esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Performance Analysis for Multiple Cameras Fall Dataset (MCFD)
#### 5.3.3 Result from STFT Module  
The selection of STFT parameters such as window type, segment size, overlap ratio, FFT length, scaling, and mode was crucial for accurately capturing and analyzing significant movements indicative of falls. We employed a Hann window for its smooth tapering, which minimizes spectral leakage. A segment size of 16 and an FFT length of 256 were chosen to balance time and frequency resolutions effectively. The overlap ratio was 93.75%, ensuring motion continuity is preserved without sacrificing computational efficiency. Density scaling was applied to normalize the spectrogram by the signal's power, assisting in the consistent interpretation of spectral density. Meanwhile, the PSD mode focused on the power distribution within the frequency spectrum.  
These parameters were not arbitrarily chosen but were determined through an extensive empirical approach, including trials with various settings. This rigorous evaluation allowed us to identify a parameter set that optimally balances sensitivity to motion with computational efficiency. Initially, the effectiveness of these parameters was confirmed through the manual verification of 112 sequences derived from high-motion events captured across two camera angles. This initial test ensured the STFT settings were accurately capturing significant motion events.  
Following the successful manual verification, the STFT module was applied to a broader dataset, extracting high-motion sequences from 448 sequences recorded across eight different cameras. This extensive application demonstrates the module's capability to consistently identify and analyze significant motion across diverse settings, as shown in Figure 7.  
Figure 6: The figure demonstrates the result of the OpenPose module for different subjects in different videos, showcasing its effectiveness even when blurring the videos.  
To ensure the statistical reliability of our findings from the STFT module, we conducted a power analysis using G*Power as shown in Figure 8. Based on an effect size of 0.5, an \(\alpha\) error probability of 0.05, and a desired power of 0.8, the analysis determined that a total sample size of 102 sequences would be necessary for robust results. With our initial manual testing covering 112 sequences and the STFT module effectively analyzing 448 sequences, our study significantly exceeds the recommended sample size.
## 2 Materials and Methods
### Methodology
#### 2.2.3 Data Processing  
In total, 26 UAV flights were performed to generate photogrammetric surveys at different degrees of water turbidity, during which flight and weather parameters were recorded. However, from flight 11 onwards, water the turbidity levels started to vary before the photogrammetric flights were conducted.  
The Pix4 Dmapper software [67] was used for photogrammetric processing, through which a point cloud and an orthomosaic were obtained. The information from which the images were generated was entered into the software to achieve this processing. The three control points on the dry ground were used to georeference the surveys, and the 2 control points underwater were used to determine the accuracy of the results [25]. Table 6 shows the accuracy found for each flight using the two underwater control points as reference. For flights 23 to 26, the control points could not be recognized due to the turbidity levels. Figure 7 shows the point cloud of the pool obtained from a photogrammetric flight (a), a side view of the depth of the pool (b), and a general view of the image processing with the location of the photographs, the ground control points, and the context of the study site (c).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Flight**} & \multicolumn{3}{c}{**Accuracy for Control Point A1 (cm)**} & \multicolumn{3}{c}{**Accuracy for Control Point A2 (cm)**} \\ \cline{2-7}  & **X** & **Y** & **Z** & **X** & **Y** & **Z** \\ \hline
1 & 0.005 & \(-\)0.004 & 0.009 & 0.000 & 0.011 & 0.047 \\
3 & 0.009 & 0.002 & 0.032 & 0.002 & 0.008 & 0.061 \\
5 & 0.002 & \(-\)0.006 & 0.050 & \(-\)0.007 & 0.014 & 0.108 \\
7 & 0.005 & \(-\)0.002 & 0.011 & 0.003 & 0.009 & 0.046 \\
9 & 0.009 & 0.001 & 0.002 & \(-\)0.003 & 0.016 & 0.048 \\
11 & 0.020 & \(-\)0.010 & \(-\)0.022 & 0.010 & 0.050 & 0.038 \\
13 & 0.010 & 0.000 & \(-\)0.072 & 0.020 & 0.020 & 0.008 \\
15 & 0.010 & 0.000 & \(-\)0.072 & 0.010 & 0.010 & \(-\)0.002 \\
17 & 0.000 & \(-\)0.010 & \(-\)0.092 & 0.010 & 0.000 & \(-\)0.012 \\
19 & 0.040 & 0.000 & \(-\)0.152 & 0.000 & 0.040 & \(-\)0.052 \\
21 & 0.040 & 0.000 & \(-\)0.132 & 0.000 & 0.030 & \(-\)0.042 \\
23 & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\
25 & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\
**Average** & 0.014 & \(-\)0.003 & \(-\)0.040 & 0.004 & 0.019 & 0.023 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy for control points underwater.  
Figure 7: Pool image processing: (**a**) top view of the point cloud; (**b**) side view of the point cloud; (**c**) image processing view.  
Post-processing was conducted to filter the noise from the point cloud. This eliminates extreme or atypical values for the surface when obtaining photogrammetric information. Because the body being assessed is a known object on which a previous topographic survey had been conducted to determine its measurements, as is seen in Figure 7, this can be used as a foundation for noise filtering [68]. For this research, point filtering was performed manually because the case study is not a large area, and the shape of the surface is known.
## 1 Introduction
[PERSON] [28] developed a subsea all-electric Christmas tree gate valve actuator and analyzed the sealing process of the gate valve. [PERSON] [25] proposed a new pressure compensation all-electric control gate valve and actuator integrated structure, which substantially reduced the high-pressure hydraulic resistance during valve closure. According to the above analysis, it is obvious that some designs and researches have been done for the all-electric subsea gate valve actuator. However, most of the existing researches on the all-electric subsea gate valve actuator do not concentrate on the low-power holding mechanism, which was the unique composition of the all-electric subsea gate valve actuator. Therefore, a low-power holding mechanism of the all-electric subsea gate valve actuator is proposed in this paper, which uses an electromagnet as a driving element and combines spiral transmission and cam-like transmission, the corresponding mechanical and kinematic models are also established, respectively. The analytic expression of the relation is derived, respectively, for the lockable maximum load of the closing spring and the permissible stroke of the locking tab with regard to the design variables. The parameter effects and the corresponding sensitivities are analyzed as well. The lockable maximum load of the closing spring and the design parameters of the low-power holding mechanism are obtained. This present work is aimed at developing a novel and feasible low-power holding mechanism of the all-electric subsea gate valve actuator, which can minimize the power consumption and cable number for control and improve the open-position keeping performance of an all-electric subsea gate valve actuator, only requiring a holding force of approximately 2%-7% of the maximum load of the closing spring to keep the valve open and with the ability to be applied to all-electric subsea gate valve actuators with various valve sizes and process pressure ratings, to bridge the gap of the low-power holding mechanism in the all-electric subsea gate valve actuator of the subsea production system. The rest of the paper is organized as follows: Section 2 briefly introduces the working process of the all-electric subsea gate valve actuator proposed, followed by a full presentation of the configuration, work principle and advantage of the proposed failsafe closing and low-power holding mechanism. Section 3 presents the mechanical and kinematic models. In Section 4, the detailed discussion of the parameter effects and sensitivity analysis is performed based on the established model and results. Finally, conclusions are given in Section 5.
# Order-Based Schedule of Dynamic Topology for Recurrent Neural Network
## 2 Theoretical Frameworks
### Long Short-Term Memory (LSTM)  
LSTM units are an extension of recurrent networks that allow them to remember their inputs for a longer period, resembling a computer's memory. It can read, write, and delete information from its cell, saving information, and preventing it to vanish through time [21].  
The cell decides whether to store or delete the information it is receiving based on a degree of importance that is assigned to it. This importance is assigned through the weights of the connections, so the LSTM learns over time to identify what part of the information is important and what is not. Implementing an internal state of the cell that will generate a long-term memory [22].  
An LSTM neuron is composed of three gates: input, forget, and output, as shown in Figure 1. Which determines if a new input is allowed, which information is eliminated, or if it is allowed to affect the output at the current time.  
The first gate that appears from left to right in Figure 1 is the forget gate, which decides what information of the cell state (\(C^{t}\)) is going to be discarded, through a sigmoid function (\(\sigma\)).  
\[f_{t}=\sigma\Big{(}W_{f}*[h_{t1},x_{t}]+b_{f}\Big{)} \tag{5}\]  
where \((h_{t-1})\) is the previous output of the unit, \((x_{t})\) is the input, \((W_{f})\) the forget gate weight matrix and \((b_{f})\) the gate bias. The output will be between a value ranged from 0 to 1, with 0 representing total elimination and \(1\) keeping the whole information. After this step, the input gate continues, which indicates what portion of the new information will be stored in the cell state. This gate is composed of two functions, one sigmoidal and the other hyperbolic tangent, which determines the degree of update of the state.  
\[i_{t}=\sigma(W_{i}*[h_{t-1},x_{t}]+b_{i}) \tag{6}\]  
\[g_{t}=\tanh\bigl{(}W_{g}*[h_{t-1},x_{t}]+b_{g}\bigr{)} \tag{7}\]  
Being \((W_{i})\) and \((W_{g})\) the weight matrix for the input gate and \((b_{i})\) and \((b_{g})\) their respective bias. Thus obtained, the new state of the unit, by eliminating the unnecessary information and updating it with the new information acquired.  
\[C_{t}=[C_{t-1}*f_{t}]+[i_{t}*g_{t}] \tag{8}\]  
Finally determine the output of the unit \((h_{t})\), which will be a version of the cell state filtered by the current input and the previous output. Where \(W_{o}\) and \(b_{o}\) are the weight matrix and bias for the output gate.  
\[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{9}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{10}\]
## 6 Proposed System Architecture and Components
### Proposed System Components  
Our proposed architecture consists of two components and software modules. The ultimate objective of this system is to provide a high level of safety and increase the efficiency of the transportation system. The following points summarize the system components:  
* Kappa Architecture: KA is a real-time streaming component that can process the real-life data that is obtained from a cluster (segment) head node. This is a one pipeline machine learning process that consists of two main layers: The streaming and serving layers.
* Distributed machine learning model: It is the proposed model that is built based on different random forest algorithms with different hyper-parameters values. The Stacking ensemble learning approach is utilized to build our proposed model.
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 3 An Overview of LoRaWAN and SD-WAN Technologies
### WAN and SD-WAN Technologies  
Due to the constant development of technology and the need for a better quality of service both in data transfer and real-time communication, we need to increase the development of new technologies while upgrading existing ones. With the current known WAN technology, companies have managed over time to obtain reliable communication between the central points and their branches without any problems [31]. As computer and telecommunication networks size up devices such as routers and switches through interconnections, many organizations, and businesses are turning to SD-WANs as a solution [32].  
WAN technology was a widely distributed technology approximately 30 years ago. The term \"Data Centers\" was an unknown concept, and network administrators had to manipulate servers manually, wherever they were, as these devices needed to be configured on-site one by one [33]. WAN networks had two disadvantages, as they were expensive and rigid. At the first point, WAN network equipment was costly because of the specialization scope that had to be completed. Secondly, these devices had the necessary information for the local system's network and were not responsible for the global network. Overall, due to their low flexibility, any new deployments for adding new WAN extensions and capabilities were not easy to perform [34]. Table 3 depicts a performance comparison of the most common WAN technologies [3; 26].  
SDN technology was the most recognized subject in the previous decade in the network world because of the solution that promised to provide for large scaled networks. Centralized monitor control using software plans can increase network speed and provide a better quality of service, both in voice and data transmission [35; 36]. Compared to classic networks, SDN can provide improved network architecture of three layers under the same infrastructure. The \"application\" layer is responsible for monitoring and load balancing, the \"control\" layer regards network topology, and the \"data plane\" layer is responsible for the network equipment's physical connections [37].
# Formation Mechanism of Metro Rail Corrugation Based on Wheel-Rail Stick-Slip Behaviors
## 4 Analysis of Wheel-Rail Stick-Slip Characteristics
### Wheel-Rail Stick-Slip Characteristics of Large Radius Curve
Meanwhile, it should be noted that compared with the adhesion coefficient of 0.069 required for the lateral torsional stick-slip vibration of the outer rail-outer wheel of the driven wheelset on the small radius curve, the adhesion coefficient of 0.066 that is required for the lateral torsional stick-slip vibration of the outer rail-outer wheel of the driven wheelset on the large radius curve is slightly smaller, which shows that with the increase of the curve radius, the intensity of the lateral torsional stick-slip vibration of the outer rail-outer wheel of the driven wheelset is enhanced, but the corresponding adhesion coefficient is still large, so the outer rail is not easy to produce corrugation when the rail surface is smooth. In addition, compared with the inner rail-inner wheel of the driven wheelset on the small radius curve, the inner rail-inner wheel of the driven wheelset on the large radius curve has a slight lateral torsional stick-slip vibration, which shows that with the increase of the curve radius, the contact stick-slip behaviors of inner rail-inner wheel of the driven wheelset have a certain effect on the formation of inner rail corrugation, but this effect is much smaller than that of the inner rail-inner wheel of the guiding wheelset. The wheel-rail longitudinal adhesion coefficient of the driven wheelset is positively correlated with the longitudinal creepage, so the longitudinal adhesion characteristics have little influence on the formation of the inner and outer rail corrugations. It can be seen from the above that the lateral torsional stick-slip vibration of the inner rail-inner wheel of the guiding wheelset is easy to lead to the generation of inner rail corrugation on the curve track with a large radius, but the degree of inner rail corrugation decreases compared with that on the curve track with a small radius. Corrugation is still not easy to be formed on the outer rail of large radius curve, but if the rail surface is not smooth, it may cause the appearance of the outer rail corrugation.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 5 The Proposed BSPGH
### Handover Preparation  
This phase occurs before the handover, preparing the necessary key materials for the first MTCD handover authentication and group handover authentication. This phase is shown in Figure 3.  
\[Step\text{-}1:s\text{-}gNB\to AMF:\{TID_{s}\}\]  
Figure 2: Initial registration.  
The s-gNB sends a handover request to the AMF containing the neighboring gNB list and the temporary identity identifiers \(TID\)s of all group members.  
\[Step\text{-}2:AMF\to s\text{-}gNB/t\text{-}gNB:\Big{\{}\{TID_{1}||\ldots||TID_{i }\},\Big{\{}GID,TS_{f}\Big{\}}_{\delta^{{}^{\prime}}}MAC\Big{\}}\]  
After receiving all TIDs from the group members, the AMF computes the group key \(GID=H(\sum_{i=1}^{n}TID)\). The AMF encrypts the timestamp \(TS_{i}\) and generates the MAC as \(MAC=H\Big{(}GID\Big{|}|TS_{f}\Big{)}\). This message is then broadcasted to the base stations. Upon receiving the message, the t-gNB queries the public key information of MTCDs stored in the blockchain within the group and stores the queried information locally.  
\[Step\text{-}3:s\text{-}gNB\to\text{MTCD}:\Big{\{}GID,TS_{f}\Big{\}}_{\delta^{ {}^{\prime}}}MAC\Big{\}}\]  
s-gNB broadcasts the message to all MTCDs within the group. Each MTCD decrypts the message to obtain the \(GID\) and verifies the received message's timestamps and \(MAC\) to determine its authenticity.
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 3 Methodology  
### System Overview  
We propose an approach which gradually removes image areas classified as non-flood, based on binary masks constructed from color and edge information. Our method is fully unsupervised, meaning that there is no training process involved, and thus no need of ground truth labeling. We use the labels provided by the datasets only for evaluation purposes of our method. A repetitive process, consisting of the same algorithmic steps, is applied over each of the components extracted from the color image in order to identify areas that are not affected by floods. For each component, as described below, a binary map is obtained in which areas identified as non-flood are discarded, leading to a final mask of potential flood areas (PFAs), refined by simple morphological operations. The flood's dominant color is calculated by weighting the potential flood area pixels and hysteresis thresholding yields the final segmentation. An overview of our proposed methodology is graphically depicted in Figure 3. In the following, we analytically present the proposed methodology.  
Figure 2: Sample images from the Flood Semantic Segmentation dataset (**top**) and their corresponding ground truths (**bottom**).
## 2 Materials and Methods
### Methodology
#### 2.2.2 Artificial Data Generation  
Specifically, the unit under test is considered to have a cuboid geometrical shape, with dimensions \(L\times W\times H\) (0.5 m \(\times\) 0.5 m \(\times\) 0.5 m). So in order to generate random dipoles, the six necessary parameter values were drawn via a uniform distribution bounded in the intervals \([-L/2,L/2]\), \([-W/2,W/2]\), and \([0,H]\) for the x, y, and z dipole positions and in the interval \([-p_{max^{*}},p_{max}]\) with \(p_{max}=5\cdot e^{-14}C\cdot m\) for the electric moment [6]. With this in hand, employing Equations (1)-(3), the corresponding \(3\times 14\) electric field values (EDMnet inputs) were calculated for each dipole. These field values, along with the 3 electric dipole position values and the 3 electric dipole moment values (6 EDMnet output values), assembled a 48-element vector as a sample of the dataset.
## 3 Stability Analysis under Weak Grid  
In this section, the influence of the grid impedance variation on the stability of three-phase LCL-filtered inverter controlled by two presented current controllers (the direct grid current control method and the LQR-based integral-resonant state feedback control method) is investigated. Furthermore, the comprehensive assessments are conducted considering all possible filter parameter designs which are commonly classified according to the relative value of the resonance frequency (\(\forall_{R}=\omega_{R}/2\pi\)) and critical frequency defined as \(1/6\) of the switching frequency. Particularly, three LCL filter designs can be considered: the LCL filter has the resonance frequency higher than the critical frequency, around the critical frequency, and lower than the critical frequency. The system stability is analyzed by investigating the closed-loop eigenvalues of the inverter system with two presented controllers under the grid impedance variation in distorted weak grid. Finally, the design guidelines for the presented current controllers are given to achieve both system dynamic performance and strong robustness under weak grid conditions. It is worth noting that other negative effects of weak grid such as unbalanced grid voltages or grid frequency variation are beyond the scope of this paper.
## 2 Materials and Methods
### Test with Synthetic Background Noise Data  
In order to test the effect of the frequency-Bessel transform in extracting multi-order dispersion curves from background noise data, we designed a theoretical model of four layers of horizontally layered media to synthesize noise data. The P-wave and S-wave velocity, density, and depth of each stratum are based on the borehole data collected in the paper. The specific parameters of physical properties are shown in Table 1.  
By the simulation method proposed by [PERSON] et al., in the paper, the center frequency of 6-10 Hz. Reyker wavelet was selected as the source time function, and the discrete wavenumber method was used to randomly generate 1000 vertical noise signals. The source intensity was between 0.001 and 1, with a signal sampling interval of 6 ms, and acquisition time of 60 s. The observation system (Figure 1) adopted a linear arrangement of 60 stations for reception, represented by red dots with a spacing of 2.5 m. The randomly distributed noise is located between the inner and outer circles, represented by blue dots, with an inner circle radius of 500 m and an outer circle radius of 1500 m.  
The synthesized noise data were segmented by 5 s, and the dispersion energy superposition was calculated by the spatial autocorrelation method and the frequency-Bessel transform algorithm, respectively (Figure 2). Among them, the dispersion spectrum calculated by the spatial autocorrelation (a) had a well-developed fundamental dispersion, mainly concentrated at 7-20 Hz, but there was no higher-mode dispersion information in the dispersion spectrum. The dispersion spectrum calculated by the frequency-Bessel transform (b) developed multi-order mode dispersion, with the dispersion of fundamental mode being concentrated at 7-20 Hz, and the higher-mode dispersion also being clear and continuous. It can be seen that the information of the dispersion spectrum provided by the frequency-Bessel transform is more abundant. The synthetic noise data also confirms the theoretical basis and computational effectiveness of the frequency-Bessel transform in extracting multi-order dispersion information.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer No** & **Depth (m)** & **P-Wave Velocity (m/s)** & **S-Wave Velocity (m/s)** & **Density (g/cm\({}^{3}\))** \\ \hline
1 & 0 & 225 & 130 & 1.72 \\
2 & 4 & 450 & 260 & 1.91 \\
3 & 10 & 658 & 380 & 1.97 \\
4 & 27 & 1593 & 920 & 2.87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters.  
Figure 1: Observation system of synthetic background noise.