esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Characteristics in Mountainous Valleys Obtained through Field Measurement
## 3 Results
### Turbulence Characteristics
#### 3.2.3 Correlation between Turbulence Intensity and Gust Factor  
Some correlation usually exists between the gust factor and turbulence intensity. In this study, the time interval for calculating the gust factor was taken as 3 s. Based on the samples obtained from the measured data, one-dimensional linear regression analysis was performed on the gust factor \(G_{u}\) and turbulence intensity \(I_{u}\) at the two measurement points on the northeast and southwest sides, and the results are shown in Figure 23. The gust factor \(G_{u}\) increases with the increase in the turbulence intensity \(I_{u}\), and the slope is larger on the southwest side. The regression equation for the northeast side is \(G_{u}=0.98+2.19~{}I_{u}\). The regression equation for the southwest side is \(G_{u}=0.95+2.41~{}I_{u}\).
## 4 Experiments and Discussion
### Landmark Detection Results
#### 4.3.1 ISBI Test Dataset
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Comparisons of SDR**} \\ \hline
**Method** & **2 mm (\%)** & **2.5 mm (\%)** & **3 mm (\%)** & **4 mm (\%)** \\ \hline  & \multicolumn{4}{c}{Testset1} \\ \hline [PERSON](2015) [8] & 71.7 & 77.4 & 81.9 & 88.0 \\ [PERSON] [7] & 73.7 & 80.2 & 85.2 & 91.5 \\ [PERSON] [9] & 75.4 & 80.9 & 84.3 & 88.3 \\ [PERSON] [11] & 82.5 & 86.2 & 89.3 & 90.6 \\
**Proposed:** & **86.4** & **91.7** & **94.8** & **97.8** \\ \hline  & \multicolumn{4}{c}{Testset2} \\ \hline [PERSON] [8] & 62.7 & 70.5 & 76.5 & 85.1 \\ [PERSON] [7] & 66.1 & 72.0 & 77.6 & 87.4 \\ [PERSON] [9] & 67.7 & 74.2 & 79.1 & 84.6 \\ [PERSON] [11] & 72.4 & 76.2 & 80.0 & 85.9 \\
**Proposed:** & **74.0** & **81.3** & **87.5** & **94.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SDR of the proposed method compared with other benchmarks for ISBI 2015 challenges on cephalometry landmark detection Testset1 and Testset2.
## 2 Calculation of Damping Reduction Factors  
Damping reduction factors (DRFs) are essential for seismic design and the evaluation of structures. They are used to adjust the elastic response spectra for a 5% damping ratio to other higher or lower damping levels [39]. These factors are defined as follows [40]:  
\[\text{DRFa}=\frac{SA(\xi,\text{T})}{SA(5\%,\text{T})},\text{DRFv}=\frac{SV( \xi,\text{T})}{SV(5\%,\text{T})},\text{DRFd}=\frac{SD(\xi,\text{T})}{SD(5\%, \text{T})} \tag{1}\]  
where \(SA(\xi,\text{T})\), \(SV(\xi,\text{T})\), and \(SD(\xi,\text{T})\), are the spectral accelerations, velocity, and displacement corresponding to a specific level of damping, respectively. The notations of \(SA(5\%,\text{T})\), \(SV(5\%,\text{T})\), and \(SD(5\%,\text{T})\) are the spectral accelerations, velocity, and displacement corresponding to 5% damping, respectively.  
The first research to identify DRFs was carried out by [PERSON] and [PERSON] [41], and this groundbreaking work highlighted that these factors rely upon the natural vibration period [42]. This study inspired scientists in this field, and afterward, DRFs were adopted in many seismic design codes. Table 1 shows the different DRF relationships for different design codes. In Figure 1, the code-based DRF relations determined using the equations given in Table 1 are presented based on different viscous damping ratios. The figure shows significant differences among the code-based period-independent DRF definitions. The lowest values for the DRFs have been suggested in the Japanese seismic design code. The great differences between the design codes imply the importance of studies on DRFs.
# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
## 3 Results
### Data Processing on the PS Terminal in ZYNQ
#### 3.1.1 Fault Type That Can Be Detected  
The faults detectable using this program include fuel injection timing advance, fuel injection timing retardation, decreased efficiency of the turbocharger (turbocharger impeller carbon deposit), decreased efficiency of the intercooler (intercooler pipe blockage), as well as normal operation. Therefore, the program assigns the following results: normal operation is denoted as 0, fuel injection timing advance as 1, fuel injection timing retardation as 2, decreased efficiency of the intercooler (intercooler pipe blockage) as 3, and decreased efficiency of the turbocharger (turbocharger impeller carbon deposit) as 4, totaling five types of fault results.  
Advance fuel injection may cause premature ignition, incomplete combustion, increased emissions, decreased fuel efficiency, and may lead to diesel engine knocking, potentially resulting in piston damage and engine shutdown when severe. Similarly, the retardation of fuel injection is also unfavorable as it leads to incomplete combustion, lower fuel efficiency, increased smoke emissions, and environmental pollution.  
Decreased turbocharger efficiency leads to reduced engine power, lower fuel economy, and may cause engine overheating, resulting in more severe damage to the diesel engine.  
Decreased intercooler efficiency leads to insufficient engine cooling and engine overheating which in turn leads to decreased engine performance, and may even cause cylinder detonation when such problems occur.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 4 Results  
### Aquaculture Mapping Results for Coastal Asia  
The results of the object-based pond aquaculture area extraction for the entire coastal zone and selected coastal study areas are presented in Figure 9. Due to the small object size of the ponds, it is difficult to display the entire object-based mapping result for the complete study area. Therefore, we illustrate some exemplary coastal spots located along the shoreline of Asia, with the respective mapped aquaculture objects illustrated in blue color (see Figure 9). We calculated the accuracies for the two classes (aquaculture pond and non-aquaculture pond) for the entire coastal study region. In order to quantify the agreement between the mapping result and the reference data set, the user and producer (UA and PA) and the overall accuracy (OA) as defined in [55] were computed (see Table 4). The aquaculture ponds in the coastal zone were mapped with a mean overall accuracy of 91.90 percent. In more detail, the overall accuracies per macro-region range from 92.69 percent in East Asia to 84.31 percent in Northeast Asia.  
The bar plot in Figure 10 shows that the highest amount of aquaculture pond area is concentrated in close proximity along the coastline of Asia (15 km distance to the coastline) of the respective study area. The total area of the mapped aquaculture ponds within the 200 km coastal buffer zone in Asia accounts for over 2 million ha (2,000,852 ha). More than one-fifth (21 percent) of the total pond area is located within 1 km distance from the coastline, and 45 percent can be found within 5 km distance from the coastline. This indicates that pond aquaculture is clearly concentrated in close proximity to the shores.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{3}{c}{**Reference**} \\ \cline{2-6}  & Aquaculture Ponds (AP) & AP & Non-AP & Sum & UA \\  & Non-Aquaculture Ponds (Non-AP) & 6483 & 1017 & 7500 & 86.44 \\  & Non-Aquaculture Ponds (Non-AP) & 198 & 7302 & 7500 & 97.36 \\ \cline{2-6}  & Sum & 6681 & 8319 & 15,000 & \\ \cline{2-6}  & PA & 97.04 & 87.77 & & 91.90 & OA \\ \hline \end{tabular}
\end{table}
Table 4: Accuracy assessment for the two classes of aquaculture and non-aquaculture for the entire coastal study region. Producer’s accuracy (PA), user’s accuracy (UA), and overall accuracy (OA).  
Figure 9: Extracted aquaculture ponds for the entire coastal zone of Asia. Coastal spots: **(A)** Guangdong Province, China; **(B)** Hainan Province, China; **(C)** Irrawaddy Delta, Myanmar; **(D)** Andhra Pradesh state, India; **(E)** Chachoengsao Province, Thailand; **(F)** Sumatra, Indonesia; **(G)** Java, Indonesia; **(H)** Red River Delta, Vietnam.
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 3 Results and Discussion
### Developing Artificial Neural Networks
#### 3.4.3 ANN-Generated UPV  
In the case of the UPV (ultrasonic pulse velocity) of stabilized and unstabilized soil samples developed by an artificial neural network, the Pearson correlation matrix can be used to examine the relationship between the input parameters and the UPV values predicted by the ANN model. Table 5 shows the Pearson correlation matrix between the input data and the values of the UPV predicted by the ANN model. The strong Pearson correlation observed between the predicted and experimental ultimate compressive strength demonstrates the ability of artificial neural networks (ANN) to accurately capture the distinct physicochemical interactions caused by the presence of marble powder.  
Previous research has explored the utilization of marble powder in concrete to model its ultrasonic pulse velocity (UPV) [47]. However, this study represents the pioneering attempt to employ artificial neural networks (ANN) to simulate UPV in stabilized soil. Additionally, this approach integrates artificial neural network (ANN) modeling with a systematic optimization technique to analyze the effects of curing and composition parameters on ultrasonic pulse velocity (UPV).  
The proven artificial neural network (ANN) technique presents a novel predictive tool for customizing soil mixtures stabilized with marble powder. This tool aims to obtain specific ultrasonic pulse velocity (UPV) values associated with desired levels of strength and integrity.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 3 Results  
### Analysis of LULC Distribution and Changes in Maseru  
In Figure 2, we show the LULC classification across Maseru, Lesotho for the periods 1988, 1993, 1998, 2003, 2008, 2013 and 2019. Our results show a clear landscape pattern, with built-up areas dominating the central northeast section of the study area. From 1988, this spread steadily and, in 2019, consolidated the interior and further south of the city (Figure 2). Against this expansion, the clear decrease in agricultural areas and grassland areas over the years is remarkable. Relatively smaller waterbodies occurred mostly from the central section and northern parts flanked by built-up areas. There were few waterbodies and woody vegetation in the study area (Figure 2). There was also a rise in areas with bare soils from 1993 to 2019 (Figure 2).  
Figure 2: LULC classification maps from 1988 to 2019 for the study area.
# A Multi-Node Detection Algorithm Based on Serial and Threshold in Intelligent Sensor Networks
## 2 SCMA System Model  
The SCMA uplink model consists of transmitter, transmission channel and receiver, as shown in Figure 1. Assuming that the number of sensor nodes is j and the number of time-frequency resource blocks is K (J>K), the binary bit data stream bj (bj, bj,..., bj) is obtained after the sensor node information uj (u1, u2,..., uj) is encoded by the source and the channel. Then data enters the SCMA encoder, which maps it to the sparse SCMA codebook x=f(bj); the mapping process of SCMA [37,38] can be defined as f: Blog2M \(\rightarrow\chi\). B is the set of binary numbers, \(\chi\) is the sensor node's codebook, and M is the size of the codebook. Due to the channels of each layer of the upper link being different, the channel factor is different, here assuming that the channel factor is hj (hj, hj,..., hj).  
The size M of the codebook depends on the number of bits of binary data. Sensor node data is divided into several groups according to a-bit. For a-bit user data, the size M of the codebook is shown in Equation (1):  
\[\text{M}=2^{\text{a}}. \tag{1}\]  
The ratio of the number of sensor nodes that can be carried on a certain time-frequency resource block and the size of a codebook in the SCMA system is defined as the overload factor, and its calculation method can be expressed as Equation (2):  
\[\lambda=\frac{\text{J}}{\text{M}}. \tag{2}\]  
If six sensor nodes occupy four time-frequency resource blocks, the overload ratio is 150%. As shown in Figure 2, the bit information of six sensor nodes is mapped to the codewords of different  
Figure 1: Uplink sparse code multiple access (SCMA) communication system model.  
codebooks [39], and each sensor node has a unique codebook. Each codebook contains two non-zero elements and two zero elements, so the codebook length is 4. In the factor graph, 1 represents the non-zero element and 0 represents the zero element, then the user information can be expressed by a matrix as in Equation (3):  
\[\mathrm{F_{4\times 6}=\left[\begin{array}{cccccc}0&1&1&0&1&0\\ 1&0&1&0&0&1\\ 0&1&0&1&0&1\\ 1&0&0&1&1&0\end{array}\right]. \tag{3}\]  
The K resource blocks are loaded on the subcarrier for transmission, and the signals on the K subcarrier at the receiving end are expressed as Equation (4):  
\[\mathrm{y}=\sum_{j=1}^{J}\mathrm{diag(h_{j})x_{j}+n,} \tag{4}\]  
where, \(x_{j}=[x_{1,j},\,x_{2,j},\,\ldots,\,x_{K,j}]^{T}\) is the k-dimension SCMA code of sensor node \(j\), and the non-zero element is K. The SCMA code \(x_{j}\) has sparsity, which can reduce the codeword conflict at time-frequency resource \(k\). The vector \(h_{j}=[h_{1},\,h_{2},\,\ldots,\,h_{K}]^{T}\) is the receiver channel factor vector. The vector \(n_{j}=[n_{1},\,n_{2},\,\ldots,\,n_{K}]^{T}\) is the White Gaussian Noise vector added to the channel with distribution \(N(0,\,\sigma^{2}l)\)[40]. The vector \(y_{j}=[y_{1},\,y_{2},\,\ldots,\,y_{K}]^{T}\) is the signal received by the receiver.
# A Review on the Importance of Microbial Biopolymers Such as Xanthan Gum to Improve Soil Properties
## 4 Xanthan Gum Biopolymer
### Effects of Xanthan Gum on Soil Properties for Geotechnical Engineering
#### 4.3.1 Mechanical Properties
These differences in the behaviour are probably due to the fact that the bonds promoted by the xanthan gum are less exposed to the environment in a clayed soil than in pure sand, where a faster interaction of xanthan gum with water occurs [40]. As previously mentioned, the increase in the mechanical properties over time is related to dehydration of the hydrogels associated with the formation of a soil-xanthan gum matrix, which promotes the aggregation of soil particles [17; 34]. Besides the positive effect on the stiffness, compressive and shear strength when using xanthan gum to stabilize soils, the results of direct tensile strength tests [41] and splitting tensile strength tests [40] carried out with air-dried specimens of two sandy soils show an increase in the tensile strength with the increase in the xanthan gum content (from 1.5 to 3%) and with the curing time (up to 30 days). This effect is justified by the combination of higher suction and hydrogel bonding [41]. Recent research about the behaviour of xanthan gum biopolymer and sand interaction during drying indicates that this biopolymer promotes a reduced effect on shear strength when the water content is high, independently of the biopolymer content [42]. On the other hand, the continuous evaporation of the water induces a significant increase in the shear strength of the xanthan gum treated soil and this effect is higher with the increase in the xanthan gum content. Additionally, the strengthening is more significant when the drying was carried out in an oven at 40 \({}^{\circ}\)C than at a room temperature of 20 \({}^{\circ}\)C. Infact, the latter drying conditions bring about a reduced increase in shear strength, since under these conditions the drying and the crystallization (promoted by the xanthan gum) is fundamentally superficial, while, in the inner part of the sample, the water content remains high enough to only generate weak bonds [42].
## 4 Performance Analysis
### SINR Comparison of All Beamformer with Respect to Disparity Angle
#### 4.2.3. SINR Comparison of the VDL-Based Beamformer with Respect to the Disparity Angle  
In Figure 3c, the SINR of the LCAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is 38.95 dB, 36.09 dB, and 33.35 dB, respectively. The SINR of the LCPAA beamformer is 40.41 dB, 41.29 dB, and 40.09 dB with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) differences, respectively. With 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) differences, the SINR of the LRAA beamformer is 40.18 dB, 39.43 dB, and 38.33 dB, respectively. The SINR of the LCCAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is 40.41 dB, 40.29 dB, and 40.09 dB, respectively. The SINR of all beamformers without disparity is 40.45 dB. From the above discussion, it is concluded that the LCAA beamformer has a better SINR performance.
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 2 Related Work
### DAttention Mechanism in CD  
In the change detection task, the attention mechanism is increasingly used, which can automatically focus on the key features in the changed region while suppressing the interfering information in the unchanged region, thus enhancing the detection capability of the model. For example, the JoinAtt module in the dual-branch multi-level spatio-temporal network (DMINet) cleverly implements feature interactions to direct attention to the real change [14]. The DSIFN proposed by [PERSON] et al. [15] utilizes multi-scale features combined with an attention module to improve feature fusion. The SNUNet proposed by [PERSON] et al. [16] improves the modeling capability of contextual information by integrating the channel attention modeling capability of contextual information, which makes the intermediate feature representation richer. To address the category imbalance problem, the MFPNet [17] introduces adaptive channel weights to better focus on changing regions. In addition, for the pixel-level long-distance dependency problem, some works try to model spatio-temporal contextual relationships through the self-attention mechanism, which achieves ideal results but faces the problem of rising computational complexity.  
Therefore, we designed a high-frequency attention module (HFAM), which improves the detection accuracy of the edges of the change region by combining the two phases of spatial attention and high-frequency enhancement and effectively enhances the change detection performance of the model, and a spatio-temporal disparity attention module (SDAM), which learns the global change information of the bi-temporal features and the fine-grained local content, respectively, by adopting a dual-branching structure, utilizing the coordinates of the attention to capture the spatio-temporal differences while extracting local context information through cascading convolution, thus enhancing the representation of the change region and suppressing interference noise.
# Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
## 3 Methods
### Comparison and Accuracy Evaluation  
To evaluate the differences in accuracy of the retrieved snow depth among different models and compare the retrieved HY-2B SMR snow depth with the GCOM-W1 AMSR-2 snow depth product, we used three statistical parameters: mean deviation (MD), mean absolute deviation (MAD), and root mean square deviation (RMSD), as defined in Equations (5)-(7). These parameters are commonly used in the field due to their effectiveness in quantifying the dispersion of data points around the mean ([PERSON] et al., 2000). They provide a comprehensive measure of the model's performance by evaluating the bias, precision, and overall error, respectively [34].  
\[\text{MD}=\frac{\sum_{i=1}^{n}(SD_{i}-h_{i})}{n}, \tag{5}\]  
\[\text{MAD}=\frac{\sum_{i=1}^{n}\lvert SD_{i}-h_{i}\rvert}{n}, \tag{6}\]  
\[\text{RMSD}=\sqrt{\frac{\sum_{i=1}^{n}(SD_{i}-h_{i})^{2}}{n}}, \tag{7}\]  
where, \(SD_{i}\) represents the snow depth on sea ice retrieved from HY-2B SMR data, \(h_{i}\) refers to the average snow depth obtained from in situ observations conducted by the R/V Xuelong after spatial and temporal matching, or the snow depth product from GCOM-W1 AMSR-2, and n represents the number of pixels involved in the comparison.
## 2 Mathematical Model
### Boundary Integral Equations  
Following the reasoning given in [23], we put a simple layer potential on the outer surface \(\Sigma_{1}\) and a simple layer and double layer potentials on the inner boundary \(\Sigma_{2}\). Thus, the field of velocities and pressures in the regions \(\Omega_{1}\), \(\Omega_{2}\) are sought in the following form:  
\[\mathbf{v} =\
abla W_{\lambda_{1}}[\Sigma_{1},h]+\
abla U_{\lambda_{1}}[ \Sigma_{2},g]+\
abla W_{\lambda_{1}}[\Sigma_{2},h], \tag{15}\] \[p =-\alpha_{i}\big{(}W_{\lambda_{1}}[\Sigma_{1},h]+U_{\lambda_{1}}[ \Sigma_{2},g]+W_{\lambda_{1}}[\Sigma_{2},h]\big{)}+\chi_{\Omega_{2}}p_{v}. \tag{16}\]  
Here, \(\lambda_{1}=0\) in \(\Omega_{1}\), \(\lambda_{2}=\sqrt{L\alpha_{2}}\) in \(\Omega_{2}\), and \(\chi_{\Omega_{2}}\) is the domain indicator function for \(\Omega_{2}\). The advantage of this representation is that the velocity and pressures are expressed by the same operators in different domains.  
The velocity and pressure in the integral representations (15) and (16) satisfy system (1). The integral representations (15) and (16) are substituted into the boundary conditions (2)-(5). We construct a system of integral equations for the unknown densities of the simple and double layer potentials \(h\), \(g\) in which each equation corresponds to one of the boundary conditions (2)-(5).  
\[\frac{h}{2}+\mathbf{n}\cdot\
abla W_{0}[\Sigma_{1},h]+\mathbf{n}\cdot\
abla U_{0}[ \Sigma_{2},h]+\mathbf{n}\cdot\
abla W_{0}[\Sigma_{2},h]-\xi=f_{0},\quad x\in \Sigma_{1}, \tag{17}\]  
\[W_{0}[\Sigma_{1},h]+U_{0}[\Sigma_{2},h]+W_{0}[\Sigma_{2},h]=-\frac{\psi}{ \alpha_{1}},\quad x\in\Sigma_{1}, \tag{18}\]  
\[\alpha_{1}W_{0}[\Sigma_{1},h]-\alpha_{2}W_{\lambda_{2}}[\Sigma_{1 },h]+\frac{\alpha_{1}+\alpha_{2}}{2}g+\\ +\alpha_{1}U_{0}[\Sigma_{2},h]-\alpha_{2}U_{\lambda_{2}}[\Sigma_{ 2},h]+\alpha_{1}W_{0}[\Sigma_{2},h]-\alpha_{2}W_{\lambda_{2}}[\Sigma_{2},h]=-p_ {v},\quad x\in\Sigma_{2}, \tag{19}\]  
\[\mathbf{n}\cdot\big{(}\
abla W_{0}[\Sigma_{1},h]-\
abla W_{\lambda_{2}}[ \Sigma_{1},h]\big{)}+\mathbf{n}\cdot\big{(}\
abla U_{0}[\Sigma_{2},h]-\
abla U_{ \lambda_{2}}[\Sigma_{2},h]\big{)}-\\ -h+\mathbf{n}\cdot\big{(}\
abla W_{0}[\Sigma_{2},h]-\
abla W_{\lambda _{2}}[\Sigma_{2},h]\big{)}=0,\quad x\in\Sigma_{2}, \tag{20}\]  
The solution functions \(h\), \(g\) of the boundary integral equation system allow calculation of the velocity and pressure fields in integral form (15) and (16) in the filtration domain \(\Omega\).