esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Formulation
### Outer Loop of Dec-MPMAB Framework  
#### 4.1.1 Basic MAB and Dec-MPMAB Framework  
The multi-armed bandit (MAB) framework is a classical model designed to address scenarios characterized by decision making under uncertainty, exploration, and exploitation. In the context of the MAB problem, an agent is presented with a set of \"arms\", where each arm represents a distinct choice or action that the agent can take. The primary objective of the agent is to maximize its cumulative reward over time, all while contending with uncertainty regarding the rewards associated with each individual arm. The challenge lies in finding an optimal strategy that balances exploration (trying different arms to learn their rewards) and exploitation (choosing the arm believed to yield the highest reward based on current knowledge) to achieve the overall goal of maximizing the cumulative rewards.  
Decentralized multi-player multi-armed bandit (Dec-MPMAB) problems [24] extend the traditional multi-armed bandit (MAB) framework to encompass situations where multiple players interact with a shared set of arms or actions. In the Dec-MPMAB framework, multiple players engage in decision making simultaneously, and they may either compete or cooperate in the allocation of limited resources. In the upcoming section, we present the Dec-MPMAB formulation tailored to address the challenges posed by our specific problem.
## 4 Discussion
### Degradation Pathway  
The types of biodegradation transformation products differed very little compared with air oxidation in Figure 5, and the pathway of UDMH oxidation did not change in biodegradable participation. A universally applicable degradation pathway is proposed in Figure 11. In the earlier study, (CH\({}_{3}\))\({}_{2}\)NN\(\cdot\) H was thought to be an important intermediate in the degradation mechanism of UDMH, which was the source of redox reactions [45; 56; 53]. According to the quantum chemical calculation, the first stage forming (CH\({}_{3}\))\({}_{2}\)NN\(\cdot\) H was an exothermic process releasing energy of 181.96 KJ\(\cdot\) mol\({}^{-1}\)[57]. This process was a no barrier process with a reaction rate constant of 5 \(\times\) 10\({}^{7}\) L\(\cdot\) mol\({}^{-1}\)\(\cdot\) s\({}^{-1}\)[17]. Then (CH\({}_{3}\))\({}_{2}\)NN\(\cdot\) H lost an H atom and rearranged into (CH\({}_{3}\))\({}_{2}\)N\({}^{+}\)=N, releasing energy of 2896.4 KJ\(\cdot\) mol\({}^{-1}\)[57]. FADMH, which was found in high concentration in Figure 5, could also prove that this process was proceeding easily. Therefore, the production of (CH\({}_{3}\))\({}_{2}\)NN\(\cdot\) H was crucial for accelerating the degradation rate of the UDMH oxidation process. According to the mechanism inferred by [PERSON], (CH\({}_{3}\))\({}_{2}\)NN\(\cdot\) H was the precursor of a series of degradation intermediates [56]. Consequently, (CH\({}_{3}\))\({}_{2}\)NN\(\cdot\) H was very important for the processes of biodegradation and air oxidation. It could be further oxidized into different compounds with lower toxicity that could provide the necessary carbon and nitrogen for bacteria to grow. In solution, it had been found that the oxygen content was positively correlated with the oxidation rate of UDMH. In soil, the oxygen content was lacking so that the air oxidation of UDMH was slower than in an aqueous solution. At this time, biodegradation played bigger role than air oxidation. It could be deduced that the biodegradation and the air oxidation of UDMH were synchronous, and had a mutually reinforcing relationship.  
Figure 10: The influence of extra carbon source on UDMH degradation for P4 strain in soil.
# Properties of Vector Embeddings in Social Networks
## 6 Conclusions  
This work has tackled graph embeddings to investigate network topological properties such as degree, closeness, betweenness, and eigenvector centrality. Empirical evaluation on real-world social networks indicated that each embedding technique can retain a different combination of network properties. We studied and reported recent existing methods of inspecting embeddings. We also  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Centrality** & **Average Value** & **Std** & **Input of the Model** & **RMSE** & **NRMSE** & **CV (RMSE)** \\ \hline \multirow{4}{*}{degree} & & & deepwalk & 0.01848 & 0.09898 & 1.46908 \\  & & & loc & 0.01681 & 0.09000 & 1.33579 \\  & & & node2 vec(1) & 0.02074 & 0.11108 & 1.64857 \\  & & & node2 vec(2) & 0.02226 & 0.11919 & 1.76898 \\  & & & LINE & 0.03132 & 0.16772 & 2.48925 \\ \hline \multirow{4}{*}{closeness} & & & deepwalk & 0.04755 & 0.22282 & 0.18795 \\  & & & loc & 0.03647 & 0.17088 & 0.14413 \\  & 0.25302 & 0.02319 & node2 vec(1) & 0.05906 & 0.27674 & 0.23342 \\  & & & node2 vec(2) & 0.06260 & 0.29331 & 0.24740 \\  & & & LINE & 0.04695 & 0.21998 & 0.18555 \\ \hline \multirow{4}{*}{betweenness} & & & deepwalk & 0.01630 & 0.06905 & 15.44306 \\  & & & loc & 0.02024 & 0.08573 & 19.17428 \\  & & & node2 vec(1) & 0.01568 & 0.06639 & 14.84949 \\  & & & node2 vec(2) & 0.01554 & 0.06583 & 14.72446 \\  & & & LINE & 0.01761 & 0.07458 & 16.68014 \\ \hline \multirow{4}{*}{eigenvector} & & & deepwalk & 0.02412 & 0.25284 & 2.32737 \\  & & & loc & 0.02724 & 0.28549 & 2.62788 \\ \cline{1-1}  & & & node2 vec(1) & 0.02664 & 0.27918 & 2.56986 \\ \cline{1-1}  & & & node2 vec(2) & 0.02731 & 0.28625 & 2.63491 \\ \cline{1-1}  & & & LINE & 0.02534 & 0.26561 & 2.44495 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE) and Coefficient of Variation of the RMSE for approximating centrality values in the Facebook graph.  
presented an approach to approximate centrality values using neural networks. Our results revealed that closeness centrality is only centrality, and can be approximated in a more efficient time. For future work, we will analyze and compute the runtime of closeness centrality approximation precisely. We believe that there are some promising research directions exploiting embeddings to approximate some complex measures such as length of shortest path between two nodes.  
This work is being supported by the German government in the BMBF-project 'Provenance Analytics'. The authors thank an anonymous colleague for proof-reading the mathematical equations and for his technical support while preparing the manuscript.  
The problem definition, theoretical analysis, and algorithm design were performed jointly by all authors; [PERSON] derived the theorems and [PERSON] implemented the algorithms depicted in the paper. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.
## 5 Simulation Experiments and Analysis
### Comparative Analysis of Algorithm Performance
#### 5.3.2 UGVs Reward Analysis  
The reward function changes in the three UGVs were compared and analyzed when the SARSA algorithm, Q-learning algorithm, speedy Q-learning algorithm, and OWS Q-learning algorithm were planned from the starting point to the target point. The reward changes in each UGV in the four algorithms in three scenarios are shown in Figure 11.  
Figure 11: The reward changes in each UGV of the four algorithms in three scenarios.  
Among them, columns 1, 2, and 3 are the reward change result graphs of scenario1, scenario2, and scenario3. Rows 1, 2, 3, and 4 are the UGV changes under the SARSA algorithm, Q-learning algorithm, speedy Q-learning algorithm, and OWS Q-learning algorithm.  
From the results of Figure 11, in the three experimental scenarios, the rewards of the 3 UGVs of the SARSA algorithm in 1800 iterative learning did not converge, and the reward function under each round fluctuated greatly, and the training effect of UGV in the process of finding the path was unstable. The reward of the 3 UGVs in the 1800 iterative learning of the Q-learning algorithm also did not converge, but compared with the SARSA algorithm, the fluctuation range of the reward function of the 3 UGVs was alleviated. Under the speedy Q-learning algorithm, the reward of 3 UGVs in 1800 learning iterations also did not converge but compared with the SARSA algorithm and Q-learning algorithm, the fluctuation range of each UGV reward function slowed down. In scenario1, the reward function converges to around 0 after about 850 iterations of learning for three UGVs in the OWS Q-learning algorithm. In scenario2, the reward function converges to around 0 after about 800 iterations of learning for 3 UGVs in the OWS Q-learning algorithm. In scenario3, the reward function converges to around 0 after about 1000 iterations of learning for 3 UGVs in the OWS Q-learning algorithm.  
In general, compared with the changes in reward function under the SARSA algorithm, Q-learning algorithm, and speedy Q-learning algorithm, the OWS Q-learning algorithm proposed in this paper has a significant convergence trend of reward function in multi-UGV path planning problems.
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 2 Accurate Modeling of SINR in the Presence of RFI
### RFI Impact Quantitative Analysis Model
Since RFI comes from other spaceborne SARs, according to the bistatic radar equations [38], RFI power affecting the pixel after SAR processing can be written as  
\[P_{RFI}=G_{RFI}\frac{P_{ti}G_{i}\sigma_{B}A_{s}A_{rs}}{\left(4\pi\right)^{2}R_ {i}^{2}R_{s}{}^{2}} \tag{6}\]  
where \(P_{ti}\), \(G_{i}\) and \(R_{i}\) are the peak transmitted power, antenna gain and slant range of the RFI source SAR system, respectively. \(\sigma_{B}\) is the bistatic scattering coefficient, \(A_{rs}\) is the antenna aperture area of the disturbed SAR, \(A_{s}\) is the receiving aperture area, and \(G_{RFI}\) is the gain obtained by RFI in SAR processing.
## 3 Results
### MuRXLS Framework Implementation
#### 3.1.1 System Architecture Overview  
The MuRXLS framework operates through three integrated components designed to optimize example selection for cross-lingual summarization tasks, as illustrated in Figure 1. The system processes source articles through multilingual embedding, retrieves semantically similar examples, and constructs contextually relevant prompts for target language models.  
Figure 1: Overview of the MuRXLS (Multilingual Retrieval-based Cross-lingual Summarization) framework. Our approach leverages a multilingual datastore processed through BGE-m3 for retrieval. For each article in the source language, we identify semantically similar examples based on cosine similarity scores. The selected examples and the test article are then combined in a prompt for the target LLM, which generates the cross-lingual summary.  
The framework addresses the fundamental challenge in cross-lingual summarization by moving beyond random or length-based example selection to semantic relevance-based retrieval. Unlike previous approaches that prioritize fitting examples within context length constraints, our method ensures that each article is paired with contextually appropriate demonstrations that better guide the LLM in generating accurate cross-lingual summaries.
## 5 The COGNIFOG Project
### Architecture  
The COGNIFOG architecture, as shown in Figure 2, has been conceived to be modular by design and is composed of a set of distinct yet interconnected building blocks distributed across four primary layers:  
* The modelling layer empowers users to conceptualize and define the main characteristics of the CEI continuum before its actual deployment. It includes designing the network topology, outlining hardware and software specifications, creating service level agreements (SLA) models, defining application data flow models, and planning infrastructure frameworks.
* The DevOps layer facilitates the automation of the software development and deployment process across the various stages of the software lifecycle, utilizing a comprehensive continuous integration/continuous delivery (CI/CD) framework.
* The runtime layer encompasses the actual environments in which applications function, including on-premises setups, cloud infrastructures, edge devices, and any locations within the COGNIFOG architecture. This layer integrates essential components such as servers, operating systems, containers, serverless environments, databases, and other critical elements required for hosting and executing applications.
* The governance layer focuses on the implementation of tools and practices for effective orchestration, monitoring, logging, auditing, and reporting of the COGNIFOG framework. It also ensures compliance with established processes, enforces policies, and prioritizes software security and data protection.
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
###### Abstract  
The recycling of waste materials has become an important topic worldwide. Wastes can be effectively used in concrete to improve its characteristics. This study aimed to research cement mortar's physical properties, mechanical properties, and durability. In a cement mortar with a fixed water-to-binder ratio (W/B) of 0.5, waste polyethylene (PE) was added at sand volume ratios of 0%, 1%, 2%, 3%, and 4%. Cement was replaced with 0%, 10%, and 20% ground granulated blast furnace slag (GGBFS). The results showed that the slump and flow of mortar tended to decline as the added amount of waste PE increased, but they also increased with the increased replaced amount of GGBFS. The setting time of mortar was shortened as the waste PE increased but delayed as the amount of GGBFS increased. In terms of mechanical properties, the compressive strength of mortar declined as the replaced amount of waste PE increased. Using the GGBFS to replace part of the cement can improve the later mortar strength. This study found that when the added waste PE was within 2% and the replacement amount of GGBFS was 10%, the goal of recycling waste was reached most effectively, while maintaining the concrete's mechanical properties.  
recycling of waste materials; polyethylene; ground granulated blast furnace slag; mechanical properties +
Footnote †: journal: Applied  
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Footnote †: journal: Applied  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Machine Learning Applications for Jet Tagging in the CMS Experiment
## 2 Jet Flavour Tagging
### The DeepCSV Tagger  
The DeepCSV algorithm was developed with a Deep Neural Network (DNN) with more hidden layers and more nodes per layer in order to improve the CSVv2 b-tagger. The input is the combination of the IVF secondary vertices and up to the first six track variables, taking into consideration all the jet-flavour and vertex categories. Variable preprocessing is used to speed up training and centres the distributions around zero with a root mean square equal to one. The jet \(\mathrm{p_{T}}\) range used in training goes from 20 GeV up to 1 TeV and remains within the tracker acceptance by also using the preprocessed jet \(\mathrm{p_{T}}\) and \(\eta\) as input.  
Figure 2: Distribution of the CSVv2 discriminant for jets of different flavour in tf events: the output for the version with (**a**) IVF reconstruction and with (**b**) AVR reconstruction. The distributions are normalised to unit area. Jets without a selected track and secondary vertex are assigned a negative discriminator value. The first bin includes the underflow entries [18].  
The neural network, developed with _KERAS_[20], uses four fully connected hidden layers, and each layer has 100 nodes. The activation function of each node is a rectified linear unit that defines the output of the node, with the exception of the last layer, for which the output is a normalised exponential function interpreted as the probability of flavour \(f\) of the jet (P(\(f\))). Five jet categories corresponding to the nodes in the output layer are defined: one for b hadron jets, at least two for b hadrons, one for c hadron and no b hadron, at least two for c hadron and no b hadron, and other jets. Figure 3 shows the DeepCSV probability P(\(f\)) distributions.  
The DeepCSV tagger is used also for c tagging, which combines the probabilities corresponding to the five categories. In particular, the DeepCSVCsB discriminant is used to discriminate c jets from b jets and is defined as:  
\[DeepCSVCsB=\frac{P(c)+P(c\bar{c})}{1-P(udsg)}, \tag{1}\]  
Figure 3: Discriminator distributions of (a) DeepCSV P(b), (b) DeepCSV P(b\(\bar{b}\)), (c) DeepCSV P(c), (d) DeepCSV P(c), (e) DeepCSV P(usdg), and (f) DeepCSV P(b)+P(b\(\bar{b}\)) [18].  
where \(1-P(usdg)\) is the probability of identifying an a, b, or c jet. In the same way, DeepCSVCvsL is defined to discriminate c jets from light jets:  
\[DeepCSVCvsL=\frac{P(c)+P(c\bar{c})}{1-(P(b)+P(b\bar{b}))}, \tag{2}\]  
and the denominator is the probability of identifying a c jet or a light jet.
# Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning
## 2 Preparatory Knowledge
### Graph Neural Networks
For instance, analyzing identity addresses can help determine the causes of cryptocurrency price fluctuations and their association with specific types of accounts, which is essential in safeguarding the blockchain's ecological integrity and establishing standardized transaction protocols. Traditional methods of identity recognition mainly include manual annotation and source code analysis. The former requires considerable manpower and is virtually impossible to accomplish for hundreds of millions of identity information. Although source code analysis for address identification (which involves analyzing the source code of a smart contract to identifypotential backdoors or vulnerabilities) is more accurate, this approach is difficult to implement and many smart contracts do not disclose their source code. In contrast, graph neural network technology offers a novel solution. [PERSON] et al. (2020) designed a blockchain address identification method based on graph deep learning, and its framework is shown in Figure 2.  
This model primarily comprises three major modules. The graph construction is based on node representation matrices, adjacency matrices and temporal density matrices to construct a directed weighted graph, generating distinctive feature representations for each node. In this framework, the node representation matrix includes information about the nodes' out-degree and in-degree, as well as the node type. The adjacency matrix is constructed with four types of edges based on transactions, contract calls, rewards, and other methods. The time density matrix is built according to the frequency and timing of interactions between account addresses. The model employs graph convolutional neural networks for learning and ultimately uses the softmax function to predict the node types for anomaly identity detection. [PERSON] et al. developed a model named I2 BGNN ([PERSON] et al., 2019), an end-to-end network specifically designed for processing the graph structure of blockchain transaction data. The I2 BGNN model learns and captures patterns within transaction subgraphs and associates these patterns with user identities to enable de-anonymization. This approach infers user identities from transaction behaviors by analyzing transaction subgraphs, transforming the task of identity inference into a problem of graph classification. The model architecture of I2 BGNN is shown in Figure 3:  
Figure 3: I2 BGNN Model Framework. Figure 2: GNN Identity Recognition Framework (Numbers represent trading entities). The model initially constructs a graph network through blockchain transaction information, then samples labeled accounts, extracting subgraphs centered around the target accounts as input for the model. Finally, it trains a GNN model and evaluates the results. Experiments conducted on the EOSG and ETHG datasets demonstrate that this method achieves superior results in the domain of identity inference.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 6 Simulation Analysis
### Handle Exceptions
#### 6.3.2 Communication Abnormity  
In actual flight, the communication topology may be temporarily interrupted. At this time, a communication event, \(k=0\), is insufficient to meet the expected formation, and formation interval distance correction is required again.  
In this simulation, a formation switch was performed from an inverted V-shape to a V-shape. It was assumed that in the initial communication event, namely \(k=0\), the communication failure rate reached 18%, and in the second communication event, namely \(k=1\), the communication failure rate reached 9%. Other parameters were as shown in Table 1, and the results are shown in Figures 19 and 20.  
Figure 17: Formation reconstruction.  
Figure 18: Formation reconstruction process.  
It can be seen in Figures 19 and 20 that in the case in which some UAVs and other UAVs were lost in the communication network, the first formation interval distance correction could not obtain the desired formation. In the second correction, the communication network still had local faults, but eventually formed the expected formation. So, the formation-switching algorithm was effective in dealing with communication abnormity.
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 3 Results
### Comparing Proximal and Remote Sensing Indices against Tree Performances
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Irrigation**} & **Predictive** & \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**B**} & \(\boldsymbol{p}\)**-Value** & \multirow{2}{*}{**R2 adj**} & \multirow{2}{*}{**F (df)**} & \(\boldsymbol{p}\)**-Value** & **SEP** \\  & & & **(\(\boldsymbol{t}\)-Test)** & & & & **(Regression)** & **(kg/Tree)** \\ \hline \multirow{2}{*}{FI} & Intercept & \(-\)36.7 & 0.021 & \multirow{2}{*}{0.48} & \multirow{2}{*}{16.5 (16)} & \multirow{2}{*}{0.001} & \multirow{2}{*}{3.92} \\  & NDVI & 103.2 & \(<\)0.001 & & & & \\ \hline \multirow{2}{*}{DI} & Intercept & \(-\)48.1 & \(<\)0.001 & \multirow{2}{*}{0.61} & \multirow{2}{*}{31.9 (37)} & \multirow{2}{*}{\textless{0.001}} & \multirow{2}{*}{2.96} \\  & NDVI & 85.5 & \(<\)0.001 & & & & \\ \cline{1-1}  & \(\text{ECa}_{1}\) & 0.5 & 0.002 & & & & \\ \hline \multirow{2}{*}{RF} & Intercept & 100.5 & 0.038 & \multirow{2}{*}{0.55} & \multirow{2}{*}{10.7 (14)} & \multirow{2}{*}{0.001} & \multirow{2}{*}{1.98} \\  & \(\text{ECa}_{1}\) & 0.54 & 0.001 & & & & \\ \cline{1-1}  & TC\_gamma & \(-\)0.26 & 0.035 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of forward stepwise regressions calculated for the fruit yield and the three irrigation regimes (FI: Full irrigated; DI: Deficit irrigation; RF: Rainfed, no irrigation). Legend: B, coefficient of the regression; \(p\)-value, value of the \(t\)-test of the coefficient, \(\text{R}^{2}_{\text{adj}}\), adjusted \(\text{R}^{2}\); F, F-test value of the multiple regression and degrees of freedom; SEP, Standard Error of Prediction. Figure 6: The relationship between soil \(\text{ECa}_{1}\) and the NDVI of trees grown in the soil area under full irrigation (**A**,**D**), deficit irrigation (**B**,**E**), and rainfed conditions (**C**,**F**). Each point represents one tree (and the respective allocated soil area). Coefficients of determination were calculated within each irrigation treatment. Regression equations: NDVI = 0.42 + 0.005 \(\text{ECa}_{1}\) (**A**,**D**); NDVI = 0.44 + 0.004 \(\text{ECa}_{1}\) (**B**,**F**); NDVI = 0.44 + 0.003 \(\text{ECa}_{1}\) (**C**,**F**).