esa-sceva/satcom-chunk-collection huggingface dataset:
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 5 Architecture and Implementation
### Model Training Component  
At first, the data are read into dataframes, labeled and cleaned. Duplicate \(x\) and \(y\) positions are discarded, since the getevent tool may have reported the same values for multiple events. Event values collected from the touch input stream are not considered for training the classifier. More complex touch gestures, which can be extracted using those event codes, are not in focus for the model training process. Therefore, the prediction performance from multi-touch gestures is not evaluated and the classifier is relying on the _x_position_, the _y_position_, timestamps, and the device orientation when performing single stroke inputs.  
The \(x\)- and \(y\)_positions_ of the user input are visualized onto a figure, which was stretched to the size of the display, used for capturing the data. When observing the figure illustrating multiple touch gestures, it can be seen that the user does only use a specific portion of the display, for most of their inputs. Therefore, this may also suggest that different users are using different areas of the display for input, depending on the length of their finger, how they hold their device, and other metrics.  
Furthermore, various single data point inputs can be observed, which may have an impact on prediction performance. In Figures 2 and 3, it can be seen that for the beginning and the end of the single swipe gesture, more data points were captured than in between those points. This may indicate, that the beginning and the end of a swipe gesture, is carried out slower than the rest, because the finger first has to get into motion. However, this may not be the same for all users, thus different features can be extracted from the touch input alone. Some potentially viable features were already determined in [26], together with the mutual information that can be gained from them.  
Figure 2: A single gesture visualized at display scale, indicating that more input points are available when starting and finishing the gesture.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 5 Research Methodology
### Confusion Matrix in Binary
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & \
\begin{tabular}{c} **Features** \\ **Number** \\ \end{tabular} & **Accuracy** & **FNR** & **FN** & **TP** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} NSL\_KDD \\ \end{tabular} } & 13 & 9000 + 2280/12,600 = 0.89 & 605/(605 + 9000) = 0.06 & 605 & 9000 \\  & 30 & 9714 + 2885/12,600 = 0.99 & 0/(0 + 9714) = 0 & 0 & 9714 \\  & 41 & 9500 + 2480/12,600 = 0.95 & 405/(405 + 9500) = 0.04 & 405 & 9500 \\  & 13 & 1500 + 400/2470 = 0.76 & 344/(344 + 1500) = 0.19 & 344 & 1500 \\ UNSW\_NB2015 & 35 & 1701 + 744/2470 = 0.99 & 25/(25 + 1701) = 0.01 & 25 & 1701 \\  & 49 & 1525 + 630/2470 = 0.87 & 201/(201 + 1525) = 0.11 & 201 & 1525 \\  & 13 & 492,176/565,562 = 0.87 & 62,736/(62,736 + 434,615) = 0.123 & 62,736 & 443,615 \\ CIC\_IDS2017 & 40 & 564,844/565,562 = 0.99 & 396 + 453,916 + 0.0008 & 396 & 453,916 \\  & 78 & 524,106/556,562 = 0.92 & 24,741/(24,741 + 437,550) = 0.053 & 24,741 & 4,377,550 \\  & 13 & 492,180/572,302 = 0.86 & 72,634/(72,634 + 543,813) = 0.1178 & 72,634 & 543,813 \\  & 40 & 564,921/565,203 = 0.995 & 401/(401 + 513,918) = 0.000779 & 401 & 513,918 \\  & 78 & 524,204/563.660 = 0.93 & 30,132/(30,132 + 473,271) = 0.0598 & 30,132 & 473,271 \\  & 91 & 513,987/577,513 = 0.89 & 17,543/(17,543 + 514,821) = 0.0329 & 17,543 & 514,821 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: HABBA accuracy by various FSs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**id** & **Attack Type** & **F-Score** & **Accuracy** \\ \hline
0 & DDoS & 99\% & 98\% \\
1 & SQL injection & 99\% & 100\% \\
2 & Brute Force & 100\% & 98.4\% \\
3 & DoS Hulk & 99\% & 99\% \\
4 & DoS Golden Eye & 99\% & 98.9\% \\
5 & Botter & 100\% & 100\% \\
6 & DoS Slow Loris & 99\% & 99\% \\
7 & FTP Patator & 99\% & 98\% \\
8 & Web Attack & 99\% & 100\% \\
9 & XSS & 100\% & 100\% \\
10 & Infiltration & 99\% & 99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Attack F-score and accuracy when using the reimplemented system. all classes using the reimplemented system are shown in Table 8, with Brute Force, XXS, and Botnet achieving 100%. This suggests that the number of characteristics is the right one for recognizing all types of attacks.
# A Simplified CityGML-Based 3D Indoor Space Model for Indoor Applications
## 3 Simplified 3D Indoor Space Model (3 DSISM) Based on CityGML
### Simplified Representation for Indoor Spaces
#### 3.2.1 Principles for the Simplified Representation  
Indoor navigation requires not only geometric data with certain precision and clear semantic definitions but also topological relationships among indoor spaces. To meet the requirements of indoor navigation, this paper proposes the following principles for the simplified description of indoor spaces.  
**Principle 1:** Appropriate accuracy. The indoor space environment of a high-rise building is exceptionally complex. The simplification of indoor spaces is a generalized and abstract expression of building components; thus, there is consequent loss in the geometric accuracy of indoor spaces. For indoor applications, such as indoor navigation, the locations in indoor spaces are required to be accessed; thus, simplification of indoor spaces should ensure the accuracy of indoor positioning. Currently, the accuracy of indoor positioning varies depending on the signal transmitted, principle of positioning and indoor environment. As an example, the low cost, high accuracy Ultrawideband (UWB) can achieve an average indoor positioning accuracy of 15 cm [52]. In addition, the CityGML LODs specify different accuracies for spatial features representation. In CityGML LOD4, the proposed absolute accuracy requirement for a 3D point should be 0.2 m or less. Therefore, the geometric simplification of building components should meet the corresponding accuracy requirements.  
**Principle 2:** Reflection of the characteristics of indoor spaces. The geometric properties of building elements may change after geometric simplification, which may result in changes in the shape of indoor spaces. Simplification of indoor spaces should, therefore, reflect the geometric characteristics and layouts of indoor spaces, following the indoor LOD definition mentioned above.  
**Principle 3:** Appropriate semantic description. Changes in the geometric properties of building components can result in inconsistencies in semantics after simplification. The semantic descriptions of building elements and indoor spaces should keep the original semantic properties, and if necessary, appropriate semantics should be reassigned according to the semantic classes of the LOD description in Section 3.1.2.  
**Principle 4:** Topological consistency. The topological relationships of indoor spaces should be taken into account to avoid topological inconsistency, such as the topological relationship of adjacent rooms on the same story and/or upstairs and downstairs.  
**Principle 5:** Applicability. Different buildings have different building components and spatial shapes. The simplified descriptions of building components should have a certain applicability, not just for a single building.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 3 Experiments and Results
### Results of Indoor Navigation Network Extraction  
The results of the extracted indoor navigation network from the experiments are shown in Figures 14 and 15. In the extracted indoor navigation network, blue node represents general space such as office, conference room, and storage; red node represents connection space such as door and virtual door; and yellow node represents transition space such as hallway and stairwell. The edges connecting the nodes are shown in green.  
Figure 11: The self-collected 3D data and its corresponding HoloLens 2 movement trajectory: (**a**) floor 0, (**b**) floor 1, (**c**) floor 2, (**d**) floor 3, and (**e**) floor 4. The trajectory starts at red and ends at blue.  
Figure 12: Color-labeled visualization of basic indoor units of our self-collected data: (**a**) floor 0, (**b**) floor 1, (**c**) floor 2, (**d**) floor 3, and (**e**) floor 4. In this case, stairwells are depicted in orange, elevator lobbies in yellow, hallways in blue, and kitchens in green.  
Figure 13: Indoor navigation elements were extracted from various spaces in our self-collected dataset, with floor 1 as an example.  
To verify that the distribution of nodes and edges corresponds to the actual spatial locations, we used point cloud data as a background map and overlaid the extracted navigation network onto it for visualization. This way provides an intuitive way to check if the extracted navigation network aligns with the actual spatial layout.  
Figure 14: The results of the extracted indoor navigation network with S3 DIS dataset: (**a**) Area_1, (**b**) Area_4, and (**c**) Area_6.  
Figure 15: _Cont._  
Additionally, to validate the efficiency of the indoor navigation network extraction algorithm, we recorded the runtime for extracting navigation networks across different datasets. For each scene, we performed the network extraction algorithm five times and took the average runtime as the evaluation reference. Moreover, we recorded the total input file size and output JSON file size for each scene. All navigation network extractions were conducted on a standard macOS laptop with a 2.3 GHz Quad-Core Intel Core i5 processor and 8 GB of RAM. The statistics are shown in Table 2.
# Stabilization Methodology in Foundation Soils by ERT-3D Application in Estepona, South Spain
###### Abstract  
The paper proposes a novel methodology for the stabilization of shallow foundations, with a simplified model combined with 3D electrical resistivity tomography (ERT-3D and consolidation injections. To determine its usefulness, the method has been applied in a case located in Estepona (southern Spain). The chosen tomography model is the dipole-dipole configuration, with an optimized distance between electrodes of 0.80 m for a better visualization of the foundation subsoil; with this parameterization, a total of 72 electrodes were installed in the analyzed case. In this work, the depth of the anomaly in the building's supporting subsoil was detected ranging from 2.00 m to 3.90 m deep. The study also delineates areas of high resistivity variations (50-1000 \(\Omega\) m) in the middle and eastern end of the field. These data have been validated and corroborated with a field campaign. The results of the ERT-3D monitoring are presented, once the investment data has been processed with the RES3 DINV software, from the beginning to the end of the stabilization intervention. The novelty occurs with the interaction between the tomography and the foundation consolidation injections, until the final stabilization. This is a very useful methodology in case of emergency consolidation, where there is a need to minimize damage to the building. Thus, people using this combined system will be able to practically solve the initial anomalies of the subsoil that caused the damages, in a non-invasive way, considerably lowering the value of the resistivities.  
Keywords:electric tomography; three-dimensional; electrodes; seat control; foundations; stabilization methodology +
Footnote †: journal: Applied
## 3 Results and Discussion
### Application of the Method to Surface Water Samples  
The developed method was tested on both river and lake water samples and its accuracy was evaluated. Spiking of formaldehyde was performed in four replicates at each of three concentrations, 0.5 ng mL\({}^{-1}\), 5.0 ng mL\({}^{-1}\) and 10.0 ng mL\({}^{-1}\). The extraction efficiency of the method was calculated based on comparing the amount of formaldehyde extracted from the spiked samples with that determined in the non-spiked samples. However, as there was no formaldehyde detected in the lake and river water samples only the spiked amount was used for the calculation of recovery. A satisfactory recovery of formaldehyde from tested samples was shown (relative recovery from 78.0 to 99.1%). The repeatability for the medium and high levels was good (RSD in the range of 3.2-9.9%) but it was slightly worse for the low level--in the range of 14.6-18.8% (Table 3). Nevertheless, lower repeatability at the low level is acceptable because it is at the LOQ of the method. Thus, the developed method can be considered suitable for the determination of formaldehyde in surface waters.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} OFDM & orthogonal frequency division multiplexing \\ IM & index modulation \\ IRCS & integrated radar and communication systems \\ UAV & unmanned aerial vehicle \\ MIMO & multiple-input multiple-output \\ HAD & hybrid analog-digital \\ DL & deep learning \\ ICI & intercarrier interference \\ IFFT & inverse fast Fourier transform \\ CP & cyclic prefix \\ GI & guard interval \\ ISI & intersymbol interference \\ DFT & discrete Fourier transform \\ CRB & Cramer-Rao bound \\ SMC & sequential Monte Carlo \\ SNR & signal-to-noise ratio \\ PSLR & peak sidelobe ratio \\ MSE & mean-square error \\ BER & bit error rate \\ ML & maximum likelihood \\ MMSE & minimum mean square error \\ \end{tabular}
## 2 Background and Preliminary
### Anomaly Types in MTS
#### 2.2.1 Point-Wise Anomalies  
Global point anomaly: Global point anomaly is an anomaly in which a single data point is significantly different from other data. They are typically spikes in the entire sequence. Considering a threshold \(\delta\), it can be described as:  
\[|x_{t}-\hat{x}_{t}|>\delta \tag{4}\]  
Figure 1: The overall classification framework diagram for multivariate time series anomaly types.  
where \(x_{t}\) is expected value and \(\hat{x}_{t}\) is the output of the model, \(\delta=\lambda*G(\mathbf{x}_{t})\), \(G\) represents the variance of the context over \(\mathbf{x}_{t}\), and \(\lambda\) is the threshold adjustment coefficient.  
Local point Anomaly: Local point anomaly refers to abnormal situations that occur within specific environments or contexts. Detecting local point anomaly typically involves analyzing both the point and its associated contextual information to identify behavior that deviates from the expected norms within a given context. This type of anomaly can be defined as:  
\[\delta\approx\lambda*L(\mathbf{x}_{t-k}:\mathbf{x}_{t+k}) \tag{5}\]  
where \(\mathbf{x}_{t-k}:\mathbf{x}_{t+k}=(x_{t-k},x_{t-k+1},...,x_{t+k})\) refers to the context of the data point \(x_{t}\) with a window size \(k\), \(L\) represents the variance of this local context. An example of point anomalies are shown on Figure 2a, global point anomalies typically stand out against the backdrop of the entire time series, while local point anomalies contrast more with the data points immediately adjacent to them.
## 3 Results and Analysis  
### Temperature-Time Curve  
The temperature inside the furnace was controlled to increase at a rate of 8 \({}^{\circ}\)C/min at the start stage. Once the furnace temperature reached the target test temperature, it was kept constant by the furnace controller. The temperature-time curve of the furnace is shown in Figure 5.  
The temperature-time curve of the bond zone between rebar and SFRC is shown in Figure 6. Because concrete is a poor conductor of heat, there was a significant temperature difference between the bond zone and the furnace at the start stage of the test, which gradually decreased with the increase in furnace temperature. The temperature of the bond zone between rebar and SFRC remained essentially constant after 6 h.  
The following can be observed in Figure 6.  
1. When the heating time reaches about 100 min, there is an apparent horizontal section of the temperature time curve at the target test temperature of 400 and 600 \({}^{\circ}\)C. The temperature of the horizontal section is approximately 140-180 \({}^{\circ}\)C. The reason for this is that the free water inside the concrete evaporates when the specimen's internal temperature reaches the boiling point of water. Some of the heat from the specimen is absorbed due to the evaporation of free water; thus, the temperature of the bond zone increases slowly. The temperature rate of the bond zone gradually increases after water evaporation in the specimen is complete.
2. The temperature-time curves for NC and SFRC are similar for each high-temperature environment. As \(V_{f}\)increases, the temperature between rebar and SFRC rises more rapidly and the temperature difference between the bond zone and furnace is gradually reduced.  
Figure 5: Temperature–time curve of the furnace.
# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 2 System Model
### Performance Metrics
More specifically, the revenue comes from the service data rate provided to the users and the expenditure mainly comes from the number of RBs used and the amount of computing resources used, i.e.,  
\[\psi_{R}=\varrho\sum_{s\in\mathcal{S}}r^{s} \tag{20}\]  
\[\psi_{E}=\sigma_{1}\sum_{s\in\mathcal{S}}\sum_{u\in\mathcal{U}_{s}}\sum_{k\in \mathcal{K}}x_{s,u}^{k}+\sigma_{2}\sum_{s\in\mathcal{S}}\sum_{j=1}^{t_{s}}c_{j }^{s} \tag{21}\]where \(r_{s}\) as defined in (4) is the total achieved rate of Slice \(s\); \(c_{j}^{s}\) as defined in (9) is the computing resource used by the \(j\)-th VNF of Slice \(s\); \(\sum_{s\in\mathcal{S}}\sum_{u\in\mathcal{U}_{s}}\sum_{k\in\mathcal{K}}x_{s,\mu}^ {k}\) is the total number of RBs used, with \(x_{s,\mu}^{k}\) defined in (1); \(\varrho\) represents the unit price of the service rate; and \(\sigma_{1}\) and \(\sigma_{2}\) represent the unit price of an RB and the computing resources, respectively.
## 2 Materials and Methods
### Test Equipment  
The layout of the test system is shown in Figure 2, which consists of the test loading system and the data image acquisition system. The TAW-2000 (Chang Chun City Chao Yang Test Instrument Co., Ltd., Changchun, China) electro-hydraulic servo rock test system of the College of Civil Engineering of Liaoning Technical University can complete rock tests such as the uniaxial compression test, triaxial compression test, cyclic loading test and seepage test. It is an advanced rock test system both at home and abroad, which can automatically collect the stress-strain data of sandstone over the whole process of the uniaxial compression test. The stiffness of the testing machine reaches 1600 KN, the maximum confining pressure that can be applied is 70 MPa and the measuring range of displacement is \(\pm\)4 mm, which can fully meet the requirements of this test. The SMT DIC-3D (Beijing SMAT Technology Co., Ltd., Beijing, China) full-field strain measurement and analysis system is composed of the surface speckle image acquisition system (CCD camera) and the related calculation program. The strain measurement range of the system is 0.01-1500%, and the strain measurement accuracy is \(\leq\)20 \(\mu\epsilon\). The image acquisition rate is 2 frames/s, and the image resolution is 1920 pixels \(\times\) 1280 pixels. During the test, the CCD camera was adjusted so that the camera lens was parallel to the surface of the specimen, and the camera lens was 0.8 m away from the observation surface of the specimen. The time of the rock loading testing machine was strictly consistent with the time of CCD camera data acquisition.
## 4 Parameter Estimation
 \tag{23}\]  
Correspondingly, \(\mathcal{I}^{-1}(\hat{\Theta})\) can be obtained as follows:  
\[\mathcal{I}^{-1}(\hat{\Theta})=\frac{1}{\hat{\mathcal{I}}_{11}\hat{\mathcal{I }}_{22}-\hat{\mathcal{I}}_{12}\hat{\mathcal{I}}_{21}} \begin{pmatrix}\hat{ \mathcal{I}}_{22}&-\hat{\mathcal{I}}_{12}\\ -\hat{\mathcal{I}}_{21}&\hat{\mathcal{I}}_{11}\end{pmatrix}  \tag{24}\]  
Since the hypothesis of interest is only involved with the parameter \(\eta\), we could carry out the [PERSON]'s test procedure easily with the following test statistic:  
\[\sqrt{W}=\frac{\hat{\eta}-1}{\sqrt{\text{Var}(\hat{\eta})}}, \tag{25}\]  
where, from (16) and (17), \(\text{Var}(\hat{\eta})=\frac{\hat{\mathcal{I}}_{11}}{\hat{\mathcal{I}}_{11}\hat{ \mathcal{I}}_{22}-\hat{\mathcal{I}}_{12}\hat{\mathcal{I}}_{21}}\). We conducted the simulations to assess the type 1 error performances and the power performances of both tests under different scenarios. For all the simulation scenarios, r = 10,000 samples were generated from the EEP density provided in (11). The R package'mist' [25] was utilized to generate the random samples. To evaluate the Type 1 error rates of the two GoF tests, we designed twelve different simulation scenarios with different \(\theta\) values and sample sizes. The details of the simulation scenarios are listed in Table 1. Table 1 shows the type 1 error performance of both tests under twelve different scenarios. We noticed that both tests can control the type 1 error rate well for all the simulation scenarios. To assess the power performances of the two GoF tests, we designed various simulation scenarios. The simulation scenarios are described as follows:  
* Three different sample sizes: \(n=50,100,200\). * Four different true \(\theta\) values: \(\theta=0.01,0.1,1,10\). * Eleven true \(\eta\) values: \(\eta=0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5\). In total, we generated 132 simulation scenarios. The power comparison of two tests are provided in Figure 6. It can be observed that the power of the tests drops when \(\eta\) increases from 0.5 to 1 and rises when \(\eta\) increases from 1 to 1.5. When the sample size becomes larger, the power of the tests increases. It is also noteworthy that, when the sample size is 50 or 100, the power of the LRT was slightly better than [PERSON]'s test. When the sample size increases to 200, both tests demonstrated similar performance. However, the value of \(\theta\) did not affect the power of both tests significantly, under all the simulation scenarios. Figure 6: Power comparison of the Likelihood ratio test and asymptotic [PERSON]’s test (the red line represents the asymptotic [PERSON]’s test, and the black line represents the LRT).
## 3 Results and Discussion
### FE-SEM Analysis  
FE-SEM images of various MnO\({}_{2}\) nanostructures are shown in Figure 4. It is clearly shown in these images that all the synthesized MnO\({}_{2}\) phases were in the nano range.  
Figure 4a,b show the SEM images of \(\alpha\)-MnO\({}_{2}\), Figure 4c,d show images of \(\beta\)-MnO\({}_{2}\), and Figure 4e,f show images of \(\gamma\)-MnO\({}_{2}\). The FE-SEM images of \(\alpha\)-MnO\({}_{2}\) revealed the formation of nanowires having lengths in the micrometer range (average 2.368 \(\upmu\)m) with a 20-30 nm diameter. For \(\beta\)-MnO\({}_{2}\), nanorod-like structures with a length of 1.6 \(\upmu\)m were revealed.  
In \(\gamma\)-MnO\({}_{2}\), these nanorods folded into urchins with average diameters of 5 \(\upmu\)m, as shown in Figure 4e, and this may be due to the mild temperature conditions. The size of the phase morphology was calculated using ImageJ software Version 1.44. Temperature and synthesis parameters affect various morphologies of MnO\({}_{2}\)[20]. As the temperature increased, these urchins unfolded into nanorods or nanowire-like structures, as shown for \(\alpha\)- and \(\beta\)-MnO\({}_{2}\). At high temperatures, the nanoparticles' energy increased, encouraging the nucleation route; hence, these nuclei clustered into longer nanofibers. These longer fibers have more active sites for intercalation/de-intercalation of charges on their surface. Therefore, from the surface morphology, we can comment that \(\alpha\)-MnO\({}_{2}\) had a higher electrochemical performance compared to \(\beta\)- and \(\gamma\)-MnO\({}_{2}\).