esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Analysis  
### Experimental Verification  
The measured signal was collected by the ST5000A rolling bearing vibration test bench, as shown in Figure 6. The rolling bearing vibration test bench was installed with SKF-6000 deep groove ball bearings. The KSWL-3806-20 encoder and the tachometer were installed at the end of the rotating shaft in the test bench. The TDGC2-0.5k VA contact voltage regulator was used to control the speed of the AC motor. Finally, the CA-YD-1181 acceleration sensor, YE6231 data acquisition card, and matching acquisition software were used to collect the signals synchronously.  
Figure 4: The separated time domain waveform.  
Figure 5: The envelope spectrum of inner and outer ring faults.  
The measured vibration signals mainly included outer ring faults and eccentric fault. The environmental noise and noise from the acquisition system itself are mixed into the vibration signal during the acquisition process. The outer ring crack of the bearing was artificially faulted by electrical discharge machining (EDM). The load was an eccentric flywheel. The sampling frequency is 24 kHz, and the rotation speed of the bearing \(f_{r}\) is 3600 rpm, and the geometric parameters of the bearing are shown in Table 2. The characteristic frequency of the outer ring fault \(f_{o}\) was 152.9 Hz, and the characteristic frequency of outer ring fault \(f_{e}\) was 60 Hz.  
The time-domain waveform of the collected vibration signal is shown in Figure 7. The characteristic waveforms of the outer ring fault (impact-waveform) and the eccentric fault (sine-waveform) cannot be distinguished from Figure 7.  
The test signal was separated by the method proposed in this article, and the separation result is shown (Figure 8). The waveform of the signal corresponding to the outer ring fault is a series of impact-waveforms, and the waveform of the eccentric fault signal is a continuous sine-waveform from Figure 8. The waveforms of these two signals are consistent with the actual situation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Type & 6000 \\ Number of Rolling Elements & 7 \\ Contact Angle & 0\({}^{\circ}\) \\ Pitch Diameter D/mm & 17.65 \\ Ball Diameter d/mm & 4.8 \\ Characteristic Frequency of Outer Ring Fault \(f_{o}/\mathrm{Hz}\) & 2.548\(f_{r}\) \\ Characteristic Frequency of eccentric Fault \(f_{e}/\mathrm{Hz}\) & 1/\(f_{r}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The bearing structure parameters.  
Figure 6: The bearing experimental bench ST500A.  
Figure 7: The time domain waveform of the collected vibration signal.  
In order to analyze the components of the separated two fault vibration sources, their envelope spectrums were calculated, as shown in Figure 9. \(f_{o}\) is the characteristic frequency of the outer ring fault, and \(f_{e}\) is the characteristic frequency of the eccentric fault. From Figure 9, there are peaks at the first harmonic and the second harmonic of fault characteristic frequency in the envelope spectrums. The method presented in this article proved to be effective in separating the eccentric and outer ring crack fault signals.
###### Abstract  
Human body communication (HBC) is a wireless communication method that uses the human body as part of the transmission medium. Electrodes are used instead of antennas, and the signal is transmitted by the electric current through the human body and by the capacitive coupling outside the human body. In this study, direction of electric field lines and direction of electric current through the human body were analyzed by the finite-difference time-domain method to clarify the signal path, which is not readily apparent from electric field strength distribution. Signal transmission from a transmitter on the subject's wrist to an off-body receiver touched by the subject was analyzed for two types of transmitter electrode settings. When both the signal and ground electrodes were put in contact with the human body, the major return path consisted of capacitive coupling between the receiver ground and the human body, and the electric current through the human body that flowed back to the ground electrode of the transmitter. When the ground electrode was floating, the only return path was through the capacitive coupling of the floating ground. These results contribute to the better understanding of signal transmission mechanism of HBC and will be useful for developing HBC applications.  
human body communication; wireless body area networks; electromagnetic field analysis; capacitive coupling; electric-field distribution; finite-difference time-domain +
Footnote †: journal: Applied Sciences
# Advanced Hysteretic Model of a Prototype Seismic Isolation System Made of Polymeric Bearings
## 5 Numerical Analysis
### Single-Storey Structure Model
\right\}\!,\dot{\mathbf{u }}(\mathrm{t})=\left\{ \begin{array}{c}\dot{\mathrm{u}}_{\mathrm{b}}(\mathrm{ t})\\ \dot{\mathrm{u}}_{\mathrm{1}}(\mathrm{t})\end{array} \right\}\!,\,\mathbf{u}( \mathrm{t})=\left\{ \begin{array}{c}\mathrm{u}_{\mathrm{b}}(\mathrm{t})\\ \mathrm{u}_{\mathrm{1}}(\mathrm{t})\end{array} \right\} \tag{15}\]  
Numerically obtained acceleration-time history plots for the single-storey structure model, both fixed-base and base-isolated considered as 1-DOF and 2-DOF systems, respectively, are compared with the experimental data (see [28]) in Figures 5 and 6. Moreover, computed peak lateral accelerations recorded at the top of the analyzed single-storey structure model, with and without the PBs, are briefly reported in Table 3. Additionally, reduction levels in lateral accelerations are also presented and compared with the results of the previously performed shaking table investigation [28]. Figure 5: \(Cont.\)  
Figure 5: Experimentally determined (**left**) and numerically obtained (**right**) time-acceleration history plots for the fixed-base single-storey model during different ground motions: the 1940 El Centro earthquake (**a**,**b**), the 1971 San Fernando earthquake (**c**,**d**), the 1989 Loma Prieta earthquake (**e**,**f**), the 1994 Northridge earthquake (**g**,**h**), and the 2002 Polkowice mining tremor (**i**,**j**). Figure 6: _Cont._  
Figure 6: Experimentally determined (**left**) and numerically obtained (**right**) time-acceleration history plots for the base-isolated single-storey model during different ground motions: the 1940 El Centro earthquake (**a**,**b**), the 1971 San Fernando earthquake (**c**,**d**), the 1989 Loma Prieta earthquake (**e**,**f**), the 1994 Northridge earthquake (**g**,**h**), and the 2002 Polkowice mining tremor (**i**,**j**). \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Dynamic Excitation**} & \multicolumn{3}{c}{**Numerically Computed Peak Lateral**} & \multirow{2}{*}{**Reduction Based on**} & \multirow{2}{*}{**Reduction Based on**} \\  & \multicolumn{3}{c}{**Acceleration for the Single-Storey**} \\  & \multicolumn{3}{c}{**Structure Model (m/s\({}^{2}\))**} & \\ \cline{2-5}  & **Fixed-Base** & **Base-Isolated** & \multirow{2}{*}{**Numerical Analysis (\%)**} & **Shaking Table** \\  & **(1-DOF System)** & & & \\ \hline
1940 El Centro earthquake & & & & \\ NS component, original record & 11.39 & 7.21 & 36.70 & 38.7 \\ with PGA = 3.070 m/s\({}^{2}\) & & & & \\ \hline
1971 San Fernando earthquake & & & & \\ Pacoima Dam station, N74\({}^{\circ}\)E & & & & \\ component, scaled record with & 15.64 & 9.66 & 38.24 & 32.8 \\ PGA = 5.688 m/s\({}^{2}\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the seismic tests for the single-storey structure model.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 1 Introduction
### Contributions  
To mitigate the high cost of the inverse operation of MMSE detection, this work employs a low-complexity iterative approach for signal identification based on solving linear equations. The following is a summary of this work's main contributions:  
1. An improved Gauss-Seidel iterative algorithm based on conjugate gradient and Jacobi (CJ) joint preprocessing is proposed, which can be described as the CJGS iterative method. The proposed algorithm attempts to combine CG and Jacobi iteration to accelerate the convergence of GS. Then the GS detector is employed to converge faster and iterate less.
2. A well-chosen initialization technique can lower computing cost and increase algorithmic accuracy. The best appropriate initialization technique for the advised approach is chosen by contrasting the three initialization strategies.
3. Software simulation and data analysis are utilized to provide more detailed examples of the suggested algorithm's advantages in terms of complexity and performance. Simulation representations demonstrate that, independent of channel correlation, the CJGS iterative scheme surpasses both the enhanced GS-based iterative program and the conventional Gauss-Seidel repeated approach in terms of BER ability. Because of its reduced complexity, the MMSE detection ability may be attained with fewer iterations. As a result, the recommended strategy performs better in terms of complexity and detection effectiveness.
###### Abstract  
The reduction in disaster risk in urban regions due to natural hazards (e.g., earthquakes, landslides, floods, and tropical cyclones) is primarily a development matter that must be treated within the scope of a broader urban development framework. Natural hazard assessment is one of the turning points in mitigating disaster risk, which typically contributes to stronger urban resilience and more sustainable urban development. Regarding this challenge, our research proposes a new approach in the signal processing chain and feature extraction from microtremor data that focuses mainly on the Horizontal-to-Vertical Spectral Ratio (HVSR) so as to assess liquefaction potential as a natural hazard using AI. The key raw seismic features of site amplification and resonance are extracted from the data via bandpass filtering, Fourier Transformation (FT), the calculation of the HVSR, and smoothing through the use of moving averages. The main novelty is the integration of machine learning, particularly stacked ensemble learning, for liquefaction potential classification from imbalanced seismic datasets. For this approach, several models are used to consider class imbalance, enhancing classification performance and offering better insight into liquefaction risk based on microtremor data. Then, the paper proposes a liquefaction detection method based on deep learning with an autoencoder and stacked classifiers. The autoencoder compresses data into the latent space, underlining the liquefaction features classified by the multi-layer perceptron (MLP) classifier and eXtreme Gradient Boosting (XGB) classifier, and the meta-model combines these outputs to put special emphasis on rare liquefaction events. This proposed methodology improved the detection of an imbalanced dataset, although challenges remain in both interpretability and computational complexity. We created a synthetic dataset of 1000 samples using realistic feature ranges that mimic the Rif data region to test model performance and conduct sensitivity analysis. Key seismic and geotechnical variables were included, confirming the amplification factor (Af) and seismic vulnerability index (\(K_{g}\)) as dominant predictors and supporting model generalizability in data-scarce regions. Our proposed method for liquefaction potential classification achieves 100% classification accuracy, 100% precision, and 100% recall, providing a new baseline. Compared to existing models such as XGB and MLP, the proposed model performs better in all metrics. This new approach could become a critical component in assessing liquefaction hazard, contributing to disaster mitigation and urban planning.  
**Keywords:** microtremor data; HVSR; soil liquefaction; stacking model; autoencoder; Rif region of Morocco
## 4 Evaluation
### Results and Discussion
#### 4.2.1 Performance Evaluation in the Skudai Scenario
The number of central nodes recorded by the routing techniques is shown in Figure 14. CCAF records about 30% improvement with respect to PRoPHET, while FLARoute records more than twice PRoPHET's value. Table 4 summarises the impact of CCAF and FLARoute on PRoPHET routing protocol. We discuss these results next. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Performance Metric**} & \multirow{2}{*}{**PRoPHET**} & \multicolumn{2}{c}{**Incorporated Solution**} & \multirow{2}{*}{**Comment on FLARoute as Compared**} \\ \cline{3-4}  & & **CCAF** & & **FLARoute** & **with CCAF** \\ \hline Throughput (\%) & 65.3 & 76.2 & 85 & Higher throughput and routing fairness \\ \hline Routing fairness (\%) & 95.8 & 96.2 & 97.9 & is due to locality-awareness \\ \hline Average delivery delay (h) & 5.5 & 5 & 8.2 & More delay is incurred in order to deliver messages traversing longer distances \\ \hline Energy distribution (\%) & 17.1 & 11.3 & 43.3 & Energy consumption is more evenly distributed among the two sets of nodes \\ \hline Transmission overhead & 1002.2 & 93.7 & 265.2 & Added transmission overhead and average energy consumption is due to improved fairness \\ \hline Average energy consumption (Joules) & 178.6 & 19.2 & 61.5 & More number of central nodes contributing to increased fairness and throughput \\ \hline Average central node count & 57 & 74 & 131 & More number of central nodes contributing to increased fairness and throughput \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the impact of CCAF and FLARoute on PRoPHET routing protocol. Figure 11: Throughput (in %) of the different routing techniques. Figure 12: The average delivery delay (in h) of PRoPHET, and that of CCAF and FLARoute when incorporated into PRoPHET.
# An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis
## 3 The Strategies for Selecting High-Fidelity Samples
We define the projection error of low-fidelity response as the deviation between the original low-fidelity response \(s_{j}^{L}\) and the projected response,\(\Phi^{L}\overset{\rightarrow}{\alpha}\), which is as follows:  
\[\overset{\rightarrow}{\varepsilon}_{j}^{projection}=\Phi^{L}\overset{ \rightarrow}{\alpha}-s_{j}^{L} \tag{5}\]  
The dimensionless two-norm projection error is defined as follows:  
\[\delta_{j}^{projection}=\frac{\left\|\overset{\rightarrow}{\varepsilon}_{j}^{ projection}\right\|_{2}}{\left\|s_{j}^{L}\right\|_{2}} \tag{6}\]  
The prediction error is defined as \(\overset{\rightarrow}{\varepsilon}_{j}^{prediction}=\Phi^{H}\overset{ \rightarrow}{\alpha}-s_{j}^{H}\), and the dimensionless two-norm prediction error is as follows:  
\[\delta_{j}^{prediction}=\frac{\left\|\overset{\rightarrow}{\varepsilon}_{j}^{ prediction}\right\|_{2}}{\left\|s_{j}^{H}\right\|_{2}} \tag{7}\]  
For an incomplete sample, the high-fidelity response \(s_{j}^{H}\) is unknown; therefore, it is impossible to evaluate the prediction error of the Gappy-POD method. However, considering that in a CFD field, for the same set of model parameters, there is usually a certain correlation between high- and low-fidelity responses. [PERSON] et al. [23] posited a strong correlation between prediction errors on high-fidelity responses and projection errors on low-fidelity responses. That is, when \(\delta_{j}^{projection}\) is large, \(\delta_{j}^{prediction}\) is also likely to be large. Although this cannot be strictly proven mathematically, it was confirmed in the subsequent test cases. Therefore, in this paper, we utilize projection error \(\delta_{j}^{projection}\) as an estimation of prediction error. In the adaptive refinement process, we traverse incomplete samples and compare the maximum projection error with a pre-set value to determine whether the existing complete sample set is sufficient; if the maximum projection error is higher than the pre-set value, we add the sample with the largest projection error to the complete sample set.
###### Abstract  
An optical system consisting of a straight-line polarization grating (SPG) and two parabolic-line polarization gratings (PPGs) is presented for the characterization of optical vortices (OVs) with arbitrary states of polarization (SoPs). The PPG is capable of converting an OV with a specific SoP into a bright bar-like spot with 100% efficiency. The number of dark lines and their orientation respectively correspond to the magnitude and sign of topological charge (TC) of the incident OV, thereby enabling characterization of OVs with high efficiency. Furthermore, on combining an SPG with PPCs, the present system can characterize the TC of incident OVs regardless of their SoP. The feasibility of the system was demonstrated in experiments using gratings fabricated by applying the photoalignment method and employing films of a photo-crosslinkable polymer liquid crystal. The experimentally obtained efficiency is 70.2%. We furthermore demonstrate the system's capability to characterize polarization vortices.  
optical vortex; polarization vortex; liquid crystal polymer; hologram; polarization grating +
Footnote †: journal: Applied Sciences  
0000-0002-29 August 2022000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-000  
throughput because over a given diffraction order (specifically, the +1 st order diffraction spot), all of the beam's energy is transferred into a beam that forms a bright bar-like spot. In addition, its ease of use is advantageous because it is not sensitive to the relative location of the beam axis and the grating center. Moreover, by combining the parabolic-line amplitude and phase gratings, a near 50% diffraction efficiency for diffracted light of +1 st order has been indicated from the theory. However, in view of applications, the diffraction efficiency of the spot for OV characterization should ideally reach 100%. Although a phase-type spatial light modulator (SLM) only acts on linearly polarized light for which the polarization azimuth aligns along the director of the liquid crystal layer of the SLM, a parabolic-line grating cannot be used for arbitrary states of polarization (SoPs). To overcome this problem, we proposed an alternative method that exploits two kinds of polarization gratings, called straight-line polarization gratings (SPGs) and parabolic-line polarization gratings (PPGs). Based on the way the PPG functions, our method can characterize the TC of incident OVs with 100% efficiency. Furthermore, by adding an SPG, the method can be used regardless of the SoP of the incident OV.
# DataMatrix Code Recognition Method Based on Coarse Positioning of Images
## 3 Method of Adaptive Sampling Grid Partitioning  
The division of the sampling grid is usually based on the finder pattern of DM codes [2], but when the finder pattern encounters pollution or is missing, it is no longer possible to divide the sampling grid by using this method. This section proposes an adaptive sampling grid division method, and the overall workflow is shown in Figure 3. By calculating the gray deviation value of the DM code image in one direction and accumulating it in the vertical direction, the two-dimensional barcode is converted into a one-dimensional waveform for analysis to obtain the sampling grid. The experimental results show that this algorithm can effectively divide the sampling grid of DM code images with problems such as wear, corrosion, geometric distortion, and strong background interference.  
Figure 3: The workflow of the adaptive sampling grid division method.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 5 Details of Prototype System
### Cab Dispatch Scenario: A Sequence of Events  
The cab dispatch scenario unfolds through a series of orchestrated interactions between key system components: the passenger application, MQTT broker, dispatch service, and driver application. Figure 5 visualizes these interactions using a UML sequence diagram, highlighting the chronological flow of events:  
1. Cab Requested: Initiated by the passenger application, this event signifies a ride request and includes details like passenger ID and desired destination. The passenger application publishes this event to the MQTT broker and subscribes to the topic for receiving cab acceptance notifications.
2. Cab Request Broadcasted: Upon receiving the CabRequested event, the MQTT broker acts as a message intermediary, broadcasting it to all subscribed entities, including the dispatch service and driver applications.
3. Driver Assigned: The dispatch service receives the broadcasted request and, applying predefined rules like driver proximity and availability, assigns the request to the most suitable driver.  
Figure 5: The sequence diagram of the cab dispatch system.  
Cab Request Accepted: The assigned driver acknowledges the request by publishing a CabRequestAccepted event to the MQTT broker.
* 5. Acceptance Notification Broadcasted: Similar to the request, the broker relays the acceptance notification to all subscribers, including the passenger application, confirming the assigned driver.
* 6. Cab Arrived: The driver application simulates the driver's journey to the passenger's location and, upon arrival, publishes a CabArrived event to notify the system.
* 7. Arrival Notification Broadcasted: The broker forwards the arrival notification to subscribed entities, informing the passenger that the driver has arrived at the pickup point.
* 8. Trip Started: Once the passenger boards the cab, the driver application publishes a TripStarted event to mark the commencement of the trip.
* 9. Trip Start Notification Broadcasted: The broker disseminates the start notification, informing all subscribers, including the passenger application, that the trip has begun.
* 10. Trip Ended: Upon reaching the destination, the driver application publishes a TripEnded event to signify the completion of the trip.
* 11. Trip End Notification Broadcasted: The broker broadcasts the final notification to all subscribers, informing them of the trip's completion.
* 12. Cab Availability Notification: The driver application updates its status to available by notifying the dispatch service, allowing it to assign subsequent ride requests.  
This sequential flow exemplifies the event-driven nature of the system, where individual events trigger specific actions and state transitions, orchestrating the entire cab dispatch process.
## 3 Materials and Methods
### Preliminaries and Notations
#### 3.1.3 Value of Information  
Immediate detection of regions of interest in early stage can provide sufficient time to take corresponding actions. Hence, we adopt the concept of value of information which evaluates information in terms of timeliness. Hence, the later a packet is forwarded to the sink, the lower its value is. Therefore, the \(VoI\) of a packet can be expressed as Equation (2),  
\[VoI(p_{i,t})=ke^{-at_{i}},\ t_{i}\in[0,TTL] \tag{2}\]  
Figure 1: An Underwater Wireless Sensor Network (UWSN) with passive mobility.  
where \(p_{i,t}\) represents the \(p\)-th packet in \(v_{i}\) at time slot \(t\), \(t_{l}\) indicates the living time duration of packet \(p_{i,t}\) since it is generated, \(\alpha\) is the decay factor, \(k\) is the discount coefficient and \(TTL\) is the maximum life of the packet, i.e., time to live.  
\(VoI(p_{i,t})\) is a key factor of the decision making of a sensor node as to which packet should be relayed. If the living duration of a packet approaches its \(TTL\), it will be discarded immediately.