esa-sceva/satcom-chunk-collection huggingface dataset:
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 8 Discussion  
In this section, we compare the compression performance of the proposed method with some recently proposed CS-based image processing methods [48; 49; 50]. It is worth noting that here we select methods that use chaotic measurement matrices or other types of measurement matrices that are generated by deterministic means, similar to the proposed methods.  
We conduct experiments using the same images used by [48; 49; 50]. The original images and their reconstruction results are exhibited in Figure 11. The related PSNR values are listed in Table 10. From data shown in Table 10, we can infer that, although the PSNR values vary when using different original images, the proposed method could archive similar reconstruction quality as Refs. [48; 49; 50], when the compression ratios reach 0.5. More importantly, the proposed method could achieve multi-level reconstruction for users in different groups. Namely, restricted-authorized users could merely reconstruct images with concealed critical information, whereas full-authorized users could reconstruct the entire images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Compression Rate 0.75**} \\ \hline \multirow{2}{*}{**Image**} & **Proposed** & \multirow{2}{*}{**Ref. [48]**} & **Ref. [49]** & **Ref. [50]** \\ \cline{1-1}  & & & \\ \hline lena & 31.23 & 34.19 & 29.56 & - \\ cameraman & 28.90 & 30.85 & 28.93 & - \\ peppers & 32.51 & - & - & 31.25 \\ woman & 36.16 & - & - & 33.92 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Compression performance comparison via PSNR values (Unit: dB).  
Figure 11: The first column shows the original images named lena, cameraman, peppers, and woman, respectively. The second, fourth, and sixth columns show the reconstructed images obtained by restricted-authorized receivers under compression rates 0.75, 0.5, and 0.25, respectively. The third, fifth, and seventh columns show the reconstructed images obtained by full-authorized receivers under compression rates 0.75, 0.5, and 0.25, respectively.
# Projected Augmented Reality Intelligent Model of a City Area with Path Optimization
## 3 Experiments and Results
### Population Size and Mutation Rate  
Table 2 shows simulation results for different population sizes and mutation rates. For all the experiments, elitism was maintained in 5 and crossover was done using the OX method.  
The results show the best is a population of 25 individuals, with a mutation rate of 1%. Lower mutation rates cause the algorithm to require more generations and require more time. Higher mutation rates, as expected, may impair convergence and delay the algorithm.
## 3 Results
### Turbine Performance at a Rotor Outlet Pressure of 200 kPa  
At an outlet pressure of 200 kPa from the rotor, the results were compiled exclusively for a rotational speed of 3000 rpm (Figure 23). Under these conditions, the turbine efficiencies ranged from 64% to 67.3%, with power outputs varying from 24 kW to 107 kW. Significantly higher efficiency values were obtained at lower inlet pressure values, indicating that the design point of this flow system can be assumed to be close to the assumed pressure values. A comparison of the performance at an outlet pressure of 170 kPa with that at 200 kPa revealed a decrease in power output by several kilowatts.  
Figure 24 demonstrates that at an inlet pressure of 380 kPa, the aerodynamic expansion process is less intense. The gradual pressure gradient observed from the inlet to the outlet indicates that the flow is relatively stable, with lower aerodynamic forces acting on the rotor blades. The diminished aerodynamic loading on the blade surfaces minimizes the potential for flow separation or recirculation zones, thereby promoting a more laminar flow behavior. In contrast, Figure 25, which depicts a significantly higher inlet pressure of 780 kPa, demonstrates a markedly stronger aerodynamic process. The steeper pressure gradient from the inlet to the outlet highlights the rapid acceleration of the working fluid as  
Figure 23: Comparison of efficiency and power output at varying inlet pressures.  
Figure 22: Comparison of efficiency and power output at varying inlet pressures and rotational speeds.  
it passes through the turbine. The high-pressure difference across the blade passages creates stronger aerodynamic forces, driving a more intense expansion process. Consequently, the working fluid experiences significant increases in velocity and kinetic energy. This results in higher Mach numbers and enhanced flow acceleration, characteristics typical of turbines operating under high-loading conditions. It should be noted that the aforementioned conditions result in elevated levels of aerodynamic losses, predominantly due to an increase in exit velocity losses.
## 3 Results
### Magnetic and Electronic Properties  
To determine the magnetic ground state of the MnCrNO\({}_{2}\) monolayer, we construct a monolayer supercell with 4-unit cells (4 \(\times\) 1 \(\times\) 1) and consider one ferromagnetic (FM) and all possible antiferromagnetic (AFM) configurations within this supercell. The initial on-site magnetic moments are set as 4 \(\mu_{\text{B}}\) for each Mn and Cr atom and as nonmagnetic for each N and O atom. Figure 2 shows the FM configuration and the two AFM configurations at the magnetic ground states. Among these three magnetic configurations, the FM state takes the lowest energy and is thus the ground state, while the AFM1 and AFM2 configurations take the higher energy, i.e., 406.89 meV and 585.84 meV higher than that of the FM configuration. The monolayer MnCrNO\({}_{2}\) at the FM state exhibits a total magnetization of about 6.0 \(\mu_{\text{B}}\) per primitive cell, with 3.4 \(\mu_{\text{B}}\) attributed to the magnetization of the Mn atom and 3.0 \(\mu_{\text{B}}\) from that of Cr, indicating their dominant roles in the magnetism of MnCrNO\({}_{2}\). Additionally, the N and O atoms contribute small negative magnetic moments (approximately \(-0.4\)\(\mu_{\text{B}}\)), primarily due to the spin polarization of their p orbitals caused by the p-d hybridization with neighboring magnetic Mn and Cr atoms, as evidenced by the spin-resolved projected density of states (PDOS) in Figure S1.  
In order to further comprehend the relationship between the magnetic and electronic properties, the spin-polarized band structures and density of states are then calculated based on both PBE functional and hybrid-functional HSE06, as depicted in Figure 3. Near the Fermi level are the spin-majority bands, exhibiting a semiconducting characteristic, which is unlike that of the previously reported metallic MXenes, with an indirect bandgap of 0.69 (1.49) eV, as determined by the GGA-PBE (HSE06) functional. In other words, both the valence band maximum (VBM) and the conduction band minimum (CBM) are spin-up states despite their different locations. The spin-resolved PDOS in Figure S1a shows in more detail that the VBM bands are determined by the significant overlap between the Cr-3d and O-2p orbitals over a large energy range. In contrast, for the CBM bands, there is a similar 3d-2p overlap, but with a greater proportion accounted for by the Mn-3d orbitals. Overall, these electronic properties remain similar for Hubbard U = 1 eV, 2 eV, and 3 eV for Mn and Cr, except for the small and predictable changes in the bandgap, as shown in Figure S2. The different roles played by 3d transition metal atoms (Mn and Cr) in the VBM or CBM bands of monolayer MnCrNO\({}_{2}\) demonstrate the potential and effectiveness of energy band engineering for Janus MnCrNO\({}_{2}\) through stacking configurations, pressure, strain, and other methods.
## 4 Materials and Methods
### Bioinformatics Analysis  
The raw paired-end reads were processed with in-house scripts to remove the adapter sequences, poly-N sequences, and low-quality reads. The trimmed reads were then mapped on the publicly available _S. Iycopersicum_ (tomato) genome (GCA_000188115.3) using the Hisat2 v2.1.0 short-read aligner, with the parameters, \"-dta-cufflinks. FeatureCounts v1.5.0-p3 was used to count the reads mapped to each predicted gene, and the FPKM method was applied for the calculation of the gene expression levels. The raw read number was normalized to correct for the sequencing depth, and a statistical model was used to calculate the hypothesis test's probability (\(p\)-value). Multiple hypothesis test corrections were applied to obtain False Discovery Rate (FDR) values. The DEGs were classified after a cut-off of an absolute value of log2 fold change \(\geq 1\) and an adjusted \(p\)-value \(\leq\) 0.05. The assignment of the readings on a transcript level was performed using the htseq-count program, and the differential expression was performed using the DESeq2 R package (1.20.0). The fasta file of the predicted gene sets (version ITAG4) was downloaded from the FTP server of the Solanaceae Genomics Network ([[https://solgenomics.net/ftp/tomato_genome/annotation/](https://solgenomics.net/ftp/tomato_genome/annotation/)]([https://solgenomics.net/ftp/tomato_genome/annotation/](https://solgenomics.net/ftp/tomato_genome/annotation/)), accessed on August 10, 2022). Enrichment analysis (clusterProfiler) was applied to identify significantly enriched biological functions or pathways. Gene Ontology (GO) term functional analysis was employed based on the Pfam terms, using the hhmscan tool and the Pfam-A.hmm database, while the GO terms were retrieved from the Pfam2 GO. GO terms with a \(p\)-value of less than 0.05 were considered significantly enriched. In addition, KOBAS software 3.0 was employed to assign genes to their corresponding KEGG Orthology (KO) terms and conduct pathway enrichment analysis.
## 3 Methodology
### Dataset Segmentation  
After obtaining the static dataset \(SF\) and the dynamic datasets (i.e., \(DFD\), \(TS\), and \(TSF\)), the four datasets are split into training, validation, and testing sets. In the dynamic dataset, there is a correlation between the samples generated from the dynamic data of the same ship (e.g., \([x_{1},x_{2},\dots,x_{k}]_{1}^{i_{m}T}\) may be similar to \([x_{1},x_{2},\dots,x_{k}]_{n}^{i_{m}T}\)). If we randomly split the dynamic datasets, samples from the same ship can simultaneously appear in training, validation, and testing sets, which will cause data leakage and the overestimation of the classifiers' performance. In this paper, the datasets are split by the MMSL. Taking \(TSF\) as an  
Figure 8: Process of extracting dynamic features distribution.  
example, we divide \(TSF\) at the level of \(TSF^{lm}\) rather than \([x_{1},x_{2},\dots,x_{k}]_{n}^{in\,T}\), i.e., once the \(TSF^{lm}\) is assigned to the training set, the feature vectors belonging to \(T^{lm}\) can only appear in the training set.  
Ideally, the trajectory \(T^{lm}\) and the static feature sample \(s^{lm}\) are one-to-one correspondence. Due to the processing of removing outliers from static data in Section 3.1, there may be no \(s^{lm}\) corresponding to \(T^{lm}\). Furthermore, the dynamic data used in this paper is only a part of the whole dataset, which may result in no \(T^{lm}\) corresponding to \(s^{lm}\). To solve this problem, the MMSI in AIS data is divided into three parts, which are the MMSI that only appears in dynamic data (MMSI_D), the MMSI that only appears in static data (MMSI_S), and the MMSI which exists in both dynamic data and static data (MMSI_C), as shown in Figure 9. The MMSI_C is then divided into the MMSI training set (MMSI_TR), MMSI validation set (MMSI_V), and MMSI testing set (MMSI_T) by the stratified sampling of different types of ships. The static data, which is the MMSI in MMSI_S and MMSI_TR, forms the training set of static feature samples. The dynamic data, which is the MMSI in MMSI_D and MMSI_TR, forms the training set of dynamic feature samples. The static data, which is the MMSI in MMSI in MMSI_V and MMSI_T, forms the validation set and the testing set of dynamic feature samples, respectively.  
For a trajectory \(T^{lm}\), if \(i_{m}\) in MMSI_C, we can generate one \(DFD^{lm}\), \(n\)\(TS_{n}^{in}\), and \(n\)\(TSF_{n}^{in}\), which corresponds to one \(s^{in}\). The number of static samples and dynamic samples with the same MMSI is different. To solve this problem, we copy \(DFD^{lm}\) and \(s^{in}\)\(n\) times in \(DFD\) and \(SF\), respectively.
## 2 Materials and Methods
### Binary Semantic Segmentation
#### 2.1.3 Training Strategies  
Three distinct training strategies were assessed and compared for U-Net and SegNet. The first strategy, referred to as _axial_ training, is the traditional 2D training method. Axial slices of 512 \(\times\) 512 pixels were employed to train the developed networks. This strategy does not take into account any 3D information, and to overcome this, the so-called 2.5D training strategies were considered.  
The second strategy is majority voting (MV), in which separate convolutional neural networks (CNNs) are trained for each of the three orthogonal slices. The prediction for a voxel is determined by aggregating predictions from all three CNNs, and the voxel is considered part of the foreground if at least two of the CNNs predict it as the foreground.  
Lastly, an augmented 2D training strategy was evaluated, referred to as _multi-planar_ training (MPT). In this approach, the network is trained with a dataset composed of slices from all three orthogonal planes (axial, sagittal, and frontal). Each batch, during the training process, contains images from the three views. The networks under evaluation include six variations: U-Net with axial training, MPT, and MV and SegNet with axial training, MPT, and MVtraining, as shown in Figure 2.  
A workstation with a GeForce RTX 2070 SUPER (NVIDIA Corporation, Santa Clara, CA, USA) was used for the training. We employed an Adam Optimizer, with an initial learning rate of \(10^{-3}\). The networks were trained for 250 epochs using a batch size of 16. Early stopping and learning rate decay were integrated into the network as callbacks. Before training, a normalization of the pixel intensity values to standardize the input images was performed. Extensive data augmentation techniques were applied during  
Figure 2: Comparison of training strategies for U-Net and SegNet: the diagram illustrates axial, multi-planar, and majority voting training methods for both U-Net and SegNet.  
training. These techniques included random rotations, flips, shifts, and zooms, as well as the addition of noise and adjustments to brightness and contrast. Moreover, the dataset was arranged according to volumes to prevent data leakage, making sure that every slice from a given volume was either in the training set or the test set. In this way, the training and test sets' data independence was preserved, which is helpful for the generalization of the model's performance and an accurate model evaluation. Four volumes were used for testing and eight volumes for training. Moreover, two volumes distinct from the training and test sets were considered to be the validation set.
###### Abstract
However, most existing studies focus on coarse spatial resolution or temporal aggregation, which limits their ability to capture the spatial heterogeneity and localized dynamics of sediment accumulation. This hinders their practical application in detailed sediment risk assessment and channel maintenance planning. To address this gap and align with the evolving trend of intelligent waterway management, this study explores the potential of machine learning for fine-scale spatial prediction of sediment deposition. The study area is the downstream channel of the Shihutang navigation power hub on the Ganjiang River, which is characterized by dynamic sedimentation and strong seasonal hydrological variability. An RF regression model is developed using multi-source inputs, including bathymetric surveys, hydrological observations, and meteorological statistics, to predict monthly siltation volumes at a 30 m spatial resolution during the period from July to December 2024. By revealing localized siltation patterns under data-limited conditions, this approach aims to inform more precise dredging and risk mitigation strategies. The results contribute to the broader goal of intelligent and adaptive inland waterway management in the face of increasing environmental uncertainty.
## 3 OLSR Attacks
### Incorrect Control Traffic Relay  
Real-time reception of OLSR-TC messages is essential for maintaining network efficiency, adaptability, fault tolerance, and security. It enables routers to make informed routing decisions, respond quickly to changes, detect failures promptly, and proactively defend against potential attacks. OLSR TC messages can be exploited by attackers to carry out various attacks on the routing control plane. Indeed, if TC messages are not relayed correctly, connectivity will likely be lost. In the same way, when a node is unable to transmit data packets, a loss of connectivity may occur. Blackhole attacks, replay attacks, and wormhole attacks are attacks that can have severe consequences for network performance, security, and reliability.  
* Blackhole Attack: A node (the attacker) can delete received routing messages instead of relaying them. Consequently, the amount of routing information accessible to other nodes diminishes. This is called the \"black hole attack\" by [PERSON] et al. [13] and is considered a \"passive\" and straightforward method to carry out a denial of service attack. When a node fails to forward TC messages, it can result in connectivity problems throughout the network. Figure 7 shows us how this attack is done, and we can see that node C won't relay the TC messages. Such behavior disturbs the overall functioning of the network because it can lead to long paths and a loss of connectivity between entities [14].
* Replay Attack: Old control messages, in the case of a topology change, describe a network configuration that no longer exists. Thus, an attacker can force other nodes to update their routing tables with stale routes [15] by advertising to them old control. This attack is generally successful even if control messages are digitally signed without a timestamp. A TC message cannot be replayed \"as is,\" as it will be rejected due to the ANSN value. Therefore, an attacker must conduct an ANSN attack by increasing the ANSN value of the TC message. Figure 8 illustrates the general operation of this attack.
* Wormhole attack: It is a quite severe attack [16] and entails capturing traffic from one section of the network and reproducing it \"as is\" in a separate area. An intruder node X can artificially generate a foreign link between nodes A and B by intercepting control messages exchanged between them, thus creating a wormhole. A more extensive wormhole can be established by two explicit intruders, X and X0. This newly created link falls entirely under the attacker's control. To exploit the wormhole successfully, the attacker must wait until nodes A and B have exchanged a sufficient number of HELLO messages (through the wormhole) to establish a symmetrical link. Prior to this  
Figure 8: Replay attack process.  
Figure 7: Blackhole Attack.  
establishment, other tunneled control messages would be rejected. This situation is caused by the fact that OLSR states that TC/MID/HNA messages will not be treated if the relay node (the \"last hop\") is not a symmetric neighbor. Nevertheless, once created, the link connecting Nodes A and B is under the control of the attacker.
## 6 Performance Evaluations
### Simulation Results
#### 6.3.1 Evaluation of Packets Delivery Ratio (PDR)
In this experiment, the number of sink nodes is varied from one to three, while the average traffic rate is three packets per second. Figure 5 illustrates the network PDR with varying numbers of sink nodes in the second scenario. This figure shows that additional sink nodes have improved network PDR. This improvement is due to adding additional sink nodes to the network, reducing data transmission paths. As a result, the impact of the environmental conditions on the data routing decreases, which improves the network PDR. However, it can be easily obtained that the proposed algorithm performs best compared with the others. In addition to the reasons mentioned above, which made the proposed approach outperforms the other algorithms in the first scenario, the proposed bypasses the danger zones and discovers safer paths for the data transmission through its modified environmental impact metric than those computed by SMRP. In the case of EERP, the environmental impact on the routing paths is not considered, making them easy to cut off due to environmental reasons, resulting in a low delivery ratio. This reveals why the EERP performs worse in terms of PDR than that of SMRP and the proposed algorithm. Figure 5: Increasing the number of sink nodes effect on packet delivery ratio under the second scenario.
## 5 Results and Discussion
### Qualitative Results  
Figures 7 and 8 present T1 WI and T2 WI reconstructions from the fastMRI brain dataset under different undersampling conditions: an \(8\times\) acceleration factor with a one-dimensional random sampling pattern and a \(12\times\) factor with a one-dimensional equispaced sampling pattern. These illustrations compare the performance of various reconstruction methods. At the same time, the error map between the real image and the reconstructed image and the enlarged image of the reconstructed details is also shown in the figure. The error map has a numerical range of \([-0.4,0.4]\), and the color depth represents the quality of the reconstruction. The results show that the zero-filled image leads to a large error and a serious loss of details. It is obvious from the figures that our proposed method achieves the lowest error in T1 WI and T2 WI reconstruction and effectively preserves the key details. Figure 9 shows the reconstruction results of knee images using different methods with a \(10\times\) acceleration factor using a one-dimensional Cartesian random undersampling pattern. Among the multi-modal reconstruction methods, MINet achieved unsatisfactory results in the reconstruction results of PDs and PDFSs, and it seems that its effect on processing fastMRI data is not as good as HR. From observation of the figure, we can find that our method achieves the lowest error in PDs and PDFSs reconstruction, which verifies the effectiveness of our use of multi-hierarchical feature interaction to learn complementary information.
## 3 System Model
### Communication Model  
The wireless channels in the UAV-assisted SEC network exhibit distinct characteristics for UAV and satellite links. In this paper, we consider radio frequency (RF) wireless communication channels for both ground-to-UAV and ground-to-satellite links [28], rather than free space optical (FSO) channels [29]. The choice of RF communication is motivated by its robustness to weather conditions and lower pointing requirements compared to FSO, which is particularly important for mobile UAVs and fast-moving LEO satellites.