esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 5 Experiment
### Study Area and Implementations
#### 5.1.3 Experimental Design  
Figure 6 illustrates four strategies of coastal land cover classification in the experiment, including binary classification, multi-class classification, multi-label classification, and the proposed method. Binary classification, multi-class classification, multi-label classification and the proposed method have been conducted by a state-of-the-art deep learning model called PNASNet and a state-of-the-art foundation model involving SAM and ViTAE-RVSA.  
In detail, we selected the top-score class as the outputs of multi-class classification; otherwise, we selected the top-t score class as the outputs of multi-label classification and the result of semantic analysis as the outputs of the proposed method. We assumed we have C total land-cover forms and L total labels (or variables) assigned to the content of a remote-sensing image. These four classifications are outlined as follows.  
* Binary classification addresses the classification problem when \(\mathrm{C}=2\) and \(\mathrm{L}=1\). For example, the sample image in Figure 6 is labeled merely as either coastline or non-coastline.
* Multi-class classification deals with the classification problem when \(\mathrm{C}\geq 2\) and \(\mathrm{L}=1\). For example, the sample image in Figure 6 is classified into one of four land-cover types.  
Figure 6: Comparison of the result generated by different designs.  
* Multi-label classification assigns multiple classes to the content of a remote-sensing image with no restrictions on the total number of land-cover forms labeled. Multi-label classification addresses the classification problem when \(\text{C}=2\) and \(\text{L}\geq 1\); for example, labels of sample images that contain all coastal land cover forms recognized from this image.
* The proposed method addresses the classification problem when \(\text{C}\geq 2\) and \(\text{L}\geq 1\), and each L is weighted. In Figure 7, all of the generated coastal land cover types are quantitatively normalized as scores within the interval 0-1.  
Then, we would evaluate the proposed approach by the following experimental group.  
1. Experimental groups: * Coastal land use similarity analysis; * Coastal land use retrieval.
2. Evaluative comparison * Comparison of binary classification, multi-class classification, multi-label classification and the proposed method with deep learning models; * Comparison of binary classification, multi-class classification, multi-label classification and the proposed method with foundation models; * Comparison of deep learning models and foundation models for each classification.
## 3 Results
### Verification of Dimensional Stability of Bonded Joint  
When assessing the feasibility of using composite materials in optical instruments, it is crucial to consider not only manufacturing precision but also the dimensional stability during the instrument's operation. The aim of stage three of the study is to experimentally verify the dimensional stability of bonded joints during environmental testing based on the standards ISO 9022-1:2016, [55] ISO 9022-2:2015, [58] ISO 9022-3:2015 [59] and ISO 9022-8:2015 [60].  
The noncompliant samples from the previous stage required calibration by turning to achieve the necessary accuracy before undergoing environmental testing. As shown in Figure 6, the total runout of all the samples met the required standards after calibration, as indicated by the highlighted portion of the columns in comparison to the previous values in lighter color.  
Following the calibration of the samples, two cycles of thermal resistance tests and one cycle of mechanical resistance tests were carried out. The measured total runout values after each test are detailed in Table 4. Throughout and after the tests, there were no observed issues related to adhesion, cohesion, or visible damage.  
Figure 6: Values of total runout after calibration.  
The initial assessment of the data indicates that 14 out of 15 samples (93.3%) meet the required total runout tolerance of 0.05 after passing all environmental test cycles. However, the development of the change in total runout that occurred during the tests is more significant, especially the fact that eight of the 15 samples (53.3%) did not exceed the total runout of 0.005 mm throughout environmental tests. This suggests that under certain conditions, this type of the bonded joint exhibits sufficient stability for application in sports optics. It is important to highlight that five out of the 15 samples (33.3% of the total samples, 71% of the noncalibrated samples) exceeded the deviation of 0.01 mm at least once during the environmental tests, with the largest deviation measured at 0.036 mm for sample 12. This underscores the heterogeneous behavior of the sample set, where some samples meet the stability requirements, while others exceed them significantly.
## 2 Materials and Methods  
### Geometry and Materials  
A real coronary model helps to guarantee the accuracy of the study results. In this study, the 3D model of coronary arteries was constructed according to coronary CT data. Since the left coronary artery is more prone to plaque growth than the right coronary artery, the left coronary artery was selected for modeling in this research [11]. Diagonal branches are often neglected in coronary artery analysis. However, it has been demonstrated that the presence of side branches alters the blood flow distribution [12], so the diagonal branches were retained in the coronary model in this study. The model was set with 50% stenosis, and plaques were manually added to the left main coronary (LM). The four coronary models with different circumferential distributions of plaque are shown in Figure 1. In Model 1, the plaque was located on the side away from the heart; in Model 2, the plaque was located on the side close to the left; in Model 3, the plaque was located on the side close to the left circumflex branch (LCX); and in Model 4, the plaque was located on the side away from the LCX. Blood is considered to be an isotropic, incompressible, homogeneous, laminar, non-Newtonian fluid, and the blood density \(\rho_{f}\) was taken to be 1060 kg/m\({}^{3}\)[13]. Considering the shear thinning behavior of the blood, the Carreau viscosity model [14] was applicable, which is defined by Equation (1).  
\[\mu=\mu_{\infty}+(\mu_{0}-\mu_{\infty})[1+\big{(}\lambda\cdot\dot{\gamma}\big{)} ^{2}]^{\frac{n-1}{2}} \tag{1}\]  
where \(\mu_{0}\) is the viscosity at a zero shear rate, 0.056 Pa-s; \(\mu_{\infty}\) is the viscosity at an infinite shear rate, 0.0035 Pa-s; \(\lambda\) corresponds to the relaxation time, 3.313 s, \(n\) is the power index, 0.3568; and \(\dot{\gamma}\) is the shear-rate of strain, s\({}^{-1}\).  
Because the purpose of this study was to investigate the effect of plaque location, plaque material properties had little influence on the conclusions, and the plaque was considered to be consistent with the vessel wall material property. The vessel wall was assumed to be an isotropic, homogeneous, nonlinear elastic material with a uniform thickness of 0.4 mm [15]. In this study, the 5-parameter Mooney-Rivlin hyperelastic model was used to represent the mechanical properties of the coronary vessels [16]. The present constitutive equations were as follows:  
\[W=c_{10}(I_{1}-3)+c_{01}(I_{2}-3)+c_{20}(I_{1}-3)^{2}+c_{11}(I_{1}-3)(I_{2}-3)+ c_{02}(I_{2}-3)^{2}+\frac{1}{d}(J-1)^{2} \tag{2}\]  
where \(W\) is the strain energy-density function, \(I_{1}\) and \(I_{2}\) are the first and second stress invariants, \(c_{10}\) (\(-\)4.02 MPa), \(c_{01}\) (4.321 MPa), \(c_{20}\) (18.401 MPa), \(c_{11}\) (\(-\)51.856 MPa), and \(c_{02}\) (39.105 MPa) are the hyperelastic constants describing the deformation, \(d\) (2.434 MPa\({}^{-1}\)) is the incompressible parameter, and \(J\) represents the elastic volume ratio. The density of the vessel wall \(\rho_{w}\) was taken as 1120 kg/m\({}^{3}\).
# Application Layer Protocol Identification Method Based on ResNet
## 4 Experimental Results and Discussion
### Evaluation Metrics  
For the specific application protocol being analyzed, we have selected the following three indicators for further analysis:  
(1) True Positives (TP): Each packet is identified as the corresponding protocol, and the identification result matches the label result.  
(2) False Positives (FP): Each packet is identified as the corresponding protocol but does not actually belong to that protocol.  
(3) False Negatives (FN): Each packet is identified as not belonging to the application protocol but belongs to that protocol.  
(4) True Negatives (TN): The situation where each packet identified as not belonging to the application protocol does not belong to that type.  
Based on these data, we selected four indicators: accuracy, accuracy, recall, and F1 score, to evaluate the performance of the algorithm.  
\[accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{1}\]  
\[precision=\frac{TP}{TP+FP} \tag{2}\]  
\[recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1=2\times\frac{precision\times recall}{precision+recall} \tag{4}\]
## 2 Model Setup
### Model Establishment
#### 2.2.4 Full-Ring Model Calculation  
In the full-ring model, rotational stiffness and radial shear stiffness are considered for the longitudinal joints while radial shear stiffness and tangential shear stiffness are considered for the circumferential joints. The stiffness matrix is \(K=[K_{l\theta},K_{ls},K_{cn},K_{cs}]\) with \(K=[K_{l\theta 1},K_{l\theta 2},\cdots,K_{l\theta 12}]\) representing the rotational stiffness matrix of the moment-rotation curves of the longitudinal joints JF1 to JF12, \(K_{ls}=[K_{ls1},K_{ls2},\cdots,K_{ls12}]\) representing the radial shear stiffness matrix for the longitudinal joints JF1 to JF12, \(K_{cn}=[K_{cn1},K_{cn2},\cdots,K_{cm}]\) representing the tangential shear stiffness matrix of the circumferential joints, \(K_{cs}=[K_{cs1},K_{cs2},\cdots,K_{csn}]\) representing the radial shear stiffness matrix of the circumferential joints. The number of springs in the circumferential joints can be defined as needed. The positions of the joints, key sections (JM) and key deformation points (ends of line segments D) for the structure are shown in Figure 6.  
Figure 5: Illustration of the characteristics of the rigid zone.  
Figure 6: Key sections and deformation points for the model calculation.  
The key sections, except for JM11, are the sections with a relatively large bending moment, which will be decisive for the reinforcement area in the corresponding segments. JM11 experiences a large axial force rather than bending moment and is selected as a key section to represent the stress condition in the interior column. The key deformation points are used to calculate the structural convergence deformation. This convergence deformation is the relative deformation of a pair of specific measurement points. For circular or quasi-rectangular tunnels, the measurement points are usually selected at the ends of the axes crossing, the center of the circular section horizontally and vertically. Positive values correspond to outward deformations, while negative values correspond to inward deformations. According to the Chinese Code for Design of Metro [14][15], this convergence deformation should be maximum 3% of the corresponding axis length. This means that the deformations of D1, D2 and D3 should be maximum 3% of the lengths of D1, D2 and D3, respectively.
## 3 Modelling, Analysis Tools and Experimental Support for Stability Analysis  
This section aims at defining the models and tools dedicated to the stability analysis of the powertrain. Three analysis means are successively proposed:  
1. First, a nonlinear state model is built, representing the transient behavior of the system. This analytic state model can be simulated in the time domain on Matlab Simulink and is compared for validation with another time simulation based on the Saber circuit solver [32].
2. Second, the previous analytic nonlinear state model can be small-signal linearized to set the root locus of the powertrain, in order to characterize the stability domain.
3. Finally, a reduced power scale test bench was developed in order to validate theoretical analysis set from both previous approaches.
# Classification of Heart Sounds Using Convolutional Neural Network
## 2 Materials and Methods
### Multi-Feature Extraction
#### 2.3.3 Features in the Energy Domain  
There are two kinds of features in the energy domain--(1) the energy ratio of the band-passed signal to the original signal; and (2) the energy ratio of one state to another.  
For (1), we considered the frequency bands with an initial bandwidth of 10 Hz and an incremental bandwidth of 10 Hz. We obtained 42 frequency bands from 10 Hz to 430 Hz ([10 20] Hz, [20 30] Hz, [30 40] Hz,..., and [420 430] Hz). So, there were 42 features for each of these bands. First, we applied the Butterworth filter to the raw signals. The output \(y_{i}\) of each fifth-order Butterworth filter applied to each band signal was calculated using Equation (1).  
\[\mathbf{y}_{i}=\text{filter}\;(\mathbf{b}_{i},\mathbf{a}_{i},\mathbf{x}),\;i=1,2,\ldots,42, \tag{1}\]  
where \(\mathbf{b}_{i}\) (the numerator) and \(\mathbf{a}_{i}\) (the denominator) denote the coefficients of the Butter-worth IIR filter. Then, the energy ratio of each band was calculated using Equation (2).  
\[Ratio\_energy\_band_{i}=\frac{\sum\left|y_{i}\right|^{2}}{\sum\left|x\right|^{ 2}}\;,i=1,2,\ldots,42. \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Domain** & **Num. of Features** \\ \hline
1 & Time & 20 \\
2 & Amplitude & 12 \\
3 & Energy & 62 \\
4 & High-order statistics & 16 \\
5 & Cepstrum & 65 \\
6 & Frequency & 308 \\
7 & Cyclostationary & 4 \\
8 & Entropy & 10 \\ Total & – & 497 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the numbers of features extracted from eight domains.  
We also needed to consider the energy ratio between any two states. Taking as an example the energy ratio of S1 to the cycle period, if we have \(N\) cycles in a PCG recording and each cycle contains \(n\) discrete time indices, then the \"Ratio_energy_state\" can be defined as in Equation (3).  
\[Ratio\_energy\_S1\_cycle=\frac{\sum_{n}\abs{S_{1i}(n)}^{2}}{\sum_{n}\abs{c_{i} (n)}^{2}},\ i=1,2,\ldots,\ N. \tag{3}\]  
In this study, the mean and the standard deviation (SD) of \(Ratio\_energy\_states_{S1\_cycle}\) in the \(i\)th cycle were calculated as two features, namely, m_Ratio_energy_S1_cycle and sd_Ratio_energy_S1_cycle. We also obtained the energy ratios of state S1 to the S2, systole and diastole states, respectively. So, for state S1, eight features were extracted. Similarly, we extracted six, four and two features for the S2, systole and diastole states, respectively. All of the features from this domain are listed in the Appendix A (Table A3).
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
Finally, a comprehensive analysis of the impact of ionospheric delay correction on multi-GNSS performances is performed.
# Introducing Cement-Enhanced Clay-Sand Columns under Footings Placed on Expansive Soils
## 3 Experimental Program  
### Sample Preparation of Mixtures  
In order to conduct this study, some of the parametric factors of soil, such as the initial water content and dry density, were kept constant and similar to enable comparison and help in examining the effect of cement. In this study, four different percentages of regular Portland cement content were used: 1, 2, 4 and 8% by the dry weight of sand. The cement content range was chosen based on the known trials in the literature; however, more than 8% cement was expected to form a rigid non-workable paste. Five distinct sand-expansive clay (Qatif) combinations containing 10, 20, 30, 40 and 60% expansive clay were prepared. There were four distinct curing times used for each mixture: 1, 7, 28 and 90 days. The expansive clay content range was selected to cover all practical ratios that are likely to be present in soils showing expansive behavior. The clay was air-dried, ground into a powder, and sieved through sieve No. 40. To create a uniform blend, the desired ratios  
\begin{table}
\begin{tabular}{c c c} \hline
**Property** & **Value** & **Standard Used** \\ \hline Liquid limit (\%) & 140–150 & ASTM D4318 [27] \\ Plastic limit (\%) & 45–50 & ASTM D4318 [27] \\ Plasticity index (\%) & 95–100 & ASTM D4318 [27] \\ Specific gravity & 2.76 & ASTM D854 [25] \\ USCS classification & CH & ASTM D2487 [26] \\ Swelling potential (\%) & 20–25 & ASTM D4546 [28] \\ Swelling pressure (\(\mathrm{kN/m^{2}}\)) & 500–600 & ASTM D4546 [28] \\ \hline \end{tabular}
\end{table}
Table 2: Physical properties of Al-Qatif clay.  
\begin{table}
\begin{tabular}{c c} \hline
**Chemical Compound** & **Percentage (\%)** \\ \hline Na\({}_{2}\)O & 0.50 \\ MgO & 4.92 \\ Al\({}_{2}\)O\({}_{3}\) & 15.82 \\ SiO\({}_{2}\) & 55.86 \\ K\({}_{2}\)O & 5.22 \\ CaO & 1.74 \\ FeO & 9.39 \\ \hline \end{tabular}
\end{table}
Table 3: Chemical composition of Al-Qatif clay.  
Figure 2: The XRD profile for Al-Qatif expansive clay.  
of oven-dried sand, clay, and cement were manually combined in a dry environment. As advised by ASTM Standard D698 [29], the desired water content was then added as shown in Table 4, well mixed, and the plastic bags containing the mixtures were then stored in a humid environment for at least 16 h to give the soil particles time to hydrate under a controlled relative humidity greater than 95% and an ambient temperature of 23 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C. In order to achieve the desired unit weights, samples were prepared by static compaction in a stainless-steel odometer ring of 63.5 mm in diameter and 20 mm in height. To lessen the side friction, silicon grease was put on the ring's inner surface. The compacted sample was topped and bottomed with filter paper and a porous stone. The ultimate sample height of 16 mm was maintained, with a 4 mm gap to accommodate sample expansion.
## 5 Performance Analysis
### Probability of Successful Information Delivery  
WSN nodes are known for their high-energy efficiency. A node's battery life is limited because of the tiny size of its dry cell. These nodes have a limited quantity of energy and thus optimizing energy in these nodes is particularly useful to the long-term viability of a WSN. The three energy-consuming processes for the WSN nodes are sensors, communications, and motor control. The probability of successfully transmitting the packet to the BS is an essential aspect for optimizing the energy in WSNs. Grounded on the average number of packets obtained by the BS, this metric points to how well the packet is transmitted to the intermediate nodes. According to Figure 9, our suggested protocol can transmit 85% of packets to BS and has a success rate of 89% as compared with others as their success rate is LEACH at 34%, CPCP at 71%, and CACP at 63%. A packet's delivery to BS is more likely to be successful if the CH is effectively selected, increasing the network's density. Packet drops are reduced by using the right count of clusters and the right number of CHs in a network [53; 54; 55; 56; 57; 58; 59; 60; 61].  
Figure 8: Average residual energy vs. rounds.
# Intra-Mode Decision Based on Lagrange Optimization Regarding Chroma Coding
## 5 Experimental Results
### Coding Complexity  
In order to verify the effect on coding complexity, the proportion of time increment is calculated as follows:  
\[\Delta T=\frac{T_{p}-T_{o}}{T_{o}} \tag{19}\]  
where \(T_{p}\) and \(T_{o}\) represent the total coding time of the proposed algorithm and the original algorithm, respectively. The results of the complexity comparison are given in Table 4, where the average values of \(\Delta T\) are approximately 0.030 for the All-Intra configuration and 0.014 for the Random-Access configuration. It can be observed that the computational complexity has a slight increase compared to the original algorithm. Despite the fact that the chroma RDO process in the LM, LM_T, and LM_L modes consumes additional coding time, the increment in complexity is not excessive. Based on the above evaluation, it can be verified that the proposed method provides improved coding performance with an acceptable computation complexity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sequences** & \(\Delta\)**Bitrate (\%)** \\ \hline Class B & \(-1.23\) \\ \hline Class C & \(-1.24\) \\ \hline Class D & \(-1.22\) \\ \hline Class E & \(-1.26\) \\ \hline Average & \(-1.23\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The RD performance of the proposed algorithm compared to the original algorithm in the All-Intra configuration.  
Figure 8: The RD curve of the performance in comparison with the sequence of RaceHorses\(\_832\times 480\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sequences** & \(\Delta\)**Bitrate (\%)** \\ \hline Class B & \(-1.03\) \\ \hline Class C & \(-1.04\) \\ \hline Class D & \(-1.03\) \\ \hline Class E & \(-1.05\) \\ \hline Average & \(-1.03\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The RD performance of the proposed algorithm compared to the original algorithm in the Random-Access configuration.
# Characteristics of Deep Groundwater Flow and Temperature in the Tertiary Pohang Area, South Korea
###### Abstract  
The geothermal characteristics of specific areas on Earth can be identified using geophysical and lithological logs based on deep boreholes, such as those more than 1000 m in depth. Based on the combined analyses of geophysical logs (temperature, caliper, electrical conductivity (EC), and natural gamma-ray logs) and lithological logs, as well as 1D steady-state heat transfer model, the deep groundwater flow and temperature were characterized in four deep boreholes (BH-1-BH-4) roughly 2000 m deep in the area of Heunghae-eup, Pohang, South Korea. The estimated thermal gradients from the temperature profiles are as follows: 22.37-30.77 \({}^{\circ}\)C/km for BH-1, 35.67-64.52 \({}^{\circ}\)C/km for BH-2, 40.85-46.44 \({}^{\circ}\)C/km for BH-3, and 33.33-35.71 \({}^{\circ}\)C/km for BH-4. According to the geophysical logs and lithology profiles, the groundwater mainly flows into and out of the boreholes through the basic dyke, nhydite, and sandstone/mudstone. Evidently, the groundwater flows moving through the fractures and faults induce nonlinear temperature changes. The upward and downward groundwater flows passing through fractures and faults can be estimated using a 1D steady-state heat-transfer equation, by considering a fracture angle based on the lithological and geophysical profiles. To determine the direction (up/down) and rate of groundwater flow, the values for the parameter \(\beta\) were estimated as follows: -1.95 to 5.40 for BH-1, -13.48 to 4.87 for BH-2, -1.76 for BH-3, and -3.39 to 14.15 for BH-4.  
deep groundwater flow; geothermal property; geophysical logs; \(\beta\) value; one-dimensional heat transfer equation; Pohang area +
Footnote †: journal: Applied Sciences