esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensitivity Analysis for Gaussian-Associated Features
## 5 Sensitivity Matrix Associated with a Least-Squares Sphere Fit to Data
### Analytical Approximations for a Patch of a Sphere  
We can use the principle of Monte Carlo integration (15) to determine analytical approximations for matrices used to construct the sensitivity matrices. It is convenient to work in spherical coordinates \((x,y,z)=(\cos\theta\cos\phi,\sin\theta\cos\phi,\sin\phi)\), where \(\theta\) is the azimuth angle about the \(z\)-axis and \(\phi\) is the angle of elevation above the \(xy\)-plane. For points approximately uniformly distributed on the sphere on the patch determined by \(-\pi\leq\alpha_{1}\leq\theta\leq\alpha_{2}\leq\pi\) and \(-\pi/2\leq\beta_{1}\leq\phi\leq\beta_{2}\leq\pi/2\) we have  
\[\frac{1}{m}J^{\top}J\approx\frac{1}{A}H_{A},\]  
where \(H_{A}\) is the symmetric matrix with  
\[H_{A}(1,1) =\int_{a_{1}}^{a_{2}}\cos^{2}\theta\mathrm{d}\theta\int_{\beta_{1 }}^{\beta_{2}}\cos^{3}\phi\mathrm{d}\phi,\] \[H_{A}(1,2) =\int_{a_{1}}^{a_{2}}\sin\theta\cos\theta\mathrm{d}\theta\int_{ \beta_{1}}^{\beta_{2}}\cos^{3}\phi\mathrm{d}\phi\] \[H_{A}(1,3) =\int_{a_{1}}^{a_{2}}\cos\theta\mathrm{d}\theta\int_{\beta_{1}}^ {\beta_{2}}\sin\phi\cos^{2}\phi\mathrm{d}\phi,\] \[H_{A}(1,4) =\int_{a_{1}}^{a_{2}}\cos\theta\mathrm{d}\theta\int_{\beta_{1}}^ {\beta_{2}}\cos^{2}\phi\mathrm{d}\phi,\] \[H_{A}(2,2) =\int_{a_{1}}^{a_{2}}\sin^{2}\theta\mathrm{d}\theta\int_{\beta_{1 }}^{\beta_{2}}\cos^{3}\phi\mathrm{d}\phi,\] \[H_{A}(2,3) =\int_{a_{1}}^{a_{2}}\sin\theta\mathrm{d}\theta\int_{\beta_{1}}^ {\beta_{2}}\sin\phi\cos^{2}\phi\mathrm{d}\phi,\] \[H_{A}(2,4) =\int_{a_{1}}^{a_{2}}\sin\theta\mathrm{d}\theta\int_{\beta_{1}}^ {\beta_{2}}\cos^{2}\phi\mathrm{d}\phi,\] \[H_{A}(3,3) =(\alpha_{2}-\alpha_{1})\int_{\beta_{1}}^{\beta_{2}}\sin^{2}\phi \cos\phi\mathrm{d}\phi,\] \[H_{A}(3,4) =(\alpha_{2}-\alpha_{1})\int_{\beta_{1}}^{\beta_{2}}\sin\phi\cos \phi\mathrm{d}\phi,\] \[H_{A}(4,4) =(\alpha_{2}-\alpha_{1})\int_{\beta_{1}}^{\beta_{2}}\cos\phi \mathrm{d}\phi.\]  
These integrals can be evaluated according to the formulae in Appendix A. The elements of \(H_{A}\) take into account the change of variables from Cartesian to spherical coordinates and involve an additional \(\cos\phi\) term.
# Application of a Local Dynamic Model of Large Eddy Simulation to a Marine Propeller Wake
## 3 Verification and Comparison  
### Verification of Adequate Domain Size  
The propeller wake is influenced by the size of the computational domain, so the computational domain setting must be large enough to reduce channel blockage effects. As a rule of thumb, if the blockage (defined as the ratio of two areas of propeller disk and domain cross section) less than 0.1, the rotor wake can be deemed unconfined and effects of finite domain size are negligible. This verification is clearly shown in Figure 6. At \(r/R=6\), the perturbation of axial velocity is less than 1% and can be basically ignored. At \(x/D=-1.8\) upstream of the propeller, the radial variation in axial velocity can also be ignored. Taking into account the axial symmetry of the five-blade propeller, the figure shows the normalized mean value of sampling points of every \(9^{\circ}\) within an angle of \(72^{\circ}\). From the results we can conclude that the present choice of domain size is proper and adequate. From the results, we can conclude that the selection of domain size is appropriate and sufficiently large, and its impact on the evolution of propeller wake can be ignored.
## 2 Materials and Methods
### Genetic Path Planner
#### 2.1.5 Fitness  
The fitness operation consists in assigning a performance value to each chromosome through the computation of the utility function, which involves as contributions the informativeness of the path and a variable assessing the coverage of the area to inspect. The informative content of a chromosome \(P_{i}\) representing a path is evaluated using an information function \(I(P_{i})\), which utilises as input the covariance map obtained by the GP estimate. In particular, the information function employed within this work is an averaged version of the DE function \(h(x)\), which depends only on the variance of the GP evaluated in the generic point \(x\) within the target area. Considering each point in the path as a test location \(x^{*}\), the predicted distribution over it is given by \(f_{*}\sim\mathcal{N}(\bar{f}_{*},\mathbb{V}[f_{*}])\) and the corresponding DE is [42]  
\[h(x^{*})=\frac{1}{2}log(2\pi e\mathbb{V}[f_{*}]) \tag{2}\]  
where \(\mathbb{V}[f_{*}]\) is the variance obtained from the GP estimate in the test location \(x^{*}\), defined as in Appendix A.3. The information function \(I(P_{i})\) utilises Equation (2) to evaluate how much informative a specific path is, by averaging the DE, as follows:  
\[I(P_{i})=\frac{\sum_{x_{ij}\in P_{i}}h(x_{ij})}{m} \tag{3}\]  
where \(P_{i}\) is a specific chromosome, \(m\) is the number of genes in a chromosome and \(j\) is the gene's index.  
The information value obtained by \(I(P_{i})\) is exploited as input of the utility function \(U(P_{i})\), together with the distance between the initial and the final points \(d_{02e}\) of the path. In fact, \(d_{02e}\) has been utilised as indicator of the extension, over the area of interest, of  
Figure 3: Example of mutation operation.  
the paths generated by the GPP. Thus, the formulation of the utility function can then be formalised as:  
\[U(P_{i})=g(I(P_{i}),d_{02e},\beta)=\beta I(P_{i})+(1-\beta)\frac{d_{02e}}{c_{d}} \tag{4}\]  
where \(c_{d}\) is a scaling factor used to normalise the distance with respect to the maximum reachable distance for the specific area under inspection and \(\beta\in[0,1]\) is a parameter which tunes the trade-off between informativeness and coverage.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 3 Mobile Ad Hoc Networks
### Security Issues in MANETs  
Security requirements in MANETs differ slightly from traditional networks. While the core principles remain the same, MANETs necessitate additional considerations due to the following [24; 25]:  
* Resource constraints and security trade-offs: Unlike traditional wired networks, nodes in a MANET have limited battery life, processing power, memory, and bandwidth. Implementing robust security measures can significantly strain these already limited resources. Therefore, it is crucial to find a balance between security requirements and resource availability to ensure optimal network performance and expected lifetime.
* Vulnerability of wireless medium: Compared to wired networks with a clear physical layer boundary, MANETs are inherently more vulnerable. All nodes communicate wirelessly, eliminating the need for physical access to launch attacks. This vulnerability necessitates additional security measures beyond what is typically employed in wired networks.
* Challenges of centralized management: Security in wired networks often relies on centralized management for tasks like data encryption with key distribution. However, MANETs lack a central authority. While partial or selective encryption techniques can be implemented to reduce resource consumption, the absence of a central repository for key management presents a significant challenge [26].
* Impact of mobile topologies: The dynamic nature of MANETs, with nodes freely moving in and out of the network, creates challenges for both routing and security. Detecting malicious nodes becomes difficult due to the constant changes in the network topology caused by legitimate node mobility  
Figure 4: Route request transmission and route reply.  
Figure 3: AODV routing process.
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 3 Supposed Method
### Classic Transformer Encoder
#### 3.2.2 Stacked Transformer Encoder Layers  
Each encoder layer comprises Multi-Head Self-Attention (MHSA), Layer Normalization (LayerNorm), Feed-Forward Network (FFN), and residual connections. For the \(l\)-th layer (\(1\leq l\leq L\)):  
Figure 3: The figure shows a typical encoder layer structure, which adopts the Transformer design. Its core process includes a multi-head attention layer (Multi-Head Atten.), a normalization layer (Norm), a multi-layer perceptron (MLP), and another normalization layer (Norm).  
**Multi-Head Self-Attention (MHSA)**: The input \(\mathbf{z}_{l}\in\mathbb{R}^{N\times D}\) is transformed into queries \(\mathbf{Q}\), keys \(\mathbf{K}\), and values \(\mathbf{V}\) via learnable weights \(\mathbf{W}_{i}^{\mathbf{O}},\mathbf{W}_{i}^{K},\mathbf{W}_{l}^{V}\in\mathbb{R} ^{D\times d_{k}}\) for each head \(i\) (\(1\leq i\leq h\)), where \(d_{k}=D/h\). The attention output is computed as follows:  
\[\text{head}_{i}=\text{softmax}\left(\frac{\mathbf{Q}_{i}\mathbf{K}_{i}^{\top} }{\sqrt{d_{k}}}\right)\mathbf{V}_{i}, \tag{2}\]  
\[\text{MHSA}(\mathbf{z}_{l})=\text{Concat}(\text{head}_{1},\ldots,\text{head}_ {h})\mathbf{W}^{\text{O}}, \tag{3}\]  
where \(\mathbf{W}^{\text{O}}\in\mathbb{R}^{D\times D}\) is the output projection matrix [32].  
**Residual Connection and Layer Normalization**: The MHSA output is combined with the residual input and normalized:  
\[\mathbf{z}_{l}^{\prime}=\text{LayerNorm}(\mathbf{z}_{l}+\text{MHSA}(\mathbf{z }_{l})), \tag{4}\]  
where LayerNorm is defined as \(\text{LayerNorm}(\mathbf{x})=\gamma\odot\frac{\mathbf{x}-\mu}{\sigma}+\beta\), with \(\mu\) and \(\sigma\) as the mean and standard deviation along the feature dimension, and \(\gamma\), \(\beta\in\mathbb{R}^{D}\) as learnable parameters.  
**Feed-Forward Network (FFN)**: The normalized features undergo a nonlinear transformation via a two-layer MLP with Gaussian Error Linear Unit (GeLU) activation:  
\[\text{FFN}(\mathbf{z}_{l}^{\prime})=\text{GeLU}(\mathbf{z}_{l}^{\prime} \mathbf{W}_{1}+\mathbf{b}_{1})\mathbf{W}_{2}+\mathbf{b}_{2}, \tag{5}\]  
where \(\mathbf{W}_{1}\in\mathbb{R}^{D\times 4D},\mathbf{W}_{2}\in\mathbb{R}^{4D \times D}\), and \(\mathbf{b}_{1},\mathbf{b}_{2}\) are bias terms [33].  
**Second Residual Connection and Normalization**: The final output of the layer is computed as follows:  
\[\mathbf{z}_{l+1}=\text{LayerNorm}\big{(}\mathbf{z}_{l}^{\prime}+\text{FFN}( \mathbf{z}_{l}^{\prime})\big{)}. \tag{6}\]
## 4 Conclusions  
We have demonstrated a very simple, low-cost and large-scale production method for preparing a Si/C nanocomposite anode material through a high-energy mechanical milling and carbonization process. The XRD and TEM analyses revealed the nanocomposites comprise nano-sized Si particles in a carbon matrix phase and there were no significant contamination peaks observed. The role of the carbon layer was to form a stable solid electrolyte Interface (SEI), accommodate the Si volume changes during cycling and maintain good electrically-conducting pathways. The Si/C nanocomposite electrode delivered a remarkable capacity of 1181 mAh g\({}^{-1}\) at 100 th cycle with a coulombic efficiency of 99.2%. Such high capacity and coulombic efficiency were attributed to the size reduction of Si particle to nanometer scale and uniform distribution of nano-sized active Si particles in carbon matrix phase, which provides good mechanical integrity and electrical contact between the Si nanoparticles. Thus, the carbon coated-silicon nanocomposite prepared via a simple and cost-effective methods of high energy mechanically milling and carbonization process, is a promising candidate for high capacity anode material in the lithium ion rechargeable batteries. However, further studies are necessary to verify the suitability of Si/C nanocomposite anode material in the lithium-ion battery full cells.  
Conceptualization R.M. and K.-S.L.; Methodology R.M. and W.J.C.; Investigation and Writing original draft R.M.; Writing-Review & Editing C.L. and K.-S.L.  
This research was funded by Basic Science Research Program through the National Research Foundation of Korea (NRF) the Ministry of Education (NRF-2017R1D1A1A02019022).  
The authors declare no conflict of interest.
## 4 The Geometries of the Proposed Transmitters  
### The Geometry for the Single-LED Transmitter Case  
Since the distance between the two tailligths is 150 cm, the two transmitters of the single-LED configuration are located at \(\text{P}(\pm 75\;\text{ cm},0,0)\). Each transmitter is a single LED with a half FOV \(\phi_{\frac{1}{2}}\) of \(30^{\circ}\). Their power is focused in a circular area of radius \(R=d\tan\phi_{\frac{1}{2}}\) in the plane \(y=d\), where \(d\) is the y-location of Rx. The power drops to half its maximum  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **X (cm)** & **Y (cm)** & **Z (cm)** \\ \hline Tx1 & \(-75\) & 0 & 0 \\ Tx2 & 75 & 0 & 0 \\ Rx & \(0,350\) & y & \(0,30\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transmitter and receiver coordinates.  
Figure 2: Samsung’s PixCell LED headlight module [42].  
value at the boundary of this circle. At a particular y-distance \(d\), the two circles will be tangent. This distance is found to be \(d=130\) cm using  
\[2d\tan(\phi_{\frac{1}{2}})=150\;\mathrm{cm} \tag{18}\]  
The geometry of the single-LED transmitter case is illustrated in Figure 3.
## 2 Method
### Predictor  
The predictor is responsible for generating the optimized result for a given input data point. Its ANN-architecture consists of multiple hidden layers, convolutional layers and output layers (see Figure 5).  
All parameters that can be changed during training, such as the bias, the slope of the parametric rectified linear unit (PReLU) as well as the weights of the hidden layers, are generally referred to as trainable parameters in the following. They are collected in the matrix \(\mathbf{W}_{\text{p}}\). The operations performed by the predictor can be represented by function \(f_{\text{p}}\) as follows:  
\[\mathbf{x}=f_{\text{p}}(\mathbf{W}_{\text{p}},\mathbf{r}_{\text{k}},\mathbf{ r}_{\text{s}},M_{\text{tar}}). \tag{7}\]  
The predictor's topology is shown in Figure 5 in a simplified form. An input data point (top left) is processed by several successive hidden blocks and then passed on to some residual network (ResNet)-blocks. In order to reduce the resolution to a lower level \(\Lambda\), average pooling is used.  
Figure 4: Nodes and elements at different levels \(\Lambda\) (resolutions). The boundary conditions do not change.  
In Figure 5, the hidden block is the combination of a hidden or fully connected layer and an activation function call. The ResNet-block is the combination of two (convolutional) layers and a shortcut that is added as a bypass to the output of the layers. The ResNet-block allows for faster learning but also reduces the error [27].  
The PReLU function [28] is used as the activation function \(f_{\text{a}}(z)\) in the hidden and convolutional layers. The PReLU function is the equivalent of the rectified linear unit (ReLU) function [28]:  
\[f_{\text{ReLU}}(z)=max(0,z)=\begin{cases}0&\text{if }z<0,\\ z&\text{otherwise},\end{cases} \tag{8}\]  
with the difference of a variable negative slope \(\alpha\), which can be adapted during training as follows:  
\[f_{\text{a}}(z)=f_{\text{PReLU}}(z)=\begin{cases}\alpha z&\text{if }z<0,\\ z&\text{otherwise}.\end{cases} \tag{9}\]  
The sigmoid function  
\[\text{sig}(z)=\frac{1}{1+e^{-z}} \tag{10}\]  
is well suited as an activation function for the output layer because it provides results in the interval \((0,1)\); see Figure 6. This makes the predictor's output directly suitable to describe the density values of the geometry.
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
###### Abstract
Furthermore, the dielectric technique has been widely used for soil moisture content measurement because of its stable performance and simple operation for nondestructive testing [19]. Numerous equations have been proposed on the relationship between soil dielectric constant and moisture content [20; 21; 22]. [PERSON] et al. [23] determined the moisture content of sew-age sludge composite materials by TDR using coated probes. TDR probes were calibrated as a function of dielectric properties that included temperature effects. The relative error was 0.92 \(\pm\) 0.72% when comparing the measured values of TDR of the samples with those of the weight method, indicating that the measured values of TDR have high accuracy. Meanwhile, compost has a similar structure to the soil, and [PERSON] et al. [24] investigated the capability of the resistivity measurement technique in soil moisture estimation. The result showed that the resistivity method achieved better results in monitoring soil moisture over large areas with a root mean square error of 4.4% vol/vol on average. In summary, dielectric techniques have potential applications in detecting the moisture content of corn composite materials. Moisture detectors developed based on dielectric properties have been applied to cereal crops and soils. Limited studies have been conducted on the dielectric properties of compost materials. As a rapid method to detect the moisture content of materials, dielectric technology has many influencing factors, including the temperature, moisture content, and bulk density. If the data processing technique is imperfect, measurement accuracy decreases [25]. Furthermore, the measurement method of dielectric parameters is technologically complex. Therefore, in this study, the effects of frequency (1 to 100 kHz), moisture content (5% to 35%), temperature (25 to 65 \({}^{\circ}\)C), and bulk density (683.8 to 829.1 kg/m\({}^{3}\)) on the dielectric constant \(\varepsilon^{\prime}\) and the loss factor \(\varepsilon^{\prime\prime}\) were investigated. The variation in dielectric properties and its causes were also analyzed. A mathematical model of the dielectric parameters and their primary influencing factors was established for evaluating the feasibility of the dielectric properties in predicting the moisture content of compost. Thus, a feasible method with sound scientific basis was proposed for developing a low-cost and high-efficiency compost moisture content detector.
## 4 Results and Discussion  
### System Level Simulation  
Principal simulation parameters are listed in Table 2. BS and UEs use UPAs with 256 and 16 elements, respectively. BS height is assumed to be 30 m and UEs with 1.5 m height are uniformly distributed in the single cell with radius of 20 m, as shown in Figure 1. SNR, given for the antenna elements linking BS and UE, is assumed to be 10 dB. Assuming a Rician fading channel with \(K\) = 10 dB, the multipath component is modeled as 11 path exponential decay with 3 dB attenuation for each 10 ns as per [32]. Spatial correlation, i.e., \(\mathbf{R}_{tx,i}\), \(\mathbf{R}_{rx,i}\), and LoS channel, change with UE rotation on the horizontal plane. CSI is updated every 1.3 ms. This corresponds to 200 symbols, each with 6.67 \(\upmu\)s symbol duration. CSI estimation error due to the receiver noise is excluded in order to evaluate the impact of the outdated CSI. We compare the following three schemes:  
* Case 1: Four stream per UE via BD;
* Case 2: One stream per UE via BD (1 st Eigenmode); and
* Case 3: One stream per UE via simplified beamforming (proposal).  
In Case 1, UE is assumed to perform minimum mean square error (MMSE) detection at every symbol reception. Cases 2 and 3 simply steer the beam to the BS array by using CSI at the update instance. We evaluate achievable signal-to-interference plus noise power ratio (SINR) and throughput performance. Throughput is determined based on modulation coding scheme (MCS) matches as the received SINR [33; 34]. The relationship is defined in Table 3. Since the original relationship was defined for an additive white Gaussian noise (AWGN) channel, we added a margin of 6 dB to ensure a conservative evaluation. User scheduling effect is taken into account by separating the UEs by least 3 m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Precoding Scheme** & **Complexity** \\ \hline Proposed scheme & 10_Nc_/_Np_ \\ SVD & 2_Nc_(_N_N_/_N_/_N_/_N_/_N_/_N_/_N_/_N_/_N_/N_/_N_
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
###### Abstract  
The practical constitutive model UBCSAND2, which combines two-mobilized planes--a maximum shear stress plane and a horizontal plane within a framework of classical plasticity approach--is used to incorporate shear-induced effects in both loading and unloading as well as principal stress rotation effects. UBCSAND2 was calibrated by capturing cyclic direct simple shear (CDSS) test results of Pohang sand, which was collected from liquefied paddy fields due to the 2017 Pohang earthquake (\(M_{\text{w}}=5.4\)) in South Korea. The model procedure focuses on simple shear condition because it best simulates field conditions under earthquake loading. The calibrated UBCSAND2 model is then used to assess the liquefaction-induced damages that occurred at the quay wall and backfill layer in Youngil-man port near the epicenter of the Pohang earthquake. The numerical results show that liquefaction mostly occurred in silty sand layers, in which the excess pore pressure ratio reached almost one. The estimated displacements of the quay wall and the predicted settlement of reclaimed area obtained from the analysis were in good agreement with those obtained from field measurements.  
s amd; liquefaction; constitutive model; effective stress; Pohang earthquake; Youngil-man port +
Footnote †: journal: Applied Sciences  
_Article_
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Composite Relative Measurement Technology of Spacecraft Formation
#### 3.6.8 Laser + Infrared Coupling Measurement Technology  
The principle of the coupling infrared and laser measurement technique is that infrared measurements perform target capture and guide the laser for target measurement [73]. The target search part is completed using the large-angle tracking pendulum mirror system and the infrared measurement system. The orientation information of the target spacecraft is input into the laser system, and the laser is guided to point at the target spacecraft. Since the infrared measurement accuracy is in the order of a milli-arc, the minimum guided laser-beam width is sub-milli-arc. Figure 29 shows a typical measurement system combining LiDAR and infrared measurement, including a laser rangefinder, infrared thermal imager, infrared goniometer, and information-processing components. The aim of the method is to track and locate the target spacecraft via infrared. The infrared camera obtains the angle information and the laser rangefinder obtains the distance information. The information-processing system fuses and calculates the relative position and altitude with centimeter- and arcsecond-level accuracy.