esa-sceva/satcom-chunk-collection huggingface dataset:
# Fiber Bragg Gratings Based Cyclic Strain Measuring of Weld Toes of Cruciform Joints
###### Abstract  
The real weld toe geometry is generally not mathematically perfect, resulting in obvious stress concentration effects, both on the weld section and along the longitudinal direction of the weld toe. The true stress-strain state at the local weld toe directly affects the fatigue performance and behavior of the welded structure. Therefore, a Fiber Bragg Grating (FBG) sensor based method for testing the cyclic strain at the weld toe was proposed. Cruciform welded joints were fabricated as specimens on which FBG sensors were arranged at several characteristic points along the weld toe curve. Strains at all the characteristic points under cyclic tensile load were measured and recorded, which showed the proposed measuring method could accurately obtain the complete local strain time histories along the weld toe. The strain time histories clearly reflected the cyclic hardening phenomenon in the early stage and the plastic yielding phenomenon in the final stage. Furthermore, based on the cyclic stress-strain constitutive model of the weld material, the stress-strain response curves of all the characteristic points were drawn. Combined with the fatigue fracture morphology, the mechanism of the unsynchronized initiation of the multiple cracks in the weld toe was investigated.  
cruciform joints; FBG sensors; cyclic strain; weld toe multiple cracks; fatigue test +
Footnote †: journal: Applied Sciences
## 2 Literature Review
### Online Learning in Saudi Arabia
Madrasati is an internet-based platform that functions as a virtual classroom, facilitating simultaneous virtual meetings between instructors and students, or at their convenience, via pre-recorded lessons. An online random sampling technique was employed to survey 3000 students enrolled in grades 1 through 12 across various schools. The research found that system, service, and content quality, along with technological infrastructure, security considerations, and training, are pivotal in enhancing student engagement with the platform. Additionally, [PERSON] et al. [31] conducted a study aimed at demonstrating the effects of various factors--such as time constraints, lack of support, technical difficulties, internet accessibility issues, and insufficient technical skills--involved in distance learning in Saudi Arabia amid the COVID-19 epidemic. Furthermore, they investigated how online learning affected students' stress and anxiety levels. Employing a structural equation model, the scholars analyzed data collected from 260 students. The results indicated that factors like time constraints, lack of support, and technical issues negatively influenced online learning. Moreover, the study demonstrated the significant influence of internet accessibility and costs on online learning. Additionally, the research identified online learning as a being a significant predictor of stress and anxiety during the pandemic. Drawing from the preceding discussion, all universities within Saudi Arabia underwent and implemented online learning during the COVID-19 period. Consequently, several institutions initiated the offering of online learning following the pandemic [5]. Nonetheless, the majority of prior research was centered on examining online learning before or during the COVID-19 period, even in recently published studies. There have been limited quantitative studies into the present state of online learning despite the widespread adoption of this educational approach by most Saudi universities. Therefore, it is essential to assess students' satisfaction with online learning in the post-pandemic period. Such an investigation into student satisfaction holds the potential to improve the effectiveness and quality of distance learning.
# A Blockchain Protocol for Authenticating Space Communications between Satellites Constellations
## 7 Conclusions  
This study aimed to introduce a blockchain protocol called Proof of space transactions (PoST) to model, manage and authenticate satellite transactions within various constellations (or swarms). The main contribution of this study showed that tokenizing space transactions in the form of space digital tokens (SDT) and processing them using the proposed PoST protocol is a promising solution for managing and authenticating space transactions based on blockchain technology. This study has identified the effectiveness of the PoST protocol based on some simulation experiments that proved the effectiveness of the proposed Blockchain protocol in managing and authenticating satellite transactions in P2P networks.  
According to the obtained results, it could be concluded that this study appears to be one of the first mature attempts to thoroughly investigate tokenizing space transactions and processing them using Blockchain. So, these results are expected to improve space communications management and develop a new generation of Blockchain-based satellite systems.  
This study could be further enhanced by conducting additional experimental simulations and evaluations to investigate the effectiveness of the proposed PoST protocol for studying two important problems in this study, firstly, managing and authenticating TDRS's satellites, and ground station transactions. Secondly, immunizing a satellite constellation against space debris collisions based on the sensing orbital data between satellites and space debris.  
Conceptualization: M.T.; data curation, M.T. and E.G.; Formal Analysis: M.T. and T.G.; Investigation: M.T. and T.G.; Methodology: M.T.; Senior Administration: A.E.H.; Software: E.G.; Supervision: A.E.H.; Validation: M.T. and T.G.; Visualization: M.T.; Writing--original draft, M.T.; Writing--review and editing: T.G. and V.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded with the support of the Slovak Operational Programme Integrated Infrastructure in the frame of the project: Intelligent systems for UAV real-time operation and data processing, code ITMS2014+: 313011V422 and co-financed by the European Regional Development Fund.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 2 Mathematical Models of the System Components
### Electrolyzer  
To supply the hydrogen fuel for the operation of the _FC_, an electrolyzer is used. It generates hydrogen from water via electrolysis. The chemical reaction in an electrolyzer is the reverse of that in an _FC_. The power absorbed by the electrolyzer and the generated hydrogen mass are related by the expression below [9]:  
\[P_{EL}=Bq_{H_{2},\ non}+Aq_{H_{2},\ gen} \tag{8}\]  
where \(P_{EL}\) denotes the power consumed by the electrolyzer system, \(q_{H_{2},\ non}\) denotes the nominal hydrogen mass flow generated by the electrolyzer, while \(q_{H_{2},\ gen}\) symbolizes the actual generated hydrogen mass flow (kg/h). A and B are the consumption coefficients of the electrolyzer power curve where A = 10 kW/kg and B = 40 kW/kg were used in this paper.
## 2 Materials and Methods
### Datasets
#### 2.2.2 Satellite Data  
The RTSIF data obtained from the Sentinel 5P TROPOspheric Monitoring Instrument have been proven to have good temporal and spatial resolution [31; 32]. In previous studies, we first coupled multiple indicators for comparative analysis and found that there was still some room for improvement in using RTSIF data for GPP estimation in the arid regions of Xinjiang. Subsequently, we utilized MATLAB 2022a software to comprehensively convert the RTSIF data format. We also used ArcPro 3.3.1 software for mask cropping and resampling. Then, we combined land-use data and applied canopy and linear precision improvement methods. Finally, we generated a higher-precision RTSIF dataset with a consistent temporal and spatial resolution within the study area [24]. The comprehensively generated higher-precision RTSIF data were recorded every 8 days with a resolution of 0.05\({}^{\circ}\). This study was based on the 2020 data from this dataset.  
There are differences in the basic information obtained from MODIS GPP and five environmental factor satellite products (PAR, VPD, Tsoil, Tair, and SWC) (Table 2). The time scale used for these data is also 2020.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station** & **Coordinates** & \begin{tabular}{c} **Area and** \\ **Underlying** \\ **Surface** \\ \end{tabular} & **Elevation** & **Instruments and Accuracy** & **Data Application** \\ \hline \
\begin{tabular}{c} Ulan Usa \\ Station \\ \end{tabular} & 44\({}^{\circ}\)16\({}^{\prime}\)59\({}^{\prime\prime}\) N & Northern Slope & 406.9 m &
\begin{tabular}{c} The CO\({}_{2}\) and H\({}_{3}\)O data in the eddy covariance system were measured by an open-path LT500 infrared gas analyzer. \\ \end{tabular} & In this study, environmental factor data (Tair, VPD, Tsoil, SWC, and PAR) from 2020, along with the corresponding eddy covariance and radiation data from each data were collected by the CRX000 data logger at frequencies of 10 Hz/2 Hz, with time frequencies of 10 \(\mathrm{s}\), 1 mm, 30 min, and 1 h. The radiation and gradient data measured by the radiation and gradient system had the same time frequency and collection frequency of 1 Hz. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information on the observation stations for the three ecosystems in the study area.
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 3 Human Activity Signatures Measured by Using a SISO FMCW Radar System
### Scenarios of Human Activities  
The indoor environment is a laboratory cluttered with fixed items such as wooden furniture, computers, routers, and other miscellaneous laboratory tools and electronics. The direction of activities of a person relative to the SISO radar system are depicted in Figure 1. To elucidate the direction of human motion in an experiment, we refer throughout this section to a \(3\times 3\) grid illustrated in Figure 1, in which three different scenarios of human movement are represented by different markings. The human activities have been carried out in three different directions in order to demonstrate the limitations of the SISO radar system in the context of HAR. Scenario 1 is a trivial scenario, where a person moves towards the SISO radar system. In this case, the human motion is parallel to the radar boresight, implying that the SISO radar system does not encounter any problem capturing the micro-Doppler signatures with high accuracy. Most of the research on HAR is limited to merely Scenario 1 with the SISO radar systems in a monostatic configuration. However, the shortcomings of SISO radar configuration come to the surface if we consider a human activity that is perpendicular to the boresight of the SISO radar system. Thus, in Scenario 2, when a person moves perpendicular to the radar boresight or moves from the position \((x_{2},y_{3})\) to \((x_{2},y_{1})\), then the SISO radar system captures a completely different micro-Doppler signature that is suboptimal for HAR. In Scenario 3, the person moves diagonally in the \(3\times 3\) grid of Figure 1, either from \((x_{3},y_{3})\) to \((x_{1},y_{1})\) or from \((x_{1},y_{1})\) to \((x_{3},y_{3})\). We expect to acquire adequate human activity signatures in Scenario 3, but not as good as the human activity signatures of Scenario 1. For each of Scenarios 1, 2, and 3, we consider a walking and falling activity. The results of the recognized human activities by employing the monostatic SISO radar system are shown in the next subsection for Scenarios 1, 2, and 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Symbols** & **Values** \\ \hline RF cable lengths & \((L_{1}^{T_{x}},L_{1}^{R_{x}})\) & \((0.3,0.3)\) m \\ Carrier frequency & \(f_{c}\) & 24.125 GHz \\ Radar’s bandwidth & BW & 250 MHz \\ Sweep time & \(T_{sw}\) & 500 μs \\ Pulse repetition freq. & PRF & 2 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters of the SISO experimental setup.
# Tunable Plasmonic Talbot Effect Based on Graphene Monolayer
## 3 Calculation Results and Discussion
### The Talbot Effect Distance \(Z_{t}\) as a Function of the Multiple Parameters
In order to further understand the behavior of \(Z_{T}\) shown in Figure 4a, the real part of effective index denoted by \(\text{Re}\Big{(}n_{eff}\Big{)}=\text{Real}(\beta/k_{0})\) and the imaginary part of effective index described as \(\text{Im}\Big{(}n_{eff}\Big{)}=\text{Imag}(\beta/k_{0})\) are plotted as a function of chemical potential from 0.2 to 0.7 eV at a given wavelength of 7 \(\upmu\)m as shown in Figure 4b. It can be seen that the real part of the effective index \(\text{Re}\Big{(}n_{eff}\Big{)}\) decreases with the increasing chemical potential, which suggests the increasing effective wavelength along the graphene sheet, and finally results in the decreasing Talbot effect distance \(Z_{T}\), according to Equation (9).
# Real-Time Egress Model for Multiplex Buildings under Fire Based on Artificial Neural Network
## 4 Egress Model for Multiplex Buildings in Fire
### Application of Proposed Egress Model  
In this study, the optimal evacuation route for the simulated fire scenario was derived using the proposed egress model. As shown in Figure 18, it was assumed that a fire occurred at No. 1 position, and the walking speed of the occupant was set at 1.272 m/s. The visibility and gas measurement sensors were installed at No. 2-7 nodes, and the emergency exits were expressed as Exit 1 and Exit 2.  
Table 12 shows a summary of the available safe egress time (ASET), movement time between adjacent nodes (\(t_{mov}\)), and margin of safety (\(\text{ASET}-t_{mov}\)) calculated using the proposed model. The last column of Table 12 shows whether or not the evacuation path is safe. In other words, if the calculated margin of safety is greater than zero, the value is '1', otherwise it is '0'. If the occupant considers only the shortest route for evacuation, the evacuation route is \(1\to 2\to 4\to 6\to\text{Exit 1}\), as shown in Figure 18. On the other hand, since the proposed model considers not only the movement time of occupants but also ASET at the fire compartment and nonfire compartments, it can offer the evacuation route with the highest margin of safety, which would be \(1\to 2\to 3\to 5\to 7\to\text{Exit 2}\). In other words, although the evacuation time of the shortest route was 40.88 s, whereas that of the route provided by the proposed model was 47.17 s, the margin of safety was 108.12 s for the shortest route, but was 161.83 s for the proposed route. This suggests that the proposed route can lead to the safe evacuation of more occupants. Therefore, it is expected that the use of the proposed egress model can contribute to reducing the number of casualties when a fire occurs in a multiplex building.  
Figure 17: Calculation of margin of safety.  
Figure 18: Application example of the egress model.
# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
## 3 Methodology
### Traditional Line-Time Normalization
Assuming that the number of inner CCDs is \(N\), the largest scan lines among all inner CCD images is \(R_{N}\); then, the normalized sampling rate \(f_{N}\) can be calculated by  
\[f_{N}=\frac{T_{e}-T_{s}}{R_{N}} \tag{3}\]  
Generally, the imaging period for all inner CCDs is nearly the same, while the sampling rates differ from each other. Therefore, we can derive the imaging time after normalization for the \(n\)th row of the normalized image as follows:  
\[T_{c}=f_{N}\cdot n+T_{0} \tag{4}\]  
In addition, \(T_{c}\) can be obtained using the raw image data before normalization, and the normalized image ca then be obtained by interpolating the raw data based on the relationship between the original and normalized imaging times of each scan line. Initially, pixels located in different segments may have different sampling intervals in the along-track direction due to the change in the line-time. After normalization of the line-time, all inner CCD images are resampled with the same imaging frequency, as shown in Figure 6b. This method was developed based on the assumption that the line-time  
Figure 5: Rigorous sensor model of stitched CCD imagery: (**a**) rigorous model in object-space and (**b**) rigorous model in image-space. in each segment remains stable, which suggests an ideal design. In fact, the sampling intervals in image-space of different scan lines differ considerably from each other, leading to telescopic differences between pixels.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
### Research Contribution  
Accurate wind data prediction is essential for various applications [39; 43]. A reliable multi-time horizon forecast of wind speed and power can help schedule energy delivery to the grid in advance, reducing operating costs. The research aims to provide a comprehensive evaluation of these algorithms' accuracy and computational efficiency for decision-makers and policymakers to manage the city electricity supply. The novelty of this paper lies in developing an integrated multi-time horizons forecasting system for WSF and WPF, optimizing both prediction accuracy and computational cost. By assessing the strengths and weaknesses of these models, the study seeks to identify the most effective algorithms for forecasting wind energy. The results will offer valuable insights for simulating, analyzing, and managing wind energy scenarios based on informed decision-making.
## 2 Materials and Methods
### Image Features Extraction  
Image feature extraction is the process of automatically identifying and extracting notable and meaningful information or representations from digital images. It can also be defined as reducing a large amount of raw data into smaller and more manageable representations [25]. Feature extraction involves identifying and describing an image's key visual characteristics or features, such as edges, corners, textures, shapes, colors, and patterns [26].  
Image feature extraction aims to reduce the complexity of an image by extracting the most important and relevant information that represents the original image [27]. Feature extraction is beneficial for various applications, such as object recognition, image retrieval, medical imaging, video surveillance, and other related tasks [28].  
Multiple techniques and algorithms are used for image feature extraction, including edge detection, corner detection, blob detection, and texture analysis. In feature extraction, different techniques can be employed individually or in combination to extract a wide range of features [29]. The extracted features can be used as inputs to machine learning algorithms for image classification, object detection, and other problems. In our case, extracting color and texture features have been adopted. After capturing images of ten apple varieties, undertaking necessary preprocessing steps, color moments, and a textural feature, a histogram of oriented gradient (HOG), was extracted.
# Studies on a Piezoelectric Cylindrical Transducer for Borehole Dipole Acoustic Measurements
## 2 Principles and Numerical Analyses
### BENDER Bar and Its Vibration Principles
For the metal layer at both sides (\(L_{p}/2<|x|<L_{m}/2\)), the vibration equation is:  
\[D_{m}\
abla^{2}\
abla^{2}w+\rho_{m}H_{m}\frac{\partial^{2}w}{\partial t^{2}}=0, \tag{4}\]  
where \(D_{m}=\frac{E_{m}H_{m}^{3}}{12(1-\sigma_{m}^{2})}\) is the stiffness coefficient of the metal layer. Suppose that the usual excitation is symmetric, the symmetrical vibration modes of bender bar are excited. For Equation (3) of the composite layer, the displacement is denoted as:  
\[w_{c}(x)=A_{1}\cosh\frac{\omega}{v_{1}}x+C_{1}\cos\frac{\omega}{v_{1}}x, \tag{5}\]  
where \(v_{1}=\sqrt[4]{\frac{\omega^{2}D}{\rho H}},A_{1},C_{1}\) are constants to be determined. For Equation (4) of the isotropic metal part, the displacement is denoted as:  
\[w_{m}(x)=A_{2}\cosh\frac{\omega}{v_{2}}x+B_{2}\sinh\frac{\omega}{v_{2}}x+C_{2 }\cos\frac{\omega}{v_{2}}x+D_{2}\sin\frac{\omega}{v_{2}}x, \tag{6}\]  
where \(v_{2}=\sqrt[4]{\frac{\omega^{2}D_{m}}{\rho_{m}H_{m}}},A_{2},B_{2},C_{2},D_{2}\) are constants to be determined. Considering the continuity boundary condition at the junction between the composite layer and the metal layer, the physical function such as the transverse displacement, the shear force, the rotation angle, and the bending moment are continuous [15]. Moreover, the fixed boundary conditions need to be met at both of the ends of the bender bar. After substituting transverse displacement expressions in Equations (5) and (6) into continuity and fixed boundary conditions, we obtained the following coefficient matrix equations:  
\[a_{ij}A_{j}=c_{i},i,j=1,2,\cdot\cdot\cdot 6. \tag{7}\]  
According to the coefficient determinant value \(\left|a_{ij}\right|=0\), we calculated the resonant frequencies and corresponding vibration modes. For the long and short benders that we used to actuate our cylindrical oscillating transducer, the resonant frequencies were 402 Hz, 3692 Hz, 767 Hz and 4705 Hz, calculated by our theory. The first two frequencies corresponded to the first and third orders of the bending modes of the long bender, and the latter two frequencies corresponded to those of the short bender. We used the finite element method to simulate vibration modes of the bender bar, as shown in Figure 2.
## 2 Materials and Methods
### Methods
#### 2.2.5 Evaluation of Model Performances  
We assessed the accuracy of the phenology model based on two metrics: RMSE and Pearson correlation coefficient (\(r\)) [10; 28]. The original CASA output \(NPP_{org}\) and the phenology-modified CASA output \(NPP_{mod}\) were compared with the MODIS gap-filled yearly NPP dataset (MOD17A3 HGF) using the coefficient of determination (R2) and RMSE. Additionally, the performance of NEP estimation was also evaluated using R2 and RMSE by comparing NEP flux data with NEP data produced by either the original CASA or the phenology-modified CASA [11].