esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Wind-Induced Soil Erosion
#### 4.1.3 Effect of Application Rate  
The experimental results showed that irrespective of the application rate tested, all the biopolymer treatments considerably reduced wind-induced soil loss for both tested soil types (Figures 6 and 7). This suggests that at their respective plateau concentrations, all the biopolymers were able to effectively agglomerate particles on the sample surface to a crust,already at a low application rate of 0.2 L/m\({}^{2}\). Similar trends have also been reported by [PERSON] et al. [13] and [PERSON] et al. [40] (Table A6). Both studies found that increasing the application rate only slightly reduced the soil loss. However, similar to this study, the lowest application rates tested in their studies already resulted in marginal soil loss. Contrary to these studies, [PERSON] et al. [12] showed that increasing the application rate to 0.5 wt% in Acacia gum treatments significantly reduced the soil loss, whereas equally weighted treatments with Sodium alginate and Pectin already displayed high wind erosion resistance at the lower application rate. Likewise, [PERSON] et al. [16] also found that higher application rates significantly improved wind erosion resistance in a study that evaluated food processing by-products as dust suppressants. Thus, it can be concluded that increasing the application rate generally increases the soil's wind erosion resistance. However, this effect was not evident in this and previous studies [12; 13; 40], as low application rates already resulted in very low soil loss. It is believed that more challenging testing conditions, such as higher velocity, repeated wet-dry cycles, or testing inclined samples, would have revealed the effect of the application rate more distinctively.  
For medium-grained sand, the results of Phase 2 showed that the soil loss slightly increased at application rates >0.4 L/m\({}^{2}\), with the two-way ANOVA indicating that the application rate has a significant effect (\(p\) < 0.001) (Table 4). This minor trend contradicts the existing literature and is likely related to soil surface disturbances caused by spraying the biopolymer solution onto the samples, resulting in some sand particles not sufficiently adhering to the soil matrix. By contrast, such a trend was not evident for the fine-grained silica sand samples (\(p\) = 0.568), likely because its finer and more uniform grain size distribution results in a more homogeneous surface less susceptible to wind erosion. Nevertheless, as this trend was only very marginal, it is not considered to be relevant for future potential field applications.
## 4 III-V-on-Silicon Devices for Optical Communication Applications
### 1550 nm Resonant Cavity Mirror Lasers
The device cross-section is illustrated in Figure 17a whereas the light-current-voltage characteristics under continuous operation, measured at 25 \(\,\mathrm{\SIUnitSymbolCelsius}\) can be seen in Figure 17b. VCSELs with different oxide aperture diameters of three, five, seven, and nine \(\upmu\)m were characterized. Threshold currents of 0.3 mA and 1.2 mA, respectively were measured with smallest and largest oxide aperture. A nine \(\upmu\)m oxide aperture diameter VCSEL produces a maximum output of 1.6 mW at 845 nm wavelength, limited by thermal rollover at 6.0 mA bias current. These hybrid VCSEL structures show \(\sim\)3 times higher thermal impedance than ordinary oxide-confined VCSELs, because of the low thermal conductivity of the dielectric DBR. At present the performance of VCSEL is mainly limited by high thermal impedance, which can be further improved by integrating a metallic heat spreader. Figure 16: (**a**) Longitudinal cross-section of the resonant cavity mirror laser. (**b**) Transversal cross section of the electrically pumped device. Reproduced from [53,54].
## 2 Materials and Methods
### Remote Sensing Data
#### 2.2.2 Gross Primary Productivity (GPP)  
GPP refers to the amount of energy fixed, or organic matter produced by photosynthesis per unit area of green vegetation per unit time [49; 50]. This is an important link in the carbon exchange between the land and the atmosphere. Mastering the dynamic changes in GPP is important for maintaining a global carbon balance; thus, understanding the impact mechanism of global climate change on land vegetation is necessary. In this study, annual growth changes in different vegetation types were analyzed. The MOD17A2 dataset was downloaded from the Moderate Resolution Imaging Spectroradiometer (MODIS) website ([[https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)]([https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)), accessed on 8 December 2021) with a temporal resolution of 8 days and a spatial resolution of 500 m. Using GPP data, we explored the correlation between SO\({}_{2}\) stress and vegetation characteristics in different months of the season.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 2 Related Works
### Model-Based Intent Recognition Methods
#### 2.1.3 Bayesian Networks  
Bayesian networks employ probabilistic graphical models to represent dependencies between variables, allowing intent inference under uncertainty. Dynamic Bayesian networks (DBN) are widely used for intent recognition of complex missions, which can infer intent from time series data under uncertain conditions.  
[PERSON] et al. proposed a DBN-based human-computer interaction system that enables robots to infer human intentions in real time and adjust their behavior through an intent-action-state modeling mechanism, thus achieving a more natural interaction [27]. To solve the problem of target intention recognition in air combat, a robust method based on dynamic Bayesian networks is proposed [28]. The method combines self-organizing feature mapping to pre-process the target track information, and uses the tactical features of the target to construct a dynamic Bayesian network, which realizes the effective recognition of the enemy target's tactical intent. Bayesian networks are also used in dynamic behavior analysis, combining conditional dependence and time series modeling to achieve real-time inference of multi-objective intentions [29]. To solve the dynamic intention recognition problem in complex battlefield environment [30], the extended multi-entity Bayesian network (EMEBN) model is proposed.  
Bayesian networks excel in intent recognition due to their strong capability to handle uncertainty by integrating prior knowledge with dynamic observations, making them highly suitable for complex environments [31]. They offer transparent inference through directed acyclic graphs (DAGs), which provide an intuitive representation of causal relationships, and their ability to model temporal dependencies using extensions like dynamic Bayesian networks further enhances their applicability in dynamic scenarios. However, they face challenges such as high computational complexity as the number of nodes and state space increase, and a strong reliance on initial network structure and conditional probability tables, which require extensive domain knowledge and data support.
## 2 Literature Review
### Existing Optimization Models for Hinterland Connectivity  
This section reviews optimization models for enhancing hinterland connectivity. It highlights various strategies and tools used to improve transport efficiency between ports and their hinterlands, focusing on addressing connectivity and capacity challenges.  
The study [21] examines the use of automated ground vehicles (AGVs) to improve port hinterland connectivity. It proposes a robust optimization approach to evaluating the time and cost efficiency of AGV platoons in container pickup and delivery tasks. The research introduces a bi-objective mixed-integer programming model to minimize time, cost, and emissions, addressing uncertainties in vehicle availability. Case studies of the Port of Rotterdam and Port of Valparaiso show that AGV platoons offer significant benefits, including reduced dwell times (56% on average) and lower carbon emissions (10% on average), with increased cost savings as travel distance grows.  
This study [22] explores the development of a dedicated corridor system to enhance the connectivity between hinterlands and seaports in the Bohai Rim region of China. It employs the shortest path model and forecasting techniques to evaluate and select optimal routes for multimodal transport systems. By assessing various transport modes and their performance, the study aims to identify cost-effective strategies to improve regional development. The results highlight that an efficient multimodal transport network can significantly strengthen the connection between hinterlands and seaports, contributing to the overall growth of the Bohai Rim region.  
Another study [23] addresses the intermodal routing problem for regional freight transportation in the ECOWAS region of West Africa, focusing on optimizing freight flow quantities and transportation modes across transit corridors in countries like Mali, Burkina Faso, and Niger. Using a linear programming model solved with Lingo Mathematic Application, the research aims to minimize inland transportation costs. The results indicate that current freight logistics often do not follow the most efficient paths, leading to longer transit times and higher costs than geographically necessary.  
Also, the study [24] focuses on coordinated scheduling in Yangtze River ports, aiming to reduce berth deviation costs, shorten ship scheduling times, and maximize berth utilization in river-sea intermodal transportation. By introducing factors like berth preferences, seagoing ship inspections, and planning cycles, the research applies an optimized non-dominated sorting genetic algorithm III (NSGA-III). Using a seven-day dataset, the model improved objective values by 30.81%, 13.73%, and 12.11%, outperforming traditional NSGA-III and NSGA-II methods. The findings demonstrate the model's ability to lower operational costs, enhance ship and berth scheduling, and improve clearance efficiency for port authorities.
# Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest
## 2 Significance of the Research Study  
Accurately predicting the axial bearing capacity of pile is of crucial importance because of many possible advantages and contributions to foundation engineering. Numerical or experimental approaches in the available literature still face some limitations, for instance, the lack of dataset samples ([PERSON] et al. [44] with 36 samples; [PERSON] and [PERSON] [45] with 50 samples; [PERSON] et al. [46] with 37 samples), accuracy assessment and improvement of the ML algorithms or comparison with classical prediction methodologies. Therefore, the contribution of the present work could be highlighted through the following ideas: (i) the largest dataset, to the best of the author's knowledge, was used for the construction of ML models, including 2314 experimental tests; (ii) a comparison of two ML algorithms, namely ANN and RF, was conducted and compared with classical MVR and five formulas in the literature to fully assess the prediction performance of each approach; (iii) the performance of ML algorithms was evaluated under the presence of random splitting dataset, which could truly find out the efficiency of ML algorithms; and (iv) a sensitivity analysis was performed to reveal the role of each input parameters in predicting the axial bearing capacity of piles.
## 3 Experimental Results
### Quantitative Evaluation  
In this sub-section, quantitative evaluation is performed to evaluate the effectiveness of TongueNet a numerical way. To assess the performance in different aspects, five metrics are utilized, which are Intersection over Union (IOU) [31], Pixel Accuracy (PA) [25], Precision [32], Recall [32], and F-score [33]. We first give an introduction to these five metrics. Then, we compare TongueNet and REF under the metric of IOU, PA, F-score, Precision and Recall on a subset of 20 test samples covering all circumstance summarized in Section 1 (refer to Figure 1). Then, we provide comparisons between TongueNet and other state-of-the-art methods by calculating the average value of all test samples. Afterwards, an efficiency comparison between REF and TongueNet is carried out.
## 2 Theoretical Background for the Nonlinear Analysis of End-Plate Connections
### Geometrical Nonlinear Analysis  
The mechanical response of end-plate connections is frequently characterized by large deformations. To accurately model the real behavior of such structural configurations, geometric nonlinearity must be incorporated into the simulation. This section outlines the theoretical framework used for geometrically nonlinear finite element analysis.  
In this study, the second Piola-Kirchhoff stress tensor and the Green-Lagrange strain tensor are employed to capture the nonlinear stress-strain relationship [44]. Accordingly, the Green-Lagrange strain tensor is defined as follows:  
\[\epsilon_{ij}=\frac{1}{2}\Big{(}u_{ij}+u_{j,i}+u_{k,j}u_{k,j}\Big{)} \tag{8}\]  
where \(u\) denotes the displacement field, and the indices \(i\), \(j\), and \(k\) refer to the spatial coordinates in a Cartesian system. Furthermore, in the finite element formulation, the relationship between the incremental strain and displacement is expressed as follows [44]:  
\[d\mathbf{\epsilon}=\mathbf{B}(\mathbf{U})d\mathbf{U} \tag{9}\]  
where \(\mathbf{B}(U)\) is the strain-displacement finite element matrix, which depends on the current displacement field \(\mathbf{U}\) and maps the incremental nodal displacement vector \(d\mathbf{U}\) to the corresponding incremental strain \(d\mathbf{\epsilon}\).  
In the context of the second Piola-Kirchhoff stress and Green-Lagrange strain measures, the linear elastic constitutive relationship--consistent with the previously introduced Equation (5)--is expressed as follows:  
\[s_{ij}=C_{ijkl}\epsilon_{kl} \tag{10}\]  
where \(s_{ij}\) represents the components of the second Piola-Kirchhoff stress tensor; \(C_{ijkl}\) denotes the material's fourth-order elasticity tensor; and \(\epsilon_{kl}\) is the Green-Lagrange strain tensor.  
The residual force vector \(\mathbf{R}(\mathbf{U})\), which quantifies the deviation from equilibrium, is defined as follows [44]:  
\[\mathbf{R}(\mathbf{U})=\mathbf{P}-\int_{V}\mathbf{B}^{T}\mathbf{s}dV \tag{11}\]  
where \(\mathbf{P}\) is the external force vector and \(\mathbf{s}\) is the second Piola-Kirchhoff stress tensor. Equilibrium is achieved when the residual force vector reduces to zero. This condition is solved iteratively using the Newton-Raphson method, which is expressed as follows [44]:  
\[\mathbf{K}_{T}=\frac{\partial\mathbf{R}}{\partial\mathbf{U}} \tag{12}\]  
where \(\mathbf{K}_{T}\) denotes the tangent stiffness matrix.
## 2 Spectral Clustering Algorithm
### Demapping Strategy Correcting Label Inconsistency  
Figure 4 shows the different stages of the spectral clustering algorithm. In real-time data stream processing, the stages that process matrices consume memory and can generate bottlenecks. Our proposal involves processing data samples that are buffers to store a portion of the data stream. As mentioned above, the algorithm has a final demapping stage that relabels the symbols with the correct value of the ideal constellation.  
The process for addressing the potential labeling inconsistency or the correct mapping of symbols is explained as follows:  
1. The first step involves selecting the mapping for the ideal constellation, which is used as the reference for the correct labeling of the symbols, as shown in Figure 5a.
2. A specified quantity of data \(d_{act}\) is received, as illustrated in Figure 5b.
3. The data stored in the window \(w_{n}\) are clustered using the spectral clustering algorithm.  
Figure 3: Label inconsistency.  
Figure 2: Effect of k-means and spectral clustering algorithm on a synthetic data set. (**a**) Clustering synthetic data by directly applying k-means. (**b**) Clustering of the data by applying k-means on the space described by the matrix Y. (**c**) Clustering of the original synthetic data set.  
4. Independently of the memberships returned by the spectral clustering algorithm, the demapping step calculates the average of the coordinate values within each identified cluster.
5. The demapping algorithm calculates the distance between the coordinates of the reference points assigned in step 1 and the averages of cluster coordinates obtained in step 4.
6. The labels from step 1 are assigned to the coordinates identified in step 4 closest to the reference points of the ideal constellation assigned in step 1.
7. The labels specified in step 6 are assigned to the symbols from which the average coordinates were obtained in step 4.  
Upon completing these seven steps, data clustering is achieved (as depicted in Figure 5c) that maintains consistent labels regardless of the number of algorithm runs. The removal of label inconsistencies constitutes the final phase of the algorithm.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 4 Experiments  
This section evaluates our proposed SISnet on the spacecraft segmentation dataset we labelled. In Section 4.1, we introduce the dataset and annotation details we used. In Section 4.2, we introduce the implementation details of our network and evaluation indicators. In Section 4.3, we compare our proposed method with representative methods in the segmentation domain: U-Net [15], HRNet [55], DeepLabv3+ [13], PSPNet [43], EMANet [53]. In Section 4.4, we conducted a comparative experiment on the backbone and a comparative experiment on the parallel-ASPP structure. In Section 4.5, we conducted an ablation study to demonstrate the effectiveness of our backbone, attention module, and decoder improvements.
## 3 Problem Definition
### Occlusions  
In some situations, cameras may not be able to view the entire FoV owing to occlusions such as the planes of surveillance targets. Figure 3 shows the situation in which the occlusion could interfere with the FoV of a camera. The blue area within the normal FoV of a camera is covered by the camera sensor; the red area represents the invisible area occluded by edges and planes of the grey box. In this  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Camera Types** & **A** & **B** & **C** \\ \hline Vertical reachable region (\(\phi_{\text{o}}\)) & \([-45^{\circ},45^{\circ}]\) & \([-60^{\circ},60^{\circ}]\) & \([-75^{\circ},75^{\circ}]\) \\ Horizontal reachable region (\(\phi_{h}\)) & \([-90^{\circ},90^{\circ}]\) & \([-90^{\circ},90^{\circ}]\) & \([-90^{\circ},90^{\circ}]\) \\ Working distance with zooming (\(R\)) (meter) & 60 & 120 & 180 \\ Estimated cost (USD) & 4000 & 8000 & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of three PTZ cameras: A, B, and C.  
Figure 2: FoV of three pan-tilt-zoom (PTZ) cameras.  
study, we regard it as static occlusions, which are known to affect the given cameras in the same manner at any time [23]. The consideration of occlusions is a fundamental step in the development of efficient algorithms considering the actual coverage of the camera because the occluded zone that is not visible from a given camera could be covered by other cameras.  
To check whether a target point \(O\) is covered, we define the line of sight (\(\overline{SO}\)) between the target point and the camera position \(S\). If there exists an intersection point between the defined line and a plane (as shown in Figure 3), the point is excluded.
## 2 Materials and Methods
### Energy Balance
Thus, when calculating the heat \(Q_{v}\), the number of cycles must be considered by multiplying the heat for one cycle by the total number of cycles N. To estimate the heat spent on forming the vapor-gas shell in one cycle, the geometry and volume of the evaporating near-cathode zone must be specified. According to [21; 22], the thickness of the vapor-gas shell ranges from 50 to 200 \(\upmu\)m, depending on the voltage and electrolyte modes. In this work, an average shell thickness parameter of \(\delta=100\)\(\upmu\)m was adopted for the assessment. Considering this thickness for calculations, the cathode evaporation region can be approximated as a volume. This volume was formed by extending the shell from all external boundaries of the sample by a thickness \(\delta\) and deepening along the normal by the same value. Therefore, if the dimensions of the sample are \(a\times b\times h\), the volume of the shell can be estimated as  
\[V_{page}=(a+2\delta)\cdot(b+2\delta)\cdot\delta, \tag{5}\]  
where \(\delta\)--thickness of the vapor-gas shell; \(a\) and \(b\)--length and width of the sample, respectively. This approximation provides insight into the zone where the phase transition of liquid into steam occurs due to local heating. Consequently, the heat of evaporation in one cycle can be determined using the following formula:  
\[Q_{v}^{(1)}=\rho\cdot V_{page}\cdot L, \tag{6}\]where \(\rho\)--density of the electrolyte; \(L\)--specific heat of vaporization of the electrolyte solution. Therefore, the total amount of heat for electrolyte evaporation in the cathode region during hardening can be estimated as  
\[Q_{v}=N\cdot Q_{v}^{(1)}=N\cdotp\cdot V_{\text{\emph{page}}}\cdot L. \tag{7}\]
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 3 Review on MLE-Based Positioning
### Position Estimation  
The MLE cost function is determined by the synchronization parameters \(\tau\) and \(f\). These parameters can be calculated with the user PVT parameters. The elements in \(\tau\) and \(f\) are given by [26]:  
\[\tau_{i}=\frac{1}{c}\|\mathbf{u}^{(i)}-\mathbf{u}\|+t_{u}-t^{(i)}+\mathbf{e}^{(i)}, \tag{8}\]  
\[f_{i}=-\frac{f_{T}}{c}[(\mathbf{v}^{(i)}-\mathbf{v})\mathbf{1}^{(i)}+t_{u}-\mathbf{l}^{(i)}]. \tag{9}\]  
Here,  
* \(c\) is the speed of light;
* \(\mathbf{u}^{(i)}\) is the coordinates of the \(i\)-th satellite in Earth-centered earth-fixed (ECEF) coordinate system;
* \(\mathbf{u}=[x_{u},y_{u},z_{u}]^{T}\) is the coordinates of the user in ECEF coordinate system;
* \(t_{u}\) is the offset of the user clock from system time;
* \(t^{(i)}\) is the offset of the \(i\)-th satellite clock from system time;
* \(\mathbf{e}^{(i)}\) denotes ionospheric and tropospheric delays and errors induced by relativistic effects; these errors can be eliminated by models or differential techniques;
* \(f_{T}\) is the frequency of the transmitted satellite signals;
* \(\mathbf{v}^{(i)}\) is the velocity of the \(i\)-th satellite in ECEF coordinate system;
* \(\mathbf{v}=[x_{v},y_{v},z_{v}]^{T}\) is the velocity of the user in ECEF coordinate system;
* \(\mathbf{1}^{(i)}\) is the unit vector pointing along the line of sight from the user to the \(i\)-th satellite and can be expressed as Equation (10);
* \(l_{u}\) is the drift rate of the user clock relative to system time;
* \(i^{(i)}\) is the drift rate of the \(i\)-th satellite clock relative to system time.  
\[\mathbf{1}^{(i)}=-\frac{\mathbf{u}^{(i)}-\mathbf{u}}{\|\mathbf{u}^{(i)}-\mathbf{u}\|}. \tag{10}\]  
It can be seen that \(\tau\) and \(f\) are determined by the user PVT parameters \(\mathbf{\rho}=[\mathbf{u},\,t_{u},\,\mathbf{v},\,l_{u}]\). When at least four satellites are in view, \(\mathbf{\rho}\) can also be resolved by \(\tau\) and \(f\). Hence, the ML estimates of the user PVT parameters can be obtained by maximizing \(J\) based on the invariance principle of MLE [23].