esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Posture Adjustment Range
### Workspace Analysis  
The position workspace, i.e., the set reached by the moving platform at different positions in the \(x,y,z\) direction, is solved as shown in Figure 4.  
1. The initial parameters of the center point of the moving platform of the 4-SPPP parallel mechanism are given, i.e., the initial values of \(\alpha,\beta,\gamma\) are set as \(\alpha=0^{\circ},\beta=0^{\circ},\gamma=0^{\circ}\). Here \(\alpha,\beta,\gamma\) represents the posture angle of the moving platform, similar to \(x,y,z\) represents the position of the moving platform.
2. The maximum range of \(x,y,z\) is given. Perform the search within a certain range, i.e., any boundary search in the \(x,y,z\) direction must satisfy the following constraints: \(-50<x<50,-50<y<50,-50<z<50\).
3. \(x_{I},y_{I},z_{K},\alpha,\beta,\gamma\) are substituted to the position inverse solution to calculate the displacement values of the drive pairs. Judge whether it is within the permitted range; if it is, record this point; if not, continue to search until the whole workspace is covered.
4. The three-dimensional diagram of \(x_{I},y_{J},z_{K}\), is drawn to obtain the position workspace of the mechanism.  
For the 4-SPPP mechanism, the travel limits of the linear actuators and the angular constraints of the spherical pairs jointly determine the size and shape of the workspace. Therefore, the relevant structural parameters of the parallel mechanism need to be set first. In this paper, the maximum trajectory of the horizontal prismatic pairs is set to \(s_{\rm max}=200\) mm, the minimum trajectory to \(s_{\rm min}=-200\) mm, the maximum trajectory of the vertical prismatic pairs to \(Z_{\rm max}=150\) mm, and the minimum trajectory to \(Z_{\rm min}=-150\) mm. Therefore:  
\[\begin{array}{l}-150\ {\rm mm}<l_{ij}<150\ {\rm mm},i=1-4,j=1\\ -200\ {\rm mm}<l_{ij}<200\ {\rm mm},i=1-4,j=2-3\\ -10^{\circ}<\lambda_{i}<10^{\circ},i=1-4\end{array} \tag{8}\]  
The workspace of the 4-SPPP parallel mechanism can be obtained by MATLAB R2021b and the boundary search method.  
The posture workspace, i.e., the set reached by the moving platform at different postures in the \(\alpha,\beta,\gamma\) direction, is solved similarly to the position workspace, and the flowchart is shown in Figure 5.  
Thus, the initial position and posture workspace can be obtained as shown in Figure 6.  
Figure 4: Flowchart of position workspace solving.  
Figure 5: Flowchart of posture workspace solving.  
Figure 6: Workspace.
# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 4 Experiments
### Model Interpretability  
In the proposed DMT-GIN, the SHapley Additive exPlanations (SHAP) [56] method is employed to dissect the basis of model predictions. The SHAP method, grounded in Shapley values from game theory, assigns an importance value to each feature, reflecting its contribution to the prediction outcome. The central idea of SHAP involves iterating over all possible combinations of model inputs, computing the average contribution of each feature to the prediction result. The strengths of this method lie in its fairness, as the importance scores of features are derived based on all potential combinations of features, ensuring comprehensive and unbiased evaluations. Moreover, SHAP scores not only offer  
Figure 5: Loss value plot for different tasks during the training process.  
\begin{table}
\begin{tabular}{l r r r r r r} \hline  & **D** & **B** & **C** & **E** & **C** & **P** \\ \hline
**D** & 1.00 & 0.75 & \(-0.56\) & 0.67 & \(-0.75\) & 0.82 \\
**B** & 0.75 & 1.00 & 0.45 & \(-0.52\) & 0.63 & \(-0.72\) \\
**C** & \(-0.56\) & 0.45 & 1.00 & 0.71 & \(-0.59\) & 0.49 \\
**E** & 0.67 & \(-0.52\) & 0.71 & 1.00 & 0.84 & \(-0.76\) \\
**C** & \(-0.75\) & 0.63 & \(-0.59\) & 0.84 & 1.00 & 0.87 \\
**P** & 0.82 & \(-0.72\) & 0.49 & \(-0.76\) & 0.87 & 1.00 \\ \hline \end{tabular}
\end{table}
Table 5: Correlation analysis among the six features.  
the importance of features, but also indicate the direction of their impact on the prediction outcome, granting us a more profound comprehension of the predictive behavior of the model.  
This methodology was applied not only to the multi-task graph isomorphism network, but also to other models like the GCN and GIN. We identified the top five brain regions that contributed the most to the [PERSON]'s disease classification task (see Table 6). It was interesting to note that despite the different models used, some brain regions consistently emerged among the top contributors. For instance, the Precentral gyrus and the Middle frontal gyrus were identified as crucial by all three models. These brain regions may play a vital role in the pathogenesis of [PERSON]'s disease and hold potential as biomarkers. This highlights the robustness of these regions in contributing to [PERSON]'s disease classification, reinforcing their potential as focal points for future research.
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 7 Conclusions  
Microencapsulation of polyphenols offers innovative opportunities in the application of these compounds in the food industry, enabling better utilization of their health-promoting properties. Polyphenols are widely valued for their antioxidant, anti-inflammatory and anti-cancer activities, but their instability to light, temperature and low bioavailability pose challenges in their direct use in food. Microencapsulation technology solves these problems by protecting polyphenols from external agents and enabling their controlled release in the digestive system. The use of different carriers, such as polysaccharides, proteins and lipids, provides a wide range of possibilities to modify the properties of the capsules, adapting them to different food products, including bakery products, dairy products and functional beverages.  
The advantages of microencapsulation include increasing the stability of polyphenols, extending their shelf life in food products, and improving their bioavailability, which contributes to higher nutritional value and better sensory acceptance. In addition, microencapsulation allows for the controlled release of polyphenols, which can enhance their biological efficacy. Disadvantages of this technology, however, include high production costs, the need for specialized equipment, and the risk of incomplete protection or too rapid a release of active ingredients in some food matrices. Despite these challenges, microencapsulated polyphenols have the potential to become an important ingredient in modern food products, joining the trend of functional and health foods.  
Conceptualization, W.B.; methodology, W.B.; investigation, W.B.; resources, W.B.; data curation, W.B.; writing--original draft preparation, W.B., A.S. ([PERSON]) and A.S. ([PERSON]); writing--review and editing, W.B., A.S. ([PERSON]), and I.W.-K.; visualization, W.B.; supervision, A.S. ([PERSON]) and A.P.; project administration, W.B.; funding acquisition, A.S. ([PERSON]) and I.W.-K. All authors have read and agreed to the published version of the manuscript.  
Research financed by the Polish Ministry of Science and Higher Education within funds of the Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS), for scientific research.  
Not applicable.  
The authors declare the availability of the data and material.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery
## 3 Network Model  
This section describes the implementation of the MIMO-OFDM intelligent receiver. For the original information bit stream with \(M\) bits at the transmitter side, it is transmitted to the receiver side through a series of operations at the transmitter side via a wireless fading channel. The receiver side performs feature extraction on the received \(IQ=(\text{Re}[y(k)],\text{Im}[y(k)])\) signal, and then the extracted feature vector is subjected to multi-label classification to finally achieve the recovery of the original information bit stream. Convolutional neural network, as an outstanding technology to promote the rapid development of deep learning, has significantly improved the feature extraction ability of neural network by superposition of convolutional layers. ReseNet [24] is proposed to solve the performance degradation problem caused by the accumulation of convolutional layers. Similarly, the introduction of DenseNet [25] pushed the feature extraction capability of convolutional neural networks to a new level. With the expansion of CNN in terms of depth and width, the ability of the network model to extract features from data becomes more outstanding, but the demand of the network model on computational resources also becomes more obvious, which makes the training process of convolutional neural networks difficult and time-consuming. In particular, DensNet has a serious feature redundancy problem, which makes the network model training very difficult. Therefore, [PERSON] etal. improved the feature aggregation model on the basis of DenseNet and redesigned the activation function and finally proposed VOVNet [26], which significantly reduces the inference speed of the model. In response to the demand for instant mine communication systems, we used VOVNet to build a mine intelligent receiver model.
## 4 Learning-Based Anomaly Detection
### Data Preprocessing  
Since the data preprocessing process directly affects the analysis results, it is important to obtain data that is refined in order to develop good algorithms, since the correct analysis results require the correct data to be entered [47; 48]. Data preprocessing consists of data cleaning, integration and transformation, and a total of 84,850 time series are obtained as the outcome of this pre-processing.  
1. Data Cleaning In this process, data is refined by filling missing values, filtering excessive noises, removing outliers, and resolving inconsistencies. As explained in Section 2, the difference between the actual position and the reference position, and the actual velocity are used as input data. To match the time stamp of the two different data sources, a linear interpolation technique is used. A linear interpolation of reference data, which is relatively low noise compared to the actual signals, is  
Figure 2: Anomaly detection model for swarm-drone system.  
performed to derive a difference value from the actual data at the same time stamp. Excessively large values that may be caused by noise aberrations are removed from the training data set. In addition, some portion of data exhibiting noises are cut out to reduce noise values across the data.
2. Data Integration Data from multiple drones are integrated into a single dataset with vehicle IDs. The vehicle ID is not explicitly used in the learning process but is useful in analyzing the performance of the learned model. Then, the input data includes 1 label information, 1 time stamp, and 6 kinematic variables.
3. Data Transformation Normalization is performed to bring the range of values of the selected parameters within a certain level. The well-known standardization method is used in this paper. The standardization method normalizes each data sequence by calculating the mean value (\(X_{mean}\)) and the standard deviation value (\(X_{stdev}\)): \[X_{standard}=(X-X_{mean})/X_{stdev}\] (6)
## 3 Potential Security Implications of IoT in 6G Environment
### Network- or Device-Level Vulnerabilities
#### 3.1.4 Software and Firmware Vulnerabilities  
Many IoT devices operate with outdated or unpatched software and firmware, making them vulnerable to known exploits. This reliance on outdated software creates persistent backdoors that attackers can exploit [46]. Addressing this issue will be particularly challenging in the context of 6G, where ensuring timely updates across trillions of interconnected devices is crucial for maintaining security.
## 4 Distributed Hotspot Network Architecture
### Distributed and Decentralized Hotspot Network Layer
#### 4.2.5 Types of Hotspots  
Based on the earlier discussion, we can state that the dew servers can act as: (i) full hotspot miners, (ii) light-weight hotspot miners, and (iii) data-only hotspots. Anyone can deploy the dew servers to act as one of three hotspots in the aforementioned architecture. In doing so, the range of the hotspot network coverage can be expanded to several hundreds of miles which can transmit IoT data in a fraction of the cost and energy with respect to standard cellular networks. All types of miners should follow the same software and packet format so that homogeneity is perceived [97; 98]. Besides acting as a hotspot facilitator, dew severs can act as miners too. Such miners can also earn a given cryptocurrency standard (e.g., bitcoin, Ethereum, tether, USD, XRP, terra, Binance, etc.) or one that is indigenously (newly) developed from the network. Here, we discuss the three types of hotspots:  
* Full hotspot: Dew servers, which can be configured as full hotspots, can be eligible to perform coverage facility to the IoT devices that are in their vicinity and also participate in all types of potential crypto reward scenarios inside the network. The Proof-of-Coverage (PoC) can be seen as a dominant algorithm in such an aspect as this. However, it should be maintained that such full hotspots should have prior approval from the underlying network authority, with high-standard and failure-proof subjunctives. We apprehend that the higher specification of servers with a complex processor design and memory capacity are mandatory for such type of hotspots.
* Light-weight hotspot: Such hotspots can be configured to replace full hotspots when moderate-load and moderate computations are needed, seamlessly. Dew servers have great potential to become converted into light-weight hotspots. They can perform regular hotspot coverage and perform header-wise synchronization with the associated blockchain. In this context, when overloading is perceived on the light-weight hotspots, they can transfer some consensus work to the full hotspots which are then expected to act as the validator of the light-weight hotspot. The use of light-weight hotspots can simplify the network structure and it enables the network ecosystem to grow rapidly.
* Data-only hotspot: This type of hotspot can only perform network data transfer. The transfer of data that are related to crypto awards may be earned by the use of such hotspots. We do not expect that the data-only hotspot would participate in the PoC-aware reward. Thus, a permissionless approach may be incorporated in the blockchain. users start earning crypto tokens as and when they are allowed to add blocks to the blockchain.
## 3 Materials and Methods
### Backward Multi-Scale Feature Fusion Module (BMFM)  
Due to the huge differences in morphology and scale of colorectal polyps, some tiny polyps are difficult to segment, and relying solely on features of a single scale often cannot effectively pay attention to both global and local feature information. The high-level feature map output by the encoder contains the semantic and location information of the polyps, while the low-level feature map contains hidden information such as polyp edges and textures. In order to capture the spatial feature information of colorectal polyp images based on feature maps of different scales, the BMFM is proposed inspired by [28] to fuse high- and low-level feature maps to generate attention maps to enhance the image's ability to capture multi-scale features.  
As shown in Figure 1, the BMFM is mainly composed of three spatial attention map (SAM) blocks, and the structure of the SAM is shown in Figure 2. Firstly, the high-level feature map \(M_{i+1}\) generated by the aforementioned encoder is upsampled to the same size as the low-level feature map \(M_{i}\). Then, \(M_{i}(i\in\{1,\ 2,\ 3\})\) and \(M_{i+1}\) are transmitted to the SAM block to generate a spatial attention map, which is multiplied with \(M_{i}\) to obtain \(D_{i}\). In SAM, the channel dimensions of \(M_{i}\) and \(M_{i+1}\) are adjusted by a convolution block (ConvBlock), respectively, which includes a \(1\times 1\) convolution, \(BN\) and \(ReLU\) operations. Subsequently, \(M_{i}\) and \(M_{i+1}\) are added element by element and \(ReLU\) activation is performed to complete the fusion of feature information, and the fusion feature is expressed as \(\alpha\), as shown in Equation (1).  
\[\alpha=ReLU(ReLU(BN(Conv_{1\times 1}(M_{i})))+ReLU(BN(Conv_{1\times 1}(M_{i+1}))) \tag{1}\]  
Figure 1: Structure of HPANet.  
Next, the following steps are taken: perform a \(1\times 1\) convolution, BN (batch normalization), and sigmoid activation operation on the fused feature \(\alpha\), map the feature values to the [0, 1] interval, generate a spatial attention weight map, and multiply the attention weight map element by element with the initial \(M_{i}\), so that the multiplied feature map \(D_{i}\) can assign higher weights to key regions in low-level feature maps based on the semantic information contained in high-level feature maps, improve the ability to capture important features, and suppress the interference of irrelevant information. The calculation steps of \(D_{i}\) can be summarized using Equation (2) as follows:  
\[D_{i}=SAM(M_{i},M_{i+1})=M_{i+1}*Sigmoid(BN(Conv_{1\times 1}(\alpha))) \tag{2}\]
# Experimental Study on Settlement Behavior of Ballasted Tracks with Polymer Compound-Coated Gravel
## 2 Performance Tests on the Coated Ballast  
### Composition of the Coated Ballast  
In the proposed design, the exterior of the track bed ballast was coated by a polymer compound material at a uniform thickness so that friction and wear between ballast pieces is minimized while maintaining the exterior shape of the ballast itself to increase impact resistance and elasticity, making it possible to semi-permanently maintain the elastic capacity of the track bed layer that supports a ballasted track. The coating material used was polyvinyl chloride (PVC). PVC is a thermoplastic polymer currently widely used in many aspects of life and has become a universal polymer. PVC resins are generally mixed with other additives, such as impact modifiers and stabilizers, providing hundreds of PVC-based materials with a variety of engineering properties. Owing to their durability, dependability, light weight, and flexibility, PVC coatings help to maintain the integrity of the coated object. The soil type of PVC raw material was melted and mixed with other additives such as impact modifiers and stabilizers processed into liquid form. Then, the coated ballast was completed by immersing (dipping process) the railway ballast in PVC processed in liquid form, coating the ballast with about 2 mm, and curing it by post-heat treatment. In this study, the type of stone used as a ballast aggregate was acquired from the crushing of rocks. Extrusive igneous rocks (rheolite, andesite, and basalt) are the most suitable parent rocks for the railway ballast. Figure 3a shows the cross-section of the coated ballast, and Figure 3b shows a comparison of the coated ballast and the normal ballast before and after roughing. Coating the ballast with the polymer compound, as shown in Figure 3b, endowed it with an elastic effect (reduced impact and cushioning effect), and the friction angle and friction coefficient between pieces of ballast were improved by adjusting the coating layer's surface roughness. By doing so, ballast wear, friction, and the risk of damage, such as breaking, were markedly reduced, and the overall performance of the ballasted track can be improved by increasing the durability (ballast shape maintenance) and elasticity of the ballast itself.
# Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
## 4 Materials and Methods
### Cost Analysis  
#### 4.3.1 Test Cost  
To form the set of models, we train each model individually, where the largest model with a 1280-pixel resolution tends to generate a training time in dataset 1 (Section 4.1.1) of 24 h on the GPU specified in Section 4. Given that we are using an ensemble of several models, the total training time is close to 5 days. Once all the models were trained, we carried out threshold optimization by running PSO 10 times, involving different combinations of vote numbers and penalty values. We defined a range of number of votes from 5 to 9 and the penalty values as 1.5 and 2.5.
## 6 Conclusions  
To enhance the dispatch potential of users and maximize the operational benefits of the expanded VPP through competitive agent interactions, this paper proposes a novel two-layer user energy management strategy for the VPP based on multi-agent reinforcement learning. A novel two-layer optimization framework is established, where the upper layer is applied to coordinate the scheduling and benefit allocation among various stakeholders and the lower layer is applied to execute intelligent decision-making for users. An HG-multi-agent reinforcement-based method is applied to accelerate the game-solving process. To  
Figure 12: Energy consumption of major appliances with DR in a house.  
Figure 13: Energy consumption of major appliances without DR in a house.  
validate the effectiveness and superiority of the proposed strategy, simulation verification was conducted, leading to the following conclusions:  
1. Compared to the conventional framework for optimized operation, the proposed framework and strategy can increase VPP aggregator benefit by over 56.2%, reduce user energy costs by over 12.9%, and increase ESS benefit by over 7.8%.
2. The proposed stepped DR mechanism helped the VPP reduce peak load by approximately 39.33%. Moreover, it safeguards users' interests in cases of load adjustment deviations, increasing their willingness to respond to the power company's policies actively.
3. Compared to conventional methods, the HG-multi-agent reinforcement learning method can reduce calculation time by over 23.8% and lower user costs after DR compensation by over 8.8%.  
Validation, W.J. and H.Z.; formal analysis, T.L. and Q.X.; writing--original draft, S.T.; writing--review and editing, S.T., T.L. and Q.X.; supervision, Z.W., J.Q. and Q.X. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Science and Technology Project of the State Grid Corporation of China (5700-202418337A-2-1-ZX).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Authors [PERSON] and [PERSON] were employed by the company China Electric Power Research Institute. Authors [PERSON] and [PERSON] were employed by the company Nanjing Power Supply Company, State Grid Jiangsu Electric Power Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.