esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Result Analysis and Discussion  
The statistical results (presented in Table 2) indicate the superior performance of the proposed Wet-ConViT model across various land cover classes and overall accuracy metrics. The proposed model achieved the highest accuracies for bog (94.79%), fen (90.57%), swamp (89.04%), and marsh (89.91%), which are challenging wetland classes. In comparison, other models showed lower accuracies for these classes, highlighting the effectiveness of the proposed hybrid approach in capturing both local and global features essential for accurate wetland classification. As discussed earlier (on both classification maps and UMAPs in Figures 6 and 7), the classes of water and urban are the most discernible categories, achieving accuracies surpassing 97% across all models. Particularly, the proposed model demonstrated exceptional potential, achieving accuracies exceeding 99% for both of these categories. Conversely, the marsh and swamp land covers presented to be the most challenging classes to be distinguished in both classification maps and UMAPs and consistently yield the lowest accuracies. The overlap in spectral features between marsh, swamp, and water reflected in the spectral feature overlap seen in UMAP results (Figure 7), as well as their proximity to each other (shown in classification maps in Figure 6), led to confusion among these categories. Accordingly, both state-of-the-art and proposed models struggled with these classes and resulted in the lowest accuracies among all the land covers. Despite these challenges, the proposed model attained accuracy rates exceeding 89% for these wetland categories. Among the non-wetland land covers, grassland showed the lowest accuracy, which only the proposed model could achieve above 90% accuracy. As evident in classification maps (Figure 6), grasslands are the dominant land covers in the northern part of the study area, where they coexist along the urban area of the city of St. John's. The separability of these land covers is limited by the spatial resolution of the input data. The presence of mixed pixels, where more than one land cover type is present within a single pixel, further complicates accurate classification. Moreover, grassland-extracted features overlap with the pasture category (as evident in UMAP Figure 7). This inherent heterogeneity and spatial distribution of land covers add complexity to the classification task. The spectral similarity between certain classes, as well as the spatial resolution of the input data, influenced the classification accuracy. The overlap in spectral features often led to confusion between these classes. Furthermore, the spatial distribution and heterogeneity of land covers, especially in transitional zones, posed additional challenges. The use of
## 4 Computational Results
### Randomly Generated Instances Studies
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Instance** & \multicolumn{3}{c}{**GIH2**} & \multicolumn{3}{c}{**GIH-F**} \\ \hline \(n\) & \(e\) & \(m\) & \(\textbf{T}\textbf{C}_{H}\) & \(\textbf{C}_{H2}\) (s)_ & \(\textbf{T}\textbf{C}_{F}\) & \(\textbf{C}_{F}\) (s) & \(R\) \\ \hline \multirow{4}{*}{500} & 1.2 & 250.5 & 43,909.1 & 53.0 & 43,909.1 & 0.219 & 242.0 \\  & 1.5 & 315.0 & 38,637.8 & 52.2 & 38,637.9 & 0.187 & 279.1 \\  & 2.0 & 417.0 & 34,417.5 & 56.3 & 34,417.5 & 0.082 & 686.6 \\  & 3.0 & 628.5 & 28,948.1 & 56.9 & 28,948.0 & 0.093 & 611.8 \\ \hline \multirow{4}{*}{1000} & 1.2 & 504.0 & 87,500.3 & 244.2 & 87,500.3 & 1.802 & 135.5 \\  & 1.5 & 628.5 & 77,598.3 & 230.3 & 77,597.0 & 0.873 & 263.8 \\  & 2.0 & 840.0 & 69,199.2 & 294.1 & 69,199.2 & 0.432 & 680.8 \\  & 3.0 & 1256.5 & 57,923.1 & 250.7 & 57,923.1 & 0.485 & 516.9 \\ \hline \multirow{4}{*}{2000} & 1.2 & 1002.5 & 176,681.2 & 3910.8 & 176,680.7 & 15.701 & 249.1 \\  & 1.5 & 1255.5 & 155,205.3 & 3114.3 & 155,206.2 & 6.503 & 478.9 \\  & 2.0 & 1669.0 & 137,774.1 & 4316.2 & 137,774.1 & 3.346 & 1290.0 \\  & 3.0 & 2501.7 & 115,661.0 & 1785.8 & 115,661.0 & 3.574 & 499.7 \\ \hline \multirow{4}{*}{3000} & 1.2 & 1511.7 & 263,511.1 & 19,136.9 & 263,511.6 & 46.551 & 411.1 \\  & 1.5 & 1880.0 & 231,954.6 & 14,429.7 & 231,954.9 & 25.560 & 564.5 \\  & 2.0 & 2483.3 & 205,368.8 & 19,759.8 & 205,368.1 & 11.219 & 1761.3 \\  & 3.0 & 3780.0 & 173,630.6 & 6571.9 & 173,630.6 & 12.432 & 528.6 \\ \hline \multirow{4}{*}{4000} & 1.2 & 1991.7 & 352,975.8 & 59,016.2 & 352,977.0 & 107.610 & 548.4 \\  & 1.5 & 2511.7 & 306,983.3 & 43,971.5 & 306,983.3 & 66.669 & 659.5 \\ \cline{1-1}  & 2.0 & 3335.0 & 275,694.8 & 60,539.8 & 275,694.8 & 26.281 & 2303.6 \\ \cline{1-1}  & 3.0 & 4986.7 & 231,148.4 & 17,014.0 & 231,148.4 & 29.728 & 572.3 \\ \hline \multirow{4}{*}{5000} & 1.2 & 2498.3 & 438,717.9 & 136,314.9 & 438,718.6 & 168.581 & 808.6 \\ \cline{1-1}  & 1.5 & 3131.1 & 386,546.1 & 101,071.7 & 366,548.1 & 106.764 & 946.7 \\ \cline{1-1}  & 2.0 & 4161.1 & 341,504.3 & 139,122.7 & 341,504.3 & 50.931 & 2731.6 \\ \cline{1-1}  & 3.0 & 6257.8 & 291,685.7 & 51,471.0 & 291,685.7 & 58.821 & 875.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computational results for the large-size instances. environments. Thus, according to Rule 2, all the jobs do not have to traverse on-peak periods, and then \(CT_{H2}\) is greatly reduced. Conversely, when \(e\) is set as 1.2, the number of periods decreases and the jobs are arranged very tightly. There will be many jobs inserted into the periods with higher electricity prices. Therefore, our algorithm should filter more positions with lower electricity prices and constantly judge whether the job needs to be moved. Obviously, all these operations may increase the computation time. Thus, when dealing with large-size instances and setting \(e\) to 1.2 or 1.5, our algorithm runs longer, but the computation time is still far less than GIH2.
## 5 Results
### Use Cases
#### 5.2.1 Pipe Inspection  
The pipe inspection task shown in Figure 12 was developed in joint experiments with the University of the Balearic Islands (UIB) using a Lanty stereo camera [29]. This a use  
Figure 11: Network congestion image transfer protocol controlled by the operator.  
Figure 10: Network congestion image transfer protocol in automatic control mode.  
case where the underwater robot navigates around a delimited area while the operator inspects the area using frontal and downside cameras. Most of the time, these inspection routines are performed using a tether cable, basically because this allows the operator to receive real-time video of the area. But if the area is complex to navigate, the possibilities of tether entanglement increase. In this case, using a VLC modem to communicate can be a great option to perform the mission, avoiding tether management problems.  
Network congestion image transfer was implemented, and the HRI was adapted to the pipe inspection experiment. Figure 13 shows two scenarios. The top left shows that the bandwidth is high enough to show two high-scale and high-quality RGB images. The bottom left shows the second case where the bandwidth is constrained and the images deployed in the HRI are low-quality grayscale images. It is important to mention that the HRI offers the option to change the controller's mode, give priority to the scale and quality, or just change the mode to the adaptive or manual mode as explained in Section 3.1. Another important feature of the HRI is that if the operator clicks on any of the images, the image gets maximized, offering a better view. On the right side of Figure 13 two images are shown: one where the bandwidth is high (top) and the second one where the bandwidth is constrained (bottom).  
Figure 12: Pipe inspection use-case experiment.  
Figure 13: HRI (**left**) and different compressed images depending on the bandwidth (**right**).  
The results of the network congestion image transfer control algorithm are shown in Figure 14. This use case consists of transferring data from two cameras (frontal and downside cameras) through the VLC modems. This means that the bandwidth is more likely to become congested, resulting in numerous delays in the time required for the image to arrive (RFL). As explained in Section 3.1, the operator can give more priority to an image at some specific moment. This is the case for the downside image in practically the whole experiment, principally because it is the one that helps the operator to detect the pipe. The only instance when the operator changes the priority occurs between 20 s and 390 s, during which the mode of the downside camera was switched to manual. This adjustment fixed the values for quality and scale. The frontal camera (bottom of Figure 14) shows numerous spikes in the RFL, mainly because the controller tried to adjust the quality and the scale of the frontal image, although the priority was the downside camera. At the same time, there were some changes in the desired TBF causing a noisy response.
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 4 Simulation Results and Discussions
### Travel-Time Prediction Using Greenshields Model Based on Cascaded Fuzzy Logic Systems  
A simulation test was conducted to evaluate travel-time prediction using cascaded fuzzy logic systems for segments of the Sun Yat-Sen Highway, specifically from the Dingjin System heading north to the Chiayi System during morning rush hours on a typical working day. The route consists of four segments in sequence: Dingjin to Rende, Rende to Tainan, Tainan to Xiaying, and Xiaying to Chiayi. Each segment is equipped with a dedicated Greenshields model-based fuzzy logic system designed in advance for vehicle speed prediction. These fuzzy logic systems receive real-time traffic data--including traffic density and flow--from the Traffic Control Center for their respective segments [42]. Based  
Figure 26: Defuzzification of the proposed fuzzy logic system in congested mode with traffic flow at 21% and traffic density at 89%.  
Figure 25: Defuzzification of the proposed fuzzy logic system in non-congested mode with traffic flow at 34% and traffic density at 20%.  
on these inputs, each system predicts the vehicle speed for its segment. Using the predicted speeds and the known lengths of the segments, the estimated travel time for each segment is calculated. The total estimated travel time for the entire route from Dingjin to Chiayi is then determined by summing the segment travel times. The simulation results are presented in Table 12, showing the predicted travel times for each segment along with the corresponding traffic conditions. It is important to note that these estimates vary with changing road conditions and must be updated accordingly. The simulation confirms both the feasibility and adaptability of the cascaded fuzzy logic approach. Since each prediction relies on short-range, segment-specific traffic data, the overall travel-time estimate for the planned route demonstrates a high level of accuracy.
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 2 Literature Review
Recent works also highlight performance gains when hybridizing Fast R-CNN with Bi-LSTM and hierarchical models, enabling superior temporal analysis and semantic inference [65; 66]. Furthermore, a growing body of research emphasizes the strategic use of synthetic data and transfer learning in industrial settings. To reduce the dependency on large annotated datasets, works by Eversberg et al. (2024) [67]; Ouarab (2024) [68] and Ouarab et al. (2023) [69], have explored deep active learning combined with synthetic imagery, significantly enhancing model performance in object detection for industrial robotics and SCARA machine deployment [70]. Other contributions include robust single-pass architectures that streamline training while maintaining model generalizability under factory-level visual variation [71]. Additionally, YOLO-based architectures such as those proposed by Rhee et al. (2023) [72] have been successfully integrated into industrial safety systems, enabling real-time monitoring of critical zones and enhancing situational awareness in hazardous environments. These insights validate the strategic potential of vision-based deep learning architectures--particularly YOLO and Fast R-CNN variants--for deployment in complex, real-time, and safety-critical applications such as mining. Given the operational challenges in open-pit mining--ranging from equipment collision risks to unauthorized intrusions--the proposed autonomous mobile station leverages the YOLO11 architecture for its balance between accuracy, scalability, and latency, as demonstrated in prior industrial validations. This technological convergence positions the current research within a frontier of applied AI aimed at enabling real-time risk mitigation and equipment monitoring in highly dynamic and unstructured environments.
###### Abstract  
To examine the applicability of the nacelle transfer function (NTF) derived from nacelle light detection and ranging (LIDAR) measurements to wind turbine power performance testing without a met mast, wind turbine power performance measurement was carried out at the Dongbok wind farm on Jeju Island, South Korea. A nacelle LIDAR was mounted on the nacelle of a 2-MW wind turbine to measure wind conditions in front of the turbine rotor, and an 80-m-high met mast was installed near another wind turbine to measure the free-stream wind speed. The power measurement instruments were installed in the turbine tower base, and wind speeds measured by the nacelle anemometer of the turbine were collected by the SCADA (Supervisory control and data acquisition) system. The NTF was determined by the table method, and then the power curve drawn using the NTF by the nacelle LIDAR (PC\({}_{\text{NTF, NL}}\)) was compared with the power curves drawn in compliance with International Electrotechnical Commission (IEC) standards, 61400-12-1 and 61400-12-2. Next, the combined standard uncertainties of the power curves were calculated to clarify the magnitude of the components of the uncertainties. The uncertainties of annual energy production (AEP) were also estimated by assuming that wind speed is a Rayleigh cumulative distribution. As a result, the PC\({}_{\text{NTF, NL}}\) was in good agreement with the power curves drawn in accordance with the IEC standards. The combined standard uncertainty of PC\({}_{\text{NTF, NL}}\) was almost the same as that of the power curve based on IEC 61400-12-2.  
l 17 February 2019; Accepted: 15 March 2019; Published: 21 March 2019
## 3 Methods
The density of ice and sea water has been assumed as 917 kg/m\({}^{3}\) and 1027-1030 kg/m\({}^{3}\), respectively, in many studies on thickness estimation of the Antarctic ice shelves from the freeboard measurements by satellite altimetry [25; 36; 37; 38; 35]. We used 917 kg/m\({}^{3}\) and 1027 kg/m\({}^{3}\) as the density of ice and water. Snow density of 300 kg/m\({}^{3}\)[34] was used in this study. The change in firn densification should also be considered in the estimation of iceberg thickness change from the freeboards. However, snow depth predicted by MERRA-2 used in this study does not fully reflect the firn densification and there is no information about changes in firn densification of iceberg A68A. Nevertheless, we estimated the changes in thickness of iceberg A68A, ignoring the changes in firn densification. This is because the change in firn densification around the West Antarctic ice shelves is approximately only \(-2.7\) cm/year [39] which is significantly smaller than the freeboard changes in iceberg A68A (see Section 4). The volume loss of iceberg A68A caused by the thickness changes was also analyzed in this study. Changes in the area, drift speed, rotation angle, and freeboard of iceberg A68A were analyzed with the environmental factors predicted from the MERRA-2 reanalysis fields and seabed topography from Bedmap2. This enabled us to assess the possible causes of iceberg evolution.
## Appendix C Redfield Equation Derivation in Brief  
In the Redfield approach, physical information of the system-bath interaction is retained. In fact, by defining \(C_{ij}=\text{Tr}(\tilde{B}_{i}\tilde{B}_{j}\tilde{\rho}_{B})\) and \(C_{ji}=\text{Tr}(\tilde{B}_{j}\tilde{B}_{i}\tilde{\rho}_{B})=C_{ij}^{*}\), and dropping the time dependence by assuming the stability of the bath in (17). If \(t_{0}\to-\infty\) and then performing \(t^{\prime}\to\tau=t-t^{\prime}\), (13) becomes:  
\[\dot{\tilde{\rho}}_{S}(t)=-\frac{1}{\hbar^{2}}\sum_{i=1}^{N^{2}-1 }\left(\left[\gamma_{i}\tilde{S}_{i}(t),\left(\sum_{j=1}^{N^{2}-1}\gamma_{j}C _{ij}\int_{0}^{\infty}\tilde{S}_{j}(t-\tau)d\tau\right)\tilde{\rho}_{S}(t)\right]\right. \tag{17}\] \[\left.-\left[\gamma_{i}\tilde{S}_{i}(t),\tilde{\rho}_{S}(t)\left( \sum_{j=1}^{N^{2}-1}\gamma_{j}C_{ij}^{*}\int_{0}^{\infty}\tilde{S}_{j}(t-\tau )d\tau\right)\right]\right)\]  
which is reduced to its classical form (after to return to the [PERSON] picture):  
\[\dot{\tilde{\rho}}_{S}=-\frac{i}{\hbar}[H_{S},\rho_{S}]-\frac{1}{\hbar^{2}} \sum_{\alpha}[\Sigma_{\alpha},\Lambda_{\alpha}\rho_{S}-\rho_{S}\Lambda_{\alpha }^{\dagger}] \tag{18}\]  
by introducing:  
\[\tilde{\Sigma}_{i} = \gamma_{i}\tilde{S}_{i}(t) \tag{19}\] \[\tilde{\Lambda}_{i} = \sum_{j=1}^{N^{2}-1}\gamma_{j}\int_{0}^{\infty}C_{ij}(\tau) \tilde{S}_{j}(t-\tau)d\tau \tag{20}\]  
which physically describes the coupling with the environment.
## 3 Results
### Evaluation of Landslide Susceptibility Models  
#### 3.3.1 Seed Cell Area Index  
We evaluated the achieved susceptibility models based on the SCAI index; however, our evaluation was performed for split areas (\"Lososina\" and \"Grodek\"). This allowed for the better investigation of possible changes (the modeling and validation splitting strategy are described in Section 2.5). If the values of the SCAI decrease from very low to very high classes of LS, the model is regarded as excellent [13]. Table 4 presents percentage of each susceptible class, landslide observed in this specific class and SCAI index for both models. We can observe that based on the evaluation of the LSM in the modeling area, the SCAI index was extremely high (2491) and extremely low (0.42) for the \"very low\" and \"very high\" susceptible zones, respectively. These are exceptionally good results. A somewhat lower performance can be observed for the LSM based on \"Grodek\" and evaluated in the \"Grodek\" area; however, even its results were largely correct. Moreover, based on the evaluation of our results in the validation area, the SCAI index for the \"very low\" class in both cases (\"Lososina\" and \"Grodek\" model) was higher than 50, and it was below 0.5 for the \"very high\" susceptibility class, which is also considered appropriate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \
\begin{tabular}{c} **Model/** \\ **Area** \\ \end{tabular} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{
\begin{tabular}{c} **Validation** \\ **Area** \\ \end{tabular} } & \multirow{2}{*}{**Pixels in** **Domain**} & \multirow{2}{*}{\%} & \multicolumn{2}{c}{**Landslide Pixels within**} & \multicolumn{1}{c}{**Percentage**} & \multirow{2}{*}{**SCAI**} \\  & & & & & **[km\({}^{2}\)]** & & **[\%]** \\ \hline \multirow{6}{*}{Lososina} & Very low & \multirow{6}{*}{Lososina} & 630,917 & 0.06 & 46 & 0 & 2491.70 \\ \cline{2-6}  & Low & & 2,064,901 & 0.18 & 11,417 & 0.01 & 32.86 \\ \cline{2-6}  & Moderate & & 2,900,312 & 0.25 & 213,545 & 0.10 & 2.47 \\ \cline{2-6}  & High & & 3,515,091 & 0.31 & 841,964 & 0.41 & 0.76 \\ \cline{2-6}  & Very High & & 2,323,889 & 0.20 & 1,010,439 & 0.49 & 0.42 \\ \cline{2-6}  & Very low & & 877,306 & 0.04 & 1645 & 0 & 66.82 \\ \cline{2-6}  & Low & & 6,874,753 & 0.35 & 144,129 & 0.06 & 5.98 \\ \cline{2-6}  & Moderate & & 6,843,835 & 0.34 & 907,641 & 0.37 & 0.94 \\ \cline{2-6}  & High & & 4,534,307 & 0.23 & 1,173,528 & 0.47 & 0.48 \\ \cline{2-6}  & Very High & & 713,775 & 0.04 & 259,295 & 0.10 & 0.34 \\ \hline \end{tabular}
\end{table}
Table 4: Landslide susceptibility model evaluation based on modeling and validation study areas.  
Figure 7: Distribution of susceptible classes across the study area for the (a) “Lososina”-based model and (b) “Grödek”-based model.
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 7 Suggested Improvements
### Software Advancements  
One of the key enhancements in software approaches is the usage of an Intrusion Detection System (IDS), as shown by [PERSON] et al. [20]. It can monitor network traffic in real time for suspicious activities, such as unauthorized access attempts or data manipulation.  
* **Anomaly Detection**: the IDS can be trained to recognize normal traffic patterns for the AEB system and alert it to deviations that might indicate an attack.
* **Rapid Response**: in the event of malicious activity detection, the IDS can trigger rapid response measures, such as isolating compromised components or halting suspicious communications.  
Other approaches aimed at enhancing the overall security of AEB system components, such as the Electronic Stability Control ESC and Engine Management System EMS are related to regular software updates to patch known vulnerabilities.  
* **Secure Update Mechanisms**: this involves implementing secure update mechanisms to ensure that software updates come from trusted sources and are not tampered with.
* **Thorough Testing**: this involves rigorously testing software updates before deployment to avoid compatibility issues or new vulnerabilities.
* **Controlled Communication**: The gateway can facilitate controlled communication by managing the CAN frames sent between AEB components, ensuring that only authorized and properly formatted messages are transmitted. This prevents unauthorized devices from injecting malicious frames into the network.
* **Traffic Monitoring**: By monitoring the CAN traffic passing through the gateway, potential threats can be detected early. The gateway can analyze frame patterns and identify anomalies, such as unexpected frame types or transmission rates, enabling prompt action to mitigate risks.
* **Protocol Translation**: The gateway can support different communication protocols, including the CAN, to ensure seamless interoperability between various components. It can convert CAN frames to other protocols used in the architecture, allowing diverse components to communicate effectively while maintaining the integrity of the data.
* **Error Handling and Frame Validation**: The gateway can implement error handling mechanisms to ensure the reliability of communication. It can validate incoming CAN frames against predefined criteria, rejecting frames that do not meet security or functional requirements.
* **Logging and Auditing**: The gateway can log all CAN traffic for auditing purposes, providing a record of communication events. These data can be used to analyze potential security incidents or to verify compliance with established protocols.
## 3 Security Weaknesses of Cao et al.'s Scheme
### Informal Security Analysis
#### 3.2.1 UE Impersonation Attack  
During UE registration phase, the UE receives SC\(=\{ID_{UE_{i}},T_{u_{i}}(K_{uu_{i}}||ID_{UE_{i}})\ \text{mod}\ p\), \(u_{i}\}\) from KGC. According to Section 1.2, [PERSON] et al. present the threat model and analyze security of the proposed scheme using their threat model. However, if a malicious attacker \(A\) compromise the UE and extracts the data stored in the UE's memory, \(A\) can successfully generate the access request \(\{ID_{SN_{j}},T_{x_{i}}(K_{sn}||ID_{SN_{j}})\ \text{mod}\ p,E_{1},MAC_{1}\}\) and the response message \(\{MAC_{3}\}\) because the secret data of the UE's memory is directly stored without employing any cryptographic method. Further, \(A\) can also generate the session key \(SK_{ij}\). Therefore, their scheme is vulnerable to UE impersonation attacks and a detailed description of the processed involved in this attack is shown in Figure 1.