esa-sceva/satcom-chunk-collection huggingface dataset:
# A Local Information Perception Enhancement-Based Method for Chinese NER
## 2 Related Work
### Graph Attention Network  
Recently, the successful application of lexical information and graph neural networks has demonstrated the effectiveness of graph neural network models in enhancing the performance and sophistication of NER. [PERSON] developed a graph network that utilizes lexical information and constructed the Chinese NER project to classify nodes in a graph problem [25]. The advantage of graph attention networks is the application of a multihead attention mechanism. Hence, they can summarize the features of the graph by allocating respective weights to adjacent nodes or correspondence edges [26]. [PERSON] found that the majority existing methodologies relied on static weighting methods in calculating character  
Figure 4: Overall architecture of the SoftLexicon model. By constructing the SoftLexicon features, the model adds the lexicon matches to the begin, middle, and end of each character, as well as single positions. Then it inputs these enhanced character embeddings into the sequence encoding layer and the conditional random field (CRF) layer to obtain the final prediction results.  
Figure 5: SoftLexicon method.  
word set embeddings. This leads to an inaccurate utilization of lexical information, which seriously affects the performance of NER. For this reason, [PERSON] proposes a polymorphic graph attention network (PGAT) [27]. It constructs a graph for each of the four lexical sets of \"BMEs\" [23]. It dynamically captures the relationship between characters and matching words from multiple dimensions, thus more fully utilizing lexical information. Furthermore, by utilizing large-scale grammatical information, neural network models can achieve improved performance [28; 29]. However, these methods typically rely on external NLP tools to build dependency parsing trees, which can lead to error propagation issues [7; 29; 30], since word embeddings already available in lexicon can provide lexical semantic information. In addition, the graph attention mechanism can dynamically adjust the importance of word meanings based on contextual information, thereby achieving word sense disambiguation [26]. On the other hand, GAT establishing a joint representation of the character-word method helps us incorporate the semantic relationship between characters and words in their neighborhood into the model. For this reason, we constructed a graph attention network for Chinese NER to capture local feature information and improve the recognition rate of named entities.
## 4 Traffic Filtering-Based Techniques
### Port Locking (PL)
#### 4.3.3 Advantages and Disadvantages  
The PL approach reduces redundant unicast traffic in HSR networks by filtering unicast traffic for unused DANH rings. Since the PL approach does not use control messages for filtering traffic, it avoids adding more control overhead in the HSR networks.  
However, the PL approach has some disadvantages. The PL approach does not prune unicast traffic for unused QuadBox rings. Additionally, unicast frames are still duplicated and circulated in all QuadBox rings.
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 2 Background and Related Work
### Directed Grey-Box Fuzzing  
Directed grey-box fuzzing differentiates itself from general coverage-guided grey-box fuzzing by specializing in testing specific code locations. DGF is driven by the code coverage and distance feedback to reach the target code locations. Traditional practices of DGF mainly employ distance metrics for providing runtime feedback to guide the testing direction through seed scheduling. For instance, AFLGo, the first directed grey-box fuzzer, defines the distance of a basic block as the harmonic mean of its shortest path lengths to all target sites. In the shortest path, the path length is the sum of the weighted inter-procedural CG distance from the caller function to the target function and the intra-procedural CFG distance from the basic block to the call site's basic block. During a fuzzing trial, a directed fuzzer obtains the execution trace of a test case and computes the average distance of the executed basic blocks as the seed distance. It then uses the distance for the seed scheduler to choose the best (seed with the shortest path) to fuzz in the next fuzzing iteration. Nevertheless, the shortest-path seed selection strategy exhibits inherent limitations, as it fails to consider the node complexity in real execution environments.
# A Link Prediction Method Based on Neural Networks
## 3 Problem Description and Formula  
Link prediction refers to predicting the possibility of a link between two nodes that have not yet been connected in the network through known nodes and structural information [37]. The undirected scale-free network \(G\) (\(V\), \(L\)) is studied in this paper, where \(V\) and \(L\) denote sets of nodes and links, respectively. In this section, we introduce link prediction models and evaluation indexes for the undirected scale-free networks. Link prediction methods need to provide a score (\(S\)) to predict the probability of a link existing between a pair of unconnected nodes. For example, \(S_{xy}^{CN}\) represents the score of possible links between two nodes \(x\) and \(y\) in a network calculated by the Common Neighbors (CN) model. Similarly, \(S_{xy}^{RA}\) is the score of the Resource Allocation (RA) model, and \(S^{MF}\) is a matrix of the scores of all links obtained using the Matrix Forest (MF) model. All links not generated in the original network are sorted in decreasing order according to their scores, and the links at the top are most likely to exist.
## 3 Methodology
### Evaluation Metrics
#### 3.9.3 WER (Word Error Rate)  
The study uses WER to assess the alignment of predicted and reference translations, contrasting SpBLEU and ROUGE, which rely on n-gram matching and recall, with lower WER indicating better alignment. WER is computed by counting the substitutions, deletions, and insertions needed to match the sequences with the following formula:  
\[\text{WER}=\frac{S+D+I}{N} \tag{15}\]  
where \(S\) is the number of substitutions (incorrect words in the predicted output). \(D\) is the number of deletions (missing words in the predicted output). \(I\) is the number of insertions (extra words in the predicted output). \(N\) is the total number of words in the reference translation [37,38].  
These metrics offer a comprehensive evaluation of the model's ability to generate translations that are lexically accurate (via SpBLEU), contextually fluent (via ROUGE), and edit-wise correct (via WER). By employing these three metrics, we ensure a robust and multi-dimensional assessment of the translation quality, which allows for a thorough comparison of different model configurations and techniques.
## 2 Problem Setting
### Contact Mechanics
#### 2.2.1 Signorini Conditions  
The non-penetration condition in the contact surface is mathematically formulated using the normal gap function \(g_{n}\) defined as:  
\[g_{n}=-\mathbf{x}\boldsymbol{\cdot}\mathbf{n}\geq 0 \tag{6}\]  
Figure 1: The reference and the deformed configuration of a deformable body in contact with a rigid plane.  
The gap function could be written using the displacement \(\mathbf{u}\)  
\[\begin{split} g_{n}(\mathbf{u})&=-(\mathbf{X}+\mathbf{u })\cdot\mathbf{n}\\ &=g_{0}-u_{n}\end{split} \tag{7}\]  
where \(g_{0}>0\) is the initial gap. To prevent the body from penetrating the plane, a non-positive contact pressure arises in the contact zone.  
\[\Pi_{n}\leq 0 \tag{8}\]  
Finally, if a point is not in contact, then \(\Pi_{n}=0\), meanwhile \(\Pi_{n}<0\) if not. This leads to the non-penetration non-adhesion condition:  
\[\Pi_{n}g_{n}=0 \tag{9}\]  
The force acting on an element of area in the deformed configuration is defined via  
\[d\mathbf{f}=\mathbf{\sigma}\mathbf{nd}\Gamma^{t}=\mathbf{PN}\mathrm{d}\Gamma \tag{10}\]  
The set of the presented conditions are called Hertz-Signorini-Morea or Karush-Kuhn-Tucker conditions [17]:  
\[g_{n}\geq 0,\qquad\quad\Pi_{n}\leq 0,\qquad\quad g_{n}\Pi_{n}=0\quad\text{ on } \ \Gamma_{c} \tag{11}\]  
These conditions provide the basis to treat frictionless contact problems in the context of constraint optimization.
## 3 Model Verification on a Bearing Test Rig  
### Bearing Test Rig Setup  
To validate the accuracy of the dynamic model, a vibration test was carried out on a bearing test rig, which had two deep groove ball bearings to support the shaft. The type of tested bearing was 6206 ZZ, which was placed at the drive end of the shaft, as shown in Figure 5. The measurement system, such as sensors, data acquisition device, load device, motor and control unit can be found in Figure 5a. In addition, the sampling frequency was set at 96 kHz and each frame of signal was recorded for 20 s, which allowed sufficient precision to be achieved in waveform and in the spectrum analysis.  
To study the influence of clearance on vibration characteristics, two groups of bearings with different clearances, named CN and C4, were tested, representing \"normal\" clearance and \"large\" clearance, respectively. The clearances for these two types of bearings are listed in Table 3. During the experiment, the rig ran at a shaft speed of 1500 rpm and different radial loads, namely, 0, 10, 20 and 30 bar, were applied with hydraulic equipment. With the diameter of the piston as 45 mm, the above loads, i.e., 10, 20 and 30 bar, applied on the testing bearing could be calculated as 800, 1600 and 2400 N, respectively.  
Figure 5: Experimental setup: (**a**) illustrative diagram and (**b**) schematic drawing.
## 4 Simulations Results Using Actual Industrial Data
### Parameters Training  
The 50 sets of the entire 164 sets of original data after data screening are used as the testing data, and the remaining 114 sets and 886 sets of generated data are used as  
Figure 4: Strategy diagram of the proposed optimization combining CBR with NSGA-II.  
the training data. Then, by performing multiple simulations in the laboratory, the initial population number is chosen as 200, and evolutionary algebra is set to 80. Figures 5 and 6 show the Pareto optimal fronts for ore and water feeding errors of the first generation and the 80 th generation. Inspecting the figures, we can find that training is stabilized in the 80 th generation and NSGA-II shows excellent convergence in two optimal objects. The solutions along the Pareto front are equally optimal and the TOPSIS method is applied to select the most appropriate solution.  
The optimization results of different case features are shown in Table 3. \(\omega_{1}\) to \(\omega_{7}\) describe the importance of each case feature in case retrieval, respectively, and \(\omega_{8}\) represents the importance of the generated cases.  
Figure 5: Simulation diagram of the first generation.  
Figure 6: Simulation diagram of the 80 th generation.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \(\mathbf{\omega_{1}}\) & \(\mathbf{\omega_{2}}\) & \(\mathbf{\omega_{3}}\) & \(\mathbf{\omega_{4}}\) & \(\mathbf{\omega_{5}}\) & \(\mathbf{\omega_{6}}\) & \(\mathbf{\omega_{7}}\) & \(\mathbf{\omega_{8}}\) \\ \hline
0.067 & 0.358 & 0.179 & 0.126 & 0.047 & 0.043 & 0.18 & 0.83 \\ \hline \end{tabular}
\end{table}
Table 3: Weights distribution.  
As can be seen from the figures and table, the results obtained by CBR accurately vary with the given values. Although there are some deviations, the deviation values are much smaller than other algorithms. At the same time, compared with other algorithms, the CBR have almost no abnormal points. The range of change is smaller and the MAPE is much smaller for both ore and water feeding. The reason is that, compared with other machine learning algorithms, CBR assigns human experience to different case descriptions and places more emphasis on important variables. What is more, the case solutions are jointly derived from similar cases and less affected by outliers. The training process is also simpler than other machine learning algorithms and does not suffer from overfitting or underfitting. Therefore, this method is very suitable for application in practical engineering.
## 5 Discussion
### On Controlling VR Experience with Gestures  
The game industry has adopted VR technology extensively and rapidly because players are familiar with interaction controls in the virtual world. Console games with controllers simulate gestures in the real world, and they are similar to hand controllers in VR. However, market analysis has identified device compatibility limitations as a challenge that poses a barrier to the industry's growth [48]: different VR hardware developers have designed their controllers with a varying number of buttons and other control methods. Although some of these methods are similar, these differences necessitate onboarding when the user begins using a new controller device. If the user has never used a VR controller, the onboarding time will increase. In contrast, basic hand gestures, as our results show, promote feelings of freedom and immersion while also making the interaction convenient for the user. This is possible when the gesture-tracking technology works well and the user interface is well designed. Some participants in our evaluation noted issues with gesture tracking (gesture synchronization). Several tracking issues of Leap Motion have been identified in other studies, including losing hand tracking [49], confusing hands/fingers [49; 50], and synchronization errors such as latency and misplaced hands [49; 50]. To mitigate these issues in gesture-based VR contents that require robust tracking, we recommend using HMDs with accurate built-in tracking or VR gloves, some of which can also provide haptic feedback for increasing immersion [37]. The lack of sensory feedback (statement 9 in Figure 5) is a possible reason for the participants of our study finding the gesture-based interface to not resemble real-world interaction (statement 1 in Figure 5).  
Gesture-based VR experiences can be broadly divided into room-scale, seated, and standing VR experiences. When the prerequisites for these three VR setups are compared in terms of using hand gestures, room-scale and standing VR allow more freedom in movement and viewing angles, so the user's hands must be systematically restricted from moving out of sight. Although seated VR has more disadvantages than room-scale and standing VR [51], it has an advantage for gesture-based interaction in that the user's posture is mostly predictable, and the positions of the hands are expected to remain in front of the users, thus making it the safest way to experience a hand-gesture interface. However, the physical limitations of seated VR experiences can limit the freedom of movement in a gesture-based UI.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data
### Design Management
#### 3.1.5 Cloud Storage Structures  
The structure of cloud storage plays a significant role in determining the level of data privacy and security within the cloud environment. Different organizations and individuals have diverse requirements when it comes to storing and managing their data, which has led to the emergence of various cloud storage structures, including public cloud, private cloud, and hybrid cloud. Among these structures, the cloud storage structure specifically designated for implementing privacy-preserving measures becomes crucial. This structure defines how data is stored, accessed, and protected within the cloud. It serves as the foundation for deploying privacy-enhancing techniques and safeguards. Two primary storage structures commonly used in cloud storage are the single-cloud structure and the multiple-cloud structure, and each one has its own merits and demerits. Initially, organizations and individuals often relied on a single-cloud infrastructure to meet their data storage and service requirements. This approach simplified data management and offered convenience, but it also had limitations in terms of scalability, redundancy, and potential vendor lock-in [51].  
On the other hand, the multi-cloud structure has gained popularity due to its ability to distribute data across multiple cloud providers. This approach provides increased resilience, fault tolerance, and flexibility by leveraging different cloud platforms and service offerings. It allows organizations to choose the most suitable cloud providers for their specific needs, avoiding dependence on a single vendor and mitigating the risks associated with potential service disruptions or data breaches. While the multi-cloud structure offers enhanced reliability and diversity, it also introduces complexities in terms of data synchronization, interoperability, and security coordination across multiple cloud environments. Organizations must carefully evaluate their requirements, budget constraints, and risk tolerance to determine the most suitable cloud storage structure for their data privacy needs. Hence, selecting the appropriate cloud storage structure is a critical decision for organizations and individuals seeking to protect their data privacy [52]. Whether opting for a single-cloud or adopting a multi-cloud approach, it is essential to consider factorssuch as scalability, security, vendor lock-in, and the specific privacy-preserving measures that can be implemented within the chosen structure. By making informed choices, stakeholders can ensure their data remains confidential, secure, and accessible while leveraging the benefits of cloud storage services. However, as the CC industry advanced and cyber world applications continued to proliferate [53], the adoption of multi-cloud infrastructure became inevitable. Multi-cloud technology refers to the utilization of multiple CC and storage services within a single heterogeneous design [36].
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 2 HVDC Transmission System Model
### Voltage Stability Constraints  
Constraints (15) and (16) are added in the proposed model to ensure the static voltage stability margin of load center is greater than \(\eta_{\min}\). The reason is that the load center of the power grid always has weaker voltage supporting ability. Besides that, HVDC also needs enough voltage support capacity to operate steadily. Therefore, enough generators close to inverter substation could start up by the voltage stability constraints and provide necessary reactive power by following constraints.  
\[\eta_{VSM}>\eta_{\min} \tag{15}\]  
\[\eta_{VSM}=(P_{ls}-P_{l0})/P_{l0} \tag{16}\]
## 2 Framework and Methodologies
### Noise Reduction  
As a popular CSI noise reduction algorithm, DBSCAN clusters objects based on their density in the dataset, identifying anomalies. In the DBSCAN algorithm, we need to preset two parameters: the neighborhood radius \(\varepsilon\) and the density threshold minPts. These parameters generally rely on empirical values [30; 40]. However, inappropriate selection of these parameters can impact the noise reduction effect. By analyzing data samples' structural properties, we introduce A-DBSCAN, a method that adaptively determines parameters for noise reduction. The pseudo-code for Algorithm 1 is as follows:  
Figure 1: Positioning systems framework.  
```
Input: Amplitude information \(X\) of a single R-T link, iteration count of \(K\) Output: Denoised amplitude \(X^{*}\)
1. for\(i\gets 1\) to \(K\) do
2.\(D\leftarrow\) Compute Euclidean Distance Matrix(\(X\));
3.\(\varepsilon\leftarrow\) Compute Average Distance For INearestNeighbors(\(D\), \(i\));
4. MinPts \(\leftarrow\) round(Average Number of Points Within Epsilon(\(D\), \(\varepsilon\)));
5. clusters \(\leftarrow\) DBSCAN(\(X\), \(\varepsilon\), MinPts);
6. end for
7. count \(\gets\) 1; sequence Length \(\leftarrow\) 10; current Number \(\leftarrow\) results [1]; best \(K\leftarrow\) NULL;
8. for\(i\gets 2\) to length (results) do;
9. if results[\(i\)] = current Number then
10. count \(\leftarrow\) count + 1;
11. else
12. count \(\leftarrow\) 1; current Number \(\leftarrow\) results[\(i\)];
13. end if
14. if count = sequence Length then
15. best \(K\gets i\)-sequence Length + 1;
16. end if
17. end for
18. clusters \(\leftarrow\) DBSCAN (\(X\), \(\varepsilon\) derived from best \(K\), MinPts derived from best \(K\));
19. Replace noise data in \(X\) with average of the remaining data to get \(X^{*}\);
20. return \(X^{*}\);
```  
**Algorithm 1**2-3 uses A-DBSCAN to remove CSI noise
# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 4 Results and Discussion  
### Evaluation of PV Generation Forecasting Model Performance with Missing Data Imputation  
The purpose of this study is to determine the degree to which the missing data imputation affects the PV forecasting. In practice, both the training data and test data can be applied to the missing data imputation method at the same time. However, if both datasets are used for missing data imputation simultaneously, it is difficult to understand which missing data imputation most affects the solar prediction. Therefore, we categorized the simulation into two cases: one is the case wherein the missing data imputation is applied only to the training dataset (Section 4.1.1), while the other is case wherein the missing data imputation is applied only to the test data (Section 4.1.2).
## 5 Case Study: Problem Formulation  
The proposed design methodology is implemented and validated on the case study of a healthcare system for multi-environmental monitoring of elderly people living alone in the Silesia region in Poland. The designed system can be used not only to support and localize the elderly people in their multi-room apartments located in multi-story buildings, but even outdoors in the buildings' neighborhood.