esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Exploring SDN, NFV, Policies, ML, Network Slicing and Telecom Security
### Network Slicing
#### 2.3.3 Challenges in Network Slicing for Future Networks  
Besides the conflicting requirements discussed, the use of network slicing in the next generation of networks brings forth a myriad of challenges. These challenges are present in different areas of network slicing implementation, namely resource management and scalability, inter-slice handover, integration of AI tools, such as ML, DL, and expert systems, and the security aspects of its implementation in such networks.  
In the case of resource management and scalability, the integration of various network segments, space, air, and ground, into a cohesive Space-Air-Ground Integrated Network (SAGIN) requires sophisticated coordination. The dynamic nature of such segments, especially with the mobility of satellites and unmanned aerial vehicles (UAVs), complicates resource allocation and scalability [49]. Efficient management of these heterogeneous resources is essential to meet the diverse and stringent QoS requirements of 6G applications.  
Inter-slice handover is another critical aspect of 6G network slicing. As 6G networks are envisioned to support a wide variety of applications with varying QoS requirements, ensuring seamless handover between slices is necessary. Current methods often require re-running the entire authentication process during a slice switch, which is resource-intensive and time-consuming. The work in [50] introduces a cloud-native orchestration framework for network slice federation, which aids in the maintenance and continuity of the service across different network domains without significant overhead.  
As will be disseminated further along in this review, the integration of AI tools in the current and future mobile networks is a common subject of discussion, and in the case of network slicing, it is no different. The integration of these tools into network slicing can optimise resource allocation and predict network demands, but they also require substantial computational resources and add complexity to network management. Ensuring real-time processing and decision-making through these tools within the constraints of such networksis a formidable task [51]. Furthermore, constructing customised network slices to support the emerging services provided by these tools, such as DL applications, involves managing new QoS requirements like data quality and inference accuracy.  
Lastly, the security aspects of network slicing in future 6G networks are a focal point in discussion within the current literature. The survey presented in [52] highlights various security issues such as slice lifecycle security, inter-slice and intra-slice security, and the need for robust slice isolation to prevent unauthorised access and attacks. Their survey emphasises the importance of addressing novel security and privacy challenges, such as impersonation attacks, DDoS attacks, and data breaches, which are critical in maintaining the integrity and reliability of network slicing in future network environments.
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
## 4 Simulations Environment and Performance Evaluation
### Packet Loss Due to Queue Overflow  
One of the negative effects of congestion in WSNs is queue overflow, which leads to packet loss. The number of dropped packets due to queue overflow reflects the congestion level in the network. Figures 6 and 7 present the ratio of lost packets in log scale compared to the total number of generated packets in the network for 5 pkts/s/node and 10 pkts/s/node, respectively. Note that we obtained 0 lost packets for scenarios with 1 pkt/sec/node for all protocols.  
Results of Figure 6 show that the number of lost packets due to queue overflow for ABORt and CoLBA is almost null. In the worst case of simulated scenarios, CoLBA loses 0.042% and ABORt 1.09% of the generated packets. CoLBA is a routing protocol in single channel network; thus, all nodes in the network communicate using the same channel. This facilitates broadcasting an alert message to the transmitting nodes when the receiving node queue is nearly full. However, ABORt uses multiple channels; thus, the broadcast support is more complex and nodes do not get the alert message at the same time. This is the main reason why CoLBA outperforms ABORt in the scenario of 80 nodes with 5 and 10 packets per second. The number of lost packets due to queue overflow of RPL, M-HopCount and M-HopCount-Sync is higher. RPL is a single channel routing protocol, so there are no parallel transmissions in the same communication range. With high traffic load, the contention level becomes higher and generated or forwarded data packets spend more time in queues, which increases the risk of overflow. Moreover, the load balancing approach of RPL is only metric based, which is not enough to fairly distribute data traffic on per hop basis. Some nodes are overloaded leading to queue overflow while others are under-loaded. M-HopCount and M-HopCount-Sync use static routing metrics. This overloads nodes that are part of the shortest paths of many nodes towards the sink. When the packet generation is high, this leads to queue overflow. Moreover, unlike ABORt and CoLBA, M-HopCount and M-HopCount-Sync do not use an alert message to inform transmitting nodes when the receiving node is suffering from queue overflow.
## 2 Methodology
### Evaluation Metrics
#### 2.6.2 Root Mean Squared Error (RMSE)  
The Root Mean Squared Error (RMSE) is the square root of the average of the squared differences between actual observations and forecasted values. The formula for its calculation is as follows:  
\[\text{RMSE}\,=\,\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}\,-\,\hat{y_{i}})^{2}} \tag{35}\]  
where \(y_{i}\) is the actual observation value of the i instance, \(\hat{y_{i}}\) is the corresponding forecasted value, and \(n\) is the number of forecasting periods. The smaller the value of the RMSE, the better the performance of the forecast model, and since the RMSE gives greater weight to larger errors, it can also reflect the stability of the forecast model.  
By comparing and contrasting the magnitudes of these evaluation metrics, this paper can comprehensively assess the performance of the proposed model. After comparing with other baseline forecasting models, the superiority of the model proposed in this paper can be further highlighted.
## 2 Materials and Methods  
### Raw Materials and Analysis  
The cement used in this test is Conch P.O42.5 ordinary Portland cement (OPC). Table 1 shows the basic performance parameters of this study.  
The SS is a converter steel slag produced by a steel company in Guangxi, with a density of 3.30 kg/m\({}^{3}\) and a specific surface area of 667.5 m\({}^{2}\)/kg.  
The RM is Bayer red mud discharged from an aluminum company in Guangxi, with a density of 3.02 kg/m\({}^{3}\) and a specific surface area of 880.40 m\({}^{2}\)/kg.  
The EMR is discharged from a company in Quanzhou, Guangxi, with a density of 2.59 kg/m\({}^{3}\) and a specific surface area of 823.85 m\({}^{2}\)/kg. The chemical composition of the raw materials was analyzed by an X-ray fluorescence spectrometer (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{\
\begin{tabular}{c} **Density** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Specific Surface Area** \\ **(m\({}^{2}\)/kg)** \\ \end{tabular} } & \multicolumn{3}{c}{**Flexural Strength**} & \multicolumn{3}{c}{**Compressive Strength**} \\  & & & **(MPa)** & & **(MPa)** & \\ \cline{3-8}  & & & **3 d** & **7 d** & **28 d** & **3 d** & **7 d** & **28 d** \\ \hline OPC & 3.15 & 356.50 & 5.2 & 6.4 & 7.2 & 24.2 & 35.6 & 55.1 \\ \hline \end{tabular}
\end{table}
Table 1: The basic properties of the concrete parameters.  
Figure 1 depicts the particle size distribution of the raw materials, where the particle size of OPC ranges from 5.3 to 164.8 \(\upmu\)m, with an average size of 67.7 \(\upmu\)m. The particle size of SS ranges from 0.2 to 29.5 \(\upmu\)m, with an average size of 3.79 \(\upmu\)m. The particle size of RM ranges from 0.5 to 9.4 \(\upmu\)m, with an average size of 1.29 \(\upmu\)m. The particle size of EMR ranges from 0.54 to 13.19 \(\upmu\)m, with an average size of 1.46 \(\upmu\)m. It can be judged that the D50 of the raw material particles are ranked from largest to smallest: OPC, SS, EMR, and RM, which is consistent with the experimental results of specific surface area.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \(\mathbf{Fe_{2}O_{3}}\) & \(\mathbf{Al_{2}O_{3}}\) & \(\mathbf{TiO_{2}}\) & \(\mathbf{SiO_{2}}\) & \(\mathbf{ CaO}\) & \(\mathbf{MgO}\) & \(\mathbf{Na_{2}O}\) & \(\mathbf{K_{2}O}\) & \(\mathbf{MnO}\) & \(\mathbf{SO_{3}}\) & **Other** \\ \hline OPC & 3.71 & 5.64 & 0.30 & 24.76 & 60.25 & 0.88 & 0.16 & 0.70 & 0.32 & — & 3.28 \\ SS & 24.15 & 5.66 & 0.57 & 26.95 & 29.67 & 5.33 & 0.09 & 0.08 & 5.39 & — & 2.11 \\ RM & 30.58 & 20.39 & 7.80 & 13.49 & 15.82 & 0.33 & 9.15 & 0.19 & 0.14 & — & 2.11 \\ EMR & 5.45 & 7.43 & 0.55 & 28.21 & 18.73 & 3.66 & 0.04 & 2.43 & 4.18 & 28.52 & 0.80 \\ \hline \end{tabular}
\end{table}
Table 2: The chemical composition of the raw materials (wt%).  
Figure 1: Particle size distribution of raw material particles: (**a**) particle size distribution; (**b**) cumulative distribution.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 9 Miniaturized Element FSSs
Table 5 shows a summary of different miniaturized-element bandpass FSS structures with their overall thickness and fractional bandwidth. Figure 11: The first miniaturized element FSS. **(a)** 3D view of FSS, **(b)** the unit cell of the structure. (Picture taken from [112]). Figure 12: **(a)** Lumped-element circuit model for miniaturized elements, **(b)** Comparison between EM and circuit model simulations with \(L=1.08\) nH and \(c=0.15\) pF (results are adopted from [112]). Figure 13: Scan angle performance of miniaturized-element FSS; **(a)** TE Polarization; **(b)** TM Polarization (results are adopted from [112]).
## 4 Results
### Evaluating Numerical Efficiency
#### 4.2.1 Cohesive Zone Model  
In the CZM simulation, the non-linear equation systems were solved using the Abaqus explicit solver. A mesh size of 0.05 mm was used, resulting in 180,151 solid elements and 361,390 CIEs for the 2D benchmark model.  
Figure 12 presents the reaction force-displacement curve, comparing the CZM model with the hybrid and CPFM models. The results show a strong agreement between the models, with only a minor deviation observed in the softening zone. This behaviour is probably due to the need for careful calibration of numerous numerical parameters, in particular the TSL properties assigned to the CIEs in different phases. However, the main limitation of the CZM model is its very high computational cost. As can be observed in Figure 13, by utilizing 15 CPUs, it took 258 h and more than 33,000 increments to achieve a successful CZM simulation of the benchmark model. In contrast, the CPFM and hybrid simulations used only five CPUs with a much lower computational cost. Within the utilized workstation used for FE simulation, each CPU had \(2.43\times 10^{8}\) FLOPS (Floating-Point Operations) per core per GHz.
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
## 4 Experiments and Evaluations
### Compare the Authenticity of the Channel-Response Estimates  
We first verified the fitting ability of the proposed algorithm to the real channel sample after channel estimation. We compared the amplitude of their channel responses under the KAU1 and NOF1 channels separately, as shown in Figure 8. We can see that compared to the NOF1 channel, the KAU1 channel was more complex and the multipath structure was more complex. It was observed that the channel responses estimated by our proposed algorithm could fit well to the real channel samples.
# Visual-Inertial Navigation System Based on Virtual Inertial Sensors
## 2 System Overview  
This paper proposes a VIMU-aided visual-inertial SLAM system to address the high hardware requirements and poor robustness in dynamic environments of existing approaches, such as VINS-Fusion. The main workflow is illustrated in Figure 1.  
This method begins by classifying the inertial sensor information into gait phases using a DeepConvGRU neural network model. The DenseNet-GRU hybrid model is used to extract the nonlinear mapping relationship between the torso and foot inertial sensors, thereby constructing the VIMU. The pose information is estimated using the loosely coupled INS algorithm, and cumulative errors are corrected using the ZUPT algorithm. This method does not require the introduction of additional IMU hardware as a data source. Instead, data are still collected by a camera and a single IMU. The IMU data are mapped to the VIMU data using a deep learning model, which is then corrected for zero velocity to obtain a virtual inertial navigation sensor. After pre-integration processing, the VIMU data are used together with the pre-integration results of the camera and IMU for initialization.  
The visual part uses the KLT sparse optical flow tracking method to obtain the feature points of the latest image, while the IMU part performs the pre-integration calculationsbetween the adjacent keyframes. The pose information is updated by iteratively minimizing the nonlinear least squares cost function, with additional optimization information provided by the ZUPT constraint included in the cost function. The calibration of physical quantities is completed by combining the attitude obtained by visual SFM with the IMU pre-integration. Subsequently, back-end sliding window optimization is performed, with visual constraints, IMU constraints, and loop closure constraints included in a large objective function for nonlinear optimization. Marginalization is introduced to convert the related constraints into priors when removing poses, with the Marg residual part, IMU residual part, and visual reprojection error part handled separately. The entire local mapping of VINS is completed by the VIO fusion module, which uses IMU and camera observations as constraints for BA optimization.  
Next, loop detection is performed. When multiple loops occur within the sliding window, all corresponding features retrieved from the loop frames are used to optimize the state estimation results. This guides the subsequent nonlinear optimization of VIO, and performs tightly coupled optimization of feature observations and IMU pre-integration values through nonlinear optimization within the sliding window, while also having the ability to perform re-localization. Finally, pose graph optimization is performed, utilizing the re-localization data to load the past pose graph into the global map for optimization and reuse, thereby eliminating localization and mapping drift.
## 4 Results
### Experimental Results
To further compare the performance of different algorithms on new and former categories data, we separately output the accuracy of the aforementioned algorithms on both new and former categories data as shown in Figure 7a,b. Figure 7b shows that, except for our method, the new class accuracy of other methods remains at a relatively high level (within the range of 0.9 to 1) during stage 2 and stage 3, while the new class accuracy of our method drops to around 0.8. On the other hand, all other methods, except for the joint method and ours, exhibit significantly lower accuracy in the old classes. Our method trades a slight, acceptable drop in new class accuracy for a greater retention of old knowledge. However, according to Figure 7a, our method shows a significant decline at stage 5, which is caused by the combined influence of multiple factors. The most important of all, because each CIL training begins from the training result at the last CIL step, the impact bring by catastrophic forgetting accumulates over every incremental learning step, which leads to a noticeable decrease in CIL performance at stage 5. For our proposed CIL method, dataset and experiment settings in this paper, according to Table 2 and Figure 7, the proposed CIL method is effective within three incremental steps, beyond which the performance degradation becomes more noticeable. To explore the cause of the performance decline of our method on old classes at stage 3, the t-SNE visualization results for our method and LWF at stage 3 are shown in Figure 7c,d. Figure 7: Comparison of algorithms on accuracy for former and new classes. (**a**) Comparison of algorithms on accuracy for former classes. (**b**) Comparison of algorithms on accuracy for new classes. (**c**) T-SNE visualization result of our method. (**d**) T-SNE visualization result of LWF. Our feature distributions of the former classes (classes 1-7) show more compact intra-class distributions and better inter-class separability compared to those of LWF, particularly for classes 6 and 7. In the output of LWF, class 7 exhibits more overlap with the new class, and the new class features are more tightly clustered. In contrast, our method presents a trade-off in the clustering of the new class features, which provides better preservation of former knowledge. It is worth noting that the features used for t-SNE visualization here are taken from the input to the final classifier layer. Our method further reduces the impact of the overlapping region between the new class and class 7 on the classification accuracy of class 7 by applying regularization in the final classifier, distributing the impact across both class 7 and class 8. This prevents a significant decline in the accuracy of the old classes, though it also contributes to the suboptimal classification performance observed in the newly added class. In other words, our method achieves a better balance between stability and plasticity, leading to overall superior performance in the CIL process.
## 2 Materials and Methods  
### Overview of the YOLOv5 Algorithm  
YOLOv5, an efficient and precise single-stage target detection network, was introduced by [PERSON] in 2020. It processes faster than two-stage networks and has become widely applied in various target detection tasks. We utilize the fastest YOLOv5s as the base model among the four versions provided by the small, medium, large, and extra-large models of YOLOv5. The network's architecture comprises four main components, the input, backbone, neck, and head, as depicted in Figure 1.  
Figure 1: Network structure of YOLOv5s.  
The backbone, a critical component of YOLOv5s, efficiently extracts spatial features from input images. It primarily consists of the CBS (convolutional layer, batch Normalization, and SiLU activation function), C3 (three convolutional blocks), and SPPF (Spatial Pyramid Pooling Fast) modules. The CBS module lays the foundation for feature extraction and assists the C3 module. The C3 module further enhances the structural depth of the network. The SPPF module, an optimized version of the SPP, refines feature representation by reducing the pooling window scale and simplifying the process. Thus, the inference of the network model is accelerated. This design aids in extracting features at multiple scales while preserving the spatial hierarchy.  
The neck structure, comprising the Feature Pyramid Network (FPN) and the Path Aggregation Network (PAN), processes feature maps of varying sizes obtained from the backbone. These features are fused and upsampled, generating new multi-scale feature maps for detecting objects of different sizes. The detection head uses anchor boxes to depict the confidence level and bounding box regression, and it generates a multi-dimensional array containing the target category, confidence level, and bounding box dimensions. The detection results are then refined using a confidence threshold and non-maximum suppression (NMS).  
The integration of the FPN and PAN in YOLOv5s facilitates an effective fusion of top-down semantic and bottom-up positional information flows to enhance the network's multi-scale target detection capability. This design allows YOLOv5s to efficiently predict targets of various sizes across different feature resolutions, making it suitable for computation-limited environments.
## 1 Introduction
To effectively address this limitation, we propose the generation of an extensive dataset of virtual degraded images from pristine originals using optical simulations. This simulation-based approach allows us to accurately replicate degradation phenomena, including diffraction effects, that are specific to UDC systems. The resulting synthetic dataset provides diverse and comprehensive training data, reducing the need for laborious data acquisition and enabling the creation of datasets with varying PSFs to represent different UDC imaging scenarios. By leveraging virtual data augmentation, we facilitate the expansion of the dataset to encompass a broad spectrum of UDC scenarios, making the training process both efficient and representative real-world conditions. This use of synthetic data addresses the fundamental challenges of limited paired datasets in UDC image restoration tasks. The effectiveness of our trained AI models has also been evaluated in restoring experimentally obtained degraded images. This validation indicates the utility and robustness of the proposed image restoration methodology in UDC systems. The remainder of this paper is organized as follows: Section 2 outlines the proposed methodology, including the process of generating virtual degraded images through optical simulations and the design of the hybrid frequency-spatial domain learning model. Section 3 presents the experimental setup, dataset preparation, and results of the image restoration tasks, comparing the performance of the proposed model with other existing approaches. Section 4 serves as the conclusion of this study, encompassing the implications of the findings, and potential future directions for enhancing image restoration in UDC systems. It summarizes the key contributions and highlights the significance of introducing hybrid frequency-spatial domain learning as a robust solution for addressing the unique challenges of UDC imaging.