esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Research Challenges and Future Directions in Unstructured Outdoor Environment Navigation
### Research Challenges
The domain gap refers to the significant deviations in environment attributes of the known or trained domains relative to the actual operating domains of the robot. These differences in application domain can occur due to variations in weather, texture, previously unseen terrain conditions, environment context changes (e.g., static to dynamic) or different factors in the robot operating environments. Deep learning architectures such as reinforcement learning can generate robust performance in one target domain but may subsequently fail due to an unforeseen drastic change in that environment. Robots can be designed to be more adaptable if they are trained on large and diverse real-world data. However, the collection of real-world data to achieve such diversity can be challenging. Thus, there is a requirement for the development of more efficient data collection and data augmentation techniques. One option to address this is to train robots in computer simulators using data collected synthetically instead of training on real-world data. This method has benefits such as a reduction of the number of real-world experiments and a more economical and safer deployment in comparison to real-world systems. However, the trained models might have low adaptation to the real world due to overfitting to the simulation environment conditions. If the physics and the environments of simulators are significantly different from the target domain, the learned policies and loss functions in the simulator domains might not be directly applicable to the real world, thus leading to low performance. Novel techniques are needed to ensure that learning-based models, policies, and loss functions are transferable to the application domain to bridge this gap. Real-time performance is a critical aspect of robots working effectively in unstructured environments. The models trained in simulators might be unnecessarily complex for application in the real world. Therefore, techniques of domain adaptation must be transferable, economically sustainable, and computationally efficient in making decisions. In safety-critical applications, domain adaptation must be performed while ensuring safety, which requires careful validation and testing to ensure that the adapted model behaves safely in the real world.
## 3 Results and Discussion
Therefore, since our experimental spectral bandwidth was of 56 cm\({}^{-1}\), when we set the laser beams to excite the 2845 cm\({}^{-1}\) lipid band, we are exciting from (FWHM) 2817 to 2873 cm\({}^{-1}\); in the same way, when we set the laser beams to the protein band at 2940 cm\({}^{-1}\), effectively we excite the range 2912-2968 cm\({}^{-1}\) (FWHM). In Figure 7, the spectral bandwidth derived by the chirping of the pulses (56 cm\({}^{-1}\)--continuous lines) and two simulated Raman bands with two peaks at a distance of 95 cm\({}^{-1}\) with a width of about 100 cm\({}^{-1}\) (red circles and green diamonds) are illustrated. The overlap between two excited bandwidths is highlighted with blue lines; however, the contribution to the Raman signal from this region can be neglected considering that the intensities are lower than FWHM values and, therefore, they can be considered under the threshold. Thus, we can conclude that, using 56 cm\({}^{-1}\) chirped pulses, the 2845 cm\({}^{-1}\) channel is essentially related to the lipid signal and the 2940 cm\({}^{-1}\) channel is principally due to protein content. Definitely, considering the retrieved experimental spectral resolution (about 56 cm\({}^{-1}\)) that is lower than the Raman band of lipids and proteins (about 100 cm\({}^{-1}\)) and since the overlap region, obtained when lipids and proteins are sequentially excited, can be considered negligible, our SRS microscope is suitable to image molecular specificity such as lipids (CH\({}_{2}\)) and proteins (CH\({}_{3}\)) in the C-H region, as already demonstrated in our previous paper [23].
## 4 Results: Design and Evaluation of LSHT Machines
### The 16-Pole LSHT Designs Obtained Through Multi-Objective Optimization  
The raw optimization data for the 16-pole machines are presented in Figure 9. The geometry of stator and rotor structures of the best 16-pole LSHT machines is shown in Figure 10, and the mesh considered for the fault assignments in the 2D FE model is illustrated in Figure 11. Further details regarding the dimensions and materials of the machines are provided in Appendix A.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Symbol** & **PMSM** & **SynRM** & **SCIM** \\ \hline Mean Torque & \(\mathrm{T_{mean}\;[Nm]}\) & 1360.9 & 716.8 & 700.1 \\ Torque Ripple & \(\mathrm{T_{rp}\;[\%]}\) & 4.50 & 42.9 & 3.9 \\ Efficiency & \(\mathrm{\eta\;[\%]}\) & 97.3 & 94.4 & 94.2 \\ Active Losses & \(\mathrm{L_{cp+fe+edd}\;[W]}\) & 2329 & 2650 & 2699 \\ Power Factor & \(\mathrm{pf\;[-]}\) & 0.98 & 0.47 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main performance comparison of the optimized 10-pole LSHT machines.  
Figure 8: The 2D FE model used for the evaluation of 10-pole LSHT machines: (**a**) meshed best PMSM model; (**b**) meshed best SynRM model; (**c**) meshed best SCIM model. A quad-layer airgap was considered in all FE models to enhance accuracy.  
Figure 7: The resulting optimized 10-pole LSHT machines in 3D sketches: (**a**) PMSM; (**b**) SynRM; (**c**) SCIM.  
The performance of the best designs obtained after solving this optimization process is summarized in Table 3, focusing on rated performance.  
Figure 11: The 2D FE model used for the evaluation of 16-pole LSHT machines: (**a**) meshed best PMSM model; (**b**) meshed best SynRM model; (**c**) meshed best SCIM model. A quad-layer airgap was considered in all FE models to enhance accuracy.  
Figure 10: The resulting optimized 16-pole LSHT machines in 3D sketches: (**a**) PMSM; (**b**) SynRM; (**c**) SCIM.  
Figure 9: Optimized LSHT 16-pole machines: (**a**) PMSM; (**b**) SynRM; (**c**) SCIM.  
The evaluation results show that the PMSM continues to be the best performer in terms of torque capacity, efficiency, and power factor, significantly outperforming the SynRM and the IM. In contrast with the results for the 10-pole machines, the 16-pole IM has a slight advantage in torque capacity when compared to the 16-pole SynRM. In addition, the IM significantly surpasses the SynRM in torque ripple, achieving a value below 6%. The SynRM continues to offer relatively high efficiency but suffers from a significantly low power factor. In summary, among rare-earth-free topologies, the IM outperforms the SynRM in torque capacity, torque ripple, and efficiency for the 16-pole configuration.
# Stability Assessment of Gob Side Entry at the Steeply Inclined Mining Face
## 3 Results  
### Geostress  
The geostress components are shown in Table 3. The elevation of measurement locations #1 and #2 are both \(-\)650 m, with maximum principle stresses \(\sigma_{1}\) of 21.3 MPa and 20.2 MPa, respectively, resulting in an average of 20.75 MPa. The significant disparity  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Lithology** & **Bulk/GPa** & **Shear/GPa** & **Cohesion/MPa** & **Friction/\({}^{\circ}\)** & **Tension/MPa** & **Density/(kg/m\({}^{3}\))** \\ \hline Packsand & 7.88 & 4.90 & 4.76 & 44.1 & 4.31 & 2750 \\ \hline Siltstone & 6.77 & 3.76 & 4.85 & 39.9 & 5.36 & 2570 \\ \hline Coal & 2.23 & 0.98 & 1.64 & 33.6 & 1.48 & 1450 \\ \hline Mudstone & 4.33 & 2.84 & 0.76 & 39.9 & 3.18 & 2460 \\ \hline Fine sandstone & 9.24 & 5.57 & 2.28 & 42.0 & 5.44 & 2650 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical parameters of the rock.  
Figure 4: Layout of monitoring sections.  
Figure 3: M-C yield criterion.  
between the \(\sigma_{1}\) and the vertical stresses \(\sigma_{\rm v}\) reveals the predominance of horizontal stresses in the mine.  
The is direction of the \(\sigma_{1}\) at measurement points #1 and #2 approximately 120\({}^{\circ}\), with a dip angle of 5 to 10\({}^{\circ}\). The \(\sigma_{1}\) values range from 20 to 22 MPa, exceeding the \(\sigma_{\rm v}\). The direction angle of the minimal principal stress \(\sigma_{3}\) ranges from 210\({}^{\circ}\) to 215\({}^{\circ}\), the dip angle is around 13\({}^{\circ}\), and the stress magnitude varies within 10 MPa. The disparity between the \(\sigma_{1}\) and \(\sigma_{3}\) is significant, with \(\Delta\sigma\) measuring 13.4 MPa and 10.2 MPa, respectively, whereas the \(\sigma_{1}\)/\(\sigma_{3}\) are 2.0 and 2.7. The significant disparity in stress alterations will result in substantial deformation of the surrounding rock. The angle between the intermediate principal stress \(\sigma_{2}\) and the horizontal plane is predominantly around 75\({}^{\circ}\), with the stress magnitude ranging from 12.4 to 14.7 MPa.  
The measurement location at the elevation of \(-\)920 m yields a bigger value, with the direction of \(\sigma_{1}\) recorded at 161.4\({}^{\circ}\). The value of \(\sigma_{1}\) is 35 MPa, and the \(\sigma_{3}\) is 19.7 MPa, resulting in a difference of 15.3 MPa and a ratio of 1.78. The maximal primary stress is oriented horizontally, with a magnitude about 1.3 to 1.52 times the gravity load, considerably affecting the mining configuration.
## 2 Results and Discussion
### Volatile Organic Compounds (VOCs)
** & **Compounds** & **RT/min** & **CJ** & **NJ** & **50J** & **PJ** & **NM** & **PM** & **Descriptor** & **Thresholds (ug/L)** \\ \hline A1 & 1-Heanol & 10.85 & 0.05 & 0.09 & 0.06 & 0.06 & 0.12 & 0.09 & Resin, green & 8000 [31, 32] \\ B1 & \(\gamma\)-Dcamole & 46.30 & 42.72 & 30.12 & 45.16 & 32.27 & 60.19 & 24.35 & Peach & 11 [33] \\ D1 & Linazol & 21.18 & 37.02 & 39.84 & 44.33 & 38.25 & 52.78 & 34.45 & Floral (Fruity, musky) & 15[12, 34] \\ D2 & Gernanyl acetone & 24.18 & 0.09 & 0.32 & 0.13 & 0.16 & 0.81 & 0.05 & Tropical fruits & 60 [32] \\ D3 & a-Terpinol & 27.00 & 0.43 & 0.46 & 0.43 & 0.43 & 0.52 & 0.26 & Pleasant, sweet, arise & 250 [31, 35] \\ D4 & Myrtional & 28.76 & 0.00 & 2.95 & 2.27 & 2.63 & 1.13 & 0.28 & Floral, ninety & 7 [34] \\ D6 & (E,E)-Framesol & 34.66 & 0.39 & 0.33 & 0.35 & 0.61 & 0.33 & 0.35 & Lemon, anise, floral, honey, & 100 [58] \\ D8 & Trans-nevidold & 38.99 & 0.15 & 0.09 & 0.14 & 0.40 & 0.30 & 0.29 & Floral, ninety, orange, light flavor & 700 [37] \\ E1 & Methyl salicate & 28.83 & 0.16 & 0.29 & 0.32 & 0.80 & 0.40 & 0.52 & Holly & 40 [38] \\ F1 & 1-butanol, 3-methyl- & 2.34 & 0.39 & 0.42 & 0.38 & 0.27 & 0.46 & 0.46 & Whiskey, harsh bitter & 300 [34, 53] \\ F3 & 1-Octanol & 20.85 & 0.47 & 0.56 & 0.68 & 0.42 & 1.97 & 1.70 & Rosenes, citrus & 120 [37] \\ F4 & 2-Nonland & 20.68 & 0.00 & 0.07 & 0.07 & 0.10 & 0.10 & 0.11 & Fat wax, rose, citrus & 50 [37] \\ G1 & 1-butanol, 3-methyl-, acetate & 5.05 & 2.45 & 2.84 & 3.27 & 2.50 & 0.75 & 0.47 & Banana & 1500 [39] \\ G3 & Acetate acid, methyl ester & 13.43 & 51.50 & 17.67 & 55.06 & 65.14 & 6.21 & 3.79 & Apple, banana, pear & 10 [37] \\ H1 & Hexanoic acid, ethyl ester & 12.09 & 112.47 & 21.06 & 24.02 & 26.76 & 152.57 & 125.71 & Apple, strawberry & 14 [31, 32] \\ H4 & Heptonacid acid, ethyl ester & 17.11 & 0.04 & 0.11 & 0.12 & 0.11 & 0.09 & 0.06 & Pineapple, fruity & 220 [37] \\ H6 & Octanoic acid, ethyl ester & 21.66 & 759.46 & 1007.75 & 1180.29 & 1333.33 & 440.28 & 234.82 & Pnaepple, brundy & 22 [37] \\ H11 & Decanoic acid, ethyl ester & 29.77 & 1.78 & 3.16 & 4.18 & 5.70 & 1.76 & 0.72 & Pear, brundy & 200 [32, 37] \\ H12 & Ethly 1-decononon & 30.98 & 0.75 & 4.73 & 7.59 & 6.72 & 2.56 & 0.22 & Fruity & 100 [40] \\ H4 & Dodecane acid, ethyl ester & 36.97 & 0.04 & 0.06 & 0.33 & 0.25 & 0.07 & 0.02 & Oil, floral & 400 [40] \\ L1 & Styrene & 8.08 & 5.26 & 6.85 & 7.12 & 6.34 & 5.09 & 4.36 & Flowery & 65 [41] \\ L2 & Benzaldehyde & 19.19 & 0.42 & 0.10 & 0.23 & 0.13 & 0.09 & 0.09 & Almond & 2000 [41] \\ L3 & Acetophenone & 25.17 & 1.21 & 0.33 & 1.05 & 0.57 & 1.03 & 0.69 & Almost & 65 [42] \\ L5 & Preshylethyl Alcohol & 30.40 & 0.20 & 0.23 & 0.28 & 0.10 & 0.35 & 0.08 & Honey, rose & 14,000 [31, 32] \\ L6 & Acetic acid, 2-phenylethyl ester & 31.95 & 7.51 & 4.61 & 6.96 & 2.82 & 2.45 & 0.94 & Floral & 250 [37] \\ L7 & Benzoic acid, ethyl ester & 26.44 & 0.05 & 0.11 & 0.14 & 0.08 & 0.32 & 0.10 & Holly oil and fruit & 575 [32] \\ L8 & Benzenoacetic acid, ethyl ester & 31.25 & 0.03 & 0.10 & 0.10 & 0.06 & 0.10 & 0.05 & Floral, honey & 73 [37] \\ L9 & Benzenenenpro
## 3 A General Framework for Grid Partitioning Techniques and Grid Renumbering Methods
### Grid Renumbering Framework
#### 3.2.1 RCM Algorithm  
After careful algorithm design, the RCM algorithm was shown to be able to adapt to large-scale distributed memory parallelism [28] and has been widely used in software such as OpenFoam and MatLab. When the numerical stage of sparse matrix computation is reached, the matrix is usually distributed. At this point, the RCM algorithm can improve the sparse structure of the sparse matrix, making the non-zero elements more concentrated, which can theoretically improve the storage efficiency of the computer.  
The RCM algorithm uses BFS to traverse the nodes in the graph. At each layer of the graph, nodes are sorted according to their degree labels for the renumbering of that layer. In this paper, due to the modular nature of the numerical simulation framework and the complexity of the case, the algorithm's feature of sorting nodes according to their degrees is weakened to reduce the time cost of partially traversing the nodes. Algorithm 1 shows the overall program structure.  
```
0:\(mesh_{0}\)
0:\(mesh_{n}\)
1: Select the initial unit to be numbered, set to \(mesh_{k}\). Set its number to the value of the maximum number of units
2: Arrange the unnumbered neighbors of \(mesh_{k}\) in the container \(L\) according to the descending order of degree
3: Set the first element of \(L\) to \(mesh_{k}\) and renumber it. The value is the maximum number of cells minus one
4: Implementation Step 2
5: Implementation of step 3, and the numbered values in decreasing order
6: Repeat until the numbering is complete
```  
**Algorithm 1** RCM numbering algorithm  
Note that the initial numbered cells of the original RCM algorithm are selected by traversing the entire grid of the computational solution domain. The degree of meshes is used to achieve the desired optimization. Here, we require that the initial elements be selected as boundary cells or close to the boundary. This method prevents excessive time overhead due to the large and complex size of the case. It makes the algorithm more adaptive in increasingly complex cases. The key step is the numbering. The RCM algorithm sorts the meshes in reverse according to the sequence of degrees, from \"nElement -1\" to \"0\".
# Seismic Blind Deconvolution Based on Self-Supervised Machine Learning
## 2 Methodology
### Loss Function  
Before introducing the loss function, we first explain the relative sparsity process on the reflectivity predicted by the reflectivity inversion network. Due to the contamination of noise and the band-limited nature of seismic data, the blind deconvolution problem is ill-posed and underdetermined. Numerous deconvolution results can fit the same seismic data well. Hence, an additional process is generally needed to achieve the optimal solution. Inspired by [10; 23], we impose a relative sparsity constraint with a variable relative sparsity that increases with the training epochs defined by Equation (7) to obtain the optimal reflectivity inversion result:  
\[\mathbf{\tilde{R}}_{\text{pred}}\ =\ \left\{\begin{array}{l}\mathbf{R}_{\text{ pred}}\,\left|\mathbf{R}_{\text{pred}}\ \right|\geq\max\!\left(\text{abs}\!\left(\mathbf{R}_{\text{ pred}}\right)\right)\times\mu,\\ 0,\left|\mathbf{R}_{\text{pred}}\ \right|<\max\!\left(\text{abs}\!\left( \mathbf{R}_{\text{pred}}\right)\right)\times\mu,\end{array}\right. \tag{6}\]  
where \(\mathbf{R}_{\text{pred}}\) is the output of the reflectivity inversion network, \(\mathbf{R}_{\text{pred}}\) is the sparsity-promoted reflectivity, and \(\mu\) is defined as follows:  
\[\mu=\left\{\begin{array}{l}0.01,r_{\text{epoch}}\ <0.1,\\ \frac{r_{\text{epoch}}\ -0.1}{0.9-0.1}\times 0.1+\frac{0.9-r_{\text{epoch}}}{0.9-0.1} \times 0.01,0.1\leq r_{\text{epoch}}\ \leq 0.9,\\ 0.1,r_{\text{epoch}}\ >0.9,\end{array}\right. \tag{7}\]  
where \(r_{\text{epoch}}\) is the ratio of the current epoch to the number of all epochs during the training process.  
Figure 4: Wavelet phase inversion network structure.  
Then, the sparsity-promoted reflectivity \(\tilde{\mathbf{R}}_{\text{pred}}\) is convolved with the phase-rotated seismic wavelet \(\mathbf{w}_{\text{pred}}\) to generate the reconstructed seismic data \(\tilde{\mathbf{Y}}_{\text{pred}}\). And then, \(\tilde{\mathbf{Y}}_{\text{pred}}\) further participates in the calculation of the loss function with observed seismic data \(\tilde{\mathbf{Y}}_{\text{obs}}\):  
\[\text{loss}\Big{(}\mathbf{Y}_{\text{obs}},\tilde{\mathbf{Y}}_{\text{pred}}\Big{)} =\Big{\|}\mathbf{Y}_{\text{obs}}-\tilde{\mathbf{Y}}_{\text{pred}}\Big{\|}_{2} ^{2}=\Big{\|}\mathbf{Y}_{\text{obs}}-\mathbf{T}_{\mathbf{w}_{\text{pred}}}\, \tilde{\mathbf{R}}_{\text{pred}}\Big{\|}_{2}^{2}. \tag{8}\]  
It is worth noting that our proposed method obtains reflectivity and seismic wavelet from seismic data simultaneously, which is a completely blind process.
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 2 Background and Related Work
### Incident Response with NIDS, SIEM, and SOAR
#### 2.1.2 Siem  
SIEM solutions are crucial to enhance the security posture of an enterprise network [27]. The goal of these solutions is to collect, integrate, and analyze security-related data from multiple sources, including logs from firewalls, IDSs, and endpoint security tools. As a result, SIEM solutions provide a comprehensive view of the activities occurring in the IT infrastructure, and they can detect potential security incidents before they cause significant damage. In addition to threat intelligence, many SIEM solutions employ user and entity behavior analytics [28] to monitor network activities and detect and mitigate attacks. This helps simplify security operations and reduces the administrative burden of incident response, allowing organizations to enhance their overall security posture and respond to security incidents effectively and efficiently. Recently, increasing interest has been placed on deploying SIEM solutions for security threat detection and response [29]. However, various challenges, e.g., false positive alerts and alert fatigue, remain, and efforts are being made to address these issues using ML and incident response workflows.
## 3 Preliminaries
### System Model  
In terms of the communication process, the VANETs' architecture is categorized into two layers, namely the physical layer and the application layer, in which case the physical layer is comprised of the vehicles, the RSUs situated on designated points of the road. Vehicles on the roads are embodied with OBUs as a communication enabling device to connect with other vehicles, RSUs or other advanced smart city facilities. [49]. The OBU is equipped with a TPD device to secure stored sensitive information like secret key and the global positioning system (GPS). As such the vehicle is securely able to carry out advanced VANETs communications in smart cities including V2X, V2V and V2I, which are enabled by a dedicated short range communication (DSRC) protocol specifically identified as IEEE 802.11p. On the other hand, the application layers are comprised of the key generation center (KGC) and the tracing authority (TRA) application server, which are the major components undertaking the TA roles in a conditional privacy preserving VANETs based system. The design and the interplay of these main entities in the system is illustrated in Figure 1, where close range networks are facilitated by wireless communication technology such as IEEE802.11p, mid-way network communication is aided by long range communication technology coupled with high bandwidth such as WiMax. Whereas, the backbone network system is empowered by wired communication which is mostly assumed to be secure as it controlled by the public utility company. It is the wireless communication that is supposed to be secured by security algorithm that ensures authentication and integrity on all communications amongst the concerned entities. The TRA is the responsible authority for RSUs and issuing pseudo-identities to vehicles, and can do real identity revocation whenever necessary. In a like manner, the KGC is responsible for public and partial private keys' generation for both RSUs and vehicles. So in VANETs schemes, it is usually assumed that the KGC and TRA are trusted parties and hence assumed honest but curious [50]. Both KGC and TRA have sufficient computation power but the OBUs and RSUs are the one with limited computation and storage capabilities hierarchically with RSUs as most powerful one [23; 29; 51]. However, OBUs and RSUs are not trusted entities and therefore any communication initiative originating from them must be authenticated. Thus, this inspires the devising of security protocols for VANETs with suitable computation requirements for OBUs and RSUs.
## 3 Challenge-Based CIDNs
### Assumption Analysis  
Challenge-based CIDNs are shown to be robust against several common insider attacks in prior studies [6; 7]. However, challenge mechanisms rely on two major assumptions, causing CIDNs to be problematic in real implementations.  
* _First assumption_. Challenges should be sent randomly to ensure they are hard to be identified from normal messages.
* _Second assumption_. Malicious nodes always behave untruthfully to other nodes, that is, by sending untruthful answers to the received messages.  
The first assumption indicates that an IDS node cannot distinguish a challenge from normal messages, ensuring that it has a small possibility for a malicious node to give manipulated feedback to challenges. However, this assumption still leaves a chance for attackers to figure out the challenges in practice. In literature, [PERSON] et al. [8] designed an advanced attack, which can distinguish normal requests from messages with a high probability and enable nodes to send untruthful feedback only to normal requests without decreasing their trust levels.  
The second assumption attempts to ensure that malicious nodes always behave abnormally, which helps to decrease their reputation in a fast manner. [PERSON] et al. [5] summarized this assumption as a _maximal harm model_, in which a malicious node chooses to give untruthful answers with the purpose of making the most negative influence on the target nodes. However, in a practical scenario, a malicious node can choose a different harm model and send malicious answers in a dynamic way to keep the trust level.  
DiscussionThe above assumptions are realistic in some scenarios in which an intruder is naive and willing to use the maximal harm model. However, many intruders may choose a different and dynamic strategy to attack CIDN nodes. For instance, a malicious insider node can give untruthful feedback to some nodes while behaving normally to other nodes. On the whole, the assumptions adopted by existing challenge mechanisms are too strong for real-world implementations, leaving a chance for advanced attackers to compromise the CIDN security.