esa-sceva/satcom-chunk-collection huggingface dataset:
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
## 2 Materials and Methods
### Dataset  
The training dataset for the base (pre-trained) AE model was the ImageNet-1K image dataset. This dataset consists of 1,128,167 training images, 50,000 validation images, and 100,000 test images divided into 1000 labeled classes [40]. The Vision Transformer (ViT) model pre-trained using the MAE method was obtained from the HuggingFace model hub.  
To fine-tune the model parameters and for validation, a custom dataset was built. We acquired 317 satellite images using the Mapbox API v3.1.0 [41]. The images were later divided into two parts, one representing the normal sample dataset (197 samples) and the other representing the anomaly dataset (120 samples). Regarding the train-test split, the dataset was divided in such a way that the test set consisted of 120 samples from each class. Only the remaining normal samples were used for training.  
The data was then preprocessed using TensorFlow's 2.17.0 image preprocessing layers, and a data augmentation step was performed for the fine-tuning training phase. With random horizontal and vertical rotation flips, random rotation and auto-contrast applied, the training dataset was artificially enlarged to balance the train-test ratio. All the operations were performed using the torchvision.transforms module. RandomRotation with an angle in the range of (0, 45) degrees was applied to each image. RandomAutocontrast was applied with default probability \(p=0.5\). RandomHorizontalFlip was applied with probability \(p=0.2\). The same probability was reused in RandomVerticalFlip. Two samples from the dataset are presented in Figure 2, divided into anomaly and normal classes.  
To divide the dataset into normal and abnormal classes, the following rules were applied. Pictures with homogeneous environments, included forests, deserts, and full maritime areas. Pictures depicting planes, starting lanes, ships deployed on full seas, and changes or gaps within crops were identified as anomalies. Examples of classification are shown in Figure 2. However, the division rules are subjective. They reflect the specificity of real-life applications where anomalies occur sparsely, meaning there is also a requirement for an unbalanced dataset.
## 3 Data Fusion in the Integration of SINS/DVL/MCP/PS
### Data Fusion of SINS/DVL/MCP/PS  
The estimated vectors \(\mathbf{\hat{X}}_{\text{SINS/DVL}}\), \(\mathbf{\hat{X}}_{\text{SINS/MCP}}\), and \(\mathbf{\hat{X}}_{\text{SINS/PS}}\) from the subsystems should be further fused in the federated Kalman filter, named the main filter, to acquire the optimal estimation. As shown in Figure 2, the federated filter with non-feedback to the subsystems is chosen. The main advantage of this structure lies in its low complexity and non-cross interaction between each subsystem.  
With no feedback, the main filter performs data fusion without filtering. The algorithm running in the main filter is as follows:  
\[\left\{\begin{array}{c}\boldsymbol{P}_{G}=\left(\boldsymbol{P}_{\text{SINS/DVL} }^{-1}+\boldsymbol{P}_{\text{SINS/MCP}}^{-1}+\boldsymbol{P}_{\text{SINS/PS}}^{- 1}\right)^{-1}\\ \boldsymbol{\hat{X}}_{G}=\boldsymbol{P}_{G}\left(\boldsymbol{P}_{\text{SINS/DVL} }^{-1}\boldsymbol{\hat{X}}_{\text{SINS/DVL}}+\boldsymbol{P}_{\text{SINS/MCP}}^{-1} \boldsymbol{\hat{X}}_{\text{SINS/MCP}}+\boldsymbol{P}_{\text{SINS/PS}}^{-1} \boldsymbol{\hat{X}}_{\text{SINS/PS}}\right)\end{array}\right. \tag{21}\]  
The above equation requires that all subsystems are independent and that there is cross coupling. The DVL, MPC, and PS in this paper are run independently and satisfy this requirement.
# A New Design Method of Shield Tunnel Based on the Concept of Minimum Bending Moment
## 4 Cross-Section Design and Case Analysis for Minimum Bending Moment Shield Tunnel in Soft Soil Area  
### Brief Introduction for Shanghai Metro Shield Tunnel  
Taking the Shanghai soft soil area as an example, the minimum bending moment shield tunnel is designed according to its stratum characteristics, as shown in Figure 10. The circular cross-section tunnel lining of the shield tunnels with non-staggered installation, which is widely used in Shanghai's single-line metro, is assembled with non-staggered installation precast segments. The outer diameter of the segment ring is 6.2 m, and the thickness and width of the segment are 0.35 m and 1.2 m, respectively. The segment is precast using high-strength concrete (C55 grade), of which the Poisson's ratio and thr elastic modulus are 0.18 and 35.5 GPa, respectively. The segment ring is composed of six segments, which is shown in Figure 11. Straight bolts, with a mechanical property grade of 5.8, are adopted for the circumferential and longitudinal connection of the segment rings.  
Shanghai, as the representative saturated soft soil ground area in China, has shield tunnels that mainly go through soil 3, 4, 5, 6, especially soil 3, 4. Table 1 shows the physical parameters measured by some engineering test of corresponding stratum. These data indicate that the main soil that the shield tunnel goes through features large water content, large compressibility, and low strength. Such soil also features high sensibility. When a shield tunnel is constructed in such soil, the horizontal soil resistance is very small [27,28], which is one of the reasons why the beyond-limit oval deformation of the shield tunnel easily occurs. So, it is necessary to try to design a rational cross-section of shield tunnels constructed in soft soil with less bending moment.  
Figure 11: Diagram for structure parameters of shield tunnel in Shanghai (unit: mm).  
Figure 10: Critical parameters for vertical elliptical cross-section shield tunnel.  
Suppose the coefficient of lateral earth pressure \(k\) is calculated as Equation (20).  
\[k=\frac{\mu}{1-\mu} \tag{20}\]  
where \(\mu\) is the Poisson's ratio.  
Then the coefficient of lateral earth pressure is 0.4286-0.4925.
# Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)
## 5 Proposal of SADFIR
### Network Settling Phase (NSP) of SADFIR
So, the intra-cluster communication cost is calculated by the following equation:  
\[Ira-C-Cost=\sum_{f=1}^{F}\sum_{i=1}^{N}\sum_{j=1}^{N}Z_{ij}\Gamma_{if}\Psi_{if} \varrho_{f} \tag{6}\]  
Similarly inter-cluster communication cost of \(SCC_{EFDCHs}\) is calculated by:  
\[ler-C-Cost=\sum_{f=1}^{F}\sum_{j=1}^{N}l_{\varrho_{f}}Z_{js}\Psi_{if} \tag{7}\]  
The overall network total communications cost is calculated as:  
\[Total-C-Cost=Intra-C_{C}ost+Inter-C_{C}ost \tag{8}\]  
\[Total-C-Cost=\sum_{f=1}^{F}\sum_{i=1}^{N}\sum_{j=1}^{N}Z_{ij}\Gamma_{if}\Psi_{ if}\varrho_{f}+\sum_{f=1}^{F}\sum_{j=1}^{N}l_{\varrho_{f}}Z_{js}\Psi_{if} \tag{9}\]  
\[Total-C-Cost=\sum_{f=1}^{F}\sum_{j=1}^{N}\Psi_{if}(l_{\varrho_{f}}Z_{js}+\sum_{ j=i}^{N}Z_{ij}\Gamma_{if}) \tag{10}\]  
where right side of the equation is constant for any node \(i\) to transmit \(f\) reports through \(j\)\(SCC_{EFDCHs}\), thus it can be replaced by constant:  
\[Total-C-Cost=\sum_{f=1}^{F}\sum_{j=1}^{N}\varsigma_{if}\Psi_{if} \tag{11}\]  
The distributed SDN controllers coordinates with each other to minimize the cost which can be represented by the following inequalities:  
\[Min-Total-C-Cost=\sum_{f=1}^{F}\sum_{j=1}^{N}\varsigma_{if}\Psi_{if} \tag{12}\]  
\[subject-to=\sum_{j=1}^{N}\Psi_{if}=1,\forall f\epsilon[1,0] \tag{13}\]  
\[\Psi_{if}=0,1\forall j\epsilon[1,N] \tag{14}\]  
The above problem description holds the property of binary integer program, and it contains the class NP-Hard complexity in general. For these problems, greedy algorithm executions are needed to experiment with all possible solutions testing every combination. At this particular instance, we focus on processing capabilities by specific quantitative values, which are denoted by \(Fmax\). Now SDN controller's analytical operation will be more restricted;  
\[Min-Total-C-Cost=\sum_{f=1}^{F}\sum_{j=1}^{N}\varsigma_{jf}\Psi_{jf} \tag{15}\] \[ler-C-Cost=\sum_{f=1}^{F}\sum_{j=1}^{N}l_{\varrho_{f}}Z_{js}\Psi_{if} \tag{7}\]0 (16) \[ler-C-Cost=\sum_{f=1}^{F}\sum_{j=1}^{N}l_{\varrho_{f}}Z_{js}\Psi_{if} \tag{7}\]1 (17) \[ler-C-Cost=\sum_{f=1}^{F}\sum_{j=1}^{N}l_{\varrho_{f}}Z_{js}\Psi_{if} \tag{7}\]2  
This settling phase of SADFIR routing algorithm achieves effective cost reduction and results in prolong network lifetime by introducing the most economical utilization of residual resources. In the algorithm below, we represent the functionality of the settling phase of proposed SADFIR. In particular, in Algorithm 1, \(N\) number of sensor nodes are deployed in the network where, SADFIR selects FECHs and non-FECHs. The whole criteria for selecting the FECHs and non-FECHs is divided into 5 conditions proved in the equations above.
# Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image
## 2 Materials and Methods
### Spectroradiometer  
This study used the equipment of ASD FieldSpec 4, and the performance of the equipment is shown in Table 4. The reflectance of each land cover was investigated using the related equipment. When measuring reflectance using a spectrometer, errors may occur due to the influ-ence of weather, such as the amount of light and humidity at the time of measurement. In this study, the white standard was used, as shown in Figure 8, and the correction was made just before the measurement of the land cover. The reflectance of each land cover was investigated using a calibrated spectrometer.  
\begin{table}
\begin{tabular}{c c} \hline
**Contents** & **Specification** \\ \hline Spectral range & 350–2500 nm \\ \hline Spectral resolution & 700 nm (3 nm VNIR) \\  & 1400/2100 nm (8 nm SWIR) \\ \hline Spectral sampling (bandwidth) & 1.4 nm (350–1000 nm) \\  & 1.1 nm (1001–2500 nm) \\ \hline Scanning time & 100 milli seconds \\ \hline Stray light specification & VNIR 0.02\%, SWIR 1 \& 2 0.01\% \\ \hline Wavelength reproducibility & 0.1 nm \\ \hline Weight & 5.44 kg \\ \hline Channels & 2151 \\ \hline \end{tabular}
\end{table}
Table 4: Specification of ASD FieldSpec4 spectrometer.  
Figure 8: Calibration of ASD FieldSpec4 spectrometer.  
Figure 7: Orthoimage results for each camera image.  
Figure 9 is a field photograph measuring reflectance by wavelength using an ASD FieldSpec4 spectrometer for tree, grass, basketball court, waterproof coating roof, and road.
## 2 Numerical Model
### Theoretical Equations
RANS could not only simulate fluid movement in complex geometric shapes, but also has a fast calculation speed and accurate simulation results, which provides a powerful tool for us to research the wind-blown sand disasters along the railway [13; 14; 17].The wind-blown sand two-phase flow can be described by the turbulent transport equations of the standard \(k\)-\(\varepsilon\) model [24]:  
\[\frac{\partial}{\partial t}\big{(}\alpha_{g}\rho_{g}k\big{)}+\frac{\partial} {\partial x_{i}}\big{(}\alpha_{g}\rho_{g}ku_{gi}\big{)}=\frac{\partial}{ \partial x_{j}}\Bigg{[}\alpha_{g}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{k}}\bigg{)} \frac{\partial k}{\partial x_{j}}\Bigg{]}+\alpha_{g}G_{k}+\alpha_{g}\rho_{g} \sigma_{k}+S_{d}^{k}, \tag{6}\]  
\[\frac{\partial}{\partial t}\big{(}\alpha_{g}\rho_{g}\varepsilon\big{)}+\frac{ \partial}{\partial x_{i}}\big{(}\alpha_{g}\rho_{g}\varepsilon u_{gi}\big{)}= \frac{\partial}{\partial x_{j}}\Bigg{[}\alpha_{g}\bigg{(}\mu+\frac{\mu_{t}}{ \sigma_{\varepsilon}}\bigg{)}\frac{\partial\varepsilon}{\partial x_{j}} \Bigg{]}+\alpha_{g}\frac{\varepsilon}{k}\big{(}C_{1}G_{k}-C_{2}\rho_{g} \varepsilon\big{)}+S_{d}^{\varepsilon}, \tag{7}\]  
where, \(k\) and \(\varepsilon\) are the turbulent kinetic energy and turbulent dissipation rate, respectively; \(u_{gi}\) and \(u_{gi}\) are the velocity components in the \(x\)-and \(y\)-directions; \(\sigma_{k}\) and \(\sigma_{\varepsilon}\) are the Prandtl constant corresponding to the turbulent kinetic energy and turbulent dissipation rate (1.0 and 1.3, respectively); \(G_{k}\) denotes the turbulent kinetic energy caused by the average velocity gradient; \(S_{d}^{k}\) and \(S_{d}^{\varepsilon}\) are the turbulent kinetic energy and turbulent dissipation rate caused by particle motion; \(\mu_{t}\) is the turbulent viscosity:  
\[\mu_{t}=\rho_{g}C_{\mu}\frac{k^{2}}{\varepsilon} \tag{8}\]  
the following values are used as model constants: C\({}_{1}\) = 1.44, C\({}_{2}\) = 1.92 and C\({}_{\mu}\) = 0.09.
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 5 Simulation
### Comparison of the Angular Resolution  
In Figure 6, we compare the angular resolution of the evaluated methods with 5 impaired sensors, where the two sources \((\theta_{1},\phi_{1})\) and \((\theta_{2},\phi_{2})\) are placed closely. Here, the source \((\theta_{1},\phi_{1})\) is fixed at \((20^{\circ},40^{\circ})\), and \((\theta_{2},\phi_{2})\) is generated with an angular spacing of  
\[\delta=\sqrt{\left|\theta_{2}-\theta_{1}\right|^{2}+\left|\phi_{2}-\phi_{1} \right|^{2}} \tag{29}\]  
to \((\theta_{1},\phi_{1})\). The evaluated methods are identify to distinguish the two sources if  
\[\sqrt{\left|\delta_{k,n_{\text{MC}}}-\theta_{k}\right|^{2}+\left|\delta_{k,n_ {\text{MC}}}-\phi_{k}\right|^{2}}<\delta \tag{30}\]  
for each trial. The successful rate will be calculated from the number of successful trials over the \(N_{\text{MC}}=1000\) Monte Carlo trials. The SNRs of sources are set to 15 dB, the number of snapshots is fixed at \(T=100\), and the angular spacing varies from \(2^{\circ}\) to \(8^{\circ}\).  
Obviously, the angular resolution of the proposed algorithm is higher than the other methods. Such improvement is benefited from the enforced structural-missing tensor completion, which enables to utilize the entire URA aperture for high-resolution DOA estimation. In contrast, for other methods, the URA aperture is not fully exploited due to the impaired sensors, resulting in degraded angular resolution of DOA estimation. Note that the proposed algorithm can still identify source DOAs with the angular spacing of only \(2^{\circ}\), whereas the compared methods almost fail.  
Figure 6: Comparison of angular resolution.
# Ground-Borne Vibration Model in the near Field of Tunnel Blasting
## 2 Dimensional Analysis
### Derivation of Sadovsky Formula Based on Dimensional Analysis Principle  
The Sadovsky formula is derived from the principle of dimensional analysis under the condition of concentrated charge blasting. In the process of deriving the Sadovsky formula, the main factors affecting the blasting vibration velocity are considered as follows: maximum charge per delay \(Q\), distance from the detonation center \(R\), rock density \(\rho\), and rock longitudinal wave velocity \(c_{r}\).  
The function expression of the ground-borne vibration is:  
\[F(V,Q,R,\rho,c_{r})=0. \tag{1}\]  
Since the rank of the matrix obtained from all quantities according to the basic dimensions is 3, the \(Q\), \(R\), and \(c_{r}\) can be taken as the basic dimensions, and the dimensions are \(\left[\mathrm{M}\right]\), \(\left[\mathrm{L}\right]\), and \(\left[\mathrm{L}\cdot\mathrm{T}^{-1}\right]\), respectively. Then, the number of zero-dimensional quantities is \(5-3=2\), which can be represented as:  
\[\pi_{1}=V/(Q^{\beta_{1}}R^{\beta_{2}}c_{r}{}^{\beta_{3}}),\ldots\ \pi_{2}=\rho/(Q^{\beta_{1}}R^{\beta_{2}}c_{r}{}^{\beta_{3}}). \tag{2}\]  
According to the principle of dimensional consistency, the solutions are \(\partial_{1}=0\), \(\partial_{2}=0\), \(\partial_{3}=1\); \(\beta_{1}=1\), \(\beta_{2}=-3\), \(\beta_{3}=0\), i.e., \(\pi_{1}=V/c_{r}\), \(\pi_{2}=\rho/(QR^{-3})\).  
Then, in terms of the \(\pi\) theorem \(f(\pi_{1},\pi_{2})=0\), thus \(\pi_{1}=f_{1}(\pi_{2})\), as required. Furthermore, the \(\pi_{1}\) can be replaced by \(\pi_{1}=V/c_{r}=f_{1}\left(\rho/\left(QR^{-3}\right)\right)=k\left(\sqrt[3]{Q} /R\right)^{k}\rho^{\vartheta^{\prime}}\).  
The rock density \(\rho\) and rock longitudinal wave velocity \(c_{r}\) are constants in a certain blasting engineering, so the commonly used form of the Sadovsky formula can be obtained,  
\[V=K(\sqrt[3]{Q}/R)^{\vartheta} \tag{3}\]  
where \(K=k\rho^{\vartheta},c_{r}\) is characteristic constant of the blasting site, and \(\vartheta\) is the attenuation coefficient.
# A Novel System of Mixed RF/FSO UAV Communication Based on MRR and RIS by Adopting Hybrid Modulation
## 2 System and Theoretical Model
### RF Link  
In this mixed RF/FSO system, the RF channel experiences Rayleigh distribution, the RIS and the RF signal generator are put together as the RF source, which implies that the channel attenuation between them can be ignored. Thus, the probability density function (PDF) of the signal-to-noise ratio (SNR) is given by [31]:  
\[f_{\gamma_{1}}(\gamma_{1})\approx\frac{\gamma_{1}^{N-1}e^{-\frac{\gamma_{1}}{B _{1}}}}{(B\tilde{\gamma}_{1})^{N}(N-1)!}, \tag{1}\]  
where \(B=1+(N-1)\Gamma^{2}(\frac{3}{2})\), \(\gamma_{1}\) and \(\tilde{\gamma}_{1}\) are the instantaneous SNR and the average SNR of RF link, respectively. \(\Gamma(\cdot)\) is the Gamma function, and \(N\) is the number of passive reflective elements in the RIS. Further, the cumulative distribution function (CDF) of the RF link SNR is written as [22]:  
\[F_{\gamma_{1}}(\gamma_{1})\approx 1-e^{-\frac{\gamma_{1}}{B\tilde{\gamma}_{1}}} \sum_{m=0}^{N-1}\frac{\gamma_{1}^{m}}{(B\tilde{\gamma}_{1})^{m}m!}. \tag{2}\]
## 4 Decomposition into Geometric Primitives
### Matching Procedure  
Matching of the point cloud of a scan with the auxiliary point cloud of an STL triangulation is done by finding the number of geometric primitives that are common to the scan and to the STL model. Unfortunately, calculating this number is computationally demanding. Namely, it has a combinatorial complexity as it needs to find proper permutations of the pairwise-feature matrices.  
Consider the plane-distance matrices \(D^{\text{pl,scan}}\) and \(D^{\text{pl,model}}\) of the scan and the model, respectively, and the related plane-angle matrices \(A^{\text{pl,scan}}\) and \(A^{\text{pl,model}}\). We take sorted nonzero entries of the first column of \(D^{\text{pl,scan}}\) and the first column of \(A^{\text{pl,scan}}\). We look for the column index in \(D^{\text{pl,model}}\) and \(A^{\text{pl,model}}\) such that the columns are the same up to a tolerance. In case where we succeed, we find the first common planar primitive; we remove the affected rows and columns from \(D^{\text{pl,scan}}\), \(A^{\text{pl,scan}}\), \(D^{\text{pl,model}}\), and \(A^{\text{pl,model}}\); and we continue to search for the second common planar primitive. In cases where we fail, we remove the first column from \(D^{\text{pl,scan}}\) and from \(A^{\text{pl,scan}}\) and repeat the procedure with the next (originally second) column of \(D^{\text{pl,scan}}\) and \(A^{\text{pl,scan}}\). This way, we proceed until we remove all the columns from either matrix. Finally, we repeat the procedure for the cylindrical feature matrices \(D^{\text{cyl,scan}}\), \(A^{\text{cyl,scan}}\), \(D^{\text{cyl,model}}\), and \(A^{\text{cyl,model}}\) and add the number of common cylindrical primitives to the number of common planar primitives. We store this total number of common primitives.  
The procedure is performed for all models from the database. The model with the maximal number of common primitives is recognized as successfully matched to the scan.
# NAS-CRE: Neural Architecture Search for Context-Based Relation Extraction
## 5 Discussion
### Time Complexity of RNN  
The time complexity of an RNN mainly depends on the length of the input sequence \(T\) and the size of the hidden layer \(H\). Suppose we have an input sequence of length \(T\) and the hidden layer size is \(H\), the time complexity of the RNN can be broken down into the following parts:  
**Single-step computation complexity**: At each time step \(t\), the RNN needs to compute the hidden state \(h_{t}\) and possibly the output. The main computational cost comes from the matrix multiplication. Assuming the hidden layer size is \(H\), and the input dimension is \(I\), the computation complexity at each time step is \(O(H\cdot(I+H))\), where \(H\) is the size of the hidden layer, and \(I\) is the input dimension.  
**Effect of sequence length**: Since RNN processes the sequence step by step, the time complexity for an input sequence of length \(T\) is computed \(T\) times.  
Therefore, the total time complexity of the RNN is:  
\[O(T\cdot H\cdot(I+H)) \tag{14}\]  
where \(T\) is the length of the input sequence, \(H\) is the size of the hidden layer, and \(I\) is the dimension of the input data.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 4 Future Trend of ML in IVLC
### Intelligent Physical Layer
#### 4.1.2 Distributed Channel Equalization  
As mentioned above, existing research on machine learning in VLC has mainly focused on the standalone receiver or transmitter side. However, the available computational resources of the terminals and the central office are apparently different, so that leaving the computational complexity at the receiving or transmitting end only is not a reasonable solution. Therefore, ML-based distributed channel equalization at both the transmitting and receiving end is worthy of investigation. In [65], predistortion based on K-means is proposed to mitigate nonlinear impairment in VLC systems. A spatiotemporal neural network-based predistortion equalizer has also been proposed in RF communication systems in order to compensate for the nonlinearity caused by the power amplifier [95]. CNN has also been proposed to be applied for behavioral modeling and digital predistortion, and the model's coefficients can be significantly reduced [96]. Both NN-based distortion and postequalization do promise great performance.  
However, an essential premise for pre-equalization is that the channel information state (CSI) is known to the transmitter. The CSI is usually obtained by training sequence. Unfortunately, the VLC channel is usually not static, and there is a mismatch between the estimated CSI and the actual CSI, as the statistic characteristics of the transmitted signal may be changed after pre-equalization or predistortion. The entire channel equalization should be computed simultaneously at the transmitter and receiver side in a distributed manner, taking into account the computational resources of both the receiver and transmitter as well as performance optimization. The CSI mismatch can be compensated since the training sequence is available at the receiving end. Therefore, distributed channel-equalization methods are required in the future VLC system.