esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Classifiers for Binary Detection and Multiple Faults
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Attitude** \\ **Mode** \\ \end{tabular} } & \multirow{2}{*}{**Sensor**} & \multicolumn{4}{c}{**SVM**} & \multicolumn{4}{c}{**ANN**} \\  & & & **Quadratic** & **Cubic** & **Gaussian** & **Narrow** & **Medium** & **Large** & **Bilayered** & **Trialyered** \\ \hline \multirow{4}{*}{Pointing} & CYRO & 89.6 & 92.5 & **95.5** & **92.5** & 97 & 97 & 95.5 & **98.5** & 94 \\  & MAG & 94 & 91 & 91 & 91 & 95.5 & 94 & 95.5 & 95.5 & 95.5 \\  & SUN & 91 & 91 & 71.6 & 89.9 & 94 & 94 & 91 & 91 & 94 \\  & ACTS & 89.6 & 92.5 & 91 & 79.1 & 91 & 91 & 92.5 & 95.5 & 95.5 \\ \hline \multirow{4}{*}{Slew} & CYRO & 89.6 & 92.5 & 92.5 & 82.1 & 88.1 & 91 & 91 & 88.1 \\  & MAG & 86.6 & 97 & **95.5** & 83.6 & 95.5 & **97** & 95.5 & 92.5 & 94 \\  & SUN & 94 & 100 & 85.1 & **92.5** & 95.5 & **97** & 94 & 97.7 & **98.5** \\  & ACTS & 92.5 & 94 & 92.5 & 83.6 & 95.5 & 92.5 & 92.5 & 97 & 92.5 \\ \hline \multirow{4}{*}{No Control} & CYRO & 89.6 & 94 & 88.1 & 91 & 88.1 & 92.5 & 91 & 91 & 89.6 \\  & MAG & 89.6 & 95.5 & 91 & 88.1 & 95.5 & 95.5 & 92.5 & 95.5 & 95.5 \\ \cline{1-1}  & SUN & **95.5** & 95.5 & 61.6 & 91 & **98.5** & 95.5 & **97** & 95.5 & 92.5 \\ \cline{1-1}  & ACTS & - & - & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}  
* Results reported in bold are the highest accuracies reached by each classifier in this example.  
\end{table} 
Table 6: Binary classifiers training: test batch % accuracy 1 on a 2-class classification. Each sensor in each attitude mode is validated and tested on a balanced dataset of 672 total entries.
## 4 Performance Evaluation
### Simulation Setup
#### 4.2.4 Node's Number Impact  
This approach relies on the network contextual information. The node's number impact study is crucial as it affects the network density, \(\lambda\). In fact, this latter is a key parameter studied in different WSN applications. [PERSON] [38] proposed Equation (22) to calculate \(\lambda\) as  
\[\lambda=\frac{N\pi R^{2}}{A}, \tag{22}\]  
where \(N\) is the number of nodes in area \(A\), and \(R\) is the nominal range of each node. However, the authors in [39] propose a more precise equation as  
\[\lambda=\lim_{|A|\to 0}\frac{N(A)}{|A|} \tag{23}\]  
where density is measured in nodes per m\({}^{2}\).  
A higher node density introduces less distance between nodes; thus, the impact of this network parameter is crucial in this study since it affects the number of neighbor nodes within a communication range.  
The CRSSA is tested with a different number of nodes without changing the deployment area. Boxplots in Figure 6 show the simulation results for different \(N\), by illustrating how errors are extended. Simulation parameters are listed in Table 3. As observed, the maximal NRMSE value does not exceed 0.065 for \(N=100\). An important observation is drawn from Figure 6: the median localization error denoted by red lines remains stable when varying \(N\), where it changes from 4.1% to 4.2% of the range. Once more, the results indicate a plateau in performance gains and validate the effectiveness of the approach.
## 4 Results and Discussion
### Radiological Hazards Parameters
#### 4.3.1 Radium Equivalent Activity (Raeq)  
The radium equivalent content (Raeq) can be used to detect the nonuniform distribution of \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K in the Wadi sediments. The values of Raeq must be smaller than 370 Bq kg\({}^{-1}\), which identify the same annual dose of 10, 7, and 130 Bq kg\({}^{-1}\) for \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K, respectively. The data of Raeq can be computed by the following Equation (1) [2,46]:  
\[\text{Raeq}=\text{A}_{\text{U}}+1.43\ \text{A}_{\text{Th}}+0.077\ \text{A}_{\text{K}} \tag{1}\]  
where A\({}_{\text{U}}\), A\({}_{\text{Th}}\), and A\({}_{\text{K}}\) refer to the activity concentration of \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K, respectively. Based on Equation (1), the Raeq mean value (255.7 Bq kg\({}^{-1}\)) of the investigated Wadi sediments was observed to be less than the approved level (370 Bq kg\({}^{-1}\)) for public dose. However, the maximum value (433.8 Bq kg\({}^{-1}\)) was found to be greater than the approved level, which indicates the derived Wadi sediments of the surrounding rocks contain high concentrations of uranium and thorium, while the minimum value 107.6 Bq kg\({}^{-1}\)was detected in the investigated Wadi sediments.
# A Novel Framework for Cross-Cluster Scaling in Cloud-Native 5G NextGen Core
## 3 Comparison between ETSI MANO and Kubernetes
### Scaling in Kubernetes Multi-Cluster and Multi-Cloud Deployments  
From security and compliance considerations in large environments such as a tele-com network, one Kubernetes cluster is generally not sufficient; hence, a multi-cluster infra management solution is needed. There are many existing open-source and vendor-provided solutions to manage multiple Kubernetes clusters, i.e., Rancher, OpenShift [66], Crossplane [67], Cluster.API, etc.  
Liqo [15] is an open-source project enabling dynamic Kubernetes multi-cluster topologies. Liqo leverages the Liquid computing paradigm [68] for scalability purposes in both hybrid-cloud (i.e., the combination of on-premise and public cloud) and multi-cloud approaches, which aim for high availability, geographical distribution, and cost-effectiveness, enabling telcos to become vendor agnostic in terms of cloud provider. Liqo makes use of the virtual node abstraction as an extension of the _Virtual Kublet_ project [16]. In Kubernetes, the kubelet is the primary node agent, responsible for registering the node with the control plane and handling the scheduling of the pods. The virtual upelet replaces a traditional kubelet for a physical node through the standard Kubernetes APIs with both the local and the remote clusters [69].  
Moreover, the virtual node summarizes and abstracts the amount of resources shared by a given remote cluster. In terms of vertical scaling, one key role that the Virtual Kublet has is to offload the local pods scheduled on the virtual node to the remote cluster and to allocate the amount of resources (e.g., CPU, memory) shared by the remote cluster. It also automatically propagates the negotiated configuration (Services, ConfigMaps, Secrets, Storage) into the capacity required for the proper execution of the offloaded workloads, a mechanism that is called _resource reflection_. All the available resources that exist in a particular namespace and are selected for offloading are automatically propagated into the corresponding twin namespaces created in the selected remote clusters.  
Liqo also has an incorporated failover mechanism for the custom resource, i.e., _ShadowPod_ to ensure remote pod resiliency even in case of temporary connectivity loss between the local and remote clusters. The local copy of each resource is considered the source of trust leveraged to synchronize the content of the reflected remote shadow copy. The Virtual Kublet also ensures a remapping of certain information (e.g., endpoint IPs) that guarantees the uniqueness of different configurations for different clusters.
## 3 System Design
### Layered Topology Optimization  
The system constructs a layered overlay based on node reputation values, where high-reputation nodes are strategically positioned closer to the content source to form a stable transmission backbone network [29]. The objective function for the simple chain topology optimization is  
\[\min\sum_{i=0}^{n}\sum_{j=0}^{n}w_{ij}\cdot d_{ij}\cdot(1-R_{j}) \tag{9}\]  
with the constraints  
\[\begin{cases}\sum_{j=1}^{n}w_{ij}=1\\ w_{ij}\in\{0,1\}\end{cases} \tag{10}\]  
where \(w_{ij}\) denotes the traffic weight from node \(i\) to node \(j\), whose potential values are just 0 or 1; \(d_{ij}\) is the distance reflected by factors such as ping latency, physical distance, bandwidth, and network accessibility; and \(R_{j}\) is the reputation value of node \(j\). By solving this optimization problem, the system can construct an optimal transmission topology that considers both network performance and node reliability. This cost function balances path distance and node reliability. We also tested alternative forms using hop count or delay-only metrics, but the proposed function showed superior performance in maintaining stream quality under dynamic conditions.
## 3 Results and Discussion  
Results of the effect of the studied factors on the surface tension are shown in Table 4. Application of both adjuvant types resulted in a decrease in surface tension. This reduction was to almost half of its original value (control water sample without adjuvants) when adding the recommended concentration (100%) of the adjuvants. This trend was observed for both types of adjuvants, and for both types of water. Decreasing the value of surface tension would improve the deposition and wetted area, and the latter would enhance the spray coverage on the target, as reported in [11]. There was a slight numerical difference in the surface tension values between the two types of water, with or without adjuvants. The results of the droplet size characteristics are shown in Table 5. Numerically, there was a slight decrease in the values of \(\mathrm{D}_{\mathrm{v0.5}}\) after adding both types of adjuvants (especiallythe HY adjuvant) to the WS-type water, when compared to the control treatment (without adjuvant). Although this decrease was small, it may be important because it occurs on droplets below 200 \(\upmu\)m size. This droplet size is responsible for spray drift. The opposite trend was observed when adding both types of adjuvant to the WUP-type water, except when adding the HY adjuvant with 50% concentration--where the D\({}_{\text{V0.5}}\) slightly decreased. It is known that decreasing the surface tension of a certain spray results in a decrease in the droplet size (D\({}_{\text{V0.5}}\)) of this spray. Here, the decrease in surface tension contributes to the reduction in the droplet size. At the same time, increasing the density of the liquid causes the formation of larger droplets. These outcomes demonstrate the variable influence of the adjuvant on the drop formation [6; 7]. In examining the data listed in Table 4, it is noticeable that there were decreases in surface tension of 29 and 28% when adding HY and OL adjuvants (with a concentration of 50%), respectively, to the WS water type, whereas decreases of 50 and 49% were observed when HY and OL adjuvants were added (with a concentration of 100%), respectively, to the WS water type. A similar percent decrease in the surface tension values resulted from using the WUP water type. When the concentration of 50 or 100% was employed, the differences in the percent decrease in the surface tension values were not reflected in the droplet size (D\({}_{\text{V0.5}}\)) results, as shown in Table 5. Here, we observed a noticeable decrease in the values of D\({}_{\text{V0.5}}\) when both types of adjuvants with 100% concentration were added, as compared to the 50% concentration, except for OL in the WS water type, as D\({}_{\text{V0.5}}\) was found to be less sensitive to the variation in the OL additive. This type of adjuvant changes the fine droplets in the D\({}_{\text{V0.1}}\) range by emulsifying the liquid with oil, which increases their diameter and stabilizes D\({}_{\text{V0.5}}\) However, this tendency was reflected in the results of SMD values, which are relevant in applications where the active surface area is important. When we added an oil adjuvant
## 3 Data-Based Framework for Analyzing Sleep Data
#### Integration of the LSTM Model into the Framework for Analyzing Sleep Data  
In this expanded version of the framework, an LSTM (Long Short-Term Memory) model is integrated to enhance the detection of apnea events and better understand the temporal dynamics of sleep disorders. The LSTM model is particularly well suited for time-series data like polysomnographic signals, which involve sequences of data points over time. Appendix A gives a detailed explanation of LSTM model and architecture.  
To integrate the LSTM model, the existing framework is first adjusted to ensure compatibility with sequential data inputs. This involves preprocessing the polysomnographic signals by extracting relevant features such as heart rate, oxygen saturation, and respiratory rate, which are then formatted into sequences that the LSTM model can process. The data are normalized and split into training and test sets with temporal dependencies taken into account.  
The LSTM model is then trained to recognize patterns indicative of apnea events, which can be difficult to detect with traditional methods. This new addition enables the framework to detect not only anomalies in isolated data points but also to capture complex temporal relationships between various sleep parameters. The result is a more accurate and dynamic system for monitoring sleep disorders, providing personalized insights and improving the overall diagnostic process.
## Appendix A Covariance Matrices \(R_{k}^{T}\) and \(Q_{k}^{T}\)  
In this appendix, the covariance matrices \(R_{k}^{T}\) and \(Q_{k}^{T}\) used in this work are declared. Regarding the matrix \(R_{k}^{T}\), the first three values of the diagonal are related to the covariance of the velocity of the wheel (assuming contact with the ground). This covariance was calculated from a histogram of the steady-state response of the velocity. The value obtained is 0.014. The fourth value of the diagonal is related to the orientation output, and the rest of values are connected to the position measurements. The covariances of these variables were obtained by following the manufacture's manual (mentioned in Section 4.2) for the absolute orientation sensor and for the beacon-based indoor positioning system, where the precision of these devices is indicated. The values obtained are 0.005 and 0.0025, respectively. Finally, the matrix \(Q_{k}^{T}\) is tuned by hand, according to the desired uncertainty for the model.  
\[Q_{k}^{T}=\begin{bmatrix}0.025&0&0&0&0&0\\ 0&0.025&0&0&0&0\\ 0&0&0.025&0&0&0\\ 0&0&0&0.1&0&0\\ 0&0&0&0&0.1&0\\ 0&0&0&0&0.1\end{bmatrix}\]
## 4 Case Study
### Remarks
This means that, for the same hysteretic model, the EDR predictions obtained with the TH method are essentially independent of the code used to select the reference cycle. By analyzing the EDR trend as a function of the hysteretic variable, for both the Capacity Spectrum Methods and the TH method, it is possible to notice that (i) the EDR shows a quasi-linear trend in the range 0.3 < \(\beta_{\textit{-HYS}}\) < 1.2, (ii) the EDR shows a spike in the range 1.2 < \(\beta_{\textit{-HYS}}\) < 1.5, and (iii) the energy dissipation due to the roof diaphragm is not significant once the EDR exceeds \(\beta_{\textit{-HYS}}\) = 1.5. In fact, in the range 1.5 < \(\beta_{\textit{-HYS}}\) < 2.1, the EDR is almost constant, or it decreases, as occurs for ATC-40 and the TH method referring to ATC-40. This means that the design of an over-stiffness roof diaphragm does not represent a useful seismic solution to improving the transversal response of a historical single-nave church. This can be also extended to historical churches with a central nave and two lateral \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Model** & **EDR [\%]** \\ \hline FEMA 356 & SD & \(\simeq\)16.8 \\ ATC-40 & SD & \(\simeq\)25.0 \\ TH in relation to FEMA 356 & SD & \(\simeq\)15.2 \\ TH in relation to ATC-40 & SD & \(\simeq\)14.8 \\ FEMA 356 & SSD & \(\simeq\)16.0 \\ ATC-40 & SSD & \(\simeq\)16.3 \\ TH in relation to FEMA 356 & SSD & \(\simeq\)12.5 \\ TH in relation to ATC-40 & SSD & \(\simeq\)12.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: EDR values for the case study for \(\beta_{\textit{HYS-opt}}\) = 1.40.  
aisles when the first vibration mode shape is characterized by a significant percentage (about 50-60%) of the total mass of the construction. The obtained EDR is related to the energy dissipation in the panel-to-panel and panel-to-wall steel connections. This energy dissipation is caused by the plasticization of the steel elements, which can be damaged during the earthquake. Therefore, after the seismic event, the steel connections should be inspected to verify their condition. Two scenarios can occur. The first scenario concerns a connection with only cylindrical shank elements (e.g., steel screws). In this case, the plasticization of the screws also implies the damage of the surrounding wood. Therefore, after the seismic event, a careful inspection of all of the CLT panels should be carried out and, in the case of panels that are too damaged, it may be necessary to replace some of them. In the second scenario, the connection usually also includes a thick perforated steel plate as a coupling strip. In this case, the connection behaves as an elastic-plastic device, where the screws remain in the elastic field, while the energy dissipation is localized in the perforated steel plate. As a consequence, after the post-earthquake inspection, only the substitution of the steel plate may be required. In both cases, the amount of damage in the masonry walls could be limited by the hysteretic behavior of the retrofitted roof structure.
## 3 Modeling of a Transmission Tower with a PTMD
### Simulation of the PTMD  
A PTMD can be obtained by the combination of a TMD and an impact damper, which has double the vibration reduction characteristics. The proposed PTMD is shown in Figure 6. The PTMD includes a cable, a mass block, a limiting device, and viscoelastic material, and the mass block is covered with viscoelastic material. The PTMD device is installed at the top of the tower by using the connecting plate, and the connecting plate is fixed on the angle steel of the tower by bolts. When the earthquake loads are small, the PTMD can be regarded as TMD. When the earthquake loads are large enough, the mass block will impact on the limiting device. Due to bounding energy dissipation, the PTMD has double the reduction characteristics, and the vibration reduction effect depends on the mass block, collision, and viscoelastic material.  
The PTMD is simulated in ABAQUS. The mass ratio is 2%, and the mass of mass block is 404.7 kg. The mass block and limiting device are simulated by S3R elements. The spring element is adopted to simulate the cable, and the axial stiffness of the spring is 1900 kN/m. The axial stiffness of the spring is large enough so that the axial deformation can be ignored. The gap between the mass block and the limiting device is 0.02 m. The Mooney-Rivlin model is used for the viscoelastic material, and the  
Figure 5: Vibration mode shapes of the transmission tower. (**a**) The first modal shape in the \(Y\) direction; (**b**) The second modal shape in the \(Y\) direction; (**c**) The third modal shape in the \(Y\) direction; (**d**) The first modal shape in the \(X\) direction; (**e**) The second modal shape in the \(X\) direction; (**f**) The third modal shape in the \(X\) direction.  
mechanical constant C1 and C2 are 3.2 \(\times\) 10\({}^{6}\) Pa and 8.0 \(\times\) 10\({}^{5}\) Pa, respectively. The contact is defined as the surface-to-surface contact, and the penalty contact method is used as the contact algorithm. The length of the cable is determined by the natural period of the structure, which can be obtained from \(l=T^{2}g/4\pi^{2}\). Design guidelines of the optimal parameter of the PTMD are described in Figure 7.  
To verify the accuracy of the finite element simulation of the PTMD, the finite element model of the transmission tower with the PTMD is compared to [PERSON]'s simplified model [16]. Figure 8 shows the time history curve of the top displacement of the transmission tower with the PTMD under the conditions of the El Centro earthquake. It can be seen that the two time history curves are slightly different, and the trend and maximum displacement are the same. Therefore, the finite element model of the PTMD is more accurate and can be used for further analysis.  
Figure 8: Displacement response of the transmission tower with the PTMD under the conditions of the El Centro earthquake.  
Figure 6: Schematic diagram of the PTMD.  
Figure 7: Design guidelines of the optimal parameters of the PTMD.
# Gait Phase Estimation Based on User-Walker Interaction Force
## 4 Experiment
### Data Analysis  
In this study, we consider a normalized gait phase as the reference gait phase, which linearly increases from 0 to 2\(\pi\) in one step cycle. The phase of one step instead of one stride was used to evaluate the estimation performance because we considered asymmetric gait. The instant when the force between the participant's heel and the ground sharply increased was set as the HS instant. The instant when the estimated gait phase reached 2\(\pi\) was considered as the estimated HS instant. Figure 8 shows the difference between the reference and estimated gait phases (i.e., estimation error), and the average root mean square error (RMSE) across the experiment was used to quantify the accuracy of gait phase estimation. The HS error determines the estimation performance because the HS is the most important gait event. During the experiment, gait phase estimation failed to detect HS in some cases, as shown in Figure 9. The HS misdetections were discarded to calculate the estimation error. The number of misdetections was recorded for evaluation. For all the trials, the first five strides were discarded to exclude the synchronization period between the AO and interaction force, and the other steps were considered for evaluation.  
We also compared the proposed method with the direct use of an AO to determine the effectiveness of the proposed adaptive rule and switching mechanism for estimation. The results of the proposed method were obtained in real time, while those of the AO  
Figure 7: Walker used in experiment.  
were obtained offline in MathWorks MATLAB from the same experimental data and same parameter settings (\(\mu=1,\ \epsilon=0.1,\ \sigma=1,\ \delta=1,\) and \(\lambda=0.3\)). All of the parameters are tuned by simulations in Matlab/Simulink and confirmed in the preliminary stage of the experiment.  
A one-way analysis of variance was applied to analyze significant differences in the estimation of HS instants. [PERSON]'s chi-squared test is applied to evaluate the significant difference in the number of misdetections. The threshold for the significance was set to 0.01.  
All the analyses were implemented in MATLAB 2019a.
# Multipath Error Fusion Modeling Methods for Multi-GNSS
## 2 Materials and Methods  
As the multipath error is related to the observation environments within a certain range around the station, which is the observation platform where the GNSS antenna is located, and the multipath error cannot be eliminated or mitigated by differential technology, it has been a major error source that affects the accuracy and reliability of multi-GNSS data processing [18]. The inter-frequency bias should be estimated for ambiguity resolution because the frequency division multiple access (FDMA) signal mode is adopted in GLONASS, which leads to the application limitation of GLONASS in high-precision data processing [19]. In addition, there are only three GLONASS satellites broadcasting code division multiple access (CDMA) signals currently [20]. So, the experimental analysis is limited to the other three systems.