esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 2 Proposed Methods
### Real-Time Level Selection of Negotiation Granularity
#### 2.3.2 Neural Network Training  
The neural network training is divided into two steps: finding the best activation function and determining the neural network structure. In the first step, the two-layer model is set to be the initial neural network structure. The output layer's activation function is set to be the _purelin_ model. There are 14 activation functions and different node numbers of hide-layers are traversed to find out the best three activation functions. In the second step, the three-layers structure and also the different node numbers in each layer are selected and traversed, including 4, 6, 8, 16, 32, 64, 128, and 256. By comparing the MSE, the best structure of the neural network is found.
## 3 Results  
### Classification Experiments  
Table 4 shows the results for the 2-class classification based on PCA for the three surgical tasks. Overall, the accuracy for Tasks 5 and 6 is higher than for Task 7. The specificity, representing the true negative rate, is superior to the sensitivity across all tasks, denoting better classification for the End of training class. When analyzing each task separately, the performance differences across the algorithms are minor, but certain algorithms present better performance. Specifically, in Task 5, SVM (Linear) is the best-performing algorithm with an accuracy of 97.1%, while LDA shows the lowest accuracy (89.2%). Additionally, QDA achieves the highest sensitivity (98.1%), whereas LDA exhibits superior specificity (99.2%). In Task 6, SVM (Linear) maintains the highest accuracy (96.9%) while GNB scores the lowest accuracy (92.5%). SVM (Linear) demonstrates the highest specificity (98.9%), while RF and QDA excel in sensitivity (95.3%). In Task 7, lower performance scores are presented across all models. LDA scored the highest accuracy (75.7%) while LR performed the poorest (63.6%). In terms of specificity, LDA outperforms the other algorithms (87.8%), whereas QDA achieves the highest sensitivity (81.1%). Table 5 presents the results for the 2-class classification experiment, utilizing [PERSON]'s score, which seem aligned with those based on PCA, but with slightly lower values. Accuracy is higher for Tasks 5 and 6 compared to Task 7. Furthermore, sensitivity percentages are consistently higher than specificity across all tasks, indicating superior classification ability for the Beginning of the training class. For Task 5, the best performing algorithm is LR (93.6%) while QDA has the lowest performance (83.6%). Similarly, for Task 7, LR achieves the highest accuracy (72.1%), while QDA the lowest one (52.9%). Lastly, SVM (RBF) stands out as the best performing algorithm in Task 6, achieving an accuracy of 95.3%. Figure 2: Model design process: (**a**) data acquisition from the VR simulator, (**b**) z-normalization data scaling, (**c**) division of the main dataset for algorithm training and testing, (**d**) application of dimensionality reduction techniques, (**e**) model training using the training set, and (**f**) performance evaluation based on metrics using the test set. Figure 3 presents the normalized confusion matrices for the most effective algorithm, based on PCA, for the 2-class classification experiment. The matrices, which were generated by summing the raw confusion matrices from the twenty folds, reveal better classification performance for the End of the training class across all three tasks. The confusion matrices of all other algorithms can be found in the Supplementary Materials, Figure S1. The results for the 3-class classification (Table 6) were produced following a specific experimental protocol. Initially, the feature dimensionality was reduced using [PERSON]'s score, resulting in 6-7 final features including the feature _Trial number_. Further reduction was performed based on PCA, resulting in 4-6 principal components.
## 2 Methodology
### Long Short-Term Memory Network  
An LSTM network is a type of RNN architecture introduced by [51]. LSTM was designed to handle the vanishing gradients issue for long-time sequences in traditional RNNs. These vanishing gradients make it difficult for the RNN to learn long-term dependencies effectively.  
LSTMs can keep information over a longer period by incorporating a memory cell. An LSTM controls the short-term and long-term cell states via three gates. These are the forget, input, and output gates, operating in the specified order. The basic structure of an LSTM cell is shown in Figure 2. The input of the current cell is \(x_{t}\) and the short-term and long-term states of the previous cell are \(h_{t-1}\) and \(c_{t-1}\), respectively. During the operation of the gates, three signals are generated from the gates and denoted as \(f_{t}\), \(i_{t}\), \(g_{t}\), and \(o_{t}\), respectively.  
Figure 1: One-dimensional convolution operation for multivariate time series [49].  
Figure 2: The basic structure of an LSTM cell.  
The forget gate controls how much of the previous long-term cell state \(c_{t-1}\)'s information is retained or forgotten. The forget gate signal is generated through a sigmoid function \(\sigma(.)\) using the short-term state of the previous cell, namely, \(h_{t-1}\), and input of the current cell, namely, \(x_{t}\):  
\[f_{t}=\sigma\Big{(}W_{f}x_{t}+R_{f}h_{t-1}+b_{f}\Big{)} \tag{4}\]  
where \(W_{f}\) and \(R_{f}\) are the weights and \(b_{f}\) is the bias.  
The input gate controls the influence of the new information on the current cell with Equation (5):  
\[i_{t}=\sigma(W_{i}x_{i}+R_{i}h_{t-1}+b_{i}) \tag{5}\]  
where \(W_{i}\) and \(R_{i}\) are the weights and \(b_{i}\) is the bias. The current cell state candidate values \(g_{t}\) are created, and the previous long-term cell state is updated into a new state using the following equations:  
\[g_{t}=tanh\big{(}W_{g}x_{t}+R_{g}h_{t-1}+b_{g}\big{)} \tag{6}\]  
\[c_{t}=f_{t}\otimes\ c_{t-1}+i_{t}\otimes\ g_{t} \tag{7}\]  
where \(W_{g}\) and \(R_{g}\) are the weights, \(b_{g}\) is the bias, \(\otimes\) is element-wise multiplication, \(tanh\) is the tangent hyperbolic activation function, and \(c_{t}\) is the updated long-term cell state. The output of the LSTM cell is obtained with the following two equations:  
\[o_{t}=\sigma(W_{o}x_{t}+R_{o}h_{t-1},b_{o}) \tag{8}\]  
\[h_{t}=o_{t}\otimes\ tanh(c_{t}) \tag{9}\]  
where \(W_{o}\) and \(R_{o}\) are the weights and \(b_{o}\) is the bias.  
The updated long-term cell state \(c_{t}\) and the short-term cell state \(h_{t}\) are then passed to the next time step as input. This process of updating the cell and hidden state is repeated at each time step of the input sequence.
## 3 Results
### Extraction Performance of the SBA-15-SPME Arrow  
The extraction efficiency of the SBA-15-SPME Arrow was compared with commercially available SPME Arrows with different coatings (Carbon WR/PDMS, DVB/PDMS, PDMS, DVB/Carbon WR/PDMS, and Polyacrylate) under their optimal extraction and desorption conditions (Figures S3-S7) in a mixed standard solution (50 ng mL\({}^{-1}\)). As shown in Figure 6a, the DVB/PDMS SPME Arrow demonstrated the highest extraction capacity among the five commercial SPME Arrows. However, the SBA-15-SPME Arrow exhibited significantly higher extraction efficiency for all analytes except thiabendazole. Additionally, the extraction performance of the SBA-15-SPME Arrow and the DVB/PDMS SPME Arrow was compared for the five analytes in milk samples. As shown in Figure 6b, the SBA-15-SPME Arrow exhibited much higher extraction capacity and selectivity for amantadine, clenbuterol, sulfamethazine, and ratcopamine, while the DVB/PDMS SPME Arrow showed slightly higher extraction efficiency for thiabendazole in milk samples. Overall, the SBA-15-SPME Arrow demonstrated excellent extraction ability and selectivity for the five veterinary drugs compared to the commercially available SPME Arrows, confirming its superior extraction efficiency. This enhanced performance is crucial for improving the precision of on-site analytical methods, especially in complex food matrices where interference from other compounds can be problematic. The ability of SBA-15 to provide a higher extraction capacity and greater selectivity improves the accuracy of quantitative analyses, enabling faster and more reliable results in the field. These findings highlight the potential for broader adoption of SBA-15-SPME Arrows in regulatory food safety monitoring, where rapid and accurate detection of contaminants is essential.
## 7 Applications in SDN-based VANETs
### Efficient Data Dissemination  
Profoundly modified information spread of SDN-based VANETs is a striking highlight. The information dispersal gives a take-off to numerous administrations, for example, crisis communicate administrations, versatile communicate interim time, security of clients, and so on. [PERSON] et al. [118] built up a versatile signal-based information spread convention which scatters the admonition messages to vehicles close by when a risky circumstance happens without tossing an additional heap on the channel. The data dissemination depends on the safe and measured public key infrastructure (PKI), vehicular gatherings, the hybrid trust model, the CRL, and the board framework [110].
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 3 New Compact Dual Source CRB for Delay and Doppler Estimation with Band-Limited Signals  
The main goal of this contribution is to derive a new compact analytic form of the delay-Doppler CRB for the dual source model in (5).
## 3 Results and Discussion
### Analysis of Formation, Propagation and Width of Cracks  
Monitoring of the formation, propagation and width of cracks in concrete was carried out visually by drawing on the previously hatched side surface of the tested beams. The cracks were marked with different colors in relation to the moment of their formation, i.e., the phases of their emergence (Figure 6).  
Based on the \"crack map\", shown in Figure 6, the flow and propagation of cracks as a function of load are observed. From the presented sketches, one may observe the emergence and the extent to which cracks have developed. In the zone around the middle support (above the support itself), as well as under the force, cracks were formed vertically, while to the left and right of the middle support, cracks were formed diagonally (at an angle of 45\({}^{\circ}\)). In the part between the end supports and the force, vertical cracks were formed, while between the force and the middle support, cracks were formed directed towards the force at an angle of 45\({}^{\circ}\). Vertical cracks occur as a consequence of the bending moment, while cracks at an angle of 45\({}^{\circ}\) are a consequence of shear stress, where the normal stress is zero or close to zero, so the main tensile stresses are at an angle of 45\({}^{\circ}\), leading to the emergence of these cracks.  
Analysis of test results obtained with LVDT 3 and LVDT 4 is possible because the strain in concrete until the emergence of the first cracks has a negligibly low value. With the increase of the load on the  
Figure 5: Load vs. strain curves for concrete and tensioned reinforcement for: (**a**) Section I; (**b**) Section II; (**c**) Section III.  
beam, new cracks are formed, while with the onset of yielding in the reinforcement, new cracks are formed, while the existing ones suddenly expand in the zones of reinforcement plasticization.  
In addition to the visual method, the crack width as a function of load was measured continuously (every second), by means of a displacement transducer (LVDT), with a measurement base of 100 mm, in Section I, in the middle of the first beam span (LVDT 3), and in Section III above the middle support (LVDT 4). Comparative load-crack width diagrams obtained using LVDT 3 and LVDT 4 are shown in Figure 7.
# A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x
## 3 Method
### Incorporating the Spatial Attention Mechanism CBAM  
CBAM (convolutional block attention module) is a more comprehensive approach to feature attention, as illustrated in Figure 8. Unlike SENet, which only considers channel attention, CBAM focuses on both channel and spatial attention. The channel attention mechanism assigns appropriate weights to different channels to help them focus more effectively on key information. The module consists of two parts: global maximum pooling and global average pooling. The input features are processed through a shared fully connected layer; the resulting output is fed into two separate branches for channel and spatial attention. The output of each branch is then combined through summation and passed through a sigmoid function to obtain a value between 0 and 1, which is then used to weight the original features and obtain the new feature F1.  
The spatial attention mechanism of the CBAM module focuses on the most salient regions in the feature map and assigns different weights to different regions to reduce  
Figure 8: CBAM module.  
Figure 7: Simple-BiFPN network structure.  
the proportion of irrelevant regions. When the feature map F1 passes through the spatial attention mechanism, it undergoes global maximum pooling and global average pooling; these two results are concatenated channel by channel. The resulting feature map is then processed by a 1 \(\times\) 1 convolution to adjust the number of channels. A sigmoid function is used to obtain weights between 0 and 1, which are then multiplied element-wise with the input features. The resulting weighted feature map is then used as the input for the next layer, F2.  
As illustrated in Figure 2, the CBAM module is inserted between the backbone network and the feature network, adding three attention mechanism modules in total. By incorporating the attention mechanism between the backbone network and the feature network, the network can leverage the rich feature representations generated by the backbone network while selectively focusing on the most relevant regions of the image. This includes key contextual information such as the outline and posture of pedestrians, as well as the rainy background and environmental factors. This allows the network to generate more accurate predictions by considering only the most pertinent information in the image.  
The integration of the CBAM module between the backbone and feature fusion networks proves highly effective. CBAM enhances feature quality, refines spatial context, improves information flow, and notably contributes to the network's improved performance, especially in challenging conditions such as rainy weather.
# A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data
## 1 Introduction
Elman neural network is used as a weak predictor to train Elman neural network to form a strong predictor, and an algorithm of tool wear prediction based on Elman_AdaBoost strong predictor is established to make the system have Elman's adaptability to time-varying characteristics. In order to verify the effectiveness of the algorithm, this paper uses the data provided by the PHM society in the data competition held in 2010 to test and verify the algorithm. In addition, the process of machine tool processing usually needs to use a variety of tools, so the process of tool replacement, the cutting force of different tools, vibration signals, acoustic emission signals, and other sensor signals are different. Therefore, before evaluating tool wear, it is necessary to identify the type of tool currently in use. Aiming at the problem of tool type identification, this paper presents a tool identification method based on spindle current. The rest of this paper is organized as follows. In Section 2, the framework and algorithm of tool wear prediction system are introduced. The tool recognition scheme based on spindle current proposed in this paper is described in Section 3. In Section 4, the tool wear prediction based on Elman_AdaBoost model is introduced and the results are analyzed. Finally, Section 5 is the summary of the paper.
## 5 Simulation and Results
### Performance Evaluation  
The performance is evaluated by comparing FMSTC scheduling time to scheduling time in a linear topology (scheduling time is optimal in a linear topology). Figure 8 shows the relationship between power, the number of nodes and optimal time slots in a linear topology.  
We notice that the number of time slots required increases as the number of nodes is increased and the allocated power is decreased, as shown in Figure 8. Notice that the number of time slots is increased monotonically with respect to the variations of power and node numbers. Figure 9 shows the relationship among power, the number of nodes and time slots in the general topology.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Power**} & \multicolumn{8}{c}{**Number of Nodes**} \\ \cline{2-11}  & 10 & 20 & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ \hline
3 & 55.00 & 115.23 & 165.23 & 199.33 & 285.53 & 335.40 & 422.50 & 493.43 & 530.63 & 588.93 \\
6 & 45.53 & 103.77 & 156.43 & 185.83 & 258.27 & 297.57 & 398.17 & 458.17 & 490.73 & 531.30 \\
9 & 30.83 & 96.80 & 143.23 & 180.50 & 230.77 & 286.93 & 387.57 & 448.10 & 480.57 & 514.37 \\
12 & 29.53 & 75.77 & 135.70 & 171.17 & 224.50 & 276.43 & 382.87 & 435.70 & 471.30 & 512.17 \\
15 & 23.20 & 56.13 & 120.53 & 153.67 & 194.17 & 267.97 & 355.97 & 416.23 & 459.00 & 503.13 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Scheduling time in the general topology.  
Figure 8: Power _vs._ the number of nodes _vs._ optimal time slots in a linear topology.  
Figure 9: Power _vs._ number of nodes _vs._ time slots in the general topology.  
We notice that the number of time slots required in the general topology is increased as the number of nodes increases and the allocated power decreases, as shown in Figure 9. Be aware that there exists a little bit of a steep increase between nodes Number 60 and 70, due to the additional sub-spanning trees made during the simulation. This steep increase, however, does not affect the overall performance of scheduling allocation with respect to the linear topology. We will show that in the following table.  
Table 10 represents the ratio with the result of the simulation and the optimal scheduling time in the linear topology.  
As shown in Table 10, when the power is three with 10 nodes, the ratio is 3.24, meaning that in the general topology, it needs 3.24 times more time slots than the linear topology to get all the messages from each node. The average of the ratio is 2.69 when the power is three, 2.80 when the power is six, 2.78 when the power is nine, 2.77 when the power is 12 and 2.57 when the power is 15. The average ratio for the scheduling times of the steep increase is 2.55 for node Number 60 and 2.87 for node Number 70, respectively.  
The ratio difference is negligible, as is shown in Table 10. The highest ratio is when the power is six with 10 nodes (3.25), and the lowest ratio is when the power is 15 with 50 nodes (2.28). The overall average ratio is 2.72.
## 3 Result and Discussion
### Improvement of Prediction Performance by Data Augmentation
#### 3.2.2 With Bone Density
This achievement is a dramatic improvement over the conventional analysis method, as shown in Figure 3. This analysis method, which can link artificial bone and cadaveric bone, will reduce the number of clinical trials in which data accumulation is difficult, and will contribute to the expansion of implant strength diagnosis through vibration analysis. Figure 7: Analysis results of a three-fold cross validation using a mixed dataset of artificial and cadaveric bones with the introduction of bone density: (**a**) the relationship between measured and predicted insertion torques; (**b**) Bland–Altman plot. The limitation of this study is the use of micro-CT, and it is necessary to verify whether equivalent results would be obtained if multislice CT or DEXA results were used. In addition, this study used monoaxial screw analysis, and it was necessary to verify the performance when a polyaxial screw was used. The introduction of artificial bone test data was verified against the background of difficulty in obtaining test data with cadaveric bones. Approximately 35% of the dataset was obtained from cadaveric bones, thereby demonstrating the validity of this method. The reduction in the amount of cadaveric bone data is a future challenge, and the amount of required data is the most essential limitation of the L-RFA for the diagnosis of implant stability. In addition, whether this dataset can be used in other facilities should be verified. If this trained model is available regardless of location, it could be a useful tool for hospitals without experimental infrastructure. If this analysis method is introduced into routine clinical practice, it will improve the accuracy of implant placement procedures and contribute to better patient outcomes. In the future, it is most important to validate the analysis method through clinical trials as it is introduced into clinical practice.
# Blockchain Architecture for Lightweight Storage
## 3 Reputation Model
### Types of Nodes  
For improved organization and management, LSB categorizes nodes into client nodes, consensus nodes, and candidate nodes, with consensus nodes further classified into agent nodes and verification nodes.  
* **client node:** The client node, as a specialized entity in the blockchain, does not store data or participate in information retrieval; rather, it primarily serves to transmit data to other nodes. In this paper, client nodes are characterized as honest nodes established by the system.
* **agent node:** Agent nodes are selected from light nodes with high reputations to gather transaction information and generate blocks according to a predetermined schedule but do not participate in block verification.
* **verification node:** In LSB, lightweight nodes store only part of the structure of the whole chain; therefore, a single lightweight node's ability to verify transactions is limited. A full node holds a complete copy of the ledger and can independently verify the legitimacy of a block; thus, a full node within the network is chosen as the verification node.
* **candidate node:** Nodes not selected to participate in the bookkeeping for the current consensus cycle become candidate nodes. These candidate nodes are ranked based on their reputation, and the top-ranked candidates have the potential to transform into agent nodes or verification nodes.
* **malicious nodes:** nodes that have engaged in malicious behavior repeatedly are flagged by the system and are excluded from consensus participation.