esa-sceva/satcom-chunk-collection huggingface dataset:
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 7 Conclusions  
In this paper, we proposed a new UAV-assisted routing protocol, called the Q-LBR, that uses a Q-learning algorithm to handle UAV relay traffic. The proposed protocol uses an URPA mechanism when considering the traffic priority and the existence of a route independently from ground network routing. We also proposed an RCF for rapid learning feedback of the reward values in consideration of a dynamic network environment. Q-LBR adjusts the reward value according to the URN load and ground network congestion. Performance evaluation using the Riverbed Modeler showed that Q-LBR achieved a significantly better network throughput and latency compared to that of existing algorithms. As a continuation of this work we plan to continue research on implementation of actual equipment and additional algorithms linked to DRL.  
**Author Contributions:** Conceptualization, B.-S.R. and M.-H.H.; methodology, B.-S.R.; software, M.-H.H.; validation, K.-I.K., B.-S.R., and J.-H.H.; formal analysis, M.-H.H.; investigation, B.-S.R.; resources, J.-H.H.; data curation, B.-S.R.; writing--original draft preparation, B.-S.R.; writing--review and editing, B.-S.R.; visualization, M.-H.H.; supervision, K.-I.K.; project administration, J.-H.H.; funding acquisition, J.-H.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Agency for Defense Development.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Cases Study
### Portugal Arch Bridge Case Study
#### 4.2.2 Scenario 2 (Corrective Maintenance Scenario)  
The second degradation scenario considers the impacts of remedial maintenance operations, which were modeled as an immediate boost in dependability and a slowing of the degradation rate. Although the bridge remained in generally good shape, needed remedial repair was performed on it. For 13 years, it was presumed that the same corrective activities would be conducted periodically (Figure 20), resulting in a more reliability improvement (\(\gamma=0.53\)).  
Availability, affordability, and security were all given qualitative assessments (Figure 21). During the corrective activity, availability was reduced; thus, we anticipated a gradual decline in availability over the duration, even as we anticipated a minor boost in performance almost immediately. The cost of the repair was expected to be manageable. The drop in user safety was predicted to be gradual, with a temporary uptick just after the change was implemented.  
Figure 21: Key Performance Indicators and their progression in the second scenario (_source: COST Action TUI406_).  
Figure 20: Reliability of Semi-Quantitative Performance Indicators in the second scenario (_source: COST Action TUI406_).
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
###### Abstract  
Phishing remains a persistent cybersecurity threat, often bypassing traditional detection methods due to evolving attack techniques. This study presents a Reinforcement Learning (RL)-based phishing detection framework, leveraging a Deep Q-Network (DQN) to enhance detection accuracy, reduce false positives, and improve classification performance. The model was trained and evaluated using a real-world dataset comprising 5000 emails (2500 phishing and 2500 benign) and externally validated against a synthetic phishing dataset of 1000 samples simulating unseen attacks. It achieved a 95% accuracy, 96% precision, 94% recall, and a 2% false positive rate on the real-world dataset and a 93% accuracy, 94% precision, and a 4% false positive rate on the synthetic dataset. Area Under the Curve (AUC) analysis yielded a score of 0.92, confirming excellent classification separability and alignment with the model's high accuracy and low false positive rate. This work contributes to scalable, real-world phishing defense by addressing the limitations of static detection systems and improving detection reliability.  
Keywords:phishing detection; RL; cybersecurity; DQN; adaptive security; AI-based threat detection
## 1 Introduction
This technique avoids some limitations of data-driven approaches (e.g., neural networks, support vector machines) and produces better models of performance [44; 45]. Precision and digital agriculture both rely on the acquisition, processing, and exploitation of large amounts of data, including the soil environment of plant growth and the operation of agricultural machinery. Dynamic field probes measuring the mechanical parameters of agricultural soils play a special role here. Some of them allow for the measurement of soil compactness--understood as cone penetrometric resistance in the vertical profile and in the horizontal plane--over several layers [46]. Others, such as the probe developed by the team of [PERSON] et al. [47] make it possible to measure soil shear resistance at different depths of the soil profile of a plowing. Unfortunately, these concepts are not yet commercially available. The research into new solutions for monitoring and predicting soil shear stress, compactness, and penetrometric resistance seems justified. Soil shear stress, compaction, electrical conductivity, and magnetic susceptibility correlate significantly with related soil parameters such as soil texture (clay content), bulk density, moisture content, and organic matter content. Therefore, it is reasonable to use ECa and MS scanning as an indirect method to estimate soil shear stress and soil compactness. On the other hand, the prediction of soil shear stress from indirect measurements and other physico-mechanical parameters of soils in agriculture and agrophysics is not a new approach. [PERSON] et al. [48] invented a method to predict soil shear strength parameters (cohesion and internal friction angle) by combining cone penetration test (CPT) data and soil properties such as bulk density and water content. Using methods of machine learning for prediction, they obtained a prediction error of R = 0.87. [PERSON] et al. [49] attempted to predict shear strength on the basis of soil suction. Thirdly, soil electrical conductivity and magnetic susceptibility measurement platforms and probes are currently the most widely used and adopted in agricultural advisory practices. Research into their alternative uses is, therefore, the subject of this study. This research aimed to compare the performance of multiple linear regression, two types of artificial neural networks (ANN), and support vector machines to predict soil compaction and the shear stress of clay soil based on apparent conductivity and magnetic susceptibility. As a result of the prediction of these parameters, soil traction performance can be estimated. In addition, energy loss resulting from the transfer of tractor driving force on the soil can be minimized. It can be used in practice, e.g., in controlled traffic farming.
## 8 Open Challenges and Research Directions
### Stability and Interconnectivity  
One of the primary challenges in the realm of MEC-enabled VNs revolves around achieving and maintaining stability and interconnectivity. Many vehicular applications, such as video streaming, platooning, and autonomous driving, critically depend on a stable and uninterrupted connection between vehicles and the infrastructure. Given the proliferation of connected vehicles, particularly within the context of 5G infrastructure, the demand for frequent connections and seamless handovers across multiple cells is evident. The ability to control and manage such a dynamic and densely populated environment is paramount to ensuring network stability. Moreover, preserving service continuity during handovers, whether vertical (between different access technologies) or horizontal (between cells of the same cellular network), poses a significant challenge. Future research in this domain should aim to develop robust algorithms, protocols, and network architectures that can effectively handle the intricacies of vehicular mobility and the demands of diverse applications. This includes addressing issues related to handover management, QoS guarantees, and the dynamic allocation of network resources to meet the varying needs of vehicular applications. Additionally, exploring the potential of next-generation technologies such as 6G and beyond could open up new avenues for achieving enhanced stability and interconnectivity in MEC-enabled VNs. While MEC has already made significant progress  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Methodology** & **Mobility Consideration** & **Focused Connection** \\ \hline
[101] & Cooperative vehicle-assisted task offloading & Yes & V2I \\
[185] & Cooperative task offloading with vehicles, MEC, and cloudlet & Yes & V2V-V2I \\
[186] & Offloading service with best-relaying vehicle to & Yes & V2X \\  & requesting vehicle & & \\
[188] & Cooperative task offloading to VEC and MEC servers & Yes & V2I \\
[189] & Collaborative EdgePV, parked vehicle resources & No & V2X \\
[184] & Moving vehicles with a resource pool & Yes & V2I \\
[190] & Collaborative EdgeGA, parked vehicle resources & No & V2V-V2I \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of recent works on vehicles augmenting MEC capabilities.  
in transforming VNs, there is ample room for innovation and exploration in ensuring the stability and interconnectivity necessary to support a wide range of mission-critical vehicular applications in an increasingly connected and mobile world.
###### Abstract  
The time series of GNSS coordinates contain signals caused by the age-related movement of tectonic plates, the deformation of the Earth's surface, as well as errors at different time scales from sub-daily tidal deformation to the long-term deformation of the surface load. Depending on the nature of the signal, specific approaches are used for both the visual interpretation and pre-processing of time series and their statistical analysis. However, none of the present software analyzes the nature of the residual errors but assumes their random nature and obedience to the classical normal distribution. One of the methods for analyzing the time series of coordinates with residual, unaccounted-for systematic errors is the non-classical error theory of measurements. The result of this work is a developed software solution for analyzing the time series of GNSS coordinates to test their normality, or in other words, to test whether a particular GNSS station is subject to the influence of small, unaccounted-for errors. Conclusions: After testing our software on four reference stations in Europe, we concluded that none of the chosen stations followed the normal law of distribution; thus, it is vital to perform such tests before conducting any experiments on the time series from reference stations.  
GNSS time series; spatio-temporal filtering; statistical analysis; Pearson-Jeffreys distribution; non-classical error theory; Python (programming language) +
Footnote †: journal: remote sensing
## 5 Conclusions and Future Work
In Proceedings of the 2022 5 th World Symposium on Communication Engineering (WSCE), Nagoya, Japan, 16-18 September 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 33-37. * [PERSON] et al. (2020) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A VLC channel model for underground mining environments with scattering and shadowing. _IEEE Access_**2020**, \(8\), 185445-185464. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A novel three-dimensional indoor positioning algorithm design based on Visible Light Communication. _Opt. Commun._**2017**, _392_, 282-293. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] High-speed 3D indoor localization system based on visible light communication using differential evolution algorithm. _Opt. Commun._**2018**, _424_, 177-189. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] VLC-based indoor positioning algorithm combined with OFDM and particle filter. _China Commun._**2019**, _16_, 86-96. * [PERSON] and [PERSON] (2002) [PERSON]; [PERSON] The particle swarm-explosion, stability, and convergence in a multidimensional complex space. _IEEE Trans. Evol. Comput._**2002**, \(6\), 58-73. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] An overview of variants and advancements of PSO algorithm. _Appl. Sci._**2022**, _12_, 8392. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimized PSO algorithm based on the simplicial algorithm of fixed point theory. _Appl. Intell._**2020**, _50_, 2009-2024. [CrossRef]
* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON]; [PERSON] High-accuracy VLC-based indoor positioning system using multi-level modulation. _Opt. Express_**2019**, _27_, 5768-5784. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Indoor high-precision 3D positioning system based on visible-light communication using improved whale optimization algorithm. _Photonics_**2022**, \(9\), 93. [CrossRef]  
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# Comparative Analysis of Derivatization Reagents for Catecholamines and Amino Acids
## 2 Materials and Methods
### 3 Results  
The detected masses of TMPy-labeled standard DA (_m_/z 258.2), L-DOPA (_m_/z 302.2), L-Dopa-(phenyl-d\({}_{3}\)) (_m_/z 305.2), GABA (_m_/z 208.1), and glycine (_m_/z 180.1) increased by 105.0 Da compared with their original masses (MW 153.1, 197.1, 200.1, 103.1, and 75.0) (Figure 2a-e). The detected masses of DPP-labeled standard DA (_m_/z 368.2), L-DOPA (_m_/z 412.2), L-Dopa-(phenyl-d\({}_{3}\)) (_m_/z 415.2), GABA (_m_/z 318.2), and glycine (_m_/z 290.1) increased by 215.1 Da compared with their original masses (Figure 2f_j). The detected masses of FMP-10-labeled standard DA (_m_/z 421.2), L-DOPA (_m_/z 465.2), L-Dopa-(phenyl-d\({}_{3}\)) (_m_/z 468.2), GABA (_m_/z 371.2), and glycine (_m_/z 343.1) increased by 268.1 Da compared with their original masses (Figure 2k-o). The detected masses of TPP-labeled standard DA (_m_/z 444.2) and GABA (_m_/z 394.1) increased by 291.1 Da compared with their original masses (Figure 2p_s).  
Figure 1: Chemical structure of derivatization reagents and fragmentation of derivatized reagents.  
Theoretically, the signals of TPP-labeled L-DOPA, L-Dopa-(phenyl-d\({}_{3}\)), and glycine are _m/z_ 488.1, 501.1, and 366.1, respectively. However, these signals were not observed, indicating that TPP did not react with L-DOPA, L-Dopa-(phenyl-d\({}_{3}\)), and glycine due to steric hinderance. We confirmed reproducibility six times for the derivatized reaction. When derivatization was not used, dopamine and L-DOPA were detected in negative ion mode, while GABA and glycine were detected in positive ion mode (data not shown). Thus, one of the benefits of using derivatization reagents is that all derivatized target molecules can be detected in positive mode, thereby eliminating the requirement to perform measurements under both polarities. The MS spectra of TMP-derived targets (Figure 2a-e) showed sharp peaks and low minor peaks compared with those of the other derivatization  
Figure 2: Mass spectra of TMPy-labeled dopamine (DA) (a), L-DOPA (b), L-Dopa-(phenyl-d\({}_{3}\)) (c), GABA (d), and glycine (e). DPP-labeled DA (f), L-DOPA (g), L-Dopa-(phenyl-d\({}_{3}\)) (h), GABA (i), and glycine (j). FMP-10-labeled DA (k), L-DOPA (l), L-Dopa-(phenyl-d\({}_{3}\)) (m), GABA n), and glycine (o). TPP-labeled DA (p), L-DOPA (q), L-Dopa-(phenyl-d\({}_{3}\)) (r), GABA (s), and glycine (t). ND = not detected.  
reagents. Thus, we evaluated detection efficiency using S/N values. An additional merit of using derivatized reagents is that specific fragmented ions can be detected in tandem MS. Thus, tandem MS confirmed the fragmentation ions of TMPY, DPP, FMP-10, and TPP, respectively. Fragmented ions of the pyridine ring (_m/z_ 122.1) moiety from TMPY, 2,4-diphenyl-pyranylium (_m/z_ 232.1) moiety from DPP, anthracene-9-yl pyridinium moiety (_m/z_ 269.1) from FMP-10 and triphenyl-pyranylium (_m/z_ 309.1) moiety from TPP were cleaved and observed from all derived target molecules (Figure 1). The S/N ratios of derivatized signals were improved relative to the non-derivatized targets (Table 1).
## 4 Performance Evaluations
### Experimental Results  
In this section, we validate the performance of LSRR-LA through a practical experiment. As shown in Figure 10, a small vacant patch of land is chosen as the experimental field, which is about 25 \(\times\) 25 m\({}^{2}\). Some bushes, regarded as 'obstacles', are growing in the center of this field. A total number of 20 nodes were used in the experiment, and each node was mounted on a pole with a height of 1.3 m. All of the nodes are equipped with omnidirectional antennas and IEEE 802.15.4 compatible TI CC2450 transceivers operating in the 2.4 GHz ISM band.  
Figure 8: The localization results of the six algorithms in random deployment networks.  
Figure 9: Comparison of 3D Localization results (\(R=150\) m, \(M=70\)).  
The distribution of nodes is shown in Figure 10(a). We assign a number to each node. The transmission power is set to 0 dBm; thus, in this environment, each node can communicate range over a distance of 8 m. The connected graph of the entire network is shown in Figure 10(b).  
In the experiments, the anchor ratio is varied from 10% to 30%. The localization performance is compared using box plots in Figure 12. On each box, the central mark indicates the median while the bottom and top marks represent the minimum and maximum values, and the bottom and top edges of the box indicate the 25 th and 75 th percentiles, respectively.  
Figure 11: The network topology.  
Figure 10: The real experimental scene.  
Figure 12a-c show the localization results in which the anchor ratio is 10%, 20%, and 30%, respectively. The boxes represent the results of DV-hop, Amorphous, MDS-MAP, PDM, LSVR, and LSRR-LA, in that order.  
From the figures, it is evident that the positioning accuracy depends proportionally on the number of anchor nodes for the compared localization algorithms DV-hop, Amorphous, MDS-MAP, PDM, LSVR, and LSRR-LA. It also proves that our proposed method has much better performance than the compared methods.  
We also use the error cumulative distribution function (CDF) to represent the performance of our proposed algorithm, as shown in Figure 13. If anchor ratio is 10%, we can see from Figure 13a that the CDF is 63.8%, 85.5%, and 98.3% when the location error is 0.5 m, 0.8 m, and 1.5 m, respectively. If the anchor ratio is up to 30%, we can see from Figure 13b that the CDF is 67.1%, 92.8%, and 100.0% when the location error is 0.5 m, 0.8 m, and 1.5 m, respectively. As a comparison, the LSRR-LA performs best due to its advantage of the regression model.
## 3 Results and Discussion
### Relationship between PAH and Trace Metals Concentrations and Occupant Activities
#### 3.3.2 Metals  
In terms of residents' activities, smoking indoors was associated with increased concentrations of Cu (593 vs. 216 ug/g, \(p\) < 0.05), Sr (141 vs. 89 ug/g, \(p\) < 0.05) and Zn (422 vs. 379 ug/g, \(p\) = 0.058.1). Frequent cleaning activities (\(>\) 5 activities/week) were associated with increased Al concentrations (2801 vs. 1918 ug/g, \(p\) < 0.05) and the use of cleaning sprays was associated with increased levels of Pb (75.3 vs. 19.5 ug/g, \(p\) < 0.05). The use of air conditioning appeared to increase the concentrations of Cu (765 vs. 120 ug/g, \(p\) = 0.070.1), Zn (540 vs. 321 ug/g, \(p\) = 0.092) and Cd (1.0 vs. 03 ug/g, \(p\) = 0.0090.1). Finally, in terms of different cooking methods, frying was associated with increased levels of Ni (34.0 vs. 25.3 ug/g, \(p\) < 0.05) and Mn (147 vs. 100 ug/g, \(p\) = 0.076) in house dust, while frequent boiling (using of pot) was linked with higher levels of As (6.1 vs. 3.2 3 ug/g, \(p\) < 0.05) and Cd (1.1 vs. 0.3 3 ug/g, \(p\) < 0.05).
## 3 Results
### Effect of AMF Content on Performance of Machine-Made Sand Concrete
#### 3.1.2 Effect of AMF Content on the Compressive Strength of Machine-Made Sand Concrete  
The compressive strength of the concretes with different AMF contents is shown in Figure 3. Although there was no significant difference between the compressive strength of the concretes at 7 days, the results at 28 days showed a similar pattern as in Figure 2. When the content of AMF was 10%, the compressive strength of concrete at 28 days was the highest, which was 12.1% higher than that without AMF. When the AMF filled the pores, it also increased the tightness and compactness of the concrete. The hardened concrete had a higher packing density and a higher strength. Excessive AMF resulted into a poor workability, poor compactness, and thus a decrease in strength.
## 4 Experiments  
### Experimental Setup  
NS2 Simulator is used for analyzing the efficiency of the proposed method. The results are compared with state-of-the-art algorithms discussed in Section 2. The comparison is made based on metrics such as End-End Latency, 1-HOP Latency, CB-overhead, Rate of success, and admission rate. Table 2 describes the parameters set up for the simulation.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 4 Experimental Setup
### Ablation Experiment  
To further demonstrate the structural efficacy of the proposed AKE-PWD model, three ablation experiments were conducted:  
1. Three classifier models (RF/GBT/FCN-Transformer): This experiment evaluated each base learner independently, testing their performance when predicting separately.
2. Soft Voting Ensemble Learning model (RF + GBT + FCN-Transformer): The ensemble learning strategy employed a soft voting mechanism, integrating RF, GBT, and FCN-Transformer. This experiment analyzed the improvements in prediction accuracy offered by the ensemble approach compared to the individual models, i.e., RF/GBT/FCN-Transformer.
3. Adaptive KNN-Ensemble model (KNN + Soft Voting Ensemble (RF + GBT + FCN-Transformer)): Utilizing the KNN algorithm, this model identified the cluster of physiological features relevant to the input data. Based on these clusters, corresponding ensemble models were selected for feature processing. This approach was tested to evaluate its effectiveness in enhancing model generalizability and accuracy by considering individual physiological differences.