esa-sceva/satcom-chunk-collection huggingface dataset:
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 5 Experiments
### Running Time Analysis  
We used MATLAB 2018b to run each clustering method independently and recorded the running time of each clustering method under one hyperparameter combination on each data set in Figure 3. From these results, one can see that FeatConcate is the fastest among most of multi-view data sets. To explore the consistency or complementary information of the multi-view data, these multi-view clustering methods generally need a relatively long running time to produce the final clustering. Our LGOMSC method is faster than Co_reg_c, Co_reg_p, LMSC, FMR, MLRSSC, RMKMC, mPAC, PMSC, and COMVSC on most data sets. On the Caltech101 data set, our LGOMSC method is 4.35 s slower than MLRSSC, and on the Wikipedia data set, our LGOMSC method is 1.89 s slower than MLRSSC and 0.21 s slower than RMKMC. On these data sets, LMVSC and GMC are the fastest, and they are more suitable for solving large-scale clustering problems. They are more concerned with efficiency rather than effectiveness. Therefore, the clustering performance is relatively poor. Although our proposed method is slower than the methods designed specifically for large-scale scenarios, our method is still comparable to LMVSC and GMC for the NottingHill data set. In Table 7, MVSCLG costs more running times than our method. In the NottingHill data set, LGOMSC costs 4744.49 s under one hyperparameter combination. However, since MVSCLG contains three hyperparameters, we need to conduct the grid search strategy to select the optimal one from 420 hyperparameter combinations, which may need 553.52 h. Hence, we ignore it.  
Figure 3: Running time of different methods on nine multi-view data sets.
## 4 Quantum and Classical Image-Processing Algorithms  
Further reduction of the acquisition speed and the optimization of the elaboration time is addressed by exploiting dedicated quantum and classical image processing, as well as novel mathematical methods coming on one hand from compressive sensing, and on the other hand from quantum tomography and quantum Fisher information.
# A 245 GHz Real-Time Wideband Wireless Communication Link with 30 Gbps Data Rate
## 7 Conclusions  
A 245 GHz real-time wideband wireless communication link with a 30 Gbps data rate is presented in this paper. The 16 QAM modulation scheme was used in four base-band channels with low-complexity algorithms and FPGA resource consumption. IF modules operating at 15.65 GHz were applied to combine the base-band signals for further frequency conversion and signal transmission at 245 GHz. THz BPF was also adopted in the system to reduce the interference from the mirror image signal and background noise. The measured EVM at the base-band, IF, and THz front-end reached 4%, 10.3%, and 12.7%, respectively, demonstrating a high-quality wireless communication link at a distance of 1.2 m. The proposed THz wireless system demonstrates its potential for the application of next-generation communication systems, namely indoor wireless data transfer and inter-satellite high-speed communications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Freq (GHz)** & **Bandwidth (GHz)** & **Data Rate (Gbps)** & **Modulation Scheme** &
\begin{tabular}{c} **DAC/ADC** \\ **Sampling Rate (Gsps)** \\ \end{tabular} & **Distance (m)** \\ \hline
[18] & 300 & 1.5 & 3 & QPSK & 2.45 & 60 \\
[19] & 220 & 5.2 & 20.8 & 16 QAM & 6.4 & 0.3/1030 * \\
[20] & 220 & 5.2 & 31.2 & 64 QAM & 6.4 & 0.05 \\ This work & 245 & 12.5 & 30 & 16 QAM & 2.5 & 1.2 \\ \hline \hline \end{tabular}
* 0.3 m link presented in a lab environment with antenna gain at 22 dBi, and 1030 m in a field test with antenna gain at 58 dBi.  
\end{table}
Table 2: Comparison of published THz real-time wireless communication links.  
Figure 10: Measured constellation of the 245 GHz wireless communication system.  
Figure 9: Photograph of the 30 Gbps THz communication demonstration platform.  
**Author Contributions:** Conceptualization, X.H.; Data curation, H.Z.; Funding acquisition, J.D.; Investigation, T.Z., H.Z. and H.S.; Methodology, T.Z., H.Z., X.H., J.P. and K.S.; Project administration, T.Z. and J.D.; Supervision, J.D. and J.G.; Validation, H.Z.; Visualization, K.S.; Writing--original draft, T.Z. and H.Z.; Writing--review & editing, X.H., H.S., J.P. and K.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Commonwealth Scientific and Industrial Research Organisation (CSIRO) Space Technology Future Science Platform Project ST-R2-03.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments and Results
### Setting Hyperparameters  
The hyperparameters for the training were obtained from preliminary experiments. Patch sizes of 64 \(\times\) 64 were chosen based on the optimal balance, training time, and data storage. The learning rate was defined as \(10^{-3}\), with a decay factor of 0.1 for every time the validation loss function did not yield a significant improvement for 20 epochs. We defined an offset of 30 epochs for training to terminate in the absence of improvement. Adam was selected as the optimizer, as it yields substantially faster convergence and better quantitative results than SGD [64].  
The preliminary experiments also helped to define the approach regarding single or multiple views. Using all available channels as input, the dice metrics of the test dataset (Figure 8) indicate that the sagittal and axial views led to the worst results, whereas the coronal and combined views led to the best results. We also note that the aggregated view yielded the best AHD in terms of both average and standard deviation, indicating better and more stable segmentation behavior. This occurred because the aggregation of views corrects any misclassified voxels that appear far from the segmented thalamus. Accordingly, view aggregation was chosen as the approach to be used.
## 2 Numerical Details and Layouts for Analysis
### Richardson's Extrapolation  
Richardson extrapolation is a method to attain a higher-order estimation of the continuum value from a series of lower-order discrete values [24] and is used as a computational tool for increasing the accuracy of numerical simulations. Moreover, this method can be used to validate the simulation results [25; 26]. Four different mesh structures with 0.5, 1, 2 and 4 million cells are generated by increasing the number of grids. This grid structure sequence is generated with a constant refinement ratio (\(r\)) of 2. The extrapolated exact numerical solution is estimated using [PERSON]'s method. [PERSON] [27] suggested the Grid Convergence Index (GCI) to estimate the required grid resolution. The GCI is defined as:  
\[\text{GCI}=\frac{F_{\text{s}}|\epsilon|}{r^{p}-1} \tag{1}\]  
where \(F_{\text{s}}\) is a safety factor, \(\epsilon\) is the relative error between the two grids, \(r\) is the grid refinement ratio and \(p\) is the order of convergence. [PERSON] [27] suggested safety factor (\(F_{\text{s}}\)) of 1.25 for such mesh study, confirming convergence of the mesh.  
Using the GCI, the error band is calculated as follows:  
\[\text{Error band}=f_{\text{exact}}\pm f_{\text{exact}}\cdot GCI_{12} \tag{2}\]  
in which \(f_{\text{exact}}\) is the approximate exact solution and 1 and 2 are grid structures.  
The sensitivity of changes with number of cells on the thermal resistance (\(R_{t}\)) is shown in Figure 6 along with the estimated exact solution and the error band estimated.  
As shown in Figure 6, the thermal resistance \(R_{t}\) is calculated by the mesh having 4 million cells within the error band and therefore this grid resolution is used for the rest of the simulations to obtain a valid solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Max Element Size** & **Min Element Size** & **Max Element Growth Rate** \\ \hline Fluid domain & 5.12 mm & 0.64 mm & 1.45 \\ IGBT module domain & 1.5 mm & 0.448 mm & 1.15 \\ Wetted walls & 0.829 mm & 0.0897 mm & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Geometric values of mesh configurations.  
Figure 6: Sensitivity of changes with number of cells on thermal resistance (\(R_{t}\)).
## 1 Introduction
The framework's low-level structure is illustrated in Figure 1. By \"end-to-end XAI framework\", we specifically mean that we address all stages, including loading the dataset, preprocessing the data, training black-box AI models, generating global and local XAI graphs, extracting top intrusion features based on XAI methods, and providing explanations on local and global scales using the extracted features and XAI graphs. We implemented our framework to examine three network intrusion datasets, each with distinct characteristics. The first two datasets, CICIDS-2017 [33] and NSL-KDD [34], are widely acknowledged in the field of network intrusion detection as testbeds or controlled-environment datasets. In contrast, the RoEduNet-SIMARGL2021 dataset [35] comprises realistic network traffic. Our framework is capable of furnishing both global and local explanations of the AI model. This entails elucidating how features in the network traffic data collectively influence the outcome and explaining the decision-making process of the AI model for specific data instances. Furthermore, we demonstrate that our framework furnishes human-understandable explanations for the decision-making of the AI model, in stark contrast to black-box models that offer no justifications for their decisions. Additionally, we illustrate that the benefits derived from XAI explanations do not entail a significant time overhead for generating such explanations for most AI models. Finally, we conduct a benchmark comparison with two prior solutions for feature selection in network intrusion detection systems [35; 36]. Our findings reveal that most AI models exhibit superior performance when utilizing the top significant features selected by XAI-IDS compared to baseline methods. **Summary of Contributions:** Below, we detail our key contributions:  
* We present a comprehensive end-to-end framework designed to improve the explainability of AI models used in network intrusion detection tasks. This framework provides both overarching and detailed explanations for AI models dedicated to intrusion detection. * We extract important features, both model-specific and intrusion-specific, across various classes of AI models and different types of network intrusions. * We offer human-understandable explanations and justifications for the decision-making process of the AI model, aiding the security analyst. * We apply seven black-box AI models to one real-world and two emulated network intrusion datasets, presenting diverse performance metrics. * We provide our source codes to the community, establishing a foundational XAI framework for network intrusion detection and promoting further exploration with additional datasets and models (the URL for our source codes is [[https://github.com/ogarreche/XAI_NIDS](https://github.com/ogarreche/XAI_NIDS)]([https://github.com/ogarreche/XAI_NIDS](https://github.com/ogarreche/XAI_NIDS)) (accessed on 9 April 2024)).
## 5 Result Analysis
### Effect of Helix Angle on Sealing Performance  
Based on Figure 5a,b for \(\lambda_{c}\) = 48, when the spiral groove \(\theta\) is in the range of 20 to 30\({}^{\circ}\). the average vapor phase volume fraction increases with an increase in the spiral angle \(\theta\). In addition, the opening force gradually decreases with an increase in the spiral angle \(\theta\). An increase in the helix angle \(\theta\) leads to the reduction in the working surface area on the leeward side of the spiral groove. Furthermore, a sudden and simultaneous increase in the pressure gradually decreases the low-pressure zone of the groove. However, its area gradually increases, which continuously increases the vaporization phase transition area. Consequently, this leads to a decrease in the opening force between the two seal faces and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Ranges** \\ \hline helix angle & 20–30\({}^{\circ}\) \\ groove-diameter ratio & 0.1–0.9 \\ groove-weir ratio & 0.1–0.9 \\ groove depth & 3–10 \(\mu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter range.  
Figure 4: Variation curve of average vapor phase volume fraction with mass transfer coefficient.  
an increase in the average vapor phase volume fraction. On the contrary, the opening force decreases continuously with an increase in the phase change. This, however, is inconsistent with the enhancement of the lifting ability of the phase change to the opening force mentioned in [21]. According to Figure 5c, with an increase in the helix angle \(\theta\), the leakage rate has a certain change, and first increases and then stabilizes. However, the magnitude of this change is negligible compared to the change in leakage caused by other structural parameters.  
When \(\lambda_{c}\) = 0.1, the average vapor phase volume fraction is relatively small, and the change in the helix angle has almost no effect on its value. This observation is significantly different from the obtained results when \(\lambda_{c}\) = 48. However, the variation of the opening force and the leakage rate with the helix angle is approximately the same as when \(\lambda_{c}\) = 48. The maximum error of the opening force is 3.7%, which is almost negligible, and the leakage is reduced by 3.7 to 9.6%.  
Figure 5: Cont.
## 5 Results and Discussion
### Considerations upon Measurements' Results Undertaken inside Room A and B  
By the results, as commented in Sections 5.1 and 5.2, it has been shown that the values of the scattering coefficient, related to the measurements undertaken inside Room A, are consistently high. This is due to the microphones that have recorded much more reflected sound energy than what was expected. This phenomenon has been created by the small distance between the microphones and the vertical walls of the room, whose reflected energy has been overlapped with the sound energy scattered by the QRD. This statement is confirmed by the concentration of sound energy in the corners of the room, related to microphone positions 1 and 25.  
In summary, the values registered by the tests performed inside Room A should not be subject to consideration, because they were impacted by extraneous factors not related to the samples.  
The results obtained by testing the panels inside Room B are considered valid because the samples were tested in an appropriate environment in accordance with the guidelines. The differences between the measurements undertaken in Room A and B are shown by the graphs in Figure 13, related to the same sample (i.e., plywood QRD in the horizontal configuration, without any backing reflecting panel).  
Figure 12: Directional diffusion coefficient related to the samples tested inside Room B.  
Figure 13 shows that the scattering coefficients in Room A have comparable levels at 500 Hz across all the microphones, with downward peaks up to 10 dB, related to channel 4 and 22. Results obtained in Room B indicate a noticeable difference of up to 15 dB between the central channels (8 to 19) and the laterals, approximating the reference curve of the theoretical model introduced in Section 6. A similar trend has been found in the graph related to 2 kHz, where the central channels of Room B revealed higher sound energy than that tested in Room A, while the values obtained at the lateral channels (i.e., 1 to 7, and 19 to 25) are very comparable.  
Curves indicated in the graph related to 1 kHz accentuate the difference between the results obtained in Room A and B, including the lateral channels for a level difference comprised between 10 and 15 dB.  
The graph in Figure 14 reports the comparison among the Horizontal Plywood Panel with back, tested in Room A and Room B. The light blue line reports the values of the reflecting panel (alone), whilst the dotted line reports the scattering coefficient values of the Horizontal Plywood Panel without reflecting panel tested in Room B. From the graph, the Horizontal Plywood Panel--Room B scattering coefficient (dotted line) resulted higher than 0.4 for all the frequencies, except for 250 and 125 Hz, while for the other frequencies the scattering coefficient increases with increasing frequency. The values obtained for the Horizontal Plywood Panel with back--Room A gave inconsistent results due to the size of Room A.  
Figure 13: Comparison of the scattering coefficient based on measurements undertaken inside Room A and B.
## 2 Background  
### Finnish-Swedish Ice Class Rules and IB Waiting Time  
The aim of the Finnish-Swedish Ice Class Rules (FSICR) is to ensure that ships operating on the northern Baltic Sea, to and from Finnish and Swedish ports, have sufficient ice-going capability to maintain safe and efficient navigation year-round [13]. To this end, the rules, which have been developed jointly by the Finnish and Swedish maritime authorities based on accumulated experience and research, specify five ice classes: IA Super, IA, IB, IC, and II. Enforcement is through port-specific restrictions determining the minimum ice class and deadweight needed to be eligible for IB assistance [13].  
The demand for IB assistance in a region depends among others on the prevailing ice conditions, the amount of maritime traffic, and the ice-going capability of the ships operating there. If the demand for IB assistance exceeds the available icebreaking resources, the waiting time for IB assistance will increase. To maintain smooth and efficient maritime traffic, the goal of the Finnish IB service is to limit the average waiting time to four hours [14]. To this end, the FSICR determines ice class-specificperformance requirements. These are determined as per [5] in terms of the minimum ice conditions in which a ship must be able to maintain a speed of at least 5 knots.
## 3 Results and Discussion  
### pH  
The pH values of wild red deer meat treated with different levels of high hydrostatic pressure (HHP) and stored for 14 days at 4 \(\pm\) 1 \({}^{\circ}\)C are summarized in Table 1.  
On Day 1, pH values ranged from 5.60 in the control group (0 MPa) to 5.94 at 600 MPa. Samples treated at 300, 350, 400, 450, and 550 MPa exhibited significantly higher pH values than the control (\(p\) < 0.05), while the 150, 200, and 250 MPa groups were not significantly different. The highest pH value at 600 MPa was significantly different from all other treatments (\(p\) < 0.05). These increases are consistent with previous findings under high-pressure conditions, often attributed to ion redistribution and structural changes in proteins [21; 22].  
By Day 14, a general decrease in pH was observed in most treatments. The control group exhibited a significant decline from 5.60 to 5.13 (\(p\) < 0.05), likely due to lactic acid and other metabolites accumulating during microbial activity, as reported in previous studies on vacuum-packaged meat [14]. Significant pH declines were measured at 150, 200, and 250 MPa between Day 1 and Day 14 (\(p\) < 0.05), likely due to continued microbial activity and post-mortem glycolysis leading to acid accumulation. In contrast, significant pH increases were observed at 400, 450, 500, and 550 MPa (\(p\) < 0.05), which may be attributed to pressure-induced denaturation of proteins and suppression of acid-generating microbial and enzymatic activity. pH remained stable at 300, 350, and 600 MPa (\(p\) > 0.05). The 600 MPa group retained a pH of 5.90 on Day 14, nearly identical to its initial value of 5.94. This stability could be explained by the suppression of microbial activity and enzymatic processes at higher pressure levels, as reported in similar studies on red meat [23].
## 2 The Elephant in the Room: Great Diversity
### Protocols Involved within the Application Layer: CoAP and REST
#### 2.4.2 REST: Representational State Transfer  
REST leverages the existing HTTP infrastructure to create or access resources using standard HTTP methods. It is widely adopted for web APIs, enabling straightforward and flexible interactions with services. RESTful interfaces are favored for their scalability and ease of integration across diverse platforms, making them suitable for complex IoT ecosystems where different devices and services need seamless communication [38].  
Both CoAP and REST are integral to the IoT application layer, offering distinct advantages for device-to-device and device-to-service communication. CoAP excels in constrained environments, while REST is preferred for broader web integration, highlighting the importance of selecting the right protocol based on the specific requirements of an IoT deployment.
## 5 Discussion  
### The Role of the Grid Resolution  
The grid resolution was a key factor in the strain rate calculation. To understand the sensitivity of the grid resolution of the flood distance, three cases of BM and nine cases of MBM were performed with the resolutions varying from dx: 1.8-2.2, dy: 1.8-2.2, and dz: 0.8-1.2 m. Figure 14 shows the deposited boundary of the tailings predicted by BM and MBM. The results show that BM was less sensitive to the resolution than MBM. The results from MBM show that the deposited boundary was more sensitive with dz than dx and dy. The inundation distance was shorter with dz = 1.2 m (blue lines) and longer with dz = 0.8 m (red lines). To give an overview, the inundation width and inundation distance were convergent as dz \(<\) 1.2 m.  
Figure 14: The stoppage boundary profiles of FGT66 for grid resolution analysis. (**a**) Bingham model (BM); (**b**) modified bi-viscosity model (MBM). Red lines: dz = 0.8; black lines: dz = 1.0; blue lines: dz = 1.2; solid lines: dx = dy = 1.8; dashed lines: dx = dy = 2.0; dash–dot lines: dx = dy = 2.2.  
Figure 13: Snapshots of strain rate profile on the centerline cross-section of the breach (y = 220 m). (**a**) Bingham model (BM); (**b**) modified bi-viscosity model (MBM).