esa-sceva/satcom-chunk-collection huggingface dataset:
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 3 Model Framework
### Reinforcement Learning Decision and Strategy Mechanism
#### 3.1.3 Reward Design and Decision-Making Mechanism  
The reward function is used to measure the edge side accuracy improvement after the migration operation, and it is defined as  
\[r_{c}=acc_{c}^{cloud}-acc_{c}^{edge} \tag{7}\]  
When a category \(c\) is selected to perform the migration operation, that is, action = 1, and distillation training is performed on the category, the updated edge and cloud detection accuracy is calculated. If the accuracy difference is less than the set threshold \(\theta\) = 0.1, the migration of this category is considered effective, and this category is regarded as a positive experience. This category enters the \"frozen\" state, and migration will not be repeated in the subsequent training process to avoid resource waste. If action = 0, migration of this  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Input Dimension & \(3(Sc=(acc_{c}^{edge},acc_{c}^{cloud},\Delta_{c}))\) \\ Output Dimension & \(2(a_{c}\in\{0,1\})\) \\ Number of Hidden Layers & 2 layers \\ Neurons Per Layer & 64 neurons per layer \\ Activation Function & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 1: Q-Network architecture.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline Learning Rate (lr) & \(1\times 10^{-3}\) & Initial learning rate for the Adam optimizer \\ Discount Factor (\(\gamma\)) & 0.99 & Degree of consideration for future rewards \\ Initial \(\varepsilon\) Value & 1.0 & Initial exploration rate (fully random) \\ Minimum \(\varepsilon\) Value & 0.05 & Lower bound of \(\varepsilon\) to maintain some exploration \\ \(\varepsilon\) Decay Factor & 0.995 & \(\varepsilon\) is multiplied by this factor after each update \\ Replay Buffer Size & 10,000 & Stores past state-action transitions for replay \\ Batch Size & 64 & Number of samples per training iteration \\ Target Network Update Frequency & 50 & Synchronize target network parameters every 50 steps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reinforcement learning algorithm parameters.  
category is skipped, and the reward value of the previous round is retained in the current round to maintain the continuity of the strategy.
# Hydropower Functional Zoning with Crowdsourced Geospatial Data: A Case Study in Sichuan Province
## 4 Discussion
### Development Suggestions Based on Hydropower Functional Regions
#### 4.2.3 Insufficient Discharge Ecological Flow  
The ecological flow is an important index for maintaining the ecological functions of rivers and lakes, and for controlling the development intensity of water resources. It is the important foundation for the overall planning of life, the production of aquatic ecological resources, and the optimization of the allocation of water resources. For example, hydropower avoids discharge ecological flow in the branch ditch of the Moxi River, a tributary of the Dadu River, resulting in a serious water reduction in the Yanzi River and the Bingchuan River. The riverway is almost dried up (Department of Ecology and Environment of Sichuan Province 2021).  
Control over the discharge-ecological-flow volume mainly occurs in middle-sized and small-sized hydropower stations, as well as in the affected zones and core affected zones of medium-sized hydropower stations, such as the core affected sub-zone of medium-sized hydropower stations in the middling mountainous forest zone of the major Jialing River Basin (6-1-1), the core affected subzone of medium-sized hydropower stations in the middling mountainous meadow zone of the major Jialing River Basin (6-2-1), and the core affected subzone of medium-sized hydropower stations in the high mountainous forest and urban zone of Min River Basin (9-1-3), the discharge-ecological-flow volume should be systematically examined. The discharge-ecological-flow volume shall be controlled through a pool-level limit of scouring sluice, basic operation discharge, a bottom barrier and slotting, a fixed-gate limit, and other engineering measures.
## 2 Related Work
### Graph Neural Networks  
In the early stage, among the works concerning CD approach, graph theory-based approaches have been extensively used for CD [54; 55]. For instance, in [56], a weighted graph was built to measure changes for CD with SAR images. [PERSON] et al. proposed an iterative robust graph for unsupervised heterogenous images CD [57]. This method constructs a robust K-nearest neighbor graph of bitemporal images, and calculates the difference image by comparing the graphs.  
With the development of deep learning, a variant of CNNs, graph neural networks (GNNs), has received sustained attention in many applications [58; 59; 60]. In particular, graph convolutional networks (GCNs) have been successfully applied in the fields of remote sensing, such as remote sensing image retrieval [61], remote sensing image semantic segmentation [62], and hyperspectral image classification [63]. Specifically, GCNs are able to efficiently process graph-structured data by modeling the relationships between samples (or vertices). Therefore, GCNs can be naturally used to model remote spatial relationships in remote sensing images, which is not considered in CNNs. Recently, considering the previous GCNs-based research in the field of remote sensing, these methods have been developed and applied to CD tasks. For example, [PERSON] et al. promoted a multi-scale GCN to detect land cover changes for CD in homogeneous and heterogeneous remote sensing images [64]. This approach constructs graph representations through object-wise high-level features generated by a pretrained U-Net. In [65], a multi-scale dynamic GCN was employed to mine the short-range and long-range contextual information. These GNNs-based methods have been initially applied to solve remote sensing image CD. However, there are still few CD methods for GNNs, and a large number of systematic theoretical studies and applications are still lacking. Therefore, further development of GNN-based CD methods has potential value.
# Data Lake Architecture for Smart Fish Farming Data-Driven Strategy
## 4 Big Data Technologies for Fish Farming Use Case
### Data Processing: Apache Spark  
Apache Spark is an open-source distributed data processing engine. Unlike the MapReduce framework, Spark uses in-memory caching to optimize performance. In addition, Spark enables complex processing on huge datasets through development APIs in Scala, Python, or Java. Indeed, Apache Spark relies on the resilient distributed dataset (RDD) concept, which is a constructed collection of data from a source system or another RDD stored in memory. In case of failure, Spark can reconstruct the RDD thanks to a direct acyclic graph that describes the sequence of operations, hence the resilience of RDDs. It has to be noted that while working on DataFrames and datasets, Spark implicitly transforms them to RDD before performing any processing. The Spark framework includes many components, such as those listed below [33]:  
* Spark Core: It is the cornerstone of the platform as it is responsible for managing memory, distributing tasks, interacting with HDFS, and fault recovery.
* Spark SQL: It is a distributed engine allowing fast, interactive querying compared to the MapReduce model. Moreover, it can write the output RDD to Hive tables and use HiveQL.  
* Spark Streaming: It is a real-time engine allowing streaming data analysis. Mainly, it relies on the micro-batch data ingestion concept and enables data processing with almost the same logic as batch processing.
# A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices
## 3 Original Data
### Intersection-Related Crashes  
We employed the spatial data of crashes provided by the Iowa Department of Transportation's public platform ([[https://data.iowadot.gov/](https://data.iowadot.gov/)]([https://data.iowadot.gov/](https://data.iowadot.gov/))), which has statewide data of general traffic crashes from the previous 10 years. The crash data contain 49 types of information (e.g., crash_date, district, county_num, literal, light, weather, rdtype, xcoord, and ycoord) and met the requirements of this research. For this research, a dataset of intersection-related crashes that occurred in Spencer city was selected and analyzed.  
We extracted spatial data of crashes in Spencer city from the Iowa statewide crash data using the ST_Intersects function provided by the PostGIS extension to clip the intersection-related crashes within the Spencer city boundary. A total of 1149 intersection-related crashes occurred in Spencer city, as shown in Figure 8.  
Figure 8: Distribution of crashes in Spencer city.  
Figure 7: Distribution of intersections in Spencer city.  
Note that it is difficult to determine the crash hotspot intersections from the figure above because there are several overlapping coordinates of crashes. Therefore, the proposed approach is needed to identify the crash hotspot intersections (CHIs).
## 4 Results  
### SAR Geophysical DCA Correction  
Taking a SAR image acquired on 30 April 2020 along the Gulf of Mexico as an example, Figure 4a-c show the observed Doppler frequency \(f_{DC}\), the predicted Doppler frequency \(f_{DP}\), and the Doppler centroid anomaly \(f_{DCA}\), respectively. In Figure 4c, the uncorrected DCA is significantly affected by the scalloping effect, primarily manifested as distinct banded variations in the range direction, displaying a regular pattern of brightness changes. To reduce the impact of the scalloping effect, we employ a scalloping effect removal method similar to that in reference [2]. Firstly, we calculate the average in the azimuth direction for each swath to obtain a one-dimensional DCA signal profile, as shown by the black dashed line in Figure 4d. Then, a fifth-order polynomial fit is applied to this averaged DCA profile (red solid line in Figure 4d) and then subtracted, resulting in a periodically varying signal known as the effect term \(f_{DCA}^{sca}\) (blue solid line in Figure 4d). Subsequently, the DCA, after removing the scalloping effect, is shown in Figure 4e. Finally, by averaging and removing the DCA over land in the range direction, large-scale systematic biases can be eliminated [13]. After this processing, the remaining signal is the geophysical DCA, as shown in Figure 4f.
# Educational Organization's Security Level Estimation Model
## 3 Proposed Security Level Estimation Model  
To simplify the evaluation of education organization security level, a quantitative model would allow the possibility of modeling different situations and comparing them, analyzing the impact of some security influencing factors. At the same time, the model application would be more accessible in case of automated or at least no specific security knowledge requiring data gathering about the organization. Considering that fully automated solutions cannot provide data on the organization's security policies and processes, a manual data presentation about the organization is selected. By adopting the model to use discrete input values, which evaluation does not require specific security knowledge, even small organizations, which has no security specialists in it will be able to model organizations security level situations.  
A multi-criteria decision-making approach is used for the security level estimation model to assure all the mentioned features will be taken into account. The base principle of MCDM usage in the model is presented in Figure 2. Model developers estimate the set of criteria, which define the education organization security level and can be discretely evaluated. The criteria are selected to reflect both organization security and safety, i.e., third parties related risks as well as individual, employee impact to the organization. Then, to evaluate the importance of each criterion, security experts execute a pair-wise comparison between criteria. This kind of comparison allows an estimation of expert's opinion consistency ratio. Therefore, only data of security experts with a consistent opinion are used for criteria importance estimation.  
The security experts do not analyze the organization's data, they only define the importance of each criterion. Therefore, the education organization might provide the criteria values, describing the organization's current or modeled situation, without the use of security experts, as the criteria values are discrete and require no security level interpretation. By using criteria importance coefficients (estimated by security experts opinion), education organization data (provided by the organization), and criteria normalization methods (defined by model developers), the organization's security level is calculated.
## 4 Experiments
### Performance Evaluation
#### 4.2.2 HPO for CNN  
In this experiment, we try to optimize the hyperparameters of the LeNet [35] automatically. The LeNet-5 designed for handwritten font recognition is one of the most popular CNN models. LeNet-5 saves a lot of computational cost by cleverly designing the network to extract features using convolution operation, parameter sharing, and pooling, then using an FC layer for classification recognition. Moreover, many recent novel CNN models are inspired by this network structure.  
**Search Space:** We consider the five hyperparameters of LeNet as shown in Table 4. Experiments were carried with LeNet on MNIST and Fashion MNIST datasets, which are commonly used to test the performance of ML and DL models.  
**Results:** The results for average accuracy and search time of our method and other conventional methods are tabulated in Table 5. As can be seen in this table, our method outperforms other methods in terms of accuracy and time. The search time is greatly reduced since utilizing the surrogate model. Among RS, TPE, hyperband and BOHB, for MNIST and Fashion MNIST datasets, BOHB takes the shortest time, and TPE has the highest accuracy. Compared to BOHB, our method can reduce the time by about 15% to 20%, demonstrating that the surrogate model can reduce the search time. For MBRL-SDP, although its time is similar to our method on these two datasets, it does not perform well on the test set. This also illustrates the effectiveness of intrinsic reward. This experiment demonstrates that our method not only achieves high accuracy on large datasets, but also requires much less time.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Name** & **Type** & **Ranges** \\ \hline conv\_size & int & \([2,7]\) \\ hidden\_size & int & \([124,1024]\) \\ batch\_size & int & \([16,32]\) \\ dropout\_rate & float & \([0.5,0.9]\) \\ learning\_rate & float & \([0.0001,0.1]\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters of LeNet  
Figure 6: Average validation accuracy across five trials on the winequality_white dataset over time.  
Figure 7 shows the best validation set accuracy over time for all methods on the MNIST dataset. Each method was run five times and the average of the five accuracy was calculated. By observing the black line, we can see that our method can find a good hyperparameter configuration much faster compared to MBRL-SDP. Since hyperband and BOHB use a small budget (e.g., epoch) in the initial stage, these two methods will run multiple sets of hyperparameter configurations initially, and it is possible to find hyperparameter configurations that perform well. Therefore the speed of finding good configurations is faster than our method. Although TPE and our method will find good configurations simultaneously, our method converges faster in the initial stage. Moreover, our method has significant advantages over RS and MBRL-SDP. This experiment demonstrates that RFEPPO not only achieves high accuracy on large datasets, but also requires much less time.
# Dynamic Stability Finite Difference Time Domain Analysis of Landfill Based on Hypergravity Test
## 5 Analysis of Dynamic Stability
### The Impact of the Values of Solid Waste Strength Parameters on Stability  
Figure 10 shows the safety factor of a high-water-level landfill under high-intensity earthquakes when determining the strength parameters of MSW via destruction strains of 10% and 20%, respectively. Under the earthquake of \(k_{h}=0.34\), different stable results were observed for the centrifugal model of a high-water-level landfill when using strength parameters determined via destruction strains of 10% and 20%. When studying and analyzing with strength parameters determined via a destruction strain of 10%, instability occurred in the high-water-level landfills when negative excess pore pressure almost completely dissipated after an earthquake. However, when analyzing with strength parameters determined via a destruction strain of 20%, the stability of the landfill under an earthquake was good, and no instability occurred. Under the earthquake of \(k_{h}=0.46\), there was a significant increase in the safety factor at the same time step when analyzing with strength  
Figure 9: Time history curves of the safety factor of high-water-level landfills and their corresponding relationships with the seismic response of pore pressure and displacement under various ground motions.  
parameters corresponding to a destruction strain of 20%, but instability also occurred. It suggests that if the strength parameters for MSW are determined based on breaking strain, further research is still needed on selecting appropriate values for breaking strains.
# Kohonen Mapping of the Space of Vibration Parameters of an Intact and Damaged Wheel Rim Structure
## 3 Research Results and Analysis
### Spectral Analysis of Vibrations
After the introduction of the presented diagnostic method for the quality control system of wheel rims, an unnaturally high percentage of damaged wheel rims was noted. It should be stressed that the production system of wheel rims is automated. At the same time, random verification of damaged wheel rims indicated by the diagnostic system, performed by quality controllers with an individual evaluation of vibration spectra made it possible to classify the wheel rims as being serviceable. That, in turn, was also confirmed with the VT and RTG methods. Thus, it was concluded that using only four diagnostic symptoms, each described by 20 features, is insufficient for an unequivocal evaluation of the technical condition of new wheel rims. Consequently, the search began for a rapid, adaptable method capable of analysing the entire spectrum, rather than just selected features. After evaluating the available methods for their suitability, innovation potential, software availability, and the feasibility of development and integration with existing diagnostic systems within a manufacturing company, the integration of the proposed diagnostic system with Kohonen neural networks was selected.
# Stability Index of Surrounding Rock during Deep Rock Excavation Considering Energy Release Speed
## 1 Introduction
[8] adopted the local energy release rate (LERR) index proposed by the energy difference before and after rock failure. [PERSON] et al. [17], [PERSON] et al. [18], and [PERSON] [19] took the time factor into consideration, but the method adopted to determine the coefficient in the equation that describes the rockburst process is insufficient in theory and can only be obtained by experience. It is still impossible to describe the dynamic process of the system and the distribution of released energy in detail. However, it demonstrates the concept of the \"rate\" of energy change during the rock damage process, which is significant for the development of energy theory [20]. Various research was conducted to explore the stability of rock mass affected by the release speed of energy. For instance, [PERSON] et al. [21] combined numerical simulation and statistical analysis of the seismic energy of a slope to verify that increasing excavation speed is detrimental to the stability of surrounding rocks and increases the energy level of the tremor. [PERSON] et al. [22] emphasized that the process of energy release should be considered in the analysis process rather than just the total plastic zone volume or total energy release amount in a region, which may not necessarily indicate the worst stability. Large-scale SCTO (single-cleavage trapezoidal open) specimens were used by [PERSON] et al. [23] to conduct impulse tests, and the results showed that the crack growth speed, dynamic fracture toughness, and energy release rate increased with the loading rate, while the delayed cracking time decreased. It can be seen that the process of energy release of rock has effects on the state of the rock mass, and the faster the energy release of the rock mass, the more unstable it becomes. However, most of the research on energy release induced by excavation was focused on the stress unloading of original rocks, with the amplitude of unloading being considered the main factor influencing the stability of surrounding rocks. The research on the change in energy is related to the state before and after excavation or between different excavation stages. There was little research on the time factor of energy release of surrounding rocks during excavation. In this study, an index that considers the energy release rate of surrounding rocks during the excavation of deep tunnels and its influence on the stability of the surrounding rock mass was proposed. Through numerical analysis and validation with monitoring data from Canadian underground laboratory test tunnels, the index was shown to effectively indicate the location and strength of potential impact areas. The contribution of this study lies in the incorporation of energy release speed in the evaluation of rockburst tendency, providing a more comprehensive understanding of the stability of surrounding rocks during the excavation of high-stress rock.