-processing approaches to extract semantic information from the reconstructed 3D models, the obtained results are uncertainty, and are evenly incorrect. Thus, it is necessary to explore or develop a novel 3D reconstruction approach to automatic recover 3D geometry models and obtained semantic information in simultaneous.  
The rapid advent of deep learning brought new opportunities to the field of semantic 3D reconstruction from photo collections. Deep learning-based methods are not only able to extract semantic information but can also be used to enhance some fundamental techniques in semantic 3D reconstruction: those fundamental techniques include feature matching or tracking, stereo matching, camera pose estimation, and multiview stereo. Moreover, deep learning techniques can be used to extract priors from photo collections, the obtained information in turn can improve the quality of 3D reconstruction.  
The aim of this Special Issue is to provide a platform for researchers to share innovative work in the field of semantic 3D reconstruction, virtual reality, and augmented reality, including deep learning-based approaches to 3D reconstruction, and software platforms of deep learning for virtual reality and augmented reality.
# Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
## 3 Method
### Algorithm Design
#### 3.2.2 Domain Discriminator  
The domain discriminator is responsible for aligning the marginal distributions of the source and target domains in the proposed algorithm. The idea is inspired by the Domain Adversarial Neural Network (DANN) [37] and consists of two main components: a gradient reversal layer (GRL), shown in Figure 4, and a fully connected classifier that distinguishes whether the feature (\(f\)) comes from the source or target domain. After the data (\(x\)) are processed by the feature extractor, the resulting feature (\(f\)) first passes through the GRL, after which the fully connected classifier determines whether the data are from the source or target domain. The result is represented by \(\hat{d}\) in Figure 4. The domain discriminator is a supervised learning classifier, since it only needs to classify data into two categories (the source or target domain). Both source domain data and target domain data are used in the training of the domain discriminator. The corresponding loss function is the cross-entropy loss (\(L_{g}\)), represented as follows:  
\[L_{g}=\frac{1}{n_{s}+n_{t}}\sum_{x_{i}\in D_{s}\cup D_{l}}L_{d}\Big{(}\hat{d},d_{i}\Big{)} \tag{9}\]  
\(\hat{d}=G_{d}\Big{(}R_{\lambda}\Big{(}G_{f}(x_{i})\Big{)}\Big{)}\), where \(n_{s}\) and \(n_{t}\) are the numbers of source and target domain samples, respectively. The GRL acts as an identity function during forward propagation but reverses the gradient during backpropagation, as expressed in Equations (10) and (11), where III is the identity matrix. The role of the GRL is to propagate the gradients in the opposite direction during backpropagation, ensuring that the feature extractor is updated in a direction that maximizes the adversarial training effect:  
\[R_{\lambda}(x)=x \tag{10}\]\[\frac{dR_{\lambda}}{dx}=-\lambda I \tag{11}\]
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 3 Preliminaries
### Basic Idea  
This basic idea of the mobility management is to propose a cooperation strategy of cloud and cloudlet to reduce the transmission time and the packet loss rate.  
Based on the proxy MIPv6-based FMC result of [24], all data packets are tunneled between MAG (Mobile Access Gateway) and LMA (Local Mobility Anchor) to keep the high connectivity property. As mentioned before, a cloud-cloudlet (\(CL\)) set is \(CL=\{c_{1}\to l_{1},c_{2}\to l_{2},\cdots,c_{s}\to l_{d},\cdots,c_{d}\to l_{s}, \cdots,c_{n}\to l_{n}\}\). When \(UE\) attached to a different MAG, four kinds of cooperation of cloud-cloudlet (\(CL_{s\to s}\)), cloud-cloudlet (\(CL_{s\to d}\)), cloud-cloudlet (\(CL_{d\to s}\)), and cloud-cloudlet (\(CL_{d\to d}\)) are defined below. The comparison of of proxy MIPv6-based FMC proposed by [PERSON] et al. [24] and our the handover protocol using follow me cloud-cloudlet is illustrated in Figure 3.  
**Cooperation of cloud-cloudlet \(CL_{s\to s}\)**: Before the handover event of UE, assumed that \(c_{s}\) and \(l_{s}\) are the current serving cloud and cloudlet of the current serving F-RAN. Packets \(p_{s}\) from the CN in data center of \(c_{s}\) are transmitted to UE through F-AP\({}_{s}\). For instance as shown in Figure 3(a), the cooperation of cloud-cloudlet \(CL_{s\to s}\) is provided.  
**Cooperation of cloud-cloudlet \(CL_{s\to d}\)**: When UE moves to a different region, and initiate the handover procedure, some packets are still transmitted from the current serving data center of \(c_{s}\) to the new serving cloudlet \(l_{d}\) if the data migration procedure is still not initiated. For instance as shown in Figure 3(b), the cooperation of cloud-cloudlet \(CL_{s\to d}\) is provided.  
**Cooperation of cloud-cloudlet \(CL_{d\to s}\)**: After UE is moving to a different region, a data migration operation is executed from \(c_{s}\) to \(c_{d}\), it also means that CN is migrated from \(c_{s}\) to \(c_{d}\). Packets are then transmitted from the new serving data center of \(c_{d}\) to the previous cloudlet \(l_{s}\), and then be re-forward to the new cloudlet \(l_{d}\) to UE, before the new route path from \(c_{d}\) to \(l_{d}\) is not re-calculated in FMCC. For instance as shown in Figure 4(a), the cooperation of cloud-cloudlet \(CL_{d\to s}\) is provided.  
**Cooperation of cloud-cloudlet \(CL_{d\to d}\)**: After the new route path is re-routed from the new serving data center of \(c_{d}\) to the new cloudlet \(l_{d}\) which is determined by FMCC, packets \(p_{d}\) are transmitted from \(c_{d}\) to UE through F-AP\({}_{d}\) of \(l_{d}\) by using the new re-calculated route path. As shown in Figure 4(b), an example of the cooperation of cloud-cloudlet \(CL_{d\to d}\) is given.  
Figure 4: Basic operations of (**a**) cooperation of cloud-cloudlet \(CL_{s\to s}\) and (**b**) cooperation of cloud-cloudlet \(CL_{s\to d}\).  
Figure 3: Comparison of (**a**) PMIPv6-based follow me cloud and (**b**) our proposed scheme using follow me cloud-cloudlet.
## 2 Study Area and Data
### Remote-Sensing Datasets
#### 2.2.2 Landsat Dataset on GEE  
Landsat, a joint program of the USGS and NASA, has been observing the Earth continuously, from 1972 through to the present day [60]. Today, the Landsat satellites image the entire Earth's surface at a 30-m resolution about once every two weeks, including multispectral and thermal data. The Google Earth Engine (GEE) is a cloud computing platform designed by Google Inc., Mountain View, CA, USA [61]. It has been used in global Earth observations and regional-scale surveys, including vegetation mapping and monitoring [62; 63; 64; 56; 57; 65; 66], land-cover mapping [65; 65], and agricultural applications [66; 53]. Two Landsat collections are available on GEE. Landsat Collection 1 consists of level-1 data products generated from the Landsat 8 operational land imager (OLI)/thermal infrared sensor (TIRS), Landsat 7 enhanced thematic mapper plus (ETM+), Landsat 4-5 thematic mapper (TM), and Landsat 1-5 multispectral scanner (MSS) instruments. Landsat Collection 2 is an improved version made by the USGS. It includes Landsat level-1 data since 1972, as well as level-2 surface reflectance (T1_SR) and surface temperature products from 1982 to the present.  
The Landsat Collection 2 data (T1_SR) were used to derive persistent water and non-water distributions over study sites. A total of 1777 scans (965 scans from the WeiFang site and 812 scans from the Ji'An site) were used for calculating historic water probabilities.
# A Microscopic Traffic Flow Data Generation Method Based on an Improved DCGAN
###### Abstract  
Microscopic traffic flow data, an important input to virtual test scenarios for autonomous driving, are often difficult to obtain in large quantities to allow for batch testing. In this paper, a neural network for generating microscopic traffic flow scene fragments is proposed, which is improved by adding Gate Recurrent Units (GRU) to the discriminator of the Deep Convolutional Generative Adversarial Network (DCGAN) to enable it to better discriminate continuous data. Subsequently, this paper compares individual sample motion trajectories of the generated data using Grey Relational Analysis (GRA) and Dynamic Time Warping algorithm (DTW) at the microscopic scale, and evaluates the overall scenes generated using averaged statistics at the macroscopic scale. The results show that the method proposed in this paper can generate realistic microscopic traffic flow data very well and that the neural network proposed in this paper can generate better near-realistic microscopic traffic flow data than the original DCGAN under the evaluation metrics used in this paper.  
generative adversarial networks; microscopic traffic flow; autonomous vehicles; gated recurrent unit
## 6 Conclusions  
This paper addresses the challenge of weld seam tracking in thick plate welding by presenting a solution that enhances the imaging accuracy and measurement range of line laser vision sensors through the application of the Scheimpflug principle. A comprehensive imaging model was developed to support this enhancement, providing a robust framework for improved sensor performance. Additionally, an effective image processing method was devised to enhance image quality and extract key weld seam feature points. This method includes an improved grayscale extreme centroid method for the precise extraction of the laser stripe center. The V-shaped weld seam feature extraction algorithm was further refined by integrating the maximum distance search method with least-squares fitting. This combination resulted in enhanced accuracy and robustness, even in noisy environments.  
A cylindrical filtering method was also introduced to isolate and remove noise from the 3D weld seam data points, thereby improving the overall data quality. Subsequently, a Non-Uniform Rational B-Spline (NURBS)-based algorithm was employed for smooth trajectory generation and posture interpolation, ensuring precise weld seam tracking. Experimental evaluations demonstrated that the proposed sensor and algorithms significantly improved feature point detection accuracy and weld seam tracking precision, achieving sub-millimeter deviations from the actual weld seam trajectory. These results demonstrate that the proposed solution successfully overcomes the limitations of traditional vision sensor systems caused by restricted depth of field, which impedes their application in thick plate welding. Building on this foundation, this study introduced robust and high-precision detection and tracking algorithms, providing a comprehensive and efficient solution for the intelligent transformation of thick plate welding processes.  
Conceptualization, L.Z., W.R. and J.L.; Methodology, L.Z., W.R., F.L. and J.L.; Software, R.W. and J.L.; Validation, F.L., J.F. and Y.L.; Formal analysis, W.R. and Y.L.; Resources, J.F. and J.Z.; Writing--review & editing, F.L.; Supervision, R.W. and J.F.; Project administration, J.Z.; Funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the infrastructure technology and equipment engineering research center of the China State Construction Engineering Corporation (CSCEC-PT-017).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
Authors [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company China Construction Second Engineering Bureau Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Principle
Then, the parameter \((-\alpha+\kappa_{R}(z))\) can be expressed as:  
\[(-\alpha+\kappa_{R}(z))=\frac{dP_{s}(z)}{dz}. \tag{8}\]  
In Equation (4), the influence on signal transmissions of the RIN transfer is described by \(\kappa_{R}(z,T^{\prime})\). In the numerical method, the difference between \(T\) and \(T^{\prime}\) can be modeled by the variable elements shift. In Equation (4), the distributed noise \(N(z,T)\) mainly includes the SpRS noise and DRB noise in the distributed FRA systems. The power of the SpRS noise within a segment can be expressed as:  
\[P_{SpRS}(\Delta z_{i},T)=(g_{R}2 hv_{s}\Delta v)\cdot\Big{[}\Big{(}P_{p}^{+}(z _{i},T)+P_{p}^{-}(z_{i},T)\Big{)}\cdot\Delta z_{i}\Big{]}. \tag{9}\]  
Based on the power of the SpRS noise, we generated a complex Gaussian noise sequence as the optical field of the SpRS \(E_{SpRS}(\Delta z_{i},T)\). According to the generating progress of the DRB noise, we took the Raman gain and the two-times Rayleigh backward scatterings into account to get the optical field of the DRB noise. Assuming that the once and twice back reflection locations are \(l_{1}\) and \(l_{2}\), respectively, the slowly varying field envelope of the DRB noise can be expressed as follows:  
\[ \begin{array}{ll}E_{DRB}(z_{i},T)&=\int_{0}^{z_{i}}dl_{1}\int_{0}^{l_{1}}dl_{2 }\Phi(l_{2},l_{1})\eta(l_{2})\Phi(l_{1},l_{2})\eta(l_{1})\\ &\times\Phi(0,z_{i})A\Big{(}T-\frac{z_{i}+l_{1}-2l_{2}}{v_{s}},l_{1}\Big{)}. \end{array}  \tag{10}\]  
In Equation (10), \(\eta(l)\) is the Rayleigh backscattering (RB) coefficient with the unit (1/km), which can be modeled as a complex white Gaussian zero-mean process. \(\Phi(l_{1},l_{2})\) is the transform function of the electrical field amplitude when the DRB field propagates along the fiber from \(l_{1}\) to \(l_{2}\). Neglecting the influence of the nonlinear effects and the dispersion on the DRB field propagation, \(\Phi(l_{1},l_{2})\) can be expressed as:  
\[\Phi(l_{1},l_{2})=\sqrt{\frac{P_{s}^{+}(l_{2})}{P_{s}^{+}(l_{1})}} \tag{11}\]  
\(P_{s}^{+}(l_{1})\) and \(P_{s}^{+}(l_{2})\) are the signal powers at \(l_{1}\) and \(l_{2}\), which can be calculated using Equation (7). Therefore, the field of additive distributed noise generated in one fiber can be written as:  
\[N(\Delta z_{i},T)=E_{SpRS}+[E_{DRB}(z_{i},T)-E_{DRB}(z_{i-1},T)]. \tag{12}\]  
Except for the SpRS and DRB noises, the ASE noise of the EDFA at the transmitter was regarded as the centralized additive complex Gauss noise. The complex Gauss noise was added to the link at the positions of the EDFAs.
## 3 Methods
### Input Data  
Depending on the ROI and the expected dynamics that could constrain the selection of multi-temporal image pairs, all available images within the specified time period should be reviewed, for example using the _PlanetExplorer_ web interface. It is advisable to prioritize scenes with a finalized or standard processing state. Additionally, cloud coverage should be minimal, ideally \(\leq\)10%. The data must be downloaded as Level 1B products, which include metadata in JSON format required for image-pair selection and RPCs necessary for reconstructing the image geometry during subsequent bundle-block adjustment and stereo processing. The approximation DEM is required to be in geographic coordinates with ellipsoidal heights above the WGS84 reference ellipsoid. Highly variable areas, such as glaciers, should be excluded during the re-alignment of the generated 3D point clouds. This can be achieved by utilizing vectorized mask data, such as those provided by Randolph Glacier Inventory (RGI) [30].
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 4 Experiment on a Lab-Scale Caisson-Foundation System  
### Vibration Test on a Lab-Scale Caisson System  
Figure 7a shows an Oryuk-do caisson-foundation system investigated by [PERSON] et al. [12]. The submerged breakwater system consists of 50 caisson units and has a total length of 1004 m. Each caisson unit is 20 m in width, 20 m in length, and 20.78 m in height. As detailed in Figure 7b, the concrete caisson in the lab-scale experiment was scaled down to 1/20 of the real size, with measurements of 1200 mm in width, 1100 mm in length, and 1300 mm in height. The foundation mound of the lab-scale system is made of a sand layer of 480 mm and an armor gravel layer of 100 mm.  
Figure 8 shows the experimental setup for the vibration test on the lab-scale caisson-foundation system. The foundation consisted of a sand mound and gravel armor. The sand mound was precisely dimensioned and compacted to guarantee the stability of the caisson system. The caisson was placed on top of the mound, featuring shear keys to ensure precise alignment and secure interlocking. To further reinforce the structural integrity, a 100 mm layer of gravel was applied on top of the sand mound.  
Accelerometers (model PCB 393B04) (i.e., ACC x, ACC y, ACC z) were installed on top of the caisson via steel cubes to measure vibrations along the x, y, and z directions. In this setup, two sensors were placed in the x-direction (labeled as 2x and 3x), one sensor in the y-direction (labeled as 2y), and two sensors in the z-direction (labeled as 1z and 2z).  
The data acquisition system comprised a signal conditioner (model 481A, PCB Piezotronics, New York, United States), a terminal block (BNC 2090A), a DAQ card (model 6036E), and a laptop. A steel block suspended from a 1300 mm rope was swung freely from a distance of 400 mm to simulate the impact load on the caisson. The time-history responses were collected in 15 ensembles. The obtained time-history responses were then concatenated according to the previously described methodology.
## 2 Materials and Methods
### One vs. Rest Classifier  
In the case of a multi-class problem, the symbolic expression obtained with the GPSC algorithm must correctly classify to one of the following outputs 0--Healthy, 1--Hepatitis, 2--Fibrosis, and 3--Cirrhosis. To do that the One vs. Rest Classifier was used. However, when One vs. Rest Classifier was used with GPSC it did not produce the symbolic expression and there was not any way to access the estimator inside the OneVSRestClassifier function. To overcome this problem the One vs. Rest Classifier had to be built from scratch for GPSC to build the system. In the case of 4 different classes, the dataset had to be slightly modified i.e., in the case of detecting healthy patients from the entire dataset the healthy class is labeled as 1, and the remaining three (1--Hepatitis, 2--Fibrosis, and 3--Cirrhosis) as 0. The following dataset modification including the previously described case are:  
* 0--Healthy labeled as 1 vs. (1--Hepatitis, 2--Fibrosis, 3--Cirrhosis) labeled as 0,
* 1--Hepatitis labeled as 1 vs. (0-Healthy, 2--Fibrosis, 3--Cirrhosis) labeled as 0,
* 2--Fibrosis labeled as 1 vs. (0-Healthy, 1--Hepatitis, 3--Cirrhosis) labeled as 0, and
* 3--Cirrhosis labeled as 1 vs. (0-Healthy, 2--Fibrosis, 3--Cirrhosis) labeled as 0.  
After the best symbolic symbolic expressions are obtained for each case the multi-class detection system will consist of 4-different symbolic expressions. The result of each symbolic expression goes through the Sigmoid decision function and the end result is multiplied by the real class number. For example, the third symbolic expression detects 2--Fibrosis patients from the dataset. The output of the Sigmoid function can be 0 or 1. If it  
Figure 9: The procedure of performing GPSC with random hyperparameter search with 5-fold cross-validation.  
is 1 then the system has detected the patient from 2--Fibrosis and to obtain the real dataset class then the output of the Sigmoid function is multiplied by 2.