esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 7 Open Challenges and Future Directions  
Despite the significant progress of the research in GAN and amodal completion in the last decade, there remain a number of problems that can be considered as future directions.  
1. **Amodal training data**: Up until now, there has been no fully annotated generic amodal dataset with sufficient ground-truth labels for the three sub-tasks in amodal completion. Most of the existing datasets are specific to a particular application or task. This not only makes training the models themselves more difficult, but verifying their learning capability as well. In many cases, there is no sufficient labeled amodal validation data to establish the accuracy of the model. We present the challenges related to each sub-task in amodal completion. For amodal segmentation, the current datasets do not contain sufficient occlusion cases between similar objects. Hence, the model cannot tell where the boundary of one object ends and the other one begins. The existing real (manually annotated) amodal datasets have no ground-truth appearance for the occluded region. This makes training and validating the model for amodal content completion more challenging. As for the case of order recovery, some occlusion situations are very rare in the existing datasets. On the other hand, it is impossible to cover all probable cases of occlusion in the real datasets. Nevertheless, in the future, the current datasets can be extended through generated occlusion to include more of those infrequent cases with varying degrees of occlusion.
2. **Evaluation metrics**: There are several quantitative and qualitative evaluation measures for GAN [59]. However, as it can be noticed from the results, there is no standard and unanimous evaluation metric for assessing the performance of GAN when it generates the occluded content. Many existing works depend on the human preference judgement which can be biased and subjective. Therefore, designing a consensus evaluation metric is of utmost importance.
3. **Reference data**: Existing GAN models fail to generate occluded content accurately if the hidden area is large. Particularly, when the occluded object is non-symmetric, such as the face or the human body. The visible region of the object may not hold sufficient relevant features to guide a visually plausible regeneration. As the next step, reference images can be used along the input image to guide the completion more effectively.  
In addition to the above-mentioned problems, the challenges in the stability and convergence of GAN remain open issues [28].
## 3 Constitutive Model of Rock Mass Under THMC Coupling
### Multi-Field Coupled Constitutive Model of Rocks  
#### 3.2.1 Multi-Field Coupled Constitutive Model  
The flow-stress coupling constitutive models enable theoretical and quantitative predictions of rock behavior by integrating mathematical and mechanical principles, verified through experimental and numerical methods. These models, categorized into six types--the equivalent continuum model, dual-medium model, fracture network model, continuous damage mechanics model, fracture mechanics model, and statistical model-- highlight different facets of flow-stress interactions [11]. The first three, based on viscoelastic-plastic behavior, focus on water flow's impact on rock structure, while the latter three capture the complex coupling effects of flow and stress on rock damage and internal structure changes due to damage evolution.
## 3 Results
### Amino Acid, VOCs and Aroma Profile of Antao Vaz White Wines
Data were also treated using LDA analysis, a supervised statistical analysis used when the groups are known in advance. First, the method measures the distance from each point to each group's centroid. Then, it classifies the point to the closest group considering the variance and covariance between the variables [9; 10; 28; 29; 61; 62]. The significantly different compounds responsible for the discrimination of the wine's samples--determined by one-way analysis of variance (ANOVA)--are AA, such as Asp, Leu and Trp, and also various groups of VOCs such as seven esters: Ethyl acetate (1), isoamyl acetate (6), ethyl hexanoate (9), (Z)-3-hexenyl acetate (11), ethyl lactate (13), heptyl acetate (14), and ethyl dec-9-enoate (isomer) (27); and four relevant unknown compounds, such as, Unknown 10 (71), Unknown 11 (72), Unknown 12 (73) and Unknown 16 (77). The linear discriminant analysis (LDA), obtained from the combined data of AAs and volatile compounds (Figure 2), showed a clear separation between all Antao Vaz wines. A clear separation between Mix* and the other two group wine samples were obtained along F1; separation between unripe wines (UM*) and T* wines was possible along F2. The results of the flavour profile of Antao Vaz wines are shown in Figure 3. As can be observed, the spider graph represents the six major chemical group compounds present in these wines. Esters represent the major abundant group of VOCs, followed by alcohols; in contrast, ketones are the least abundant group. The results clearly showed a different volatile profile for the Mix* wines compared to the T* and UM* wines concerning the aldehydes and ketones content. Moreover, concerning the carboxylic acid content, Mix* and UM* wines present higher contents compared to T* wines. Nonetheless, the three wines have similar amounts of the other three chemical groups. These results support the analytical results from the ANOVA test, Table 3, where no significant differences were found between total content of VOCs of Antao Vaz wines, and the PCA obtained for the separation by chemical function groups, Figure 1.  
Figure 2: Score plot of the two first discriminant functions obtained after linear discriminant analysis (LDA) analysis of the different Antão Vaz wines.
## 6 Application to a Real Dataset
### Performance Comparison  
The MCDO model exhibited superior forecasting performance compared to SARIMA, achieving a lower RMSE (32.42 vs. 37.59) and MAPE (5.79% vs. 6.21%), indicating higher accuracy in point predictions. Figure 17 shows the interval forecast comparison for this model. While MCDO produced a wider prediction interval (96.92 vs. 83.26), it maintained a significantly higher coverage probability of 91.67% compared to SARIMA's 66.67%. This suggests that MCDO's intervals, though broader, are more reliable in capturing actual values, making them more informative in practice. In real-world applications, such intervals offer users greater confidence in forecasted ranges, which is particularly valuable for decision-making under uncertainty. Rather than relying solely on narrow intervals, practitioners can interpret these as indicators of both predictive accuracy and model trustworthiness.  
### Performance Comparison  
The MCDO model exhibited superior forecasting performance compared to SARIMA, achieving a lower RMSE (32.42 vs. 37.59) and MAPE (5.79% vs. 6.21%), indicating higher accuracy in point predictions. Figure 17 shows the interval forecast comparison for this model. While MCDO produced a wider prediction interval (96.92 vs. 83.26), it maintained a significantly higher coverage probability of 91.67% compared to SARIMA's 66.67%. This suggests that MCDO's intervals, though broader, are more reliable in capturing actual values, making them more informative in practice. In real-world applications, such intervals offer users greater confidence in forecasted ranges, which is particularly valuable for decision-making under uncertainty. Rather than relying solely on narrow intervals, practitioners can interpret these as indicators of both predictive accuracy and model trustworthiness.  
Figure 16: Lowest 25 RMSEs for MCDO configurations for real data.  
Figure 17: Interval forecasts comparison for airline passenger test dataset.
###### Abstract
To overcome the limitations of traditional path planning algorithms that rely on high-precision maps and lack generalization and obstacle avoidance capabilities in unknown environments, this study proposes a path planning approach for autonomous vehicles based on DRL algorithms. DRL algorithms exhibit generalizability and do not require pre-built maps or corresponding state-transfer equations. They can be trained and interact with the environment to make path planning decisions. Building upon the traditional DQN algorithm and its derivatives, this study enhances the network structure, algorithmic components, and content to improve network convergence and enhance algorithm generalization performance. Using the Ubuntu operating system and Robot Operating System (ROS), we created obstacle-free, obstacle-rich, and complex dynamic simulation environments in the Gazebo platform. Comparative experiments were conducted among the improved DQN, traditional DRL, and traditional map-based path planning algorithms.
# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 3 Proposed Model Architectures
### Mixer U-Net  
Figure 4 illustrates the methodology of the first proposed model, namely, the mixer U-Net. The model adopts the U-shaped structure of the U-Net model consisting of four encoding and decoding stages with a bridge that connects them. As mentioned in Section 2.1.4, unlike in the U-Net, in the proposed mixer U-Net, all standard \(3\times 3\) convolutions are replaced with ConvMixer inception block except the first encoding stage. Thus, it is called Mixer U-Net. At the first stage, the input proceeds through an embedding layer, which is a standard \(3\times 3\) convolution followed by a ConvMixer inception block. At each of the next stages of encoder, the resolution of the input is, first, halved by the \(2\times 2\) max-pooling operation and then convolved by two ConvMixer inception blocks. The number of feature channels, as in U-Net, increases from 64 at the first stage of the encoder and eventually reaches 1024 at the bridge. It should be noted that the resolution of the input image should be divisible by 16 as it is downsampled by two at each stage with 16-times in total by the time it hits the bridge.  
In the U-Net model, the feature maps are upsampled by the transposed convolution (also known as deconvolution) operation [12]. However, there are two bottlenecks in using this method. First of all, as the transposed convolution is also a convolution operation that maps lower resolution input into higher resolution output, it has learnable parameters, which means additional computational cost. On top of that, it can result in \"_unseen overlap_\" in some pixels of the upsampled feature map or so-called \"_checkerboard artifact_\" [41; 42]. In order to avoid these effects caused by the deconvolution operation, the bilinear interpolation method is exploited in the proposed mixer U-Net. Thus, at the end of every stage of decoder, including the bridge, the extracted feature map is upsampled with bilinear interpolation before concatenating it with the output of the mirroring encoder stage. Next, the concatenated feature map is convolved with two ConvMixer inception blocks. At the final stage, after convolution operations, \(1\times 1\) convolution is used for extracting the binary segmentation map.
# Effect of Water Content on Light Nonaqueous Phase Fluid Migration in Sandy Soil
###### Abstract  
Contamination from light nonaqueous phase fluids (LNAPLs) and their derivatives during mining, production, and transportation has become a concern. Scholars have extensively studied LNAPL contamination, but the role of water content variation on its migration process in the unsaturated zone has not been sufficiently researched. The specific issue addressed in this study is the impact of water content on the migration of light nonaqueous phase liquids (LNAPLs) in sandy soils, a critical yet under-researched aspect of subsurface contamination. To tackle this, we employed indoor simulated vertical, one-dimensional, multiphase flow soil column experiments, utilizing the orthogonal experimental method to systematically evaluate the effects of varying water contents on the occurrence state and migration rate of LNAPLs. The experimental results indicate the following: (1) The migration rate of LNAPL exhibits an L-shaped trend during subsurface imbibition and a nonlinear relationship with migration time. The migration rate and migration time of surface infiltration have a linear growth relationship. (2) The residual rate of LNAPL is negatively correlated with water content and positively correlated with oil content in the homogeneous non-saturated state. With the increase in the amount of leaked oil, 40% of the leaked LNAPL is solved within the soil. (3) When the water content of the test medium is below 14%, and the oil content is below 11%, LNAPL appears in the unsaturated zone in a solid phase. As the water content increases, the adsorption rate of the oil phase gradually decreases and eventually reaches the oil saturation point. (4) When the water content of the medium exceeds 8%, over time, LNAPL will be subject to oil-water interfacial tension, and the rate of LNAPL movement first decreases and then increases, displaying nonlinear growth. The innovation of this work lies in the comprehensive analysis of LNAPL migration under controlled laboratory conditions, providing results that enhance the understanding of LNAPL behavior in sandy soils. These quantitative insights are crucial for developing targeted remediation strategies for LNAPL-induced pollution in the unsaturated zone.  
light nonaqueous phase fluids (LNAPLs); adsorption rate; water content; oil content; unsaturated state +
Footnote †: journal: Applied Sciences
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 5 Performance Evaluation
### Simulation Setup  
We utilize our proposed protocol to provide an implementation in OMNET++ [41] simulation environment. Traffic flows are generated using SUMO [42] traffic simulator. We extend the Veins [43] framework which models the vehicular communication to integrate network edges. We placed a set of edges which are assumed to collect traffic data. At the end of a predefined time cycle, each edge computes the traffic condition factor of each link it supervises, and then updates its local adjacency matrix as described in Section 4.3. These factors are utilized to control data dissemination in the vehicular network.  
Al Ain city map is selected for an urban road network representation. Figure 5 shows a SUMO NetEdit map with traffic generation scenarios. Each pair of colored nodes shown in the figure illustrates an Origin-Destination pair. To set the physical and the MAC layer, we utilize the implementation of IEEE 802.11p available in Veins. We rely on four traffic flow generation rates to represent different traffic scenarios ranging from low to high density. Simulation settings are summarized in Table 1.  
Safety messages are generated by the network edge, which disseminates them to nearby vehicles. Receiving vehicles proceed disseminating data in multi-hop fashion via V2V communication. For performance comparison, we ran each of the selected protocols under different traffic scenarios firstly in VANET environment and then with the aid of MEC.
# Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking
## 2 Literature Review
### Ultra-WideBand Technology
The proposed method was able to reduce the prediction error of the ranging distance by approximately 34.44% compared to the existing AI regression method, resulting in improved UWB positioning accuracy with an average positioning error of 0.20 m in seven different environments [61]. Another study examined the use of bias and deviation-weighted graph searches for NLOS calibration. The results demonstrate that the proposed algorithm has an average bias positioning error of 0.077 m under LOS conditions [62]. The limitation of this method is that environmental changes, such as the addition of more racks or large machines, necessitate system recalibration (i.e., the calculation of a new deviation-weighted graph of the new area). Finally, in the literature, we identified methods that combine some of the already referenced methods and technologies. For example, one group of researchers tried to create an RTLS with a combination of a UWB module and an IMU sensor [63]. The study also examined the MPU-9250 sensor positioning error with the DWM1000 trilateration positioning error. The IMU sensor's error is lower than the DWM1000 module's error. However, the IMU calculates tag's position based on acceleration and rotation measurements, and this results in an increase in the positioning error as time passes. The positioning accuracy of the approach that combines UWB with IMU outperforms (by 2.5 to 5 times) the approach using UWB sensors alone. Moreover, research on the PDR tracking category has been con ducted on a multi-floor area with the use of a backtracking particle filter, and the mean error from these experiments was 1.6 m [64]. Furthermore, some researchers have tried to improve the UWB RTLS accuracy by applying a ray-tracing algorithm; they observed that the overall RMSE was below 0.25 m on every axis [65]. A similar approach has been stud cording to the technology categorization scheme, most of them implement and propose a system for indoor tracking by deploying an IPS based on one of the above-mentioned technologies.
# NAGNE: Node-to-Attribute Generation Network Embedding for Heterogeneous Network
## 5 Experiment
### Node Clustering  
By applying the K-Means algorithm to the derived node embeddings, we cluster nodes into classes, evaluating the clustering outcomes via normalized mutual informa  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DBLP**} & \multicolumn{2}{c}{**IMDB**} & \multicolumn{2}{c}{**ACM**} & \multicolumn{2}{c}{**Aminer**} \\  & **Micro-F1** & **Macro-F1** & **Micro-F1** & **Macro-F1** & **Micro-F1** & **Macro-F1** & **Micro-F1** & **Macro-F1** \\ \hline DeepWalk & 90.21 & 89.52 & 56.53 & 55.34 & 82.22 & 81.77 & 83.23 & 82.91 \\ Metapath2 Vec & 92.50 & 91.87 & 52.38 & 51.15 & 83.56 & 82.31 & 84.43 & 83.65 \\ HIN2 Vec & 84.77 & 83.45 & 49.57 & 48.71 & 55.37 & 49.18 & 78.76 & 77.45 \\ MAGNN & 92.92 & 92.50 & 59.44 & 58.63 & 83.42 & 82.80 & 84.65 & 83.92 \\ HAN & 93.31 & 92.22 & 58.35 & 54.22 & 82.86 & 81.67 & 84.27 & 83.81 \\ GTN & 94.16 & 93.63 & 61.24 & 59.45 & 81.23 & 80.53 & 85.31 & 84.87 \\ HGNN-AC & 93.53 & 92.57 & 60.96 & 59.27 & 80.33 & 79.66 & 84.54 & 83.48 \\ HGSL & 93.07 & 92.56 & 60.05 & 59.25 & 80.22 & 79.68 & 84.08 & 83.45 \\ Megnn & 93.35 & 92.64 & 60.85 & 58.91 & 82.30 & 81.92 & 84.37 & 83.52 \\ NAGNE & 95.02 & 94.32 & 62.42 & 60.38 & 83.86 & 82.61 & 85.72 & 85.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation of multi-label classification.  
tion (NMI) [35], with results in Table 3. NAGNE again surpasses others in most cases, corroborating its effectiveness.
## 3 Mechanical Properties and Carbonation Depth  
### Mechanical Properties  
The mechanical properties of each specimen were evaluated at 3, 7, 28, and 59 days after casting. In addition, the mechanical properties at the age of the accelerated carbonation test (112 days after curing) were also measured. The compressive strength, elastic modulus, and splitting tensile strength were measured based on KS F 2405 [24], KS F 2438 [25], and KS F 2423 [26], respectively.  
In this study, the target compressive strength was 50 MPa for all mixture designs to remove the compressive strength effect on the carbonation resistance. As shown in Figure 2, the compressive strengths at 28 days after curing were 49.5, 52.7, 53.2, and 50.2 MPa for the OPC, FA15, SF35, and NS20 samples, respectively. Thus, it can be confirmed that the compressive strengths of the test specimens were almost identical, regardless of pozzolanic material type or content. Figure 3 represents the relationship between the elastic modulus and the curing age for the test specimens. There was no significant difference in the type of pozzolanic materials, and the change in the elastic modulus was not significant. The splitting tensile strength evolutions are shown in Figure 4. Similar to the compressive strength and elastic modulus, the splitting tensile strengths of the test specimens were similar to each other. The splitting tensile strengths at 28 days were 3.79, 3.97, 4.31, and 3.90 MPa for the OPC, FA15, SF35, and NS20 specimens, respectively.  
Figure 3: Elastic modulus vs. age.  
Figure 2: Compressive strength vs. age.
# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 5 Result
### Model Training  
There are two main training methods, namely deterministic and stochastic methods [31] (see Table 10). Deterministic methods look for a single parameter vector while the stochastic methods search for the distribution of the model parameters with the purpose of minimizing the model error [27]. In a more detailed division, local methods (L) that often work on gradient information and global optimization approaches are two kinds of deterministic methods. Gradient methods can be further sub-divided into first-order methods or second-order methods. Deterministic methods based on gradient information have been widely used in model training algorithm. The Levenberg-Marquardt algorithm, a second-order method, was most widely used in deterministic methods. The Levenberg-Marquardt method combines the advantages of BP and Newton algorithm, and its training speed is obviously faster than BP and momentum algorithm [81]. However, it has the disadvantage of being incompatible with regular terms, and requires a lot of memory when datasets are large. Sixty-two of the papers did not provide details about the model training algorithm. Seven categories of local methods (see line 1 to line 7 in Table 10) are summarized. Relatively speaking, there were few works on network training using Bayesian [27] and the Adam optimization methods [57].
# Wave Propagation in Periodic Metallic Structures with Equilateral Triangular Holes
## 3 Results
### Applications
Thus, square holes arranged in a glide-symmetric configuration are good candidates for the design of EBG (electromagnetic bandgap) structures, because of the widest stopband they offer. Circular holes are easier to fabricate and cost-effective compared to triangular and square holes, specially if the drilling technique is considered. For this reason, they have been widely used in the design of EBG structures, despite square and triangular holes offer a wider stopband. Additionally, the frequency dispersion of the structure with circular holes is lower compared to triangular and square geometries. Furthermore, Table 1 shows that the glide-symmetric configurations with triangular and square holes are denser (higher equivalent refractive index) than the configuration that uses circular holes. This feature is very suitable for the design of graded-index metalenses (metamaterial lenses). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**HOLE SHAPE**} & **Anisotropy (\%\%)** & **Refr. Index (\%10 GHz)** & **Dispersion (\%\%)** & \multirow{2}{*}{**Bandgap (\%)**} \\ \hline
**Triangular** & 16.20 GHz & 1.31 & 25.71 GHz & 39.50 \\
**Circular** & 28.86 GHz & 1.17 & 46.04 GHz & 0 \\
**Square** & 22.98 GHz & 1.21 & 32.10 GHz & 6.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison among different metastructures possessing glide symmetry for the maximum hole size. For each line, the frequency values in the columns “Anisotropy” and “Dispersion” correspond to the maximum frequency that shows a deviation less than 5% with respect to the value at \(f=0\). The geometrical parameters of the unit cells are: \(h_{\rm hole}=1.5\,\)mm, \(g=0.2\,\)mm, and \(d_{x}=d_{y}=4.5\,\)mm.