esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Subject Anonymization  
The proposed anonymization system was tested using the videos from the LE2I dataset to evaluate the algorithm's effectiveness. With regard to performance, in terms of detection accuracy, utilizing the YOLOv8 model (yolov8s-pose.pt), the system was able to reliably detect the person's location in over 99% of the video frames. However, challenges arose in scenarios with significant occlusion, where more than half of the subject's body  
Figure 7: Proposed classification model using residual causal convolutional network.  
was obscured by environmental objects such as desks or tables. In these instances, the model occasionally encountered difficulties in detecting keypoints with high confidence. Nonetheless, it consistently achieved success in identifying the upper body's bounding box, even under such challenging conditions. Limitations were notably observed in rare cases when parts of the subject exited the camera's field of view, resulting in detection failures. To mitigate these limitations, future implementations could consider the strategic placement of cameras within the environment or the integration of multiple cameras to avoid blind spots, thereby enhancing the system's overall robustness and reliability in diverse settings.  
To visually demonstrate the anonymization process utilized in this study, four images are presented to illustrate the stages: an original video frame is shown in Figure 8a, showing a scene prior to any processing. Figure 8b displays the privacy frame, where the person has been removed from the scene using their bounding box. The skeleton representation, as predicted by the YOLOv8 model's pose detection, is shown in Figure 8c. Finally, Figure 8d presents the 2D avatar superimposed onto the scene, representing the person's movements in an anonymized manner. Additionally, another action, a person sitting, is shown in Figure 9. Both Figures 8 and 9 originate from the \"lecture room\" video of the LE2I dataset. This scene is well-lit, offering a baseline for fair comparison. Furthermore, two frames from the \"home\" scene of the LE2I dataset, which is moderately lit, are presented in Figures 10 and 11. Despite varying lighting conditions, scene setups, and the actions of the person, the proposed method effectively detected the person in the scenes and superimposed the 2D avatar, demonstrating its ability to perform consistently across different environments and lighting conditions.  
Figure 8: Subject anonymization process in “lecture room” scene with person walking: (**a**) original frame, (**b**) foreground removal, (**c**) skeleton representation, and (**d**) 2D avatar.  
Figure 10: Subject anonymization process in “home” scene with person walking: (**a**) original frame, (**b**) foreground removal, (**c**) skeleton representation, and (**d**) 2D avatar.  
Figure 9: Subject anonymization process in “lecture room” scene with person sitting on a chair: (**a**) original frame, (**b**) foreground removal, (**c**) skeleton representation, and (**d**) 2D avatar.
# Gradient-Based Aerodynamic Optimization of an Airfoil with Morphing Leading and Trailing Edges
## 2 Methodologies
### Feasible Morphing Airfoil Geometry Parameterization
#### 2.2.3 Morphing Leading and Trailing Edge Algorithm
In other words, the chord-wise coordinate of distinct points \(\psi_{\text{b}}\) is located from \(\psi_{\text{front}}\) to \(\psi_{\text{year}}\), where \((\cdot)_{\text{front}}\) and \((\cdot)_{\text{near}}\) represent front and rear spars of the wing-box. Those locations of wing-box points are known to the designer as morphing always starting from a baseline airfoil. Meanwhile, the coefficient vector \(A\) is partitioned into \(A=\left(A_{\text{b}}^{\intercal}\,A_{\text{P}}^{\intercal}\right)^{\intercal}\), in which the subsets \(\psi_{\text{b}}\) and \(\psi_{\text{P}}\) represent box and perturbed aerodynamically driven terms, respectively. Consequently, the inverse fitting problem to solve is adjusted to  
\[\left[ \begin{array}{cc}\mathbf{T}_{b}(\psi_{b})&\mathbf{T}_{p}(\psi_{b})\\ \mathbf{T}_{b}(\psi_{m})&\mathbf{T}_{p}(\psi_{m})\end{array} \right]\left\{ \begin{array}{c}A_{\text{b}}\\ A_{\text{P}}\end{array} \right\}=\left\{ \begin{array}{c}f(\psi_{b})\\ f(\psi_{m})\end{array} \right\} \tag{10}\]  
The coefficients with regard to the wing-box constraints \(A_{b}\) are solved by evaluating the first row:  
\[\mathbf{T}_{b}(\psi_{b})A_{\text{b}}=f(\psi_{b})-\mathbf{T}_{p}(\psi_{b})A_{ \text{P}} \tag{11}\]  
An example of a deformed RAE 2822 airfoil is shown in Figure 4. In this case, the morphing shape is obtained in a sequential way. Firstly, the intermediate airfoil with a morphing trailing edge is generated by changing the morphing trailing edge aerodynamic design parameters and taking the baseline airfoil as a reference with wing-box of \(\psi\in[0,\psi_{\text{near}}]\). The  
Figure 3: Morphing algorithm for maintaining the wing-box and the length of the skin. length of upper skin is kept constant through solving a nested single value optimization problem. As a result, the chord length slightly decreases. Then, the obtained intermediate airfoil with morphing trailing edge is taken as the reference airfoil, and the leading edge morphing is achieved by perturbing the leading edge aerodynamic design variables. Now, the wing-box region is \(\psi\in[\psi_{\text{front}},1]\), and the wing-box constraint is preserved by re-evaluating the subset of CST coefficients by solving an inverse fitting problem. Meanwhile, the chord-wise coordinate of leading edge point \(x_{\text{LE}}\) varies to keep the length of leading edge skin constant.
## 5 Comparison of Different Topologies
### Topology Comparison Based on Energy Efficiency
In a cluster-based topology, where the cluster members reach the cluster head in a single hop, only the cluster heads can be used for data aggregation/pre-processing. For a tree-based topology, sensor nodes get more opportunity to aggregate/pre-process data. For example, a parent node can process the data received from its child node(s). Finally, chain-based architecture is inherently amenable to in-network processing. In this topology, each single node can be used for in-network processing of its predecessor's data. It decreases communication traffic and communication frequency via data aggregation progressively at each leader in the chain by processing and filtering the possibly redundant data received from other chain members. Unlike the cluster topology, the leader of a chain is not responsible for all data aggregation. Every member of a chain participates in the process of data aggregation. This actually balances the load among the nodes of a chain and thus energy  
Figure 6: Communication patterns for different topologies of WSNs. (**a**) cluster topology, (**b**) chain topology, and (**c**) tree topology. consumption is evenly distributed. This is one of the important reasons why chain-based topology offers longer lifetime of WSNs. In summary, chain-based topology performs best regarding energy efficiency. On the other hand, flat topology is the least energy efficient topology. Cluster-based topology is ahead of tree-based topology with respect to energy efficiency. However, the energy efficiency of cluster-based topology primarily depends on the cluster formation algorithm.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis
### Test in Hardware Platform
#### 4.2.4 Test of AOS Frame Switching Scheme  
Node 2 also connects to node 1 through port 2 (Figure 17). Therefore, node 2 should receive services from port 2 and then forward these to port 4, according to the LFIB with the OutPort of 4 (Figure 21). So, we monitored port 2 and port 4 of node 2 and captured AOS frames.  
Figure 23 shows the sequence diagram of the same AOS frame captured by port 2 and port 4 of node 2. As can be seen, the AOS frame was received by port 2 with the INLABEL of 0x 0201 (Figure 23a) and then sent by port 4 with the OUTLABEL of 0x 0301 (Figure 23b). Referring to the LFIB configured in the network manager (Figure 21), we could see that the label in the AOS frame was replaced and the AOS frame was forwarded to port 4, according to the LFIB. That is to say, the AOS frame switching scheme is feasible and well implemented. It can be seen that for an IPv6 service, the only information needed to transmit the service is the labels, which are irrelevant to any information of the service itself. That is to say, the AOS frame switching scheme can indeed provide transparent transmission for services.  
Figure 21: Network manager configuration screen for LFIB.  
Figure 22: DCN Sequence Diagram.  
Similarly, we can configure all the FIBs/LFIBs of the LSP (label switching path) to the corresponding nodes and built a static LSP by using the network manager.
## 3 Review of WBASN MAC Protocols for IEEE 802.15.4 and IEEE 802.15.6
### Superframe Modification Approaches  
These approaches improve the slotted CSMA/CA protocol of IEEE 802.15.4 / IEEE 802.15.6 by proposing modifications in the superframe structure. An emergency beacon service is used to manage the normal, emergency and periodic data transmission services in CAP through a contention access mechanism [94]. The coordinator is responsible for the transmission of the emergency beacon to handle the emergency services in the CAP period by modifying the superframe structure. The energy consumption analysis shows that the existing superframe structure is not enough to manage emergency traffic. The priority-based load adaptive MAC (PLA-MAC) protocol [95] offers four data classes, including ordinary, delay, reliability and critical data packets. Few dedicated slots are introduced in the superframe to handle emergency data traffic. Low-delay traffic-adaptive medium access control (LTD-MAC) [96] extends the contention periods in CFP. However, this protocol stops the transmission after all channels are occupied, which causes data loss. Adaptive and real-time GTS allocation (ART-GAS) [97] handles the differentiated services by adaptively introducing GTS slots in the superframe duration. It is noticed that the performance of other nodes with normal data traffic suffers by frequently using these GTS slots. Differentiated service classes have been proposed, including emergency-TDMA (ETDMA), medical contention access periods (MCAP) and normal-TDMA (NTDMA). This scheme works well; however, managing slot assignment for different nodes that desire to access the channel is complex and energy-consuming. Fuzzy control medium access (FCMA) [98] decides on slot assignment in CAP and CFP on the bases of fuzzy rules, which are made on priority and data rates. It works in three phases, including data acquisition, fuzzy-logic control and implementation. Priority-based adaptive timeslot allocation (PTA) [99] divides the CAP channel into chunks. Different slots are assigned to the nodes according to the priorities; however, the mechanism is expensive in the context of limited latency and energy consumption. To conclude the above discussion, there are different optimisation mechanisms to improve the IEEE 802.15.4 / IEEE 802.15.6 protocol. Table 7 summarises the categorisation of optimisation approaches with their possible advantages and disadvantages. Table 8 presents and summarises some of the proposed protocols based on the categorisation presented in Figure 6. Moreover, we have shown indications as delay (D), reliability I, energy efficiency I), throughput (T), collision handling (C), priority (P), mobility (M), interference (I) and scalability (S).
## 3 Results and Discussion
### Results and Analysis of the Hydraulic Pressure Regulation Test  
As shown in Figure 9a, when the water injection pressure increased gradually from 0 to 0.16 MPa, the filler pressure at each measuring point showed a linear growth trend before reaching 0.15-0.18 MPa. The filler pressure was almost identical at all measuring points when the water injection pressure was lower than 0.04 MPa. As the water injection pressure continued to increase, the filler pressure at two peripheral measuring points (4-2 and 5-2) was consistent, while it increased quickly at the central point (1-2) and increased slowly at the peripheral measuring point (2-2). With a water injection pressure of 0.16 MPa, the filler pressure at the measuring points of the two peripheral sides was 14% lower than that at the central measuring point and 6% higher than that at the distant measuring points. When the water injection pressure varied within the range of 0.04-0.16 MPa, the difference in pressure between different measuring points could be estimated based on the linear difference value. The reason for the pressure difference in different positions of filler is the various seepage path. The mean value of filler pressure results in measuring points can be calculated as the normal reference values of filler pressure.  
The filler pressure at each measuring point along each filler pressure monitoring section decreased linearly with the decline in pressure at the water discharge opening from 0.1 to 0.01 MPa (Figure 9b). The curves of the measuring points at these two peripheral sides (4-2 and 5-2) overlapped with the distant peripheral measuring point (2-2). The filler pressure at the central measuring point (1-2) declined at a high decreasing rate, but its pressure difference from other measuring points gradually narrowed with depressurization until the difference was negligible. The results in Figure 9 show that when the water injection port was pressurized by one bar or when the water discharge opening was depressurized by one bar, the mean value of the filler pressure on each monitoring section increased or decreased by one bar.
# Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation
## 5 Case Study: Spatio-Temporal Alignment of Indicators
### Tools
#### 5.2.2 Ontology Matching  
Ontology matching tools take as an input a pair of ontologies in OWL and return alignments in RDF. The _Ontology Alignment Evaluation Initiative_ ([[http://oaei.ontologymatching.org/](http://oaei.ontologymatching.org/)]([http://oaei.ontologymatching.org/](http://oaei.ontologymatching.org/)) (accessed on 20 October 2020)) (OAEI) is probably the most known international initiative for the enhancement and evaluation of ontology matching tools. Outstanding results from the OAEI community are presented yearly in the _Ontology Matching Workshop_ (OM), which is co-located with the prestigious _International Semantic Web Conference_ (ISWC).  
We adopted two of the most popular tools within the OAEI community:  
* **LogMap** ([[https://github.com/ernestojimenezruiz/logmap-matcher](https://github.com/ernestojimenezruiz/logmap-matcher)]([https://github.com/ernestojimenezruiz/logmap-matcher](https://github.com/ernestojimenezruiz/logmap-matcher)) (accessed on 20 October 2020)) [67] is a highly scalable ontology matching tool. It performs an iterative process based on structural matching. To achieve scalability, LogMap relies on lexical and structural indexes of the input ontologies.
* **AML** ([[https://github.com/AgreementMakerLight/AML-Project](https://github.com/AgreementMakerLight/AML-Project)]([https://github.com/AgreementMakerLight/AML-Project](https://github.com/AgreementMakerLight/AML-Project)) (accessed on 20 October 2020)) [41] also approaches ontology matching looking at scalability and, additionally, provides a comprehensive user interface. AML combines different individual matchers to optimize the process.  
We adopted more than one tool in the experiment to highlight the possible impact of tools on the final outcome. More concretely, we adopted the online web interface versions of LogMap ([[http://krrwebtools.cs.ox.ac.uk/logmap/](http://krrwebtools.cs.ox.ac.uk/logmap/)]([http://krrwebtools.cs.ox.ac.uk/logmap/](http://krrwebtools.cs.ox.ac.uk/logmap/)) (accessed on 20 October 2020)) and AML v3.1 ([[https://github.com/AgreementMakerLight/AML-Project/releases](https://github.com/AgreementMakerLight/AML-Project/releases)]([https://github.com/AgreementMakerLight/AML-Project/releases](https://github.com/AgreementMakerLight/AML-Project/releases)) (accessed on 20 October 2020)), which also include graphical user interfaces.  
It is critical to highlight the constant evolution of the underlying technology. Indeed, in line with more generic trends, recent advances are based on (or include) large language models (LLMs), such as in [68]. While expectations are significant, we consider that such a technological trend is still at an early stage. Therefore, we have focused on the more consolidated technological landscape (pre-LLM).
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 1 Introduction
### Noise Interference in EEG Signal Acquisition  
Noise interference presents a considerable challenge in EEG signal acquisition. It creates noise artifacts that are embedded in the raw EEG data that are collected with electrodes and leads to significant challenges in the interpretation of the real brain's state patterns by practitioners in the fields of Neurology and Neuroscience [15; 16]. EEG captures the brain's electrical activity indirectly through scalp electrodes because electrodes capture the voltage fluctuation in the skin that is caused by the projection of an electromagnetic field, causing internal neural activity [17]. Unfortunately, along with the brain's lead field projection in the electrode, the signal that the electrodes capture is also corrupted with unwanted noise artifacts, such as compound muscle movement artifacts, electromagnetic interference, etc. [18]. The purpose of the EEG device is to digitize the least amount of possible noise artifacts that are contained within the original raw EEG signal collected from the electrodes while keeping intact the brain-related component of the raw signal [19; 20].  
Figure 1: EEG signal categorization by frequency sub-bands.  
As it becomes apparent from the above, obtaining clear and precise EEG signals is often difficult due to numerous sources of noise interference [17; 21].
# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
## 4 Results and Experiments
### Experimental Results
The model achieved an average precision, sensitivity, and F1 score of 0.95. The model attained a precision of 0.98 and demonstrated a good sensitivity of 0.97 for the MCI classes. These findings demonstrate the model's capability to attain a high level of accuracy in diagnosing the disease, particularly in the early stages of [PERSON]'s disease, such as the MCI stage. For the macro-average scores of all evaluation metrics, it is evident that the Xception + PPC model outperformed the other models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Class** & **Precision** & **Sensitivity** & **F1-Score** & **Accuracy** \\ \hline \multirow{5}{*}{VGG-16 + PPC} & AD & 0.78 & 0.93 & 0.85 & \\  & CN & 0.91 & 0.79 & 0.85 & \\  & MCI & 0.95 & 0.95 & 0.95 & \\  & Macro Average & 0.88 & 0.89 & 0.88 & \\ \hline \multirow{5}{*}{VGG-19 + PPC} & AD & 0.80 & 0.90 & 0.85 & \\  & CN & 0.95 & 0.87 & 0.91 & \\ \cline{1-1}  & MCI & 0.95 & 0.95 & 0.95 & \\ \cline{1-1}  & Macro Average & 0.90 & 0.91 & 0.90 & \\ \hline \multirow{5}{*}{ResNet-50V2 + PPC} & AD & 0.88 & 0.93 & 0.90 & \\  & CN & 0.94 & 0.90 & 0.92 & \\ \cline{1-1}  & MCI & 0.95 & 0.96 & 0.96 & \\ \cline{1-1}  & Macro Average & 0.92 & 0.93 & 0.93 & \\ \hline \multirow{5}{*}{**Xception + PPC**} & **AD** & **0.92** & **0.94** & **0.93** & \\ \cline{1-1}  & **CN** & **0.95** & **0.95** & **0.95** & \\ \cline{1-1}  & **MCI** & **0.98** & **0.97** & **0.97** & \\ \cline{1-1}  & **Macro Average** & **0.95** & **0.95** & **0.95** & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of different deep TL architectures with various metrics. Figure 5: Accuracy ratios in the test set for all the models. Figure 6 shows the confusion matrix, which provides a detailed overview of the class-wise results for the Xception + PPC model. By examining the confusion matrix, we assessed the number of correctly classified and misclassified images for the specific classes. From the confusion matrix, we can infer that the Xception + PPC model misclassified only seventeen images from the test dataset. Of the 406 tests, 389 were accurately classified by the model, demonstrating a high accuracy. Therefore, based on the evaluation metrics accomplished by the proposed model, it can be inferred that Xception + PPC outperformed the other models in all aspects. We want the area to be 1 in an ideal receiver operating characteristic (ROC) curve and aim to move away from the FPR value as the TPR value increases. Within this context, the suggested approach demonstrated superior classification capability compared to alternative methods, as evidenced by an average AUC of 0.97, specifically achieving an 0.98 AUC for MCI prediction (Figure 7). The effectiveness of this method becomes apparent when the confusion matrix is examined.
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
###### Abstract
However, hash algorithms are inherently sensitive to data tampering, with even minor modifications leading to significant changes in the hash value. This lack of robustness poses challenges in reconstructing unique identifiers for disrupted data. There is a critical need for a new method to generate unique identifiers that combine strong uniqueness with exceptional robustness, enabling them to reliably represent vector maps on blockchain platforms. To address these issues, this paper proposes a blockchain-based copyright protection model using unique identifiers (BCPM-UI). This model embeds watermark information into vector maps using a distance ratio-based quantization watermarking algorithm. Unique feature identifiers are subsequently constructed based on both the geometric and topological characteristics of the watermarked vector maps. Geometric features are extracted by calculating angles between geographic entities using techniques such as Delaunay triangulation, forming a geometric feature vector that represents the map. Topological features are derived by analyzing spatial relationships--such as proximity, connectivity, and containment--between geographic elements. The Hausdorff distance is employed to evaluate the spatial configuration and closeness of features, ensuring robust topological representation. The combined geometric and topological features are used to generate a unique identifier for each vector map. This identifier, along with watermark information, timestamps, and user details, is recorded on the blockchain instead of the full vector map. This approach reduces blockchain storage requirements by a factor of 200 while maintaining the confidentiality of vector maps. The unique identifiers are robust against common geometric transformations, such as rotation, scaling, and translation, ensuring their reliability. In cases of suspected infringement, unique identifiers are constructed from the infringing data and matched with those stored on the blockchain to identify the corresponding original map. Watermark information is extracted and compared between the suspected infringing data and the original map files to verify their correlation. This combined use of unique identifiers and watermark information provides definitive evidence for determining infringement and supports subsequent legal arbitration. The structure of this paper is as follows: Section 2 reviews related work, while Section 3 introduces the preliminaries. The proposed BCPM-UI model is detailed in Section 4. Experimental results and discussions are presented in Sections 5 and 6, respectively, and Section 7 concludes the paper.
# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 3 Proposed Routing Protocol  
In this section, we introduce a dynamic decision-making routing approach based on node relationships and combine it with the algorithms introduced in Section 2 to propose and elaborate a multi-decision dynamic intelligent routing protocol.