esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Framework: SCHISM  
We utilized the latest version (v5.9.0) of the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) [22], a derivative product based on the original SELFE model (v3.1 dc) [23]. The Navier-Stokes equations in the hydrostatic form are solved using the semi-implicit finite-element/-volume method, allowing stable simulations. Due to its location within the typhoon belt, the SIS is highly susceptible to extreme weather conditions, such as massive freshwater influxes, strong winds, and high waves caused by typhoons. Therefore, the high stability of SCHISM allows simulations performed in the SIS to be maintained relatively robustly.  
The fundamental equations within the hydrodynamic model, SCHISM, are the continuity equation, the momentum equation, and the transport equation, expressed in sequence as follows:  
\[\
abla\cdot\mathbf{u}+\frac{\partial w}{\partial z}=0 \tag{1}\]  
\[\frac{D\mathbf{u}}{Dt}=\frac{\partial}{\partial z}\left(\
u\frac{\partial \mathbf{u}}{\partial z}\right)-g\
abla\eta+\mathbf{F} \tag{2}\]  
\[\frac{DC}{Dt}=\frac{\partial}{\partial z}\left(\kappa\frac{\partial C}{ \partial z}\right)+F_{h}+Q \tag{3}\]  
where \(\mathbf{u}\) is the horizontal velocity with Cartesian components \((u,v)\), \(w\) is the vertical velocity, \(h\) is the bathymetry, \(\eta\) is the free surface elevation, \(g\) is the acceleration of gravity (m s\({}^{-2}\)), \(C\) is the concentration of tracers, such as salinity and temperature, \(\
u\) is the vertical eddy viscosity (m\({}^{2}\) s\({}^{-1}\)), \(\kappa\) is the vertical eddy diffusivity of tracers (m\({}^{2}\) s\({}^{-1}\)), \(\mathbf{F}\) is a momentum forcing, such as the air pressure, Coriolis effect, or horizontal viscosity, \(F_{h}\) is the horizontal diffusion, and \(Q\) is the source/sink term for the mass of tracers.  
Advection and viscosity schemes in SCHISM include several options, such as the Laplacian viscosity, biharmonic viscosity, and 5-point Shapiro filter, for enhancing momentum stabilization. Without these tools, inertial spurious modes of velocities or temperature can develop during the simulations. Each option demands a diffusion-number-like dimensionless parameter that can be controlled depending on the instability of the model runs. Here, we adopted the 5-point Shapiro filter [24], the application of which can be controlled by a parameter (\(\gamma\)) ranging in value from 0 to 0.5 as follows:  
\[\widetilde{u_{0}}=u_{0}+\frac{\gamma}{4}(u_{1}+u_{2}+u_{3}+u_{4}+-4u_{0}) \tag{4}\]  
where \(u_{0}\) is the velocity at side '0' between two elements (here, triangular) and \(u_{\pi}\) (n = 1, 2, 3, 4) is the velocity at the other two sides of the triangle (2 \(\times\) 2 = 4). We provide node-specified parameters that vary spatially depending on the possible instability of each node (the maximum current, mesh resolution, and heavy river discharge), as is further explained in the following model setup section.
# Dependence Representation Learning with Convolutional Neural Networks and 2D Histograms
## 3 Case Studies
### Dependence type Classification
#### 3.1.2 Results  
Quantitative results. Figure 4 shows the results obtained with the training and test sets: (a) the cross-entropy loss and (b) the classification accuracy of the CNN on the training and test sets. The loss and accuracy values are recorded every 100 iterations. The test accuracy of the CNN reached 100% after only 600 iterations (or updates) even though there was a slight performance degradation at 800  
Figure 3: Examples of the synthetic datasets used to classify the types of dependency. Top: Scatter plots of the synthetic feature pairs. Bottom: 2D histograms of the synthetic feature pairs.  
iterations. More importantly, there was no sign of overfitting since the training loss was always higher than the test loss, as shown in Figure 4a.  
Qualitative results. Figure 5 shows visualizations of the dependence representations for the test set. The 1743-dimensional dependence representations learned with dependence type classifications are reduced into 2-dimensional vectors using t-stochastic neighbor embedding (t-SNE) [27] and principal component analysis (PCA) [28], which are visualized on the 2-dimensional space of each subfigure, respectively. In order for the classification of dependence types to be accurate, the learned results of dependence representations should be clustered. As shown in the figure, the representations are perfectly clustered by their dependency type. In particular, in the right subfigure of PCA visualization, 'Polynomial3' and 'Polynomial4' types of representations are positioned closely because they look actually similar, even though they are of different types. We interpret this figure as indicating that the dependence representations can not only capture similar dependence types, but also classify them with slight differences.
## 4 Radial-Shaped Geo Clustering and Angular Routing
### CH Selection in RSC  
The clusters are partitioned in each sector of the radial-shaped virtual ring, and the nodes identify their position. For each cluster, a Euclidean distance is calculated for all the intracluster nodes as local coordinates, and the node closest to the midpoint of a cluster becomes the initial CH. Subsequently, the re-election process starts whenever the CH's battery level is less than the set threshold. The sensing node with the highest battery level becomes the CH of the cluster.  
The procedure to select the CH is presented in Algorithm 2. The size of randomly deployed nodes Size (A), midpoints Size (Grp), side length of a cluster (a) and the radius (r) are taken as the input parameters. From the lines 1 to 4 in the Algorithm 2, the midpoint of the sector is taken as the minimum threshold value and calculates the \(x,y\) coordinates for each node to compare with the maximum of \(x\), \(y\) coordinates in each sector. The lines Nos. 6 to 12 measure the radius, the angle and the distance of each node within the sector. The node which is nearest to the midpoint with maximum energy is taken as a CH in each sector according to the lines Nos. 15 to 19 in Algorithm 2. This procedure continues until all the CHs are selected in the deployment area.  
The CH sends data towards the next lower layer of the ring, which in turn sends the data to the next lower layer and so on. This action terminates when the sink node receives the data. The CH may also act as the relay node. In this strategy, the CH is assumed to be simple and efficient, which minimizes the intracluster communication and reclustering costs of the repetition process.  
Furthermore, the CH broadcasts its head message to all the other members of the clusters. First, it reduces the intracluster communication cost as the CH is located at the midpoint of the clusters. Second, the reclustering frequency is minimized as it happens only when no node is available in the midpoint of the cluster area.  
```
1:Input: size(A), size(grp), a, r
2:Output: Each cluster with CH
3:for\(i=1\) to Size(Grp)
4: Find the min. and max. of \(x\), \(y\) coordinates for each sector with a
5:\(k=1\)
6:for\(j=1\) to Size(A)
7:if\(\min(x)\ \leq\) node.\(x\leq\max(x)\) and \(\min(y)\ \leq\) node.\(y\leq\max(y)\)
8:\(C_{k}(i):=\) node.id
9:\(radius_{C_{k}}=r/2\)
10:\(angle(C_{k}(i))=\ \theta/2\)
11:\(Dist_{i}(j)=\ \text{the distance between the node location}\)
12:endif
13:endfor
14:endfor
15:for\(l=1\) to node.count do
16:\(Min_{i}(l)=\min(Dist_{i}(l))\)
17:if\((Min_{i}(l)==Min_{i}(l-1))\)
18: Verify the level of energy and select the node with max. energy \(Ch_{i}(k)=Min_{i}(node.id)\)
19:endif
20: k = k + 1
21:endfor
```  
**Algorithm 2** CH selection in radial-shaped clusters.
# Miniaturized PIFA for 5G Communication Networks
## 3 Planar Inverted-F Antenna  
In this paper, the multi-band internal antenna for WCDMA800, WCDMA850, GSM900, DCS, PCS, WCDMA2100, LTE2100 mobile communication services, and 5G communication networks is modeled, and the antenna characteristics are simulated with the coordinate system. This antenna is a flat antenna with a smaller area of rectangular patch plate on top of the ground surface of the plate, consisting of a ground surface, patch, feeder, short-circuit pin (or short-circuit strip). PIFA acts as a radiation element as the patch is resonated with the ground surface by current electric charge. Moreover, the height, width, length and position of the short-circuit wires determine bandwidth, gain and resonant frequency. PIFA can be built into a mobile phone because it does not have any protrusions, and two antennas can be attached to either side of the mobile phone or overlaid for dual mode of two frequency bands to improve its orientation. To improve the narrowband problem of existing PIFA, this paper suggests a modified PIFA form that positions the short-circuit between the radiant element and the ground surface. It also has the radiation patch to have wider bandwidth. The desired bandwidth (5.2%) was obtained with the minimum height of the antenna (\(h=0.015\lambda\)) for use as built-in to the handset in the proposed manner. To make the antenna slim, this paper investigated how the location (\(Z_{s}\)), width  
Figure 1: Structure of the Planar Inverted-F Antenna (PIFA).  
Figure 2: Fabricated PIFA.  
(\(W_{\rm s}\)), length (\(d\)), short strip height (\(h\)), and dielectric change (\(\varepsilon_{r}\)) affected the antenna. With these factors, this paper also compared the bands with the existing PIFA antenna and represented the proposed pattern as H and E plane.
# Numerical Simulation of Seakeeping Performance on the Preliminary Design of a Semi-Planing Craft
## 3 Data Analysis  
Prior to conducting numerical simulation, this study used the specifications of the series 62 craft for reference. Next, 3D computer-aided design (CAD) was performed using the commercial software Rhinoceros 3D, and the ship hull was cut into equally spaced strips. This study then set the draft as well as hull posture, and determined the strip nodes as well as coordinates. The node coordinates, hydrostatic performance, and speed of each hull strip underwater were made into input files, and sea states with significant wave heights (\(H_{1/3}\)) and the average period (\(\overline{T}\)) were calculated. Subsequently, a ship motion simulation program was installed and calculations were performed. After completing the calculations, the results were presented in graphic form to illustrate the RAOs of the hull motion responses. Additionally, the speed polar diagrams of the semi-planing craft with five degrees of freedom under specific sea states were displayed, revealing the seakeeping performance of the craft.
## 3 Numerical Simulation of Direct Shear Test of Bonded and Naturally Rough Concrete-Rock Interfaces
### Cohesive Frictional Model
Once the damage initiation criterion is met at a local point, the damage of the cohesive bonds starts to increase. Consequently, the contribution of the cohesive behavior (\(\tau_{coh}\)) decreases and is partially compensated by the increase in friction (\(\tau_{fric}\)) following Equations (3)-(5). \[\tau=\tau_{coh}+\tau_{fric} \tag{3}\]  
\[\tau_{coh}=(1-D)\tau \tag{4}\]  
\[\tau_{fric}=\mu D\sigma \tag{5}\]  
where \(\mu\) is the friction coefficient and \(D\) the damage parameter, which varies between 0 (no damage or degradation of the cohesive bonds) and 1 (complete failure of the cohesive bonds). The evolution of the parameter \(D\) is defined in the model using Equation (6) assuming an exponential softening of the stress in the second phase, between \(B\) and \(C\)[53]. \[D=1-\left\{\frac{\delta_{B}}{\delta_{m}^{max}}\right\}\left\{1-\frac{1-exp \left(-\alpha\left(\frac{\delta_{m}^{max}-\delta_{B}}{\delta_{C}-\delta_{B}} \right)\right)}{1-exp(-\alpha)}\right\} \tag{6}\]  
where \(\delta_{B}\) and \(\delta_{C}\) (refer to Figure 15) are, respectively, the shear displacement at the peak shear strength and the shear displacement when the residual shear strength is reached, \(\delta_{m}^{max}\)is the maximum shear displacement attained during the loading history, \(\alpha\) is a non-dimensional parameter that governs the rate of damage evolution. In the third phase (between \(C\) and \(D\)), the damage parameter \(D\) reached a value of 1 such that the shear behavior is only controlled by the frictional part of the model according to Equations (3)-(5).
## 2 A General Summary of Food Biocompatible SLNs and NLCs: Composition and Preparation Methods
### Structural Composition  
SLNs and NLCs are composed of lipid matrix (solid and/or liquid lipids), emulsifier, co-emulsifier, water and, if any included, bioactive compounds (or drugs). The lipid matrix can be a single solid lipid with a highly crystallized state or a combination of different lipid classes with different physical properties, e.g., melting temperatures. The SLNs or NLCs are generally stabilized by a surfactant layer, which may consist of a single surfactant, or be composed of a mixture of surfactants (emulsifiers and co-emulsifiers). The mobility of incorporated bioactive compounds in these nanoparticles is largely determined by the physical state of the lipid matrix [19; 20]. The selection of lipid matrices for the  
Figure 1: Schematic representation of the composition structures of lipid nanoparticles: SLNs and NLCs. (Adapted from [19]).  
formulation of SLNs or NLCs as delivery systems for bioactive compounds should consider the following aspects:  
1. the solubility of bioactive compounds in the lipid phase, and the efficiency of their incorporation;
2. oxidative stability of the lipid phase, and storage stability of crystallized lipids in the nanoparticles;
3. use of food biocompatible lipid components, from the viewpoint of acceptable toxicological evaluation.
# Advanced Bayesian Network for Task Effort Estimation in Agile Software Development
## 4 Application of BN Model in Another Company  
The BN model is also tested on the empirical data of two companies, set A and set B. Set A is the data used in [23] and obtained from a micro software company that developed and improves an ERP system. For the development and improvement of that ERP system, the company uses Scrum agile methodology and many agile practices [46].  
Set B is from another software company. It is a small software company engaged in software development for air traffic. The integrated software system supports the airport's key business process, i.e., passengers and aircraft handling. The software is constantly updated and upgraded. The software is developed according to the principles of agile development and the set of used agile practices is selected according to the situation of the actual project. The following agile practices were used in the examples: daily meetings, simple design, testing, shared code ownership, ongoing integration, common room, sustainable pace, off-site customer, request prioritization, and request management [46].  
During the work, the developers recorded the time spent on the development of each task. The project manager subsequently classified this information according to the rules set out by the authors. Thus, a 34-instance set was obtained and named set B.  
Set B was used to test the new BN model. Although there were a small number of instances, the results were good. The prediction accuracy for the set B was 97.06%. Only one task was wrongly classified into an adjacent class.  
Set B was added to set A (160 tasks) and the accuracy of the model was checked. In set A (7 output levels), two tasks were wrongly classified. By adding set B to set A, the number of misclassifications remained the same: the same two out of (now) 194 tasks were wrongly classified (Figures 5 and 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Old Model (5 Output Levels)** & \multicolumn{3}{c}{**The New Model (7 Output Levels)**} \\ \hline
**Class** & **Probability** & **Class** & **Probability** \\ \hline
0–2 & 0.96 & 0–1 & 0.91 \\ \hline
2.1–10 & 0.01 & 1.1–2 & 0.015 \\ \hline
10.1–25 & 0.01 & 2.1–5 & 0.015 \\ \hline
25.1–40 & 0.01 & 5.1–10 & 0.015 \\ \hline \(>\)40.1 & 0.01 & 10.1–25 & 0.015 \\ \hline  & & 25.1–40 & 0.015 \\ \cline{2-4}  & & \(>\)40.1 & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The probability of each class of the selected instance.
## 3 Methods  
### Overview of the PSANet Structure  
PSANet is designed to accurately segment building areas from SAR images. In the encoding stage, PSANet enhances its ability to encode building area features by combining different CNNs, which is crucial for accurate segmentation. In the decoding stage, transposed convolutions and skip connections methods are used to effectively integrate the encoded features into the decoding process, reducing the loss of detailed information resulting from upsampling. The general structure of PSANet is made up of four primary components: a Pseudo-Siamese fusion encoding network, atrous spatial pyramid pooling, a hierarchical decoder, and a feature refinement module, as presented in Figure 1.
# Experimental Study on the Efficacy of Water Infusion for Underground Mining of a Coal Seam
###### Abstract  
The main purpose of this study is to evaluate the efficacy of the water infusion method for various coalfaces of a coal seam by an experimental study. First, laboratory tests were performed on several coal samples taken from actual coalfaces to determine the general characteristics, especially the strength properties, with respect to the moisture content and time. The results obtained from the laboratory were employed to evaluate the appropriate parameters (e.g., water injection time and the optimum moisture content) for water infusion works in the field. A field test was then performed in order to assess the efficacy of water infusion for underground mining. The spalling depth (i.e., longwall face failure of coal wall, involving the stability of underground mining coalfaces) and mining velocity (i.e., involving the cost-effectiveness of mining constructions) were monitored at various coalfaces for both case studies (i.e., with and without water infusion). Expectedly, the field test results revealed that the spalling depth decreased significantly, whereas the mining velocity sped up considerably, at coalfaces using water infusion compared to at those without using the water infusion method. In conclusion, the promising findings obtained from the field test reinforced the efficacy of water infusion for underground mining coalfaces.  
Keywords:experimental study; efficacy of water infusion; spalling depth; mining speed +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Study Area  
Inner Mongolia (Inner Mongolia Autonomous Region) is located on the northern border of China (37\({}^{\circ}\)24\({}^{\prime}\)-53\({}^{\circ}\)23'N, 97\({}^{\circ}\)12\({}^{\prime}\)-126\({}^{\circ}\)04\({}^{\prime}\)E) and it represents the third largest province and region in China, with an area of 118.3 \(\times\) 10\({}^{4}\) km\({}^{2}\). Inner Mongolia belongs to a temperate continental climate with cold winters and hot summers. It has complex and diverse landforms, with the terrain inclining from southwest to northeast, sequentially covering the Mu Us sandy land, Otiindag sandy land, Horqin sandy land, and Da Hinggan Ling Mountains (Figure 1c). As an important animal husbandry production base in China, Inner Mongolia has rich and diverse vegetation types, with grassland being the most preva
# Linear and Nonlinear Modelling of the Usable Area of Buildings with Multi-Pitched Roofs
## 3 Results
### Linear and Nonlinear Models Based on the Existing Buildings in Koszalin
#### 3.3.3 Prediction of Usable Area of Properties in a Test Set  
The ultimate test of the model is its application in practice, while in the modelling process, the equivalent of such a practical test is the application of the model on data that were not used to determine the parameters of the model (validation). Such a validation of the models was performed in both cases. A comparison of the quality of the explanatory variable (estimates) obtained in this way is also an appropriate method of comparing the results of different models.  
Figure 4: Values observed in relation to the expected usable area of existing buildings dataset—circles. Red line is the trend line. The dashed line indicates the 95% confidence interval. Source: own calculation. (**a**) Linear model. (**b**) Nonlinear model.  
In the design dataset, another 10 observations were obtained on building designs that were not included in the model training group and a validation sample was created from them. A total of 201 observations were used throughout the process, including 191 in the training sample and 10 in the validation set. On the other hand, in the case of data on existing buildings in Koszalin, the dataset was divided into a set of 48 training observations and a validation set consisting of five elements already at the stage of data preparation. Thus, a total of 53 observations were used in the modelling process.  
Each of the models obtained in Sections 3.3.1 and 3.3.2 were verified by forecasting the usable area for the elements of the relevant validation test. Forecasts of the \(A_{U}\) variable were determined and the results were summarized using \(MAE\) and \(RMSE\) measures. The results are presented in Tables 7 and 8.  
The \(A^{+}\) linear model showed the best predictive capabilities, and the \(B^{+}\) model is not much worse. Model \(B\), described in Section 3.3.1, was slightly less successful in forecasting the area of new buildings. It should be noted that the validation sample itself included three observations, which accounted for most forecast errors. The remaining seven predictions were relatively accurate in each case.  
In this case, the best predictions were obtained using Model \(C\), indicated as the best in Section 3.3.2, followed by Model \(A_{n}\) and Model \(A\). A significant part of the prediction errors was due to one observation in the validation set where the usable area was smaller than covered area by 15 m\({}^{2}\) although this was a two-storey building. The house, however, has a double garage attached which affected the covered area. For the remaining objects, the usable area was estimated at a satisfactory level by most models.
# Application of Multi-System Combination Precise Point Positioning in Landslide Monitoring
## 2 PPP Model and Evaluation Index
### Root Mean Square Error  
The Root Mean Square Error (RMSE) reflects the difference between the estimated value and the reference value, and is expressed as:  
\[RMSE=\sqrt{\frac{\sum\limits_{i=k}^{n}\left(P_{i}-\hat{P}\right)^{2}}{n-k}} \tag{6}\]  
where:  
\(k\) = initial epoch at the time of convergence time;  
\(n\) = total epoch;  
\(P_{i}\) = parameter to be estimated;  
\(\hat{P}\) = the reference value.
## 4 Imaging Experiment and Analysis  
### Network Training Details  
The computational imaging processing module is deployed on a workstation computer equipped with an Intel i7-1070k CPU at 5.1 GHz, 32 GB memory, an Nvidia GeForce RTX 3060 Ultra W 12G, operating on Windows 10. The training set of images used for network training was acquired in a laboratory darkroom, following the method described in [4]; high-resolution real images H were projected onto the screen; and images were captured by SHDCIS to obtain low-resolution degraded images. The training dataset contained 1800 images. The DDZMR network is built based on Python3.8, PyTorch1.9.0, and CUDA10.2. The network optimizer used the Adam algorithm, with \(\beta_{1}=0.9\), and the initial learning rate was set to \(1\times 10^{-14}\). Furthermore, the learning rate was then halved every 10 k iterations. It took about 45 h to train the network and about 431 milliseconds (ms) to reconstruct an image using DDZMR. We have accelerated training using a CUDA-linked graphics processing unit (GPU). It is necessary to use a GPU in deep learning networks to improve the operation speed, which can improve the learning efficiency of the network in massive data.