esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 3 Limitations and Challenges
### Smart Grid Limitations
#### 3.1.1 Security-Related Limitations
* Weak cryptography exposes sensitive data and communications to unauthorized interception, manipulation, and exploitation by malicious actors, compromising the confidentiality, integrity, and availability of critical information and operational processes within the grid infrastructure. This requires strong encryption standards to mitigate these risks effectively [70]. * Data disruptions compromise real-time monitoring, decision-making, and system reliability. Breaches in data integrity can lead to incorrect operational decisions and system failures, while compromised privacy can expose consumer information, undermining trust and regulatory compliance. As a result, robust data protection measures and secure data handling practices are essential in smart grid operations [71]. * Insider threats include the misuse of credentials to manipulate data, disrupt operations, or steal sensitive information, posing significant risks to grid security and reliability. Effective monitoring, access controls, and employee training are essential to mitigate these insider threats and maintain the smart grid's data integrity [72]. * Physical breaches lead to disruptions in power supply, equipment damage, and potential safety hazards for personnel and the public, emphasizing the importance of robust perimeter security, surveillance systems, access controls, and resilience planning in safeguarding critical infrastructure against physical threats [8].
# Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL
## 1 Introduction
After eliminating the DC component of the collected pressure signals, the wavelet packet denoising method was used to denoise the signals. After denoising, the time- and frequency-domain characteristic indices of the signals were extracted and preliminarily screened. Subsequently, the kernel PCA (KPCA) was used to analyze the screened time- and frequency-domain characteristic indices. The first and second principal components were extracted to identify the performance degradation index of a gear pump. Finally, based on the degradation index, a prediction model based on the k-vector nearest neighbor (k-VNN) JITL method was established for the RUL prediction of a gear pump. The method proposed in this paper was compared to the traditional JITL method based on k-nearest neighbor (k-NN). The results show that the proposed RUL prediction model of a gear pump has a higher prediction accuracy than the traditional method.
## 3 Proposed Method
### System Architecture  
The TEDDY framework consists of two primary components: the base station and the distributed edge devices, as illustrated in Figure 2a. The base station serves as the central control unit and is responsible for scheduling, coordination, and maintaining global tracking consistency. Edge devices are distributed across the system and perform localized target tracking using lightweight processing algorithms. The system operates in iterative cycles, with the base station and the edge devices collaborating to optimize tracking performance and resource efficiency.  
At the beginning of each iteration, the base station evaluates the target's predicted position and trajectory. Based on this evaluation, it determines which edge devices should be activated or deactivated during the upcoming cycle. The base station sends scheduling information to the selected edge devices and receives target position updates after each iteration. This division of labor ensures that computationally intensive tasks, such as trajectory prediction and scheduling, are handled by the base station, while the edge devices focus on localized, lightweight processing of image data. The edge devices are equipped with cameras and local databases that store the necessary tracking information for the current iteration. Unlike traditional systems that continuously upload raw data to a centralized server, the TEDDY framework processes data locally at the edge. This approach reduces bandwidth usage and energy consumption and enhances data privacy by minimizing the transmission of sensitive data.
# Directly Synthesized Graphene-Based Photonics and Optoelectronics Devices
## 4 Future Perspectives  
Despite the long history of graphene and its unique electronic and optical properties, practical demonstration and production methods for synthesizing a promising quality of graphene have not yet been achieved. The conventional methods of graphene synthesis have faced difficulties, resulting in reduced process efficiency. Graphene synthesis, as a result, has not yet reached its full potential. The most important development driving research of direct graphene growth has been in the area of transfer-free growth. Directly grown graphene is a favorable solution, as it could elevate the performance of chip-scale integration for graphene-based optical devices with a minimum footprint. In addition, it can be applied to both ultrafast optical and nanoelectronic device manufacturing, and thus develop prevailing technology in the fields of nanoelectronics, photonics, optical communications, and optical computing.  
The critical challenge for realizing optoelectronic and photonic devices with directly synthesized graphene is its quality. The current methods for direct growth of graphene are not sufficiently precise to apply industrially, as the quality of the resultant graphene is inferior to that of the quality of conventionally synthesized graphene. Thus, a breakthrough to develop high-quality graphene from direct growth mechanisms, as well as further related studies, is required. Furthermore, the controllability of the nano-graphene nucleation dynamics during synthesis is an engaging area of development.  
Figure 9: (**a**) Photoresponse of the graphene/Si, SiNHs, and conformal graphene/SiNH photodetectors; (**b**) Photoresponse of the proposed photodetectors at 1, 100, and 2500 Hz of frequencies. (**a**), (**b**) Reproduced with permission from [108]. Copyright American Chemical Society, 2019.  
Another major issue is the growth temperature for graphene synthesis; the available reports on directly synthesized graphene discuss high temperature growth, while low temperature growth results are poor in quality and insufficient for implementation on a device scale. Further, application-based investigations on the synthesis of directly grown graphene are scarce. The progress towards growing graphene directly in the required quality and size at low temperatures on a non-catalytic substrate is therefore painstaking. It might be possible to reduce the production cost of graphene by reducing the growth temperature; however, crystallinity, uniformity, quality, and size of the resultant graphene are the major points to be considered. Therefore, developing new schemes to achieve the ultimate goal of a direct, defect-free graphene-based optical device should be the primary focus point. Efficient light-graphene interaction is another crucial factor in the production of ultrafast photonic devices, which cannot be achieved from 2D graphene structures. Moreover, to minimize scattering loss during the light-graphene interaction, a high-quality thin film of graphene is desired.
## 2 Model Structure
### Suitability of Seq2 Seq and LSTM for Traffic Demand Prediction  
It is acknowledged that combining Seq2 Seq and LSTM architectures is not in itself a novel approach. However, the innovation of this study lies in applying these architectures to the specific problem of predicting traffic demand for satellite mobile terminals. This application addresses the unique challenges of satellite communication, where traditional models often struggle to capture the dynamic and non-stationary nature of traffic data influenced by user mobility and geographic factors.  
The integration of Seq2 Seq and LSTM frameworks in this study is not arbitrary. These architectures are chosen for their exceptional suitability in addressing the sequential and time-dependent nature of satellite traffic demand prediction. Seq2 Seq, with its ability to map input sequences to output sequences, is particularly adept at capturing the temporal dependencies that are essential for accurate future traffic demand predictions based on historical data. Its structure is inherently designed to handle sequential information, making it ideal for modeling the dynamic changes in satellite communication traffic.  
LSTM networks are employed for their advanced memory capabilities, which are crucial for managing long-term dependencies in time series data. The use of LSTMs ensures that the model can effectively learn and retain significant patterns over extended periods, thus providing a robust solution for forecasting traffic demands in the satellite communication domain, where long-term trends and patterns are vital.  
In the context of satellite communication, traffic demands are influenced by various temporal factors, including daily and seasonal variations, as well as user movement and behavior. These factors create complex dependencies that require a sophisticated approach to modeling. The combination of Seq2 Seq and LSTM architectures is well suited to capturing these dependencies and providing accurate predictions over different time horizons. This integration allows the model to generalize across various scenarios and adapt to the dynamic changes in traffic demands that are characteristic of mobile satellite systems.  
By leveraging the strengths of both Seq2 Seq and LSTM, this study presents a tailored approach that addresses the specific challenges of predicting satellite traffic demand for mobile terminals. This innovative application demonstrates significant potential for improving the accuracy and stability of long-term traffic demand predictions in the satellite communication industry.
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 3 Methodology
### Explainable Artificial Intelligence (XAI) Used
#### 3.5.4 Accumulated Local Effects (ALE)  
ALE is a technique used to interpret the predictions of machine learning models, specifically focusing on understanding how features affect predictions on average. ALE addresses the limitation of global methods that average effects over the entire data distribution, which can be misleading if there are interactions between features. This method was developed to provide a better understanding of the feature effects by averaging them locally and thus avoiding the introduction of such interactions, as described by [28].  
The ALE of a feature is given as \(X_{j}\) at a particular value \(x_{j}\), which is calculated by integrating the marginal effects of \(X_{j}\) over the range of the dataset, while marginalizing over the distribution of other features. For a given feature \(X_{j}\), the ALE is computed as follows:  
\[ALE\big{(}x_{j}\big{)}=\int_{x_{j},min}^{x_{j}}\Bigg{(}E\Bigg{[}\frac{\partial f (x)}{\partial x_{j}}|X_{j}=z\Bigg{]}-E\Bigg{[}\frac{\partial f(x)}{\partial x_ {j}}\Bigg{]}\Bigg{)}dz \tag{6}\]  
where \(f(x)\) is the prediction model, \(\frac{\partial f(x)}{\partial x_{j}}\) is the partial derivative of the model prediction with respect to the feature \(X_{j}\), \(x_{j,min}\) is the minimum value of the feature \(X_{j}\) in the dataset, and \(E\big{[}\big{[}X_{j}=z\big{]}\) is the conditional expectation over the distribution of the dataset given the feature \(X_{j}=z\).  
Consider a dataset where we aim to understand the impact of 'Class Duration' on the adaptability level of students. We can calculate the ALE for 'Class Duration' by following these steps:  
1. Discretize 'Class Duration' into a grid of values.
2. For each interval in the grid, compute the difference in the model's prediction as 'Class Duration' changes within that interval.
3. Accumulate these local effects across the grid to estimate the average effect of 'Class Duration' on the model's prediction.  
Presume after computing the ALE, we find that increasing 'Class Duration' from 1 h to 3 h increases the model's prediction for 'High' adaptability by 0.2 on average. This implies that longer class durations are associated with a higher likelihood of students being classified as highly adaptable.  
The ALE plot for 'Class Duration' might show a positive slope, which indicates that as 'Class Duration' increases, so does the adaptability prediction. Educational stakeholders can interpret this to mean that policies or interventions designed to extend class durations could potentially improve student adaptability levels. By integrating ALE into the predictive modeling process, educators and policymakers can gain a more detailed understanding of the factors that influence student outcomes. This understanding is crucial for designing educational environments that foster adaptability, a key component of student success in dynamic learning contexts.
# Intelligently Counting Agricultural Pests by Integrating SAM with FamNet
## 2 Materials and Methods  
The experimental data in this study comprise a collection of RGB images that capture various diseases and pests from different angles and distances. To analyze and infer abundant feature information in agricultural images a segmentation-agnostic model called the segment anything model (SAM) is utilized. SAM demonstrates strong contextual reasoning and generalization capabilities, allowing for the partitioning of images into segments. Additionally, the count everything model, which is available in the five-shot scenario, is used as a benchmark model for counting purposes. The use of 'Count Every Thing' and FamNet has been standardized throughout the manuscript to ensure that the terms are used interchangeably with a clear understanding that 'Count Every Thing' refers to the seminal paper, while FamNet denotes the network presented therein.  
In the proposed approach, agricultural images are initially fed into the unsupervised SAM network, which performs segmentation based on different semantic contents and produces vector features representing different semantic information. These features are then processed and matched for label assignment with the sample vectors that have undergone the same processing. To mitigate differences in environments or lighting conditions, query instances are generated within a single image to achieve better counting results. Based on this, a flexible design utilizing SAM is proposed in this study, which avoids the segmentation issues that SAM encounters in common dense agricultural scenes and improves the generalization capabilities of counting models due to the lack of samples. Furthermore, to enable the model to learn from historical detection records similar to GPT's knowledge acquisition from dialogue history, the proposed model incorporates feedback from the results into a knowledge base at the final layer [16]. This facilitates the automatic expansion of the external data with detection records, which will benefit future detections. The network architecture is illustrated in Figure 1.  
Figure 1: The complete network architecture diagram.
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 4 Analysis of the Improved PBFT Consensus Algorithm
### The Hierarchical Clustering Mechanism  
The topological divide-and-conquer strategy is employed to partition consensus nodes into logical subgroups, thereby reducing local communication complexity and improving overall efficiency [112]. This approach has been supported by multiple empirical studies; however, its technical implementation must address two core issues: (1) The design of node selection algorithms in dynamic environments must balance load balancing and security thresholds, where an optimal number of groups can enhance consensus efficiency. (2) Improper clustering could lead to additional resource consumption.  
To address the aforementioned challenges, future approaches could introduce adaptive dynamic grouping algorithms and reputation-based grouping mechanisms for consensus node topology. This would effectively optimize load balance and improve consensus efficiency, while also preventing resource waste caused by improper grouping. Additionally, adopting a hierarchical hybrid consensus model, which executes lightweight consensus algorithms within subgroups and employs more fault-tolerant mechanisms at the inter-group level, can reduce the overall communication complexity and enhance the scalability of consortium blockchains.
# Bacterial Aerosol in Ambient Air--A Review Study
## 3 Discussion
### Interactions with Meteorological Factors
#### 3.2.5 Rainfall  
The varying correlation coefficients (\(-0.385\), \(p>0.05\), and 0.42, \(p<0.05\)) suggest that the relationship between rainfall and total bacterial concentrations may differ depending on the context or location of the study. The negative correlation observed in study [69] indicates that increased rainfall might lead to a washout or dilution effect, thereby reducing bacterial concentrations in the air. Conversely, the positive correlation reported in study [49] suggests that rainfall could enhance bacterial growth or release, thereby increasing their airborne concentrations, particularly during the monsoon season. The significant positive correlation (0.42) provides stronger evidence for the latter relationship within the context of that specific study. Similar conclusions during the monsoon season were drawn by [PERSON] et al. [98], whose experimental results indicate that bioaerosol concentrations increase during rain events.  
This phenomenon can be explained by several mechanisms. First, bioaerosols act as ice or cloud condensation nuclei, allowing raindrops to form around bacterial particles at high altitudes, which subsequently descend to the Earth's surface during rain events, thereby increasing bioaerosol concentrations. Second, bioaerosols may be resuspended from the ground by the impact of raindrops. Rainfall produces numerous small vibrations on the surface, which can dislodge and aerosolize microorganisms with the assistance of wind. This resuspension effect, through the splashing of spores and microorganisms, can result in increased bioaerosol concentrations. Third, a reduction in solar radiation during rain events may also influence the concentrations of culturable bioaerosols. It is well established that rain clouds reduce solar radiation, and this decrease may enhance the survival rates of bioaerosols, as lower levels of solar radiation during rain events can create more favorable conditions for their persistence.  
The question of why bacterial aerosol concentrations may either increase or decrease during rain events remains unresolved. The variability in available results highlights the complexity of environmental interactions with bacterial aerosols, emphasizing the importance of considering local conditions and study contexts when interpreting such data.
## 3 Methodology
### Attributed KNN Graph
According to the correlation of the multi-modal features, as shown in Figure 1a, the matrix of each multi-modal feature was assigned the corresponding weights \(\omega_{0}\), \(\omega_{1}\), and \(\omega_{2}\). The matrices \(Mb\), \(Mp\), and \(Ms\) were multiplied by the corresponding weights, and then the multi-modal feature matrix \(C\) was obtained by adding them, and it was spliced with the KNN spatio-temporal matrix \(B\) to obtain a \((L+1)\times K\) matrix, that is, the matrix \(S\). The similarity between node \(i\) and node \(j\) calculated by heat kernel is shown in Equation (1). \[X_{i,j}=\frac{1}{L}\sum_{ \begin{subarray}{c}i=1\\ j=1\end{subarray} }^{L}e^{-\frac{|x_{i}-x_{j}|^{2}}{t}} \tag{1}\]  
where \(t\) is the time parameter in the heat conduction equation. According to Equation (1), if the target node is \(i\), the closer the similarity \(X_{i,j}\) between node \(j\) and node \(i\) is to 1, the higher the similarity between the two nodes. The closer it is to 0, the lower the similarity between the two nodes. By analogy, the similarity \(X_{i,j}\) between all nodes and the target node was calculated, and the nearest \(K-1\) nodes were selected. The last step was to construct the adjacency matrix \(A\). The \(i\) th row and \(j\) th column of the adjacency matrix \(A\) were the \(X_{i,j}\) of node \(i\) and node \(j\), respectively. Similarly, the \(j\) th row and \(i\) th column of the adjacency matrix \(A\) were also the \(X_{i,j}\) of the nodes \(i\) and \(j\), respectively. Because the similarity between the node and itself was 1, the diagonals of  
Figure 2: Attributed KNN graph construction. adjacency matrix \(A\) were all 1. Thus, we obtained a symmetric matrix with a diagonal of 1, namely adjacency matrix \(A\). The undirected attributed graph by using the spatio-temporal features and multi-modal features of cellular traffic was constructed successfully, and \(G\) was defined as \(G=(V,E,S)\), where \(V=\{v_{1},v_{2},\ldots,v_{K}\}\) is the node set. \(E=\{e_{12},e_{23},e_{34},\ldots\}\) represents the set of undirected edges, corresponding to whether each region is adjacent. \(S=\{s_{1},s_{2},\ldots,s_{K}\}\) represents the the node attribute set. The graph \(G\) adeptly integrates the spatio-temporal and geographical attributes of cellular traffic, effectively illustrating the correlation between cellular traffic nodes.
# Silicon 3D Microdosimeters for Advanced Quality Assurance in Particle Therapy
## 3 Silicon on Insulator (SOI) Detector for Microdosimetry
### Design of SOI Detectors
#### 3.1.1 The 2-\(\upmu\)m-Thick Mushroom Microdosimeter Fabricated by Planar Technology  
Due to its thin active layer, the 2-\(\upmu\)m-thick Mushroom microdosimeter was fabricated using planar technology. The 2-\(\upmu\)m-Mushroom-planar (Mushroom-\(p\)) microdosimeter consists of well-defined cylindrical SVs with a height of 2 \(\upmu\)m and a diameter of 20 \(\upmu\)m. Figure 1 shows the planar design of the microdosimeter with n\({}^{+}\) phosphorus doping in the core region and \(p^{+}\) boron doping in the circular (ring) region of the SV. The n\({}^{+}\) core and \(p^{+}\) outer ring electrodes were created using phosphorus and boron ion implantation, respectively, followed by a drive-in diffusion to a depth of the 2 \(\upmu\)m active layer of the SOI microdosimeter. Outside the SV, an additional \(p\)-stop layer was created by boron implantation and diffusion that connects to the \(p^{+}\) outer electrode to avoid the effect of positive charge build-up in the field silicon oxide. This effect generally leads to the creation of inverse channels under the silicon oxide passivation layer. In this microdosimeter design, linear arrays of SVs are connected via aluminium buses (Figure 2a) with various numbers of SVs (1, 5, 10, 20 and 25) in a given array. Figure 2b,c shows the top and cross-sectional views of SEM images of a single SV obtained via a focused ion beam imaging technique. The small thickness of the SV provides more precise microdosimetric spectra of the radiation field by reducing the number of events stopping within the SV (stoppers), especially for high LET, low-energy charged particles. Such particles are important for accurate dosimetry/microdosimetry (e.g., BNCT) as well as for separation of radiation field components in mixed gamma-neutron fields.