esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
We analyze whether a link-centric approach can improve upon a node-centric methodology for industrial structure analysis. Industrial structure analysis traditionally defines key industries by utilizing node centrality indices within a network-based approach. The detailed indices used to assess node centrality include degree centrality, betweenness centrality, and closeness centrality. However, traditional network centrality indices are limited in their ability to fully capture the influence and position of each node within the whole integrated network [1]. To address these limitations, proposals have been made for the use of weighted centrality, measured by applying weighted values to the overall network [2, 3, 4, 5, 6, 7].  
This study presumes that these studies are fundamentally limited by their node-centric analysis and attempts a link-centric approach to industrial structure analysis.  
To understand changes in individual industries driven by interdependence, this study proposes a methodology that approaches the network from an inter-industry perspective, focusing on the path of nodes in the transportation and logistics industry through a link-centric approach. The present research defines an ecosystem centered on the transportation and logistics industry, reflecting the interdependence among all industries based on the proposed methodology. Based on this, this study provides a clue for understanding the industrial ecosystem from a dynamic perspective by analyzing how changes in a specific industry are spread to others.  
The methodology so developed was validated by applying it to Korean cases using the I/O table. To this end, an input-output network was constructed using the I/O table provided by the Bank of Korea. This I/O table organizes the production input structure and production distribution structure among industries into a matrix, making it possible to understand inter-industry transactions of goods and services. This basic structure is converted into a network based on the total transaction volume between the \"transportation and logistics industry (inland, maritime and air transport services)\" and all industries, and defines an ecosystem for the transportation and logistics industry after deriving nodes affecting it through link analysis. It is expected that this process will contribute to understanding how the transportation and logistics industry is linked to other industries in the overall industrial network and to the development of the industrial ecosystem from a flow perspective.  
This study is composed as follows: First, it reviews existing studies related to industrial ecosystems using network analysis. Second, it presents a link-based network analysis methodology. Third, it presents the results of a comparative analysis of the derived important nodes in the transportation and logistics industry and the existing node-centric methodology, using Korean cases. Lastly, it defines the transportation and logistics industry ecosystem based on the derived results and discusses a new perspective on ecosystem analysis.
# Application of Collision Warning Algorithm Alarm in Fishing Vessel's Waterway
## 4 Application in Fishing Vessel's Waterway Based on Vpass Data  
This study applied the collision warning algorithm to the fishing vessel's waterway by extracting two cases based on real sea vessel traffic (Vpass) data and compared the maritime risks and alarm generation based on the vessel traffic data collected over seven days. The study used the Vpass data collected for seven days during 15-21 May in 2014 and the target area was the sea of Yeongheung Island, South Korea. Vpass is a wireless device that automatically transmits the location of a ship [30] and should be installed in small fishing vessels where AIS is not installed according to the Fishing Vessels Act [31]. Therefore, this study used the Vpass data suitable for small fishing vessels that are considered as the target ships in this study.  
However, ship navigation information is transmitted every 10 min or less in Vpass [32]. As a result of analyzing the 7-day Vpass data, intervals within 1 min accounted for 75.74%, and those within 3 min accounted for 90.74%. For accuracy, interpolation, a pre-processing step of data, was performed. The Vpass data were interpolated in chunks of 100 ms when they applied to the case study and chunks of 10 s for the effective application to risk assessment. These activities were performed using the linear interpolation method and Equation (2).  
\[\text{P(t+Itv)}=\frac{l_{1}}{I}\text{P(t)}+\frac{l_{2}}{I}\text{P(t+\alpha)} \tag{2}\]  
where  
\[\text{P(t)} :\text{Position at time }(x_{1},y_{1})\] \[\text{P(t+\alpha)} :\text{Position after time }\alpha(x_{2},y_{2})\] \[\text{P(t+ltv)} :\text{Position after interval to be interpolated}\] \[\text{l}_{1} :\text{Distance between }\text{P(t)}\text{ and }\text{P(t+\alpha)}\] \[\text{l}_{2} :\text{Distance between }\text{P(t+Itv)}\text{ and }\text{P(t+\alpha)}\] \[\text{l} :\text{Distance between }\text{P(t)}\text{ and }\text{P(t+\alpha)}<l_{1}+l_{2}=l>\]  
here, the prerequisite is \(\alpha>\text{Itv}\).  
Figure 7 shows the Vpass data plot diagrams before and after interpolation. The interpolation result plot diagrams for each date are shown in Figure A1 of Appendix B.
## 3 Results
### MuRXLS Framework Implementation
#### 3.1.2 Multilingual Datastore Construction and Retrieval Process  
The framework begins by processing the training corpus using BGE-M3 to create dense embeddings for all source articles. This creates a searchable multilingual datastore where semantically similar articles across languages are positioned closely in the embedding space. The datastore enables efficient retrieval of relevant examples regardless of source language, leveraging the cross-lingual alignment properties of BGE-M3.  
Algorithm 1 presents the detailed procedure for our MuRXLS framework. The algorithm takes as input the training dataset, query article, number of examples (\(k\)), encoder model, and target LLM, then outputs a cross-lingual summary.  
```
1:\(D_{train}=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\},\)\(x_{q},\)\(k\), Encoder, \(M\)
2:\(y_{q}\) (Generated summary in target language)
3:procedureMuRXLS(\(D_{train}\), \(x_{q}\), \(k\), \(Encoder\), \(M\))
4:\(E_{train}\leftarrow\{Encoder(x_{1}),\ldots,Encoder(x_{n})\}\)\(\triangleright\) Compute embeddings
5:\(e_{q}\gets Encoder(x_{q})\)\(\triangleright\) Embedding for query article
6:\(S\leftarrow\emptyset\)\(\triangleright\) Initialize similarity scores
7:for\(i\gets 1\) to \(n\)do
8:\(sim_{i}\leftarrow\frac{e_{q}\cdot E_{train}[i]}{|e_{q}|\cdot|E_{train}[i]|}\)\(\triangleright\) Cosine similarity
9:\(S\gets S\cup\{(i,sim_{i})\}\)\(\triangleright\) Store index and score
10:endfor
11:\(S\gets Sort(S)\)\(\triangleright\) Sort by similarity
12:\(D_{demo}\leftarrow\emptyset\)\(\triangleright\) Initialize demonstration set
13:for\(j\gets 1\) to \(k\)do
14:\(idx\gets S[j]\).index\(\triangleright\) Index of \(j\)th most similar example
15:\(D_{demo}\gets D_{demo}\cup\{(x_{idx},y_{idx})\}\)\(\triangleright\) Add to demos
16:endfor
17:\(prompt\gets ConstructPrompt(D_{demo},x_{q})\)\(\triangleright\) Construct prompt
18:\(y_{q}\gets M(prompt)\)\(\triangleright\) Generate summary using LLM
19:return\(y_{q}\)
20:endprocedure
21:procedureConstructPrompt(\(D_{demo},x_{q}\))
22:\(prompt\leftarrow\) \"Please summarize the following text in [TARGET_LANGUAGE]\"
23:for\((x_{i},y_{i})\in D_{demo}\)do
24:\(prompt\gets prompt+\) \"Text: \" \(+x_{i}+\) \"Translated summary: \" \(+y_{i}\)
25:endfor
26:\(prompt\gets prompt+\) \"Text: \" \(+x_{q}+\) \"Translated summary:\"
27:return\(prompt\)
28:endprocedure
```  
**Algorithm 1** Multilingual Retrieval-based Cross-lingual Summarization (MuRXLS)  
As detailed in Algorithm 1, for each test article, the system computes embedding using BGE-M3 encoder, calculates cosine similarity with all training examples, and selects the top-\(k\) most similar examples based on semantic relevance. This process identifies the most similar source article from the validation set and its corresponding target summary, providing the LLM with contextually relevant source-summary pairs for in-context learning.
# Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform
###### Abstract  
While real-time bidding brings a huge profit for online businesses, it also becomes a potential target for malicious purposes. In real-time bidding, the bid request traffic could be classified into two kinds: intentional and non-intentional. Intentional bid requests come from ordinal web users while non-intentional bid requests come from abnormal web users. From the perspective of a demand-side platform (DSP), the budget of advertisers should be used as effectively as possible by limiting non-intentional traffic. Therefore, it is essential to classify and predict these two kinds of bid request traffic. In this research, we propose a real-time filtering bid requests (RFBR) model to predict whether an incoming bid request is intentional or non-intentional from the DSP's viewpoint. Our model is built on three stages. In the first stage, we analyzed all potential attributes in the bid request scheme and figured out the relations between abnormal behaviors and their attributes; in the second stage, a classification model was built to classify normal and abnormal audiences by the extracted features and self-defined thresholds; in the third stage, a RFBR model was built to classify intentional and non-intentional bid requests. The experimental result shows that our system can effectively classify incoming bid requests.  
online advertising; demand-side platform (DSP); real-time bidding; anomaly detection; data mining 2022
# SBNN: A Searched Binary Neural Network for SAR Ship Classification
## 4 Experiments
### Results
#### 4.3.2 Comparison with Modern Computer Vision Binary Networks  
Table 9 lists the results of SBNN and other three modern computer vision binary networks in the three-categories task. The compared binary networks are: Bi-RealNet [25], ReActNet [26] and AdamBNN [27]. Not only is the number of weights obtained, but also the binary MAdds and the floating-point MAdds are counted separately. According to the research [25; 26], the number of total MAdds can be calculated by adding the number of floating-point MAdds to 1/64 of the number of binary MAdds. From Table 9, SBNN has the best accuracy and the lowest number of MAdds close to 30% of other binary networks. In the meantime, the size of SBNN is much smaller than other networks listed together, which benefits the implement on the tiny-size mobile devices.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **MAdds** & **Weights** \\ \hline VGG With Hybrid & & & \\ Channel Feature Loss [15] & 55.26\% & 27.68 B & 15.01 M \\ \hline Mini Hourglass Region & & & \\ Extraction and Dual-Channel & 54.93\% & \(\underset{\text{(Dynamic)}}{\geq}\)369.93 B & & \\ Efficient Fusion Network [44] & & & \\ \hline SE-LPN-DPFF [38] & 56.66\% & — & — \\ \hline SBNN & 56.73\% & 9.34 M & 0.37 M \\ with fusion (ours) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results in the six-categories Dual-Polarization SAR ship classification task.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Bulk Carrier** & **Cargo** & **Container Ship** & **Fishing** & **General Cargo** & **Tanker** \\ \hline Bulk Carrier & 60.52\% & 20.60\% & 18.45\% & 0.00\% & 0.00\% & 0.43\% \\ Cargo & 17.16\% & 70.23\% & 3.68\% & 3.15\% & 0.35\% & 5.43\% \\ Container Ship & 30.87\% & 10.78\% & 57.29\% & 0.00\% & 0.42\% & 0.63\% \\ Fishing & 0.00\% & 32.00\% & 0.00\% & 64.00\% & 0.00\% & 4.00\% \\ General Cargo & 23.81\% & 52.38\% & 9.52\% & 0.00\% & 2.38\% & 11.90\% \\ Tanker & 9.15\% & 71.83\% & 4.23\% & 5.63\% & 0.00\% & 9.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The confusion matrix of SBNN in the six-categories Dual-Polarization SAR ship.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **MAdds** & **Weights** \\ \hline Finetuned VGG [13] & 69.27\% & 13.84 B & 15.52 M \\ \hline Plain CNN [42] & 67.41\% & 2.17 B & 47.44 M \\ \hline GSESCNNs [43] & 74.98\% & — & — \\ HOG-ShipCLSNet [16] & 78.15\% &
\begin{tabular}{c} 89.46 M \\ (Not Including HOG and PCA) \\ \end{tabular} & 65.11 M \\ \hline SBNN (ours) & 80.03\% & 4.67 M & 0.37 M \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results in the three-categories SAR ship classification task.
## 4 Results
### Scenario 1
#### 4.1.1 First Example Test  
The results of one of the tests of Scenario 1 (with parameters: \(K=5,N=4,L=2\), \(QBER=3\)), are presented in Table 1 and Figure 2. The number of victims' updates in this test-presented in the second column of Table 1 and in Figure 2a-is comparable in seven of the eight update rule combinations (\(\approx\)41.5 on average), while for all parties using hebbian, it is more than two times lower (\(\approx\)17). Although prolonged synchronization has been shown to increase the likelihood of a successful attack by the attacker [38; 43; 44], this effect is not observed in the current scenario. Specifically, when the attacker is using the hebbian update rule, the level of synchronization achieved-values are presented in the third column of Table 1 and in Figure 2b-is lower if the victims utilize either the dynamic_rows or dynamic_matrix algorithm (\(\approx\)64% and \(\approx\)63.5%, respectively), compared to the significantly shorter synchronization process when all parties use the hebbian rule (\(\approx\)65%).  
While this is not the case for the random walk-oriented tests, it is worth noting that the attacker's synchronization percentage in those tests (approximately 69.5% on average) was higher compared to the tests where the attacker used a random update rule at each iteration (approximately 67% on average). This indicates that the attacker's uncertainty regarding which algorithm is being used during the synchronization process may benefit the communicating parties. In particular, this advantage is evident when the attacker either fails to recognize that the update rule is being dynamically chosen or does not deploy multiple machines to attempt synchronization.  
Figure 2: Scenario 1–\(K=5,N=4,L=2,QBER=3\).
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Internet Layer
#### 3.3.2 Routing Protocol for Low-Power and Lossy Networks  
RPL is a distance-vector routing protocol designed specifically for low-power and lossy networks (LLNs) to support efficient packet routing and reliable paths from source to destination. It is one of the initiatives observed by the IETF to solve the unique communication needs of LLNs. These networks have a unique set of characteristics, including a limited power supply (often battery-powered), high loss rates, low data rates, and a large number of devices. [79, 80]. LLNs are typically used in sensor networks, smart transportation, smart grid, home and building automation, industrial monitoring, and a host IoT application.
# Vertical Correlation and Array Gain Analysis for Vertical Line Array in Deep Water
## 2 Basic Theory  
### Conventional Beamforming Technology  
Supposing that there is a vertical line array composed of \(M\) elements and the sound source is deployed in the far-field, the output of the \(g\)th element is expressed as follows according to the plane wave theory.  
\[x_{g}(t)=s_{g}(t-\tau)+n_{g}(t)\quad\text{g}=1,2,\cdots M \tag{1}\]  
where \(s_{g}(t-\tau)\) and \(n_{g}(t)\) are received signal and noise of \(g\)th hydrophone, respectively, \(\tau~{}=~{}(g-1)d\cos\theta/c\) denotes the signal time delay between the reference element and \(g\)th element, \(\theta\) is the elevation angle of the incident sound wave, \(d\) is the element interval for the line array, and \(c\) denotes the sound speed.  
Equivalently, in the frequency domain, the form of Equation (1) can be written as  
\[X_{g}(f)=S_{g}(f)e^{-2\pi f(g-1)d\cos\theta/c}+N_{g}(f)\quad\text{g}=1,2,\cdots M \tag{2}\]  
where \(S_{g}(f)\) and \(N_{g}(f)\) are signal and noise spectrum at frequency \(f\), respectively.  
The received signal and noise of the whole line array can be expressed as  
\[\mathbf{X}(f)=\mathbf{A}(\theta)\mathbf{S}(f)+\mathbf{N}(f) \tag{3}\]  
where \(\mathbf{A}(\theta)\) is a vector of the array manifold, \(\mathbf{S}(f)\), \(\mathbf{N}(f)\), and \(\mathbf{X}(f)\) represent the received signal matrix, received noise matrix, and array output signal matrix, respectively. After passing through the delay-sum narrowband beamformer, the beam output of the line array in forms of the matrix is given by  
\[\mathbf{Y}(f)=\mathbf{w}^{H}\mathbf{X}(f) \tag{4}\]  
where \(\mathbf{w}\) is the weight vector that can be denoted as  
\[\mathbf{w}=\begin{bmatrix}1&e^{2\pi fd\cos\theta/c}\ \cdots\ e^{2\pi f(g-1)d\cos \theta/c}\end{bmatrix}^{T}\quad\text{g}=1,2,\cdots M \tag{5}\]  
Further, the power output can be expressed as  
\[P(\theta)=\left|\mathbf{Y}(f)\right|^{2}=\mathbf{w}^{H}\mathbf{R}_{xx}\mathbf{w}=\mathbf{w}^{H} \mathbf{R}_{s}\mathbf{w}+\mathbf{w}^{H}\mathbf{R}_{n}\mathbf{w} \tag{6}\]  
where \(\mathbf{R}_{xx}=E\big{[}\mathbf{X}(f)\mathbf{X}^{H}(f)\big{]}\) is the covariance matrix of the array, \(R_{s}\) and \(R_{n}\) are the signal covariance matrix and noise covariance matrix, respectively.  
In Equation (6), when the power output \(P(\theta)\) reaches its maximum value, \(\theta\) corresponds to the estimated elevation angle of the incident sound wave.
# Detecting IoT Attacks Using an Ensemble Machine Learning Model
## 2 Background and Related Work
### ML-Specific Related Work on Security and Privacy
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **ML/DL** & **Features Used** & **Analysis Performed** \\  & **Algorithm Used** & (\(\checkmark\)) **or Not (\(\times\))** & (\(\checkmark\)) **or Not Performed (\(\times\))** \\ \hline
[19; 20] & Decision Tree & E(\(\checkmark\)), S(\(\times\)), & A(\(\checkmark\)), ROC(\(\times\)), FScore(\(\times\)), \\  & + Rule Induction & N(\(\times\)), D(\(\times\)) & MCC(\(\times\)), DR(\(\times\)) \\ \hline
[7; 8] & Deep Neural & E (\(\times\)), S(\(\times\)), & A(\(\times\)), ROC (\(\times\)), FScore(\(\checkmark\)), \\  & Network (DNN) & N(\(\checkmark\)), D(\(\times\)) & MCC(\(\times\)), DR(\(\times\)) \\ \hline
[22; 23] & Optimization & E(\(\checkmark\)), S(\(\times\)), & A(\(\checkmark\)), ROC (\(\times\)), FScore(\(\checkmark\)), \\  & + DNN & N(\(\checkmark\)), D(\(\checkmark\)) & MCC(\(\times\)), DR(\(\times\)) \\ \hline
[9; 13] & SVM-ANN & E(\(\times\)), S(\(\times\)), & A(\(\times\)), ROC (\(\times\)), FScore(\(\checkmark\)), \\  & + hybrid optimization & N(\(\checkmark\)), D(\(\times\)) & MCC(\(\checkmark\)), DR(\(\checkmark\)) \\ \hline
[21] & PCA & E(\(\times\)), S(\(\times\)), & A(\(\checkmark\)), ROC (\(\checkmark\)), FScore(\(\times\)), \\  & + Random Decision & N(\(\checkmark\)), D(\(\checkmark\)) & MCC(\(\times\)), DR(\(\checkmark\)) \\ \hline
[10; 11] & Dimensionality & E (\(\times\)), S(\(\times\)), & A(\(\checkmark\)), ROC(\(\times\)), FScore(\(\checkmark\)), \\  & Reduction + DNN & N(\(\checkmark\)), D(\(\checkmark\)) & MCC(\(\times\)), DR(\(\checkmark\)) \\ \hline
[24] & GA-based Latent & E(\(\checkmark\)), S(\(\times\)), & A(\(\checkmark\)), ROC (\(\times\)), FScore(\(\checkmark\)), \\  & Dirichlet Allocation & N(\(\times\)), D(\(\times\)) & MCC(\(\times\)), DR(\(\checkmark\)) \\ \hline
[25] & Autoencoder based & E(\(\checkmark\)), S(\(\checkmark\)), & A(\(\times\)), ROC (\(\times\)), FScore(\(\checkmark\)), \\  & LSTM classifier & N(\(\checkmark\)), D(\(\checkmark\)) & MCC(\(\times\)), DR(\(\times\)) \\ \hline
[26] & Multinomial Logistic & E(\(\times\)), S(\(\times\)), & A(\(\times\)), ROC (\(\checkmark\)), FScore(\(\times\)), \\  & Regression & N(\(\times\)), D(\(\times\)) & MCC(\(\times\)), DR(\(\times\)) \\ \hline
[27] & Ensemble Learning & E(\(\checkmark\)), S(\(\times\)), & A(\(\checkmark\)), ROC (\(\times\)), FScore(\(\times\)), \\  & with XGBoost & N(\(\times\)), D(\(\times\)) & MCC(\(\times\)), DR(\(\times\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related Work. The letters E, S, N, and D stand for encoding, standardization, normalization, and dimensional reduction, respectively. Further, accuracy, Matthews correlation coefficient, and detection rate are denoted as A, MCC, and DR, respectively.
## 8 Conclusions  
This work presented an accelerated data reconstruction technique based on the least-squares method, which can reduce computational time in two aspects. The first one is based on using rSVD in computing a low-dimensional basis used in least-squares approximation. The second aspect that can speed up the reconstruction process is based on applying the DEIM greedy point selection procedure on the least-squares approximation. Accuracy and computational time reduction of the proposed method are demonstrated through three numerical experiments, which considered standard test images, as well as a set of sequential two-dimensional flow images in a similar form as video data. These numerical results show that the proposed method can speed up the reconstruction process up to 100 times with a small accuracy trade-off. This work can be extended by employing different approaches to construct the projection basis, e.g., [50; 51; 52]. It is also possible to adapt other approaches used for solving differential equations, e.g., in [53; 54; 55; 56], to select the relevant components used in the least-squares approximation for data reconstruction. These extensions are left for future investigations.  
S.C. study planning, project administration, design, conceptualization, methodology, manuscript preparation, writing--review and editing, numerical analysis, funding acquisition; S.C. and S.I. background research, paper review, writing--original draft preparation, numerical experiments. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support provided by the Faculty of Science and Technology, Contract No. SciGR 12/2565.  
Not applicable.  
The authors would like to thank the reviewers for many insightful comments to improve this paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} POD & Proper Orthogonal Decomposition \\ DEIM & Discrete Empirical Interpolation \\ LS & Least-squares \\ SVD & Singular Value Decomposition \\ rSVD & Randomized Singular Value Decomposition \\ \end{tabular}  
Figure 10: (Numerical Test 3) Reconstruction error (**a**) and CPU time (**b**) of the incomplete image from the standard least-squares approach and from the accelerated least-squares approach case (i) in Algorithm 4 using different dimensions of \(k\) and \(m=k\).