esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
## 6 Evaluation
### Using TCP  
In this simulation, we use 10 file transfer protocol (FTP) sources generating 550- and 1500-byte packets. A new source starts to generate its flow every 5 s, thus increasing the traffic load as time goes. The packet sizes correspond to the actual Internet packet distribution [31]. Figure 12 compares the network throughput of min-hop, MTM, and MERV with the increasing TCP traffic load. MARY is found to outperform both MTM and min-hop. Figure 13 shows the overall network throughput, normalized routing overhead, and end-to-end delay of the simulation.  
Figure 11: Overall network throughput, normalized routing overhead, and end-to-end delay for 40 CBR (UDP) flows.  
Figure 10: End-to-end packet delay vs. packet size.  
The control overhead of MARV is lower than both min-hop and MTM. By setting the constant value \(\alpha\) (as discussed in Section 4.2), the intermediate node can rebroadcast only once or twice. This limits the control overhead of MARV while acquiring high-throughput paths.  
One thing to note is that as the number of nodes increase, there may be issues in the MAC layer due to high contention and packet collisions. In case of the multi-rate routing, the high-density scenario could be advantageous since it will offer more choices for better high-throughput routes. On the other hand, the routing overhead may increase, but this is limited by the constant value \(alpha\) as discussed in Section 4.2.
## 3 Results
### Technical Performance Results
#### 3.1.7 Mechanical Performance
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Mortar Label**} & \multirow{2}{*}{**Flow (\%)**} & \multicolumn{4}{c}{**Compressive Strength (MPa)**} & \multicolumn{4}{c}{**Eleural Strength (MPa)**} \\ \cline{3-10}  & & **1 Day** & **7 Days** & **28 Days** & **91 Days** & **1 Day** & **7 Days** & **28 Days** & **91 Days** \\ \hline M\_PC & 81.5 & 24.9 \(\pm\) 0.6 & 56.6 \(\pm\) 1.0 & 65.1 \(\pm\) 1.0 & 69.3 \(\pm\) 0.9 & 6.5 \(\pm\) 0.3 & 10.5 \(\pm\) 1.2 & 11 \(\pm\) 0.3 & 13.0 \(\pm\) 2.0 \\ M\_QP & 91.0 & 12.6 \(\pm\) 0.4 & 36.5 \(\pm\) 1.1 & 49.0 \(\pm\) 1.3 & 55.8 \(\pm\) 3.0 & 3.1 \(\pm\) 0.1 & 7.0 \(\pm\) 0.3 & 7.4 \(\pm\) 0.2 & 8.8 \(\pm\) 0.4 \\ M\_TS1 & 95.5 & 11.8 \(\pm\) 0.1 & 37.7 \(\pm\) 0.6 & 46.2 \(\pm\) 1.1 & 55.3 \(\pm\) 1.0 & 4.1 \(\pm\) 0.2 & 8.7 \(\pm\) 0.6 & 9.3 \(\pm\) 0.8 & 10.9 \(\pm\) 0.9 \\ M\_TS2 & 106 & 10.5 \(\pm\) 0.1 & 36.8 \(\pm\) 0.4 & 47.7 \(\pm\) 1.2 & 55.5 \(\pm\) 0.9 & 3.5 \(\pm\) 0.1 & 8.1 \(\pm\) 0.2 & 9.4 \(\pm\) 0.7 & 9.9 \(\pm\) 0.5 \\ M\_TS3 & 98.5 & 12.3 \(\pm\) 0.2 & 39.4 \(\pm\) 0.4 & 47.5 \(\pm\) 0.8 & 53.6 \(\pm\) 1.2 & 4.1 \(\pm\) 0.2 & 8.8 \(\pm\) 0.5 & 9.6 \(\pm\) 1.8 & 10.2 \(\pm\) 1.6 \\ \hline \end{tabular}
\end{table} 
Table 9: The flowability, compressive and flexural strength of the mortar mixtures. grains and the surrounding paste could contribute to the tensile and thus flexural strength of the mixtures at any age. This could explain why slag-blended mixtures showed lower compressive strength but higher flexural strength compared to Q_QP. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Mortar Label**} & \multirow{2}{*}{**Slag Source**} & \multicolumn{4}{c}{**SAI at Different Ages (Days)**} \\ \cline{3-6}  & & **1** & **7** & **28** & **91** \\ \hline M\_QP & – & 0.50 & 0.64 & 0.75 & 0.81 \\ M\_TS1 & TS1: Treated S1 & 0.47 & 0.67 & 0.71 & 0.77 \\ M\_TS2 & TS2: Treated S2 & 0.42 & 0.65 & 0.73 & 0.80 \\ M\_TS3 & TS3: Treated S3 & 0.49 & 0.7 & 0.73 & 0.77 \\ Standard & EN 15167 & – & – & 0.70 & – \\ limitation & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Strength activity indexes (SAIs) of the slags at different ages. Figure 11: Compressive and flexural strength results of mortar mixtures up to the age of 91 days. (**a**) compressive strength, (**b**) flexural strength.
# Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection
## 1 Introduction
The packets whose lengths exceed the bound are delivered to the CPU rapid prefiltering; otherwise, the packets are assigned to the GPU full pattern matching directly. Higher performance can be obtained by means of reducing the payload length diversity. In the experiment, the previous methods are also implemented and compared with the LHPMA, and the results demonstrate that the LHPMA can enhance the performance. The rest of this paper is organized as follows: Section 2 describes typical pattern matching algorithms and some previous studies of software-based implementation on CPU and GPU platform applied to NIDSs. Section 3 illustrates the proposed LHPMA, including the overall architecture, components, procedure and key algorithm. Section 4 demonstrates the experiment setup and results. The conclusions and future work are presented in Section 5.
## 4 Results and Discussion
### Ablation Study
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Configuration** & **Positive Instances (\%) \(\uparrow\)** & **Data Richness \(\uparrow\)** \\ \hline EduDCM & 87 & 4.6 \\ EduDCM\({}_{\text{NO}}\)-LIM-Driven Labeling & 72 & 3.2 \\ EduDCM\({}_{\text{NO}}\)-Heuristic Alignment & 83 & 4.3 \\ EduDCM\({}_{\text{NO}}\)-Semantic Disambiguation & 82 & 4.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Impact of key components on framework performance. \begin{table}
\begin{tabular}{l c c} \hline \hline
**LLM** & **Positive Instances (\%) \(\uparrow\)** & **Data Richness \(\uparrow\)** \\ \hline GPT-3.5 & 87 & 4.6 \\ Gemini-pro & 81 & 4.1 \\ Claude2 & 85 & 4.4 \\ GPT-4o & 89 & 4.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of different LLMs in the LLM-Driven Labeling component. Replacing GPT-3.5 with Gemini-pro resulted in a notable decline in both metrics, with the positive instance proportion dropping to 81% and the data richness score decreasing to 4.1. This suggests that while Gemini-pro can provide basic annotation functionality, its capacity for capturing nuanced pedagogical interactions and generating semantically rich annotations is limited compared to GPT-3.5. Claude2 demonstrated competitive performance, achieving a positive instance proportion of 85% and a data richness score of 4.4. Although slightly below the baseline, its consistent results indicate that Claude2 is a viable alternative in scenarios where cost or computational constraints favor its adoption. GPT-4o outperformed all other LLMs, achieving the highest positive instance proportion (89%) and data richness score (4.8). This superior performance highlights its advanced ability to understand conversational context and generate precise, detailed annotations. The results suggest that GPT-4o's enhanced contextual understanding and semantic capabilities make it particularly suitable for tasks requiring deep processing and high-quality annotation generation. These findings underscore the importance of selecting an appropriate LLM for the LLM-Driven Labeling component. While GPT-4o offers the best overall performance, Claude2 provides a cost-effective alternative with near-baseline results. Gemini-pro, while functional, may require additional optimization to meet the demands of complex educational dialogue classification tasks. This evaluation further highlights how advancements in LLM technology can significantly enhance the effectiveness of frameworks like EduDCM, enabling more scalable and accurate dataset generation in low-resource settings.
## 3 Results  
### Simulated Data of the Virtual Sample with KBr  
Two virtual samples (Figure 2) with grid holes representing the contrasted biofilms at 2% \(w/v\) and 5% \(w/v\) kBr were simulated using Arion. The attenuation coefficient values of these two simulated contrasted biofilms were determined using Octopus Analysis (Section 2.9). In Figure 4, we can see that the attenuation coefficient values of 2% \(w/v\) kBr (a) and 5% \(w/v\) kBr (b) in the grid holes are 1.17 cm\({}^{-1}\) (standard deviation is 0.071) and 2.4 cm\({}^{-1}\) (standard deviation is 0.12), respectively, and the average attenuation coefficient of the surrounding water medium is around 0.45 cm\({}^{-1}\) (in correlation with the real value with standard deviation 0.03).  
Here, as an ideal case, for simulation, it was considered that the concentration of contrast agent inside the biofilms and the concentration of the initially applied KBr solution are the same.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**List of Experiments**} & **ON** & **Incubation of** & **Replacement** & **Application** & & & **Serial** \\  & **Culture of** & **Bacteria in** & **TSB 1/10 for** & **and Further** & **KBr to Fully** & **PBS** & **PBS Wash** & **Dilution and** \\  & **Bacteria at** & **TSB 1/10 for** & **Incubation** & **Developed** & **Application * & **+** & **Plate** \\  & 30\({}^{\circ}\)C & **48 h** & **for 24 h** & **Biofilms** & & & **Counting** \\ \hline Irradiation & & & & & & & \\ effect of & ✓ & ✓ & ✓ & ✓ & & & ✓ \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of all the experiments and their respective protocols for incubation of bacteria in a flow cell. (PBS application * step represents the addition of PBS for diffusion and PBS wash ** is the passage of PBS through the grid plate after contrast agent application).  
Figure 4: (**a**) Reconstructed image of the grid plate with water and contrast agent (2% \(w/v\) kBr solution). (**b**) Reconstructed image of the grid plate with water and contrast agent (5% \(w/v\) kBr solution). The bright yellow spots represent the contrasted grid holes filled with 5% \(w/v\) of kBr solution.  
However, it should be noted that the concentration of contrast agent inside the biofilm in real in-situ experiments is unknown and the scope to control it is limited. Therefore, despite the attenuation coefficient of 2% \(w/v\) KBr being 2.5 times more than the attenuation coefficient of water, slightly higher concentration i.e., 5% \(w/v\) KBr was used in the in-situ experiments to enhance the contrast gradient between the constituents in the flow cell. A comparison is drawn between the attenuation coefficient values of the biofilm present in the grid hole for the simulated and the real experiments in the discussion section.
###### Abstract  
The present work proposes an artificial neural network (ANN) to analyze vertical axis wind turbines of the Savonius type. These turbines are appropriate for low wind velocities due to their low starting torque. Nevertheless, their efficiency is too low. In order to improve the efficiency, several modifications are analyzed. First of all, an innovative blade profile biologically inspired is proposed. After that, the influence of several parameters such as the aspect ratio, overlap, and twist angle was analyzed through a CFD (computational fluid dynamics) model. In order to characterize the most appropriate combination of aspect ratio, overlap, and twist angle, an artificial neural network is proposed. A data set containing 125 data points was obtained through CFD. This data set was used to develop the artificial neural network. Once established, the artificial neural network was employed to analyze 793,881 combinations of different aspect ratios, overlaps, and twist angles. It was found that the maximum power coefficient, 0.3263, corresponds to aspect ratio 7.5, overlap/chord length ratio 0.1125, and twist angle 1125. This corresponds to a 32.4% increment in comparison to the original case analyzed with aspect ratio 1, overlap 0, and twist angle 0.  
wind turbines; VAWT; CFD; Savonius; Fibonacci; ANN +
Footnote †: journal: Applied Sciences
# Mdpj _sciences_
## 4 Formal Verification
### ProVerif
#### 4.4.2 Verification Results  
Figures A1-A4 show the verification results of the DMRN Protocol, and the results are summarized in Table 7.  
**(S1) General attacker capability:**  
Figures A1 and A5 present the results based on scenario (S1), which assumes the lowest level of adversarial capability. Specifically, the verification focused on the confidentiality of the SUPI and anchor key, as well as the authentication and freshness of the two values MAC and RES*. Consequently, it has been confirmed that SUPI concealment, confidentiality of the anchor key, mutual authentication, and secure key exchange have been successfully achieved.  
**(S2) MAC freshness issue:**  
In (S2), we tested if there was any attack caused by the lack of freshness of the MAC values included in the AUTN. As depicted in Figures A2 and A6, an adversary can reuse the captured MAC value to spoof the core network and launch subsequent attacks. Note that such attacks are possible only if the adversary's public key is injected as the HN's public key or if the HN's private key is stolen.  
**(S3) Attacks by compromised or malicious SN:**  
Although the authors of the DMRN Protocol claim to guarantee defense against compromised or malicious SNs, verification through Q7 demonstrates that it is still vulnerable to such threats, as shown in Figures A3 and A7. In more detail, by eavesdropping the RES*, an adversary (e.g., a malware installed in the compromised SN) who somehow managed to obtain the two values CONC and \(R_{SN}\) can recover earlier the victim UE's SUPI and \(K_{SEAF}\) prior to the HN's final verification of that RES*.  
**(S4) PFS unsupported issue:**  
To ensure PFS, ProVerif reveals the long-term secrets \(sk_{HN}\) and \(K\) at phase 1 following phase 0. As a result, shown in Figure A4, it has been confirmed that the anchor key \(K_{SEAF}\) remained uncompromised despite the leakage of long-term secrets (i.e., PFS holds). Moreover, mutual authentication and secure key exchange through the queries Q1, Q2, Q3, Q5, and Q6 were verified to be satisfactory. On the other hand, in such a situation, secrecy cannot be fully satisfied for the SUPI, leading to privacy  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Attacker Scenario** & **Result** \\ \hline S1: General attacker capability & True \\ \hline S2: MAC freshness issue & False \\ \hline S3: Attacks by compromised or malicious SN & False \\ \hline S4: PFS unsupported issue & True \\ \hline \hline \end{tabular}
\end{table}
Table 7: Attacker scenario result summary.  
issues. Since the SUPI is not a session key and does not derive subsession keys, this vulnerability cannot be reflected in terms of PFS. Therefore, we can conclude that the DMRN Protocol holds PFS.
# Performance Evaluation of VANET Routing Protocols in Madinah City
###### Abstract  
Traffic management challenges in peak seasons for popular destinations such as Madinah city have accelerated the need for and introduction of autonomous vehicles and Vehicular ad hoc networks (VANETs) to assist in communication and alleviation of traffic congestions. The primary goal of this study is to evaluate the performance of communication routing protocols in VANETs between autonomous and human-driven vehicles in Madinah city in varying traffic conditions. A simulation of assorted traffic distributions and densities were modeled in an extracted map of Madinah city and then tested in two application scenarios with three ad hoc routing protocols using a combination of traffic and network simulation tools working in tandem. The results measured for the average trip time show that opting for a fully autonomous vehicle scenario reduces the trip time of vehicles by approximately 7.1% in high traffic densities and that the reactive ad hoc routing protocols induce the least delay for network packets to reach neighboring VANET vehicles. From these observations, it can be asserted that autonomous vehicles provide a significant reduction in travel time and that either of the two reactive ad hoc routing protocols could be implemented for the VANET implementation in Madinah city. Furthermore, we perform an ANOVA test to examine the effects of the factors that are considered in our study on the variation of the results.  
VANET; V2X; autonomous vehicles; routing protocols; ad hoc protocols; wireless communication +
Footnote †: journal: 2022, 11, 777. [[https://doi.org/10.3390/electronics1105077](https://doi.org/10.3390/electronics1105077)]([https://doi.org/10.3390/electronics1105077](https://doi.org/10.3390/electronics1105077))
## 1 Introduction: Name Disambiguation and Collocation
### Name variation  
A single person's (or organization's) name can vary or be rendered differently in printed, electronic, and other information resources. [PERSON] and [PERSON] point out, \"The use of widely variant forms of authors' names without reference or linkage to alternatives causes hardship for searchers. End-users' search results may be inaccurate or incomplete, resulting in a decrease in the scientific integrity of the research\" [7]. Here we describe the main categories or sources of name variation.  
**Fullness of name**. Names frequently vary in fullness. For example, the name _[PERSON]. [PERSON]_ can also appear as _[PERSON]_, and it can appear as _[PERSON]_. Personal names are shortened by using initials or by abbreviating a name, such as _[PERSON]_. for _[PERSON]_. Names of organizations are frequently shortened by converting them to either initialisms or acronyms. For example, the _Southern Archeological Society_ is sometimes referred to by its initials, _SAS_. However, this shortened form matches the shortened form of the _Scandinavian Airlines System_. Shortening names increases the chances of name ambiguity. People and organizations are more likely to share a shortened form than a longer form of a name.  
**Different language or script**. The name of a single person or organization can vary by language and by script. For example, the following are all different representations of the name of the author [PERSON]:  
\begin{tabular}{l l} [PERSON] & [English] \\ \(\gamma\gamma\)\(\gamma\)\(\gamma\)\(\gamma\) & [Hebrew] \\ \(\cup\)\(\mu\)\(\mu\)\(\mu\)\(\mu\)\(\mu\) & [Arabic] \\ \(\gamma\)\(\gamma\)\(\gamma\)\(\gamma\)\(\gamma\)\(\gamma\) & [Chinese] \\ \end{tabular}  
common and rare names are hard to find, not to mention researchers with similar names doing similar research\" [6].  
Shortened forms of name, especially forenames represented only by initials, increases name ambiguity in databases. The shorter a name is, the more likely it is to match other shortened names. For example, the name _[PERSON]. [PERSON]_ represents both _[PERSON]_ and _[PERSON]_. The WorldCat.org database is an example of how using variant forms of a single name can decrease recall. This database buys much of its metadata from several different vendors, and the vendors differ on the fullness of name metadata they include. Some include complete forenames when the information is available; others routinely shorten forenames to a single initial. So name searches in WorldCat retrieve a completely different set of results depending on whether the searcher searches for the full forename or just the initial.
## 4 Positioning Algorithms
### Intersection Midpoint Algorithm (IMA) Model  
Since there are not all cases in which the triangle centroid algorithm can speculate the target position, the TOA method will instead be employed to obtain the triangular type intersection of the three circles. As shown in Figure 7, when the distance between the three anchor nodes of A, B, and C is widened, the area where the three circles intersect may have become narrow in the measurement of the tag (T). When the tag moves farther away from the C point (moving closer to the first quadrant), the three circles of the intersection will become a more and more narrow triangle, leading to a scenario where the three circles of the intersection cannot be approximated into \(\Delta\)DEF. When using the triangle centroid algorithm to estimate the target coordinates, it will lose accuracy because of the too narrow triangles. Therefore, we propose the TOA intersection midpoint algorithm positioning method to improve the positioning accuracy.
## 4 Implementation and Experiments
### Experimental Results
#### 4.4.3 NutriLLM Experimental Results  
In this section, we compare the capabilities of several standard large language models with the proposed NutriLLM, which is fine tuned on a nutrition question-and-answer dataset to generate personalized dietary advice. As illustrated in Figure 10, the generated responses exhibit varying levels of relevance and coherence across models. We employ multiple metrics to assess the divergence between the model-generated answers and the original reference responses to evaluate the performance quantitatively. As shown in Table 13, we present a detailed comparison of the text generation capabilities of different models. The table reports results across evaluation metrics, such as BLEU, ROUGE-L, METEOR, and BERTScore, providing a comprehensive analysis of linguistic quality and semantic alignment in nutritional dialogue generation.  
Figure 10: Comparison of dietary advice. NutriLLM generates the text on the left, while the text on the right is from the original dataset responses.  
\begin{table}
\begin{tabular}{c c c} \hline
**Food Category** & **MAPE** & **L1 Loss** \\ \hline Meals & 37.76 & 67.90 \\ Meat & 25.89 & 29.47 \\ Fruits and Vegetables & 27.70 & 12.25 \\ Snacks & 23.50 & 18.10 \\ \hline \end{tabular}
\end{table}
Table 12: Performance of volume estimation by food category.  
In the table above, it can be seen that NutriLLM significantly outperforms the other models across all evaluation metrics. Specifically, NutriLLM achieved a BLEU-4 score of 45.13, indicating high accuracy in generating text close to the target answers. Additionally, NutriLLM scored 53.73, 34.79, and 43.82 in ROUGH-1, ROUGH-2, and ROUGH-L, respectively, demonstrating its excellence in generating text that contains the correct key information and sentence structure. Furthermore, NutriLLM achieved a meteor score of 42.46, further proving the quality of its generated text.  
In comparison, the Llama3-8B-Instruct, Llama3-8B-Chinese-Chat, and Qwen2-7B-Instruct models performed next best on the various metrics, still showing strong text generation capabilities. However, the untuned BLOOMZ-7b1-mt model scored lower on all metrics, indicating certain limitations in generating nutrition-related text.  
These experimental results show that fine tuning the Llama 3 model using the nutrition question and answering dataset can significantly enhance its ability to generate nutrition-related text. The excellent performance of the NutriLLM model across various metrics demonstrates its potential as a dietary advice assistant that is capable of providing high-quality suggestions based on users' nutritional habits.