esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 3 Methodology
### Security Alert Generation  
In the proposed method, to generate the enterprise alert dataset, an SIEM system integrates multiple NIDSs deployed in a network, providing a central monitoring point to monitor the network status and detect cyber threats. Most security appliance vendors adopt the common event format (CEF) [53], an event interoperability standard introduced by ArcSight, Inc. Owing to the flexibility of the CEF format, alert logs issued by different security appliances often convey different types of information, making data integration  
Figure 1: Proposed system framework for AI-assisted security alert data analysis.  
difficult. To address this, a data unification component synopsizes log files from different NIDSs into a single centralized view and outputs standard JavaScript object notation (JSON) [54] objects representing the enterprise dataset for further analysis.
## 4 Experiment and Result  
### Experimental Design  
The experiment consists of four parts. First part: To evaluate the effectiveness of the PCc generation method, we designed a controlled experiment with two groups. The first group uses a 7B parameter base model without our framework, the second group uses the same base model but with our proposed framework, and the third group uses two of the latest models, DeepSeek-V3 (671B) and GPT-4-turbo, without our framework. We performed a comparative analysis to assess the effectiveness of our method under identical personalized needs. For the prompt, we use the same base prompt mentioned in Section 3.1 to generate couplets with all models. The difference is that our framework utilizes the RAG module to guide the model, so in the prompt, \(d_{e}\) will be filled.  
Second part: This part aims to validate the effectiveness of our evaluation method and further demonstrate our generation approach. We conduct a controlled experiment by selecting the couplets generated in the first part and then evaluating them using our agent debate method.  
Third part: To quantitatively analyze and prove that the quality of couplets generated by our method is optimal, we conducted a questionnaire survey and performed Analysis of Variance (ANOVA) and LSD (Least Significant Difference) post facto tests according to the statistical data.  
Fourth part: In addition to validating the method's practicability and effectiveness, we also explore its role in couplet education. We first developed a user-friendly system based on our framework. Subsequently, we recruited participants and asked them to use the system to generate couplets based on their needs. Finally, participants were asked to provide their opinions on the generation process and feedback on the quality of the generated couplets.
## 4 Energy Efficiency
### Energy Efficiency  
Based on the above detailed analysis of transmission power, power consumption by signal detection, and ADC power consumption, we now summarize the total power consumption of the system and calculate the system EE. Assuming quadrature phase shift keying (QPSK) signaling, i.e., one user transmits 2 bits per symbol. The total transmission rate of UE-\(k\) is \(R=2 BK\) bits/second. The energy efficiency is evaluated as  
\[\text{EE}=\frac{R}{\text{P}_{\text{tot}}} \tag{49}\]  
in bits/Joule, where the power consumption includes  
\[\text{P}_{\text{tot}}=\text{KP}_{\text{UE}}+\frac{\text{P}_{\text{DT}}}{\eta}+N (\text{P}_{\text{BS}}+2\text{P}_{\text{ADC}})+\text{P}_{\text{SYN}}+\text{P} _{\text{SD}}+\text{P}_{\text{FIX}}, \tag{50}\]  
where \(\text{P}_{\text{UE}}\) and \(\text{P}_{\text{BS}}\) denote the power consumed by a single radio frequency (RF) chain and the UE and BS, respectively; \(\eta\) is the efficiency of the power amplifier at the transmitter; \(\text{P}_{\text{UE}}\) and \(\text{P}_{\text{DT}}/\eta\) represent the power consumption of the circuits and power amplifiers at the transmitter side; \(\text{P}_{\text{SD}}\) is the power consumption by signal detection, which can be evaluated as (35), (42) and (45) for the respective detectors; \(\text{P}_{\text{FIX}}\) is the power consumed by the signal control, backhaul, etc.; \(\text{P}_{\text{SYN}}\) is the power consumed by the local oscillator [3].  
Note that the above model ignored certain power consumption such as the channel decoding, etc. However, it still provides useful insights in studying the influence of signal detection and ADC resolution on the EE. We have summarized the major steps for evaluating the EE of MIMO systems with the LR-SIC detector in Algorithm 1 and other detectors can be evaluated similarly.  
```
0: Number of UEs \(K\), number of receiver antennas \(N\), radius of the cell \(d_{max}\), target BER, computational efficiency \(L_{\text{BS}}\)
0: Energy efficiency: EE
1: Perform Monte-Carlo simulations based on the system model (1) and (6), and the LR-SIC detector described in Section 3.2
2: Calculate the transmission power \(\text{P}_{\text{DT}}\) in (34) averaged over \(\varphi_{k}\)
3: Calculate the power consumption due to signal detection as (42)
4: Calculate ADC power consumption as (48)
5: Compute the total power consumption as (50)
6: Calculate the EE (49)
```  
**Algorithm 1** Evaluation of Energy Efficiency of MIMO Systems Employing the LR-SIC Detector
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 3 Related Work
Regarding the latter, modeling of P2 MP D2D is entirely new, as is the modeling of mode switching in P2P D2D communications and frequency reuse in both P2P and P2 MP D2D connections. It is worth mentioning that SimuLTE is currently being used by a wide research community, that uses it to test its own research (such as schemes for mode selection [36], vehicular networks [37] or smart-grid monitoring [38]). The purpose of this paper is clearly not to discuss novel D2D-related algorithms, but to support (and possibly expand) the above community by presenting it with new models of D2D functionalities, and enabling it to assess the rationale and performance/accuracy implications of the key modeling choices.
## 3 Results and Discussion
A representation of the noise levels on a plan of Algemesi (Valencia) can be observed together with the kriging of the time average values for each measuring point, so this kriging should be representative enough for all the time periods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**\%** & **mtry** & **coefReg** & **coefImp** & **RMSE** & **Rsquared** \\ \hline
10\%/90\% & 19 & 0.505 & 0.0 & 3.5215 & 0.6890 \\
20\%/80\% & 37 & 0.505 & 0.5 & 3.5327 & 0.6862 \\
30\%/70\% & 37 & 0.010 & 0.0 & 3.5225 & 0.6880 \\
40\%/60\% & 37 & 0.010 & 1.0 & 3.5338 & 0.6859 \\
50\%/50\% & 19 & 1.000 & 0.0 & 3.5405 & 0.6855 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of Leave-One-Out-Cross-Validation test using Regularized Random Forest (RRF) method.
## 4 Experiment I--DSM Quality Evaluation  
Forest change detection based on 3D data may be able to deliver good results, but it highly depends on the quality of the 3D data. For a meaningful interpretation, having deep understanding of the quality of the height information is necessary. For a continuous change monitoring it is almost impossible to have sufficient reference data for each dataset. In addition to that the storm event in 2007 has significantly changed the forest structure in Traunstein. Therefore, we have separated the DSMs into two groups for evaluation:  
1. DSMs derived from the data acquired before the storm event in 2007; and
2. DSMs derived from the data acquired after the storm event in 2007.  
The quality analysis was carried out separately using LiDAR data from 2001 for group (a) and LiDAR data from 2010 for group (b). Profiles of the DSMs were first analyzed visually; subsequently DSM heights were plotted against the LiDAR data and the top height H100 as derived for the field plots of the forest inventory. Finally, for a quantitative assessment, the DSMs were compared at pixel level for different land cover types using several statistical accuracy features.
## 4 Channel Allocation Based on Multi-Agent Deep Reinforcement Learning
### Channel Allocation Formulation  
In this subsection, the constrained optimization problem P1 is decomposed into the following channel allocation problem P2, as shown below.  
\[\begin{split}(\mathrm{P2})&:\underset{G}{\max}\mathrm{ E}_{H}[E_{sum}]\\ s.t.& C_{1}:\sum\limits_{u=1}^{K_{m}}G_{m,u}^{n}=2,G_{ m,u}^{n}\in\{0,1\}\end{split} \tag{8}\]  
In this paper, the goal of the problem P2 is to find a channel allocation scheme that maximizes the energy efficiency of the system. To achieve this goal, we treat the channel allocation problem P2 as a process of selecting users for subchannels. Since this process is inherently discrete and reinforcement learning has significant advantages in solving discrete problems, a multi-agent deep reinforcement learning approach is proposed to select appropriate users for each subchannel.
###### Abstract  
Molten salt reactors have gained substantial interest in the last years due to their flexibility and their potential for simplified closed fuel cycle operations for massive net-zero energy production. However, a zero-power reactor experiment will be an essential first step into the process delivering this technology. The choice of the optimal reflector material is one of the key issues for such experiments since, on the one hand, it offers huge cost savings potential due to reduced fuel demand; on the other hand, an improper choice of the reflector material can have negative effects on the quality of the experiments. The choice of the reflector material is, for the first time, introduced through a literature review and a discussion of potential roles of the reflector. The 2D study of different potential reflector materials has delivered a first down-selection with SS304 as the representative for stainless steel, lead, copper, graphite, and beryllium oxide. A deeper look identified, in addition, iron-based material with a high Si content. The following evaluation of the power distribution has shown the strong influence of the moderating reflectors, creating a massively disturbed power distribution with a peak at the core boundary. This effect has been confirmed through a deeper analysis of the 2D multi-group flux distribution, which led to the exclusion of the BeO and the graphite reflector. The most promising materials identified were SS304, lead, and copper. The final 3D Monte Carlo study demonstrated that all three materials have the potential to reduce the required amount of fuel by up to 60% compared with NaCl, which has been used in previous studies and is now taken as the reference. An initial cost analysis has identified the SS304 reflector as the most attractive solution. The results of the 2D multi-group deterministic study and the 3D multi-group Monte Carlo study have been confirmed through a continuous energy Monte Carlo reference calculation, showing only minor differences.  
nuclear; nuclear reactors; reactor physics; nuclear experiments; zero-power reactors; modelling and simulation; molten salt reactors +
Footnote †: journal: Applied Sciences
# The Role of Epigenetics in Periodontal and Systemic Diseases and Smoking: A Systematic Review
## 4 Discussion
### Patients with Systemic Diseases
#### 4.1.2 Limitations and Confounding Variables  
The results of the present systematic review need to be interpreted with caution due to certain limitations. First, the number of available studies on the DNA methylation in periodontitis patients who have a systemic disease or smoke is quite limited to generalize conclusions. Most of the studies lacked information on the ethnic background of the study participants, which is an important factor that affects epigenetics. Regarding studies on smoking, there was inconsistency between the studies in identifying what a (smoker) patient was, i.e., how many cigarettes they smoked a day. These studies did not stratify smoking patients into slight, moderate, and heavy, which could affect the conclusions of the included studies.
## 2 Literature Review  
The routing protocol of WSNs should be devised with properties such as energy efficiency, scalability, robustness, and rapid convergence, compared to that of traditional networks. A large number of routing methods have been proposed. Roughly, they can be divided into four categories through the analysis of relevant literature--that is, data-centric, clustering, geographic location-based, and Quality of Service (QoS)-based routing methods.  
Data-centric routing was proposed to reduce the flooding overhead caused by transmitting query and data information. In data-centric routing, data request and collection are based on data attributes, rather than only using local interactions [12; 13]. Clustering is the most common technique used for achieving energy-efficient and scalable performance in large-scale sensor networks. Cluster formation is a process whereby sensor nodes decide which cluster head they should associate with among multiple clusters [14; 15]. The low-energy adaptive clustering hierarchy (LEACH) [15], a typical cluster-based algorithm, divides a sensor network into a set of clusters, through which energy consumption is balanced and reduced. In geographical routing, the physical location of the sensor node is used to guide the path that a packet takes in the network [16; 17]. In some cases of WSN application, a higher-communication QoS is demanded, such as reliability and real-time data transmission. The method in [18; 19] can be classified in this category.  
Routing methods based on swarm intelligence have robust, adaptive, and scalable performance, suitable for autonomous distributed systems [20; 21]. Inspired by the foraging principles of honeybees, [PERSON] et al. [22] proposed a distributed and decentralized routing protocol called the BeeSensor protocol. [PERSON] et al. [23] studied the application of the ACO metaheuristic to solve the routing problem in WSNs, and came up with an energy-efficient, ant-based routing algorithm (EEABR). [PERSON] et al. [24] improved the EEABR algorithm by applying a new scheme to intelligently initialize and update routing tables, reducing the flooding ability of ants for congestion control. In [25],a self-adaptive routing mechanism is presented to ensure reliability and efficiency during data transmission by adopting the dissemination of a pheromone as a model for dealing with dynamic changes in WSN.  
QEAs are based on the concept and principles of quantum computing, such as the quantum bit and the superposition of states. As a kind of evolutionary algorithm, a QEA is also characterized by the representation of the individual, the evaluation function, and population dynamics. Learning from the quantum rotation gate strategy of QEAs, [PERSON] et al. [26] introduced an adaptive evolution mechanism for QoS multicasting in IP/DWDM networks, which allowed each chromosome in a population to update itself to a fitter position according to its own situation.
###### Abstract  
The present study investigates the impact of the electrode surface roughness on the electrofreezing of water. This research focuses on how the electrode microstructure induced by a laser treatment affects the nucleation and growth of ice crystals under controlled electric fields. For this, electrofreezing experiments of deionized water over electrodes with varying surface roughnesses and crystalline textures were conducted. The electrodes of the Al6061 T6 alloy were microstructured via the Laser Shock Processing (LSP) method. For this purpose, the pulse densities during the LSP process were varied (900, 1600, and 2500 pulses/cm\({}^{2}\)). The increase in pulse density was correlated to the microstructural features and average roughness of the LSP-treated Al6061 alloy. A wave-like microstructure was induced upon the LSP treatment, with roughnesses between 3.5 and 6 \(\upmu\)m at the selected pulse densities. The results indicate that electrode roughness significantly influences the electrofreezing process. Rougher electrodes were found to increase the nucleation temperature, suggesting enhanced ice nucleation activity. These findings are attributed to the increased electric field concentration at the asperities of the rough surfaces and the (111) planes of the Al6061 alloy, which may facilitate the alignment of water molecules and the formation of critical ice nuclei.  
electrofreezing; water; electrodes; MD Al6061 T6; laser shock processing Article
## 6 Conclusions  
Ice shedding bounce seriously affects the safety of overhead lines. Therefore, this article analyzed the main influencing factors of ice shedding bounce under strong wind conditions, and extracted the influence of strong wind on the ice shedding bounce of crescent-shaped, fan-shaped, D-shaped, and elliptical-shaped iced conductors.  
1. The resistance mainly affects the initial ice shedding position of the overhead conductors and leads to significant differences in the lateral oscillation of the conductor after ice shedding.
2. The outer curves of crescent, and elliptical iced conductors are smooth, with less lateral displacement under wind force, and their risk of ice shedding response is mainly in the vertical direction.
3. When the wind attack angle approaches 180\({}^{\circ}\), the airflow resistance of the fan-shaped and D-shaped icing conductors significantly increases. In the process of ice shedding response of transmission lines, the lateral amplitude may exceed 20 m, which has a high possibility of causing phase-phase and phase-ground discharge.  
**Author Contributions:** [PERSON] was responsible for the overall design of the study and numerical simulation model. [PERSON] and [PERSON] contributed to the interpretation of the data and the drafting of the manuscript. [PERSON] was responsible for the analysis of the data, and the hydrodynamic model. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Supported by the research project of State Grid Xinjiang Electric Power Co., Ltd. (Program No. 5230 DK230008).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare that this study received funding from State Grid Xinjiang Electric Power Co., Ltd. The funder was not involved in the study decision, collection, analysis interpretation of data, the writing of this article or the decision to submit it for publication.
# TCCCD: Triplet-Based Cross-Language Code Clone Detection
## 5 Experiment Setup  
When it comes to cross-language code clone detection, we propose several Research Questions (RQs) to evaluate the effectiveness of our approach:  
1. **RQ1: baselines' effectiveness comparison.** To gauge our approach's competitiveness in the cross-language code-clone-detection domain, we aimed to assess its effectiveness relative to state-of-the-art methods using the metrics described in Section 4.2 as the criteria. Hence, we formulated **RQ1**: \"How does our approach perform compared to the state-of-the-art approaches in cross-language code clone detection?\"
2. **RQ2: effectiveness on specific language pairs.** In practical development scenarios, developers often require code clone detection across various programming languages. To assess the effectiveness of our approach in detecting code clones across specific, distinct programming languages, we pose **RQ2:** \"Does our approach effectively detect code clones across different programming languages?\"
3. **RQ3: impact of different components.** Our approach comprises multiple components, including triplet learning and distance-weighted sampling. By analyzing the influence of these diverse components on the effectiveness of our approach, we can gain a better understanding of which components are most crucial for cross-language code clone detection. This understanding, in turn, guided our selection and optimization. Therefore, we present **RQ3**: \"What is the impact of different components in our approach on the effectiveness of cross-language code clone detection?\"
## 2 Experimental Methods
### Data Reduction and Uncertainty Analysis
Heat transfer coefficient: The heat transfer coefficient (\(h\)) was calculated using [PERSON]'s Law of Cooling, as shown in Equation (3). \[h=\frac{q}{T_{\text{wall}}-T_{\text{sat}}} \tag{8}\]  
where \(q\) is the calculated heat flux, and \(T_{\text{wall}}-T_{\text{sat}}\) is the determined wall superheat. Combined standard uncertainty in the heat transfer coefficient: The uncertainty evaluation for the heat transfer coefficient was based on the error transmission method. Uncertainties in both the wall temperature and saturation temperature contribute to the overall uncertainty in the heat transfer coefficient. Using uncertainty propagation, the combined standard uncertainty of the heat transfer coefficient, \(u_{h}\), was calculated by accounting for the individual contributions from heat flux, wall temperature, and saturation temperature as the square root of the sum of the squares of these individual uncertainties. The formula used is:  
\[u_{h}=\sqrt{\left(u_{q}\right)^{2}+\left(u_{T_{wall}}\right)^{2}+\left(u_{T_ {sat}}\right)^{2}} \tag{9}\]  
The calculated uncertainty values for \(q\) and \(h\) are presented in Table 2. Notably, the uncertainty values tend to be larger for the test specimens with a higher wall superheat. It is important to note that \(u_{T_{wall}}\) corresponds to \(u_{b}\), which is the standard error of the intercept (\(b\)) from the linear regression used to determine the temperature gradient (\(dT/dx\)). \(u_{T_{sat}}\) corresponds to the error of the pressure gauge, particularly since the saturation temperature in this work was determined based on the saturation pressure measured by a pressure gauge.