esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Surface Reconstruction from Point Clouds Using [PERSON]'s Elastica Regularization
## 3 Proposed Variational Method
Discretizing the term \(\
abla\cdot(\frac{\
abla\phi}{|\
abla\phi|})\) in 3D based on the finite difference scheme is necessary for evolving the level set function \(\phi\) in (8). The 3D grid space of dimension \(MNL\) is represented by the symbol \(\Omega\rightarrow\mathbb{R}^{MNL}\). The following formula can be used to calculate the second-order coupled curvature term \(\
abla\cdot(\frac{\
abla\phi}{|\
abla\phi|})\) at the voxel \((i,j,k)\):  
\[ \begin{split}&\frac{\
abla_{x}^{+}\phi_{i,j,k}}{\sqrt{\left( \
abla_{x}^{+}\phi_{i,j,k}\right)^{2}+\left(\
abla_{y}^{0}\phi_{i+\frac{1}{2},j,k}\right)^{2}+\left(\
abla_{z}^{0}\phi_{i,j,k+\frac{1}{2}}\right)^{2}+ \varepsilon^{2}}}-\frac{\
abla_{x}^{-}\phi_{i,j,k}}{\sqrt{\left(\
abla_{x}^{ -}\phi_{i,j,k}\right)^{2}+\left(\
abla_{y}^{0}\phi_{i,j-\frac{1}{2},k}\right) ^{2}+\left(\
abla_{z}^{0}\phi_{i,j,k-\frac{1}{2}}\right)^{2}+\varepsilon^{2} }}\\ +&\frac{\
abla_{y}^{+}\phi_{i,j,k}}{\sqrt{\left(\
abla_{y}^{ +}\phi_{i,j,k}\right)^{2}+\left(\
abla_{x}^{0}\phi_{i+\frac{1}{2},j,k}\right) ^{2}+\left(\
abla_{z}^{0}\phi_{i,j,k+\frac{1}{2}}\right)^{2}+\varepsilon^{2} }}-\frac{\
abla_{y}^{-}\phi_{i,j,k}}{\sqrt{\left(\
abla_{y}^{-}\phi_{i,j,k} \right)^{2}+\left(\
abla_{x}^{0}\phi_{i-\frac{1}{2},j,k}\right)^{2}+\left( \
abla_{z}^{0}\phi_{i,j,k-\frac{1}{2}}\right)^{2}+\varepsilon^{2}}}\\ +&\frac{\
abla_{z}^{+}\phi_{i,j,k}}{\sqrt{\left(\
abla_{z}^{ +}\phi_{i,j,k}\right)^{2}+\left(\
abla_{x}^{0}\phi_{i+\frac{1}{2},j,k}\right) ^{2}+\left(\
abla_{y}^{0}\phi_{i,j+\frac{1}{2},k}\right)^{2}+\varepsilon^{2} }}-\frac{\
abla_{z}^{-}\phi_{i,j,k}}{\sqrt{\left(\
abla_{z}^{-}\phi_{i,j,k} \right)^{2}+\left(\
abla_{x}^{0}\phi_{i-\frac{1}{2},j,k}\right)^{2}+\left( \
abla_{y}^{0}\phi_{i,j-\frac{1}{2},k}\right)^{2}+\varepsilon^{2}}}\end{split}  \tag{16}\]  
with the small positive value \(\varepsilon\) preventing division by zero. To maintain rotation-invariant properties, the half-point difference technique is used in (16). At the voxel \((i,j,k)\), the first-order forward \(\partial_{x}^{+}\) and backward \(\partial_{x}^{-}\) discrete derivatives can be defined as follows:  
\[\
abla_{x}^{+}\phi_{i,j,k}=\phi_{i+1,j,k}-\phi_{i,j,k},\ \ \
abla_{x}^{-}\phi_{i,j,k}=\phi_{i,j,k}-\phi_{i-1,j,k}\]  
\[\
abla_{y}^{+}\phi_{i,j,k}=\phi_{i+1,k}-\phi_{i,j,k},\ \ \
abla_{y}^{-}\phi_{i,j,k}=\phi_{i,j,k}-\phi_{i,j-1,k}\]  
\[\
abla_{z}^{+}\phi_{i,j,k}=\phi_{i,j,k+1}-\phi_{i,j,k},\ \ \
abla_{z}^{-}
## 7 Conclusions and Future Work  
This study proposed and tested gaze-head input (G-HI) in an autonomous vehicle (AV) compared to the most common input interactions (touch and voice inputs). Reaction time, user's perceived workload, system usability, and subjective preferences were the evaluation criteria. G-HI showed faster reaction time and lower physical load index and appeared as a promising candidate when a driver is engaged in an NDRT to assist interaction. G-HI created higher workload in general and scored lower in system usability, however as drivers become more accustomed to it, we expect G-HI workload to decrease and usability ratings to improve. To assess the performance of the three input interactions, the immediate experience sampling method (IESM) was proposed where drivers could accurately report their satisfaction and expectations about vehicle decision making in real-time. IESM was found to be acceptable by participants, as they believed the system could enhance vehicle performance over time to match their preferences and increase feelings of control over AV actions. Generally, we believe G-HI can be a promising candidate to be used in AVs, and IESM is essential to enhance drivers' experience for the same vehicle category.  
We examined drivers' UX on AV actions through two simulated scenarios. Real-world driving scenarios are more varied and more complicated, however, and many are unknown. Therefore, future studies should include more scenarios, focusing on various NDRTs as well as automated driving scenarios. Similarly, we could seek to understand the most appropriate input interactions according NDRTs. Finally, conducting multiple tests with the same participants across various NDRTs would increase their experience and familiarity with G-HI and thus affect positively system usability results. These approaches would help establish human-centered services in future AVs.  
Conceptualization, A.A.; writing--original draft preparation A.A., W.K., A.E.; writing--review and editing, A.A., W.K., A.E., S.K.; Supervision, S.K. All authors have read and agreed to the published version of the manuscript. This research was supported in part by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) (No. 20204010600340) in 2020, and in part by the GIST Research Institute (GRI) granted funded by the GIST in 2020. The authors declare no conflict of interest. The authors declare no conflict of interest.
## 2 Principle
### Principle of Transformer  
The Transformer model is a sequence-to-sequence model based on the attention mechanism, designed to address the inefficiencies of RNNs in handling long-distance dependencies. The Transformer achieves the mapping between input and output by stacking multiple layers of encoders and decoders. Input embedding converts the input sequence into fixed-dimension vectors. Positional encoding adds positional information to the input vectors to retain the order of the sequence since the Transformer model does not inherently capture positional relationships. The multi-head attention mechanism computes the similarity between different positions in the input sequence to capture global dependencies. Each attention head independently performs the attention operation, and their results are concatenated and linearly transformed. Feed-forward network applies an independent nonlinear transformation to each position's vector. Residual connections and layer normalization mitigate the vanishing gradient problem and accelerate convergence. Each sub-layer is followed by a residual connection and layer normalization.  
Each layer of the encoder and decoder consists of a multi-head attention mechanism and a feed-forward network. The decoder also includes a masked multi-head attention mechanism to ensure that future information is not visible when predicting the next word. By leveraging the attention mechanism, the Transformer model can effectively capture long-range dependencies and global context, making it highly suitable for sequential data tasks such as VLC channel modeling for DCO-OFDM systems.  
The number of hidden layers for both BiGRU and BiLSTM is set to 2, and the learning rate is set to 0.0001 to fit the channel response more quickly and accurately. We use the Adam optimizer for optimization. We collect 262,144 sample data points and divide the OFDM signal into segments of 512 data points each to speed up the training process. The number of neurons and the number of layers in the neural network model are crucial for the model's expressive power. An overly complex model can result in a slow training process, while too few neurons may lead to insufficient expressive power. The number of layers and neurons in the neural network directly affects the model's expressive capability, learning ability, and training complexity. When designing a neural network, it is essential to select these two parameters wisely based on the specific task, data, and resources to achieve optimal model performance. The number of epochs during training is set to 6000. The neural network structure of the Transformer channel model is even more complex. We use three Transformer hidden layers, each with 4096 neurons. Due to the greater complexity of the OFDM signal, the number of attention heads is set to 64, and the input size for each training iteration is 1024. To prevent overfitting, the dropout rate is set to 0.1. The number of epochs during training is set to 6000.
## 2 Theoretical Description
### Model Derivation
If the distribution of the wavelet coefficients \(w\) follow single Laplacian pdf,  
\[y=w+n, \tag{2}\]2  
Plug the above equation into Equation (9), it can be found that  
\[y=w+n, \tag{2}\]3  
The relationship between \(\hat{w}\) and \(y\) can be represented as  
\[\hat{w}= \begin{cases}y+T,&y\leq-T\\ 0,&|y|\leq T\\ y-T,&y\geq T\end{cases} , \tag{15}\]  
where \(T=\sqrt{2}\sigma_{n}^{2}/\sigma\). The above function is the frequently used soft thresholding and the notation is often written as  
\[\hat{w}(y)=\text{soft}\Bigg{(}y,\frac{\sqrt{2}\sigma_{n}^{2}}{\sigma}\Bigg{)}. \tag{16}\]Then, if the pdf of \(w\) is a mixture of two Laplace pdfs as in Equation (3), the result is shown as  
\[\hat{w}(y)=p_{k_{1}}(y)\text{soft}\bigg{(}y,\,\frac{\sqrt{2}\sigma_{n}^{2}}{\sigma _{1}}\bigg{)}+p_{k_{2}}(y)\text{soft}\bigg{(}y,\,\frac{\sqrt{2}\sigma_{n}^{2}}{ \sigma_{2}}\bigg{)}, \tag{17}\]  
where  
\[p_{k_{i}}(y)=\frac{k_{i}g_{i}(y)}{k_{1}g_{1}(y)+k_{2}g_{2}(y)},\,\,i=1,\,2. \tag{18}\]  
As to the model \(y=w+n\), since \(w\) and \(n\) are independent of each other, the pdf of \(y\) can be calculated as the convolution of \(w\) and \(n\) according to relevant knowledge of probability theory  
\[g_{i}(y)=\frac{1}{\sqrt{2}\sigma_{i}}e^{-\frac{\sqrt{2}}{\sigma_{i}}|y|}\star \frac{1}{\sqrt{2}\pi\sigma_{n}}e^{-\frac{y^{2}}{2\sigma_{n}^{2}}},\,\,i=1,\,2. \tag{19}\]  
Through the aforementioned deductions, the pdf of \(y\) is needed to improve the efficiency of calculations. That is, it is necessary to find the pdf of \(y=w+n\), where \(w\) is the noise-free value with a Laplace distribution and \(n\) is the noise with a Gaussian distribution. As \(w\) is a Laplace random variable and assume that the standard deviation of \(w\) is \(\sigma_{w}\). The pdf of \(w\) is:  
\[p(w)=\frac{1}{\sqrt{2}\sigma_{w}}e^{-\frac{\sqrt{2}}{\sigma_{w}}|w|}, \tag{20}\]  
and \(n\) denotes zero-mean Gaussian noise, assume the standard deviation of \(n\) is \(\sigma_{n}\). The pdf of \(n\) is:  
\[p(n)=\frac{1}{\sqrt{2}\pi\sigma_{n}}e^{-\frac{y^{2}}{2\sigma_{n}^{2}}}. \tag{21}\]  
According to [PERSON] [30] and after verification, the pdf of \(y\) is:  
\[p(y)=\frac{1}{2\sqrt{2}\sigma_{w}}e^{-\frac{y^{2}}{2\sigma_{n}^{2}}}\cdot[ \text{erfc}\bigg{(}\frac{\sigma_{n}}{\sigma_{w}}-\frac{|y|}{\sqrt{2}\sigma_{n }}\bigg{)}+\text{erfc}\bigg{(}\frac{\sigma_{n}}{\sigma_{w}}+\frac{|y|}{\sqrt{2 }\sigma_{n}}\bigg{)}], \tag{22}\]  
where  
\[\text{erfc}(x)=e^{x^{2}}\cdot\text{erfc}(x), \tag{23}\]  
\[\text{erfc}(x)=1-\text{erf}(x), \tag{24}\]  
\[\text{erf}(x)=\frac{2}{\sqrt{\pi}}\int_{0}^{x}e^{-t^{2}}dt. \tag{25}\]  
The notation \(\text{LapGauss}(y,\sigma_{w},\sigma_{n})\) is used to represent the pdf in Equation (22).
## 5 Results and Discussion
### Network Training  
Figure 12 shows the changes in validation accuracy, batch accuracy, loss, and batch loss with the number of iterations during the training of the residual network. The figure clearly shows that the training process of the residual network is relatively smooth, with all metrics stabilizing between 400 and 500 iterations, indicating that the network parameters gradually converge.  
As shown in Figure 12a, both the validation accuracy and batch accuracy gradually increased and leveled off, indicating that the model's generalization ability improved. The loss and batch loss gradually decreased and stabilized, indicating that the model underwent optimization and reached its optimal state during training. These curves reflect the effectiveness and stability of the residual network in handling complex acoustic signal classification tasks.  
The training process of the model showed notable variability in the early stages, reflecting the initial adjustments of the model parameters. In the first 20 iterations, batch accuracy increased from 21.88% to 24.88%, and the batch loss decreased from 1.99 to 1.97. This phase demonstrated the model's initial learning and adaptation. As training progressed, a more consistent improvement in performance was observed. By the 100 th iteration, accuracy had increased to 51.23%, and the loss had decreased significantly to 1.21, indicating the model's enhanced ability to generalize from the training data.  
Continuing through the mid-phase of training, from the 100 th to the 200 th iteration, the accuracy further increased to 71.23%, and the loss value dropped to 0.8139. This period marked substantial refinement in the model's learning, as it effectively captured more complex patterns within the data. The latter stages of training, particularly between the 300 th and 500 th iterations, highlighted the model's approach toward convergence. By the 300 th iteration, the accuracy had reached 88.50%, with a loss value of 0.14.  
In the final phase, from the 400 th to the 500 th iteration, the model's performance approached near-perfect levels. Accuracy increased to 99.50%, with the loss stabilizing at around 0.01. This indicates successful convergence and a high degree of model performance. However, the near-zero loss value also suggests a need for caution to ensure that overfitting is avoided. The overall trend underscores the model's robust learning capabilities and its potential for high accuracy in predictive tasks.  
Figure 12: (**a**) Accuracy and (**b**) loss curves during the training process of the residual network model.
## 6 Conclusions  
In this paper, RSEI was used as an effective indicator for EE evaluation, and RSEI was calculated on the GEE. Using different methods, the variation of the EE since 2002 was analyzed comprehensively for the first time on temporal-spatial scales, and the EE impacts caused by three major human projects, GGP, coal mining activity, and urbanization, wereevaluated quantitatively. The effectiveness of the GGP policy was verified, and on this basis, the future trend of the CLP typical region was also predicted. The following is a summary of the key findings of this study:  
1. Between 2002-2022, the EE of the study area has been improving under the policy of GGP, and the RSEI value has changed from 0.50 in 2002 to 0.577 in 2022, with a PC1 contribution rate of more than 83%. The results of this study agree with the data published by the Chinese government, and the applicability of the RSEI model to the study of the EE of CLP is validated.
2. The GGP implemented since 2000 is the key factor of the EE improvement in the study area. The GGP has achieved significant results, and there is no need to significantly increase the artificial vegetation. In this study, we validated that the GGP had the biggest influence on the EE of the study area using Geodetector [58; 59].
3. The EE of the coal mining subsidence region in the Huanglong coal base deteriorated, but the negative impact of coal mining activities on the EE was small in scope and discrete in distribution; the China Western Development implemented by the Chinese government since 2000 has led to the rapid development of Guanzhong plain urban agglomeration, which significantly deteriorated the EE in and around the urban region, but the impact is also small in scope. Based on a series of environmental protection policies in recent years, the EE of the urban region is gradually improving.
4. Obvious temporal-spatial EE variation could be seen in CLP typical regions. The EE in the loess hilly-guilly region changed from rapid improvement in the early phases to slow improvement in the later phases, and was predicted to enter the ecological succession stage in the future; the EE in the river valley plain regions changed from rapid deterioration in the early phases to slow improvement in the later phases, and was predicted to tend to improve gradually in the future.  
This study provides a strong scientific basis for evaluating EE protection and the sustainable development of CLP.  
Conceptualization, J.T.; methodology, J.T., L.S., Y.D., Q.Y., R.Z. and X.Q.; validation, J.T.; data curation, J.T.; writing--original draft preparation, J.T.; writing--review and editing, J.T. and L.S.; supervision, L.S.; funding acquisition, T.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number: 52274168.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Thermal Stability Test of Tackifier DT  
A 400 mL sample of the saline-based slurry was taken, and 3% synthetic tackifier was added while stirring at 3000-6000 r/min. Note that 2%, 3%, and 4% tackifier were added to the drilling fluid, resulting in apparent viscosities of 12.5 mPa\(\cdot\)S, 28 mPa\(\cdot\)S, and 36 mPa\(\cdot\)S after 16 h of high-temperature aging. The dosage of the tackifier should be determined based on oilfield operational regulations and actual conditions. After aging at 200-240 \({}^{\circ}\)C for 16 h, the viscosity was retested using a six-speed rotary viscometer, with the results presented in Table 1.  
A comparison of the data in Table 1 reveals a significant increase in viscosity for both the brine-based slurry and the brine-based slurry after the addition of tackifier DT.  
This indicates the excellent performance of the tackifier in enhancing viscosity. Experimental observations at high temperatures (200 \({}^{\circ}\)C to 240 \({}^{\circ}\)C) for 16 h showed that the viscosity retention rate of the drilling fluid ranged from 87.1% to 187.1%, demonstrating remarkable stability. These data clearly demonstrate the tackifier's excellent characteristics regarding salt and high-temperature resistance.
## 3 Results and Discussion
### Fingerprinting for Saffron Authenticity
#### 3.1.3 Statistical Analysis to Reveal Adulterated Saffron Organic Extracts
In this case, the spectral regions containing the signals of linoleic acid (at 0.85 ppm) and crocin (at 7.33 ppm which overlaps the curcuminoid signals) could be adopted as diagnostic metabolites to detect the adulteration with safflower.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 5 Experimental Results and Discussion
### Efficiency Analysis
Figure 9: Time needed for image mosaicking in different situations: (**a**) overlapping area extraction or not (no scaling compensation or parallel operation); (**b**) different downsampling degrees of overlapping areas (no parallel operation); (**c**) different parts in parallel (no downsampling); (**d**) mosaic times of the contrast experiments in all situations. The wide image was 70,088 \(\times\) 69,500 pixels and 9.07 GB. The range of the geolocation results was from 40.083754\({}^{\circ}\) to 43.656159\({}^{\circ}\) north latitude and from 116.192071\({}^{\circ}\) to 121.062448\({}^{\circ}\) east longitude. The real width of the range swath was more than 400 km. From Figure 11g, there is no obvious intensity inconsistency among adjacent subswatths and no stitching seams in the wide image. The means and standard deviations of the pixel values among adjacent images were successfully balanced by an improved Wallis filter to make the transitions smoother. There were no apparent position offsets or image pixel coverage issues, which demonstrates that the mosaic accuracy of multiple large-area images can be ensured by the proposed method. The details of the objects in the original subimages can be found in the wide image, which means that the information and resolution of the original ScanSAR images can be successfully preserved. Thus, the proposed method can provide support for some subsequent applications of wide ScanSAR images, such as information extraction, object detection and target recognition. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Direction** & **Image \(I_{\bullet}\)** & **Image \(I_{\beta}\)** & \(r_{\bullet}\)/\% & \(r_{\beta}\)/\% \\ \hline \multirow{4}{*}{Range} & Azimuth1\_subswath1 & Azimuth1\_subswath2 & 42.16 & 45.06 \\  & Azimuth1\_subswath2 & Azimuth1\_subswath3 & 9.51 & 11.22 \\  & Azimuth2\_subswath1 & Azimuth2\_subswath2 & 42.27 & 45.11 \\  & Azimuth2\_subswath3 & 9.41 & 11.11 \\ \hline \multirow{4}{*}{Azimuth} & Azimuth1\_subswath1 & Azimuth2\_subswath1 & 7.43 & 7.48 \\  & Azimuth1\_subswath2 & Azimuth2\_subswath2 & 7.46 & 7.50 \\  & Azimuth1\_subswath3 & Azimuth2\_subswath3 & 7.21 & 7.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overlapping rates between adjacent images. Figure 10: Overlapping areas of the six images.
# Security Establishment in ADS-B by Format-Preserving Encryption and Blockchain Schemes
###### Abstract  
In the next generation modernization plan, the automatic dependent surveillance-broadcast (ADS-B) system plays a pivotal role. However, the ADS-B's low level of security and its vulnerabilities have raised valid concerns. The main objectives of this paper are to highlight the limitations of legacy ADS-B systems and to assess the feasibility of using Format-preserving (F), Feistel-based encryption (F), with multiple implementation variances (X) (FFX) algorithms, for enhancing ADS-B's security. The offered solution is implemented in a standard software-defined radio (SDR) ADS-B to be utilized in real-time applications. Furthermore, a new proposed blockchain scheme is used as a secured database to manage the cipher key. The metric of message entropy is used to assess an algorithm's ability to confuse and diffuse predictable ADS-B messages; correlation and serial correlation of plain data and cipher data are deployed to evaluate the proposed method's security level. The authors provide both MATLAB simulations and flight test outcomes to demonstrate the feasibility of this approach. Based on our security analysis, ADS-B information can be kept confidential through our scheme. The performance evaluation results reveal that the proposed scheme is achievable, compatible, and efficient for the avionics industry.  
ADS-B; NextGen; SDR; format-preserving encryption; FFX; blockchain; BladeRF Article  
Security Establishment in ADS-B by Format-Preserving Encryption and Blockchain Schemes
## 4 Experimental Results and Analysis
### The Comparison of SOH Prediction Results Using Different Prediction Models
The proposed PSO-GWO-LSSVM model combines the advantages of both global and local search strategies, effectively addressing issues such as overfitting, underfitting, and local optima. The mean errors for this model are 0.3939% (MAE), 0.5148% (MAPE), and 0.5057% (RMSE), demonstrating superior prediction accuracy and enhanced generalization capabilities.