esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Transmission State of Multiple Wireless Sources: A Statistical Mechanics Approach
## Appendix B The Replica Method
}^{R_{i}}\mathrm{G}_{l}(1-\sigma_{l})^{2}\right)\right]\] (A45) \[\mathcal{U}_{-} =\sum_{\sigma_{l}}\exp[-h_{l}]\exp\left[\ -\beta\!\left(\eta+\mathrm{G}_{R_{i}}\big{(}1-\sigma_{R_{i}}\big{)}^{2}+ \sum_{l=1}^{R_{i}-1}\mathrm{G}_{l}\Big{(}1-\sigma_{l}^{(a)}\Big{)}^{2}\right)\right]\] (A46)  
In this way, we can express \(\widehat{h}\) as  
\[\widehat{h}=\ln\!\left(\frac{\mathcal{U}_{+}}{\mathcal{U}_{-}}\right)\] (A47)  
and obtain the \(\widehat{Q}\!\left(\widehat{h}\right)\) distribution  
\[\widehat{Q}\!\left(\widehat{h}\right)=\mathbb{E}_{R_{j}}\!\left[\prod_{j=1}^{ R_{j}}\int\mathrm{d}h_{j}\,Q(h_{j})\delta\!\left(\widehat{h}-\big{[}\epsilon_{ \beta}(h)\big{]}\right)\right]\] (A48)  
which is (35) in Section 5. The total error detection probability can be calculated by adding an effective magnetic field term in the cost function, \(\mathcal{E}\), of (29), so,  
\[\mathcal{E}\rightarrow\mathcal{E}+\delta\sum_{\mu=1}^{M}s_{\mu}\widehat{s}_{\mu}\] (A49)which modifies the \(\mathcal{G}_{3}\) term of the replicated partition function in (A8), resulting in  
\[\mathcal{G}_{3} =\prod_{k=1}^{T_{v}}\int\mathrm{d}\widehat{h}_{k}\,\widehat{Q}( \widehat{h}_{k})\times\left(\frac{2\cosh\!\left(\sum\limits_{k}\widehat{h}_{k}- \beta\delta\right)}{\prod\limits_{k}2\cosh\!\left(\widehat{h}_{k}\right)} \right)^{n}\] \[=-TM-M\ln\!\left(\mathbb{E}_{T_{v}}\mathbb{E}_{\widehat{h}}\left[ \frac{2\cosh\!\left(\sum\limits_{k}\widehat{h}_{k}-\beta\delta\right)}{\prod \limits_{k}2\cosh\!\left(\widehat{h}_{k}\right)}\right]\right)^{n}\] (A50)  
After taking the partial derivative of \(\mathcal{G}_{3}\) with respect to the distribution \(\widehat{Q}\!\left(\widehat{h}\right)\), we obtain  
\[\frac{\delta\mathcal{G}_{3}}{\delta\widehat{Q}\!\left(\widehat{h}\right)}=-M \mathbb{E}_{T_{v}}\prod_{k=1}^{T_{v}-1}\widehat{Q}\!\left(\widehat{h}_{k} \right)\ln\!\left(2\cosh\!\left(\sum\limits_{k}^{T_{v}-1}\widehat{h}_{k}+ \widehat{h}\right)\right)\] (A51)  
As a final step, we take the partial derivative of \(\mathcal{F}\) with respect to \(\delta\) for \(\delta\to 0\),  
\[\frac{\partial\mathcal{F}}{\partial\delta} =\left.\frac{1}{\beta}\frac{\partial}{\partial\delta}\left( \mathcal{G}_{3}(\delta)\right)\right|_{\delta=0}=M\mathbb{E}_{T_{v}}\mathbb{E} _{\widehat{h}}\!\left[\text{sign}\left(\sum\limits_{k=1}^{T_{v}}\widehat{h}_{ k}\right)\right]\] \[=M\int\mathrm{d}h\int\mathrm{d}\widehat{h}\,\mathcal{Q}(h) \widehat{Q}\!\left(\widehat{h}\right)\!\text{sign}\!\left(h+\widehat{h}\right)\] (A52)  
which validates the results in [23] obtained using the Bethe approximation.
## 4 Discussion
In comparison to visible images, SAR sensors have the ability to record data during cloud cover. This is extremely important in the West Antarctic region. The effect of cloudiness on the availability of classic satellite images is shown in Table 1. On the basis of the data presented in Table 1, it can be concluded that in the case of Landsat, only 3.6% (7 from 192) of images recorded cloud cover below 10%; in the case of the Aster satellite none of the archival images met this minimum criterion; and in the case of Sentinel-2 images, only 2 archival images from 47 (4.3%) met this condition. It should also be remembered that browser systems classify cloudiness automatically and not all listed images meet the quality criteria for this type of interpretation. Based on the trend in satellite observation systems development, we can anticipate a widespread availability of high-resolution (spatial, spectral, and temporal) data for polar regions in upcoming years. Even now, the data can be calibrated and verified using UAVs' low-altitude remote sensing, which is becoming less expensive year by year, with increasing technical capacity. Considering the above, scientists at polar research stations should already engage in the systematic monitoring of the glaciers' dynamics using remote sensing. The following research studies can serve as examples: References [(51); 52; 53; 54]. Such activity, conducted for small glacial systems in the Antarctic, will certainly be a valuable contribution to the monitoring of the global effects of climate warming.
# Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics
## 3 Proposed Approach
### Heuristics  
A direct consequence of partial observability is that the agent cannot determine the position of \(x_{d}\). However, middle points can be determined. Consider an agent with a field of view determined by \([\phi,d_{max}]\), where \(\phi\) is an angle and \(d_{max}\) is the maximum distance that the agent can see. Let us also consider that the field of view is covered by \(n\) sensor beams, in order to emulate a LIDAR sensor (Figure 7). Under the above conditions, four states are defined:  
1. Clear. No obstacles detected. A random angle and distance in the range \([0,|\phi|\leq\frac{\pi}{2}]\) and \([d_{B},2]\) are selected.;
2. Blocked. The readings of all sensors are less than \(d_{B}\);
3. Partially clear. One condition of interest is detected in \([-\frac{5\pi}{8},\frac{5\pi}{8}]\). The angle and reading of the sensor are selected;
4. Partially blocked. Obstacles are detected in any other condition different from the ones above.  
Figure 6: Effect of changing the parameter \(k_{1}\).  
Figure 7: Detection beams of the agent. The beams are equally distributed.  
\(d_{B}\) is the minimum admissible distance for the agent to maneuver. The condition of interest is composed by some beam not detecting anything (\(d=d_{max}\)) and at least one beam at its right and its left detecting something (\(d<d_{max}\)), where \(d_{max}\) is the maximum possible reading when no objects are detected. Using these cases, it is possible to define middle target points.  
Given the previous definitions, priorities can be assigned to each type of detection, so that the agent is able to turn the APFs on and off as needed. For instance, obstacles out of the collision course can be ignored in order to shorten the path to the next target.  
The above heuristics will determine where the next local target will be located. Its purpose is to provide a model (see Figure 8) that allow agents to navigate in an unknown environment under partial-observability constraints. Neither heuristics nor Artificial Potential Fields are capable of achieving this by themselves. However, when combined, the resulting model provide interesting capabilities. Notice that there is no path-planning process involved, since only one point ahead (local target) is computed each time.  
Most recent approaches [40; 41; 42] strongly rely on geometry, previous knowledge and modified versions of the classic potential functions. Only the availability of previous knowledge, either by communication or by perception, represents a challenge in any application scope. Thus, the ability to deal with partial-observability constraints is a feature that stands out.
# An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing
## 4 Experiment and Evaluation
### Experiment Detail  
First, we calculated the Pearson correlation value of every resource metric pair of other resource metrics (CPU utilization, memory usage, etc.) with the network received throughput to select the resource metric with the highest correlation value.  
Second, we used the chosen resource and network received throughput metrics as inputs to the proposed multivariate Bi-LSTM model and compared them with the univariate one. The univariate Bi-LSTM model only has a network received throughput metric as its input. Furthermore, the multivariate Bi-LSTM model was compared with other well-known multivariate deep learning models (LSTM and CNN-LSTM) [11] to evaluate its performance. All prediction models performed prediction and output the future network received throughput data of the system. We used the Python programming language and trained the models on the tensor processing unit from Google Colab [26]. Furthermore, the code for implementing these models followed the code in the _Deep Learning for Time Series Forecasting_ book [27]. The configuration of the proposed multivariate Bi-LSTM model is presented in Table 2.  
Finally, we simulated the environment to evaluate the resource provisioning efficiency of the proposed multivariate Bi-LSTM autoscaler compared with those of other multivariate deep-learning-based autoscalers, as mentioned above. In this experiment, we used the first 200 observations in the testing set of the GWA-T-13 dataset to estimate the resource provision efficiency of each multivariate model. In addition, we assumed that each VM could handle 3300 KB/s of network received throughput, \(network_{VM}=3300\), and that the workload was balanced between VMs.
## 4 Results
### Characteristics of the Mixed OSB
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type (Unit: mm)**} & \multicolumn{2}{c}{**GPS**} & \multicolumn{2}{c}{**BDS-3**} & \multicolumn{2}{c}{**Galileo**} \\ \cline{2-7}  & **L5** & **B1C** & **B2a** & **E5b** & **E5** & **E6** \\ \hline \multirow{3}{*}{STD UD OSB} & Scheme 1 & 18.6 & 5.7 & 8.8 & 1.5 & 2.3 & 2.7 \\  & Scheme 2 & 17.2 & 1.8 & 5.4 & 1.3 & 0.9 & 2.1 \\  & Improvement & 7.5\% & 68.4\% & 38.6\% & 13.3\% & 60.9\% & 22.2\% \\ \hline \multirow{3}{*}{RMS UD OSB} & Scheme 1 & 20.3 & 5.9 & 9.2 & 1.7 & 2.3 & 2.9 \\  & Scheme 2 & 18.4 & 2.0 & 6.0 & 1.5 & 1.0 & 2.3 \\  & Improvement & 9.4\% & 66.1\% & 34.8\% & 11.8\% & 56.5\% & 20.7\% \\ \hline \multirow{3}{*}{STD ED OSB} & Scheme 1 & 0.5 & 0.5 & 0.8 & 0.4 & 0.2 & 0.4 \\  & Scheme 2 & 0.2 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 \\  & Improvement & 60.0\% & 80.0\% & 87.5\% & 75.0\% & 50.0\% & 75.0\% \\ \hline \multirow{3}{*}{RMS ED OSB} & Scheme 1 & 0.5 & 0.6 & 0.9 & 0.4 & 0.3 & 0.5 \\  & Scheme 2 & 0.2 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 \\  & Improvement & 60.0\% & 83.3\% & 88.9\% & 75.0\% & 66.7\% & 80.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical results and improvement of epoch-differenced and undifferenced OSB for GPS L5, BDS-3 B1C, B2a, and Galileo E5b, E5, and E6 signals in January 2021. Figure 10: Daily receiver pseudorange and phase time-variant mixed OSB for GPS L5, BDS-3 B1C and B2a, and Galileo E5b, E5, and E6 signals on DOY 011, 2021.
# Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
## 4 Practical Tool for Predicting the Cantilever Wall Deflection
### GUI Tool  
In recent years, geotechnical engineers have paid more attention to the development of user-friendly and practical software, because the complex mathematical equations are usually challenging for practitioners. Therefore, a graphical user interface (GUI) tool was established by MATLAB [31] to predict the cantilever wall deflection \(\delta\) in undrained clay, as shown in Figure 17. In accordance with the proposed ANN model, six considered input parameters, i.e., from \(X1\) to \(X6\), are presented in this tool. People can input the values for the excavation width \(B\), the excavation depth \(H_{c}\), the wall thickness \(t\), the at-rest lateral earth pressure coefficient \(K_{0}\), the soil shear strength ratio at mid-depth of the wall \(s_{u}/\sigma_{v}^{\prime}\), and the soil stiffness ratio at mid-depth of the wall \(E_{int}/s_{u}\). After entering the input parameters, the cantilever wall deflection \(\delta\) is immediately displayed when you click on the Start Predict button. In addition, it should be noted that the scope of the developed ANN and this GUI tool is limited to the lower and upper bound of these six input parameters because the ANN model is generally not accurate for extrapolation [25].
## 3 Results and Discussion
### PM\({}_{2.5}\) Prediction Model for Administrative Districts
Next-day PM\({}_{2.5}\) prediction models are compared in Table 12. The PM\({}_{2.5}\) prediction model developed in this study is superior to the two preceding studies in terms of R-squared and mean absolute error (MAE). However, since the data used in each study is different, it may be inappropriate to compare only R-squared and MAE. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Without Accurate Weather Forecasting** & \multicolumn{2}{c}{**With Accurate Weather Forecasting**} \\ \hline  & **In-Sample** & **Out-of-Sample** & **In-Sample** & **Out-of-Sample** \\ \hline R-squared & 0.65 & 0.55 & 0.73 & 0.65 \\ Absolute Error in 1 \(\sigma\) & 0.88 & 0.84 & 0.91 & 0.89 \\ Absolute Error in 0.5 \(\sigma\) & 0.62 & 0.58 & 0.68 & 0.65 \\ Absolute Error in 0.25 \(\sigma\) & 0.34 & 0.33 & 0.39 & 0.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: R-squared and absolute errors in n \(\sigma\) of administrative district model with/without accurate weather forecasting (month: included, hidden layer layout: 12 \(\times\) 12, threshold: 0.05). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Data** & **Methodology** & **R-Squared** & **MAE** \\ \hline This study & Precipitation, Humidity, Wind speed, Atmospheric & & & \\  & Pressure, Precipitation, Snow fall, PM\({}_{10}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), & Multi-layer Perceptron(ANN) & 0.73 & 4.59 \\  & CO, PM\({}_{2.5}\) & Multi-layer Perceptron(ANN) & 0.56 & - \\  & & & & \\  & & & & \\ \hline Sun and Sun, 2017 [35] & Temperature, PM\({}_{10}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\), CO, PM\({}_{2.5}\) &
\begin{tabular}{c} LSSVM(Least square support vector \\ machine), Cuckoo search, GRNN \\ \end{tabular} & - & 18.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Next-day PM2.5 prediction model performance comparison. Figure 9: PM\({}_{2.5}\) prediction vs. actual PM\({}_{2.5}\) for the administrative district model: (**a**) not considering accurate weather prediction and (**b**) considering accurate weather prediction.
# A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration
## 2 Modeling and Implementation
### PSF Length Deviation  
As mentioned, \(\omega_{1}\) and \(\omega_{2}\) are extracted to accurately adjust the \(L_{h}\) during the restoration process. By having rough prior knowledge of the probe size (\(L_{h}\)) and the estimated sizes (\(\hat{L}_{i}\)) from \(G\), we can define PSF length deviation as the distance between the expected and the estimations (\(|L_{h}-\hat{L}_{i}|\)). This definition converges to zero if the estimation is close to the prior knowledge, whereas it increases if \(\hat{L}_{i}\) is deviated from \(L_{h}\). Two fuzzy sets (\(A_{far}\) & \(A_{close}\)) with membership functions of \(u^{\prime}_{m}\) and \(u_{m}\) are defined to account for the probe inaccuracy and assign a degree of membership to each \(\hat{L}_{i}\) based on its deviation from \(L_{h}\). Membership functions are defined by polynomial-Z (zmf) and polynomial-S (smf). The \(A_{close}\) fuzzy set definition and its membership function is formulated in Equation (2). A thorough evaluation of fuzzy membership functions are provided in Reference [14].  
\[\begin{split} A_{close}=\{(\hat{L}_{i},\mu_{m(\hat{L}_{i})})|\ 0<\hat{L}_{i}<\infty,m(\hat{L}_{i})=\frac{2|\hat{L}_{i}-\hat{L}_{i}|}{\hat{L}_{ h}}\},\\ \mu_{m}=\begin{cases}1&m\leq a_{m}\\ 1-2\big{(}\frac{m-a_{m}}{c_{m}-a_{m}}\big{)}^{2}&a_{m}<m\leq\frac{a
## 4 Numerical Analysis of Vibration Localization Characteristics  
### Numerical Model  
The theoretical model simplifies the motion state and coupling relationship of the resonant unit. To obtain more accurate calculation results, a numerical model is established to verify the parameter influence of the vibration localization characteristics of the CMS. A discrete periodic cyclic spring-oscillators model is constructed in ABAQUS 6.14 software. The model comprises 10 spring oscillators with a 36\({}^{\circ}\) rotation angle difference between adjacent units. Nodes are used to simulate both the oscillators and the center point, connecting nodes to form line segments to simulate the springs. Mass properties are assigned to the nodes; stiffness and damping properties are assigned to the line segments.  
The main coordinate system is established at the center mass node, and the local coordinate systems are established at the oscillator nodes. In the local coordinate system, the \(y\)-axis is aligned with the direction of the main spring's length, and the \(x\)-axis and \(z\)-axis are oriented orthogonally, perpendicular to the \(y\)-axis. The stiffness and damping of the main spring act solely in the \(x\)-axis direction of the local coordinate system, whereas those of the coupling spring act solely in the direction of the spring's length. In terms of the setting of the boundary conditions, the node of the center mass is completely fixed. The oscillator is permitted to translate only along the \(x\)-axis direction of its local coordinate system, with several other degrees of freedom restricted. Figures 7 and 8 depict the cases of the resonant unit without coupling and with coupling, respectively.  
Figure 7: Numerical model of uncoupled spring oscillators.  
The numerical analysis process can be represented by a flowchart as shown in Figure 9.
## 3 Results & Discussions
### Electrical Properties  
Electrical properties of the Cr doped NiO thin films such as carrier concentration, carrier mobility, conductivity etc. at different doping times were measured through Hall measurement, and the results are tabulated in Table 3. Equations (4) and (5) explain the relationship between resistivity, carrier concentration, and carrier mobility:  
\[\sigma=q\eta\mu_{h} \tag{4}\]  
\[\sigma=\frac{1}{\rho} \tag{5}\]  
where \(\sigma\) is the conductivity, \(\rho\) is the resistivity, \(q\) is the electrical charge, \(\eta\) is the carrier concentration, and \(\mu_{e}\) is the carrier mobility. The resistivity is inversely proportional to the carrier concentration and electrical mobility.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Doping Time (s)** & **FWHM** & **Lattice Constant** & **Microstrain** (\(\epsilon\), \(\times\)**10\({}^{-3}\))** & **Dislocation Density** (line/cm\({}^{2}\)[\(\delta\)], \(\times\)**10\({}^{11}\))** \\ \hline Undoped & 0.45 & 0.4185 & 4.924 & 2.539 \\
20 & 0.43 & 0.4197 & 4.705 & 2.319 \\
40 & 0.38 & 0.4244 & 4.158 & 1.811 \\
60 & 0.47 & 0.4233 & 5.137 & 2.769 \\
80 & 0.61 & 0.4250 & 6.661 & 4.664 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural parameters of Cr–NiO along the (200) plane.  
Figure 3: Effect of doping time on the FWHM and crystallite size.  
The carrier concentration of the Cr-doped NiO films increases and resistivity decreases with an increase in doping time. The highest carrier concentration is observed at a doping time of 80 s. At doping time of 80 s, film resistivity became lower with moderate mobility. This result may be due to the presence of small impurities from Cr doping that decrease the bandgap of the Cr-doped NiO. Initially, the carrier mobility of the Cr-doped NiO thin films increased with doping time while the resistivity decreased. Carrier mobility gradually decreased after a doping time of 40 s, with continuously increasing doping time and decreasing resistivity. This result could be due to an improvement in dislocation density, as shown in Table 3 which increases carrier scattering and, as a result, lowers carrier mobility. Thus, electrical conductivity increases with doping time. Electrical conductivity varies from \(4.65\times 10^{-4}\)\(\mathrm{Scm^{-1}}\) to \(8.73\times 10^{-2}\)\(\mathrm{Scm^{-1}}\) when doping time is increased from 0 s to 80 s. Because of microstructural defects in the NiO crystal and surface chemical reactions, the electrical conductance of Cr-doped NiO thin films changes with doping time.
# Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions
## 4 PUF-Based Key Distribution for PQC
### PUF-Based Key Distribution for LWR Lattice Cryptography  
There are some similarities between LWE and LWR implementations. The seed k of the PUF is only used to generate one vectors \(s_{\text{(i)}}\), while a constant vector \(h_{\text{(i)}}\) can be generated independently. The public vector \(t_{\text{(i)}}\) is computed in a similar way: \(t_{\text{(i)}}\gets A_{\text{(i)}}\). \(s_{\text{(i)}}+h_{\text{(i)}}\).
# Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5
## 1 Introduction
proposed an Inception-CNN model and used it for aero-engine sensor fault detection, achieving 95.41% detection accuracy with the sensor failure dataset [16]. However, applications of object detection algorithms for surface defect detection of aero-engine components are limited, and the recognition efficiency and accuracy need to be improved. In order to improve the accuracy and speed of surface defect detection of aero-engine components, a defect detection model based on an improved YOLOv5 algorithm is proposed in this paper. First, a k-means algorithm was used to cluster the real labeling boxes of the experimental dataset, optimize the size of the preset anchors and increase the matching degree between the anchors and the real samples. Next, an ECA-Net mechanism was added at the end of the backbone network to enhance the feature expression ability. Then, the PANet structure of the neck network was replaced with BiFPN modules to improve the feature fusion ability of the model. The experimental results showed that the model had a high detection accuracy and small size, making it easy to deploy in mobile terminals, and could better complete surface defect detection tasks for aero-engine components.
# A Geographical Heuristic Routing Protocol for VANETs
## 3 Background
### Heuristics Adaptations for VANET Routing  
Local search with greedy selection is the predominant strategy in geographical VANET routing [3; 5; 6; 7; 19]. Since this mechanism has obtained good results, it is important to test other heuristics, like Tabu search and simulated annealing adaptations to the VANET routing. Tabu search and simulated annealing are two meta-heuristic widely used in combination with local search that obtain good solutions in too complex and big discrete optimization problems. Adaptations of both of them have been proposed in [9] to operate in multi-hop VANET routing. In this section, we summarize these adaptations.