esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
### Study on Controller Functionality  
The first part of the simulation experiments is addressed to the vehicle safety investigation on the split-\(\upmu\) surface profiles. The maneuvers are simulated on straight and curved roads. For the straight braking, the vehicle was accelerated to 100 km/h and after that the emergency braking was conducted. For curved road braking the vehicle was accelerated to 65 km/h while the transport is cornering left. Braking on a curved road with a split-\(\upmu\) surface is the most extreme situation for vehicle safety, because the yaw rate is created by the driver while cornering. The left side of the road is dry and the right side is icy in the present instance. The vehicle body and the wheel speed curves are shown in Figure 9.  
Before the ABS is activated, the ESP is already reducing the braking pressure from the left side of the vehicle (the dry surface). The slip values of the left side wheels are therefore lower compared to the right side (Figure 10). Although the left side of the road is dry, the left wheels have less than 10% of the wheel slip values, because the ESP reduces the braking pressure from the left half of the vehicle. The driver, thus, is able to control the car path to follow the road. Figure 10: Braking on a curved split-\(\upmu\) road with a half dry–half icy surface; slip profile curves: 1—FL wheel slip, 2—FR wheel slip, 3—RL wheel slip, 4—RR wheel slip. Figure 9: Braking on a curved split-\(\upmu\) road with a half dry–half icy surface; velocity profile curves: 1—FL wheel velocity, 2—FR wheel velocity, 3—RL wheel velocity, 4—RR wheel velocity, 5—vehicle velocity. The dry surface has a higher friction coefficient than the icy road. In this case, the yaw rate is extremely high due to the left cornering. The yaw rate grows after the start of the braking process (Figure 11). However, the ESP reacts very fast and the braking pressure is minimized rapidly. Therefore, the yaw rate remains almost zero during the whole braking distance on the cornering maneuvers. Hence, the driver is able to maintain lateral stability during emergency braking. When the controller is switched off (Figure 11, dashed lines), the vehicle spins left. The driver turns the steering wheel to the right until the maximum allowed angle. Nevertheless, the high yaw rate in the opposite direction makes the car spin. The simulation results with other split-\(\upmu\) road maneuvers are introduced in Table 6, the plots are represented in Figures S1-S9. The ESP safety assistance performance is also compared to the same condition simulations with the turned-off controller. The maximum yaw rate \(\psi_{\max}\) does not exceed \(26^{\circ}/\)s. When the controller is turned off the yaw rate is very high, which makes the car spin around its COG even if the driver tries to keep the vehicle following the road. The braking distance \(s\) and the average body deceleration \(-a_{average}\) for no control simulation are not introduced in the table, because in every case, when the controller is turned off, the car spins.
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 2 Related Work
The distance measure was utilised to identify the handled problem, and K-means clustering was employed to reposition sensors around the alternative cluster head  
Fuzzy-based hybrid clustering and K-means algorithm for wireless sensor networks were proposed to resolve the problem of load balancing and cluster head (CH) selection in cluster areas with minimum energy expenditure [26]. This technique proposes a hybrid methodology that uses an unsupervised machine learning-based K-means algorithm with a fuzzy logic approach to choose cluster heads. More parameters and factors can be considered to improve this technique, such as the distance between nodes and the location of CH to BS. The DCK-LEACH protocol [27] is based on K-means and Canopy optimisation. This research depends on combining the dynamic Canopy algorithm and the K-means algorithm for effective clustering. This study presents a method for managing the frequency of reclustering processes to decrease energy waste. This algorithm utilises a hierarchical approach for the election of cluster heads, which alleviates the workload of the cluster head and achieves a balanced distribution of network traffic. A study [28] proposes using an unmanned aerial vehicle (UAV) as a flying relay to receive and transfer signals using nonorthogonal multiple access (NOMA) for optimal spectrum-sharing efficiency by using the K-means unsupervised machine learning and the gap statistic approach to cluster sensors and optimise UAV location. This work presents a method to optimise UAV trajectory: the centroid-to-next-nearest-centroid (CNNC) route. The study found that the K-means algorithm is effective for deploying large and complex WSNs.
# Multi-Material Topology Optimization Taking into Account the Position of Material Interfaces in 3D
## 3 Methods
### Extended ITED  
#### 3.6.1 Approach  
The subsequent interface energy density (Extended ITED) is used to consider the material interfaces in the topology optimization in 3D:  
\[W_{itel}=\frac{1}{2}\sigma_{nn}^{t}\varepsilon_{nn}^{t}+\beta\Big{(}\frac{1}{2 }|\sigma_{ns}\varepsilon_{ns}|+\frac{1}{2}|\sigma_{nt}\varepsilon_{nt}|\Big{)}. \tag{14}\]  
Here, \(\sigma_{nn}^{t}\) is the positive tensile normal stress on the interface:  
\[\sigma_{nn}^{t}=\begin{cases}\sigma_{nn}&,\sigma_{nn}>0\\ 0&,\sigma_{nn}\leq 0\end{cases} \tag{15}\]  
and \(\sigma_{nn}=\mathbf{n}\cdot\mathbb{C}[\varepsilon(\mathbf{u})]\mathbf{n}\).  
\[\sigma_{ns}=\mathbf{n}\cdot\mathbb{C}[\varepsilon(\mathbf{u})]\mathbf{s},\quad\varepsilon _{ns}=\mathbf{n}\cdot\varepsilon(\mathbf{u})\mathbf{s} \tag{16}\]  
Similarly, \(\sigma_{nt}\) and \(\varepsilon_{nt}\) are to be calculated.  
The first term of Equation (14) is identical to [27] and penalizes material interfaces whose position is in the tensile region. The penalization occurs because material interfaces whose position is in the tensile region lead to high energy values. The minimization of this term should lead to a shift of the material interfaces into the compression regions. This fulfills requirement 1 (see Section 2) and avoids splitting and peeling stresses in the optimization results.  
The second and third terms of Equation (14) are for the consideration of the shear components of the interface stress. The shear components are evaluated in the same way as [PERSON]'s strength criterion. The ratio between the first term and the two shear components is set via the factor \(\beta\). The absolute values are required as the aim is to reduce the shear stresses symmetrically (both negative and positive). After the first term has been reduced, the shear components ensure that the shear stresses in the interface are minimized. For this purpose, the surfaces are adjusted so that surfaces loaded in shear are converted into surfaces loaded in compression, as compressive loads do not increase the extended ITED (hereinafter ITED). This fulfills requirement 2 (see Section 2).  
Figure 3: Orthonormal basis \(\{\mathbf{n},\mathbf{s},\mathbf{t}\}\) at a point on the interface \(\Gamma\). The normal vector \(\mathbf{n}\) is calculated from the corresponding LSF \(\varphi\) and the two tangent vectors \(\mathbf{s}\) and \(\mathbf{t}\) using the Householder transformation.
# Cell Selection Technique for Millimeter-Wave Cellular Systems with Hybrid Beamforming
## 5 Conclusions  
In this paper, a cell selection technique for mm-wave cellular systems with hybrid beamforming is proposed using the CBSS in the initialization stage. It is shown by simulation that the CID and BID can be detected correctly in a multicell environment with STOs, by assigning an appropriate value to the parameter (\(L\)) in the CBSS. The proposed technique with the CBSS is shown to require only 6% of the processing time for channel estimation and cell selection, compared to the conventional technique, when the number of RF transmit chains is 8. It is also shown that the proposed cell section technique can achieve 3.64 bps/Hz (UPA) gain in channel capacity or 2 dB gain in BER, when \(N_{RF}=3\). Although the cell selection technique is described in this paper for mm-wave cellular systems with hybrid beamforming in the initialization stage, it can also be applied to cell searching in the handover period.  
**Acknowledgments: This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2012-0-00559) supervised by the IITP (Institute for Information & Communications Technology Promotion) and Basic Science  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Conventional Technique** & **Proposed Technique** \\ \hline Processing Time & \(T_{\text{SS}}\times N_{I}\times N_{B}\times N_{C}\) & \((T_{\text{SS}}+T_{\text{CBSS}})\times N_{I}\times(N_{B}/N_{RF})\) \\ \hline \multirow{2}{*}{Example} & \multirow{2}{*}{3195 μs} & \(N_{RF}=1\) & 1597 μs \\  & & \(N_{RF}=2\) & 799 μs \\  & & \(N_{RF}=8\) & 200 μs \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Number of \\ complex multiplications \\ \end{tabular} } & PSS + SSS & PSS + SSS + CBSS \\  & \(((62(504/3+4)\times N_{B}N_{I})\) & \(((62(504/3+4)+N+\left\lfloor N\log_{2}N/2\right\rfloor)\times N_{B}N_{I}/N_{ RF})\) \\ \hline \multirow{2}{*}{Example} & \multirow{2}{*}{1,364,992} & \(N_{RF}=1\) & 1,411,328 \\  & & \(N_{RF}=2\) & 705,664 \\ \end{tabular}
\end{table}
Table 1: Processing time and computational complexity when the conventional and proposed techniques are used.  
Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01057628).  
**Author Contributions:** [PERSON] and [PERSON] developed a cell selection technique for mmWave cellular systems with hybrid beamforming. [PERSON] supervised the project.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Data
#### 2.2.2 The Remote Sensing Data  
The remote sensing data used in this research are satellite data recorded by Sentinel missions operated by the ESA [14] and provided through the Copernicus Data Space Service [35]. The data used were recorded by the Sentinel 2 mission with the Multispectral Instrument (MSI) with the L2A level of processing. This product contains atmospheric-corrected images. The spectral bands of interest are 8A and 12, where band 8A is in the near-infra-red wavelengths, and band 12 is from the short-wave-infra-red wavelengths. The data's spatial resolution is 20 m, and the temporal resolution is 5 days [36].  
Data recorded by the Sentinel 3 mission are the LST and FRP. Data were recorded two times a day, before noon and in the evening, with a spatial resolution of 1 km [15]. The FRP data are recorded only when fire events exist when the satellite crosses over the area of interest with a spatial resolution of 1 km. The FRP presents the estimated released thermal energy of the fire event at the moment when the satellite crosses over the fire event [15].  
The Sentinel 5p products used are the Aerosol Index (AI) and Aerosol Layer Height (LH). The AI is calculated from the 340-380 nm (AI\({}_{340/380}\)) and 354-388 nm (AI\({}_{354/388}\)) spectral band data. It can be positive and negative. Positive AI values indicate strongly absorbing aerosols in the UV spectral range (e.g., desert dust, biomass burning smoke, etc.). Near-zero and negative values indicate the presence of near-surface aerosols (e.g., sea salt). Strongly negative values indicate the presence of sulphates [37]. One of the differences in spectral bands is in bias and instrument error. The AI\({}_{340/380}\) has a higher bias and a more-minor system error than the AI\({}_{354/388}\)[37]. The Sentinel 5p data spatial resolution is 5.5 km \(\times\) 3.5 km (5.5 km in the direction of satellite flight and 3.5 km in the direction of  
Figure 1: The area of interest is Croatia (red box) and the Istra–Kvarner–Rijeka area (blue box). Position coordinates are approximate.  
perpendicular flight at the nadir). Aerosol vertical dispersion monitoring is optimal in a resolution of 250 m. The data are retrieved once a day [1; 37].  
This research also used data recorded by the SEVIRI instrument on the Meteosat Second Generation (MSG) satellite operated by EUMETSAT. It used cloud cover images from the Dust RGB product. The spatial resolution of the SEVIRI instrument images is up to 3 km, and the temporal resolution is 15 min [38].  
Information about forest fire events provided by the European Forest Fire Information System (EFFIS) [39] can also be categorized as remote sensing data. The forest fire data were originally recorded by the Sentinel 2 mission and processed by the EFFIS to obtain the forest fire time, location, and dimensions of the burnt area [39].
## 2 Materials and Methods
### Dataset
#### 2.2.2 Multi-Temporal PS-InSAR
The different InSAR techniques that are currently available can be broadly categorized as follows: (a) PS Interferometry, first developed by [PERSON] et al. [40]; (b) Small--BASeline Subsets (SBASs) and its variants by [PERSON] et al. [47]; and (c) hybrids of PS-InSAR and SBAS, such as StaMPS (Stanford Method for Persistent Scatterers) [48] and SqueeSAR [49]. PS-InSAR processing aims to identify strong and consistent signals emanating from prominent reflectors, such as man-made structures, sharp objects and buildings. There are other evaluations of different strategies in the literature that go into more detail, e.g., [39; 50]. In this paper, a standard PS-InSAR approach is proposed, focusing on the vertical deformation of the railway and the strong, persistent reflective behavior of the rails due to their metallic structure [1; 9]. This strong reflectivity characteristic can be used to generate sufficient PSs for APS estimation and the PS-InSAR approach [34]. During these PS-InSAR analyses, SARPROZ(r) software was used, and the software mainly developed in MATLAB(r) R2015a can select different sets of interferograms to combine and process long data series and offers many different options and techniques [51]. Parameters can be estimated with the traditional PS-InSAR algorithm. All details about this interferometric package can be found in [52; 53] and on the official website [54].
## 5 Experimental Results and Analysis
### Comparative Experiments
And the remaining cluster head selection process is based on the residual energy of the nodes. Node which has the higher residual energy acts as the cluster head for the corresponding rounds. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ALGORITHM** & **FND** & **HND** & **LND (90\%)** \\ \hline KM-LEACH & 440 & 1004 & 1087 \\ DBCH & 611 & 819 & 1106 \\ EESCA & 948 & 997 & 1085 \\ CFSFDP-E & 1003 & 1025 & 1031 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Network lifetime Comparison of KM-LEACH, DBCH, EESCA and CFSFDP-E.  
Figure 7: Network Lifetime comparison of ALEACH, C-LEACH, K-LEACH and CFSFDP-E. These algorithms are simulated in the same experimental situations to compare the performance. From Figure 8, it is obvious that CFSFDP-E algorithm outperforms KM-LEACH, DBCH and EESCA in terms of energy consumption equilibrium. The FND of CFSFDP-E is 1003, which is over 2 times higher than KM-LEACH, 1.6 times than DBCH and over 50 rounds larger than EESCA. We can find that the result of EESCA is very close to our proposed algorithm CFSFDP-E. However, the clustering scheme of EESCA is inflexible. It always divides all the nodes into four clusters based on their coordinates, while the CFSFDP-E algorithm divides various clusters based on changing local density, residual energy and distance of each living node in every round. From the analysis above, our proposed CFSFDP-E algorithm can balance energy consumption effectively based on every node's local density, distance and residual energy. Figure 9 shows FND, HND and LND for above algorithms. It is proved that our proposed CFSFDP-E algorithm prolongs the life span of the first dead node and half dead nodes compared to these related algorithms.
## 2 Theoretical Framework
### Wind Turbine and Generator  
The wind turbine extracts kinetic energy from the wind and transforms it into mechanical energy through the rotation of its blades. This mechanical energy is then converted into electrical power by the generator. The power available in the wind \(P_{w}\) that crosses the swept area \(A\) of the turbine rotor can be described by the following equation:  
\[P_{w}=\frac{1}{2}\rho\text{Av}^{3} \tag{1}\]  
where \(\rho\) is the air density and \(v\) is the wind speed. However, due to aerodynamic limitations, only a portion of this power can be harnessed by the turbine. The actual power extracted \(P_{t}\) is determined by the power coefficient \(C_{p}\), which is a function of the tip-speed ratio \(\lambda\):  
\[P_{t}=\frac{1}{2}\rho\text{Av}^{3}C_{p}(\lambda,\beta) \tag{2}\]  
Figure 1: Wind energy conversion system.  
\(\beta\) is the pitch angle and the tip-speed ratio \(\lambda\) is defined as:  
\[\lambda=\frac{\omega\mathrm{R}}{\
u} \tag{3}\]  
where \(\omega\) is the angular speed of the rotor and \(\mathrm{R}\) is the radius of the rotor area. In small-scale wind turbines, the pitch angle is typically fixed, so depends solely on \(\lambda\), reaching its maximum value \(\mathrm{C_{p,max}}\) when the turbine operates at the optimal tip-speed ratio \(\lambda_{\mathrm{opt}}\). In this scenario, the mechanical torque \(\mathrm{T_{t}}\) applied to the shaft is:  
\[\mathrm{T_{t}}=\frac{\mathrm{P_{t}}}{3\omega} \tag{4}\]  
Assuming a direct-drive configuration, the turbine rotor is mechanically coupled to a Permanent Magnet Synchronous Generator (PMSG). The PMSG generates a back-electromotive force proportional to the shaft speed, as expressed by:  
\[\mathrm{E_{A}}=\mathrm{K_{e}}\cdot\omega \tag{5}\]  
where \(\mathrm{K_{e}}\) is the electromotive force constant. Under steady-state operation and neglecting losses, the electrical power output \(\mathrm{P_{e}}\) at the generator terminals is approximately equal to the mechanical power \(\mathrm{P_{t}}\). In the equivalent electrical circuit of the PMSG per phase [36], it can be seen that the internal voltage source \(\mathrm{E_{A}}\) is in series with the stator resistance \(\mathrm{R_{s}}\) and synchronous reactance \(X_{s}\):  
\[\mathrm{V}=\mathrm{E_{a}}-\mathrm{I}\cdot(\mathrm{R_{s}}+\mathrm{j}\mathrm{X_ {s}}) \tag{6}\]  
This simplified model links wind speed to electrical power generation, laying the groundwork for control via a DC-DC boost converter, which dynamically adjusts the electrical load to optimize or limit power output.
## 1 Introduction
This approach has been shown to improve image fusion performance, especially in cases in which there is significant variation in the input images. Image fusion is a process that involves gathering significant details from multiple images to produce a reduced set of images, typically just one image. The resulting image is more precise and instructive than any individual image source since it contains all the essential details. The primary objective of image fusion is to have fewer images with more information and to produce images, which are easily interpretable for both humans and machines [23; 24; 25]. When various images are combined into a single image to include relevant information, it is referred to as multisensory image fusion in computer vision [26]. An image created by fusing many images contains more information than any of the individual images used to create it [27]. This paper proposed a new human fall detection system using four-stream 3D CNNs. The major contributions of the system are as follows. Each of the system's streams is associated with one of the four stages of human decline (standing or walking, falling, fell, and at rest). Each stage consists of a series of frames with a certain orientation(s). The sequences are organized into four groups based on these characteristics. Phase-wise frame feeding to 3D convolutional neural networks is a novel idea for detecting human falls. In other studies, multi-stream CNNs have been utilized, but just to represent frames in a variety of ways, not to distinguish between stages. The poor accuracy of these systems may be traced back to the fact that some earlier efforts simply fed the CNN a set number of frames without paying attention to the semantic information (human fall phases) included within them. The remaining parts of this paper are organized into five main sections. Section 2 reviews previous studies in the field of human fall detection, analyzing different approachesto the problem and identifying gaps in knowledge. In Section 3, the proposed methodology for the human fall detection system is detailed, highlighting the key features and their functions. Section 4 presents an in-depth analysis of the conducted experiments that evaluate the efficacy of the proposed system. The results are discussed in Section 5, which compares the proposed method's performance with that of other approaches. Finally, Section 6 concludes the paper, summarizing the main findings, limitations, and potential avenues for future research in related fields.
# A Moment-Fitted Extended Spectral Cell Method for Structural Health Monitoring Applications
###### Abstract  
The spectral cell method has been shown as an efficient tool for performing dynamic analyses over complex domains. Its good performance can be attributed to the combination of the spectral element method with mesh-independent geometrical descriptions and the adoption of customized mass lumping procedures for elements intersected by a boundary, which enable it to exploit highly efficient, explicit solvers. In this contribution, we introduce the use of partition-of-unity enrichment functions, so that additional domain features, such as cracks or material interfaces, can be seamlessly added to the modeling process. By virtue of the optimal lumping paradigm, explicit time integration algorithms can be readily applied to the non-enriched portion of a domain, which allows one to maintain fast computing simulations. However, the handling of enriched elements remains an open issue, particularly with respect to stability and accuracy concerns. In addressing this, we propose a novel mass lumping method for enriched spectral elements in the form of a customized moment-fitting procedure and study its accuracy and stability. While the moment-fitting equations are deployed in an effort to minimize the lumping error, stability issues are alleviated by deploying a leap-frog algorithm for the solution of the equations of motion. This approach is numerically benchmarked in the 2D and 3D modeling of damaged aluminium components and validated in comparison with experimental scanning laser Doppler vibrometer data of a composite panel under piezo-electric excitation.  
spectral cell method; spectral element method; partition-of-unity enrichment; mass matrix lumping; moment-fitting Article  
A Moment-Fitted Extended Spectral Cell Method for Structural Health Monitoring Applications. April. Sci. 2023, 13, 10367. [[https://doi.org/10.3390/appl31810367](https://doi.org/10.3390/appl31810367)]([https://doi.org/10.3390/appl31810367](https://doi.org/10.3390/appl31810367))