esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application of Generative AI Models for Context-Aware Vulnerability Scoring
### Performance Evaluation of LLMs
#### 5.3.3 Gemini Results  
Evaluation of the multiclass vulnerability classification system reveals significant performance variations across security attributes, with accuracy ranging from 63.75% (Availability) to 95.73% (Attack Vector). While the model demonstrates strong capability in majority classes such as NETWORK vectors (F1 = 97.60%) and LOW complexity (F1 = 95.24%), it exhibits critical deficiencies in high-impact minority categories. Most alarmingly, HIGH attack complexity shows near-failure performance (F1 = 15.38%), LOCAL vectors suffer substantially reduced recall (76.92%), and CHANGED scope detection remains critically low (Recall = 40.00%). Performance degradation correlates strongly with class rarity, with HIGH-privilege requirements (Support = 14) and HIGH-availability impacts (Support = 23) showing 32.15% and 16.11% F1 reductions, respectively, compared to their NONE counterparts. These results, summarized in Table 9, coupled with macro-F1 scores as low as 63.30% (Availability), highlight the fundamental challenges in class imbalance handling and high-severity vulnerability recognition that must be addressed for operational deployment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **Class** & **Precision** & **Recall** & **F1 Score** & **Support** & **Accuracy** \\ \hline \multirow{3}{*}{Attack Vector} & NETWORK & 89.13\% & 84.54\% & 86.77\% & 97 & 80.62\% \\  & LOCAL & 66.67\% & 74.07\% & 70.18\% & 27 & \\  & ADJACENT & 28.57\% & 40.00\% & 33.33\% & 5 & \\ \hline \multirow{3}{*}{Attack Complexity} & LOW & 90.55\% & 95.83\% & 93.12\% & 120 & 87.12\% \\  & HIGH & 0\% & 0\% & 0\% & 12 & \\ \hline \multirow{3}{*}{Scope} & CHANGED & 60.00\% & 30.00\% & 40.00\% & 20 & 85.25\% \\  & UNCHANGED & 87.50\% & 96.08\% & 91.59\% & 102 & \\ \hline \multirow{3}{*}{User Interaction} & REQUIRED & 66.67\% & 40.00\% & 50.00\% & 30 & 81.82\% \\  & NONE & 84.21\% & 94.12\% & 88.89\% & 102 & \\ \hline \multirow{3}{*}{Privilege Required} & NONE & 78.13\% & 83.33\% & 80.65\% & 60 & 69.75\% \\  & LOW & 66.67\% & 66.67\% & 66.67\% & 45 & \\  & HIGH & 30.00\% & 21.43\% & 24.99\% & 14 & \\ \hline \multirow{3}{*}{Confidentiality} & NONE & 72.92\% & 70.00\% & 71.43\% & 50 & 59.83\% \\  & LOW & 53.19\% & 62.50\% & 57.47\% & 40 & \\  & HIGH & 45.45\% & 37.04\% & 40.82\% & 27 & \\ \hline \multirow{3}{*}{Integrity} & NONE & 69.77\% & 66.67\% & 68.18\% & 45 & 59.28\% \\  & LOW & 56.00\% & 62.22\% & 58.95\% & 45 & \\ \cline{1-1}  & HIGH & 45.00\% & 39.13\% & 41.86\% & 23 & \\ \hline \multirow{3}{*}{Availability} & NONE & 62.86\% & 62.86\% & 62.86\% & 35 & 51.61\% \\ \cline{1-1}  & LOW & 51.43\% & 51.43\% & 51.43\% & 35 & \\ \cline{1-1}  & HIGH & 34.78\% & 34.78\% & 34.78\% & 23 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: ChatGPT evaluation.
# Hybrid Fuzzy-DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels
## 2 Materials and Methods  
In this section, we provide an overview of the methodologies employed to address the Maximum Power Point Tracking (MPPT) challenge in photovoltaic systems under partial shading conditions. Initially, we describe a rigorous mathematical model based on the single-diode equivalent circuit to accurately capture the nonlinear behavior of the photovoltaic panel. Subsequently, our proposed control strategy, which integrates fuzzy logic with Deep Deterministic Policy Gradient (DDPG), is introduced to enhance state representation and expedite convergence in complex, non-convex operational environments. Finally, a theoretical justification rooted in the contraction properties of the Bellman operator is presented to establish the stability and robustness of the hybrid approach.
## 4 Discussion and Conclusions  
In this paper, a dynamic power dividing network, which utilizes the nonlinear input capacitance of the Class-B peaking amplifier, was designed to improve the power gain and efficiency of the DPA by removing the isolation characteristics of the power divider. The dynamic power dividing network was designed to drive more power to the carrier amplifier at the low power level and to drive more power to the peaking amplifier at the peak power level. Compared to previous works, the proposed design can have higher power gain since more power is sent to the carrier amplifier at low power. The efficiency at large OBO levels is also improved, compared to the conventional case, since the peaking amplifier bias can be lowered even while maintaining AM-AM through uneven power drive.  
Also, the dynamic power divider makes the overall DPA more compact as it has a simplified structure with fewer transformers needed for impedance matching, compared to the conventional dual-drive topology, and it has the phase offset line merged into the impedance matching at the interstage network. For verification, the proposed DPA was designed for the targeted frequency band of 23.5-27.5 GHz and implemented with a 28 nm bulk CMOS technology. The measurement results exhibited a power gain of as high as 21.9 dB, saturated output power of 15.3 dBm, and PAEs of 26.7% and 17.6% at the peak output power and the 6 dB OBO, respectively, at the center frequency. The proposed DPA exhibited superior gain and efficiency performances at a given OBO level to the previously published CMOS DPA ICs at the mm-wave frequency band.  
Conceptualization, Y.C.C.; methodology, Y.C.C.; software, S.B.; validation, Y.C.C., S.B., and Y.Y.; formal analysis, Y.C.C.; investigation, Y.C.C.; resources, K.C.H.; data curation, K.-Y.L.; writing--original draft preparation, Y.C.C.; writing--review and editing, K.C.H., K.-Y.L., and Y.Y.; visualization, Y.C.C.; supervision, Y.Y.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Samsung Electronics Co., Ltd.  
Data availability Statement: Data are contained within the article.  
The EDA tool was supported by the IC Design Education Center (IDEC), Republic of Korea.  
The authors declare no conflicts of interest.
## 3 Results  
### Diverse Spring phenology in Boreal Forests at the Regional Scale  
According to Figure 2, the correlation between \(\text{SOS}_{\text{GOME-2 SIF}}\) and \(\text{SOS}_{\text{FLUX-GPP}}\) was highly significant (\(p<0.01\)) at the 3.5\({}^{\circ}\) grid scale, and it remained significant at spatial scales larger than 3.5\({}^{\circ}\). Consequently, 3.5\({}^{\circ}\) was chosen as the optimal spatial scale for GOME-2 SIF in the spring phenology calculation in this study.  
The spatial heterogeneity of SOSs estimated from the GOME-2 SIF, GOSIF, and EVI2 see [PERSON] et al. [9]. Difference analysis showed that \(\text{SOS}_{\text{GOME-2 SIF}}\) averagely differed from \(\text{SOS}_{\text{GOSIF}}\) and \(\text{SOS}_{\text{EVI2}}\) by \(-5.86\) days and \(-5.39\) days, respectively (Figure 3a,b). This meant that \(\text{SOS}_{\text{GOME-2 SIF}}\) occurred, on average, 5.86 days earlier than \(\text{SOS}_{\text{GOSIF}}\) and 5.39 days earlier than \(\text{SOS}_{\text{EVI2}}\). The average difference between \(\text{SOS}_{\text{GOSIF}}\) and \(\text{SOS}_{\text{EVI2}}\) was 0.47 days, suggesting that \(\text{SOS}_{\text{GOSIF}}\) was slightly later than \(\text{SOS}_{\text{EVI2}}\). Non-parametric tests further demonstrated that spring phenology derived from GOME-2 SIF differed slightly from that of GOSIF and EVI2 in boreal forests (Figure 3c). Moreover, spatial analysis of root mean square errors (RMSEs) in spring phenological fitting results indicated that the RMSE of GOME-2 SIF generally remained below 0.10, while the RMSE of GOSIF stayed within 0.18 (Figure 3d).  
Figure 3: Spatial differences (**a**) and frequency distribution (**b**) of spring phenology based on GOME-2 SIF, GOSIF, and EVI2. Non-parametric tests (**c**) and root mean square error (RMSE) of spring phenology (**d**). Completely different letters in (**c**) denote significant differences (\(p<0.05\)).  
Figure 2: The relationship between the corresponding SOSGOME-2 SIF and SOSFLUX-GPP at different spatial scales.  
Through comparative analysis of SOS for different conifer vegetation types in boreal forests and their corresponding temperature characteristics, it was found that SOSs estimated from GOME-2 SIF, GOSIF, and EVI2 were DOY 111 \(\pm\) 14, 124 \(\pm\) 7, and 121 \(\pm\) 8 for evergreen needleleaf forests and DOY 127 \(\pm\) 9, 129 \(\pm\) 5, and 128 \(\pm\) 5 for deciduous needleleaf forests (Figure 4). At SOSs, the daily maximum air temperatures for nearly all types of conifer vegetation exceeded 5 \({}^{\circ}\)C, and the daily maximum land surface temperatures (canopy temperatures) were consistently higher than the daily maximum air temperatures (Table 2).
## 5 Conclusions
**Author Contributions:** H.M.K.A.-[PERSON] and [PERSON] contributed to the conception, design and wrote the paper; [PERSON] and [PERSON] help in the acquisition of data and revised the paper before the final submission; [PERSON] critically revised the paper. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**  
STC Standard test conditions. \(f_{PV}\) PV derating factor in %. \(G(t)\) Hourly solar irradiance in kW/m\({}^{2}\). \(T_{amb}(t)\) Hourly ambient temperature in \({}^{\circ}\)C.  
\(I_{lp_{h}}\) Photo current in A. \(I_{lp_{1}}/I_{D2}\) Shockley diode currents due to diffusion and recombination mechanisms in A. \(I_{sh}\) Current flowing in the shunt resistance in A. \(I_{ol}/I_{o_{2}}\) Diodes reverse saturation currents in A. \(I_{g_{\rm STC}}\) Diodes reverse saturation currents at STC in A. \(a_{1}/a_{2}\) Diodes ideality factors due to diffusion and recombination. \(V_{T}\) Diodes thermal voltages in V.  
\(R_{s}/R_{sh}\) Series and shunt resistances in \(\Omega\). \(N_{s\sigma}\) Number of PV modules connected in series per string. \(N_{par}\) Number of PV strings connected in parallel. \(P_{PV}\) Rated PV power in W.  
\(P_{PV_{p}}\) Surplus PV power transferred to the hydro pumps. \(\eta_{lim}\) Efficiency of the inverter  
\(\hat{q}_{T}(t)\) Hourly hydro turbine flow rate in m\({}^{3}\)/s. \(h_{net}\) Effective head altitude in m.  
\(C_{T}\) Turbine generating coefficient in kg/m-s\({}^{2}\). \(C_{P}\) Turbine pumping coefficient in m\({}^{3}\)-s/kg. \(V_{LR}\) Volume of water stored in lower reservoir in m\({}^{3}\). \(POW\) Percentage of water in the lower reservoir (LR) in %. \(\alpha\) Evaporation and leakage loss in %. \(\eta_{T}\) Overall efficiency of the turbine-generator unit in %. \(\eta_{P}\) Overall pumping efficiency in %. \(\rho\) Density of water (1000 kg/m\({}^{3}\)). \(g\) Acceleration due to gravity (9.8 m/s\({}^{2}\)). \(q_{charge}\) Quantity of water released in the charging mode in m\({}^{3}\). \(q_{discharge}\) Quantity of water stored in the discharging mode in m\({}^{3}\). \(E_{PVg}\) Annual PV energy generated in MWh. \(E_{Tg}\) Annual hydro energy generated in MWh. \(E_{grid\_{sales}}\) Annual energy sales to the grid in MWh. \(E_{PVp}\) Annual pumped PV energy in MWh. \(E_{brown}\) Annual energy purchased from the utility grid in MWh. \(E_{renewable}\) Annual renewable energy in MWh. EF Grid emission factor measured in tCO\({}_{2}\)/MWh. T&D Transmission and distribution losses. \(L\) Decimal equivalent of T&D. WOA Whale optimization algorithm. PSO Particle swarm optimization. LPSP Loss of power supply probability. EENS Expected energy not served. LOLP Loss of load probability. IR Index of reliability.
## 4 Results and Discussion  
### Particle Size Distribution and Afterberg Limits  
Lokoundje kaolinite materials are characterized by high proportions of clay-sized materials (Table 1). This distribution points out the predominance of clay and silty clay textures. The projection of the PSD is the texture ternary diagram concluded that these raw materials are favorable for ceramic products having a low porosity and low permeability. This is probably associated with the low amounts of sand (Figure 2). The Atterberg limits in Table 1 are coherent with materials having medium plasticity, which is of interest for the workability of the pastes from these raw materials.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **BR-5** & **BR-8** & **BR-9** &
\begin{tabular}{c} **BR-** \\ **11** \\ \end{tabular} & **GR-2** & **GR-3** & **GR-6** & **GR-8** \\ \hline  & & & & & & & \\ \hline \multicolumn{10}{c}{Particle size distribution} \\ \hline Clay (\(<\)2 \(\upmu\)m) & 75.66 & 71.66 & 65.15 & 61.57 & 75.85 & 58.86 & 73.22 & 66.39 \\ Silt (2-50 \(\upmu\)m) & 24.12 & 28.09 & 33.14 & 37.45 & 23.81 & 20.02 & 25.41 & 32.63 \\ Sand (\(>\)50 \(\upmu\)m) & 0.22 & 0.25 & 1.71 & 0.99 & 0.34 & 21.12 & 11.37 & 0.98 \\ \hline  & & & & & & & & \\ \hline \multicolumn{10}{c}{Afterberg Limits} \\ \hline LL (\%) & 85.2 & 86.1 & 87.1 & 84.2 & 80.2 & 82.7 & 90.1 & 82.4 \\ PL (\%) & 44.6 & 42.8 & 43.2 & 43.8 & 41.5 & 43.5 & 46.5 & 43.1 \\ PI (\%) & 40.6 & 43.4 & 43.9 & 40.4 & 38.7 & 39.2 & 43.6 & 39.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particle size distribution and Atterberg limits.
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 2 State of Technology
### Localization without External Sensors
Although SLAM and learning approaches are fast, using them for localization has a significant drawback: The environment has to be traversed to collect features or training data before localization is possible. This training phase takes a significant amount of work as each image has to be mapped to an exact location [20]. Additionally, once the environment changes, the training has to be repeated or improved. Even day and night changes can be enough to confuse a trained network, requiring even more training data [21]. Simulated data can help to mitigate the training problem but ultimately performs worse than using real data [19]. Floor-plan matching is an approach generally limited to indoor localization. The observer generates a 2D top-down representation of the surrounding environment, including the layout of walls and possible landmarks. This representation is then matched to an existing floor plan of the building, resulting in a location relative to the building. Breaking down the problem of localization from three dimensions to only two (or 6-DOF to 3-DOF when including rotation) significantly reduces computation complexity. Of course, this approach requires an existing plan of the building in digital form. Fortunately, with the advent of BIM over the last years, the availability of comprehensive digital models makes this a nonissue in modern buildings. [PERSON] and [PERSON] [22] have used floor-plan matching with line segments representing walls to improve robot navigation. [PERSON] et al. [23] also make use of floor plans by applying SLAM to LiDAR scans but require an initial state for incremental position correction, making the localization local instead of global. Fast global indoor localization without external sensors is still an open problem.
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 4 Solution Method  
To solve EVRPTW-PR-PL, we propose an ALNS algorithm that has successfully solved a broad range of routing problems, such as pickup and delivery problems [48], the two-echelon vehicle routing problem (2E-VRP) [49], VRP with handling uncertainty [50], EVRP variants [7; 51; 52], and VRP with delivery options (VRPDO) [40]. [PERSON] and [PERSON] [52] derive a set of functions to efficiently check the feasibility of EV routes in \(\mathcal{O}(1)\) time complexity. The efficient operators for handling delivery options described in one study [40] are also utilized in our problem.  
A solution of EVRPTW-PR-PL is represented by two arrays. Array 1 represents a set of sequences that include served customers who may visit some CSs in order. Array 2 is associated with array 1, which reports the delivery methods for customers. In particular, if a customer within array 1 is assigned to the self-pickup method, then the designated parcel locker is placed in the corresponding position of the customer in array 1; otherwise, the remaining positions are copied from array 1. Based on this representation, the preferred delivery methods are always ensured for all customers, and the total demand for parcel lockers is also guaranteed. However, it is possible for infeasible solutions to arise that can violate time window, battery, and load violations, resulting in penalties (see Section 1). We note that total cost and penalties are determined based on the second array (see Section 4.4).  
Figure 2 presents the solution of the illustrative example (as shown in Figure 1). With array 1, white cells represent home delivery customers and CSs, while blue and red cells represent self-pickup and flexible customers, respectively. Array 2 is then associated with array 1, where all blue cells must be replaced by the designated parcel lockers of self-pickup customers, and red cells can represent either the customers themselves or the designated parcel lockers. For example, route 2 is represented as \(0\to 11\to\text{CS1}\to\text{P3}\to 3\to 0\), where the flexible customer 11 is assigned to the home delivery method, while another flexible customer 12 is assigned to the self-pickup method at parcel locker 3 (as indicated in array 2). In addition, when two consecutive parcel lockers are the same on a route, we can combine them when updating the routes.
## 2 Related Work
[13] explored the integration of APF into a nonlinear model predictive controller (NMPC) as a cost function, which conducted configuration changes through a modified A* path planning approach. While this policy utilizes local observations and current costs to avoid collision and dynamically adjusts geometry, it greatly depends on a limited time interval. Meanwhile, for the local minimum problem of APF, some researchers proposed an APF algorithm combined with RL algorithms. In [30], a modified APF approach based on membrane evolution was proposed for the path navigation of mobile robots. In [31], the APF method was used to solve the problem of the six-axis robot's collision from workspace. In this paper, we carry out an improved centralized training and distributed execution framework in MADRL to achieve continuous actions for path navigation to support the configuration change. To address the flexibility problem, the APF method is utilized in the actor network. In addition, as for the prediction challenges during reinforcement online learning, we integrate our previous research [15] and the DMPC method with the critic network, ensuring environment constraints for each agent. The paper is organized as follows. Section 3 describes the problems and preliminaries. The description and proof of main results for the proposed algorithm framework are given in Section 4. The results of simulation and discussions are drawn in Section 5. Section 6 concludes the entire paper and provides a glimpse into future prospects. And all the notations and symbols are shown in Appendix A.
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 4 Experimental Test and Analysis  
### Overall Test and Analysis of the System  
In order to verify the stability and reliability of electric current field signal transmission in the experimental system, the communication tests under different drilling depths (i.e., different communication distances) are carried out. The communication bandwidth is 30 kHz, and the data transmission rate is 1 kpbs. The test results are shown in Figure 11.  
Figure 11: Comparison of communication error rate under different communication distance.  
Figure 10: Physical diagram of simulation experiment platform.  
It can be seen from Figure 11 that the larger the communication distance \(H\) is, and the greater the BER is. Under the above conditions, the communication BER is less than 0.31% when the communication distance is 1-5 cm, and the BER is about 1% when the drilling depth is 9 cm. When the drilling depth is greater than 7 cm, the BER of data transmission increases, and the change rate gradually increases with the increase of distance. The main reason for the above phenomenon is that the farther the voltage is transmitted, the more serious the loss of current in the rock layer will be, which makes the transmission current reduce and is easy to be interfered by external noise, resulting in the increase of BER. This problem can be solved by increasing the transmitting voltage and controlling the communication distance within the measurement accuracy of the anisotropic magnetoresistance sensor.
# Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter
## 4 Conclusions  
Cognitive Radio networks are subject to several cyber-attacks. The primary user emulation attack is one of the most severe attacks that can impact the normal functioning of these networks. In this paper, we propose a new approach for detecting primary user emulation attacks with a non-stationary  
Figure 8: Comparison between the proposed technique and the RSS-based localization technique in terms of the probability of detection as a function of the distance.  
Figure 9: Comparison between the proposed technique and the RSS-based localization technique in terms of the probability of miss detection as a function of the distance.  
primary user. A Kalman filter based technique is used for tracking and estimating the position of the mobile primary user, then the transmitter received power is used to derive the position of the transmitter before comparing it to that estimated by Kalman filter. Thus, secondary users can verify if the transmitter is a legitimate primary user or an attacker. Several experiments were conducted and the model was extensively tested and its results were compared to those of the RSS-based location approach. The results show that the proposed approach produces satisfactory results in terms of tracking the primary user in a non-stationary environment and it outperforms the RSS-based localization technique in terms of probability of detection and probability of miss detection. However, the proposed technique has a few limitations that need to be addressed in future works. These limitations include finding the initial coordinates of the primary user, handling the uncertainty in the measurements, and detecting an attacker that is in close proximity to the legitimate primary user position.  
[PERSON], Y.A., and N.K. conceived and designed the experiments; Z.E.M. performed the experiments; [PERSON], Y.A., and N.K. analyzed the data; [PERSON] wrote the paper; N.K. revised and oversaw the project; N.K. provided the resources and financial support. H.E.G. and B.A.A.M. provided feedback.  
Wireless Communications & Networking Lab, Electrical Engineering Department, University of North Dakota, USA.  
The authors declare no conflict of interest.