esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation  
The evaluation procedure was designed based on the understanding that our aim is not to generate signals that are as close as possible to the signals that real sensors would generate when performing the respective motions. Instead we want to facilitate the generation of training data that will allow activity recognition systems to achieve performance that is as close as possible to the performance achievable with training data recorded with real sensors. These goals are related, but not identical. Generating sensor data that is very similar or even identical is certainly a sufficient condition for getting recognition results that are close to what is achievable with real sensor-based training data. However, it is not a necessary condition. Thus, if the system can replicate enough distinct  
Figure 6: Example of a subject performing some of the movements present in our generic motions dataset used for training the regression model. The video followed can be found in [46].  
features of the signal to separate the classes in a particular application then the fact that it may miss or fail to reproduce other signal features is of no significance. This is illustrated in Figure 7. The signals shown in the figure are the actual signals we used.  
As a consequence we focus on the evaluation of the performance of activity recognition trained using simulated sensor data generated by our system from respective videos and provide limited analysis of the quality of the generated sensor signal as such (see Section 5.1). The remainder of this section describes the dataset and approach used for the evaluation. The results are presented and discussed in the next section.  
Figure 7: Real signals (red) versus simulated (blue) in the regression training set (generic motions) for the norm of signals.
# Research on Optimized Energy Scheduling of Rural Microgrid
## 3 Rural Microgrid Modeling
### Load Unit Modeling
#### 3.3.1 Model of Residential Loads  
The residential loads can be divided into rigid loads and flexible loads according to the flexibility of scheduling time [27]. Among them, rigid loads, such as lamps and televisions, need to respond immediately when there is demand for electricity, so rigid loads do not participate in scheduling optimization. Flexible loads, such as washing machines and water heaters, are different from rigid loads. They have scheduling flexibility that can be scheduled in advance or later within the permitted operating range. We assume that there are a total of A flexible loads in the microgrid, and flexible load \(a\in\{1,2,\ldots,A\}\). Moreover, we divide the day into H equal time periods, and the time period \(h\in\boldsymbol{\Gamma}=\{1,2,\ldots,H\}\). The basic constraints of flexible loads are shown as follows [28]:  
\[\sum_{h\ =\ \alpha_{a}}^{\beta_{a}}s_{a}(h)\ =\ d_{a}, \tag{8}\]  
\[s_{a}(h)\ =0,\ \ \ if\ h\in\Gamma\ and\ h\
otin[\alpha_{a},\beta_{a}], \tag{9}\]  
\[s_{a}(h)\ =0\ or\ 1,\ \ \ if\ h\in[\alpha_{a},\beta_{a}], \tag{10}\]  
The binary variable \(Sa\) represents the operating state of the flexible load \(a\), \(1\) indicates that it is working, and \(0\) indicates the standby state. Flexible loads can be further divided into interruptible loads and uninterruptible loads. In addition to satisfying the above basic constraints of (8)-(10), the uninterruptible load \(b\in\{1,2,\ldots,A\}\) also needs to satisfy the following constraints [28]:  
\[\sum_{\xi\ =\ h\ +\ 1}^{h+d_{b}}s_{b}(\xi)\geq d_{b}[s_{b}(h+\ 1)-s_{b}(h)],\ \ \forall h\in\Gamma, \tag{11}\]  
Therefore, the total power of flexible loads is:  
\[P_{def}(h)\ =\ \sum_{a\ =\ 1}^{A}s_{a}(h)P_{a},\ \ \forall h\in\Gamma, \tag{12}\]  
The total electricity consumption of residential loads is:  
\[E_{resi}=\Delta h\sum_{h\ =\ 1}^{H}(P_{def}(h)\ +\ P_{ndef}(h)), \tag{13}\]
# Smart Gateway for Healthcare Networks Based on Beam Steering Technology
## 4 AOA Estimation Using a Fading Emulator
Figure 20: Two-dimensional fading emulator to realize a Rice propagation environment. Figure 22 shows the estimation accuracy of the proposed antenna as a function of the angle of the incoming wave with the K-factor as a parameter. Figure 22a indicates the estimated angle when applying Equation (5) to the phase of Figure 21, whereas Figure 22b is the difference between the estimated angle and the actual angle, i.e., the estimation error. The black lines, red lines, and blue lines show the case of \(K=10\) dB, \(0\) dB, and \(-10\) dB, respectively. It can be seen from Figure 22a that the estimated angle coincides with \(\angle E_{\Delta}\) because the reference angle \(\angle E_{\Omega}\) is constant regardless of the angle of the incoming wave, as shown in Figure 21. In addition, we can observe that the estimated angle agrees with the actual angle regardless of the K-factor. In Figure 22b, an estimation error within \(\pm 15^{\circ}\) is found regardless of the K-factor, with its features being similar to the analytical outcomes shown in Figure 10b. It is concluded from these figures that the AOA estimation using the MIQ  
Figure 21: Phase characteristics vs. angle of the incoming wave. Figure 22: Estimation accuracy of the proposed antenna obtained through the OTA testing: (**a**) estimated angle and (**b**) error of the estimated angle. method achieves sufficient accuracy for a small number of elements, even though the K-factor is very small. Hence, the ability of the AOA estimation for the proposed antenna, when operating in an indoor situation, can usefully form a beam with respect to the angle of the incoming wave.
# Simulation and Design of Three 5G Antennas
## 3 Design of 5G Microstrip Array Antennas
### Design and Simulation of 4 \(\times\) 4 Antenna Units
#### 3.2.5 Physical Measurement and Result Analysis  
First, VSWR parameters of the physical array antenna were measured. As shown in Figure 22, the measured VSWR plot of the array antenna falls below 2 within the 5G LTE band 42 (3.4-3.6 GHz) frequency range, meeting the antenna design requirements.  
Figure 21: Array antenna physical fabrication diagram. (**a**) Front view of physical fabrication and (**b**) rear view of physical fabrication.  
Figure 20: Laboratory fabrication environment image.  
Subsequently, a vector network analyzer was used to measure the return loss, and the measured S parameters are displayed in Figure 23.  
Comparing the measured S parameters of the array antenna with the simulated S parameters, it is observed that the measured results exhibit a slight overall upward shift, with a slight leftward deviation in the center frequency to approximately 3.48 GHz, resulting in a return loss of approximately \(-25.9\) dB. Nevertheless, these measured results closely align with the simulation results.  
In Figure 24, the microwave anechoic chamber measurement environment is depicted. Figure 25 displays the radiation pattern of the antenna measured in the microwave anechoic chamber at a frequency of 3.5 GHz. When compared with the simulation results, no distortion is observed, indicating that the designed 4 \(\times\) 4 array antenna exhibits favorable radiation characteristics.  
Figure 23: Measured S parameters of the array antenna.  
Figure 22: Measured VSWR plot of the array antenna.  
Figure 24: Microwave anechoic chamber measurement environment image.  
Figure 25: The measured radiation pattern of the antenna at a frequency of 3.5 GHz.
# Trustworthy Transaction Spreading Using Node Reliability Estimation in IoT Blockchain Networks
## 5 Experiments
### Performance Evaluations: Valid Transaction Sharing and Valid Block Sharing
After the final commit was completed in the PBFT consensus, we checked whether the block stored by each node in the network was valid (the number of malicious nodes was fixed at two). Figure 10 indicates the rate at which a transaction passes through a less reliable area. As the number of malicious nodes increases, the rate of transactions passing through the less reliable area generally increases. However, the trustworthy spreading method shows a significantly lower rate than the other two techniques (the network size is fixed at 50 nodes). The average time required to complete a single transaction traverse is proportional to the number of hops that the single transaction traverses. We have an action (hop size: 1-4). The hop size is applied to trustworthy transaction spreading, gossip, and random spreading. The average hops to destination nodes are 4.2 for trustworthy, 3.6 for gossip, and 5.1 for random spreading.
## 3 Methods
### Parameter Estimation  
Parameter estimation is the process of estimating unknown parameters in the population distribution according to random samples extracted from the population. Assuming that \(X=\{x_{1},x_{2},\ldots,x_{N}\}\) follows the GMM distribution, the parameters of the GMM need to be estimated for clustering. For \(X_{i}\), the probability of belonging to the Kth Gaussian distribution is calculated as follows:  
\[Ln\prod\limits_{i=1}^{N}p(x_{i}|\mu,\Sigma,\omega)=\sum\limits_{i=1}^{N}Ln \biggl{\{}\sum\limits_{k=1}^{K}\omega_{k}N(x_{i}|\mu_{k},\Sigma_{k})\biggr{\}} \tag{2}\]  
The Expectation-Maximization (EM) algorithm is often used to estimate the parameters of GMMs [28]. It has two steps: the E step obtains the probability of each component, and the M step estimates and updates parameters based on the results of the E step [14].  
Figure 4: Trip counts on weekends and weekdays.  
1. E-step: Obtain the probability \(\gamma_{k}(x_{i})\): \[\gamma_{k}(x_{i})=\frac{\omega_{k}N(x_{i}|\mu_{k},\Sigma_{k})}{\sum_{k=1}^{k} \omega_{k}N(x_{i}|\mu_{k},\Sigma_{k})}\] (3) 2. M-step: Re-estimate the model parameters with the maximum likelihood estimation based on the probability of each Gaussian distribution calculated in step E. \[\mu_{k}=\frac{\sum_{i=1}^{N}\gamma_{k}(x_{i})x_{i}}{\sum_{i=1}^{N}\gamma_{k}( x_{i})}\] (4) \[\Sigma_{k}=\frac{\sum_{i=1}^{N}\gamma(x_{i})(x_{i}-\mu_{k})(x_{i}-\mu_{k})^{T }}{\sum_{i=1}^{N}\gamma_{k}(x_{i})}\] (5) \[\omega_{k}=\frac{\sum_{i=1}^{N}\gamma_{k}(x_{i})}{N}\] (6) Repeat the two steps until the algorithm converges.
## 5 Results and Discussion
### Results for Non-Proportional (45\({}^{\circ}\) Out-of-Phase Tension and Torsion) Loading  
Figure 6a-c compares SIF obtained with the proposed method to the COD method (relative displacements of two opposite-to-crack points, Equation (14)) for modes I, II, and III, respectively, for R-031 after 33,000 loading cycles. The loading in this sample is non-proportional, with a 90\({}^{\circ}\) out-of-phase angle between axial and torque loads. For the three modes, it is seen that both methods agree in the proportional part (ramping-up and ramping-down loads). For mode I, both methods show identical results for the non  
Figure 5: Data source for (**a**) R-031 specimen, (**b**) R-033 specimen, (**c**) R-030 specimen.  
proportional loading part. For mode II, the LSM shows larger values for the COD method, whereas for mode III, the LSM method shows slightly lower values for the LSM method. In all cases, the maximum values in both methods exhibit maximum values at maximum axial load. Perhaps this is due to the maximum crack opening, which eliminates nonlinear effects such as rugosity, crack flank interlocking, and trapped debris that might hinder the development of sliding modes. Figure 6a shows the strong nonlinearity between mode I loading and unloading paths. One differential aspect of the load is the full load inversion for axial force and torque. So, one might need to crosscheck the results with the load to avoid incorrectly interpreting it as a negative \(K_{I}\) value instead of a \(\Delta K_{I}\).
# A New Model of Quasigeoid for the Baltic Sea Area
## 4 Quasigeoid Computation
### Gravity Data Gridding  
Since the gravity data set consists of point data distributed randomly, an interpolation process has to be applied to obtain a regular data grid.  
The interpolation of residual gravity anomalies (Table 2) in a grid of 1.5\({}^{\prime}\)\(\times\) 3.0\({}^{\prime}\) was made by a collocation method using a geogrid program. The method is based on an expansion of the spherical FFT method [47]. No modification of Wong-Gore Stokes function was used. The program applies for point interpolation of either weighted means or least-squares collocation (Kriging) using a second-order Markov covariance model, which was used in our study.  
For this purpose, the necessary empirical functions of covariance were calculated using the empcov program, and the results of the calculations are given in Figure 6 and Table 3.  
This enabled the calculation of residual anomalies in grid nodes 1.5\({}^{\prime}\)\(\times\) 3.0\({}^{\prime}\).  
Figure 6: Empirical covariance functions of residual gravity anomalies (BLUE LINE: empirical; RED DOTTED: approximation model) for GOCE DIR6 (a), GOCO6 (b), and EIGEN-6C4 (c).
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 9 Recommendations and Lessons Learned  
We have this opportunity to impart some recommendations from this extensive activity through SLR development and responding to the research questions. It became clear throughout the examination of the presented literature that smart healthcare is an interdisciplinary subject with roots in social and political studies in addition to health and engineering disciplines [99]. For instance, a patient should be allowed to decide whether to receive treatment from a real doctor or from an ML-based algorithm, and they should be fully informed about how and where their medical data will be utilized. From a technical standpoint, algorithms should be thoroughly examined and free of bias because otherwise, it could result in erroneous diagnoses [100]. Additionally, patients require accurate diagnoses, and emotional, and psychological support from the doctor, which may not be acceptable to the patient if a machine takes the doctor's role for most of the doctor's activities. The excessive adoption of technology may also interfere with the doctor-patient interaction, lowering expectations of care. The distribution of smart healthcare resources should initially go to underserved areas, and difficulties with the digital divide should be adequately handled, to maintain the quality of healthcare services [101]. To offer SHAs continual quality improvement, a research and review framework for partnerships at various levels should be established among healthcare stakeholders, academia, the research community, and the public.  
The lessons which we learned can be summarized as follows:  
* The most notable trend is to switch from conventional computing techniques to novel smart technologies to speed up the response time of SHAs.
* The most efficient method of computation is distributed since it is flexible and reduces delays.
* Due to their natural capacity to process enormous data contents, ML and DL algorithms are ideal for analyzing the health data that is currently available.
* Most readily accessible datasets are extremely general and have constrained access for researchers.
* Most of the evaluated literature concentrated on broad QoS issues, whereas SHA adoption requires more specialized research on its many practices and methodologies.  
In summary, our research on QoS optimization in SHAs provided important new information about QoS-conscious techniques and practices. Adopting smart technologies, concentrating on AI techniques in SHAs, and enhancing QoS are some of the key lessonsacquired. These lessons highlight the value of prompt responses and minimizing delays in SHAs. Our recommendations concentrate on the implementation of AI techniques with patients' permission and the prioritization of resource distribution, with an emphasis on underserved areas.
# Acoustic and Dynamic Response of Unbaffled Plates of Arbitrary Shape
## 3 Results
### Effect of Fluid on Dynamic Plate Characteristics
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Rectangular Simply Supported Plate**} \\ \hline  & \multicolumn{2}{c}{**Frequency (Hz)**} & \multicolumn{2}{c}{**Equivalent Damping (kg/s)**} \\ \hline  & **In vacuum** & **Incompressible** & **Compressible** & \\
**1 st mode** & 563.1 & 525.5 & 540.8 & 262.1 \\
**2 nd mode** & 1006.4 & 961.6 & 1000.0 & 514.8 \\
**3 rd mode** & 1482.0 & 1432.9 & 1479.2 & 143.9 \\
**4 th mode** & 1701.0 & 1647.9 & 1699.6 & 180.2 \\ \hline  & \multicolumn{4}{c}{**Trapezoidal Plate Simply Supported**} \\ \hline  & \multicolumn{2}{c}{**Frequency (Hz)**} & \multicolumn{2}{c}{**Equivalent Damping (kg/s)**} \\ \hline  & **In vacuum** & **Incompressible** & **Compressible** & \\
**1 st mode** & 168.2 & 148.7 & 142.9 & 26.1 \\
**2 nd mode** & 350.3 & 324.8 & 309.1 & 11.2 \\
**3 rd mode** & 448.2 & 418.8 & 410.9 & 215.1 \\
**4 th mode** & 622.2 & 592.1 & 604.8 & 183.4 \\ \hline  & \multicolumn{4}{c}{**Trapezoidal Cantilever Plate**} \\ \hline  & \multicolumn{2}{c}{**Frequency (Hz)**} & \multicolumn{2}{c}{**Equivalent Damping (kg/s)**} \\ \hline  & **In vacuum** & **Incompressible** & **Compressible** & \\
**1 st mode** & 27.5 & 24.9 & 24.9 & 0.01 \\
**2 nd mode** & 84.5 & 79.5 & 79.3 & 1.12 \\
**3 rd mode** & 147.2 & 133.6 & 133.5 & 0.14 \\
**4 th mode** & 245.1 & 232.3 & 229.9 & 1.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Natural frequency and equivalent damping values for rectangular and trapezoidal plates.
## 2 Materials and Methods
### Data Collection
#### 2.2.2 Rainfall Dataset  
A satellite-based rainfall dataset from Precipitation Estimation of Remotely Sensed Information using Artificial Neural Networks--Climate Data Record (PERSIANN-CDR) [45] was acquired for the study. The data were developed by NOAA's Centre for Hydrometeorology and Remote Sensing (CHRS) and includes daily precipitation estimates from geostationary satellites based on a neural network algorithm derived from daytime visible and infrared imagery [45]. PERSIANN-CDR dataset spatial resolution is 0.25\({}^{\circ}\) in latitude. [PERSON] [45] compared PERSIANN-CDR with ancillary rain gauge data and concluded that the former dataset can be used to monitor rainfall and assess contributing factors to changes in the rainy season due to their good performance and long temporal coverage (more than 30 years). The mean rainy season rainfall data during the study period 1990-2019 for the GSDM was downloaded ([[https://chrsdata.eng.uci.edu/](https://chrsdata.eng.uci.edu/)]([https://chrsdata.eng.uci.edu/](https://chrsdata.eng.uci.edu/)), accessed on 15 March 2022) and resampled to the spatial resolution of 0.05\({}^{\circ}\) of NDVI data using ArcGIS 10.7.
# Leveraging Blockchain Technology for Secure 5G Offloading Processes +
## 3 Blockchain-Based Secure 5G Offloading Mechanism
### Blockchain for Secure Authentication Among Operators  
As already explained in Section 3, once the event is received in the primary and selected secondary operators, the offloading process starts in the primary operator. The primary operator releases the resources dedicated to the user involved in the offloading process and issues him a verifiable credentials with information about the offloading process that is to happen. This credential will allow the secondary selected operator to securely identify the user without needing to share any pre-existing authentication information.
# Experimental and Mathematical Study of Flexible-Rigid Rail Vehicle Riding Comfort and Safety
## 3 Rail Vehicle Performance Evaluation Index
### Running Stability  
Running stability is a crucial factor for rail vehicles that determines their ability to maintain a stable and predictable motion. It is crucial for high-speed trains, wherein slight deviations from the intended path can have significant consequences. Running stability is affected by several factors, including the track parameters, the track surface condition, the train's speed, and the train's characteristics, such as its weight distribution and suspension system. To ensure running stability, rail vehicles are designed to have low centres of gravity and effective suspension systems, which help to reduce the impact of external forces and maintain the stability of the train. Additionally, track geometry and surface conditions are carefully monitored and maintained to minimize the risk of sudden changes in direction or unwanted oscillations. Advanced monitoring systems and automated control technologies are also employed to detect and correct deviations from the intended path, helping to ensure that rail vehicles remain stable and safe at all times. Running stability is critical to the safe and efficient operation of rail vehicles, and rail operators make significant efforts to maintain it at all times. This evaluation is carried out by the running stability. The stability index is shorthand for the Sperling index, broken down into vertical and horizontal stability. The Sperling Ride Index is a widely used measure of ride comfort in railway engineering. The index is based on the vertical acceleration experienced by a passenger on a train, and considers the frequency range of the vibrations [23].  
The Sperling Ride Index [24] is calculated by first measuring the vertical acceleration experienced by a passenger on a train using an accelerometer. The acceleration is then filtered to remove high-frequency noise and vibrations. The filtered acceleration data are then integrated twice to obtain the displacement of the passenger in the vertical direction. This displacement is then compared to a standardized curve representing passengers' minimum discomfort threshold. The level of ride comfort may be determined with the help of Equation (39) [3].  
\[W_{z}=0.896\sqrt[10]{\frac{a^{3}}{f}F(f)} \tag{39}\]  
where \(a\) is the acceleration of the vibration, and frequency is denoted by the letter \(f\). The weighting coefficient associated with vibration frequency is denoted by the symbol \(F(f)\). Since the vibration caused by the real vehicle moving down the track is unpredictable, the data must be categorized on the acceleration caused by the vibration in accordance with the frequency, and the total overall stability index \(W_{tot}\) must be determined. The comfort index is given in Figure 2.  
\[W_{tot}=\left(W_{1}^{10}+W_{2}^{10}+W_{3}^{10}+\cdots+W_{n}^{10}\right)^{0.1} \tag{40}\]