esa-sceva/satcom-chunk-collection huggingface dataset:
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 5 Results and Analysis
### Handover Latency (A2)  
The handover latency of the schemes is compared based on the variations of RSS error. For analysis, the variations of RSS error are taken between the range (0.0-0.5), and the respective handover latency is evaluated. The statistics of the comparison among non-location-based schemes and extensions are provided in Figure 6. While the statistics of comparison among location-based existing schemes and the proposed one are shown in Figure 7. In the comparison graphs, the horizontal axis depicts the error probability, and the y-axis represents the handover latency. RSS error probability affects the handover latency and by comparison, it is observed that location-based PMIPv6 extensions provide much less handover latency. With the existing extensions including FPMIPv6, low latency, and smart buffering, the handover latency becomes much too high for high RSS error probability.  
Figure 5: Signaling comparison among proposed and location-based schemes.  
Figure 6: Comparing handover latency among processed and non-location-based schemes.  
From the comparison, the proposed solution provides higher signaling efficiency, low handover latency, and efficient buffering performance by predicting the handover moment accurately. Furthermore, the presented scheme eliminates the multi-casting of IHR by LMA due to the utilization of target MAG-ID. As a result, the proposed solution is more reliable and has the potential for its adaptation for mobility management in upcoming future-generation communications.
# Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler
## 5 Results and Discussion  
### Compiler and Matching Module Performance  
As shown in Figure 8, the system maintains line-rate operation at average packet sizes above 700 B, which is close to the standard average packet size in typical network environments [15]. When operating below 700 B, the influx of port numbers into the port matching module for table lookups--coupled with frequent rule SID outputs from port hits--overloads the protocol and IP matching modules. This results in packet-processing  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Resources** & **ALMs** & **Registers** & **M20K Blocks** & **Block Memory** \\ \hline Design & 307,930 (33\%) & 497,889 (26\%) & 3034 (26\%) & 54.2 (24\%) \\ \hline \end{tabular}
\end{table}
Table 6: Hardware resource utilization statistics.  
delays and degraded overall performance. Consequently, subsequent experiments use 700 B as the baseline average packet size to ensure controlled variable conditions.  
Figure 9 demonstrates that the system achieves line-rate throughput when the anomaly traffic ratio is below 10%. However, throughput declines rapidly as the ratio increases. This is attributed to the Pre-Filtering module exceeding its load capacity under high anomaly ratios: (a) repeated port hits in five-tuple matching trigger intensive table lookup operations; (b) frequent hash address hits in fast-pattern matching escalate shift-or verification computations. These concurrent factors lead to progressive packet loss.  
The above data indicate that the five-tuple matching module and the fast-pattern matching module meet normal operational requirements in a 100 Gbps testing environment, supporting high-throughput line-rate operation. However, throughput loss occurs under extreme conditions characterized by smaller packet sizes and a high proportion of anomalous traffic.  
Notably, the maximum throughput here does not reach 100 Gbps because the anomaly ratio tests used a single NIC port for traffic injection, which did not saturate 100 Gbps. This design avoids interference from multi-port configurations. Subsequent tests employ dual-port injection to ensure 100 Gbps baseline conditions.  
Figure 8: Impact of average packet size on throughput.  
Figure 9: Impact of the proportion of anomalous traffic on throughput.
## 3 Challenge-Based CIDNs  
### Background  
To protect collaborative networks against insider attacks, many trust-based approaches have been proposed [24]. Challenge mechanisms are one effective approach to point out unusual nodes and measure the trustworthiness of nodes according to the received feedback [5]. Figure 1 presents a typical challenge-based CIDN with major components of an IDS node.  
In such networks, IDS nodes can choose their own collaborators or partners in terms of their prior experience, as well as maintain a list of connected partners. This list is known as a _partner list_ (or _acquaintance list_), and it can gather necessary information with other IDS nodes, for example, public keys and reputation levels. Supposing an outside node plans to join the network, it has to firstly obtain its proof of identify by registering via a trusted certificate authority (CA), including a public and private key pair. As shown in Figure 1, if node \(C\) plans to join the CIDN, it can apply to a node within the network, for example, node \(A\). After receiving a request, node \(A\) can make decisions on the basis of the predefined rules and return a list of initial partners if the request is confirmed.
# Small-UAV Radar Imaging System Performance with GPS and CDGPS Based Motion Compensation
## 2 Materials and Methods
### UAV Positioning Techniques
#### 2.2.2 Carrier Based Differential GPS  
An enhanced strategy to reconstruct UAV positioning is based on the use of CDGPS, which is a recognized technique to enhance the positioning performance accuracy of the onboard GPS and it typically exploits at least one motionless GPS receiver working as a reference station (see Figure 2b). Each GPS receiver collects observables, which are a pseudo-range and a carrier-phase measures for any tracked GPS satellite. Carrier-phase measurements show significantly reduced measurement  
Figure 1: Multicopter-UAVs (M-UAV) radar imaging system: (left) M-UAV hexacopter with onboard equipment [21]; (right) ground-based Global Positioning System (GPS) station [21].  
Figure 2: Positioning and radar data synchronization strategy (**a**) Standalone GPS; (**b**) Carrier based Differential Global Positioning System (CDGPS).  
noise (in the order of 1/100 of GPS signal wavelength, i.e., mm scale) with respect to pseudo-range, but ambiguities appear, so carrier-phase ones are biased measurements [27]. If one is able to resolve the ambiguity, very high accurate positioning is enabled. This can be achieved by differential techniques, i.e., CDGPS, where differences between the measurements collected by two relatively close receivers are computed. The CDGPS technique is, indeed, able to filter out the common errors affecting the two receivers, i.e., satellite clock errors, tropospheric and ionospheric errors, thus, achieving an accurate estimate of the relative position between them.  
Relative positioning and ambiguity resolution are herein faced in post processing by means of the open-source software RTKlib [31] Specifically, \"Post-Processing Kinematic\" (PPK) [27] is implemented by using the RTKPOST [20], which processes single-frequency differential observables. Basic equations for the problem at hand can be found in [20] Note that, depending on the working environment, platform dynamics and receiver quality, two different types of CDGPS solutions can be obtained, i.e., fixed or float solutions [32] In the experiments presented in this paper, the position of the UAV with respect to the reference station is estimated with an accuracy ranging from several cm to less than one cm. The better accuracy is achieved when the fixed solution is available, i.e., integer ambiguities are computed [33].
## 5 Results and Discussion
### \(\text{R}_{\text{AC}}/\text{R}_{\text{DC}}\) Ratio Comparison Between 2D and 3D Models  
The \(\text{R}_{\text{AC}}/\text{R}_{\text{DC}}\) ratio serves as a critical metric for evaluating the influence of skin and proximity effects on power losses in segmented cables. Table 5 summarize the \(\text{R}_{\text{AC}}/\text{R}_{\text{DC}}\) ratios for both 2D and 3D models across single-segment, three-segment, and five-segment configurations at frequencies of 0 Hz, 50 Hz, 150 Hz, and 250 Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Region** & **2D-\(BN_{\text{ERR}}\) (\%)** & **3D-\(BN_{\text{ERR}}\) (\%)** \\ \hline
1-Segment & Conductor & 0.19 & 1.72 \\
3-Segment & Conductor & 0.22 & 1.24 \\
5-Segment & Conductor & 0.30 & 1.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Discretization errors for the FE models at 50 Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Configuration** & **Material** & **Elements** & **Nodes** \\ \hline
1-Segment (3D) & Conductor & 1,109,053 & 1,581,938 \\  & Air & 468,059 & 518,748 \\
3-Segment (3D) & Conductor & 2,102,958 & 3,055,853 \\  & Air & 1,062,799 & 1,162,171 \\
5-Segment (3D) & Conductor & 5,265,862 & 7,958,575 \\  & Air & 4,107,837 & 4,509,268 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total number of elements and nodes for 3D models.  
Figure 6 graphically compares the \(\mathrm{R_{AC}}/\mathrm{R_{DC}}\) ratios obtained from 2D and 3D models across different segmentations and frequencies. The close alignment between 2D and 3D results across all configurations and frequencies underscores the efficacy of 2D modeling in accurately predicting AC losses without the computational burden associated with 3D simulations.  
The marginal differences between 2D and 3D models, particularly at higher frequencies, are attributed to the increased complexity of accurately capturing electromagnetic phenomena in three dimensions. However, these discrepancies remain within an acceptable range (<2%), reinforcing the validity of using 2D models for certain cable designs without compromising accuracy.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
###### Abstract  
The performance of visual SLAM in complex, real-world scenarios is often compromised by unreliable feature extraction and matching when using handcrafted features. Although deep learning-based local features excel at capturing high-level information and perform well on matching benchmarks, they struggle with generalization in continuous motion scenes, adversely affecting loop detection accuracy. Our system employs a Model-Agnostic Meta-Learning (MAML) strategy to optimize the training of keypoint extraction networks, enhancing their adaptability to diverse environments. Additionally, we introduce a coarse-to-fine feature tracking mechanism for learned keypoints. It begins with a direct method to approximate the relative pose between consecutive frames, followed by a feature matching method for refined pose estimation. To mitigate cumulative positioning errors, DK-SLAM incorporates a novel online learning module that utilizes binary features for loop closure detection. This module dynamically identifies loop nodes within a sequence, ensuring accurate and efficient localization. Experimental evaluations on publicly available datasets demonstrate that DK-SLAM outperforms leading traditional and learning-based SLAM systems, such as ORB-SLAM3 and LIFT-SLAM. DK-SLAM achieves 17.7% better translation accuracy and 24.2% better rotation accuracy than ORB-SLAM3 on KITTI and 34.2% better translation accuracy on EuRoC. These results underscore the efficacy and robustness of our DK-SLAM in varied and challenging real-world environments.  
mocnocular SLAM; deep learning; feature extraction and matching; loop closing 2025  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop Closing. _Appl. Sci._**2025**, 15738. [[https://doi.org/10.3390/appl5147838](https://doi.org/10.3390/appl5147838)]([https://doi.org/10.3390/appl5147838](https://doi.org/10.3390/appl5147838))  
1
## 11 Ethical AI Governance and Integrated Space-Air-Ground-Sea Networks in 6G  
### Ethical AI Governance in 6G  
Ethical AI governance in 6G [285] focuses on ensuring the responsible development and deployment of AI systems within the 6G ecosystem by addressing key issues such as transparency, fairness, accountability, and privacy. As AI becomes integral to 6G networks, explainability must be prioritized through techniques like XAI [286] to ensure that decisions made by these systems are understandable and accountable. This is particularly critical in applications such as healthcare, autonomous systems, and smart cities, where AI-driven decisions can significantly impact users' lives [287; 288]. Robust measures to detect and mitigate biases in datasets and algorithms are essential to ensure fairness and prevent discriminatory outcomes, promoting equitable benefits across diverse user groups.  
Data privacy and security are central to ethical AI governance in 6G. With the massive influx of sensitive user data from IoT devices and edge systems, strong safeguards like federated learning, differential privacy, and encryption are vital to protect user rights. Ethical frameworks should also enforce data minimization and ensure accountability through mechanisms that delineate responsibilities for AI-driven actions and decisions. Additionally, sustainability must be a core focus, with an emphasis on energy-efficient algorithms and green computing practices to reduce the environmental impact of large-scale AI systems. By fostering public trust and engaging stakeholders, ethical AI governance in 6G can establish a responsible, inclusive, and sustainable digital ecosystem.
# Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion
## 2 Materials and Methods  
Traditional fishway fish detection methods are time-consuming, incur high labor costs, and are not suitable for fishway environments. The water quality in fishway environments is often turbid and unclear, as shown in Figure 1. The use of deep learning methods can effectively meet the fishway fish detection task, enabling automated fishway fish detection and intelligent management of fishways.  
This paper proposes a novel, more intelligent fish detection model for fishway environments to address the various challenges in fish detection within fishways. First, the model's detection performance is comprehensively tested through fish dataset collection and classification in a laboratory setting. Next, the proposed model is compared with other detection models. Finally, the model is applied to real-world fishway environments for field testing. The specific structure of the model is outlined below. As shown in Figure 2,  
Figure 1: The environment around the fishway and the underwater imaging of the fishway.  
the backbone is responsible for extracting low-level and mid-level features of the fish, such as contours, colors, and textures, and the Neck is designed to merge multi-level features by optimizing the feature pyramid. Finally, the detection head takes the multi-scale features to detect fish from the low-quality images captured under poor lighting conditions. The processes are detailed in the following subsections.
# Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery
###### Abstract  
Pedestrian re-identification in complex scenarios is often hindered by challenges such as viewpoint diversity, background interference, and behavioral complexity, which traditional methods struggle to address effectively in wide-area surveillance. Unmanned Aerial Vehicles (UAVs) offer a promising solution to this problem due to their flexibility and extensive coverage capabilities. However, UAV aerial images introduce additional challenges, including significant viewpoint variations and the complexity of pedestrian behaviors. To address these issues, this paper proposes a Transformer-based model that integrates a multi-scale graph convolution network (MU-GCN) with a non-local attention mechanism to address these challenges. A MU-GCN enhances feature extraction by employing graph convolutional networks to improve feature representation after extracting detailed features at various scales through multi-scale convolution kernels. This strengthens the model's focus on local information. Meanwhile, the non-local attention mechanism enhances the model's capacity to capture global contextual information by modeling dependencies between distant regions in the image. This approach is better suited for the unique characteristics of UAV aerial imagery. Experimental results demonstrate that, compared to the baseline model, the proposed method achieves improvements of 9.5% in mean average precision (mAP) and 4.9% in Rank-1 accuracy, validating the effectiveness of the model.  
p edestrian re-identification; transformer; graph convolutional network; aerial imagery 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 2018-2018. External Links: Document Cited by: SS1.
###### Abstract  
Surface foam spraying technologies, employing natural infiltration processes, have recently been suggested to not disturb or mix contaminated soils. However, effective delivery of reactive remedial agents to the bottom area of a contaminated region using only natural infiltration processes can be a challenge. This study aimed to improve the delivery of remedial agents such as oxidants, microorganisms, and nutrients to all depths of 30 cm thick unsaturated diesel-contaminated soil using small vertical soil holes. Three vertical holes, occupying 0.8% of the total soil volume and 3% of the soil surface area, were made inside the 17.3 kg soil column. Persulfate oxidation foam and subsequent bioaugmentation foam spraying were applied for remediation of contaminated soil. Foam spraying with vertical soil holes improved the uniformity of distribution of remedial agents throughout the soil, as evidenced by the uniform pH, higher volumetric soil water content, and a microbial population of \(>\)10\({}^{7}\) CFU/g. Therefore, the total petroleum hydrocarbon (TPH) removal efficiency (88-90%) from bottom soils was enhanced compared to soil columns without holes (72-73%) and the control test (5-9%). The kinetic study revealed that relatively similar TPH biodegradation rates (0.054-0.057 d\({}^{-1}\)) can be obtained for all soil depths by using this new and simple approach.  
diesel-contaminated soil; surfactant foam; infiltration; oxidation; biodegradation; TPH Article
# Improving the Maritime Traffic Evaluation with the Course and Speed Model
## 2 Literature Review
Others used both ES models and fuzzy logic methods to study the risk factors and their weights [14]. Lastly, a study utilized machine learning to analyze traffic flow and applied the ES model to design appropriate route widths [15]. A maritime traffic flow simulation in Korea incorporated the use of the ES model. Additionally, the blocking coefficient (BC) model illustrates the potential risk of a specific ship colliding with nearby vessels or obstacles. And the subjective judgment (S) model [16] offers an objective representation of the collision risk, as perceived by the ship's operator. The determination of the S value considers factors such as whether the ship in question is a burdened vessel in crowded areas, is maintaining its course, or is an overtaking vessel. However, these models emphasize risk assessment based on subjective indexes with the direct involvement of the originator or ship operator, and do not focus on analyzing the liquidity of maritime traffic flow from a macro-perspective. Maritime traffic flow is affected by external environmental conditions, traffic condition, traffic rules, and more; therefore, it is important to adopt a macroscopic perspective [17]. Additionally, to accurately evaluate traffic flow, it is crucial to collect and analyze traffic flow data over a specific period and identify the correlations within these data. Consequently, the traffic datum accumulated by the vessel is an important reflection of the conditions at the time, and a quantitative assessment that measures the fluidity of the traffic flow itself that may be advantageous. Accordingly, this study aims to establish a standard plan based on traffic flow data, identify the deviation of each ship, and derive the quantitative indicators.
## 5 Experimental Design and Results Analysis
### System Accuracy Experimental Evaluation
Tables 1 and 2 display error metrics derived from 15 iterations of both techniques. The enhanced algorithm diminished the absolute trajectory error, reducing the mean error from 0.010691 m to 0.005951 m, a 44.33% decline; the root mean square error (RMSE) fell from 0.011777 m to 0.006580 m, a 44.15% reduction; the maximum error decreased from 0.027522 m to 0.018487 m, a 32.83% decrease; and the standard deviation was lowered from 0.004938 m to 0.002808 m, a 43.13% reduction. The enhanced algorithm reduced the mean error by 14.34% and the RMSE by 15.64% for the relative trajectory error, with a notable improvement in standard deviation, highlighting the method's advancements in stability and consistency. The thorough research confirms that the enhanced approach improves posture estimation accuracy and robustness. The enhanced algorithm substantially improves pose estimate accuracy by incorporating point-line feature extraction, optimizing reprojection error computation, and refining feature matching techniques, all while preserving real-time speed. The enhanced algorithm demonstrates greater resilience and reliability, especially when managing challenging scenarios with intricate scenes and dynamic movements. These performance enhancements offer robust fundamental support for later three-dimensional reconstruction, autonomous navigation, and other sophisticated applications.
# Light Transmission in Fog: The Influence of Wavelength on the Extinction Coefficient
## 5 Discussion
To conclude, direct spectroradiometer extinction measurements and extinction estimation using models (Mie and MODTRAN) taking into account the actual DSD showed the same trend even though they were different. The results obtained suggest that it would be possible to use a spectroradiometer measurement to estimate the DSD. Finally, the choice of using the wavelength 905 nm vs. 1550 nm in LiDARs should not be made on the basis of fog conditions: there is a small difference (<10%) between the extinction coefficients at these two wavelengths for the same emitted power in fog. Conceptualization, P.D., M.C., and F.B. ; data curation, P.D. ; formal analysis, P.D. ; funding acquisition, M.C. ; investigation, P.D. ; methodology, P.D. ; project administration, M.C. ; software, P.D. and F.B. ; supervision, F.B. ; validation, P.D., M.C., and F.B. ; visualization, P.D. ; writing, original draft, P.D., M.C., and F.B. writing, review and editing, P.D. and F.B. The research leading to these results has received funding from the European Union under the H2020 ECSELProgramme as part of the DENSEP Project, Contract Number 692449. This work has been sponsored by the French government research program \"Investissements d'Avenir\" through the IMob53 Laboratory of Excellence (ANR-10-LABX-16-01) and the RobotExEquipment of Excellence (ANR-10-EQPX-44), by the European Union through the Regional Competitiveness and Employment program, 2014-2020 (ERDF-AURAregion), and by the AURA region. The authors would like to thank [PERSON] and [PERSON] for their active participation in this publication. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results. The following abbreviations are used in this manuscript:  
Meteorological optical range  
DSD  
Droplet size distribution  
WMO  
World Meteorological Organisation  
FSO  
Free space optics  
TRL  
Technology readiness levels  
SWIR  
Short wave infrared  
ADAS  
Advance driving assistance systems  
MODTRAN  
Moderate resolution atmospheric transmission  
Lose-of-sight