esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Accuracy Measurement Metric  
Information accuracy is a fundamental requirement in any information fusion approach. Square of the difference between the estimated value at the node \(k\) and the true value is averaged for the total number of fusion estimates produced by the node \(k\)[44]. That is, for the estimated value \(\hat{\theta}_{K}\), true value \(\theta\) and number of fusion estimates \(n_{k}\), the Mean Square Error (MSE) of node k is defined using Equation (4) as  
\[MSE_{k}=(|\hat{\theta}_{k}-\theta|^{2})/n_{k} \tag{4}\]  
Network MSE (\(MSE_{network}\)) is obtained by averaging \(MSE_{k}\) for all nodes in the network. \(MSE_{network}\) is a numerical level performance metric that gives an overall insight of the information accuracy. The error sources have different levels of impacts on different bits in an information frame/packet.  
MSE is a generic accuracy measurement metric that does not depend on the actual value of the measurement parameter. However, the reliability specifications for smart grid applications intended in this context specify that the Percentage Absolute Relative Error (PARE) of the estimate must be upper bounded by 1% [50]. PARE can be calculated using Equation (5).  
\[PARE=(|\hat{\theta}_{k}-\theta|*100)/\theta \tag{5}\]  
We do not use percentage absolute relative error since it is a relative parameter depending on the value of the estimate such that the error percentage of the results, which are obtained will be depending on the actual value of the measurement as evident from Equation (5). To keep our results generic, we use sink node's MSE and network MSE instead of absolute percentage error. Therefore, we assume that the all MSE values obtained in this context corresponds to absolute percentage error less than 1 % meeting reliability requirements of SG applications targeted in this context.  
Average error probability at each bit index is a good performance metric to obtain detail bit level accuracy information at the sink node.
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 3 AI Applications in Software Engineering
### Testing and Quality Assurance
#### 3.4.2 AI in Defect Prediction and Management  
Defect prediction is a critical component of proactive quality assurance. AI models trained on historical project data can predict code segments that are likely to contain defects, enabling developers to focus their testing and review efforts more effectively [55].  
Machine learning algorithms can analyze patterns in code complexity metrics, change histories, developer activities, and previous defect occurrences to identify high-risk components. This predictive capability allows teams to allocate resources efficiently, prioritize testing efforts, and address potential issues before they manifest into significant problems.  
Additionally, AI can assist in defect management by automating the triage process and assigning bugs to the most appropriate developers based on factors such as expertise, workload, and historical performance. By automating prioritization and assignment, AI reduces bottlenecks in the defect resolution process and accelerates the delivery of fixes.  
Techniques such as random forests, support vector machines, and deep learning models have been employed for defect prediction with varying degrees of success [56]. The integration of AI into defect management systems enhances the overall quality assurance process and contributes to the development of more reliable software systems.
# Quantification of Microplastics in Plastic-Bottled Chinese Baijiu Using Micro-FTIR in Imaging Mode
## 3 Results  
### Microplastic Abundance  
The presence of microplastics in plastic-bottled Chinese baijiu, as shown in Figures 1-3, raises concerns regarding consumer health. The abundance of microplastics in plastic-bottled Chinese baijiu and Milli-Q water as a blank sample is presented in Table 2. Each sample was tested three times. All six plastic-bottled Chinese baijiu samples investigated in this study exhibited microplastic contamination. The mean microplastic concentrations varied from 173 MPs/500 mL to 944 MPs/500 mL. The relative standard deviation (RSD) for the six brands of plastic-bottled Chinese baijiu were as follows: BLT--0.029%, MP--0.084%, NLS--0.087%, HX--0.161%, HY--0.124%, and CLYJ--0.051%. These findings exhibit the satisfactory repeatability of the focal plane array-based micro-Fourier transform infrared (micro-FTIR) validation and data analysis technique.  
Figure 1: Microplastic abundance of six brands of plastic-bottled Chinese baijiu (items/500 mL).  
Figure 3: Polymer content of microplastics from Chinese baijiu in plastic bottles.  
Figure 2: Microplastic abundance of six brands of plastic-bottled Chinese baijiu (items/500 mL) with standard deviations shown.  
The most frequently found microplastics were cellulose (comprising 70.4% of the total) and PA (constituting 17.6% of the total). Other microplastic variants, namely PET, PP, PVC, and PE, were also identified, contributing 2.7%, 2.2%, 1.2%, and 0.1%, respectively. In contrast, only a small quantity of microplastics was discovered in the control groups (blank sample).  
Several studies [19; 20] indicate that synthetic textiles are the primary source of microplastics in the air, with fibers being the primary form in the atmosphere. The data show that 29% of these fibers consist of synthetic material. As a result of their low density, microplastics accumulate in the atmosphere and are transported globally, causing harmful health effects through inhalation or ingestion. Given the significant quantities of cellulose and PA detected, it is reasonable to assume that they originate from atmospheric deposition during the production, storage, and transportation of Chinese baijiu sold in plastic bottles. This study ensured strict control of air quality during the experiments conducted. Nevertheless, even in a strictly controlled environment, eliminating air contamination is an arduous task.
## 6 Numerical Tests and Discussion
### Verification of the Proposed Methods  
#### 6.2.1 Validation of SNMM  
Before using SNMM and D-vine copulas to provide scenarios for reliability assessment, we need to validate them. Firstly, we use the wind speed data at Mazongshan as an example to illustrate the performance of SNMM.  
The fitted PDF by using GMM and SNMM with eight components is depicted in Figure 6, respectively. A distinct difference between the two PDFs is marked by a red dashed circle, which shows that SNMM performs better than GMM when they have the same number of components.  
Figure 5: Wind speed PDFs of four areas.  
To present the superiority quantitatively, we compute the Chi-square values of the models fitted by SNMM, GMM [45], Weibull model and kernel density estimation (KDE) [46], as shown in Table 2.  
Apparently, SNMM holds the minimum Chi-square values among the three for all wind speed. Hence, SNMM is validated and can be further used to model the marginal PDFs of wind speed and load in reliability assessment.
## 5 Discussion
### Limitations and Future Work  
This article proposes a coordinated optimization control method for constant voltage output and transmission efficiency, but the addition of Bluetooth communication increases the complexity of the system. The future work will be adding a parameter identification algorithm that does not require communication between the WPT primary and secondary sides. Moreover, this article mainly focuses on the WPT system with LCC-S compensation. In addition to the LCC-S topology, there are many other topologies. Future work will apply the proposed control method to other compensation network topologies.
## 6 Simulation Results
### Network Performance Parameters: Definitions
#### 6.1.4 End-to-End Delay  
End-to-end delay is defined as the time taken by the data packet to reach from the source to the destination. It is measured in seconds.
## 3 Key Concepts
### Ontology-Based Multi-Agent System
Other fields such as the Communication-Act and the Conversation-ID are necessary to distinguish the message at the receiver side. The Communication-Acts are defining the ACL-message in terms of standard FIPA actions or functions, for instance a communication-act field can contain INFORM, REQUEST, CONFIRM, etc. The Conversation-ID parameter can be any unique string to distinguish or record a specific conversation topic or thread among the agents. The content field of an ACL-message contains a String data type in case of simple agent conversation. However, in a complex agent conversation, an ontology-based content will be the proper conversation method. In order to communicate via agent ontologies, every agent must deploy a content-manager. The content-manager registers a common language of conversation between the agents [25]. The FIPA Semantic Language (FIPA-SL) is not mandatory but preferable in a complex JADE conversation. FIPA-SL is a human readable language which defines the syntactic rules needed to parse or encode an ontology-based content. Also, the content-manager registers the common ontology schemas. The ontology schema is defining the abstract structure pattern and the semantics needed to construct or interpret an ontology-based ACL-message. At the sender agent side, the content-manager checks the semantics of the sent ACL-message based on a common ontology schema, and decodes that message into a stream of Bytes via FIPA-SL. At the receiver side, the content-manager parses the received ACL-message into a human readable content via FIPA-SL, and structures it based on a common ontology schema.
## 3 Point Cloud Alignment Methods
### 4 PCS Point Cloud Coarse Alignment  
The 4 PCS algorithm is a popular point cloud coarse alignment algorithm that performs a full-range search of a point cloud dataset under specified constraints. As shown in Figure 5, the basic principle of the 4 PCS algorithm is as follows: randomly select 4 points (a, b, c, d) that are coplanar but not collinear in the source point cloud set \(P\) to form a point set \(B\), where the line from ab intersects the line from cd at point e. Then, select 4 pairs of coplanar but not collinear points that are congruent with point set \(B\) from the target point cloud set \(Q\) and compute the optimal transformation matrix \(T\).  
The specific steps to implement the 4 PCS algorithm are as follows:  
Step 1: Four points that are coplanar and noncollinear are selected from the source point cloud set P to form a 4-point set B = {a, b, c, d}, and two affine invariant scale factors r\({}_{1}\) and r\({}_{2}\) are defined in this point set with the following expressions:  
\[r_{1}=\frac{\|a-e\|}{\|a-b\|} \tag{4}\]  
\[r_{2}=\frac{\|c-e\|}{\|c-d\|} \tag{5}\]  
Step 2: Based on the calculated scale factors r\({}_{1}\) and r\({}_{2}\), all the point sets B' = {a', b', c', d'} that are consistent with the point set B within the permissible range are traversed from within the target point cloud set Q, and the time complexity of the algorithm is O(n\({}^{2}\) + k) (a measure of the complexity of the execution time of the algorithm, n is the number of candidate points and k is the number of 4-point sets). Then, traverse all the point sets B' to find the rigid body transformation matrix T\({}_{i}\) of the point sets B and B' using the least squares method.  
Step 3: Through many iterations, the minimum distance between the source point cloud and the target point cloud after rigid-body transformation is calculated. The sum of the squares of the minimum distances is used as the optimal solution of the rigid-body transformation to realize the coarse alignment of the point cloud.
# Vulnerability Detection and Classification of Ethereum Smart Contracts Using Deep Learning
## 3 Methodology
### Preparation of the Dataset
#### 3.1.3 Contract Filtering and Selection  
The ScrawID and Slither datasets in their default form cannot be used, because they have an unbalanced nature and contain a lot of information that is unnecessary for the classification and detection processes. The content is modified by deleting some Solidity bytecodes whose file's length is empty [43, 44]. This will help to enhance the accuracy of the classification results. Based on that, the combined dataset consists now of 11,858 samples, 8933 of which come from the ScrawID dataset (all are vulnerable), and 2925 come from the Slither dataset (all are invulnerable).
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 6 Performance Evaluation Results
### Decision-Making Algorithm
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Combination**} & **Weight** & \multirow{2}{*}{**Weight Time**} & **Weight** & \multirow{2}{*}{**Error**} & **Error** & **Tolerant Error** \\  & **Intensity** & & & **Speed-Dev-Ratio** & & **Percentage** & \\ \hline
1 & 1 & 0 & 0 & 18/90 & 20\% & 10\% \\
2 & 0 & 1 & 0 & 24/90 & 27\% & 10\% \\
3 & 0 & 0 & 1 & 29/90 & 32\% & 16\% \\
4 & 4/12 & 4/12 & 4/12 & 15/90 & 17\% & 6\% \\
5 & 3/12 & 3/12 & 6/12 & 20/90 & 22\% & 11\% \\
6 & 5/12 & 5/12 & 2/12 & 6/90 & 7\% & 2\% \\
7 & 3/12 & 6/12 & 3/12 & 8/90 & 9\% & 2\% \\
8 & 4/12 & 5/12 & 3/12 & 10/90 & 11\% & 4\% \\
9 & 3.5/12 & 5/12 & 3.5/12 & 13/90 & 14\% & 6\% \\
10 & 4.5/12 & 5/12 & 2.5/12 & 7/90 & 8\% & 2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Congestion decision errors when changing the speed of the incident section. they provide the lowest error percentage and tolerant error. In Table 4, where the previous sections are considered, the best combinations are numbers 2, 4, and 5, which provide the best results across both error parameters. The selected combination in this case is number 4, as it is the best option in Table 3 and one of the lowest error combinations in Table 4.
# Prestack Seismic Inversion via Nonconvex L\({}_{1-2}\) Regularization
## 2 Method
### Establishment of Low-Frequency Model Constraints  
To enhance the lateral continuity and stability of the inversion results, our low-frequency P impedance model must satisfy the following equation:  
\[\mathbf{L}\mathbf{B}\mathbf{r}_{p}=\boldsymbol{\xi}_{p_{-}low} \tag{7}\]  
where \(\boldsymbol{\xi}_{p_{-}low}\) is the low-frequency P impedance model determined using the logging data, and \(\mathbf{L}\) is the low-frequency filter matrix. \(\mathbf{B}\) is the integral operator. Similarly, the low-frequency S impedance and density models must satisfy Equations (8) and (9), respectively:  
\[\mathbf{L}\mathbf{B}\mathbf{r}_{s}=\boldsymbol{\zeta}_{s_{-}low} \tag{8}\]  
\[\mathbf{L}\mathbf{B}\mathbf{r}_{\rho}=\boldsymbol{\zeta}_{p_{-}low} \tag{9}\]  
where \(\boldsymbol{\zeta}_{s_{-}low}\) and \(\boldsymbol{\zeta}_{p_{-}low}\) are the low-frequency S impedance and density models established from the logging data, respectively.  
In augmented matrix form, these three constraints are expressed as follows:  
\[\underbrace{\left[\begin{array}{ccc}\mathbf{L}\mathbf{B}&\mathbf{0}& \mathbf{0}\\ \mathbf{0}&\mathbf{L}\mathbf{B}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{L}\mathbf{B}\end{array}\right]}_{\mathbf{\Phi}} \underbrace{\left[\begin{array}{c}\mathbf{r}_{p}\\ \mathbf{r}_{s}\\ \mathbf{r}_{\rho}\end{array}\right]}_{\mathbf{r}}=\underbrace{\left[ \begin{array}{c}\boldsymbol{\zeta}_{p_{-}low}\\ \boldsymbol{\zeta}_{s_{-}low}\\ \boldsymbol{\zeta}_{p_{-}low}\\ \boldsymbol{\zeta}_{p_{-}low}\end{array}\right]}_{\mathbf{\xi}} \tag{10}\]where the simplified matrix form of this expression is  
\[\mathbf{\Phi}\mathbf{r}=\mathbf{\zeta} \tag{11}\]
# A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
## 6 Conclusions  
Historical images encompass the phenological variations of crops over time, which hold crucial significance for agricultural land segmentation. Based on this observation, this paper introduces a novel spatio-temporal encoding neural network for semantic segmentation in SITS. Specifically, the network comprises two critical encoding modules. The first one is the spatial encoding module, which constructs a high-resolution spatial feature encoding module using dilated convolutions and dense connections. This module swiftly increases the receptive field, extracts deep semantic information, and acquires rich multi-scale features without compromising spatial information. Subsequently, the temporal encoding module employs a Transformer encoder to extract the temporal features of each pixel within the feature map time series. Furthermore, we use unique position encoding to better capture the annual cyclic characteristics of crops. Extensive experiments conducted on the PASTIS dataset demonstrate that our STENN model not only significantly reduces the required training parameters but also enhances feature extraction capabilities, resulting in more satisfactory segmentation performance.  
Our team's next steps include the following two aspects:  
* Further optimization of time series models: This involves improvements to the Transformer encoder or the exploration of other architectures suitable for time series modeling. This can contribute to reducing computational requirements and memory demands.
* Integration across diverse data sources: Consider incorporating more data sources into the framework to further enhance the accuracy of semantic segmentation. For in  
Figure 10: Qualitative semantic segmentation results. We begin with a single image from the sequence represented using the RGB channels, and for which we have knowledge of the ground truth parcel’s boundaries and crop type. Subsequently, we showcase the pixelwise predictions generated by our approach, as well as those of six other comparative algorithms. The legend refers to Figure 2 for reference. Gridding effect due to downsampling (highlighted in red circles).  
stance, meteorological data, land-use data, and other sources may provide valuable information for interpreting land cover phenology.  
Conceptualization, F.Z. and Y.W.; methodology, F.Z. and Y.W.; validation, F.Z.; writing--original draft preparation, F.Z. and Y.W.; writing--review and editing, Y.D. and Y.Z.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China Joint Fund Key Project (grant number U21A2013); and Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing (grant number KLIGIP-2021B05).  
Not applicable.  
We appreciate the constructive comments and suggestions from the reviewers that helped improve the quality of this manuscript.  
The authors declare no conflict of interest.
## 3 Oscillation Analysis of FSC-VSPSU
### Rotation Speed Loop and Current Loop Parameters of the Machine-Side Converter  
Upon adjustment of the parameters associated with the rotation speed loop and the current loop of the machine-side converter, the pertinent single-input-single-output (SISO) impedances of the FSC-VSPSU are illustrated in Figure 12. In detail, the red line represents the initial FSC-VSPSU impedance, and \"\(\circ\)\", \"[\"], \"\(\diamond\)\", and \"\(\diamond\)\" represent the measured SISO impedances when changing \(K_{\text{pw}}\), \(K_{\text{iw}}\), \(K_{\text{pc2}}\), and \(K_{\text{ic2}}\), respectively. \(K_{\text{pw}}\) and \(K_{\text{iw}}\) are the proportional and integration parameters of the rotation speed loop, respectively, and \(K_{\text{pc2}}\) and \(K_{\text{ic2}}\) are the proportional and integration parameters of the current loop of the machine-side converter, respectively. As it can be seen, when changing \(K_{\text{pw}}\), \(K_{\text{iw}}\), \(K_{\text{pc2}}\), and \(K_{\text{ic2}}\), the SISO impedance of FSC-VSPSU remains almost unchanged. Therefore, the corresponding control parameters of the machine-side converter have a weak impact on oscillation occurring in the grid-connected point.  
Figure 11: Bode diagram when the integration parameter of the current loop of the grid-side converter changes.  
Figure 10: Bode diagram when the proportional parameter of the current loop of the grid-side converter changes.  
Thus, the oscillation suppression performances of the control parameters are shown in Table 1. In detail, \"\(\downarrow\)\" represents that reducing the relevant parameter can suppress the oscillation, and \"\(\times\)\" represents poor or irregular suppression of oscillation by adjusting the relevant parameter.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 4 Simulation Setup and Performance Evaluation
### Performance Evaluation
#### 4.2.3 Comparison between PUTPOSH and Existing Handoff Schemes  
Overall Service Time  
When the arrival rate of PU fits up to the threshold value, the PUTPOSH strategy switches to the reactive handoff and allows a superior rate to the proactive handoff. Similarly, when the threshold value remains or becomes higher than the arrival rate of PU, the PUTPOSH strategy allows CRU to move to the proactive handoff. In this case, PUTPOSH produces 12.80% better results than the reactive handoff (shown in Figure 7). Hence PUTPOSH, as a hybrid approach, uses the advantages of both the proactive and the reactive strategies whenever required.  
Figure 5: Distribution probability of PU detection over the signal to noise ratio.  
Figure 6: Threshold value detection to toggle among reactive and proactive handoff decisions.  
Figure 8 represents the number of handoffs executed by the CRU. The existing handoff strategies, as compared to PUTPOSH, executed an additional quantity of handoffs; during each phase, the PU is detected and the handoff is executed by proactive and reactive approaches. On the interarrival of PU, the existing approaches worked by frequently changing between channels and had no mechanism of stay and wait to execute the transmission. The PUTPOSH scheme perceptively chooses whether to execute the handoff or not, conditional to the prediction of the overall service time. The intelligent decision to handle handoff operation helps PUTPOSH to achieve 14.28% better results than the existing DFHC (reactive handoff), FLSH (proactive handoff), and DSA (hybrid handoff) approaches. However, with the help of this way, the infertile handoffs actions are evaded.  
Figure 8: Comparison of PUTPOSH with the existing handoff schemes w.r.t. the number of handoffs.  
Figure 7: Comparison of primary user traffic pattern-based opportunistic spectrum handoff (PUTPOSH) with reactive and proactive handoffs w.r.t. the overall service time.