esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Empirical Model
### The Statistical Reliability of the Proposed Model
The coefficient of determination is the closest to 1 in the author's proposition (0.96). Equations (6)-(9)  
Figure 12: Measured versus calculated damping ratio with model purposed by (**a**) authors of this article, (**b**) [PERSON] and [PERSON] [44], (**c**) [PERSON] and [PERSON] [48], (**d**) [PERSON] et al. [47], and (**e**) [PERSON] et al. [45], with Measured = Calculated (red dashed line) and \(\pm\)20% error lines (black dashed lines). have an \(R^{2}\) equal to 0.21, 0.79, 0.78, and 0.77, respectively, but the average relative error is on a high level from 28.2% for the [PERSON] and [PERSON] (2018) model to extreme, 77.3%, for [PERSON] and [PERSON] (2018), Equation (6). Thus, regard to estimation errors, Equation (5) caused the smallest, so it is best suited for the studied soils. The last statistical comparison of the analyzed models is presented in Figure 13. The picture shows the so-called box-plot chart. The smallest squares indicate the average value for the measured and calculated damping ratio. It can be seen that the mean measured damping ratio is similar to the calculated by Equations (5), (7), and (8). The mean damping ratio estimated by Equation (9) is slightly below the measured result. However, the most divergent from reality is the one from the [PERSON] and [PERSON] (2018) model. The next quantity presented in Figure 13 is average value of damping ratio plus and minus standard deviation. In this case, only the authors' model has a similar size box to the measured damping ratio. The last statistical value presented in Figure 13 is spread of results, marked with whiskers. The slightest difference between measured and calculated minimal and maximal values of damping ratio is presented by the authors' model. However, it did not reach the same maximum value as the measured results. Nevertheless, it is worth noting that, from Equations (8) and (9), the damping ratio is estimated with a minus sign, which is incorrect according to the definition. The conducted comparison clearly indicates that the best model for cohesive soil from Warsaw is Equation (5), which was proposed by the authors of this article. Damping ratios calculated by Equation (5) were the closest to the measured = calculated line (Figure 12a). Thus, the absolute and relative error was the smallest, and the coefficient of determination was the closest to 1. Moreover, the set of coefficient values calculated with the authors' model was very similar to the measured damping ratios (Figure 13). On that basis, it can be concluded that Equation (5) describes the best damping properties of the tested soils.
# Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks
## 3 Secrecy Performance Analysis
### Asymptotic Analysis
Then, based on the fact that \(\tilde{\epsilon}\) is a convex decreasing function with respect to \(N\) and \(T^{*}\) is a quasi-concave function with respect to \(N\), the optimal blocklength can be derived directly.
## 4 Performance Evaluation in Edge Scenarios
### Performance Improvement Strategies
#### 4.7.1 Optimizing Enclave Entry and Exit  
Enclave switching poses a significant bottleneck in SGX-based systems, with the overhead of a single Ecall or Ocall exceeding 14,000 cycles [28]. In comparison, a typical system call incurs only around 150 cycles. Since nearly all system calls within an enclave require switching to the untrusted application to complete, frequent transitions result in substantial performance overhead. To address this issue, approaches like HotCalls [28] and Eleos [67] have been proposed. HotCalls reduces the latency of enclave transitions by leveraging a spin-lock mechanism and shared unencrypted memory, cutting the overhead of recalls and Ocalls to as low as 620 cycles. Eleos, on the other hand, employs an \"exitless\" architecture that batches system calls, caches results and utilizes shared memory buffers to minimize the need for enclave exits, achieving a 2-3x reduction in overhead for system call-intensive workloads. Both approaches effectively mitigate the severe performance degradation caused by frequent enclave transitions, making SGX more practical for real-world, high-performance scenarios. However, our experimental evaluation reveals that the LibOS-based approach offers a more balanced solution for performance, security and usability in SGX implementations. By integrating most system calls directly within the enclave, LibOS significantly reduces the frequency of enclave switches. With a moderate increase in the TCB size, it also eliminates the need for partitioning existing code, allowing applications to be deployed into enclaves efficiently and with minimal performance overhead. Consequently, we conclude that the LibOS-based approach is particularly well suited for IoT and edge intelligence scenarios, where both performance optimization and ease of deployment are critical.
## Materials and Methods
### Data Analysis
#### 2.4.2 MVBS Comparison  
Two frequency comparisons were needed to calculate the differences in MVBS values generated from the backscatter of the same object. The process was done in order to distinguish the effect of frequency variability, which eventually can determine the dominant scatterers [29]. The difference in MVBS values for each ADCP instrument is written as:  
\[\Delta\text{MVBS}=\text{MVBS}_{750\text{kHz}}-\text{MVBS}_{307.2\text{kHz}}, \tag{1}\]  
where \(\Delta\text{MVBS}\) are the differences of MVBS from each ADCP and are also used as the threshold value for the echo intensity tracing method (dB). This method considers echo intensity from one bin obtained from each moored and mobile ADCP and also analyzes the MVBS value from time series data. The number of the tracers were selected by matching the time, depth, and location of the mobile ADCP to that of the moored ADCP. The echo intensity tracing method contains many successive echoes from the same backscattered signal of the zooplankton. The equation used to match the acoustic data is written as follows [12]:  
\[t_{c}=t_{d}-\Delta t, \tag{2}\]  
where the \(t_{d}\) value is the actual time at the beginning of the mobile ADCP ship moving past the moored ADCP location placed at the depths \(d\), and \(t_{c}\) is the end time when the ship crosses the moored ADCP. The variable time difference, \(\Delta t\), is calculated from the equation:  
\[\Delta t=\frac{\sqrt{D^{2}-d^{2}}}{v}, \tag{3}\]  
where \(D\) is the distance between the GPS antenna on the ship and the transducer (5 m), \(d\) is the depth when \(t_{d}\) time starts, and \(v\) is the speed of the ship (ms\({}^{-1}\)).
## 2 Problem Formulation and Preliminaries
### USV Model  
In this section, we first define the frames: the Earth-fixed inertial frame \(X_{0}OY_{0}\)\((i)\) and the body-fixed frame \(X_{p}OY_{p}\)\((b)\), as illustrated in Figure 1. Then, we only consider the horizontal motion of the USV, namely, neglecting roll \(p\), pitch \(q\), and heave \(w\).  
From [17], the kinematic equation of the USV can be modeled by:  
\[\left\{\begin{array}{l}\dot{x}=u\cos\psi-v\sin\psi\\ \dot{y}=u\sin\psi+v\cos\psi\\ \dot{\psi}=r\end{array}\right. \tag{1}\]  
where \((x,y,\psi)\) is the position and orientation of the USV in \((i)\) and \(u\), \(v\), and \(r\) include the frame \((b)\) surge, sway, and yaw velocities, respectively.  
Considering the actual situation of USV navigation, the [PERSON] nonlinear model [27] with respect to USV steering control is given by:  
\[\left\{\begin{array}{l}\dot{\psi}=r\\ \dot{r}=-\frac{1}{T}r-\frac{\alpha}{T}r^{3}+\frac{K}{T}\delta\end{array}\right. \tag{2}\]  
where \(\delta\) is the rudder angle, \(\alpha\) is the Norrbin coefficient, \(T\) is the time constant, and \(K\) is the gain constant.  
Combining the system (1) and (2), the nonlinear mathematical model of the USV is written as:  
\[\left\{\begin{array}{l}\dot{x}=u\cos\psi-v\sin\psi\\ \dot{y}=u\sin\psi+v\cos\psi\\ \dot{\psi}=r\\ \dot{r}=f\left(r\right)+\Delta f\left(r\right)+b\mu+d\\ y_{1}=x\\ y_{2}=y\end{array}\right. \tag{3}\]  
Figure 1: The Earth-fixed inertial and the body-fixed frame.  
where \(f\left(r\right)=-\frac{1}{T}r-\frac{\pi}{T}r^{3}\), \(b=\frac{K}{T}\), and \(y_{1}\), \(y_{2}\) denote the output signal; \(\Delta f\left(r\right)\), \(d\) represent unknown dynamics and multiple disturbances, respectively. \(\mu=\delta\) is the input of the USV. Considering the saturation characteristics of the rudder, it can be described as:  
\[\mu=\left\{\begin{array}{ll}\mu_{\max},&\quad if\ \mu_{0}>\mu_{\max}\\ \mu_{0},&\quad if\ \mu_{\min}\leq\mu_{0}\leq\mu_{\max}\\ \mu_{\min},&\quad if\ \mu_{0}<\mu_{\min}\end{array}\right. \tag{4}\]  
where \(\mu_{\max}\) and \(\mu_{\min}\) represent the amplitudes of input saturation. From [29], we know \(\mu_{\max}=35^{\circ}\), \(\mu_{\min}=-35^{\circ}\).  
**Remark 1**.: _Here, the frequency of \(d\) is low compared to the USV dynamics, and it is also filtered by a nonlinear tracking differentiator. Hence, \(d\) is considered to be slow time-varying disturbance [30]._  
Our control objective is to design a robust adaptive path following control strategy with completely unknown dynamics, disturbances, and rudder saturation, so that the USV tracks the reference path \(\left(x_{d},y_{d}\right)\) with an arbitrarily small error.
reconstruction [7; 8], the identification of hydrological and environmental anomalies [9; 10], and the reconstruction of watershed agricultural development [11]. Organic carbon burial in lake sediments is a series of biochemical processes that are closely related to factors, such as climate change, organic carbon sources, lake characteristics, and human activities [12].  
In the context of global warming, the increase in microbial respiration and the mineralization rate of organic matter in water bodies and sediments may lead to a decline in the rate of organic carbon burial, and sediments may change from \"carbon sink\" to \"carbon source\" [13]. Many studies have shown that, with the increasing intensity of human activities, lake environments and ecosystems have experienced significant changes [14; 15; 16], and in-depth study of the temporal and spatial changes of organic carbon burial in lake sediments and their relationship with lake ecology, climate environment, and human activities will not only help to understand the evolution of the lake ecological environment more deeply but also make it possible to more scientifically assess the role of lake organic carbon burial in the global carbon cycle.  
The Aral Sea region has a dry climate, a shortage of water resources, and an uneven distribution; however, the Aral Sea was once the fourth largest lake in the world [17]. Rapid population growth and socioeconomic development have led to the continuous development and utilization of natural resources, leading to multiple environmental problems [18]. The Aral Sea has shrunk sharply in recent years, and its changes have become one of the focuses of research regarding lakes in Central Asia [19; 20; 21].  
More in-depth studies have been carried out on the shrinkage of the Aral Sea [22; 23; 24], the microbiome of the Aral Sea [25; 26; 27], and the ecological environment of the basin [28; 29; 30; 31; 32]. The research on lake sediments has become more abundant, e.g., environmental reconstructions of the salt water, geological structure, etc. through biological indicators [33; 34; 35]; the use of sedimentary records of titanium changes to restore the wind intensity and frequency characteristics of western Central Asia since the Late Holocene [36]; the ecological risk assessment of potentially toxic elements in the North Aral Sea [16]; etc.  
In recent decades, the natural variations superimposed on the impact of human activities have made environmental changes more complicated. Therefore, the response of organic carbon burial in lake sediments to watershed environmental changes may show more complex changes. In this paper, by using the sedimentary records of organic carbon and its stable isotopes in lake sediments, the characteristics of organic carbon burial in the North Aral Sea since the 1950s and its relationship with environmental changes in the basin were analyzed. The results can be used not only as basic data for the long-term reconstruction of environmental changes, but also as a scientific basis for a more scientific assessment of the role of lake organic carbon burial in the regional and even global carbon cycle.
## 4 Results
### Total Bedload Transport  
The total bedload transport is the mass (or volume) of bedload that passes through a cross-section during a defined time interval. Figure 16a shows the total bedload transport at cross-section P1 computed based on Helley-Smith (HS) samples, moving-bed tests (ADCP-BTe and ADCP-BTvr), the modified ISSDOTv2 method and five empirical equations (K, MPM, E, EB and VR). The predictions were created for the three consecutive survey days. The result of the mISSDOTv2 method (776 tons/day), however, represents the mean  
Figure 15: Wavelet-spline discrimination for longitudinal bedform profile N = 26 on 1 February 2018, with heights and wavelengths of the 3 hierarchies, where h represents the original signal, i.e., the measured bed elevations (m); h\({}_{1,3}\) is the first hierarchy (small dunes); h\({}_{2,3}\) is the second hierarchy (medium and large dunes); and h\({}_{3,3}\) is the third hierarchy (bars). Flow occurs from left to right. Values are in meters.  
bedload transport between the last two days (1 February 2018 and 2 February 2018), since it uses longitudinal profiles from two DEMs generated with bathymtic data measured under two distinct hydraulic conditions.  
The average total bedload transport rates of all methods are: 528 ton/day (31 January 2018), 742 ton/day (1 February 2018) and 1182 ton/day (2 February 2018) (Figure 16a). The accuracy of the methods was evaluated in terms of a discrepancy ratio, defined as the ratio of the computed total bedload of each method and the average of all methods. Figure 16b shows the discrepancy ratios as a function of the transport stage (Equation (6)). Several authors consider the methods to give consistent sediment transport rates when the discrepancy ratio is between 0.5 and 2 [4; 27; 30; 41; 42; 43]. This condition was met for all estimates on the three days, except for the HS measurements, which were probably underestimated.  
The lowest sediment transport rates were estimated by HS on the first two days (this method was not applied on the third day) and by the MPM equation on the third day (disregarding the mISSDOTv2 method, which represents the mean bedload between the last two days).  
The maximal sediment transport rates on the first two days were accomplished with ADCP-BTe and ADCP-BTvr (which were not applied on the third day). Despite the variations in unit bedload rates computed with these two approaches, the total bedload transport was similar: (A) 31 January 2018-829 tons/day with ADCP-BTe and 794 tons/day with ADCP-BTvr; (B) 1 February 2018-926 tons/day with ADCP-BTe and 1046 tons/day with ADCP-BTvr.  
On 2 February 2018, the maximal rate was predicted by the EB equation. When the Shields parameter (\(\Psi\)) is high, the EB equation begins to consider a portion of the suspended (fine) sediment as bedload (on last day, Shields parameter was calculated as \(\Psi=1.3\)). This is the main reason for the higher bedload computed on that day, when the discharge severely increased (+43% in relation to the previous day).
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 2 Study Area and Dataset  
The research area is located in the southwest river basin of the Sichuan-Tibet area, which is renowned for its abundance of hydraulic resources, as shown in Figure 1. The study area encompasses 359,945 km\({}^{2}\) across the river basins of the Dadu River, Jinsha River, Nujiang River, and Minjiang River in the Sichuan-Tibet area. The diverse plane morphologies of old landslides encompass long-tongue shapes, ovals, trumpets, irregular formations, and more. These old landslides have typically evolved through the gradual accumulation of rockfalls, resulting in an inverted triangle, conical, or fan-shaped appearance on the plane (Figure 2). Notably, some large and extra-large old landslides have distinctive armchair landforms with clear boundaries, which have now become inhabited areas, serving as the main living places for present-day residents [36; 37]. The dataset used in this study comprised Google Earth images with a spatial resolution of 2 m and covered a time span from 2012 to 2022. According to the analysis provided by geological experts, a total of 329 landslides have been identified within the southwest river basin of the Sichuan-Tibet area. Leveraging the manipulation of Google Earth's three-dimensional perspective and adjusting the timing of image acquisition, a multitude of landslide remote sensing images were successfully captured. Subsequently, the collected image data underwent processing using the LabelImg tool to construct the dataset. To facilitate model training and evaluation, the dataset was divided into training and validation sets in an 8:2 ratio. Additionally, data augmentation techniques, such as image cropping and rotation, were employed to enhance the diversity and robustness of the data. The augmented training dataset comprised 2500 images, while the validation dataset encompassed 600 images.  
Figure 1: The Sichuan–Tibet study area.  
Figure 2: Examples of old landslides. Red lines indicate the old landslide boundary and the yellow arrow indicates the direction of slide.
## 3 Methodology
### Overview of Proposed Network  
The transformer architecture has a lot of optimization work at this stage, with the main optimization methods being linearization and sparsification. Linear transformer [19] is a variant of the famous transformer architecture widely used for natural language processing (NLP) and other sequence modeling tasks. The main difference between the linear transformer and the original transformer is how the attention mechanism is applied to the input sequence.  
Transformer is a deep learning model for natural language processing and other sequence-to-sequence problems, which was proposed by the Google team and released in 2017. Unlike traditional recurrent neural network (RNN) models, the transformer model uses a self-attention mechanism to learn relationships at various locations in the input sequence to better capture long-range dependencies in the series. The model has been widely used for multiple natural languages processing tasks, such as machine translation, language modeling and text generation. The structural flowchart of the transformer is shown in Figure 1 as follows:The transformer model based on the seq2 seq architecture can perform typical tasks in NLP research, such as machine translation, text generation, prediction, etc., while building pre-trained language models for the transfer learning of different jobs. The overall transformer architecture can be divided into four parts: the input part; output part; encoder part; and decoder part [20]. The input contains the source text embedding layer and its location encoder, target text embedding layer and location encoder. The output part has a linear layer and a softmax layer. The encoder part consists of N encoder layers stacked; each encoder consists of two sub-layer connection structures; the first sub-layer connection structure includes a multi-headed self-attentive sub-layer, a normalization layer and a residual connection; the second sub-layer connection structure consists of a feed-forward fully connected sub-layer, a normalization layer and a residual link [21]. The encoder structure is shown in Figure 2.  
The decoder part has three sub-layers: the first sub-layer includes a multi-headed self-attentive mechanism and a normalization layer and a residual connection; the second sub-layer includes a multi-headed attention mechanism and a normalization layer and a residual link; the third sub-layer consists of a feed-forward fully connected layer and a normalization layer and a residual connection.  
Figure 1: Flowchart of the structure of the standard transformer.
## 4 VSCAVC Characteristics of the Selected Design  
The design parameters and dimensions of the selected optimized 12/7 model are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Torque Density at the Rated Current Density (kNm/m\({}^{3}\))** & **Voltage Regulation Ratio** \\ \hline
12/7 & 32.1 & 96.1\% \\
12/8 & 34.1 & 81.5\% \\
12/10 & 37.4 & 25.4\% \\
12/11 & 38.4 & 23.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overall comparison of the four cases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{3}{c}{**Analytical Method**} & \multicolumn{3}{c}{FEM} \\ \cline{2-7}  & \(E_{min}\)=\(|E_{A}\)\(-\)\(E_{B}|\) & \(E_{max}\)=\(E_{A}\)+\(E_{B}\) & \(VRR\) & \(E_{min}\) (\(V\)) & \(E_{max}\) (\(V\)) & \(VRR\) \\ \hline
12/7 & 0.0477 \(\Omega\) & 1.2917 \(\Omega\) & 96.3\% & 0.0644 & 1.6539 & 96.1\% \\
12/8 & \(|\)\(-\)0.2450\(\Omega|\) & 1.4018 \(\Omega\) & 82.5\% & 0.2924 & 1.5781 & 81.5\% \\
12/10 & \(|\)\(-\)1.0984\(\Omega\) & 1.4799 \(\Omega\) & 25.8\% & 1.2410 & 1.6632 & 25.4\% \\
12/11 & \(|\)\(-\)1.2233\(\Omega|\) & 1.5504 \(\Omega\) & 21.1\% & 1.3855 & 1.8090 & 23.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analytical and FEM results of back EMF voltage regulation ratio.  
The VSCAVC steady-state characteristics of the 12/7 design are shown in Figure 14. The generator operates at a speed of 500 rpm in the beginning, and the initial phase angle of the modulator is fixed at 0\({}^{\circ}\). The amplitude of the voltage is 91 V. When the generator runs at 1000 and 1500 rpm to maintain amplitude, the modulator simultaneously adjusts to 108\({}^{\circ}\) and 120\({}^{\circ}\) in electrical degree. The simulation shows that the design model has good control over the voltage amplitude. The current curve is sinusoidal, and the torque curve is smooth, with a ripple under 5% of the average.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Value** & **Item** & **Value** \\ \hline Phase & 3 & Pole pair number of windings & 7 \\ Number of slots & 12 & Number of modulator blocks & 12 \\ Pole pair number of the rotor PMs & 7 & Outer diameter of the stator & 190 mm \\ Inner diameter of the stator & 122 mm & Length of the outer airgap & 0.5 mm \\ Length of the inner airgap & 0.7 mm & Length of the model & 50 mm \\ Thickness of the modulator & 4 mm & Embrace of the modulator & 0.63 \\ Thickness of the spoke PM & 10 mm & Length of the spoke PM & 20 mm \\ Number of turns & 100 & Rated speed & 500 rpm \\ \hline \hline \end{tabular}
\end{table}
Table 7: Design parameters and dimensions of the selected model.  
From (19), the back EMF can be pre-determined by the initial phase angle and rotating speed of the rotor, as shown in Figure 15. A lookup table can be made, which can be used for controlling. The dynamic model and the control performances of the selected model will be discussed in the future.