esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of the Model and Numerical Calculation Method
### LBM Model
\tag{13}\]  
When considering the influence of fluxion, the equilibrium distribution functions of (1)-(3) are the equation of (14)-(16). \[f_{i}^{eq}=w_{i}\rho\bigg{(}1+3\frac{\epsilon_{i}\cdot w}{c^{2}}+\frac{9}{2} \frac{(\epsilon_{i}\cdot w)^{2}}{c^{4}}-\frac{3}{2}\frac{\text{erg}}{c^{2}} \bigg{)} \tag{14}\]  
\[g_{i}^{eq}=w_{i}C_{i}\bigg{(}1+3\frac{\epsilon_{i}\cdot w}{c^{2}}+\frac{9}{2} \frac{(\epsilon_{i}\cdot w)^{2}}{c^{4}}-\frac{3}{2}\frac{\text{erg}}{c^{2}} \bigg{)} \tag{15}\]  
\[h_{i}^{eq}=w_{i}T\bigg{(}1+3\frac{\epsilon_{i}\cdot w}{c^{2}}+\frac{9}{2} \frac{(\epsilon_{i}\cdot w)^{2}}{c^{4}}-\frac{3}{2}\frac{\text{erg}}{c^{2}} \bigg{)} \tag{16}\]  
According to the analysis of Chapman-Enskog, continuity equation, N-S equation and convection diffusion equation can be obtained from the above mentioned LB equation. Fluid dynamics viscosity \(\
u\), thermal diffusivity \(\alpha\) and solute diffusion coefficient \(D\) are connected with fluxion relaxation time \(\tau_{f}\), temperature relaxation time \(\tau_{t}\) and concentration relaxation time \(\tau_{c}\) respectively (Equations (17)-(19)) [14]. \[\
u=\frac{\tau_{f}-0.5}{3}\frac{\Delta x^{2}}{\Delta t} \tag{17}\]  
\[\alpha=\frac{\tau_{t}-0.5}{3}\frac{\Delta x^{2}}{\Delta t} \tag{18}\]  
\[D=\frac{\tau_{c}-0.5}{3}\frac{\Delta x^{2}}{\Delta t} \tag{19}\]
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 4 Result and Discussion  
### Dataset Preparation  
We collected 38,387 and 7993 malicious URL samples from two independent cloud platforms, PhishTank and Any.Run, respectively, along with 205,628 legitimate URLs from DMOZ. Since the number of malicious samples is smaller than the number of legitimate samples, we merged additional data from Any.Run to enhance the malicious dataset. After collecting the datasets, we performed preprocessing steps including removing duplicate links, missing values, null values, and control characters, trimming leading/trailing slashes and whitespace, normalizing URLs to lowercase, and decoding encoded URLs into ASCII characters. Following preprocessing, the legitimate dataset contained 205,628 samples, and the malicious dataset contained 46,380 samples. This imbalance between the two classes could bias the model during training and testing. To address this, we shuffled the datasets by label and performed class balancing through under-sampling, selecting the minimum number of samples from each class to achieve balance. Before proceeding to tokenization and downstream tasks, we validated the dataset using transformation techniques and the interquartile range (IQR) to ensure quality and consistency.
## 4 Analysis of Reservation Frame Slotted-ALOHA
### Average Number of Frames in the Transient States  
In order to obtain the average time (in frames) in which the process remains in every transient state of the chain, we first order the \(K\) states as \(\left\{X_{1},X_{2},...,X_{K}\right\}\), where \(X_{i}=(c_{i},f_{i})\). Being \(X_{K}=(0,m)\)the absorbing state and the other the transient states, with \(X_{1}=(n,m)\), we can represent the absorbing Markov chain as a \(K\times K\) transition matrix \(P\) defined as:  
\[P=\left[\begin{array}{cc}Q&R\\ 0&1\end{array}\right] \tag{9}\]  
where \(Q\) is a \((K-1)\times(K-1)\) matrix that contains the transition probabilities between transient states (_i.e._, \(p_{ii}\
eq 1\) with \(i,j<K\)), \(R\) is a \((K-1)\times 1\) non-zero column vector that contains the transition probabilities from the transient states to the absorbing state (_i.e._, \(i<K\) and \(j=K\)), one is the transition probability at the absorbing state (_i.e._, \(p_{ii}=1\) with \(i=K\)) and zero is a \(1\times(K-1)\) row vector of zeros.  
The fundamental matrix \(N\) of \(P\) can be defined as:  
\[N=\left(I-Q\right)^{-1} \tag{10}\]  
where each element \(n_{ij}\) of \(N\) is equal to the average number of frames that the process remains in the transient state \(X_{j}\) (_i.e._, with \(c_{j}\) contending end-devices and \(f_{j}\) free slots for \(n_{ij}\) consecutive frames) given that it started in the transient state \(X_{i}\) with \(i,j=\{1,...,(K-1)\}\).  
The time of absorption, denoted by \(t_{absorption}\), is defined as the average number of frames until the process is absorbed (_i.e._, it reaches state \(X_{K}\)) when it started in state \(X_{1}\). The time of absorption can be expressed as:  
\[t_{absorption}=N\cdot c=\sum_{j=1}^{K-1}n_{1j} \tag{11}\]  
where \(c\) is a \((K-1)\times 1\) column vector with all ones. The values of \(n_{1j}\) with \(j=\{1,...,(K-1)\}\) are used in Section 5 to formulate the theoretical models of delay and energy consumption using RFSA.
## 4 Experiment and Analysis
To help analyze the experimental results better, we draw a probability density estimate figure shown in Figure 7. This set of figures mainly depict distribution situation of delay time for different security profiles in different lengths of data. In each of these figures, the x-axis presents the delay time in milliseconds and the y-axis represents probability density estimate value. From this set of figures, we can see that for the same probability density estimate value, the delay time value of security profile using Whirlpool and RSA is smaller than the one using SHA-1 and RSA. From this we can also conclude that the security profile using Whirlpool and RSA performs better than the security profile using SHA-1 and RSA in delay time. \begin{table}
\begin{tabular}{c c c c c} \hline
**Profile** & **1024 Bytes** & **4096 Bytes** & **8192 Bytes** & **16384 Bytes** \\ \hline none & 1.052 ms & 1.265 ms & 1.684 ms & 2.179 ms \\ WhirlpoolRSA & 1.215 ms & 1.736 ms & 2.485 ms & 4.387 ms \\ SHA1 RSA & 1.268 ms & 1.859 ms & 2.659 ms & 4.753 ms \\ basic128 rsa15 & 2.648 ms & 3.839 ms & 5.914 ms & 8.647 ms \\ basic256 & 3.473 ms & 5.164 ms & 8.627 ms & 12.304 ms \\ \hline \end{tabular}
\end{table} 
Table 1: Experiment results. Figure 7: _Cont._  
Figure 7: Probability distribution comparison of delay time in different lengths of messages. (**a**) In the length of 1024 bytes; (**b**) In the length of 4096 bytes; (**c**) In the length of 8192 bytes; (**d**) In the length of 16384 bytes.
## 2 Materials and Methods
### Data Analysis  
The technical goal of the VCMS is to enable data transmitted from the ventilator and the patient monitors to be observed simultaneously in synchronization with the central monitor and mobile applications and to enable storage, search, and analysis in the database without loss of information. To evaluate the accuracy of data communication, the ideal data transfer capacity (DTC) per second of the ventilator and patient monitor was calculated, which can be defined by Equation (1):  
\[DTC\text{:}VT\text{ or }DTC\text{:}PM=(14\times N_{wave}\times M_{wave}+32 \times N_{para})\times N_{Bed}\text{ (bps)} \tag{1}\]  
where \(DTC\text{:}VT\) and \(DTC\text{:}PM\) are the data transfer capacities of the ventilator and patient monitor, respectively. The wave and numerical data comprised 14-bit and 32-bit signed integrator arrays, respectively. Therefore, the capacity of the wave data per second can be defined by multiplying the number of wave data per second (\(N_{wave}\)), the number of matrix elements of each wave data point (\(M_{wave}\)), and 14 bits. In addition, the capacity of numerical data per second can be calculated by multiplying the number of parameters (\(N_{Pnm}\)) by 16 bits. Finally, \(DTC\text{:}VT\) and \(DTC\text{:}PM\) can be calculated by summing the DTC of the wave data and numerical data and then multiplying by the number of beds (\(N_{Bed}\)). Quantitative verification was performed by comparing the realistic \(DTC\) of the ventilator and patient monitor. Another assessment is data transmission rapidity (\(DTR\)). Depending on the size of each \(DTC\), it is necessary to verify whether data are transmitted at the correct time during serial communication from the device to the server. The error compared to the outlier can be expressed as Equation (2):  
\[TR\text{ }difference=\left(\frac{T_{M,DTC}-T_{I,DTC}}{T_{I,DTC}}\right) \times 100\text{ }(\%) \tag{2}\]  
where \(T_{I,DTC}\) and \(T_{M,DTC}\) are ideal and measured transfer times, respectively. The \(T_{I,DTC}\) values were close to one second for each \(DTC\).
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 5 Evaluation
### Heterogeneous Paths
#### 5.4.2 Observation 5  
Figure 6 illustrates the overall impact on throughput, which can be summarized as follows. The throughput of all MPTCP algorithms (including RSF) decreased with increasing delay, as expected. However, the decline was quite gradual for RSF MPTCP and quite pronounced for the other MPTCP algorithms.  
This result was mainly due to the delay required for re-transmission of lost packets, as well as the buffering delay when re-sequencing out-of-order packets received at the client. In contrast, the balanced pricing strategy adopted in our design made RSF MPTCP superior to the others in an environment with heterogeneous delay and non-negligible packet losses.  
Scenario 6 examined heterogeneous paths with different loss characteristics. We set the packet loss probability to a fixed value of 3% for one path while varying the packet loss probability for the other path. Both paths had equal bandwidth (20 Mbps) and delay (150 ms).  
Figure 7 depicts the throughput performance when downloading a file with a size of 10 MB. The results show that, with increasing packet loss, the throughput achieved by the existing MPTCP algorithms decreased. In contrast, the RSF-LB algorithm outperformed them and provided robust performance, even with increasing channel errors. This was because most existing algorithms significantly reduced congestion windows after detecting  
Figure 5: Throughput results for 10 MB file transfer in Scenario 4: heterogeneous bandwidth paths. One path was 20 Mbps, while the bandwidth of the other one was varied. Both paths had 150 ms delay and 4% random packet loss.  
Figure 6: Throughput results for a 10 MB file transfer in Scenario 5: heterogeneous delay paths. One path had 20 ms delay, while the delay of the other one was varied. Both paths had 20 Mbps bandwidth and 3% random packet loss.  
packet losses. However, the proposed RSF MPTCP could dynamically determine the best way to adjust the congestion windows using the balanced pricing approach.
# Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements
## 1 Introduction
This paper considers the use of MIMO systems with Single-Carrier and Frequency-Domain Equalization (SC-FDE) in order to allow, not only an efficient power amplification at the Mobile Terminal (MT), but also a reduction in envelope fluctuations when compared with Orthogonal Frequency Division Multiplexing (OFDM) [14; 15; 16; 17]. In addition, when using SC-FDE, there is also a performance improvement when we use iterative Frequency-Domain Equalization (FDE), such as Iterative Block Decision Feedback Equalization (IB-DFE), instead of linear FDE, as is done in this research work [18]. The characterisation of the system considered in this study is presented in Section 2 and the BER performance of the SISO systems is theoretically calculated in Section 3. Section 4 presents the results  
Figure 1: Motivation for this study. obtained through the BER performance simulation for both SISO and MIMO systems, allowing us to evaluate the impact when switching from SISO to MIMO scenarios. The model is then used with a real SISO scenario, described in Section 5 to evaluate how it would behave in a MIMO scenario. This paper ends with Section 6 where the main conclusions are drawn. The following denotation is employed in this paper: In general, the upper-case letters denote frequency-domain variables, while lower case letters denote time-domain variable. Bold upper letters represent matrices or vectors and the Hermitian matrix of \(\mathbf{X}\) is denoted by \(\mathbf{X^{H}}\). The expectation of \(x\) is represented by \(\mathbb{E}[x]\) and \(\widetilde{x}\), \(\overline{x}\), \(\hat{x}\), denotes samples--respectively, \"soft decision\" and \"hard decision\" estimation of \(x\).
## 4 Denoising and Prediction of Optical Fiber Monitoring Signals Based on EEMD-DE-SVD and DBO-LSTM-BP  
### Denoising Model  
This paper uses the actual deformation optical fiber grating monitoring data from the 5-1\(\#\) coal pillar between the 22,206 and 22,208 working faces of the Huojitu Well 2-2\(\#\) coal seam in the Daliuta Coal Mine during secondary mining as experimental data. The optical fiber grating at borehole 3 is located in the center of the 5-1\(\#\) coal pillar, i.e., the middle of the bell-shaped load distribution area, where the impact of the residual inclined coal pillar from the overlying mined-out area is more extensive and the concentrated stress effect is stronger. From the deformation monitoring strain curve of the above section coal pillar, it is evident that the initial optical fiber grating monitoring data are disturbed by noise, resulting in spikes and redundant signals, requiring the denoising of the original optical fiber grating signal. However, the optical fiber grating at boreholes 1-5 is located at different positions under the residual inclined coal pillar from the overlying mined-out area, leading to varying levels of noise interference in the deformation monitoring of the coal pillar section. Additionally, the noise interference in curves with larger strain variations can increase the complexity of the noise signal.  
Denoising steps for optical fiber signals based on EEMD-DE-SVD are as follows:  
Step 1: Collect vibration signals under various working conditions.  
Step 2: Use EEMD to decompose vibration signals under different working conditions. For example, perform ensemble empirical mode decomposition on the signal from borehole 1, measurement point 8, and obtain the first 6 IMF components as shown in Figure 7.  
Step 3: Calculate the dispersion entropy values of each IMF component using the DE algorithm, as shown in Figure 8.  
Figure 7: EEMD denoising decomposition of the signal from borehole 1, measurement point 8.  
Step 4: Calculate the ratio of adjacent IMF dispersion entropy values, as shown in Figure 9.  
Step 5: The threshold critical point M1 for the EEMD denoising algorithm is 1.700. M1 is the dispersion entropy ratio between IMF3 and IMF4. The maximum ratio indicates a sharp decrease in noise content, suggesting that IMF4 and subsequent components contain less noise. Therefore, IMF1-IMF3, which contain higher noise levels, should be discarded. Use IMF4-IMF5 and the residual component RES for data reconstruction.  
Step 6: Perform singular value decomposition (SVD) on the matrix constructed from the denoised signal using the DE algorithm, and use the results as feature vectors.
## 2 Hardware and Software Solutions  
For practical validation of the mentioned issue, it is necessary to have a mobile robotic system (MRS), to which the proposed solution can be applied. From our point of view, the MRS used is considered a black box that can be controlled using the API. Communication with the robot takes place using LoRa technology. LoRa (Long Range) is a spread spectrum modulation technique derived from chirp spread spectrum technology. This technology is a long-range, low-power wireless platform that has become the de facto wireless platform of the Internet of Things. A Lidar LD-OEM 1000 is mounted on the MRS, which performs measurements. LoRa communication technology does not allow sending data in the volume and speed required, so a Wi-Fi router is added, which communicates with lidar and an external computing unit using the TCP/IP protocol. The concept is summarized in Figure 1. The main element of this robotic system is the omnidirectional wheels. If omnidirectional wheels and a relevant control algorithm are used, then the robotic system is holonomic. The design and execution of this MRS is beyond the scope of this article and will therefore be published in our future publications.  
There are currently many open-source solutions for SLAM, so we do not consider it necessary to make new ones. We decide to use the Matlab platform, which offers its algorithm and adapts it on our own. Matlab uses the collaboration of several libraries for the SLAM solution, while the Robotic toolbox, Computer vision, Lidar toolbox, and Navigation toolbox already include specific solutions. It is possible to map the environment using a lidar sensor, RGB camera, but also using an IMU. How the algorithm works is discussed in [15; 16]. Testing the SLAM algorithm on MRS is only the first step in creating the entire ASLAM algorithm. The second step is to choose a suitable target for the next move of the MRS. The topic of ASLAM is beyond the scope of this article. If the destination to which the MRS is to be moved is selected, we can use the path planning algorithm. The next chapter focuses on path planning algorithms.
## 1 Introduction
[22] developed a mean line model using Matlab (R2018b, Mathworks, Natick, MA, USA) that includes the design and performance analysis procedure, and the results indicated that how different design choices in terms of specific speed and velocity ratio, and different working conditions in terms of expansion and turbine size may affect the efficiency of single stage radial inflow turbines in Organic Rankine Cycle (ORC) systems. [PERSON] et al. [23] proposed a new non-dimensional performance map tailored for small-scale turbines, and the results showed that the geometrical dependencies on the map had a strong impact on the shroud to tip radius ratio. [PERSON] et al. [24] proposed an optimization design approach to quickly acquire a preliminary optimal radial inflow turbine configuration, and the results indicate that the designed turbine using the proposed optimization design approach had a superior performance under design and off-design conditions. [PERSON] et al. [25] presented a design method for a supercritical carbon dioxide radial inflow turbine, and the results were consistent with each other. It can be seen from the above-mentioned references that a lot of research has been performed on radial inflow turbine design from different aspects. Some focus on aerodynamic performance simply, some aim to satisfy stress or weight limitation without deteriorating efficiency, and some devote to reduce dependency on designer's technical expertise through optimization algorithms. However,an automated and integrated solution that can fully consider all the problems is seldom seen in the open literature. This is a motivation of the present study. The preliminary design process of radial inflow turbines often requires a certain level of previous empirical knowledge and it usually makes the beginners feel a bit confused. Although it is well known that the aerodynamic performance of radial inflow turbines is dominated by loading coefficient and flow coefficient [26], the selection of these two parameters and rotational speed still needs comprehensive consideration. In this paper, as Part I, an automated Genetic Algorithm (GA) driven preliminary design procedure for radial inflow turbines is developed, and the effectiveness of the design approach is validated by the experimental data of the radial inflow turbine in Sundstrand Power Systems T-100 Multipurpose Small Power Unit [27]. This procedure not only alleviates dependency on the designer's technical expertise but also guarantees the optimal design at the preliminary design stage.
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 4 Combined Model Construction and Case Analysis Based on CEEMDAN and Machine Learning
### Case Studies  
In order to verify the superiority of the short-term traffic flow prediction model based on CEEMDAN and machine learning proposed in this paper, real traffic flow data were used to train the model, and the combined model in this paper was compared with the prediction results of the LSTM model, the IGWO-SVM model, and the KNN model to verify the performance of the combined model and the three single models.
# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 2 Background
### Hallucination Mitigation in RAG Systems  
Hallucination remains a critical challenge in neural text generation, particularly in public-sector applications, where factual reliability is paramount. Several approaches have been proposed to mitigate hallucinations in RAG settings. For instance, Self-RAG introduces a self-verification mechanism that re-ranks candidate answers using secondary queries [32], while GraphGPT leverages graph structures to impose logical constraints during generation [33].  
The TruthfulQA benchmark assesses model alignment with human factuality expectations in a wide range of question categories [34]. These efforts provide valuable building blocks for reliable text generation; however, most operate in isolated pipelines without agentic reasoning or dynamic retrieval adaptation. Our approach differs by introducing a multi-agent verification architecture (RQ3) that combines graph-based provenance tracking, web-sourced corroboration, and cross-agent answer validation, tailored for high-stakes e-government use cases.