esa-sceva/satcom-chunk-collection huggingface dataset:
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 3 Operation Principles
### State 8 (\(t_{7}<t<t_{8}\), Figure 6h)  
At the time \(t=t_{7}\), the current \(i_{Lr1}\) flows through the anti-parallel diode \(D_{m1}\) to release the energy stored in the magnetizing inductance \(L_{m1}\) back to the input power source, and the equivalent circuit is as shown in Figure 6h. The driving signal of the main power switch \(S_{m1}\) should be applied in this interval. Since the anti-parallel diode \(D_{m1}\) has been turned on, the switch \(S_{m1}\) can achieve the characteristic of ZVS turn-on. Because the forward diode \(D_{1}\) and the freewheel diode \(D_{2}\) are remained being on state, the leakage inductor current \(i_{Lr1}\) continues to increase linearly with the slope of \(V_{in}\)/\(L_{r1}\), and can be expressed as:  
\[i_{Lr1}(t)\ =\ i_{Lr1}(t_{7})+\frac{V_{in}}{L_{r1}}(t-t_{7}). \tag{27}\]  
When the current \(i_{Lr1}\) rises to be zero, this mode ends and the time it takes is:  
\[t_{8}-t_{7}\ =\ \frac{-L_{r1}i_{Lr}(t_{7})}{V_{in}}. \tag{28}\]
At the time \(t=t_{8}\), the leakage inductor current \(i_{Lr1}\) becomes positive. The forward diode current \(i_{D1}\) continues to increase linearly, and the freewheel diode current \(i_{D2}\) continues to decrease linearly. The equivalent circuit is shown in Figure 6i. In this state, the voltage \(v_{ds1}\) across the main switch and the leakage inductance current \(i_{Lr1}\) can be respectively expressed as:  
\[v_{ds1}(t)\ =\ 0, \tag{29}\]  
\[i_{Lr1}(t)\ =\ i_{Lr1}(t_{8})+\frac{V_{in}}{Lr1}(t-t_{8}). \tag{30}\]  
When the diode \(D_{2}\) is turned off at the time \(t=t_{9}\), the state ends, and one switching period of the proposed inverter is completed. The circuit operation will return to the state 1 of the next switching period, and the nine operation states are repeated in sequence. The time it takes for this stage is:  
\[t_{9}-t_{8}\ =\ \frac{L_{r1}}{V_{in}}\Big{(}\frac{l_{L0}}{n}+i_{Lm1}(t_{0})-i_ {Lr1}(t_{8})\Big{)}. \tag{31}\]  
It can be seen from the above analyses that the advantages of the proposed inverter are listed as follows:  
1. The magnetizing inductor current \(i_{Lm1}\) of the transformer is bidirectional, so the iron core is operated in the first and third quadrants of the hysteresis curve, which can improve the core utilization.
2. Replacing the traditional reset winding, the auxiliary switch \(S_{a1}\) and the clamp capacitor \(C_{C1}\) are used to reset the residual magnetic flux of the transformer. The energies of the magnetizing inductor and the clamp capacitor are mutually transferred so that the energy can be recycled to the input. Since both the magnetizing inductor and the clamping capacitor are passive components for energy storage, there is theoretically no power loss.
3. By using the magnetizing inductor and the leakage inductor of the transformer, both the main power switch \(S_{m1}\) and the auxiliary switch \(S_{a1}\) achieve the characteristics of ZVS turn-on, which can effectively reduce the switching loss.
## 3 Materials and Methods
### EEG Channels Correlation Analysis  
In this analysis, the correlation between EEG channels was computed separately for the low and high workload modes. This approach provides understanding into the interrelationship and synchronization of neural activity across different brain regions during different workload conditions [63]. By examining these correlation patterns, we can gain better insight into how cognitive workload affects brain dynamics and connectivity. Figure 4 shows the heat map of the correlation analysis of the channels transmitting low mode data. Strong positive correlations are evident between channels 6 and 7, channels 8 and 9, and channels 11 and 12, indicating robust synchronization during periods of high cognitive demand. Moderately positive correlations are observed among channels 2, 4, 8, 9, 10, 11, 12, and 13, suggesting substantial connectivity and interactions between these channels under similar conditions. Weaker positive correlations are found between channels 1, 2, 3, 7, 10, and 13, highlighting lesser but notable degrees of connectivity among these channels. Conversely, negative correlations are observed between Channels 3, 5, 6, 8, 11, and 12, indicating regions where activity patterns are divergent or inversely related during a low workload.  
Figure 5 represents the heat map of the correlation analysis of the channels transmitting high-mode data. Channels 7 and 6 demonstrate a robust synchronization, highlighting a strong interaction between these channels. Additionally, channels 8 and 9 exhibit a very strong correlation, indicating highly similar activity patterns. Moderate to high positive correlations are observed among channels 2, 4, 8, 9, 10, 11, 12, and 13, suggesting significant interconnected activity. Channels 11 and 13 show notable synchronization patterns. Conversely, some channels exhibit weak to moderate positive correlations, while others display negative correlations, indicating varying degrees of interaction and divergence in activity. These findings provide a comprehensive view of how different EEG channels interact under high-workload conditions, shedding light on both synchronized regions and areas with contrasting activity patterns.  
Figure 4: EEG channels correlation heat map (low mode).
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 2 Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
### Fusion and Decision  
After obtaining multilevel feature maps and multiscale feature maps from ResNet-50 and ASPP, the feature maps are fused to determine the detection results. For this purpose, as shown in Figure 4, the feature maps that are output from Conv3 and Conv4 in ResNet-50 and the feature maps that are output from ASPP are subjected to \(1\times 1\) convolution to adjust the channel number. The proportion of different levels of information must be balanced to avoid noise from the underlying information. Then, the channel-adjusted feature maps are concatenated, and one \(1\times 1\) convolution and one \(3\times 3\) convolution are used to fuse the feature maps. The fusion result image is restored to the original input image size by bilinear interpolation.  
We use the softmax function to calculate the result of the detection. Suppose we detect \(K\) classes, the original image input is set to \(x\), and the network weight parameters are set to \(\theta=(\theta_{1},\theta_{2},\ldots\ldots,\theta_{n})\). Then, the value at the coordinate \((i,j)\) of the feature map can be represented by a vector \(f\left(x|\theta\right)_{i,j}\) of length \(K\). The probability that the result at \((i,j)\) belongs to the k-th class is defined as \(p_{i,j,k}\). Then, the probability of the corresponding position of the pixel belonging to the k-th class is calculated by using the softmax function:  
\[p_{i,j,k}=\frac{e^{f\left(x|\theta\right)_{i,j,k}}}{\sum\limits_{k=1}^{K}e^{f \left(x|\theta\right)_{i,j,k}}} \tag{2}\]
# Image Mosaicing Applied on UAVs Survey
## 3 Stitching Methods
### Feature-Based: Global Single Transformation
#### 3.1.5 SURF  
Speeded Up Robust Features (SURF) is a scale and rotation invariant feature interest point detector and descriptor proposed by [32]. This algorithm has advantages over previous systems, such as SIFT, because it presents similar results of matching points, but its calculations are faster. The approach for interest point detection uses a basic Hessian matrix approximation by relying on integral images for image convolutions: the Hessian matrix \(He(x,\sigma)\) in \(x\) at scale \(\sigma\) as the convolution of the Gaussian second-order derivative, with the image \(I\) in point \(x\), and similarly for \(Lxy(x,\sigma)\) and \(Lyy(x,\sigma)\) to calculate the determinant of the Hessian matrix. These approximate second-order Gaussian derivatives are evaluated at a very low computational cost using integral images, and regardless of size, they allow fast calculation.
# Fractional-Order SIR Epidemic Model for Transmission Prediction of COVID-19 Disease
## 2 Preliminaries  
The SIR model is one of the simplest compartment models, and many others are derivatives of its basic form. The total population \(N\) is divided into three epidemiological subpopulations: \((S)\) The number of susceptible individuals. When a susceptible and an infectious person come into contact, the susceptible individual contracts the disease and transitions to the infectious compartment, \((I)\) The number of infectious individuals. These individuals have been infected and they can infect susceptible individuals, \((R)\), the number of removed individuals. This subpopulation consists of individuals who are immune due to natural immunity, have recovered from the disease and gained immunity, or have died. The SIR model consists of three ordinary differential equations that are presented below.  
\[\begin{split}\frac{dS}{dt}=&-\frac{\beta IS}{N}\\ \frac{dI}{dt}=&\frac{\beta IS}{N}-\gamma I\\ \frac{dR}{dt}=&\gamma I\end{split} \tag{1}\]  
where \(\beta\) and \(\gamma\) are infection and recovery rate, respectively.  
Birth and death dynamics are usually neglected in simple compartmental models, since the dynamics of an epidemic, such as the flu, are often much faster than the dynamics of birth and death, and the numerical simulations are often executed for short time intervals.  
Note that the dynamics of infectious disease depends on the following ratio:  
\[R_{0}=\frac{\beta}{\gamma} \tag{2}\]where \(R_{0}\) is the basic reproduction number. This ratio quantifies the expected number of cases that are directly generated by one individual in an infected population. When \(R_{0}>1\), the infection will be able to start spreading, causing a potential outbreak. If \(R_{0}<1\) the disease will cease to exists.
###### Abstract  
Healthcare facilities are constantly deteriorating due to tight budgets allocated to the upkeep of building assets. This entails the need for improved deterioration modeling of such buildings in order to enforce a predictive maintenance approach that decreases the unexpected occurrence of failures and the corresponding downtime elapsed to repair or replace the faulty asset components. Currently, hospitals utilize subjective deterioration prediction methodologies that mostly rely on age as the sole indicator of degradation to forecast the useful lives of the building components. Thus, this paper aims at formulating a more efficient stochastic deterioration prediction model that integrates the latest observed condition into the forecasting procedure to overcome the subjectivity and uncertainties associated with the currently employed methods. This is achieved by means of developing a hybrid genetic algorithm-based fuzzy Markovian model that simulates the deterioration process given the scarcity of available data demonstrating the condition assessment and evaluation for such critical facilities. A nonhomogeneous transition probability matrix (TPM) based on fuzzy membership functions representing the condition, age and relative deterioration rate of the hospital systems is utilized to address the inherited uncertainties. The TPM is further calibrated by means of a genetic algorithm to circumvent the drawbacks of the expert-based models. A sensitivity analysis was carried out to analyze the possible changes in the output resulting from predefined modifications to the input parameters in order to ensure the robustness of the model. The performance of the deterioration prediction model developed is then validated through a comparison with a state-of-art stochastic model in contrast to real hospital datasets, and the results obtained from the developed model significantly outperformed the long-established Weibull distribution-based deterioration prediction methodology with mean absolute errors of 1.405 and 9.852, respectively. Therefore, the developed model is expected to assist decision-makers in creating more efficient maintenance programs as well as more data-driven capital renewal plans.  
fuzzy logic; Markov chain; deterioration prediction; genetic algorithm; healthcare facilities
# Assessment of Heavy Metal Distributions in Sand Beaches in the Maltese Islands
## 3 Results and Discussion
### Chemical Analysis
#### 3.1.1 Descriptive Statistics
The highest concentrations of heavy metals were recorded in Rinella Bay in Malta and Marsalform Bay in Gozo, followed by Ballut Reserve Bay and Riviera Bay in Malta, and Ramla Bay and Xlendi Bay in Gozo. Sr concentrations \begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**Hypothesis 1:**} & \multicolumn{2}{c}{**Hypothesis 2:**} \\
**Elements** & **Distribution is the** & **Distribution is the** & **Distribution is the** \\  & **Same across** & **Same across** & **Same across Bays.** \\  & **Locations.** & **Coordinates.** & \\ \hline Cu & Rejected & Rejected & Rejected \\ Fe & Rejected & Rejected & Rejected \\ Mn & Rejected & Rejected & Rejected \\ Pb & Retained (0.729) & Rejected & Rejected \\ Sr & Rejected & Rejected & Rejected \\ Zn & Retained (0.776) & Rejected & Rejected \\  & & Test significance level is 0.05. & \\ \hline \end{tabular}
\end{table} 
Table 8: Hypotheses assessed using Mann–Whitney U-test and Kruskal–Wallis test for sand beaches reviewed. were mostly predominant in two bays located in the south-east region of Malta and which are in proximity: Pretty Bay and St. George's Bay, and in Rinella Bay which is on the eastern Maltese coast. Most of the bays are close to highly industrialized zones: namely a boatyard, a freeport terminal, and a dockyard. Oils and fuel derivatives from neighbouring anthropogenic activity are highly likely the source for such concentrations. Other bays, namely Riviera Bay, Ramla Bay, and Xlendi Bay, are high touristic attractions, thus an increased level of human activity could be the source of high heavy metal content in such locations. High Sr and Fe content could also be attributed to lithogenic sources of such elements. Sr is predominantly sourced in natural minerals, namely celestite, strontiumite, and gypsum, which is derived from the weathering of Coralline Limestone and precipitate from aqueous solutions to form sedimentary rocks [37]. Fe is also sourced in natural geology, namely sedimentary rock types including limestone, by forming different minerals in reaction to different environmental conditions [38]. This gives sand a red hue typical of Ramla Bay in Gozo.
###### Abstract  
To investigate the temperature field variation of the main tower of large-span suspension bridges, the Nanjing Xinshengwei Yangtze River Bridge was selected as the objective of the present study. The finite element model of the main tower was developed, and the analysis of the effect of the temperature field on the structure of the main tower was carried out. The calculation parameters of the temperature field of the main tower were determined, and the influence of the solar radiation temperature of the main tower within 24 h was investigated. Differences in the temperatures inside and outside the wall of the tower column were analyzed, and the thermal stress of the tower wall under the most unfavorable temperature difference was calculated. Results show that under the positive temperature difference, the area of tensile stress is mainly concentrated on the inner wall, the maximum value is located at the corner of the intersection of the tower wall, and the range of tensile stress is mainly diffused along the vertical wall. Under the action of negative temperature difference, the area of tensile stress is mainly concentrated in the outer tower wall, the maximum value is located in the upper part of the western outer tower wall, and the range of tensile stress is mainly diffused along the center of the tower wall to both sides. The maximum tensile stresses in the inner and outer tower wall are 2.8 MPa and 1.3 MPa, respectively, which meets the standard value of 2.85 MPa for the tensile strength of C60 concrete specified in the Chinese national standard.  
temperature field; main tower; thermal stress; finite element analysis +
Footnote †: journal: Applied sciences
## 3 Materials and Methods
### Equipment and Software  
The experimental setup included state-of-the-art equipment and software to ensure consistent and reliable results. The following tools were used for fabrication, post-processing, and testing:  
* FDM: Ultimaker S3 (Ultimaker BV, Geldermalsen, The Netherlands), a dual-extrusion desktop printer known for its reliability and ease of use. The printer features a heated build plate and a semi-enclosed build chamber, which are essential for printing ABS.
* SLA: Formlabs Form 3 (Formlabs Inc., Somerville, MA, USA), a high-resolution desktop SLA printer with a low-force stereolithography (LPS) system. This printer uses a flexible tank and a linear laser to achieve precise and consistent prints.
* Slicing Software:
* Ultimaker Cura (v4.8) for FDM, which provides advanced settings for layer height, infill density, support structures, and print speed.
* PreForm (v3.12) for SLA, which automatically optimizes print orientation, support placement, and resin usage.
* Measurement Tools:
* Instron 3369 universal testing machine (Instron, Norwood, MA, USA) for mechanical testing, capable of measuring tensile strength, elongation, and Young's modulus with high precision.
* Mitutoyo Surfest SJ-210 surface profilometer (Mitutoyo Corp., Kawasaki, Japan) for surface roughness measurements, with a resolution of 0.01 \(\upmu\)m and a cut-off length of 0.8 mm.
* Mitutoyo Absolute Digimatic digital caliper (accuracy \(\pm\)0.01 mm) for dimensional accuracy measurements.
* PCE-PA6000 power analyzer (PCE Instruments, Meschede, Germany) for monitoring energy consumption during printing and post-processing.  
All equipment was calibrated according to manufacturer guidelines before and during the experiments to ensure accurate and repeatable measurements.
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 2 System Model
### Time Delay Estimation Method  
In a time delay estimation method, it is assumed that the discrete transmitted signal is \(s_{T}(n)\) and the received signal transmitted through multipath channel is:  
\[s_{R}(n)=\sum_{k=0}^{N_{M}-1}\alpha_{k}s_{T}(n-\tau_{k})+\omega(n), \tag{6}\]  
where \(s_{R}(n)\) is received signal, \(s_{T}(n)\) is transmit signal, \(N_{M}\) is the number of paths in multipath channel, \(\alpha_{k}\) and \(T_{k}\) is the loss and time delay of the \((k+1)\)th path in channel, \(\omega(n)\) is noise. We express Equation (6) in matrix form:  
\[\mathbf{S_{R}}=\mathbf{A}\mathbf{\lambda}+\mathbf{W}, \tag{7}\]  
where  
\[\mathbf{S_{R}}=\Big{[}\begin{array}{cccc}s_{R}(0)&s_{R}(1)&\cdots&s_{R}(N- 1)\end{array}\Big{]}^{T}, \tag{8}\]  
\[\mathbf{A}=[\begin{array}{cccc}\mathbf{a}(\tau_{0})&\mathbf{a}(\tau_{1})& \cdots&\mathbf{a}(\tau_{K-1})\end{array}], \tag{9}\]  
\[\mathbf{a}(\tau_{i})=[\begin{array}{cccc}s_{T}(0-\tau_{i})&s_{T}(1-\tau_{i} )&\cdots&s_{T}(N-1-\tau_{i})\end{array}]^{T}, \tag{10}\]  
\[\mathbf{\lambda}=[\begin{array}{cccc}\alpha_{1}&\alpha_{2}&\cdots&\alpha_{K }\end{array}]^{T}, \tag{11}\]  
\[\mathbf{W}=\Big{[}\begin{array}{cccc}\omega(0)&\omega(1)&\cdots&\omega(N- 1)\end{array}\Big{]}^{T}, \tag{12}\]  
The covariance matrix of the received signal is  
\[\mathbf{R}=\mathrm{E}[\mathbf{S_{R}}\mathbf{S_{R}^{H}}], \tag{13}\]  
The MUSIC super-resolution method is based on eigenvalue decomposition of the covariance matrix of the preceding received signal in (7)  
\[\mathbf{R}=\mathbf{U_{S}}\mathbf{\Lambda_{S}}\mathbf{U_{S}^{H}}+\mathbf{U_{e} \Lambda_{e}}\mathbf{U_{e}^{H}}, \tag{14}\]where \(\mathbf{U_{s}}\) is signal subspace, \(\mathbf{U_{e}}\) is noise subspace, and \(\mathbf{\Lambda}\) is diagonal matrix whose elements are the eigenvalues of \(\mathbf{R}\). The following equation is given with the orthogonality between the steering vector of received baseband signal and noise subspace.  
\[\mathbf{a}(\tau_{i})\mathbf{U_{e}^{H}}=0, \tag{15}\]  
Conventional MUSIC algorithm uses the following spectral function to estimate time delay.  
\[P_{MUSIC}=\frac{1}{\mathbf{a}(\tau_{i})\mathbf{U_{e}^{H}U_{e}a^{H}}(\tau_{i})}, \tag{16}\]  
The estimated time delay is:  
\[\hat{\tau}=\underset{\tau}{\arg}[\max(P_{MUSIC})], \tag{17}\]  
The MUSIC algorithm for delay estimation has superior resolution and can improve the ranging ability of the 5G OFDM signal.
# Damage Assessment of Shear Wall Structures in an Earthquake-Blast Disaster Chain
## 2 Methodology
### Explosion Loads  
Natural gas is also known as methane gas. The lower flammability limit (LFL) of methane is \(4.6\pm 0.3\%\), and the upper flammability limit (UFL) of methane is \(15.8\pm 0.4\%\)[19]. The equation of methane combustion can be expressed as:  
\[\text{CH}_{4}+2(\text{O}_{2}+3.76\text{ N}_{2})\longrightarrow\text{CO}_{2}+2 \text{H}_{2}\text{O}+7.52\text{ N}_{2} \tag{1}\]  
According to Equation (1), with a 21% oxygen concentration in the air, the volume of methane is half of the volume of oxygen (i.e., 10.5%), which indicates a complete chemical reaction can occur. In other words, the highest pressure is generated when the methane concentration is 9.5%.  
Previous work has made it possible to approximate a gas explosion as a TNT explosion by the TNT equivalent method [20], which is widely used in the design of industrial buildings and the assessment of the consequences of gas explosion accidents. Based on the principle of energy equivalence, the energy released by the combustible gases in the explosion is used to estimate the amount of TNT explosives that can release the same amount of energy. The specific formula is:  
Figure 1: The framework for damage evaluation of shear wall structures under an earthquake-explosion disaster chain.  
\[M_{TNT}=\frac{\alpha M_{m}Q_{m}}{Q_{TNT}} \tag{2}\]  
where \(M_{TNT}\) is the mass equivalent of TNT (kg); \(\alpha\) is the efficiency factor for TNT, adopted as 0.04; \(M_{m}\) is the volume of methane in the cloud (m\({}^{3}\)); \(Q_{m}\) is the heat of combustion of methane (MJ\(\cdot\)m\({}^{-3}\)); and \(Q_{TNT}\) is the detonation energy of TNT (MJ\(\cdot\)kg\({}^{-1}\)).