esa-sceva/satcom-chunk-collection huggingface dataset:
# Neighborhood Deviation Attack Against In-Context Learning
## 5 Experiments
### Evaluation Metrics  
The evaluation metrics for generation quality in the experiment are described as follows.  
**Similarity:** This metric can be expressed as  
\[\text{Similarity}=\Phi\big{(}E(x),E(x^{\prime})\big{)} \tag{9}\]  
where \(E(\cdot)\) denotes a text encoder (for example, Sentence Transformer [51]) that converts samples into embedding vectors, and \(\Phi(a,b)\) represents the similarity computation function (cosine similarity in experiments).  
**BLEU Score [52]:** The BLEU score measures the n-gram overlap between generated text and reference text. The formula is  
\[\text{BLEU}=\text{BP}\cdot\exp\Bigg{(}\sum_{n=1}^{N}w_{n}\log p_{n}\Bigg{)} \tag{10}\]  
where \(p_{n}\) is the n-gram precision (the ratio of matched n-grams to the total number of n-grams in the generated text). BP is the brevity penalty, defined as  
\[\text{BP}=\begin{cases}e^{1-\frac{l_{\text{ref}}}{\text{gen}}}&\text{if }l_{\text{gen}}\leq l_{\text{ref}}\\ 1&\text{otherwise}\end{cases} \tag{11}\]  
where \(l_{\text{gen}}\) is the length of the generated text, and \(l_{\text{ref}}\) is the length of the reference text. \(w_{n}\) is the weight for different n-grams (typically uniform weighting, \(w_{n}=1/N\)).  
**Perplexity (PPL) [53]:** Perplexity measures the quality of a language model's predictions. The formula is  
\[\text{PPL}=\exp\Bigg{(}-\frac{1}{N}\sum_{l=1}^{N}\log p(w_{i}|w_{<i})\Bigg{)} \tag{12}\]  
where \(p(w_{i}|w_{<i})\) is the probability of predicting the \(i\)-th word given the previous words. \(N\) is the length of the text. Lower values indicate better prediction accuracy.  
**ROUGE-L [54]:** ROUGE-L evaluates the similarity between a generated text and a reference text by computing the F1 score of their longest common subsequence (LCS). The formula is  
\[\text{ROUGE-L}=\frac{(1+\beta^{2})R_{\text{LCS}}P_{\text{LCS}}}{R_{\text{LCS}}+ \beta^{2}P_{\text{LCS}}} \tag{13}\]  
where \(R_{\text{LCS}}=\frac{\text{LCS}(X,Y)}{l_{\text{ref}}}\) (recall). \(P_{\text{LCS}}=\frac{\text{LCS}(X,Y)}{l_{\text{ref}}}\) (precision). \(l_{\text{ref}}\) is the length of the generated text, and \(l_{\text{ref}}\) is the length of the reference text. \(\beta\) is a parameter to balance recall and precision (typically set to \(\beta\rightarrow\infty\) to emphasize recall).  
The evaluation metrics used in the experiment to evaluate the final performance are described below.  
**Advantage [55]:** This metric evaluates the effectiveness of attack methods and is defined as  
\[Adv=2\times(Acc-0.5) \tag{14}\]  
where \(Adv\) represents Advantage, and \(Acc\) represents the accuracy rate of the attack method.  
It quantifies the performance advantage over random guessing, providing a more accurate assessment of the effectiveness of the attack.
# Development of a Dual-Modality Gamma-ray/Fast Neutron Imaging System for Air Cargo Inspection
## 4 Conclusions  
We developed an air cargo inspection system using a D-T neutron generator and accelerator-based gamma-ray source. The neutron source generates neutrons with an energy of 14.1 MeV and a flux of 108 n/sec. The gamma-ray source produced a gamma-ray beam with an energy of 6 MeV and a dose of 1.25 Gy/min. A neutron and gamma-ray imaging system was installed to acquire radiation transmittance images of 30 materials loaded in an air cargo container. These radiations can penetrate materials several tens of centimeters thick within an air cargo container. A linear motorized translator moved the air cargo container between the radiation sources and detectors. The radiation detectors were based on plastic scintillators, silicon photomultipliers, and electric circuits. They were arranged vertically to scan the moving stage, including the cargo container. The radiation signals were counted as image pixel values, according to the PSD method. As a result, the radiation imaging system outputs neutron and gamma-ray images with transmittance. Data processing with image interpolation and registration was used to calculate R values from the transmittance data and displayed them on the color map image to differentiate between 30 types of materials: metals, plastics, organics, and others. This process validates the material classification capability.  
The R-value image is powerful in distinguishing more diverse materials than the use of a single radiation source. However, the industrial fields require more complicated systems for use in the mixed or packed material classes in the real cargo container. In the imaging system, the three-dimensional imaging system is required because the mixed materials are superposed on the beam path. Our further step in the development of the security inspection system is a tomographic inspector that uses gamma-rays and neutrons.  
Conceptualization, J.Y.P. and J.M.; methodology, J.Y.P. and J.H.L.; software, J.Y.P.; validation, Y.-H.Y. and M.C.; formal analysis, J.Y.P.; investigation, J.M.; resources, M.L.; data curation, J.Y.P.; writing--original draft preparation, J.Y.P.; writing--review and editing, J.Y.P.; visualization, J.Y.P.; supervision, J.Y.P.; project administration, N.-H.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This work has been carried out under the Nuclear R&D Program of the Ministry of Science and ICT(MIST) of Korea (Project NO. 523420-22 and NRF NO. 2019M2A2A4A05031483).  
Not applicable.  
The authors declare no conflict of interest.
# Multiple SSO Space Debris Flyby Trajectory Design Based on Cislunar Orbit
## 2 Problem Statement and Equations of Motion
### General Scenario
The orbit of the SSO space debris is presented in the J2000 ECI coordinate \(\left\{O-\hat{\mathbf{X}},\hat{\mathbf{Y}},\hat{\mathbf{Z}}\right\}\), where \(O\) is the center of the mass of the Earth, the \(\hat{\mathbf{X}}-\hat{\mathbf{Y}}\) plane is the equatorial plane, \(\hat{\mathbf{X}}\) points from \(O\) to the location of the equinox in 2000 years, and \(\hat{\mathbf{Z}}\) is the normal of the \(\hat{\mathbf{X}}-\hat{\mathbf{Y}}\) plane. The equation of motion of the SSO space debris in the J2000 ECI coordinate is as follows:  
\[\mathbf{a}_{d}=-\frac{\mu_{e}}{r_{d}}\mathbf{r}_{d}+\mathbf{a}_{I_{2}} \tag{5}\]  
where \(\mathbf{a}_{d}\) is the acceleration vector of space debris. \(\mathbf{r}_{d}\) is the inertial position vector of space debris. \(r_{d}=\mid\mid\mathbf{r}_{d}\mid\mid\). \(\mathbf{a}_{I_{2}}\) is the acceleration vector caused by the \(J_{2}\) perturbation. \(\mu_{e}\) is Earth's gravity constant.
## 3 Methodology
### Long Short-Term Memory  
The long short-term memory network stands as a seminal and highly effective model for time series prediction [39]. The memory cell of the LSTM model is shown in Figure 2.  
The LSTM model incorporates a sophisticated gate structure to mitigate the challenges of gradient vanishing and explosion. This gate structure comprises forget gates, input gates, and output gates, and each structure has its unique role. Three gate structures are represented mathematically by Equations (8)-(11):  
\[f_{t}=\sigma(W_{f}x_{t}+W_{hf}h_{t-1}+b_{f}) \tag{8}\]  
\[i_{t}=\sigma(W_{i}x_{t}+W_{hi}h_{t-1}+b_{i}) \tag{9}\]  
\[o_{t}=\sigma(W_{o}x_{t}+W_{ho}h_{t-1}+b_{o}) \tag{10}\]  
\[h_{t}=tanh(C_{t-1})\bigotimes o_{t} \tag{11}\]  
\[C_{t}=C_{t-1}\bigotimes f_{t}+tanh(W_{C}x_{t}+W_{hc}h_{t-1}+b_{C})\bigotimes i _{t} \tag{12}\]  
In Equations (8)-(12), matrices \(w_{xf}\), \(w_{xi}\), \(w_{xo}\), \(w_{xc}\) denote the appropriate input weight matrices, \(w_{hf}\), \(w_{hi}\), \(w_{ho}\), \(w_{hc}\) are the recursive weight matrices, and \(b_{f}\), \(b_{i}\), \(b_{o}\), \(b_{c}\) denote the corresponding bias vectors. The hidden state, denoted by the \(h_{t}\) in Equation (11), passes through the activation function tanh to produce a new hidden state after combining the input and the previous hidden state into a vector. Long-term memory is represented by \(C_{t}\), which is the outcome of the input gate, forget gate, and memory \(C_{t-1}\) from the previous moment combined. The symbol \(\bigotimes\) represents the multiplication of elements by elements between units. The tanh and sigmoid kernel functions are represented as tanh and \(\sigma\), respectively, and are defined mathematically as follows:  
\[tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{13}\]  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{14}\]  
The output \(y_{t}\) of the LSTM is computed using Equation (15), where \(w_{hy}\) is the recursive weight matrix and \(b_{y}\) is the corresponding bias vector:  
\[y_{t}=\sigma(w_{hy}h_{t}+b_{y}) \tag{15}\]  
Figure 2: LSTM memory cell structure.
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
## 1 Introduction
Although internet conditions do not limit the transportation of PPP-B2b messages, at least one of the three GEO satellites must be viewable to receive the corrections. For users in the Northern Hemisphere, the GEO satellites are always located south of the user, and they may be blocked when buildings are in the line of sight between users and the GEO satellites, which is called the \"south wall effect\". Similarly, users in the Southern Hemisphere will experience the \"north wall effect\" [14]. When users are in an \"urban canyon\" environment where buildings are concentrated, GEO satellite signals are easily blocked by buildings, leading to a loss of B2b signals. Figure 1: The Earth coverage areas of the three GEO satellites for 5\({}^{\circ}\), 10\({}^{\circ}\), and 15\({}^{\circ}\) elevation cut-off angles. This study attempts to analyze the quality degradation of the PPP-B2b correction over time and its impact on positioning results during outages of the PPP-B2b service. In Section 2, the process of PPP-B2b precision product recovery, the evaluation of PPP-B2b product accuracy, and the repair of missing PPP-B2b precision ephemeris data are briefly described. Section 3 analyzes the continuity of the PPP-B2b corrections, and the quality of the extended corrections. The real-time positioning accuracy maintained using the extended corrections during PPP-B2b signal interruptions is also evaluated. Finally, there is a discussion and conclusion.
## 2 Numerical Modeling
### Computational Domain and Boundary Conditions  
The computational domain consists of a single blade passage, where a rotational periodic boundary is applied at the pitch-wise direction of the passage. The inlet boundary of the domain is set to be 1.5 C\({}_{\rm x}\) upstream of the blade leading edge (LE) and the outlet boundary is 3.0 \(C_{\rm x}\) downstream of the blade trailing edge (TE). The meridian passage profiles of casing and hub are straight lines. Figure 1 indicates that the rotor blade is twisted and has a flat tip geometry. The tip clearance between the rotor tip and the casing is 1% of the span (0.4146 mm) and has a uniform distribution.  
Total pressure, total temperature and flow angles in the absolute frame are imposed at the inflow. Settings of these boundary conditions are presented in Table 1. Pressure outlet condition is set as the outlet boundary condition. No slip adiabatic smooth wall condition is imposed on the blade surface, casing, blade tip and hub wall. The hub wall is connected to the rotor blade and set as rotating wall, while other parts and casing wall are set as static wall. In the calculations, the incoming boundary layer is a uniform free-developing boundary layer at the inlet and the initial turbulence intensity at the inlet is set to 10%. A rotational periodic boundary is applied in the pitch-wise direction of the passage.  
To study the effect of the shock wave interference on the stability of the leakage vortex, different outlet static pressure varying from 750 kPa to 250 kPa are considered to cover a wide range of Mach numbers at the turbine outlet. The outlet pressure corresponding to the typical subsonic and supersonic case is 510 kPa and 300 kPa, respectively. It is noted that the former value is the turbine design point, corresponding to the outlet Reynolds number of about 5.7 \(\times\) 10\({}^{5}\) based on the mean chord length and the outlet relative velocity. In all simulations, the rotating speed of the rotor is set to 12,630 r/min and steady calculations are carried out.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Absolute Total Pressure/kPa & 1210.13 \\ \hline Absolute Total Temperature/K & 1588 \\ \hline \multirow{3}{*}{Incoming Flow Angle/deg} & Hub & 73.1 \\  & Mean & 74.2 \\  & Shroud & 75.4 \\ \hline \multirow{3}{*}{Incoming Turbulence Intensity/\%} & 10 \\ \cline{1-1}  & Rotating Speed/RPM & 12,630 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary condition setting.  
Figure 1: Computational domain of the first-stage rotor of GE-E3 HPT.
# Lp-Norm Inversion of Gravity Data Using Adaptive Differential Evolution
## 4 Simulation Tests
### Gravity Data with Gaussian Noise  
To prove that a new method works correctly, it is essential to test it on data with noise since actual gravity data may be contaminated by noise. Here, Gaussian white noise with a mean value 0 and a standard deviation equal to 10% of the maximum value of synthetic data are added. By using the proposed inversion method, fittings between the observed and model response for these noisy data are shown in Figure 7. The inversion results of noise data are shown in Figure 8. According to the results, one can conclude that the Lp-norm inversion of gravity is robust, and capable of reflecting the real shape of the field source from the noisy data.  
Figure 6: The inversion results of the model shown in Figure 2d with different \(p\) values. (**a**) \(p=1\), (**b**) \(p=1.1\), (**c**) \(p=1.3\), (**d**) \(p=1.5\), (**e**) \(p=1.7\), (**f**) \(p=2\).
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 4 Obstacle Avoidance Algorithm and Transverse Controller Design
### Analysis of Tire Cornering Stiffness  
In this paper, the tire lateral deflection stiffness of a driverless ferry vehicle is estimated by the recursive least squares method [19]. The linear regression equation between tire cornering force and tire linear cornering stiffness is as follows:  
\[Y_{m}(k)=\varphi^{T}(k)\phi(k)+e(k) \tag{18}\]  
where \(\varphi(k)=[\varphi(k)\ \varphi(k-1)\dots\varphi(k-N+1)]\) is the regression vector, and \(\phi(k)\) is the model parameter to be estimated.  
Figure 14: Schematic diagram of lateral motion control.  
Figure 13: Acceleration/braking switching rule curve.  
The goal of recursive least squares-based model parameter estimation is to minimize the squared difference between the estimated and actual values inside the study window of historical data. This difference can be represented as the cost function, \(J\):  
\[J=\sum_{i=1}^{N}\lambda_{m}^{N-i}[Y_{m}(i)-\varphi^{T}(i)\hat{\phi}(i)]^{2} \tag{19}\]  
in the formula, \(\lambda_{m}\) is the forgetting factor, \(N\) is the length of the time window, and \(\hat{\phi}(k)\) is the estimated value of model parameters.  
After deducing, the recursive formula for recursive least squares can be obtained as follows:  
\[P(k)=\lambda_{m}^{-1}[P(k-1)-K(k)\varphi^{T}P(k-1)] \tag{20}\]  
\[\hat{\Phi}(k)=\hat{\Phi}(k-1)+K(k)[Y_{m}(k)-\varphi^{T}(k)\hat{\Phi}(k-1)] \tag{21}\]  
Recursive gain:  
\[K(k)=P(k-1)\varphi(k)[\lambda_{m}+\varphi^{T}(k)P(k-1)\varphi(k)]^{-1} \tag{22}\]  
The tire lateral deflection stiffness estimation model was built in MATLAB/Simulink, the recursive least squares algorithm was compiled in the S-Function module, and the joint simulation with CarSim 2019.0 software was carried out to solve the estimated tire cornering stiffness. Figure 15 shows the tire side stiffness results estimated under different road adhesion coefficients.
## 1 Introduction
### The Study Variables and Proposed Hypotheses
#### 1.2.1 Maximum Accepted Price for a Smartphone  
The maximum price accepted is the maximum amount a consumer is willing to pay for a smartphone. The variable reflects consumers' perceptions of the perceived value of the product, its features, and value for money.  
According to the study conducted by [PERSON] and [PERSON] [20], perceived price plays a critical role in the decision-making for purchasing electronic products. Research by [PERSON] et al. [21] revealed that economic factors influence price decisions more than demographic differences such as gender. Also, [PERSON] and [PERSON] [22] showed that value perception is influenced by characteristics such as technological innovation and smartphone brands.  
In the current study, we explore whether demographic factors (gender) influence consumers' perceptions of the maximum acceptable price in the context of increasing online shopping and digitization of consumer behavior. Previous studies [1] indicate that price decisions are more influenced by economic factors than gender differences.  
**H1.**: _There are no significant differences between men and women in the maximum price they are willing to pay for a smartphone. The dependent variable is the maximum price of the next smartphone and the independent variable is the gender of the consumer._
## 3 Stability Analyses for the Initially Designed Geometry
### Preliminary Studies and Investigations  
In order to carry out stability analyses and verify the designed geometry of the steps, several types of investigations were undertaken, as follows:  
1. Studying the documentation made available by Jilt North open pit (description of the lignite deposit, the results of several geotechnical drillings, situation plan, longitudinal and cross sections, stratigraphic columns, etc.) [43], as well as previous research/service contracts, which concerned this exploitation perimeter;
2. On site investigations related to the technical condition of the slopes of the working fronts. During these field visits, discussions took place with representatives of the Jilt North open pit, regarding working technologies and problems of slope stability encountered so far. From these discussions, it was established that, in essence, the slopes exhibit some degree of instability. There are flows of material on the slopes in both dry and wet conditions. Following these field visits, some relevant findings were made: * There are some geometric non-uniformities of the working fronts caused by the morphology of the terrain and excavation technology; * Rock displacements (dry flows and material rolling) in conditions of low humidity and tendencies of plastic flow in conditions of saturation especially on the slopes of T1, T2 and T3 steps. Usually they do not involve large volumes of material (can be regarded as superficial landslides); * Displacements that affect the stability of the step over the entire height, with variable extent depending on the slope structure and the hydrometeorological conditions.
3. Determining some physical and mechanical characteristics relevant for the rocks encountered in the slopes of the working fronts through laboratory tests, retrieving data from the specialized literature and their statistical processing.  
The stability analyses were carried out with the help of Slide geotechnical software, in which the calculation section was modeled. The cross-section F-F through the working fronts in Figure 10b was taken into account (through the middle of the Jilt North open pit--considered to be the most relevant).
## 5 Experimental Results
### Results
#### 5.4.5 (Un)Projection Does Not Cause Information Loss  
One of the frequent arguments against projection methods is the loss of information when projecting a 3D shape into the 2D feature map space and subsequent information unprojection back to mesh cells. In our proposed method, there is inherently no loss of information, even on unsampled meshes. This is primarily due to local per-teeth processing, a reasonable number of views (49) and camera extrinsic setup, and sufficient resolution of the 2D feature maps (256 \(\times\) 256). (Un)projection loss of information would be reflected by the generated masks containing holes. Figure 7 shows an example of two teeth in parameterization space obtained by prediction on a mesh with fine geometry: 312,854 faces, where the dataset mean is 203,974 \(\pm\) 49,800 faces. Occasional unprojection errors are present near the tooth-gingiva boundary. The small errors in this part of the masks are driven by the complex geometry near the gingiva and the lower number of views from which a given part of the surface is visible. However, most cases contain no holes, even on meshes with high-resolution geometric details.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Topology Adjustment \& Performance**} \\  & **Reference** & **RVD** & **PFO** & ER \\  & _W-IoU_\(\uparrow\) & _W-IoU_\(\uparrow\) & _W-IoU_\(\uparrow\) & _W-IoU_\(\uparrow\) \\ \hline PointNet & \(0.921\pm 0.039\) & \((-2.4\%)\) \(0.897\pm 0.044\) & \((-1.1\%)\) \(0.910\pm 0.073\) & \((-0.7\%)\) \(0.914\pm 0.060\) \\ PointNet++ & \(0.935\pm 0.051\) & \((-3.5\%)\) \(0.900\pm 0.058\) & \((-1.3\%)\) \(0.921\pm 0.098\) & \((-0.8\%)\) \(0.927\pm 0.049\) \\ SpMeshCNN & \(0.918\pm 0.057\) & \((-2.8\%)\) \(0.890\pm 0.065\) & \((-1.8\%)\) \(0.900\pm 0.052\) & **(+0.2\%)** \(0.920\pm 0.082\) \\ Graph Cut & \(0.954\pm 0.109\) & \((-3.5\%)\) \(0.918\pm 0.133\) & \((-1.6\%)\) \(0.937\pm 0.101\) & \((-1.3\%)\) \(0.940\pm 0.113\) \\ LMVSegRNN & \(0.971\pm 0.038\) & \((-1.8\%)\) \(0.953\pm 0.086\) & \((-0.3\%)\) \(0.968\pm 0.055\) & \((-0.5\%)\) \(0.966\pm 0.092\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Robustness of methods towards various geometry tessellations. Abbreviations of topology and geometry adjustment methods: RVD: Random Vertex Displacement, PFO: Planar Flipping Optimization, ER: Explicit Remeshing. Blue text represents the performance change compared to the reference performance (without any topology or geometry changes) of given method. Presented point-based methods contain both 3D coordinates and normal vectors of mesh cells at the input. SparseMeshCNN contains edge self-attention blocks in two bottom stages of the encoder. Bold text represent the best-performing setup.