esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### iWUE* and LUE Trend during 1982-2010  
#### 3.2.1 iWUE* Trend during 1982-2010  
Figure 3 shows the results obtained with the validation datasets and the two ORCHIDEE simulations S1 and S2, in terms of temporal trends in annual iWUE* during 1982-2010. We found in the observational data a positive MTE-iWUE* trend during 1982-2010 (at 95% CI) over a large part of the African northern savannah and the African central forest. The iWUE* trend is between 5% and 20% of iWUE* per decade, which corresponds to a high and positive trend of the product GPP*VPD over the African northern savannahs and central forest (data not shown). In contrast, Kenya, Angola, and the southern part of Africa are marked with a negative and statistically significant trend of MTE-iWUE* (-5% to -10% per decade) corresponding to a negative trend of the product GPP*VPD. Compared to the MTE-iWUE* product, the simulated iWUE* trend at 95% CI is positive only over the central African forest. The geographical pattern of the 11-layer iWUE* trend at 95% CI is confined over the Central African forest and ranges from 7% to 20% of iWUE* per decade (Figure 3). The iWUE* trend from the 2-layer version is similar to that of the 11-layer version, but this version produces a statistically significant iWUE* trend covering more grid cells in the central African forest (Figure 3). The ORCHIDEE model, thus, does not reproduce the MTE-iWUE* trend over the northern savannahs. The model also does not reproduce the negative iWUE* trend in Kenyan, Angola, and the southern African areas (Figure 3).  
Figure 2: Same as Figure 1 for 1982–2010 LUE average (\(\;\mathrm{gC\cdot MJ^{-1}\cdot APAR}\) ). (**A**): MTE-LUE avg; (**B**) S1-LUE avg; (**C**) S2-LUE avg.
## 1 Introduction
To address the inherent scale inconsistency in monocular SLAM, we employ globally consistent depth estimation to transform monocular SLAM into a virtual stereo SLAM system, ensuring that pose estimation and mapping are scale-consistent. In the back end, we construct a bundle adjustment for learned point and line features to optimize pose estimation, and we develop an online binary bag-of-words (BoW) model to correct accumulated errors. We validate our proposed D3L-SLAM on both the public benchmark, i.e., the KITTI dataset, and our self-collected dataset, comparing its performance against model-based benchmarks like ORB-SLAM3, LDSO, and VISO-M, as well as the learning-based benchmark LIFT-SLAM. The experimental results demonstrate that D3L-SLAM achieves a 39.42% improvement in translation accuracy compared to the traditional handcrafted feature-based ORB-SLAM3 and a 75.95% improvement compared to the learning-based LIFT-SLAM. On our self-collected dataset, D3L-SLAM outperforms ORB-SLAM3 by 43.36% in terms of localization accuracy. In summary, our contributions are as follows:  
* We propose D3L-SLAM, a hybrid monocular visual SLAM system that integrates deep keypoints, deep pose priors, deep depth estimation, and a line detector, enabling robust operation in complex, low-texture environments. * To enhance SLAM performance in low-texture scenarios, we incorporate point-line feature constraints to optimize pose estimation and mapping through the construction of a tightly coupled point-line bundle adjustment (BA). * To ensure scale-consistent pose and map estimates in monocular visual SLAM, we employ self-supervised depth estimation with RGB images to form a pseudo-RGBD sensor and integrate a virtual baseline to create a pseudo-stereo SLAM system. * We conducted extensive experiments on both public and self-collected datasets, demonstrating that D3L-SLAM significantly outperforms representative traditional SLAM (e.g., ORB-SLAM3) and learning-based SLAM (e.g., LIFT-SLAM).
## 5 Discussion and Future Research Directions
### Traffic Management  
Traffic management is considered a key challenge in network function virtualization [65]. All the previous game-based solutions address only the resource management in the NV. There are no approaches based on game theory for the routing protocols, forwarding mechanisms, control, and management planes. The game theory can model the interactions between the end-users and the flows, the packets to be forwarded, etc., computing to be routed first. Thus, the GT needs to meet the players' requirements to reduce the delay, the end-to-end latency, the congestion, etc. However, the solution must consider the issues of the huge amount of data forwarded as well as the increasing numbers of services requester.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 2 State of the Art
### International Committees on Space Debris
Any intentional destruction of spacecraft or launch vehicle stages can generate debris that poses a risk to other space assets. If intentional break-ups are required, they should be carried out at lower altitudes to minimize the orbital lifetime of any resulting debris. Minimizing the potential for post-mission break-ups resulting from stored energy requires careful planning and design of spacecraft and launch vehicle disposal methods. This involves removing as much residual energy as possible before disposal to minimize the risk of post-mission break-ups. Finally, limiting the long-term presence of spacecraft and launch vehicle orbital stages in the LEO region after the end of their mission involves ensuring that disposal methods are effective and comply with international guidelines. This will help to minimize the amount of space debris in LEO and reduce the risk of collisions with other space assets [12]. International organizations have been researching and developing different technologies for systems of sensors and data centers to detect, catalogue, and process space debris and predict hazards to operational spacecraft, such as a potential collision with a debris object or even to any infrastructure on the ground during a reentering object. The main organizations involved in SSA are the U.S. military's JSpOC, responsible for SSN, and the ESA, responsible for the Space Surveillance and Tracking (SST). The SSN uses approximately 30 different systems with 4 main systems: satellites, radar systems, optical telescopes, and supercomputers. Continuous observational data are transmitted to the supercomputers to check the orbits of all satellites and catalogue space debris to check with days in advance, if there is any risk of future collision. The JSpOC is responsible for SSN and currently provides 72 h advance warning to satellite operators of approaches within 1 km for LEO and 5 km for GEO. One of the main limitations of the current SSA is that there is reduced space surveillance coverage of SSN in the southern hemisphere, which compromises SSA. This issue has been overcome with the newest ground radar (the Space Fence), operative since March 2020. This radar improves space surveillance due to its ability to detect small objects in LEO up to approximately 1 cm. Data from the Space Fence will feed into the SSN. The analysis to decide the avoidance maneuver strategy to mitigate the risk of collision is essential.
## 3 Results
### Energy Detection  
In this section, we exhibit the performance of the energy detector using the optimal k value. Figure 8 provides an extensive performance analysis using a dataset of 20,000 signals. Among these, 10,000 signals signify channel occupancy, while the remaining 10,000 represent spectrum availability. The subset denoting channel occupancy spans a range of SNR levels from 0 dB to 20 dB, with intervals of 5 dB. Following the application of our proposed system for channel impairment mitigation, the results reveal a false alarm rate of 3.85% and a missed detection rate of 3.06%. These quantitative findings distinctly validate the effectiveness of our approach.  
Figure 9, on the other hand, provides insights into the system's performance across varying \(k\) values. While typical receiver operating characteristic (ROC) curves depict model performance at different classification thresholds, our approach dynamically adjusts the threshold based on the \(k\) value. Given the significance of finding the optimal \(k\) value for peak performance, our ROC curve illustrates how different \(k\) values influence the results.  
Figure 8: Confusion matrix analysis of the energy detector performance using the best k value.  
Figure 9: Receiver operating characteristic (ROC) curves. (**a**) Original ROC curve. (**b**) Zoomed-in ROC curve for better visualization.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 4 Experimental Results
### Evaluation Under Attacks
The watermark also exhibited strong resilience to temporal desynchronization, with BER below 1.2% across all formats, largely due to the structured scene-based insertion and majority voting. To further assess the robustness of the proposed method, we extended our evaluation to include geometric attacks, which are known to challenge spatial-domain watermarking systems due to their pixel-coordinate transformations. Specifically, we considered three common distortions: Rotation of \(\pm 5^{\circ}\), which introduces misalignment at the block level, rescaling to 90% of the original frame size (followed by interpolation back to original dimensions), and cropping of 10% from each border (top, bottom, left, right), removing watermark-bearing regions. These attacks were applied to keyframes from seven representative videos across the three resolution classes (CIF, 4 CIF, HD). The extracted watermark was evaluated using BER, and visual degradation via SSIM. The results are summarized in Table 6.  
Despite operating in the spatial domain, the method exhibits moderate resilience to geometric transformations, largely due to its use of block-based embedding and multiframe redundancy. The watermark remained at least 95% recoverable in all cases, and visual quality degradation remained within acceptable limits (SSIM > 0.85 for most attacks). This suggests that the proposed method can tolerate mild spatial misalignments, particularly when combined with voting-based recovery mechanisms. The results validate that the proposed method effectively generalizes across various video formats. While HD content is more sensitive to compression and rotation (due to greater spatial detail), the watermark remains detectable and visually unobtrusive. In general, the scheme's suitability for blind watermarking for multimedia streaming and distribution platforms is validated by their robustness against typical video attacks, which is achieved through the combination of local perceptual embedding, spatially optimized block selection, and temporally distributed redundancy. For a better appreciation of the visual quality distortion caused by the attacks, Figure 3 presents the Suzie video sequence alongside its watermarked version. The visual quality degradation caused by Gaussian noise, H.264 and VP8 video coding, and cropping attacks are also shown in Figure 3.
## 2 Materials and Methods
### Different Scale Grid Division Strategy
#### 2.2.2 Fluent Grid Parameter Configuration  
(1) Model establishment and grid system  
The RNG K-\(\varepsilon\) model was selected in Fluent for this article, which better considers factors such as rotation and streamline bending in turbulence, thus providing more accurate simulation results in complex flow conditions. As shown in Figures 7 and 8, the Fluent model corresponds to the region depicted in Figure 6 of WRF, measuring 1 km in length, 1 km in width, and 200 m in height. Based on the layout of measurement points in WRF, which capture wind data at heights of 10 m, 50 m, 100 m, and 200 m above ground level, the CFD model is vertically divided into four layers: 0-10 m, 10-50 m, 50-100 m, and 100-200 m. Horizontally, wind data measured at specified points represent the wind distribution within a 1 km range to their right. Therefore, the CFD model horizontally subdivides each of its four sides into two faces, totaling 32 faces around the entire model. Wind speed and direction data measured at each point are then assigned to these faces to realistically simulate the natural wind field and enhance data fidelity. The model's bottom surface is set as a solid surface (ground), while the top surface is defined as a pressure outlet boundary. Intermediate rectangular prisms within the model represent high-rise buildings, each measuring 40 m in length, 10 m in width, and 100 m in height. Grid partitioning of the simulation area was conducted using ICEM, taking into account both the precision and accuracy of simulation results, as well as computational costs, resulting in a total of 1,200,000 grids.
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 4 Experiment Results
### Experimental Results on MobileNetV2 with EMNIST  
To evaluate the robustness and the extensive applicability of our proposed methodology, we conducted experiments training another network called MobileNetV2 [34] with EMNIST [35] additionally. EMNIST dataset is set of handwritten character digits derived from the NIST Special Database 19. There are six different splits provided in this dataset, and we used the EMNIST Balanced dataset which contains a set of characters with an equal number of samples per class. It was made up of 131,000 gray characters images, which were converted to 28 \(\times\) 28 pixel image format and consisted of 47 classes of letters and digits totally. MobileNetV2 is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layer. Besides, the MobileNetV2 network consisted of 52 convolutional layers, and we allowed Bayesian Optimization to select the optimal number of layers within this range. Then, we divided the EMNIST dataset to two sets which were chosen randomly and mutually exclusive. The base network which was trained with 40 classes among 47 classes was utilized and the branch network with the remaining seven classes was updated. To enable the branch network to utilize abundant knowledge acquired from the base network, we trained the base network with most of the  
Figure 8: Visualization results of the optimal sharing layer configuration through BayesOpt BayesOpt as threshold accuracy degradation is 3% less than the baseline accuracy. (60 classes/40 classes) **(a)** The result of the selected optimal number of sharing layers. **(b)** The L2 distance of the consecutive observed points. **(c)** The value of the calculated best-selected sample for every iteration.  
Figure 7: Visualization results of the optimal sharing layer configuration through BayesOpt BayesOpt as threshold accuracy degradation is 2% less than the baseline accuracy. (60 classes/40 classes) **(a)** the result of the selected optimal number of sharing layers. **(b)** The L2 distance of the consecutive observed points. **(c)** The value of the calculated best-selected sample for every iteration.  
47 classes. The baseline accuracy, where every layer of the network was updated for new tasks without any network sharing, was 78.66% and then we set the threshold accuracy degradation value to 8% less than baseline accuracy. Utilizing the proposed method, the optimal number of sharing layers was obtained as shown in Figure 9. Observing the results of experiment, the optimal number of sharing layers was the 36 th layer, having the accuracy of 70.69% with only seven iterations. That means it converged to the optimal solution that satisfies the proposed objective function with only seven layer-attempts of experiment.
# Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges
## 5 Conclusions  
WSNs have elicited much research interest in the last decade due to their numerous applications. The structure of routing protocols in WSNs is important when designing them and these protocols can be categorized into many categories based on them, with the important category of routing protocols being clustering or hierarchy routing.  
Clustering routing presents many advantages, such as network scalability, energy distribution among nodes, and reduced load on the nodes in a network. Given these advantages, many protocols that use clustering routing have been proposed.  
The LEACH protocol is considered as a common hierarchy protocol; it reduces power consumption by distributing energy among nodes in a network, but it has many disadvantages in terms of CH selection, cluster formation, and energy efficiency. Several advanced protocols have been conducted to improve LEACH.  
In this survey, a new taxonomy was proposed to categorize many LEACH variants. The new taxonomy is based on the selection methods utilized to select a CH and on the methods utilized to form a cluster. These two categories were examined in this survey for classifying LEACH-based routing protocols because of their importance and impact on the performance of networks. Moreover, these two categories are considered main operations in clustering protocols; therefore, discussing and presenting routing protocols that depend on these methods are important.  
In addition to using these two categories as the main classifying factors, this survey considered other important subcategories for further classification to make the classification clear and comprehensive. These new subcategories are residual energy, energy efficiency, distance, network coverage, and routing path. Due to their strong influence on the network lifetime, these parameters are considered in classifying the CH selection and cluster formation methods. This survey also discussed21 routing protocols based on LEACH, and these protocols were classified under the abovementioned categories and subcategories. The classification of these protocols was performed in a tabular format for easy referencing. Furthermore, a qualitative comparison of this survey with other existing surveys was conducted, with a focus on contributions and limitations.  
Moreover, future issues in proposing and developing new LEACH-based routing protocols were highlighted in this survey to help researchers by providing them useful information.  
The contributions of authors are as follows: Conceptualization: M.A.-S. and M.A.; formal analysis: M.A.-S.; investigation: M.A. and T.C.W.; project administration: M.A.-S.; resources: M.A.-S.; supervision: M.A. and T.C.W.; validation, M.A., T.C.W., and A.K.; writing--original draft: M.A.-S.; writing--review and editing: M.A. and A.K.  
This grant is supported in part by Universiti Sains Malaysia through Grant No. 304/PNAV/650817/C127.  
The authors declare no conflict of interest.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Window Size**} \\  & _100_ & _500_ & _1000_ & _5000_ & _8000_ \\ \cline{2-6}
**SCTs** & KNN,NN & KNN,NN & KNN,NN & KNN,NN & KNN,NN \\ \hline
0 & 0.9889, 0.9835 & 0.9998, 1.0 & 1.0, 1.0 & 1.0, 0.9877 & 1.0, 1.0 \\
5 & 0.8758, 0.8690 & 0.9310, 0.7470 & 0.9460, 0.6767 & 0.9439, 0.5839 & 0.9632, 0.6922 \\
10 & 0.9811, 0.9859 & 0.9987, 0.9959 & 0.9990, 0.9968 & 0.9996, 0.9658 & 0.9999, 0.9605 \\
15 & 0.9997, 0.9981 & 1.0, 0.9721 & 1.0, 0.9720 & 1.0, 0.8911 & 1.0, 0.9737 \\
20 & 0.9980, 0.9948 & 1.0, 0.9676 & 1.0, 0.9627 & 1.0, 0.9285 & 1.0, 0.9333 \\
25 & 0.9918, 0.9876 & 0.9947, 0.9079 & 0.9958, 0.9031 & 0.9974, 0.8640 & 0.9983, 0.8601 \\
30 & 0.9929, 0.9790 & 0.9969, 0.9958 & 0.9977, 0.9817 & 0.9988, 0.9767 & 0.9995, 0.9652 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: General accuracy comparison among KNN and NN classification. Figure 8: Mean, minimum, and maximum accuracy values of SCTs using 100, 500, 1000 and 5000 window samples. samples. The condition with 5 SCTs yielded the lowest classification accuracy across all three classifiers. Particularly noteworthy are the results obtained by the NN classifier, which exhibited the poorest performance with an average accuracy of 71.7%. The accuracies in 8000 samples are close to the same value. Therefore, the KNN method has general advantages over the DT method in accuracy with a small window size. The comparison among the accuracies of the classification methods is presented in Figure 9. As can be observed, the three proposed classifiers exhibit similar and high values of accuracy for all the window sizes and all the SCT conditions; however, KNN presents a slight advantage. The worst values occur in 5 SCTs applying the three classification methods; they are squared with dotted lines, regardless of the window size  
Figure 9: Graph of the classification method accuracies. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Window Size**} \\  & _100_ & _500_ & _1000_ & _5000_ & _8000_ \\ \cline{2-7}
**SCTs** & **KNN,DT** & **KNN,DT** & **KNN,DT** & **KNN,DT** & **KNN,DT** \\ \hline
0 & 0.9889, 0.9831 & 0.9998, 0.9997 & 1.0, 1.0 & 1.0, 1.0 & 1.0, 1.0 \\
5 & 0.8758, 0.8615 & 0.9310, 0.9266 & 0.9460, 0.9332 & 0.9439, 0.9653 & 0.9632, 0.9568 \\
10 & 0.9811, 0.9786 & 0.9987, 0.9988 & 0.9990, 0.9994 & 0.9996, 0.9996 & 0.9999, 0.9996 \\
15 & 0.9997, 0.9983 & 1.0, 1.0 & 1.0, 1.0 & 1.0, 1.0 & 1.0, 1.0 \\
20 & 0.9980, 0.9923 & 1.0, 0.9996 & 1.0, 0.9999 & 1.0, 1.0 & 1.0, 1.0 \\
25 & 0.9918, 0.9940 & 0.9947, 0.9961 & 0.9958, 0.9923 & 0.9974, 0.9974 & 0.9983, 0.9988 \\
30 & 0.9929, 0.9935 & 0.9969, 0.9944 & 0.9977, 0.9949 & 0.9988, 0.9991 & 0.9995, 0.9997 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: General accuracy comparison among KNN and DT classification.
## 4 Experiments
### Ablation Studies
#### 4.3.1 Ablation Analysis of All Components  
We selected YOLOv8 as the baseline model and conducted experiments for the following cases: (1) baseline; (2) baseline with the MNFM; (3) baseline with the MNFM and AFEM; (4) baseline with the MNFM, AFEM, and RFEM. We performed the ablation experiments on the VisDrone and DOTA datasets, and the results are presented in Tables 2 and 3.  
Based on the detection performance on the VisDrone dataset, our model showed an improvement of 4.51% in map compared to the baseline. The detection performance metric for small objects \(\mathrm{AP_{S}}\) improved by 4.81%. Additionally, the detection performance metrics for medium-scale objects \(\mathrm{AP_{M}}\) and large-scale objects \(\mathrm{AP_{L}}\) showed improvements of 6.75% and 6.23%, respectively.  
From the evaluation results on the DOTA dataset, our model showcased a remarkable enhancement in map of 3.44% compared to the baseline. Notably, the detection performance of small objects with high difficulty \(\mathrm{AP_{S}}\) witnessed a substantial improvement of 6.2%. Additionally, the detection performance metrics \(\mathrm{AP_{M}}\) and \(\mathrm{AP_{L}}\), which measure the performance for medium- and large-scale objects, respectively, both show commendable enhancements of 7.18% and 4.84%.