esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Depth Prediction Model Using Terrain Attributes in Gangwon-do, South Korea
###### Abstract  
Soil depth is a crucial parameter in slope stability analysis in mountainous areas. The drilling survey is the most reliable method for determining soil depth, but it requires a high cost for the vast geographical area. Therefore, this study proposes a soil depth prediction model for mountainous areas that uses Terrain Attributes (TAs) from digital maps. Gangwon-Do, a predominantly mountainous region in South Korea, is selected as the study target area. The study area is classified by parent rock type into igneous rocks, metamorphic rocks, and sedimentary rocks. The correlation with TAs is analyzed through multi-collinearity using drilling data published in the Korea drilling information database. In addition, the most suitable combination of variables is selected through multi-collinearity analysis, and the regression model using STI, TWI, and SLOPE is found to be the most appropriate model (VIF < 10). The proposed model for soil depth shows significance at \(p\) < 0.001, and the correlation coefficient (\(R^{2}\)) is figured out for igneous rock (0.702), metamorphic rock (0.686), and sedimentary rock (0.693). In addition, the reliability of the proposed model was verified by using data from regions not included in the model development, and the correlation coefficients were igneous rock (0.867), metamorphic rock (0.801), and sedimentary rock (0.814). The model proposed is more suitable for Korean topography than the existing statistical models; it can help to increase the accuracy of slope stability analysis.  
soil depth; terrain attributes; prediction model; multi-collinearity; multi-linear regression analysis +
Footnote †: journal: Applied Sciences  
10 November 2022  
1
Footnote 1: Corresponding: [EMAIL_ADDRESS]; Tel.: +82-052-259-1531
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Ship Arrivals and Departures
#### 4.4.3 Problem  
Arrival at a port and departure from a port are two separate but very important aspects of a ship's voyage. Both of these procedures are critical and involve a number of different complexities in them. Arriving at some ports may require going through narrow or busy seaways where navigation may be difficult.  
The solution to this problem is guided by the following forces:  
* Availability: berth facilities must be assigned in advance and in such a way as to minimize ship stays in the port;
* Scalability: the number of provisioned, designated locations to dock in a port should vary depending on the traffic of vessels to the port. New berths can be provisioned for loading/unloading as needed;
* Berth Scheduling: berth scheduling should be based on the priority of resource allocation and a comparison of the vessel and berth data. How to assign its berth time and position is of great importance to improving the level of port services and reducing the port's cost of production;
* Safety and Port Integrity: arrival and departure procedures should ensure the safety of the ship's arrival and departure, both for the people on board as well as those at the seaport. Most of the collisions and groundings of ships are reported during the maneuvering of the vessels in port, and hence the maneuvering operation at port arrival or departure is considered the most crucial time a ship faces in her voyage. Theavailability of marine pilots and tugboats should ensure the avoidance of physical damage [54].
## 5 Reviewed Papers on Empirical-Based Path Loss Models
### Performance Evaluation of Empirical Path Loss Models in Urban Environment  
In assessing the performance of empirical models in the mid-band and high-band frequency ranges in urban environments, several studies have been done, especially in [69], which demonstrated that path loss could be effectively modeled across the frequency ranges of 1.8, 3.5 and 28 GHz using wideband measurements. The CI model performed excellently in both bands, having the minimum RMSE value of 1.95 dB across the frequencies under consideration, as shown in Figure 4.  
The stability of the same early empirical models, including FSPL and COST-231 Hata, was examined against empirical data measurements for path loss prediction at 1.8 GHz in [42] and 2.5 GHz in [72]. The analytical results concluded that the COST-231 Hata model was the most suitable choice, exhibiting a minimum RMSE of 4.64 dB in the case of 1.8 GHz and 5.27 dB in the case of 2.5 GHz. Conversely, in various frequency bands and scenarios, CI and FI models were scrutinized, with the CI model showing exceptional performance, particularly in line-of-sight (LOS) scenarios across both mid-band (1.8 GHz and 3.5 GHz) and high-band (28 GHz) frequencies.  
Considering the empirical models for high-band frequency ranges, the stability of the FI and CI models was tested in [47,69] to determine the best path loss prediction at 28 GHz in an urban environment. In comparison to the NYUSIM model in [68], they discovered that the CI model had the lowest RMSE value for the situations they were evaluating, with 2.25 dB in [47], 3.54 dB in [68], and 1.95 dB in [69].
# Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors
## 3 Method
### Partitions for Different Domains  
We partition the dataset released in [28] by image quality or illumination, since these two indicators can be defined properly by mathematics, which avoids subjectivity in partitions.  
Clear Domain vs. Blur Domain: We employ a Laplace operator to obtain the blur length for each image [31] and use K-mean algorithms with \(K=2\) to cluster the blur lengths of all of the images, which leads to the clear domain (6278 samples) and blur domain (11,909 samples) in this paper.  
Bright Domain vs. Dark Domain: The illumination intensity cannot be defined properly as a single value like blur length, since the dark or bright background may dominate the calculation. Thus, we propose using the distribution of illumination intensity to partition. We employ L values of images in LAB (Lightness, Red/Green Value, Blue/Yellow Value) color space [32] to define the illumination intensity for every pixel. For each image, all of the L values can be seen as its distribution of illumination intensity. To describe the distribution, we use the \(0.1,0.2,\ldots,1.0\) quantiles to form the ten-dimensional feature vector. After vectorization of all of the images, we also use K-mean algorithms to cluster them with \(K=2\) and the final partition leads to the bright domain (8159 samples) and dark domain (10,028 samples) in this paper.
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 3 GNSS Tropospheric Tomography Methodology
### Tomography Parameter Estimation Method
#### 3.2.1 Relaxation Parameter and Iterative Termination Criteria  
The relaxation parameter \(\lambda\) acts as a weight in the iterative formula, and adjusts the magnitude of the correction for each iteration. If the relaxation parameter is too large, the correction will be large and the iteration will be prone to get divergence. On the contrary, small relaxation parameter may result in slow convergence. In this study, the relaxation parameter is set to be 0.015 after a series of trials.  
The ART algorithm requires multiple iterations to reach convergence and the criteria that describes the convergence behavior to terminate the iteration in an optimal manner needs to be pre-defined. However, there is no uniform criterion, and in this paper, the consistency between the estimates and observations is used define the iterative termination criteria.  
The \(k\)-th computed value \(SWD^{k}\) can be obtained by taking the estimated parameters back to the observation equation, and the difference between the computed and the observed value can be expressed as:  
\[dSWD^{k}=SWD^{k}-SWD. \tag{11}\]  
The root mean square (_rms_) of \(dSWD^{k}\) can be calculated as:  
\[rms=\sqrt{\frac{1}{m}{\sum}_{i=1}^{m}dSWD_{i}^{k}} \tag{12}\]  
where \(m\) is the number of cycles in the \(k\)-th iteration. Iteration is terminated when \(rms<0.002\) or after 200 iterations.
# An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition
## 4 Experimental Results
### Discussion  
#### 4.4.1 Results  
In this paper, using three different datasets with different characteristics, we show the performance of the proposed method against different problems that can be encountered in real-world conditions. The major problems can be listed as follows: variations of illumination, pose, facial expression, and low resolution. As mentioned in Section 4.1, FERET has four different subsets (Fb, Fc, Dup1, and Dup2) with different properties. The images in Fb have different facial expressions from the gallery set, and using MS-LZM on this set, we achieved results at the same level as the best known results. The images in Fc were recorded under different lighting conditions. As can be seen in Table 1, MS-LZM and the other methods achieved 100% performance on this set. The reason for this is that the local feature extraction methods are robust against illumination changes [14]. Dup1 and Dup2 sets are the most challenging ones in FERET, and they have different poses, scales and facial expressions. With MS-LZM, we achieved the highest results, reducing the error rates for these sets by 26.9% and 30%, respectively. In the datasets used in this study, LFW is the most challenging one in terms of pose differences. For this reason, as in many studies in the literature, frontalized images were used in this study. However, as can be seen in Figure 9, the frontalization process can lead to severe degradations in some images. Besides, the fact that there are many variations in the dataset (e.g., resolution, facial expression, and occlusion) also make this set very difficult. However, using MS-LZM, we were able to achieve a satisfactory result with a 0.9515 (AUC result) on this dataset. SCface is a dataset that was collected considering real-world conditions. The images were recorded on different days and in uncontrolled environments in terms of lighting. In addition, since the individuals were recorded in their natural state and did not look to a fixed point during recording, the dataset has pose variations and facial expression. There are also very-low-resolution images in the dataset. Since SCface is not divided into subsets for different problems, it is not easy to mention MS-LZM's performance separately on this dataset for different problems. However, since the images recorded at distance 1 have very low resolution as shown in Figure 10, we can talk about the performance of MS-LZM on low-resolution images. According to the results in Tables 3 and 4, MS-LZM had better performance for all of the subsets recorded at distance 1, and the average error rate at distance 1 was reduced by 21.4% for SCface-Vis and 10.9% for SCface-IR. On the other hand, we can say that MS-LZM was more robust than the other methods against the mentioned problems when we consider the average results obtained on all subsets of SCface. In Tables 3 and 4, we reduced the error rates by 12.5% and 11.6% (on average), respectively.
# Self-Adaptive Alternating Direction Method of Multipliers for Image Denoising
## 6 Discussion  
The S-ADMM method proposed in this paper, incorporating collaborative sparse regression, has established an effective sparse representation, advancing the exploration of structured sparsity and spatial relationships among pixels. Our adaptive approach has shown remarkable performance in mitigating the impact of penalty parameters on algorithm effectiveness. These findings hold significant implications for the field of image processing, offering insights for enhancing image quality and denoising efficacy. Future research directions could delve into the effectiveness of sparse representation methods, and extend the application of this method to other areas of image processing, thereby driving the development of image processing technologies.
## 2 Principle
### Bias Deviation with Single-Arm Driven Mode  
We then investigated the impact of bias deviation in the single-arm driven mode, as shown in Figure 1b. The modulated optical field \(E_{2}(t)\) is modified as  
\[E_{2}(t)=e^{js(t)+j\varphi}+e^{-j\varphi}\approx e^{j\varphi}[1+js(t)]+e^{-j \varphi}=C+je^{j\varphi}s(t) \tag{5}\]  
Figure 1: Transmitter configuration of (**a**) standard differential driven mode, (**b**) single-arm driven mode, (**c**) differential driven mode with amplitude mismatch, and (**d**) differential driven mode with time skew.  
At the receiver, the photocurrent \(I_{2}(t)\) can be modified accordingly as  
\[\begin{array}{l}I_{2}(t)=\left|E_{2}(t)\otimes h(t)\right|^{2}=\left|C+je^{i \varphi}s(t)\otimes h(t)\right|^{2}\\ =C^{2}+je^{i\varphi}Cs(t)\otimes h(t)-je^{-j\varphi}Cs^{*}(t)\otimes h^{*}(t)+ \ldots\\ \approx C^{2}+Cs(t)\otimes\left[je^{i\varphi}h(t)-je^{-j\varphi}h^{*}(t) \right]\end{array} \tag{6}\]  
Again, we transformed into the frequency domain, as in Equation (7), for a better understanding.  
\[\begin{array}{l}H_{2}(f)=je^{i\varphi}h(f)-je^{-i\varphi}h^{*}(f)\\ =\exp\left[j\big{(}\pi\lambda^{2}DLf^{2}/c+\varphi+\pi/2\big{)}\right]+\exp \left[-j\big{(}\pi\lambda^{2}DLf^{2}/c+\varphi+\pi/2\big{)}\right]\\ =2\cos\big{(}\pi\lambda^{2}DLf^{2}/c+\varphi+\pi/2\big{)}\end{array} \tag{7}\]  
In this case, the overall transfer function \(H_{2}(f)\) becomes dependent with the constant phase shift, indicating that the available bandwidth can be shifted as the bias deviates. Typically, when the MZM is biased at the quadrature point of \(\pi/4\), the first fading frequency is increased to \(\sqrt{3c/4\lambda^{2}DL}\), which is larger than \(\sqrt{c/2\lambda^{2}DL}\) with differential driven mode. However, biasing at the other quadrature point of \(3\pi/4\) would lead to smaller first fading frequency of \(\sqrt{c/4\lambda^{2}DL}\). Furthermore, for lower-arm single-driven mode, the transfer function can be easily modified as  
\[H_{2}(f)=2\cos\Big{(}\pi\lambda^{2}DLf^{2}/c-\varphi-\pi/2\Big{)} \tag{8}\]
# Open DGML: Intrusion Detection Based on Open-Domain Generation Meta-Learning
## 4 Experimental Setup
### Evaluation Datasets  
In our experiment, we selected four intrusion detection datasets, namely ISCX2012 [36], NDSec-1 [37], CICIDS2017 [28] and CICIDS2018 [38], which cover a variety of attack types including DoS, DDoS, brute force, penetration, web attacks, zombie networks, Heartbleed, and port scanning, as shown in Table 2. These datasets were chosen to meet the requirements of open-domain settings, with different distributions, multiple attack types, and some unique types not found in other datasets. ISCX2012 and NDSec-1 are real-world datasets, while CICIDS2017 and CICIDS2018 are simulation-based datasets. The different distributions, attack types, and sample sizes of these datasets provide a better evaluation of intrusion detection algorithms. It is important to consider various factors when selecting a dataset, such as its realism, reliability, sample size, attack types, and distribution. Additionally, proper preprocessing and feature extraction methods should be applied to ensure that the algorithm can effectively learn the features of the data and improve detection accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ISCX2012** & **NDSec-1** & **CICIDS2017** & **CICIDS2018** \\ \hline DoS & ✓ & ✓ & ✓ & ✓ \\ \hline DDoS & ✓ & ✓ & ✓ & ✓ \\ \hline Brute Force & ✓ & ✓ & ✓ & ✓ \\ \hline Infiltration & ✓ & ✓ & ✓ & ✓ \\ \hline Web Attack & & & ✓ & ✓ \\ \hline Botnet & & ✓ & ✓ & ✓ \\ \hline Heartbleed & & & ✓ & ✓ \\ \hline Port Scan & & ✓ & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of attack types of datasets.
## 4 Discussion
### The Impact of Meteorological Factors on WUEc Variation  
[PERSON] et al. analyzed global patterns and drivers of WUE from 2000 to 2013 using MODIS data and identified a negative correlation between WUE and temperature [61], while [PERSON] et al. reported a positive relationship [62]. In this study, we found that WUEc in Xinjiang during 2002-2022 was generally negatively correlated with temperature; however, about 40% of the region exhibited a positive relationship. This pattern may result from a pronounced threshold effect of temperature on WUEc: at lower temperatures, the activity of photosynthetic enzymes is suppressed, reducing GPPc synthesis; at higher temperatures, stomatal closure limits CO2 uptake, thereby constraining GPPc production and ultimately decreasing WUEc [63; 64]. In the study area, the negative effect of temperature is primarily attributed to an increased diurnal temperature range during the growing season, wherein elevated transpiration surpasses the improvement in leaf carbon assimilation. This finding aligns with previous studies on temperature threshold effects [65]. The influence of precipitation on WUE remains contentious. For example, [PERSON] et al. [66] identified a positive correlation between WUE and precipitation using eddy covariance tower measurements, whereas [PERSON] [67], employing a process-based model, observed a negative correlation. [PERSON] et al. [67] attributed this discrepancy to methodological differences between site-level observations and spatially integrated regional estimates. However, in arid regions, the influence of precipitation on WUE tends to be more consistent. For instance, [PERSON] et al. [68] found that in regions with annual precipitation below 500 mm, vegetation in most terrestrial ecosystems across China exhibited a positive WUE response. Similarly, [PERSON] [26] reported that more than 60% of the desert steppe in Inner Mongolia exhibited a positive spatial correlation between WUE and precipitation, consistent with the findings of this study. This consistency may result from the extremely low mean annual precipitation in the study area--less than 160 mm, far below the 500 mm threshold--leading to a positive response of WUEc to increased precipitation. It is generally accepted that WUE declines with increasing vapor pressure deficit (VPD) [32]. However, studies by [PERSON] and [PERSON] et al. [69; 70] suggest that WUE's negative response to VPD may only occur during specific seasons, or may even be absent under extreme environmental conditions. In the arid and semi-arid regions under study, irrigation is widely employed to sustain crop growth. For irrigated crops, transpiration rates are inversely related to atmospheric humidity, which is often represented by VPD. Therefore, WUEc generally exhibits a negative correlation with VPD under such conditions.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 5 Fiber Optics
### Fiber Bragg Grating
The proposed sensor is suitable to monitor the dynamic strain, determine the dynamic characteristics of the dam model, describe the crack position, and monitor the cracking progress during the seismic events simulated by the shaking table. The application of FBG sensors embedded in polymer composite materials was investigated by [PERSON] et al. [42]. A series of experiments were performed to measure strain under different conditions, i.e., pronounced gradient nature of strain distribution, compression and tension, a cyclic variation of strains with time and different temperatures. The results of the experiments have shown that FBG sensors are very effective in measuring strains in polymer composite materials. Temperature measurements with FBG sensing technology were performed by [PERSON] et al. [33]. A steel-concrete composite bridge located in Alaska was monitored with the aid of 11 fiber optic temperature sensors that are based on the FBG technique. Real-time temperature data were obtained and combined with other static and dynamic measurement data. These data were recorded on a computer were used to establish the relationship between the bridge response and thermal loading. Finally, FBG sensors have been implemented in monitoring damage in buildings due to vibration from rock blasting by [PERSON] et al. [43]. Two buildings were constructedand instrumented with FBG strain sensors to measure their damage when subjected to blast loading. The results showed that the sensing technique was accurate and that today's vibration guideline limit values include a large safety margin for buildings.
# UAV Application for Typhoon Damage Assessment in Construction Sites
## 3 Research Methods and Procedures
### Analytic Hierarchy Process  
Analytic hierarchy process (AHP), as a hierarchical weight analysis method for multi-objective evaluation, stratified the targets to form multiple target layers and compared the indicators in each target layer to construct a comparison matrix. By calculating the importance of each indicator layer relative to the previous one, the relative weights of all indicators were obtained. AHP complemented the qualitative method and provided a more rigorous connection to the research from a quantitative perspective [49; 50].
# Mechanical Behavior of Hollow Corrugated Sandwich Cylinders Under Inner Pressure Loading
## 2 Description of Problems
### Loading and Mass Expression  
Considering a long, hollow, corrugated sandwich cylinder subjected to a uniform internal pressure \(P_{i}\), of which the cross profile is shown in Figure 2a. Assume that the geometry axis is coincident with the physics axis, i.e., the Z-axis of the cylindrical coordinate system.  
Figure 2b is the topological configuration of the hollow corrugated sandwich cylinder. \(R_{0}\) and \(R_{i}\) are the inner radius of the inner and outer facesheets, respectively; \(t_{0}\) and \(t_{i}\) are the thicknesses of the inner and outer facesheets, respectively; \(t_{w}\) is the thickness of the trapezoidal corrugated core; and \(l_{w}\) is the length of the incline cell. \(\alpha_{i}\), \(\alpha_{o}\), \(\beta\), and \(\theta\) are central angles related to the structure of the corrugation. In contrast to the flat panel construction, the description of the topological parameters of the corrugated unit undergoes minor alterations due to the curvature. In a flat plate, the trapezoid width \(f\) (shown in Figure 1b) is constant, while in a cylinder shell, the trapezoid widths of the inside and outside facesheets are difficult to remain constant, which are expressed by \(f_{o}\) and \(f_{i}\) in Figure 2b. The mass of the outer facesheet, the inner facesheet, and the core can be calculated as follows:  
\[\left\{\begin{array}{l}m_{outer\_face}=\rho_{s}\pi(2R_{o}+t_{o})t_{o}\\ m_{inner\_face}=\rho_{s}\pi(2R_{i}+t_{i})t_{i}\\ m_{core}=\rho_{s}(2l_{w}t_{w}+f_{o}t_{w}+f_{i}l_{w})n_{core}\end{array}\right., \tag{1}\]  
where \(\rho_{s}\) is the density of steel, \(n_{core}\) is the number of the corrugations, and  
Figure 1: Topological configuration of a long, hollow corrugated sandwich cylinder shell. (**a**) Sandwich cylinder shell and (**b**) corrugated cell.  
Figure 2: Structure model of the pressurized hollow corrugated sandwich cylinder shell. (**a**) Inner pressure loading model. (**b**) Topological parameters.  
\[l_{w}=\sqrt{R_{o}^{2}+\left(R_{i}+t_{i}\right)^{2}-2R_{o}(R_{i}+t_{i})\cos\beta}, \tag{2}\]  
\[\beta=\frac{\theta}{2}-\alpha_{o}-\alpha_{i}, \tag{3}\]  
\[\alpha_{o}=\frac{f_{o}}{2R_{o}}, \tag{4}\]  
\[\alpha_{i}=\frac{f_{i}}{2(R_{i}+t_{i})}, \tag{5}\]  
\[\theta=\frac{2\pi}{n_{core}}. \tag{6}\]  
Therefore, the mass per unit length of the hollow corrugated sandwich cylinder can be expressed as  
\[\begin{array}{ll}m&=m_{outer\_face}+m_{inner\_face}+m_{core}\\ &=\rho_{s}\pi(2R_{o}+t_{o})t_{o}+\rho_{s}\pi(2R_{i}+t_{i})t_{i}+\rho_{s}(2l_ {w}t_{w}+f_{i}t_{w})n_{core}\\ &=\rho_{s}\{\pi[(2R_{o}+t_{o})t_{o}+(2R_{i}+t_{i})t_{i}]+n_{core}t_{w}(2l_{w} +f_{o}+f_{i})\}\end{array}. \tag{7}\]