esa-sceva/satcom-chunk-collection huggingface dataset:
# SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
## 6 Experimental Evaluation
These parameters are selected to assess the system's performance in realistic satellite-based agricultural applications. We compared this setup to the work of [PERSON] et al. [19], who transmitted images over ground networks. From Figure 3, we can see that under normal conditions, satellite internet image transmission is slower than the scheme proposed by [PERSON] et al. This is true regardless of the image size or quantity. However, after simulating extreme environments, SEAIS using satellite internet proves more stable. For \((1024\times 1024)\) image transmission, it takes only about 21.3% of the time compared to the traditional method. Additionally, for transmitting 20 images of \((256\times 256)\), SEAIS takes only about 59.2% of the time of the traditional transmission scheme. At the same time, transmission time increases with larger image sizes or higher image quantities. Finally, we experiment with agricultural image storage overheads, as shown in Figure 4. In the experiment, we utilize IPFS for distributed storage combined with blockchain to ensure  
Figure 3: Transmission time comparison. (**a**) Impact of variations in the image size on transmission time; (**b**) impact of variations in the number of images on transmission time. Figure 2: Time overhead of image encryption and decryption. (**a**) Impact of variations in the image size on time overhead; (**b**) impact of variations in the number of images on time overhead. data integrity. Each image is divided into blocks of 256 KB. Also, we use the SHA-256 hashing algorithm to ensure the integrity of data during transmission and storage. To test the storage overhead, the total image data size ranges from 10 MB to 100 MB, and we compare scenarios involving 10 and 20 images. The results indicate that blockchain stores the hash values of denoised and enhanced images using SHA-256, which reduces storage overhead and improves the scalability of blockchain. Additionally, when generating numerous images, the storage growth rate decreases over time due to the performance limitations of the simulation device.
# An Experimental Survey of Extended Resolution Effects for SAT Solvers on the Pigeonhole Principle
## 5 Experimental Results
### DRUP Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Instance** & **n** & **\#V** & **\#C** & **\#EV** & **\#EC** & **M** & **M+m1** & **M+m2** & **G** & **G+m1** & **G+m2** \\ \hline no shuffle & 11 & 119 & 597 & 9 & 36 & 101.48 & 51.14 & **50.4** & 105.33 & 65.76 & 78.55 \\ shuffle1 & 11 & 119 & 597 & 9 & 36 & 45.8 & 101.2 & **40.33** & 79.43 & 122.02 & 44.11 \\ shuffle2 & 11 & 119 & 597 & 9 & 36 & 98.69 & 97.87 & **40.6** & 170.46 & 105.53 & 91.8 \\ shuffle3 & 11 & 119 & 597 & 9 & 36 & 44.82 & 100.02 & **41.85** & 56.57 & 47.55 & 108.75 \\ \hline no shuffle & 12 & 142 & 778 & 10 & 40 & 2789.98 & 1774.52 & 693.75 & 1014.79 & 762.31 & **565.39** \\ shuffle1 & 12 & 142 & 778 & 10 & 40 & 2393.2 & 1765.4 & 702.2 & 615.44 & **592.57** & 1253.2 \\ shuffle2 & 12 & 142 & 778 & 10 & 40 & 2805.84 & 1815.72 & 703.13 & 622.15 & 922.09 & **611.21** \\ shuffle3 & 12 & 142 & 778 & 10 & 40 & 1728.68 & 1837.56 & 705.31 & 674.8 & **592.45** & 759.31 \\ \hline no shuffle & 13 & 167 & 993 & 11 & 44 & 10,000 & 10,000 & 10,000 & 10,000 & **742.11** & 10,000 \\ shuffle1 & 13 & 167 & 993 & 11 & 44 & 10,000 & 10,000 & 10,000 & 10,000 & **6585.62** & 10,000 \\ shuffle2 & 13 & 167 & 993 & 11 & 44 & 10,000 & 10,000 & 10,000 & 7435.61 & **5155.5** & 10,000 \\ shuffle3 & 13 & 167 & 993 & 11 & 44 & 10,000 & 10,000 & 10,000 & **6573.76** & 10,000 & 10,000 \\ \hline no shuffle & 14 & 194 & 1245 & 12 & 48 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 \\ shuffle1 & 14 & 194 & 1245 & 12 & 48 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 \\ shuffle2 & 14 & 194 & 1245 & 12 & 48 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 \\ shuffle3 & 14 & 194 & 1245 & 12 & 48 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 \\ \hline no shuffle & 15 & 223 & 1537 & 13 & 52 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 \\ shuffle1 & 15 & 223 & 1537 & 13 & 52 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 \\ shuffle2 & 15 & 223 & 1537 & 13 & 52 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 \\ shuffle3 & 15 & 223 & 1537 & 13 & 52 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 & 10,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of PHP instances with partial extension. which does not indicate that clauses including extended variables contribute to the proof. While MiniSAT with the \"m2\" method utilized them for instances with 11 pigeons, the other solvers could hardly exploit the extended variables. Even always prioritizing the extended variables (by using the \"m1\" method), partial information of the ideal extension has little impact on the overall performance, at least by our proposed methods. The complete acquisition of extension can be necessary to gain practical speedup of the solvers.
## 4 Application and Discussion
### Probability Assessment of the Coupled Risk Factors Based on the BN  
1 BN Modeling Based on the Direct-Relation Matrix in the DEMATEL Model  
The direct-relation matrix in the DEMATEL model can be utilized to construct the Bayesian network, in which the numerical relationship in the matrix can be transformed into the structural relationship of the network because of the arcs in the BN representing the direct causal relationship. The possible circulations can be eliminated to obtain a modified BN diagram. In order to reduce the analysis work, a threshold of 2.5 is determined based on expert experience. The relationship of matrix elements above the threshold is regarded as a valid arc in the Bayesian network, so as to obtain the initial causal diagram. Based on the three principles described in Section 3.4, the initial casual diagram is checked and revised to eliminate the possible circulations to obtain the modified BN diagram, as shown in Figure 6.  
Figure 5: The importance–causality diagram. The Parameterization of BN  
The prior probability of each node is calculated according to Equation (4) based on the classification diagram of the risk factors obtained in Section 4.1. The results are shown in Table 6.  
The conditional probability of each node with its parent node is calculated according to Equation (5) based on the direct-indirect matrix obtained by the DEMATEL model, and the results are shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Node (\(F_{i}\))**} & **Included** & \multirow{2}{*}{\(\sum_{i=\,1}^{t}\)\(P_{i}\)} & \multirow{2}{*}{**P (\(F_{i}\))**} & **Node (\(F_{i}\))** & **Included** & \multirow{2}{*}{\(\sum_{i=\,1}^{t}\)\(P_{i}\)} & \multirow{2}{*}{**P (\(F_{i}\))**} \\  & **Factors** & & & & & & \\ \hline \(F_{11}\) & \(Q_{3}\)\(Q_{13}\)\(Q_{17}\) & 0.64 & 0.5055 & \(F_{31}\) & \(Q_{9}\) & 0.35 & 0.35 \\ \(F_{12}\) & \(Q_{19}\) & 0.23 & 0.23 & \(F_{32}\) & \(Q_{11}\) & 0.70 & 0.5775 \\ \(F_{13}\) & \(Q_{4}\) & 0.19 & 0.19 & \(F_{33}\) & \(Q_{20}\) & 0.15 & 0.15 \\ \(F_{21}\) & \(Q_{2}\)\(Q_{8}\) & 0.33 & 0.31 & \(F_{34}\) & \(Q_{18}\) & 0.22 & 0.22 \\ \(F_{22}\) & \(Q_{7}\)\(Q_{10}\) & 0.48 & 0.426 & \(F_{41}\) & \(Q_{1}\) & 0.31 & 0.31 \\ \(F_{23}\) & \(Q_{14}\) & 0.23 & 0.23 & \(F_{42}\) & \(Q_{15}\) & \(Q_{21}\) & 0.37 & 0.3364 \\ \(F_{24}\) & \(Q_{5}\)\(Q_{12}\) & 0.29 & 0.278 & \(F_{43}\) & \(Q_{6}\) & 0.05 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of the prior probability. Figure 6: The structure of the Bayesian network (BN). 3 The Probability Assessment Based on the Inverse Reasoning Theory  
Based on the results of Tables 6 and 7, the parameterization of the Bayesian network is completed. Moreover, virtual evidence is set for the node \"Risk\" to define the probability distribution of \"occurrence\" and \"non-occurrence.\" As a result, the probability of occurrence of other risk factors considering the coupling relationship is obtained, as shown in Figure 7.
## 2 Fusion Evaluation of Semi-Physical Simulation and Field Test Data  
For the assessment of aircraft landing point accuracy, due to the multi-sourced landing test types, the coverage is very extensive. It includes not only system-level field prototype tests, but also alternative equivalent tests, such as numerical simulation tests, semi-physical simulation tests, and ground static simulation tests. Restricted by factors such as region, cost, and resources, there are few test data in the system-level field prototype test state. It is difficult to effectively evaluate the placement accuracy with only a small amount of prototype test data. At this time, the information source should be further expanded, and other alternative equivalent test data types should be fully utilized as auxiliary information. It is a common solution to carry out fusion evaluation on this basis, which is a multisource data fusion statistical method. In the evaluation process, it should be noted that the landing point data obtained from the substitute equivalent test are incomplete. Fusing directly with the system-level field prototype test data will lead to deviations in the accuracy of the established landing point fusion model. Errors are generated in the prediction of the coordinates of the landing point of the aircraft. Incompleteness of semi-physical simulation system data [37] refers to the situation of incomplete and missing information in the system information. Incomplete data information may appear in the process of information acquisition. Information loss caused by external uncertainties (such as failure of measuring equipment, failure of information recording) or information loss during information transmission reduces the reliability of the system analysis results.  
Therefore, this paper proposes a method for integrity detection and supplementary test design of semi-physical simulation data of aircraft landing point coordinates. By establishing completeness inspection criteria, it is determined whether the semi-physical simulation data satisfy the completeness requirements. If not, simulation landing point supplementation is realized by the supplementary test condition design method, improving the accuracy of subsequent multisource landing data fusion. The multisource fusion evaluation process of the semi-physical simulation and field test data is shown in Figure 1.
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 2 GPS and BDS-3 Signal Properties  
### Features of GPS and BDS-3  
The target receiver should receive both the GPS and BDS-3 satellite signals to improve localization accuracy. The signal properties of the two systems must be analyzed to check the feasibility of receiving both signals simultaneously. According to [11; 12], the two systems have the same carrier frequency of 1575.42 MHz and an antenna polarization of right-hand circular polarization (RHCP). Therefore, their RF antenna, local oscillator (LO) frequency, and receiver structure are compatible with each other without additional receiving links. However, they differ in the received power levels and signal bandwidth. In [11; 12], the received signal levels (receiver on the earth ground) of GPS and BDS-3 are \(-127\) dBm and \(-129\) dBm, respectively. In terms of bandwidth, BDS-3 requires a width of 4.092 MHz, which is twice that of the GPS. The difference in bandwidth does not affect the RF front-end circuits, but it is necessary to design an analog filter in the receiver with a tunable bandwidth. The in-band thermal noise power can be calculated by \(N=N_{T0}\times BW\), where \(N_{T0}=\mathrm{k}T_{0}\), k is the Boltzmann constant, \(T_{0}\) is the room temperature in Kelvin, the value of \(N_{T0}\) is usually \(-174\) dBm/Hz [8], and BW is the bandwidth. According to the bandwidths of GPS and BDS-3, the in-band thermal noise power levels are \(-111\) and \(-108\) dBm, respectively. Therefore, the minimum power level arriving at the antenna port for the GPS L1 band is 16 dB lower than the thermal noise. While the BDS-3 signal is about 21 dB lower than the thermal noise. The RF power levels received on the ground are below the thermal noise floor due to the far distance between the satellite and the ground [5]. Therefore, for a receiver to work for both GPS and BDS-3, the compatibility of the bandwidth and cascade gain requirements must be met.  
Figure 1: Simplified receiver system diagram.
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 4 Methods
### Tree Segmentation  
The tree segmentation was performed in three steps. First, the trees of the leaf-off TLS point cloud were automatically segmented with the _SimpleTree_ (4.33.06) software, a plugin of _Computree_ (5.0.054b) ([PERSON], 2017). Second, the automatically extracted trees were visually inspected, and falsely classified tree segments were manually corrected using _RiSCAN PRO_ software. Third, the extracted trees (i.e., leaf-off TLS) served as the basis for a semi-automatic segmentation of trees from the other datasets (leaf-on TLS, leaf-off and leaf-on MLS).  
For the latter we used an approach similar to ([PERSON], 2017). The corresponding trees in the other three datasets were segmented using a voxel-template (voxel size of 0.3 m) of the extracted trees. Since all datasets had a uniform reference system, the voxel space of the segmented trees is projected into the point clouds of the other datasets. The points within a voxel form a new tree segment. To account for inaccuracies in transformation and structural changes (leaves, vegetation growth) the points inside the nearest neighbors (1 st and 2 nd order) of the occupied voxel space are attributed to the segment as a  
Figure 5: Cross section of two MLS records (each record has two clouds captured from Scanner 1 and Scanner 2) recorded twice with 30 min time (t\({}_{1}\), t\({}_{2}\)) delay.  
buffer zone. This requires additional manual quality control within the buffer zone. Figure 6 shows the voxel tree template of two example trees within plot 1.
## 3 Results and Discussion
### Residual Modeling Using Regression Trees
This modeling strategy improves prediction accuracy by decomposing the problem into two levels: the RSM model captures the dominant trends, while the regression tree isolates complex nonlinear residual patterns that RSM cannot explain. By modeling only the residuals, rather than Ra directly, the tree avoids overfitting and focuses on local correction. This layered structure results in a better fit to experimental data, as reflected by the reduction in RMSE and the increase in R\({}^{2}\). One additional advantage of using regression trees is their interpretability. Unlike black-box models, the resulting decision rules can offer insight into specific interactions among input parameters that lead to systematic under- or overestimations in the base model. In summary, the proposed hybrid modeling approach effectively combines the transparency of statistical regression with the adaptive capabilities of machine learning. This  
Figure 8: Root mean square error (RMSE) of the RSM and hybrid models. The hybrid model reduced the RMSE by 20.85% compared to the RSM model. Figure 9: Coefficient of determination (R\({}^{2}\)) for the RSM and hybrid models. The hybrid model shows an 8.04% improvement in R\({}^{2}\) over the RSM model, indicating a better fit to the experimental data. integration results in improved prediction accuracy and enhanced generalization, even when working with a limited experimental dataset.
# Use of Generative Adversarial Networks (GAN) for
## 3 Results
### Phase 2: GAN-Augmented Sampling and Model Testing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Activation Function** & **Optimizer** & **Accuracy** & **Loss** & **F1-Score** \\ \hline VGG16 & relu & SGD & 87.02 & 0.49 & 0.84 \\  & swish & SGD & 90.82 & 0.37 & 0.84 \\  & mish & SGD & 84.6 & 0.56 & 0.84 \\  & relu & [PERSON] & 89.18 & 0.40 & 0.80 \\  & swish & [PERSON] & 83.41 & 0.77 & 0.74 \\  & mish & [PERSON] & 88.2 & 0.64 & 0.81 \\ \hline Densenet201 & relu & SGD & 81.73 & 0.57 & 0.81 \\  & swish & SGD & 83.04 & 0.53 & 0.81 \\  & mish & SGD & 82.37 & 0.47 & 0.80 \\  & relu & [PERSON] & 83.26 & 0.52 & 0.81 \\  & swish & [PERSON] & 81.03 & 0.66 & 0.81 \\  & mish & [PERSON] & 82.37 & 0.48 & 0.81 \\ \hline Jason 2 & relu & SGD & 88.99 & 0.49 & 0.83 \\  & swish & SGD & 80.29 & 1.07 & 0.71 \\  & mish & SGD & 80.5 & 0.54 & 0.72 \\  & relu & [PERSON] & 86.5 & 0.36 & 0.84 \\  & swish & [PERSON] & 69.1 & 1.16 & 0.64 \\  & mish & [PERSON] & 57.1 & 2.26 & 0.55 \\ \hline ResNet50 & relu & SGD & 88.17 & 0.42 & 0.84 \\  & swish & SGD & 89.96 & 0.29 & 0.84 \\  & mish & SGD & 89.06 & 0.32 & 0.84 \\  & relu & [PERSON] & 91.18 & 0.24 & 0.83 \\  & swish & [PERSON] & 91.29 & 0.29 & 0.87 \\  & mish & [PERSON] & 87.28 & 0.50 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy loss and F1-score of the models using the augmented dataset, according to combination of activation function and optimizer.
# Effect of Process Parameters on Distortions Based on the Quantitative Model in the SLM Process
## 3 Results
### Number of Layers and Scanning Speed  
Figure 6a shows the simulated distortions of the six components fabricated with different layers. As the number of layers increases, the distortion decreases. Figure 7 shows the residual stress distribution along the \(z\)-direction. The residual stress in a specific layer decreases as more layers are deposited. This is caused by the tightening effect of added layers on the deposited layers. In addition, the residual stresses in the top three layers are higher than that of the other layers, which plays a crucial role in component distortion. Since the layers adjacent to the newly added layer are affected by the laser heat, the constraining effect on the newly added layer is not strong. Therefore, a low plastic strain is formed in the newly added layer during the cooling stage, and the residual stress is relatively high after cooling down. Meanwhile, the plastic strain in the adjacent deposited layers increases, and the residual stress decreases due to the reheating effect. According to Equation (18), the distortion degree with different layers is obtained, as shown in Figure 8a. As the number of layers increases from four to twenty, the distortion degree decreases from 15.8 m\({}^{-1}\) to 1.4 m\({}^{-1}\). This is in good agreement with the simulation results (as shown in Figure 6a). Although the moment induced by axial stress increases with the number  
Figure 5: The residual stress comparisons between the analytical model and the simulation: (**a**) before removing the substrate; (**b**) after removing the substrate.  
of layers, the moment of inertia has a greater increasing rate. It can be concluded that increasing the number of layers is beneficial to decrease the distortion.  
Figure 6b shows distortions on the top layer of components fabricated with different scanning speeds. Since the scanning speed increases from 500 mm/s to 1000 mm/s, the distortion increases first and decreases after 700 mm/s. Figure 8b shows the distortion degree calculated according to Equation (18), with the same trend as the simulation result. The component fabricated with the scanning speed of 1000 mm/s has the minimum distortion degree.  
Figure 8: The distortion degree of the component with different (**a**) number of layers and (**b**) scanning speed.  
Figure 6: Distortions along the x-direction: (**a**) components fabricated with different layers; (**b**) components fabricated with different scanning speeds.  
Figure 7: Residual stress distributions along the vertical direction of components fabricated with different layers.
## 2 Position and Heading Estimation Approach Based on Cross-Modal Image Matching
### Fast Coarse Image Matching Based on PCA-HOG
#### 2.1.2 Searching Process Accelerating  
Coarse matching is a full-searching template matching as shown in Figure 4. The template, namely, the aforementioned corrected real-time image, needs to be compared with each candidate window in the referenced image and matched to the most similar window. The similarity of PCA-HOG can be calculated with the sum of squared differences (SSD) as follows:  
\[similarity_{(u,v)}=\sum_{x,y}SSD(H(x,y),H(x-u,y-v)) \tag{6}\]  
where \(H(x,y)\) represents the PCA-HOG feature vector of the pixel in \((x,y)\), and \((u,v)\) is the transition between the template and the corresponding candidate window in the referenced image.  
However, the searching process with per-pixel SSD calculation is very computationally expensive. To reduce the computational burden, we developed a fast convolution-based method for the template searching. First, we define the similarity of PCA-HOG feature map as the following equation:  
\[similarity_{(u,v)}=\sum_{x,y}(H^{\prime}(x,y)\cdot H^{\prime}(x-u,y-v)) \tag{7}\]  
where the operator \(\cdot\) represents the dot product, and \(H^{\prime}(x,y)\) is the normalized (by L2 norm) PCA-HOG feature vector of the pixel in \((x,y)\). Then, the full-searching process can be defined as the following convolution:  
\[(\hat{u},\hat{v})=\operatorname*{arg\,max}_{(u,v)}\sum_{x,y}(H(x,y)\cdot H(x- u,y-v)) \tag{8}\]  
Figure 4: Example of full-searching template matching with cross-modal images, the template image is a real-time infrared image and the referenced image is a satellite visible light image.  
where \((\hat{u},\vartheta)\) is the transition between the template and the referenced image estimate by the searching process. The convolution can be accelerated in frequency domain by using fast Fourier transform (FFT):  
\[(\hat{u},\hat{v})=\underset{(u,v)}{\arg max}\sum_{x,y}F^{-1}[F(\mathbf{H}(x,y))\cdot F ^{*}(\mathbf{H}(x-u,y-v))] \tag{9}\]  
where \(F\) is fast Fourier transform, \(F^{*}\) is the complex conjugate of \(F\), and \(F^{-1}\) is inverse fast Fourier transform (IFFT). IFFT and FFT are the main parts of the computation of the accelerated convolution process. Suppose the size of the template is \(n\times n\) and the size of the referenced image is \(m\times m\). The computation complexity of the searching process with SSD is \(O\left(n^{2}(m-n+1)^{2}\right)\), while the computation complexity of the searching process accelerated with FFT is \(O(2m^{2}logm)\). The accelerating ratio is varied with the size of image, which is given in Table 1 by examples. We can see that a significant computation decrease can be achieved by the accelerating method.