esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of Water Body Temperature Field Calculation Model and Analysis of Test Results
### Analysis of the Diurnal Variation Patterns of the Cold Skin Layer in Water Bodies
As a result, the net heat flux at the interface becomes negative. At night, the behavior of the interface heat flux mirrors that of Day 1, with sensible and latent heat fluxes remaining at relatively low levels, while net longwave radiation increases, causing the net heat flux at the interface to rise sharply. Figure 22 presents the calculated and measured results for the cold skin thickness on Day 2. The range of cold skin thickness is consistent with that of Day 1, predominantly remaining within 2-5 mm throughout the day. The measured results align closely with the calculated values in magnitude. Figure 23 shows the calculated and measured results for the cold skin temperature difference. During the daytime period of high solar radiation, a warm skin phenomenon occurs, but the temperature difference is relatively small, around 0.1 K. At night, after solar radiation diminishes, the cold skin temperature difference increases rapidly and stabilizes at approximately 0.2 K. The trends of the measured and calculated results are in good agreement. Day 1 and Day 2 represent two relatively typical measurement days. Over the course of a six-month field experiment, it was observed that solar irradiance significantly influences the measurement results. The analysis reveals that under conditions of high daytime solar irradiance, the temperature difference of the cold skin remains relatively small, and a warm skin phenomenon may even occur. Figure 21: Diurnal heat flux variation curve of Day 2. Figure 22: Cold skin thickness calculation and measured results of Day 2.
# Scalability of Water Property Measurements in Space and Time on a Brackish Archipelago Coast
###### Abstract  
Our paper aims at advancing global change management in marine archipelago environments. Water properties vary along temporal and vertical gradients, and studies indicate that these patterns may be site-specific, i.e., they may vary at local or regional scales. Understanding these complex processes is crucial for designing environmental monitoring campaigns or assessing the scalability of their results. To our knowledge, the four-dimensional (temporal, vertical and horizontal) patterns of water quality have not been statistically quantified. In this paper, we partition the variation in four key water property variables into temporal, vertical and horizontal dimensions, by utilising a unique pre-existing high-density dataset and multilevel regression modelling. The dataset comprised measurements of temperature, salinity, pH and chlorophyll-a concentration, sampled eight times from April to October on the SW Finnish archipelago coast. All variables were sampled along the depth gradient and at local (\(10^{2}\) m) and regional scales (\(10^{4}\) m) at 20 sites. All measured variables varied significantly along the temporal and vertical gradients, and the overall levels, temporal patterns and vertical gradients of these variables were significantly site-dependent. Our study confirms that many water properties, especially chlorophyll-a concentration, show high four-dimensional variability in the complex archipelago environment. Thus, studies on the regional dynamics of archipelago water properties call for a high sampling density in time, along the vertical gradient, and in space.  
water property dynamics; spatio-temporal variation; time series; temperature; salinity; pH; chlorophyll-a; multilevel modelling; linear mixed modelling; Baltic Sea
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 3 Proposed Artificial Neural Network (ANN) Model
### Network Training
#### 3.1.2. Levenberg-Marquardt Algorithm (LMA)  
Levenberg-Marquardt was developed to estimate the speed of second-order training without requiring the Hessian matrix to be calculated. Using the sum of the squares performance function, one may generate the Hessian matrix for training feed-forward networks.  
[PERSON] [39], in his research, optimized nonlinear least squares problems using gradient descent and an inversion of the Hessian matrix. LMA is often used to train feed-forward neural networks using back propagation. The gradient descent approach converges slower than the algorithm. Many disciplines use the Levenberg-Marquardt algorithm.  
In most cases, Levenberg-Marquardt uses the approximation of the Hessian matrix shown in Equation (8).  
\[X_{k+1}=x_{k}-\left[\mathrm{J}^{\mathrm{T}}\mathrm{J}+\mu\right]^{-1}J^{T}e \tag{8}\]  
When the scalar, \(\mu\), is 0, this is [PERSON]'s approach, using the estimated Hessian matrix. When \(\mu\) is big, this becomes gradient descent, with a short step size. As soon as it is feasible, [PERSON]'s approach should be switched to, as it is quicker and more accurate, in terms of keeping errors to a minimum. Thus, \(\mu\) is dropped after each successful step (reduction in the performance function) and raised only when a tentative step will enhance the performance function. This reduces the performance function with each iteration of the algorithm.  
Figure 7. Flow chart of a Bayesian neural network (BNN).
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 3 Methodology
### Algorithm 1
#### 3.1.1 Analytical Hierarchical Process  
To ensure fairness, the AHP technique is used as a multi-dimensional decision-making process which is capable of identifying irregularities. The reason for applying the AHP method is that it enables us to make decisions based on multiple criteria, where it may be challenging to compare two criteria directly. A hierarchy shown in Figure 3, is created for the incoming flow packets, considering three criteria such as packet volume, packet category and delay hindrance. In AHP, numerical ratings are assigned based on packet preferences.  
Table 3 shows the typical numeric evaluation used. The evaluation table leads to the calculation of the matrix consistency index (\(CI\)) which is defined in Equation (1).  
\[CI=\frac{\lambda_{\max}-n}{n-1}. \tag{1}\]  
Figure 3: Packets Grading Hierarchy.  
AHP is used as a method for prioritizing flow requests in an SD-Wi-Fi architecture that implements load balancing. The method involves a process to rank flow requests according to their significance. The numerical ranking of the alternatives chosen and their order of preference are represented by the alternatives maximal Eigenvalue (\(\lambda\)max) in the judgment matrix. To assess the consistency of the judgment matrix, the consistency ratio \((CI/RI)\) is computed, where \(RI\) is the random index representing the number of entities involved in the correlation. Once the flow requests have been ranked, they are assigned to the RCs based on their priority. The highest priority request is allocated first, followed by sequentially ranked requests. By following this procedure, the suggested SDN architecture with load balancing is guaranteed to be capable of managing incoming flow requests effectively, even when the workload on the controllers rises as a result of the increase in requests.
## 3 Results and Discussion
### Temperature Analysis
A further analysis of the results shows that the temperature between the two points H and I fell, which is because G and H where at the powder support zone where the temperature is conducted slowly outward due to the bottom of the area being powder; therefore, the molten pool at the G and H points was larger. When the laser scanned from points H to I, the thermal conductivity was approximately 100 times that of the powder because the bottom of the area was solid. This made the temperature conduct outward faster, so there was a significant drop in temperature. Similarly, the same result can be observed in Figure 8c. The scanning sequence of the third layer is also from the powder support zone to the solid support zone. In the process of laser scanning from M to N, the temperature also decreased. However, with the increase in the scanning layer, the temperature variation between the two regions became increasingly smaller when the laser passed through the boundary. Comparing Figure 8a,c, the highest temperature decreased from 2497 \({}^{\circ}\)C to 1659 \({}^{\circ}\)C. In Figure 8b,d, the maximum temperature decreased from 1821.8 \({}^{\circ}\)C to 1731 \({}^{\circ}\)C. This is mainly because the thermal conductivity of the powder support zone is improved by the manufacture of the first few layers and is equivalent to providing a thin, solid support zone for the current layer so that the heat can be transferred to the bottom layer faster than before. This situation is more obvious in Figure 8d. The increase of the highest temperature point tended to be the same among all points, and the temperature change from point R to point S was not as obvious as the temperature change between points H and I. This is also because the laser heating rate is much higher than the cooling rate in the LPBF process of creating an overhang structure [41]. Therefore, the influence of thermal conductivity on the molten pool and temperature field is more obvious. Figure 8: Temperature histories at different study points: (**a**) Layer 1; (**b**) Layer 2; (**c**) Layer 3; and (**d**) Layer 4.
# Plastic Properties of Prestressed High-Strength Steel Reinforced Concrete Continuous Beams
## 4 Test Results and Analysis
### Calculation Formula of the Equivalent Plastic Hinge Length  
With the increase in the relative compression zone height \(\xi\), the equivalent plastic hinge length \(L_{p}\) has a decreasing trend. The corresponding relationship between the relative compression zone height \(\xi\) and the equivalent plastic hinge length \(L_{p}\) of the test beam is shown in Table 8. This is because when the relative compression zone height is large, the ratio of the ultimate bending moment to the yield bending moment is small, which reduces the length of the plastic hinge zone. According to this law, the calculation formula of the equivalent plastic hinge zone length using the relative compression zone height as the independent variable is established.  
The fitting curve of the equivalent plastic hinge zone length of the prestressed high-strength steel reinforced concrete continuous beam is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen Number** & \(\mathbf{\Phi_{y}}\) & \(\mathbf{\Phi_{u}}\) & \(\mathbf{L_{p}}\) & \(\mathbf{\theta_{p}}\) \\  & \(\mathbf{10^{-6}/mm}\) & \(\mathbf{10^{-6}/mm}\) & \(\mathbf{/mm}\) & \(\mathbf{10^{-3}/rad}\) \\ \hline A\({}_{1}\)Q\({}_{3}\)S\({}_{4}\)P\({}_{2}\)R\({}_{5}\) & 16.80 & 37.86 & 167 & 3.517 \\ A\({}_{2}\)Q\({}_{4}\)S\({}_{4}\)P\({}_{1}\)R\({}_{5}\) & 19.96 & 31.80 & 139.8 & 1.655 \\ A\({}_{3}\)Q\({}_{4}\)S\({}_{4}\)P\({}_{2}\)R\({}_{5}\) & 20.44 & 33.14 & 142.7 & 1.812 \\ A\({}_{4}\)Q\({}_{4}\)S\({}_{4}\)P\({}_{3}\)R\({}_{5}\) & 21.16 & 34.52 & 152.4 & 2.036 \\ A\({}_{5}\)Q\({}_{6}\)S\({}_{2}\)P\({}_{2}\)R\({}_{5}\) & 23.10 & 33.64 & 149.6 & 1.577 \\ A\({}_{6}\)Q\({}_{6}\)S\({}_{4}\)P\({}_{2}\)R\({}_{5}\) & 24.40 & 30.62 & 129.6 & 0.806 \\ A\({}_{7}\)Q\({}_{6}\)S\({}_{5}\)P\({}_{2}\)R\({}_{5}\) & 25.68 & 29.60 & 118.7 & 0.465 \\ A\({}_{8}\)Q\({}_{6}\)S\({}_{4}\)P\({}_{2}\)R\({}_{6}\) & 24.70 & 31.32 & 133.6 & 0.884 \\ A\({}_{9}\)Q\({}_{6}\)S\({}_{4}\)P\({}_{2}\)R\({}_{8}\) & 25.10 & 32.47 & 137 & 1.01 \\ \hline \end{tabular}
\end{table}
Table 7: The measured values of the basic parameters of the plastic hinge.  
\begin{table}
\begin{tabular}{c c c} \hline
**Specimen Number** & \(\xi\) & \(\mathbf{L_{p}/mm}\) \\ \hline A\({}_{1}\)Q\({}_{3}\)S\({}_{4}\)P\({}_{2}\)R\({}_{5}\) & 0.3 & 167 \\ A\({}_{2}\)Q\({}_{4}\)S\({}_{4}\)P\({}_{1}\)R\({}_{5}\) & 0.34 & 139.8 \\ A\({}_{3}\)Q\({}_{4}\)S\({}_{4}\)P\({}_{2}\)R\({}_  
The fitting curve of the equivalent plastic hinge zone length of the prestressed high-strength steel reinforced concrete continuous beam can be calculated according to the following formula:  
\[\frac{L_{p}}{h_{0}}=\frac{0.739}{(3.28\cdot\xi-0.0378)}-0.0829 \tag{5}\]
# A Compact Numerical Scheme for the Heat Transfer of Mixed Convection Flow in Quantum Calculus
## 4 Convergence of Numerical Scheme  
The stability of the scalar case has been discussed. Here, the convergence of the proposed scheme for the system of the parabolic equation having spatial \(q\)-derivative terms will be explained. For doing so, consider the following system of the parabolic equation in Quantum Calculus  
\[\frac{\partial u}{\partial t}=a\frac{\partial^{2}u}{\partial y^{2}}+\overline {b}\theta \tag{35}\]  
\[\frac{\partial\theta}{\partial t}=c\frac{\partial^{2}\theta}{\partial y^{2}} \tag{36}\]  
where \(a,\ \overline{b}\) are constant, and \(\overline{b}\) is a function of \(\theta\) that is kept fixed due to linearization. These parameters will appear in the flow problem constructed in the later section. Before initializing the procedure of determining convergence conditions, a proposed scheme will be employed on a single vector-matrix equation instead of a system Equations (35) and (36). Re-write Equations (35) and (36) into a single vector-matrix equation gives  
\[\frac{\partial\mathbf{U}}{\partial t}=A\frac{\partial_{q}^{2}\mathbf{U}}{\partial y^{ 2}}+B\mathbf{U} \tag{37}\]  
where \(A=\left[ \begin{array}{cc}a&0\\ 0&c\end{array} \right]\), \(B=\left[ \begin{array}{cc}0&\overline{b}\\ 0&0\end{array} \right]\), \(\mathbf{U}=[u,\ \theta]^{t}\). Applying the operator \(\mathcal{A}\) on both sides of Equation (37) yields  
\[\mathcal{A}\frac{\partial\mathbf{U}}{\partial t}=\mathcal{A}A\frac{\partial_{q}^{ 2}\mathbf{U}}{\partial y^{2}}+\mathcal{A}B\mathbf{U} \tag{38}\]  
Equation (38) can be expressed as  
\[\frac{\left(\frac{\partial\mathbf{U}}{\partial t}\right)_{i+1}^{n}+b\left(\frac{ \partial\mathbf{U}}{\partial t}\right)_{i}^{n}+\left(\frac{\partial\mathbf{U}}{ \partial t}\right)_{i-1}^{n}}{b+2}=A\left(\frac{1+q}{2}\right)\delta_{y}^{2} \mathbf{U}_{i}^{n}+B\left(\frac{\mathbf{U}_{i+1}^{n}+b\mathbf{U}_{l}^{n}+\mathbf{U}_{i-1}^{n} }{b+2}\right) \tag{39}\]  
Employing the first stage of the Runge-Kutta scheme, it is obtained that  
\[\frac{\overline{\mathbf{U}}_{i+1}^{n+1}-\mathbf{U}_{i+1}^{n}}{\Delta t(b+2)}+\frac{b( \overline{\mathbf{U}}_{i}^{n+1}-\mathbf{U}_{i}^{n})}{\Delta t(b+2)}+\frac{\overline{ \mathbf{U}}_{i-1}^{n+1}-\mathbf{U}_{i-1}^{n}}{\Delta t}=\frac{A}{4}(1+q)\delta_{y}^{2} \mathbf{U}_{i}^{n}+\frac{B}{2(b+2)}\big{(}\mathbf{U}_{i+1}^{n}+b\mathbf{U}_{i}^{n}+\mathbf{U}_ {i-1}^{n}\big{)} \tag{40}\]  
Applying the second stage of the Runge-Kutta scheme on Equation (39) yields  
\[\frac{\mathbf{U}_{i+1}^{n+1}-\mathbf{U}_{i+1}^{n}}{\Delta t(b+2)}+\frac{b(\mathbf{U}_{i}^{ n+1}-\mathbf{U}_{i}^{n})}{\Delta t(b+2)}+\frac{\mathbf{U}_{i-1}^{n+1}-\mathbf{U}_{i-1}^{n}}{ \Delta t}=\frac{A}{2}(1+q)\delta_{y}^{2}\overline{\mathbf{U}}_{i}^{n+1}+\frac{B}{ (b+2)}\big{(}\overline{\mathbf{U}}_{i+1}^{n+1}+b\overline{\mathbf{U}}_{i}^{n+1}+ \overline{\mathbf{U}}_{i-1}^{n+1}\big{)} \tag{41}\]  
**Theorem 3. **_The proposed schemes (40) and (41) converges conditionally._  
**Proof.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
### Main Air Pollutants
#### 1.1.1 Nitrogen Dioxide  
NO\({}_{2}\) is mainly produced in internal combustion engines burning fossil fuels, such as cars, power plants and house heaters. Direct exposure to the skin or eyes can cause irritation to the throat and nose, and it also burns. Long-term exposure to relatively low levels of nitrogen dioxide is believed to cause bronchitis and asthma, especially in children [16]. Kampala has thousands of taxis, basically used for public transport as a major means of transportation around the city.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 2 Structure of CFRP and Its Electrical Conductivity
### Conductivity and Lightning Strike
#### 3.2.2 How to Avoid Lightning Strike for CFRP?  
For the purpose of minimizing lightning strike damage, several solutions have been developed. [PERSON] and [PERSON] have published a review of lightning strike protection of composites, providing a broad introduction to lightning strike protection of aircraft made from CFRP structure. They pointed out that since lightning strikes carry large electrical currents, aircraft LSP requires materials with a high conductivity to avoid structural damage [72]. From the perspective of electrical conductivity, LSP solutions have been adopted in novel aircrafts, including Airbus A350 and Boeing 787. The solutions can largely decrease the lightning strike damage in experimental studies [72; 77; 90].
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 4 Experimental Results
### Dataset Generation and Pre-Processing  
The experimental analyzes presented in this study utilize a systematically selected and processed subset of the MNIST dataset [37], specifically focusing on digits '0' and '1' to establish a well-defined binary classification scenario. MNIST is a widely recognized dataset comprising handwritten digit images that are \(28\times 28\) pixels in size, which is extensively employed in image recognition tasks due to its clarity and structured patterns, making it ideal for benchmarking QML algorithms.
## 2 Impact Energy Recognition Algorithm Based on Fiber Bragg Grating Strain Sensing Technology  
### Fiber Bragg Grating Strain Sensing Technology  
Fiber Bragg gratings produce a narrow-band reflection of incident light that meets the grating reflection condition. The ideal grating wavelength is:  
\[\lambda=2n_{eff}\Lambda \tag{1}\]  
\(\Lambda\) is the grating pitch, \(n_{eff}\) is the effective refractive index [20]. Due to the flare effect and the expansion and contraction of the grating pitch, the grating undergoes a change in wavelength when it is exposed to stress. Assuming that the fiber Bragg grating sensor at this time is only subjected to the tensile strain \(\epsilon\), from the grating period and the basic principles of the formula can be drawn:  
\[\frac{\Delta\lambda}{\lambda}=(1-P)\cdot\epsilon \tag{2}\]  
\(P\) is a valid flare function. From Formula (2) it can be seen that the wavelength of the fiber Bragg grating shifts linearly with the longitudinal strain at constant temperature. The effect of external low velocity impact load on the structure can cause the center wavelength of fiber Bragg grating sensor shift, hence FBG sensor can be used in the research of low velocity impact monitoring of variable thickness composite laminates.
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 4 Results and Analysis  
### Details  
We use the public dataset DOTAv-1.0 to evaluate the proposed method. The dataset contains 2806 aerial images. In these images, there are diverse objects with different scales, orientations, and shapes. The resolutions of these images range from 800 \(\times\) 800 to 4000 \(\times\) 4000 pixels.  
DOTA-v1.0 has 15 categories: plane (PL), baseball diamond (BD), bridge, ground track field (GTF), small vehicle (SV), large vehicle (LV), ship (SH), tennis court (TC), basketball court (BC), storage tank (ST), soccerball field (SBF), roundabout (RA), harbor (HA), swimming pool (SP), and helicopter (HC); it contains 188,282 instances.  
Due to the large and variable size of DOTA images, the images need to be cropped first. In this paper, we use the same segmentation algorithm as ROI Transformer and the images are cropped into 600 \(\times\) 600 patches with a stride of 100. The input images have two scales, which are 0.5 and 1. The training and test sets contain 69,337 and 35,777 segmented images respectively. After testing, the results of the segmented images are combined into a final result, which is evaluated via the DOTA online server.  
We implement our method with PyTorch. The backbone weights are pre-trained on the ImageNet dataset and the other weights are initialized under the default settings of PyTorch. We use some methods, including random flipping and cropping, for data enhancement. In addition, the experiments are performed on a Taitan RTX 24G server with a training batch size of 10, and we use Adam with an initial learning rate of 6.25 \(\times\) 10\({}^{-5}\) to optimize the total loss and train the network for about 80 epochs.
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 5 Experiment Setup
### Dataset Acquisition
#### 5.2.1 Pansori Rhythm Patterns  
We amassed a substantial collection of Pansori tracks, which were annotated to identify rhythm patterns, specifying their start and end times within each track. Traditionally, Pansori encompasses 12 distinct rhythm patterns: \"aniri\", \"anrichangzo\", \"dockseoeng\", \"hweemori\", \"jinyangzo\", \"semachi\", \"sichang\", \"utmori\", \"zajinmori\", \"zungmori\", \"zungzungmori\", and \"sijochang\". The duration of the collected audio files ranges from 30 s to 20 min, often with several rhythm patterns sequentially interleaved. For the purposes of our experiments, six common rhythm patterns were designated as known classes, while the remaining six were treated as unknown classes (refer to Table 1). The audio files were processed in mono-channel format at a sampling rate of 22,050 Hz, with a frame window length of 512 samples, a hop size of 250 samples, and 32 Mel-bins to generate the log-Mel spectrogram. For the training dataset, we randomly extracted sequences of 3200 consecutive frames for temporal dimension, \(T\), resulting in a patch size of \(32\times 3200\). Table 2 details the distribution of rhythm patterns across the training and testing phases. Notably, 20% of the data for each known rhythm pattern were reserved for validation purposes, and no data pertaining to unknown rhythm patterns were included in the training set.  
The training was conducted with a batch size of 16 and extended over 5000 epochs. We employed stochastic gradient descent (SGD) as the optimizer, with an initial learning rate of 0.001 for the model and 0.5 for the center loss. The model's learning rate was decremented by 10% every 500 iterations to promote faster convergence. The total loss computed during training was a weighted sum of cross-entropy (\(\alpha\)), center (\(\beta\)), and contrastive (\(\gamma\)) losses, as specified in Equation (1), with the respective weights set to 1, 0.0001, and 0.001, respectively.