esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 5 Experiment and Discussion
### Measurement for Evaluation  
#### 5.2.1 Single-Document Summary Evaluation Metrics  
To assess the quality of single-document summaries, we employ two metrics: F\({}_{1}\)-\(N\) (\(N\) = 1, 2) based on n-gram matching and F\({}_{1}\)-L based on the longest common subsequence (LCS).  
The F\({}_{1}\)-\(N\) metric, derived from ROUGE-\(N\) framework, quantifies n-gram overlap between the generated summary and the reference summary through Equation (10). This metric comprises two components: PRECISION-\(N\) and RECALL-\(N\). PRECISION-\(N\), as defined in Equation (11), measures the accuracy of generated content by calculating the proportion of n-grams in the generated summary that match the reference summary. RECALL-\(N\), specified in Equation (12), evaluates the completeness of key information reproduction by quantifying the coverage of reference n-grams in the generated summary.  
\[F_{1}-N=\frac{2\times PRECISION-N\times RECALL-N}{PRECISION-N+ RECALL-N}\;N\;=\;1,\;2 \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Maximum** & **Minimum** & **Average** & **Standard Deviation** & **Median** \\ \hline Judgment documents & 50,104 & 252 & 2784 & 2782 & 2605 \\ Gold-standard summaries & 988 & 39 & 273 & \(111\) & 262 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Character count statistics in maritime judgment documents and gold-standard summaries in the experimental dataset.  
\[PRECISION-N=\frac{\sum\limits_{s\in\{GeneratedSummary\}}\sum\limits_{gram_{n} \in s}Count_{match}(gram_{n})}{s\in\{GeneratedSummary\}\sum\limits_{gram_{n} \in s}Count_{s}(gram_{n})}\ N=1,\ 2 \tag{11}\]  
\[RECALL-N=\frac{\sum\limits_{s^{\prime}\in\{\Re\text{F}/Summary\}}\sum\limits_{gram_{n} \in s^{\prime}}Count_{match}(gram_{n})}{s^{\prime}\sum\limits_{gram_{n}\in s ^{\prime}}Count_{s^{\prime}}(gram_{n})}\ N=1,\ 2 \tag{12}\]  
The F\({}_{1}\)-L metric, based on ROUGE-L, assesses semantic coherence using LCS through Equation (13). It integrates two sub-metrics: PRECISION-L (Equation (14)) and RECALL-L (Equation (15)). PRECISION-L calculates the merged LCS length relative to the generated summary's word count (\(n\)), while RECALL-L computes this ratio against the reference summary's word count (\(m\)). Here, \(LCS\cup(s_{i}\), _GeneratedSummary_) represents the merged LCS length between reference sentence \(s_{i}\) and all generated sentences, \(u\) denotes the total number of reference sentences, and \(m\) and \(n\) indicate the word counts of the reference and generated summaries, respectively.  
\[F_{1}-L=\frac{2\times PRECISION-L\times RECALL-L}{PRECISION-L+RECALL-L} \tag{13}\]  
\[PRECISION-L=\frac{\sum_{i=1}^{u}LCS(s_{i},GeneratedSummary)}{n} \tag{14}\]  
\[RECALL-L=\frac{\sum_{i=1}^{u}LCS(s_{i},GeneratedSummary)}{m} \tag{15}\]  
Both F\({}_{1}\)-\(N\) and F\({}_{1}\)-L values positively correlate with summary quality, with higher values indicating superior performance.
# Underground Disposal of Coal Gauge Backfill in China
## 5 Problems and Proposed Solutions
### Prospects
#### 5.2.1 Innovative Gangue Disposal  
1. [label=(0)]
2. Slurry backfilling in the goat  
The materials of slurry backfilling in the goat consist of coal gangue from the coal preparation chamber and the water, which is prepared into a slurry with a certain concentration in the backfilling material preparation chamber. The rheological properties of slurry are excellent, stable, and convenient for pipeline transportation. The backfilling pump provides the power and transports the slurry to the roadway via the pipeline. Following this, the backfill slurry is placed into the adjacent or current goals [99; 100]. Its technical principles are illustrated in Figure 5. For adjacent goals, slurry backfilling is undertaken at a certain distance ahead of the current working face. Through the section's coal pillars between the two working faces, the slurry is injected from the roadway on the working face into the adjacent goat. For the current goals, pipeline laying and grouting are arranged at a triangular zone formed in the roadway after roof caving. As soon as the single-hole grouting is finished, the drill hole is sealed. The drill hole grouting is repeated at certain distances until slurry backfilling is completed for the entire goat.  
Slurry backfilling in the goat has the benefits of being a simple process, having a high mechanization degree, large spaces of coal gangue backfilling, and a low investment cost. This technology is suitable for coal mining without ground protection, and due to having no ground structures, slurry materials don't need great strength.
2. High-efficiency gangue backfilling in a super-long mining workface  
High-efficiency gangue backfilling in a super-long mining workface is developed based on fully mechanized mining with solid backfilling technology for the purpose of the high-efficiency backfilling of a large amount of coal gangue. This technology mainly uses the coal gangue as the backfilling materials, which enters the goat via the backfilling material vertical feeding hole system and the underground solid backfilling material transportation system. Its technical principle is shown in Figure 6.  
The length of the working face in this backfilling technology usually exceeds 275 m. The maximum disposal capacity of coal gangue is more than five million tons per year, outperforming the previous gangue backfilling technologies by more than three times. This backfilling technology achieves high-efficiency disposal of a large-scale coal gangue without affecting the mine production capacity. Besides, it can reduce strata movement and protect the environment of the mining area. Because of these benefits, high-efficiency gangue backfilling in a super-long mining workface is particularly suitable for modern large coal mines in ecologically vulnerable regions of China, such as Shaanxi and Inner Mongolia.  
Figure 5: Schematic diagram of slurry backfilling in the goat.
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 3 Methodology
### Fine-Grained Pavement Distress Detection Based on Deep-Object Detection Networks
#### 3.2.2 Deep Object Detection Network Construction  
The objective of this study is to automatically classify and localize pavement distress images based on object detection models. The object detection network is composed of three components: backbone, neck and head. The backbone acts as the main feature extractor, which takes images as input and outputs the corresponding feature maps. Accuracy, speed, and efficiency are the critical metrics in selecting the backbone part. The neck is for collecting different feature maps, such as the representatives: Spatial Pyramid Pooling (SPP), Path Aggregation Network (PAN) and Feature Pyramid Network (FPN). The head is for prediction. There are mainly two typical detectors in head-part naming: the one-stage detector and the two-stage detector, respectively. The two-stage detector goes through a regional proposal network (RPN) during the first stage and experiences a region-of-interest (RoI) pooling network at second stage. The RPN layer feeds region proposals into classifier and regressor for classification and bounding box regression. While the one-stage detector predicts bounding boxes from input images directly without a region-proposal step, the latter combines location regression and classification into a single stage.
## 1 Introduction
In this paper, the localization-free interference and energy holes minimization (LF-IEHM) protocol is proposed for UWSNs. The protocol selects forwarder nodes on the basis of the level of the water pressure. Nodes close to the water surface have low water pressure and are preferred for selection as forwarders. If two or more more expected forwarder nodes have the same pressure levels, the response time is taken into account to choose the best forwarder. The response time is a measure of the distance of a forwarder node from the source node. This strategy reduces the end-to-end delay and ensures that packets follow the shortest routes from a source to a destination. The proposed protocol uses a variable transmission range of sensor nodes. A node can increase its transmission range to include one or more live forwarder nodes for a situation in which it does not find any node within its transmission range. This controls the energy hole problem and reduces packet loss. This strategy is particularly effective under sparse conditions. Additionally, the proposed protocol does not require the position information of energy holes to be known, and it adjusts the holding time in a manner that minimizes simultaneous packets' transmission by more than one node. This reduces interference, which, in turn, further reduces packet loss. The proposed protocol contributes in two ways. Firstly, it minimizes the existence of energy holes (conditions in which sender nodes do not find any neighbor node for data forwarding or when all the neighbors are dead). A sender node with no neighbor increases its transmission range to include one or more neighbors for data forwarding. This strategy establishes a path from the bottom to the surface of water during data forwarding that, in turn, avoids packet loss. At the same time, it does not require the full-dimensional position information of the sensor nodes that the conventional routing protocols take into account. Secondly, to avoid interference during packet forwarding, a unique packet holding time is defined for every node to reduce the probability of two or more nodes forwarding packets at the same time. This ensures reduction in the loss of packets due to interference during data forwarding.
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 4 Simulations and Results
### Performance Evaluation of the Proposed Model Found on PSNR Comparison
This table indicates that JOPSS is capable to achieve well outcomes under different performance metrics. SSIM gives IQA from pixel based stage to a structure based stage while FSIM is used to understand images mainly according to low level features. It gives good prediction capability, higher accuracy and robust so in Table 6, we compare our algorithm with [52; 72; 96] based on FSIM. The obtained results show that JOPSS outperforms other existing approaches not only in PSNR but also in FSIM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**D** & **[**72**]** & **[**52**]** & **[**96**]** & **JOPSS** & **JOPSS-SAFE** \\ \hline
0.35 S & 16.80 & 16.76 & 7.39 & 18.81 & 24.93 \\ \hline
0.40 S & 18.99 & 17.84 & 7.39 & 22.5 & 27.3 \\ \hline
0.45 S & 19.01 & 18.02 & 7.39 & 23.12 & 29.51 \\ \hline
1.00 S & 25.49 & 24.72 & 7.41 & 29.67 & 31.79 \\ \hline
195 S & INF & INF & 17.1 & INF & 32.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison among our proposed JOPSS and JOPSS-SAFE with others found on PSNR. Figure 16: Color image with Error Energy (EE) image based on the proposed JOPSS & JOPSS-SAFE models; (**a,b**) JOPSS; (**c,d**) JOPSS-SAFE. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**D** & **SSIM** & **MSSIM** & **FSIM** & **PSNR** \\ \hline
0.35 S & 0.78 & 0.92 & 0.91 & 18.81 \\ \hline
0.40 S & 0.85 & 0.94 & 0.94 & 22.50 \\ \hline
0.45 S & 0.86 & 0.95 & 0.94 & 23.12 \\ \hline
1.00 S & 0.92 & 0.97 & 0.96 & 29.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparing SSIM, MSSIM, FSIM, and PSNR using JOPSS method.
# Improving IoT Data Security and Integrity Using Lightweight Blockchain Dynamic Table
## 7 Evaluation and Analysis
### Security Analysis  
Table 1 summarizes how the proposed framework met the above security requirements, as the proposed work was tested on two main types of attacks and threats to IoT. The first is a Distributed Denial of Service (DDOS) attack, where, to defeat a particular target node, the attacker uses several infected IoT devices. The second is a link attack, which puts the privacy of users at risk, as a link between multiple transactions or data books with the same PK is created by the attacker to find the real ID of an unknown user.  
Compared with using standard blockchain technology over an encrypted channel (e.g., HTTPS), the proposed solution has much better security benefits. The following points are summarizing the main key differences:  
* Using standard blockchain technology is much more costly than the proposed solution due to the hashing mechanism that is used with a standard blockchain, which is (SHA-2).
* Encrypting the transfer channel only with the HTTPS or SSL technique is not enough to secure the IoT data, because it requires encrypting data with a 256-bit AES key, which is much more costly than a 64-bit AES key. Moreover, it does not protect the network from message forwarding. The attacker can simply store and re-send IoT packets many times over the network to the server. On the other hand, the dynamic table mechanism provides a unique identity for each sent message.
* Using multi-part AES keys and a dynamic blockchain table, giving a different message each time even if the data are the same, which maximizes the encrypted data security against a brute-force attack.
* Last but not least, using the proposed solution provides a set of blocks that can be used to verify IoT nodes' data at any time, as each block is connected with a previous one.
# An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks
## 4 Experiments
### Result Analysis  
#### 4.5.1 Comparison with Baselines  
In this section, we first compare our algorithm with three classical machine learning algorithms. In order to ensure the objectivity of the results, all the conventional algorithms use their default parameters. The classification results are given in Table 2 in terms of recall, precision, and F1-score on the test data.  
Our method considers both feature set and graph structure. We can see that our method is superior to other algorithms by comparing with classical machine learning algorithms such as RF, MLP and BP, which only consider the feature set or graph structure. Especially for recall metric, our method gets a high score of 0.88, which is 15% higher than that of the MLP algorithm. The comprehensive evaluation metric of the F1-score represents the comprehensive performance of the classifier. The F1 score of our method is 15% higher than the second-ranked RF algorithm. Although the RF algorithm's precision reaches 0.88, which is close to our method, its recall value is only 0.66. This result reflects that the RF algorithm is affected by imbalanced data, and the classifier makes a judgment result that tends towards the majority class. Therefore, although the precision is high, about 34% of spam bots are classified as legitimate accounts, which will still cause unpredictable security implications for the social network, and cannot be applied to real spam bot detection. The BP algorithm classifies users from the graph structure. Due to an imbalanced dataset's influence, most users tend to be recognized as legitimate users, so the classification effect is poor.  
Compared with conventional machine learning algorithms on the same dataset, the recall and F1 scores of our method are much higher than those of other methods. It can be seen that the classification effect of the classifier based on multi-feature representation is much better than that of the classifier based only on features in both single and comprehensive evaluation.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 2 Internet of Vessels Architecture
### The Technical Architecture of the Networks Layer
#### 2.3.4 Opportunistic Networks  
The opportunistic network is a kind of network which takes advantage of the encounter brought by the movement of nodes to realize the network communication [32]. It is not necessary to establish the full path between the source node and destination node [33]. The ad hoc networks and wireless networks always lack treatment plans to deal with network interruption in difficult environments [34]. As a result, when the network is interrupted, the network performance may decrease dramatically. Furthermore, in practice, the topological structure of the adaptive wireless network may change at any time, which cannot guarantee the connection between the source node and destination node. Against this background, the opportunistic network technology has been developed.  
Now various sensors are arranged in the ocean to collect the ocean data. It can use the storing-carrying-transferring mechanism of opportunistic networks to store information in the airplanes and ships passing through these areas and transfer the information data to other places when the airplanes and ships arrive to the land [35].
## 3 Geological Characteristics of Shales in the Yanchang Formation
### Organic Geochemical Characteristics of Shales
#### 3.3.3 Maturity of Organic Matter  
Vitrinite reflectance (Ro) is the main indicator to characterize the maturity of organic matter in source rocks. The vitrinite reflectance of shales in the Yanchang Formation ranges from 0.7% to 1.3% and generally increases slowly with depth. Peak pyrolysis temperature (Tmax) refers to the maximum pyrolysis temperature of kerogen. The peak pyrolysis temperature of shales in the Yanchang Formation ranges from 445 \({}^{\circ}\)C to 460 \({}^{\circ}\)C. Shales in the Yanchang Formation predominantly stay in the late stage of the oil generation window and the peak period of the wet gas generation window, and they have not yet reached the over-mature dry gas generation stage.
# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 2 EMG Signals
### Feature extraction from an EMG signal
#### 2.2.2 Frequency Domain (FD)  
Spectral analysis, also known as representation in the frequency domain, is instrumental in studying muscle fatigue and it is influenced by the firing rate of the motor unit in frequencies lower than 40 Hz and for the morphology of the action potential in muscle fiber in frequencies higher than it [56].
# Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network
## 3 Modeling Implementation of a RBF Neural Network
### Preparation of Viscosity Data  
As previously mentioned, many experimental investigations have been published to discuss the effects of different factors including temperature and nanoparticle properties (such as type, size, concentration, and shape) on the viscosity of ethylene glycol/water based nanofluids. Considering the integrity of the measuring information, a total of 216 viscosity data involving TiO\({}_{2}\), CuO, SiO\({}_{2}\), and SiC are obtained to establish the sample sets. The detailed information of the nanofluids regarding nanoparticle diameter (\(d_{p}\)), nanoparticle volume fraction (\(\phi_{p}\)), nanoparticle density (\(\rho_{p}\)), temperature (\(T\)), and viscosity of the base fluid (\(\mu_{f}\)) and nanofluids (\(\mu_{nf}\)) are listed in Table 1. According to the modeling principle, 198 data (about 90%) are selected to train the RBF neural network, and the remaining 18 data (about 10%) are used to test the performance of the trained RBF neural network.  
To improve the learning and training performances of the RBF neural network, the following equation is used to normalize the input and output variables.  
\[x^{\prime}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{4}\]  
where \(x\) is the original value, \(x^{\prime}\) is the normalized value, and \(x_{\text{max}}\) and \(x_{\text{min}}\) are the corresponding maximum and minimum of \(x\).
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 2 Related Work
### Energy Management for EH-WSNs
Moreover, AutoSP-WSN features a prediction-based EM, while a model-free EM is proposed in this work, which suits the stringent hardware constraints of WSN devices. Most of the previously-proposed EM have been evaluated only through simulations, without any implementations on real sensor node hardware. Therefore, many practical problems are considered out of the scope of these studies. For example, accurate energy spending mechanisms and detailed harvested energy tracking are difficult to implement, and their implementation incurs significant overhead [34]; however, many theoretical energy-harvesting adaptive algorithms assume the availability of these values. In this study, the proposed EM has been experimentally evaluated using real hardware platforms. To this aim, the proposed EM only needs the current residual energy, and it can be used with various MAC protocols.
## 2 Materials and Methods  
### Matrix and Reinforcement Material  
The AZ31 alloy was selected as a matrix material that acts as a bonding element in the composite for the distribution and transfer of load from the matrix to reinforcement [21]. The composition of the AZ31 alloy is illustrated in Table 1. Table 2 indicates the properties of the AZ31 alloy. The average particle size of the AZ31 alloy is 150 \(\upmu\)m. AZ31 alloy powder is manufactured by Parasmani Metals, Mumbai, India. Graphene nanoparticles (GNP) were used as a reinforcing agent in the AZ31 matrix to improve the mechanical properties of the composite. The properties of graphene nanoparticles are listed in Table 3. The average particle size of graphene nanoparticles is 5-10 nm. The material powder is manufactured by Platonic Nanotech Pvt. Ltd., Jharkhand, India. Carbon-based nanoparticles provide a strong bonding strength in the composite subject to improvement in wear resistance and compressive strength of the composite [24].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Elements** & **Mg** & **Al** & **Zn** & **Mn** & **Si** & **Cu** & **Fe** & **Ni** \\ \hline Distribution (\%) & 95.09 & 2.80 & 1.75 & 0.20 & 0.10 & 0.05 & 0.005 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of AZ31 alloy provided by the manufacturer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No** & **Properties** & **AZ31 Alloy** \\ \hline
1. & Tensile strength & 260 MPa \\ \hline
2. & Yield strength & 200 MPa \\ \hline
3. & Purity & 99\% \\ \hline
4. & Density & 1.77 g/cc \\ \hline
5. & Elongation & 15\% \\ \hline
6. & Brinell hardness & 49 BHN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of AZ31 alloy provided by the manufacturer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**S. No** & **Properties** & **Graphene Nanoparticles** \\ \hline
1. & Purity & \textgreater{}99\% \\
2. & Tensile modulus & \textgreater{}1000 GPa \\
3. & Length & 5–10 microns \\
4. & Thickness & 5–10 nm \\
5. & Density & 2.5 g/cc \\
6. & Thermal conductivity & 2000 watt/m-k \\
7. & Surface area & 200–240 m\({}^{2}\)/g \\
8. & Number of layers & Layers \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of graphene nanoparticles provided by the manufacturer.