esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Direction  
The extensive research encapsulated in this paper has laid a foundational framework for a deep learning-based cybersecurity system in IoHT environments. This system leverages a hybrid deep learning-based IoHT attack detection model that integrates an ANN with BLSTM and GRU architectures.  
The research's key findings highlight the proposed model's exceptional performance, achieving near-perfect precision, recall, and F1-Score metrics in both multiclass and binary classification scenarios. As presented in Table 1 and visually supported by Figure 3's confusion matrix, the multiclass classification results indicate a superior ability to detect and classify a broad range of cyber-attack types with minimal misclassifications. Similarly, the binary classification scenario detailed in Table 3 and Figure 4 demonstrates unprece  
Figure 5: Comparison of recall results for different models. The references are as follows: [PERSON] et al. (2023) [27], [PERSON] et al. (2023) [28], and the proposed method.  
dented performance, with accuracy, precision, recall, and F1-Score, all reaching the ideal value of 100%.  
These findings represent a groundbreaking development in IDS for the IoHT landscape, with potential implications for patient safety and data security. The proposed model is reliable and efficient, setting a benchmark for future developments in the field.  
Considering future directions, as IoHT devices and their cyber threats become more complex, there is a growing need for adaptive models that can learn from new attacks and evolve. Incorporating unsupervised learning techniques for anomaly detection within the proposed model could enhance its ability to detect novel threats not part of the original training dataset.  
For this research article, both authors contributed equally in all sections. Conceptualization, S.A.A. and S.S.A.; methodology, S.A.A.; software, S.A.A.; validation, S.A.A. and S.S.A.; formal analysis, S.S.A. and S.A.A.; writing--original draft preparation, review and editing, S.A.A. and S.S.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Taif University, Saudi Arabia, Project No. TU-DSPP-2024-52.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Experiments
### Model Performance Comparison  
#### 4.2.1 Comparison Results with Baselines  
We conducted comparative experiments on baseline models and PESO on RMSE, MAE, and ADE. The LSTM, BiLSTM, GRU, and BiGRU in baselines predict the continuous sequences in sliding window mode, while the other four Seq2 Seq models do not. The experimental results under the metric of RMSE, MAE, and ADE are displayed in Table 2, Table 3 and Table 4, respectively. We conducted experiments in 5 scenarios where we predicted from 1 to 5 points using the previous 10 track points. We can see that our model is not the best in the first scenario. We attribute this result to the reason that some baseline models, such as GRU-GRU, have advantages in processing short-track sequences. In the next four scenarios, our model is superior to the baseline models under the three metrics. On the task of long-term prediction, PESO outperforms baselines without doubt.  
Meanwhile, considering that trajectory prediction task is also a forecasting problem of time series, we compared PESO with several time-series forecasting models, including traditional methods and deep learning models: ARIMA, Kalman Filter, VAR, and ST-Norm, and the comparison results are displayed in Table 5. As we can see from the table, our proposed model is superior to the other time-series forecasting models in four different measurements. We attribute these results to the application of more prior information and the construction of strong spatiotemporal correlation. Different from time-series models utilizing position sequences only, PESO benefits from more prior information, such as speed, travel distance, ship type, and the strong ability to construct spatiotemporal correlations of historical track points.
## 3 Results and Discussion
### Effect of Imperfections at High Stress Levels
The apparent scatter previously mentioned for these specimens is actually the effect of the location of the pores in the specimens, as reported in the literature for pores near the surface and for internal pores [37; 38]. From the previous review on fractured surfaces, for the stress level examined, it can be inferred that in the fatigue life the type and shape of the imperfections are less important than the size, quantity and location, and there was a certain order in the fatigue lives of the specimens, from the lowest to the highest: a continuous very small surface imperfection along of the width of specimen; various surface imperfections covering the width of the specimens or imperfections near to the border that produce fatigue in the lateral side, but growing semi-elliptically; surface pores or pores near to the surface in the lateral side; one big or a few small surface imperfections that do not cover the width of the specimen, that growth as one semi-elliptical crack; a condition similar to the previous one but with very small imperfections and one or various internal imperfections (pores). It should also be noted that specimens from all welded series, conditions and type of imperfections (undercuts, underfill, excess weld and pores) were included in the analysis carried out. In summary, for the as-welded condition, due to the crack-like imperfections present in the top and bottom sides of the weld beads, the fatigue starts increased at high stress levels as reported in [36; 39; 40], and the phenomena of coalescence and growth of cracks had a greater influence on the fatigue life of the welded series and therefore also the local stress concentration at underfill or excess weld can influence the crack propagation.
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 4 Simulation
### Simulation Results and Discussion
#### 4.2.3 Impact of Buffer Size of the Delivery Ratio  
Figure 6 shows the impact of buffer size of the delivery ratio. As shown in Figure 6a, when the buffer size is greater than 11 MB, the delivery ratio of the CBM strategy is higher than the other five strategies. Similarly, the delivery ratio of the CBM strategy is higher than the other five strategies when the buffer size is greater than 14 MB in Figure 6b. The message delivery ratio is also improved with the increase of node buffer. This is because when the buffer size is limited, the proposed CBM can forward messages with higher priority in the queue according to the utility value of the node and the message, and transfer the message with low utility value to the neighbor node when buffer congestion happens. Therefore, we can predict that the message delivery ratio can reach 100% in the simulation time when the buffer size is not limited. This is mainly because, when the node buffer is large enough, the node can store-carry-forward enough messages and encounter the destination node after a certain period of time.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**KAIST**} & \multicolumn{2}{c}{**SLAW**} \\ \cline{2-5}  & **Overhead Ratio** & **Average Delay (s)** & **Overhead Ratio** & **Average Delay (s)** \\ \hline CBM & 1340.55 & 1101.56 & 15,305.41 & 1292.61 \\ RED & 4009.45 & 1442.41 & 40,731.24 & 1487.67 \\ DH & 2372.80 & 1920.67 & 26,255.48 & 1316.42 \\ DT & 3972.88 & 1190.45 & 40,674.63 & 1494.95 \\ DOA & 3691.13 & 1324.83 & 42,903.37 & 1467.42 \\ DR & 4075.63 & 1691.94 & 54,101.54 & 1445.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of overhead ratio and average delay.  
Figure 5: The number of the migration messages in CBM policy.  
In short, we propose a comprehensive buffer management strategy that reasonably deals with the messages stored between nodes and effectively solves the problem of buffer congestion. It effectively avoids wasting network resources caused by the flooding of the message. Compared with the other five buffer management strategies (RED, DH, DR, DT and DOA), this strategy not only significantly improves the delivery ratio and reduces the number of forwarding hops, but also has the optimal performance in terms of overhead ratio and average delay. Therefore, the overall performance of CBM strategy is superior to other buffer management strategies.
## 3 Influence of Boundary Distance in the Integral Response Deformation Method
### Influence of the Lateral Boundary Distance
#### 3.1.1 Internal Forces and Deformations  
The errors in the internal forces and deformation results computed by the IRDM are defined as follows: \(\gamma=\frac{S-S_{\text{D}}}{S_{\text{D}}}*100\%\) where S denotes the internal forces (including the axial force \(N\), shear force \(Q\), and bending moment \(M\)) or deformation results obtained from the IRDM, and \(S_{D}\) represents the corresponding results derived from the dynamic time history method. Figure 5 illustrates the relationship between the relative errors of the internal forces and lateral deformations of the vertical structural members and the dimensionless lateral boundary distance ratio \(R_{S}\).  
Figure 5: Computational accuracy of the IRDM under different \(R_{S}\) values, where \(R_{S}\) is a dimensionless lateral boundary distance ratio \(R_{S}=L_{S}/L\).  
The results demonstrate that the errors in the internal forces across all sections of the underground structure, as well as the lateral deformations of the vertical members, decrease significantly as the \(R_{S}\) increases. Specifically, as the \(R_{S}\) increases from 0.5 to 5, the axial force error decreases from \(-17.11\%\) to \(-1.63\%\), the shear force error decreases from \(-17.35\%\) to \(-1.64\%\), the bending moment error decreases from \(-16.35\%\) to \(-2.09\%\), and the lateral deformation error of the vertical members decreases from \(-10.63\%\) to \(-2.35\%\).  
Further analysis reveals that the errors in the internal forces and lateral deformations decrease significantly as the \(R_{S}\) increases from 0.5 to 2.0, and then stabilize and remain under 5% for \(R_{S}\) > 2.0. Although minor increases in shear and moment errors occur at specific sections (e.g., Sections C and D) when \(R_{S}\) > 2.0, these deviations remain below 5%. As the calculated values slightly exceed the dynamic time history results, they provide conservative estimates from a safety perspective.
## 2 General Flow and Basic Scenario
### General Flow of the Proposed Method  
Figure 3 shows the general flow of the CEI and BDS-3 inter-satellite link-based measurement method for non-cooperative targets in cislunar space, which mainly consists of the following key steps:  
1. Firstly, the basic model is constructed, focusing on the whole process and key nodes of inter-satellite link-based irradiation of non-cooperative targets by BDS-3 satellites in each orbit, the transmission of signals reflected by targets in Earth-Moon space, and final reception by ground-based CEI arrays.
2. Then, we construct the kinetic constraint equations for the measurement of Earth-Moon space targets, the irradiation equations of targets based on the BDS-3 interplanetary link, the accurate measurement equations of targets based on CEI, and the error correction equations of the whole process.  
3. Subsequently, the link budget of the whole process is performed, especially the establishment of an analytical equation of the load-to-noise ratio of the measurement link of the proposed method.
4. Finally, the performance evaluation of target measurement is carried out, especially the reception performance evaluation of CEI receiver arrays.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 3 Experimental Setup  
### Database for Training and Evaluation  
Two public fetal ECG datasets accessed on PhysioNet [25] were employed to develop the pipeline and test existing methods, including the Abdominal Direct Fetal ECG Database (A&D FECGDB) [26] containing real data and the Fetal ECG Synthetic Database (FECGSYN DB) [27] containing synthetic data.  
The original databases were further processed to generate extra training and test datasets with different signal-to-noise ratio (SNR) levels for robustness evaluation. Each dataset is processed according to the details described in Section 2.1.
## 4 Results and Discussion
### Flame Stabilization
Since the vorticity field cannot distinguish between the vortex and shear motions, the \(Q\) criterion was used to identify the vortex. The \(Q\) criterion can be calculated by using the following formula [44]:  
\[Q=\frac{1}{2}(//\Omega^{2}-//S//^{2}), \tag{6}\]  
where  
\[\Omega=\frac{1}{2}\Big{[}\
abla U-(\
abla U)^{T}\Big{]}. \tag{7}\]  
\(\Omega\) is the rate of rotation corresponding to the pure rotational motion. \[S=\frac{1}{2}\Big{[}\
abla U+(\
abla U)^{T}\Big{]}, \tag{8}\]  
where \(S\) is the rate of strain corresponding to the pure irrotational motion [45]. \(Q\) describes a precise mathematical formulation that has an unambiguous physical interpretation [42]. This method is capable of extracting strong vortices, as when \(Q\) is larger than zero, the turbulence vertex can be identified as where the rotational tensor plays a dominant role. Therefore, the connected regions of positive \(Q\) are defined as vortices. Figure 7: Vorticity maps of different hydrogen ratios. The lines mark the flame location (contour of 10% maximum heat release) and the stagnation streamline. The flame leading edge is also indicated in the figure. \(Q\) can be calculated using a three-dimensional mean velocity field. Figure 8 shows the \(Q\) field and the flame position (i.e., 10% of the maximum heat release rate (black lines)). The value of the vortices is indicated by the pseudo color. A large area of vortices was concentrated in the inner recirculation zone for all conditions, while only a small area of vortices was found in the outer recirculation zone. There were more vortices around the flame location when the hydrogen ratio was lower (i.e., for the 00H2 and 30H2 cases). Since the vortices stress the flame, the flame is prone to lift-off at larger vortex values. In the case of 60H2, the vortices moved close to the burner axis relative to the flame location, and the flame location tended to be away from the vortices. Thus, the attachment of the flame to the bluff body was stronger for this case. For the 90H2 case, there were few vortices near the ORZ. This explains why the 90H2 flame could stabilize in the outer shear layer.
# Microbial Activity in Subterranean Ecosystems: Recent Advances
## 3 The Search of Antibiotics Produced by Subsurface Bacteria
### Why Is There a Need of New Antibiotics?  
According to the World Health Organization (WHO) [55], there is a serious lack of new antibiotics to combat the growing threat of antimicrobial resistance of pathogenic bacteria as well as an urgent need for more investment in research and development to fight against antibiotic-resistant infections. [PERSON] et al. [56] predicted more than ten million deaths of people infected with the antibiotic-resistant bacteria worldwide within the next 30 years if urgent measures are not taken now.  
In 2017, the WHO [57] reported a list of 12 bacteria for which novel antibiotics were urgently required. The list comprises three categories: critical, high and medium priority, according to the urgency of need for new antibiotics. In critical priority were included _Actinobacter baumannii, Pseudomonas aeruginosa_, and _Enterobacteriaceae_. High priority was assigned to _Enterococcus faecium, Staphylococcus aureus, Helicobacter pylori, Campylobacter_ spp., _Salmonellae_, and _Neisseria gonorrhoeae_, and finally the category of medium priority encompasses _Streptococcus pneumoniae, Haemophilus influenzae_, and _Shigella_ spp. It is remarkable that only three out of the twelve bacteria from the list are Gram-positive, which denotes the interest in having antibiotics active against Gram-negative bacteria.  
The clinical pipeline for new antibiotics currently includes only a small number of novel compounds in development. In the past 20 years, only two new antibiotic classes, both only active against Gram-positive bacteria, have received global regulatory approval by the US Food and Drug Administration and European Medicines Agency [58]. In the same time period, no new antibiotics against Gram-negative bacteria have been approved, and the quinolones, discovered in 1962, represent the last novel drug class identified to be active against Gram-negative bacteria. In addition, only two completely new drugs for multidrug-resistant tuberculosis treatment have reached the market in over 70 years [58].  
Currently, the clinical pipeline is dominated by improvements of existing products with similar chemical structures, but some level of cross-resistance and fast adaptation of target bacteria can be expected. Resistance to one specific antibiotic agent can lead to resistance to a whole related class, through exchange of genetic material between different bacteria, and can affect antibiotic treatment of a wide range of infections and diseases [59]. Ideally, research should be focused on entirely new classes of compounds, targets, and modes of action to avoid cross-resistance to existing antibiotics [60].
## 2 Materials and Methods
### General Characterisation Methods
#### 2.3.2 Thermogravimetric Analysis  
Thermal characterisation was carried out by using thermogravimetric analysis (Metller-Toledo TGA/SDTA 851e, Greifensee, Switzerland). Samples (5.0 \(\pm\) 0.1 mg) were heated from 25 to 800 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min under N\({}_{2}\) atmosphere (50 mL/min).
# Sensing Performance of a Vibrotactile Glove for Deaf-Blind People
## 5 Conclusions  
Communication of concepts through a vibrotactile device is feasible. Responsiveness is much higher with this device than letter by letter communication. A bidirectional communication system named TactileCom was developed for this purpose. The system is aimed at helping the deaf-blind population. However, other communication applications are possible, e.g., in noisy or multilingual environments. The analysed HMI is a vibrotactile glove that was tested with deaf-blind and unimpaired subjects. The achieved recognition performance is high enough to consider it a valid system: 97% for the DBS and 81% for the CS. These results were achieved without previous training with the HMIThe ability to discriminate vibrotactile patterns was more acute in the deaf-blind individuals than in unimpaired subjects.  
A dedicated experiment focused on DBS was performed in a second stage. The success rate improved from 63% to 71% on the first attempt. These results show that adaptability to the HMI is fast and learning its functioning is easy. The response of the DBS was also analysed in terms of pattern complexity, the two keying frequencies, and the sensitivity of each finger. The outcome showed a 100% success rate when the stimulus was applied to a single tactor and a lower performance when multiple tactors were activated.  
As the device is aimed primarily at aiding the deaf-blind population, the development procedure needs to take into account opinions and suggestions by these potential users. The subjects of the trials expressed their satisfaction with the general HMI operation and suggested improvements related to practical issues concerning everyday life activities and aesthetics. Their feedback led us to the design of new vibrotactile HMIs to wear under the clothing.  
For the potential user, TactileCom is a communication system of substitution that can codify concepts or ideas. This allows a faster and more efficient communication than the character-by-character coding. On the other hand, tactor usage is more efficient than those in tactile displays used to present a figure. And finally, the person who communicates with the deaf-blind individuals does not require previous knowledge of the coding language, as opposed to tactile sign language.
# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 2 Related Work
### Three-Dimensional Semantic Map Technology  
Unstructured 3D point cloud maps are crucial for perceiving surrounding environments, autonomous navigation, and autonomous scene understanding. For gardening pruning robots, unstructured 3D point cloud semantic segmentation technology is a key technology for achieving autonomous operation and navigation. Three-dimensional semantic maps can assist gardening robots in recognizing and detecting obstacles, thereby providing obstacle avoidance and motion path-planning capabilities, significantly improving the operation and production efficiency of gardening robots. After performing semantic segmentation on captured images to obtain the semantic information of different objects, directly mapping the semantic mask images to actual physical 3D coordinates can establish a 3D point cloud map of the working environment.  
In terms of integrating the construction of 3D semantic maps with deep learning methods, [PERSON] et al. integrated an improved YOLOV3 algorithm to propose a new method for constructing 3D semantic maps for mobile robots, combining it with a SLAM system for pose estimation and three-dimensional environment construction [16]. [PERSON] et al., by improving the neural network structure of RandLA-Net, constructed a deep 3D point cloud semantic segmentation model for large-scale unstructured agricultural scenes [17]. [PERSON] et al. utilized Structure from Motion (SFM) to create 3D point clouds and employed deep learning for the segmentation and classification of highway assets, addressing the issues of labor-intensive and time-consuming processes in highway maintenance and infrastructure monitoring [18].  
Regarding research on constructing semantic maps of different scales, [PERSON] et al. focused on small-scale 3D reconstruction to generate detailed, brief, and safe flight plans for unmanned aerial vehicles (UAVs) in environments, considering semantic properties through discrete optimization [19]. [PERSON] et al. proposed a method for reconstructing dense, large-scale outdoor 3D semantic maps based on monocular vision, achieving semantic segmentation through visual odometry, depth estimation, and a deep learning-based conditional random field (CRF) image segmentation system, ultimately producing dense urban environment 3D semantic maps with global consistency [20].  
It is clear that in many instances, establishing 3D semantic maps can significantly aid in improving work processes and solving many previously unresolved issues, making the construction of 3D semantic maps for lawn environments both necessary and meaningful.
## 2 System Model
### Radar Model
To ensure low cross-correlation in the beampatterns of a MIMO DFRC system, it is important to balance the system's detection functions with its communication performance. This involves defining specific optimization goals and constraints. The radar precoding matrix \(\mathbf{X}_{0}\) is obtained by decomposing the covariance matrix \(\mathbf{R}\) as \(\mathbf{R}=\mathbf{X}_{0}\mathbf{X}_{0}^{H}/L\). Once \(\mathbf{R}\) is determined using Equation (10), the DFRC transmission matrix \(\mathbf{X}\) is found by minimizing the power difference between \(\mathbf{X}\) and the reference precoder \(\mathbf{X}_{0}\). The objective is to ensure that \(\mathbf{X}\) closely approximates \(\mathbf{X}_{0}\).