esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of the Evolution Characteristics and Argillization of Clay Interbeds in Rockslides
## 2 Definition and Formation of Clay Interbed  
### Definition and Formation Conditions of Clay Interbed  
Clay interbeds are formed through the transformation of weak interlayers following weathering or tectonic activity, as well as long-term groundwater processes. The day in the interbed is between the plastic and liquid limits (Figure 1). However, scholars still have many different opinions regarding the necessary conditions for the formation of argillization.  
Based on the research of the Gezhouba hydropower project in China, it has been found that clay interbeds require specific rock foundation and formation conditions [4, 5]. The strength gap between the rocks in the lithologic group needs to be significant. And thewater-physical properties of at least one type of rock needs to be poor so that it will have enough time for weathering.  
The compressive stress of the overlying strata could be the reason for the formation of a clay interbed [6]. By comparing the connection form and arrangement order of silt under different degrees of compaction, the structure of soil with a higher degree of compaction transitions from a loose, honeycomb-like structure to a more compact aggregate or condensed structure. The condensed structure exhibited slight directivity and uniformity. It indicated the effect of the load from the overlying strata [7]. However, since increased pressure could hinder the hydration process, it is necessary to examine whether pressure has a positive effect on the entire argillization process. At the same time, it should be noted that the confining pressure controls the formation of clay interbeds when discussing the formation conditions. In addition, the difference between the actual sample and the original state should also be considered [8].  
The formation of clay interbeds is closely related to tectonism. Based on the actual distribution of clay interbeds, the bedding fault zone showed a high degree of consistency with the spatial distribution of the clay interbeds. It has been proven that argillization requires tectonism as a foundation [9]. In addition, tectonism would also promote the enrichment of clay minerals, which accelerated the process of agrillization [10].  
To summarize, the necessary conditions for the formation of clay interbeds include suitable parent rock, tectonism, and long-term groundwater action (Figure 2). And in the order of occurrence, tectonism would first cause the formation of a fractured zone, resulting in stress concentration and the retention of groundwater in the loose structure. The loose structure is conducive to the action of groundwater. Besides that, the presence of clay minerals will significantly expedite the degradation of physical properties deterioration and structural alteration in the crushed zone, facilitating the process of argillization.  
Figure 1: Typical clay interbed in a rock slope.
# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
###### Abstract  
Multiple-input multiple-output (MIMO) technology can significantly improve radar system resolution and target detection ability and become the core technology in millimeter-wave radar systems. Each antenna of a MIMO radar system generates a separate data stream, resulting in an exponential increase in the amount of data. The MIMO radar system needs to process these data efficiently and quickly to ensure the real-time performance of the system. Conventional MIMO radar engineering systems have difficulty meeting the efficient processing requirements for large data volume processing, and an efficient dynamic engineering implementation architecture for a MIMO radar system is necessary. In such cases, we analyze the MIMO radar engineering system principle and propose an efficient dynamic MIMO radar engineering implementation architecture, which reduces the data storage step after the Doppler fast Fourier transform (FFT) and improves the storage efficiency and processing speed. In addition, we validate the effectiveness of the method by implementing it on a field-programmable gate array (FPGA) platform. The results show that the method is better than the conventional MIMO radar engineering implementation architecture regarding storage resource consumption and processing speed. Specifically, for the same antenna channel data volume, the MIMO radar system with the proposed architecture achieves a 50% reduction in storage resource consumption and a 15% improvement in processing speed compared to the conventional architecture. The proposed architecture demonstrates better antenna array compatibility across different antenna arrays. This work provides new ideas and methods for the efficient engineering implementation of MIMO radar systems. This work can support the practical application of MIMO radar, especially the engineering applications of large-scale array MIMO radar systems.  
MIMO radar; storage resource; engineering implementation architecture
## 4 Results
### Data-Driven Modeling Results
#### 4.2.4 Genetic Algorithm-Emotional Neural Network GA-ENN  
In the context of this research, a hybrid approach referred to as GA-ENN was employed to predict the ultimate bearing capacity of sallow foundation. Through an array of analyses, the researchers pinpointed the optimal GA-ENN model, which is presented and described in detail. Figure 9 illustrates the comparison between the predicted and real values of the ultimate bearing capacity for the shallow foundation. The findings demonstrate the high effectiveness of this hybrid approach in accurately predicting the output.  
Table 10 presents a comprehensive evaluation of the overall performance of the best Genetic Algorithm-Emotional Neural Network (GA-ENN) model for predicting the ultimate bearing capacity of shallow foundations. The assessment was conducted on both the training and testing datasets, utilizing various performance metrics to gauge the model's predictive accuracy.  
In the training dataset, the GA-ENN model achieved a Mean Absolute Error (MAE) of 6.634, indicating that, on average, its predictions deviated by approximately 6.634 units from the actual values. The Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values of 72.503 and 8.515, respectively, signify that the model's predictions displayed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Training Database** & **Testing Database** \\ \hline MAE & 6.634 & 6.018 \\ MSE & 72.503 & 65.397 \\ RMSE & 8.515 & 8.087 \\ MSLE & 0.004 & 0.005 \\ RMSLE & 0.061 & 0.072 \\ \(R^{2}\) & 0.994 & 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Overall performance of the best GA-ENN model to predict the ultimate bearing capacity of shallow foundations for both training and testing databases.  
Figure 9: Results of proposed GA-ENN based on (**a**) training database and (**b**) testing database.  
relatively low variance from the actual values, indicating its precision in capturing the underlying patterns in the training data. The Mean Squared Logarithmic Error (MSLE) of 0.004 and the Root Mean Squared Logarithmic Error (RMSLE) of 0.061 further confirm the model's accurate handling of both small and large errors. Moreover, the high R-squared (\(R^{2}\)) value of 0.994 indicates that the GA-ENN model explains an impressive 99.4% of the variance in the training data, showcasing its robust fit to the dataset.  
In the testing dataset, the GA-ENN model performed exceptionally well, with a lower MAE of 6.018 compared to the training dataset, suggesting an even higher predictive accuracy on unseen data. The MSE and RMSE values of 65.397 and 8.087, respectively, reinforce the model's efficacy in producing predictions with minimal variability in the testing data. The MSLE of 0.005 and RMSLE of 0.072 reflect the model's consistent performance in handling errors on the testing data. Additionally, the high \(R^{2}\) value of 0.995 demonstrates the GA-ENN model's robustness in explaining an impressive 99.5% of the variance in the testing data, emphasizing its ability to generalize well to new and unseen data.
## 4 Experimental Results and Analysis
### Performance Evaluation on the CDD Dataset  
For the CDD dataset, a total of 3000 image pairs with a size of 255 \(\times\) 255 were used to evaluate the detection performance of the proposed and conventional algorithms. Figure 20 depicts the test images, ground truths, and detection results of the proposed and conventional algorithms for CDD. The proposed algorithm shows that the change map is almost the same as the ground truth. The proposed algorithm exhibits a significant improvement in the false negative (undetection) area compared with the conventional algorithms. In addition, it can detect changes even in small regions. For the conventional approaches, ATTUNet results in better segmentation than U-Net because it achieves better feature representation.  
To assess the detection performance objectively, \(F_{1}\)-score and KC metrics were used, as shown in Table 6. The proposed architecture results in better performance than the conventional approaches, achieving \(F_{1}\)-score and KC values of 0.70 and 68.74, respectively. It implies that the proposed method can identify the changes in the \"Good\" level. Table 7 shows the detection accuracy for the reverse input order. We found that the detection performance of the conventional approaches with JF is significantly degraded for reverse input order. However, the proposed method can consistently maintain the performance of change detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & \(\mathbf{F_{1}}\)**-Score** & **KC** \\ \hline JF + UNet & 0.59 & 56.45 \\ JF + ATTUNet & 0.63 & 61.46 \\ Modified-UNet++ & 0.45 & 41.97 \\ Proposed & 0.7 & 68.74 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(F_{1}\)-score and KC for CDD dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Area** & **Algorithm** & \(\mathbf{F_{1}}\)**-Score** & **KC (\%)** \\ \hline \multirow{4}{*}{Test area 1} & JF + UNET & 0.88 & 87.97 \\  & JF + ATTUNET & 0.84 & 82.97 \\  & Modified-UNet++ & 0.86 & 85.88 \\  & Proposed & 0.88 & 88.16 \\ \hline \multirow{4}{*}{Test area 2} & JF + UNET & 0.61 & 60.79 \\  & JF + ATTUNET & 0.67 & 67.07 \\  & Modified-UNet++ & 0.74 & 73.56 \\  & Proposed & 0.83 & 82.8 \\ \hline \multirow{4}{*}{Test area 3} & JF + UNET & 0.41 & 38.63 \\  & JF + ATTUNET & 0.46 & 43.62 \\  & Modified-UNet++ & 0.36 & 33.34 \\  & Proposed & 0.59 & 58.04 \\ \hline \multirow{4}{*}{Test area 4} & JF + UNET & 0.33 & 31.57 \\  & JF + ATTUNET & 0.35 & 34.00 \\ \cline{1-1}  & Modified-UNet++ & 0.45 & 44.85 \\ \cline{1-1}  & Proposed & 0.40 & 39.78 \\ \hline \multirow{4}{*}{Avg. \(\pm\) Std.} & JF + UNET & 0.56 \(\pm\) 0.21 & 54.74 \(\pm\) 22.01 \\ \cline{1-1}  & JF + ATTUNET & 0.58 \(\pm\) 0.19 & 56.91 \(\pm\) 19.26 \\ \cline{1-1}  & Modified-UNet++ & 0.60 \(\pm\) 0.20 & 59.41 \(\pm\) 21.17 \\ \cline{1-1}  & Proposed & 0.68 \(\pm\) 0.19 & 67.19 \(\pm\) 19.48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(F_{1}\)-score and KC with reverse image order for the data-augmented KOMPSAT-3 dataset.
## 3 Results and Discussion
A similar trend was observed for soybean yield prediction, in which RF consistently produced the highest prediction accuracy results, as reflected by consistently higher R\({}^{2}\) and lower RMSE, while SVM produced the lowest MAE in some cases. However, the difference in its performance compared to the final model fit may indicate its superior generalizability from phenological inputs to unseen data compared to other evaluated methods [76] but can also potentially indicate overfitting [77]. Additionally, differences in spatial or temporal variability in management practices between the crops could have contributed to model performance disparities [78]. While RF generally achieved high prediction accuracy in previous studies, the cross-validation results from this study did not correspond to the previous study based on the ground truth yield mapping approach from combine harvesters, which achieved R\({}^{2}\) values up to 0.89. This observation leaves an ambiguity in the from of a discrepancy between cross-validation and final model fit metrics, with no definite knowledge on expected prediction accuracy when using new, unseen datasets.
## 5 Application of CFD-Based Digital Twins
### Application of the Proposed Digital Twin for Emptying Procedures
#### 5.2.1 Data Analysis--Emptying Procedures  
In the case of the Digital Twin for the representation of emptying procedures, some sensitivity parameters such as the initial size of the trapped air pocket, the opening percentage of the drain valves, as well as the air intake orifice diameter of the air valves are taken into account. Figure 12 offers a sample of the identified behaviours, showing that the most critical vacuum pressures are associated with the Type II scenarios (with a value of 9.32 m-H\({}_{2}\)O in Test 3, where there are no air valves, and the valve opening is complete). A different behaviour occurs in the Type I scenario, where the opening of the drain valves is partial, generating a controlled drainage process and a damping of the vacuum pressures and thus resulting in a value of 9.73 m-H\({}_{2}\)O in Test 1. In the case of the Type III scenario, the lengths of the air pockets are longer compared to those presented in the Type II scenario, and the conditions of whole drain valve opening are maintained, thereby offering a minimum pressure of 9.51 m-H\({}_{2}\)O in Test 5.  
Figure 12: Air pocket pressure patterns in emptying procedures.  
Also, the differences in pressure patterns between the emptying procedures with and without air valves are shown. Figure 12 shows that in the emptying scenarios without air valves (Type I, II and III), pressure oscillations occur, which are dissipated because of energy damping by the trapped air and the pipe material, whereas in the scenarios with air valves (Type IV and V), the minimum vacuum pressures are less than the scenarios without air valves.  
In the Type IV scenario, an air valve with a diameter of 3.175 mm is used, and in a study test (Test 7), a minimum vacuum pressure of 9.56 m-H\({}_{2}\)O is shown. In contrast, in the V scenario, an air valve with an orifice inlet diameter of 9.375 mm is applied thus generating a minimum pressure of 10.14 m-H\({}_{2}\)O. Consequently, a rapid filling procedure takes place, since, in the other scenarios (Type I-IV), the pressure takes more than 8 s to recover the atmospheric conditions, which suggests that the emptying process for that point in time has not finished; whereas, in the Type V scenario (Test 12), the atmospheric pressure recovers its atmospheric conditions at 6.5 s, which is related to the total emptying of the water from the pipe at that point in time.
# Reconstruction of the Subsurface of Al-Hassa Oasis Using Gravity Geophysical Data
## 2 Study Area--Geological and Tectonic Settings
(**d**) The fault-zone pattern described by [PERSON] et al. [33] is depicted. (**e**) W-E section shows the geologic formations, the groundwater flow (blue and red arrows), and the subsurface deformation due to basement uplift beneath the study area (modified from [PERSON] and [PERSON] [1]), and (**f**) fault-zone field photo showing the major ENE-WSW and NNW-SSE directions observed at an outcrop within the study area. monitoring wells (Figure 1d). A notable gradient in groundwater salinity is observed across the region, escalating from approximately 300 parts per million (ppm) in the west to more than 5000 ppm along the Arabian coast. Intriguingly, a discernible abrupt shift in salinity with depth reveals a distinct stratigraphic influence. Shallow aquifers exhibit comparatively better groundwater quality, whereas deeper (around 800 m) aquifers, particularly within the Aruma Formation, manifest exceedingly saline groundwater [1]. The uplift and formation of the Ghawar anticline induced significant fracturing in the region. The Al-Shubaa outcrop and a southern one display joint systems trending in the NW-SE or NNW-SSE and E-W or ENE-WSW directions, eroded by wave-cut gorges (Figure 1e,f). Fluvial erosion later reshaped these features [27].
# Change Control Design in Product Supply Chain System Based on Radial Basis Function Neural Network
###### Abstract
Meanwhile, by utilizing the prediction capability of the RBFNN, the system is able to fit the current state based on historical data when facing network attacks (e.g., DoS attacks), which reduces the interference caused by the attacks on the control effect and improves the system response's efficiency. Finally, the effectiveness of the method is demonstrated through simulation experiments. The main contributions of this paper are as follows:  
* A novel adaptive LQR controller based on the RBFNN is developed, improving system stability and adaptability in environments with time-varying parameters. The controller's performance is theoretically validated, showing its ability to ensure system stability despite changes. * By integrating the feedforward prediction function of the neural network, when the system suffers a cyber-attack, the neural network can predict the changes in system parameters following the attack using historical data, thereby minimizing the impact of the attack on system performance and enhancing the system's robustness to external interference. * Through simulations, it is demonstrated that the proposed controller effectively addresses uncertainty in user demand and supply chain dynamics, providing significant improvements in system performance during environmental changes. The theoretical analysis confirms the effectiveness of the RBFNN-based controller in suppressing such uncertainties. The rest of this paper is structured as follows: Section 2 presents the background of the problem and models the product supply chain dynamics. Section 3 discusses the use of the optimal LQR controller as the primary controller, combined with feedforward control using the RBFNN, to deal with uncertain disturbances and cyber-attacks in the product supply chain system. Section 4 presents numerical examples and the simulation results of the product supply chain system. Finally, Section 5 provides a summary of this research and our conclusions.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 2 Approaches for Inference Time Reduction
### The Use of Small Size Decoders  
The encoder-decoder network is one of the most standard architectures of semantic segmentation. As a result, it is crucial to optimize the performance of this architecture as it influences plethora of models. In [8], it is suggested that the architecture of an encoder-decoder model can be simplified by reducing the decoder's size, in order to save computational cost. In particular, they introduced an architecture in which the encoder is larger than the decoder [8], following a different approach from the symmetric encoder-decoder architecture [9]. This novelty is based on the idea that encoder should process input data with smaller resolution of the input image. On the contrary, the one and only decoder's role is to upsample the output of the encoder by perfecting its details. Thus, reducing the decoder's size results in computational cost savings. Overall, this approach is appealing since in most of the times the reduction in the decoder's size does not affect the effectiveness.
## 3 Results of Data Training and Discussion
### Receiver Operating Characteristics (ROC) and Area under the Curve (AUC)  
The receiver operating characteristic (ROC curve) is a graphical representation that shows the relationship between the true positive rate (TPR or recall) and the false positive rate (FPR) for different classification thresholds. The area under the ROC curve (AUC) quantifies the model's ability to distinguish between classes. An AUC of 79.15% indicates reasonable performance.  
As shown in Figure 2, the decision-making threshold selected is 0.99804. This threshold influences how the model classifies instances, with an emphasis on precision.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Accuracy & 0.90 \\ Recall & 0.9916 \\ Precision & 0.9054 \\ F1-Score & 0.9447 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model performance metrics.  
Figure 2: Receiver Operating Characteristics.
# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
## 1 Introduction
### The Focus of This Study  
Pavement maintenance in high-altitude regions has some unique characteristics that make the maintenance work more complex and challenging compared with other regions. First, taking the Tibet Autonomous Region of China (Tibet, for short) as an example, the traffic flow in Tibet has obvious seasonal characteristics. During the peak tourist seasons, such as summer and autumn, the number of tourists is high, resulting in heavy traffic flow. In winter and early spring, due to cold and extreme weather conditions, the number of tourists is low, leading to reduced traffic flow ([PERSON] et al., 2018). This seasonal variation in traffic flow needs to be fully considered in maintenance planning to ensure that roads can withstand high traffic pressure during peak tourist seasons while minimizing disruption to traffic services. Second, the climatic conditions in high-altitude regions determine the seasonality of road maintenance work. Due to the low temperatures in winter, some road segments experience ice and snow, leading to harsh conditions that are unsuitable for large-scale maintenance activities. Therefore, it is more appropriate to carry out pavement maintenance work in warmer seasons, particularly summer and autumn (2019). This requires reasonable planning of maintenance work to minimize the impact of adverse weather on pavement maintenance and ensure maintenance quality and pavement technical condition. Furthermore, the ecological environment in Tibet is fragile, requiring environmental considerations in maintenance planning to minimize the impact on the ecosystem ([PERSON] et al., 2018). Additionally, due to the location of Tibet, the prices of maintenance materials are uncertain, and maintenance costs are generally high ([PERSON] et al., 2018). Some previous studies have considered reducing costs and carbon emissions during pavement maintenance, while others have considered budget uncertainties. However, previous methods did not fully address the maintenance decision needs of high-altitude regions and cannot be directly applied to such areas. To the best of our knowledge, previous studies have focused on annual maintenance plans without considering monthly maintenance plans. Additionally, most studies did not consider the duration of the maintenance process or manpower constraints. Moreover, no research has focused on the impact of unit area maintenance cost variations on maintenance plans, especially the corresponding robust maintenance plans. Inspired by the above background, this paper uses constraints such as manpower, maintenance months, budget, maintenance frequency, and maintenance approaches to analyze optimization objectives from multiple perspectives and propose methods for obtaining maintenance plans and robust maintenance plans. Specifically, the contributions of this paper are as follows: