esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Symbol and Note Component Detection  
The symbol detection algorithm acts on the image of a single staff (see Figure 3). To extract these images, we determine the bounding boxes of each known staff which are extended to the top and bottom by adding \(d_{\text{SL}}\), which is computed using the given staff lines. This line image is finally scaled to a fixed height \(h_{\text{staff}}\), which we choose as 80 px (see Section 5.3.2), so that the resolution of the input data is normalised. An example line is shown in the first row of Figure 7.  
Similar to the staff line detection, the symbol detector employs the proposed FCN-architecture to classify the input image pixel-wise into seven different classes (see Figure 8): clef-c, clef-f, accidental-flat, initial NC (start of a neume), logical connected NC, graphical connected NC and background. The GT mask for each line is generated by drawing a small box of the respective label at the centre of each symbol (compare Figure 5 and the second image in Figure 7). When training the FCN it aims to predict the correct labels pixel wise, which is why actual symbols and their position and types must be extracted in a proceeding step. For this purpose, we first assign the most probable class to each pixel to receive a label map. Afterwards, connected components are detected on a binary image that either denotes background or any symbol. Each component yields a single symbol whereby the centre of the component equates the symbol position and the type defined by majority voting on the label map.  
Figure 7: The first line shows the cut off original input image. The next one is the target mask for the FCN in which notes and clefs are marked. The next three lines show the dewarped, padded and an augmented version of the original image.  
Finally, the relative note position is computed based on the detected staff lines (see left image of Figure 9), whereby the relative distance of the \(y\)-coordinate of each symbol in the staff determines the closest space or staff line. It shows that notes are often \"on the line\" even though it is visually closer to a space which is why spaces and lines are not distributed equidistant, instead the ratio is \(2/3\) (see the left zoomed sub figure and for an example the right image of Figure 9).  
To improve the quality of a line, we implemented and tested several pre-processing steps applied to the line image before being processed by the FCN (compare third to fifth image of Figure 7). First, we dewarp the line by transforming the image so that the staff lines are straight. Furthermore, we pad extra space to the left and right of a staff to ensure that all symbols especially clefs are fully contained. We optionally use data augmentation that varies the contrast and brightness up to a factor of \(2^{[-0.1,0.1]}\) and a value of 4%, respectively. Also, the data is scaled with a factor of up to \(2^{[-0.1,0.1]}\) independently in \(x\)- and \(y\)-direction.
## 1 Introduction
### Contributions  
Motivated by the above, this paper proposes an integrated UAV-ICV networking framework to explore the potential for air-to-ground node collaboration. The contributions of this paper are summarized as follows:  
* First, this paper investigates the application of opportunistic transmission in integrated UAV-ICV networks, where the \"store-carry-forward\" mechanisms are adopted. Then, considering the selfish behavior of communication nodes due to factors such as energy, cache, and other practical constraints, this paper defines the types of node cooperation and analyzes the probability of encounters between nodes.
* Second, an opportunistic transmission scheme for integrated UAV-ICV networks, enabled by node cooperation, is proposed. By evaluating the forwarding capabilities of each relay node, this paper initially floods the information to be transmitted. On this basis, this paper uses UAVs to assist with delivery.
* Finally, to demonstrate the advantages of the designed scheme, this paper compares it with the state-of-the-art schemes [21; 22; 23] via simulations. Real map data are used for the simulations. The simulation results indicate that the designed scheme outperforms the state-of-the-art schemes in terms of the delivery ratio and average delivery delay. Furthermore, the effect of the proportion of selfish nodes and the cooperation threshold on the performance of integrated UAV-ICV networks is fully discussed.
## 7 Conclusions  
The results obtained by different experiments in the \(10^{14}\) to \(10^{18}\) eV region can be summarized as follows:  
* Knee energy region 1. All experiments observe an all-particle knee at \(\approx\)4 \(\times\)\(10^{15}\) eV. 2. The absolute fluxes are in good agreement with each other and with the direct measurements. 3. The elemental composition is conflicting. Experiments located at high and extreme altitude (BASJE-MAS, Tibet AS\({}_{7}\), ARGO-YBJ, EAS-TOP and CASA-MIA) reported evidence that the knee of the (p+He) component is below 1 PeV and that the composition at the all-particle knee energy is dominated by heavier nuclei. Experiments located deeper in atmosphere (KASCADE, KASCADE-Grande, IceTop/IceCube, Tunka-133) reported evidence that the proton knee is at the same energy of the all-particle knee. 4. A \(10^{-3}\)-\(10^{-4}\) Large Scale Anisotropy (LSA) amplitude is found at TeV energies [76]. 5. A \(10^{-4}\) Medium Scale Anisotropy (MSA) amplitude is observed at TeV energies [76].
* Transition region \(10^{16}\)-\(10^{18}\) eV 1. The all-particle energy spectrum measured by different experiments are in good agreement within the systematics and with the measurements of UHE experiments. 2. A concave region is observed above 2 \(\times\)\(10^{16}\) eV with a steepening at \(\sim\)\(10^{17}\) eV. 3. The dipole component of the LSA is smaller than \(10^{-2}\).  
The observed features in the all-particle energy spectrum seem to be consistent with the bending of different components in a rigidity-based scenario. However, rigidity models can be  
* _rigidity-acceleration_ models, that is, the knee can be an acceleration feature, a source property, related to the maximum energy of particle acceleration inside the CR sources;
* _rigidity-confinement_ models, that is, the knee is related to inefficient confinement of particles in the galaxy. In this case, the galaxy could contain _'super-PeVatrons'_, astrophysical objects able to accelerate particles well beyond the PeV.  
The first PeVatrons observed in the northern hemisphere by the LHAASO experiment show that SNRs are probably not the main sources of PeV CRs in our galaxy. The observation of sources emitting photons above the PeV in the North suggests the need of a wide field of view instrument in the Southern Hemisphere to monitor the Inner Galaxy and the Galactic Center looking for super-PeVatrons (SWGO [77], STACEX [78]) to operate with CTA-South [79].  
In the coming years, the LHAASO experiment is expected to be able to measure the energy spectra of different mass groups up to \(10^{17}\) eV and to determine the energy of the proton knee, thus clarifying the origin of the knee in the all-particle spectrum. The suite of independent instruments that will be operated will also allow a deep study of the characteristics of the hadronic interaction models and to investigate the uncertainties related to the main techniques used to recover the elemental composition.  
This research received no external funding.  
Not applicable.
## 2 Theory
### The Proposed Electrical Conductivity Imaging Method  
Inspired by the modified finite difference scheme [41], we can use the divergence properties, which is \(\
abla\cdot(ab)=(\
abla a)\cdot b+a(\
abla\cdot b)\); then, we can transform Equation (1) into:  
\[\
abla\cdot\Big{(}\rho\
abla\varphi^{tr}\Big{)}=2\omega\mu_{0}. \tag{5}\]Equation (5) is free of the calculation of the Laplacian operator of the transceive phase. Given the central difference method, the discretized Equation (5) in an image region is:  
\[\begin{split}\frac{\rho_{i+1,j}\big{(}\frac{2\phi^{pr}}{\phi y} \big{)}_{i+1,j}-\rho_{i-1,j}\big{(}\frac{2\phi^{pr}}{\phi y}\big{)}_{i-1,j} \big{(}\frac{2\phi^{pr}}{\phi y}\big{)}_{i-1,j}+\rho_{i,j+1}\big{(}\frac{2\phi^{ pr}}{\phi y}\big{)}_{i+1,k}-\rho_{i,j-1,k}\big{(}\frac{2\phi^{pr}}{\phi y} \big{)}_{i,j-1,k}}{2\Delta x}\\ +\frac{\rho_{i,j+1}\big{(}\frac{2\phi^{pr}}{\phi y}\big{)}_{i,j+1} -\rho_{i,j+1}\big{(}\frac{2\phi^{pr}}{\phi y}\big{)}_{i,j,k-1}}{2\Delta z}=2 \omega\mu_{0}.\end{split} \tag{6}\]  
Equation (6) can be transformed into the same linear inverse problem as Equation (3) inside the imaging region. The formula \(A\rho=b\) is used to represent the procedure result of the proposed method. Given that the diagonal of Matrix \(A\) is 0, the results cannot be obtained by the inverse matrix. Subsequently, the problem is expressed as an optimization problem, and second-order TGV regularization is adapted to stabilize the result and improve the accuracy of construction. The TGV regularization can preserve edges while suppressing the staircase effect and is proven as a favorable regularization for medical imaging problems [42; 43; 44]. As previously noted [43], the discrete \(TGV_{\alpha}^{2}(\rho)\) can be written as the following equivalent form:  
\[TGV_{\alpha}^{2}(\rho)=\underset{v}{\text{min}}\alpha_{1}\|\
abla\rho-v\|_{1} +\alpha_{2}\|\varepsilon(v)\|_{1}, \tag{7}\]  
where \(\
abla\rho=(D_{1}\rho,D_{2}\rho,D_{3}\rho)\), \(D_{1}D_{2}\),\(D_{3}\) are the finite difference operators along the \(x\)-axis, \(y\)-axis, and \(z\)-axis, respectively; \(v\) represents an approximation of \(\
abla\rho\) and is a matrix field with the same size of \(\
abla\rho\); and \(\varepsilon(v)=(1/2)\big{(}\
abla v+\
abla v^{T}\big{)}\) denotes symmetrized spatial derivatives.  
The use of TGV as a regularization term leads to the following reconstruction model:  
\[\rho=\underset{\rho,v}{\text{argmin}}\frac{1}{2}\|A\rho-b\|_{2}^{2}+\alpha_{1 }\|\
abla\rho-v\|_{1}+\alpha_{2}\|\varepsilon(v)\|_{1}, \tag{8}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are the regularization parameters.  
The reconstruction problem is non-smooth and convex, and thus, it is difficult to solve through common methods such as the conjugate gradient (CG) method. The ADMM method is used to solve the optimization problem [39]. The key idea of ADMM is to derive an equivalent problem with separable sub-problems by introducing auxiliary variables. The ADMM method does not depend on the smoothness of the problem and converges quickly to a reasonable accuracy result.
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 3 Methodology
### End-to-End Feature Pyramid Network  
The optical flow network can forecast the movement trend of the target more precisely. This paper performs end-to-end offline pre-training on the optical flow network. The network structure is displayed in Figure 2. First, based on a coarse-to-fine scheme, this paper uses pyramid features instead of ordinary images as input. Then, FlowNet2 [27] is fine-tuned, using FlowNetC as the first block followed by two FlowNetS blocks to crop the optical flow map in accordance with the target location, and finally, for the purpose of reduce the number of parameters, the image features are distorted to output 4 different scales, 64 \(\times\) 64 \(\times\) 2, 32 \(\times\) 32 \(\times\) 2, 16 \(\times\) 16 \(\times\) 2, 8 \(\times\) 8 \(\times\) 2. Based on our findings, we found that end-to-end trained networks perform outstanding than untrained networks.
## 2 Materials and Methods
### Methods
#### 2.3.2 PNPP and HNPP  
PNPP symbolizes potential vegetation conditions under the hypothesis that NPP is affected by climate change only. The Miami model [38] is empirical, derived through correlated mean annual temperature and precipitation values in conjunction with NPP and widely used for analyzing global productivity patterns. The model also acts as a baseline for comparisons [17]. The employed Miami model was used to simulate PNPP. The basic principles of the Miami Model are described by Formulas (2) and (3).  
\[\text{NPP}=\text{min}(\text{NPP}_{\text{T}},\text{NPP}_{\text{P}}), \tag{2}\]  
With  
\[\text{NPP}_{\text{T}}=3000/\left(1+\text{e}^{1.315-0.119t}\right) \tag{3}\]  
where t is the annual mean temperature (\({}^{\circ}\)C), and p is annual precipitation (mm).  
HNPP, reflecting the impact of human activities on ecosystems, can be quantified as the difference between PNPP and actual NPP (see Formula (4)) [39]. Positive HNPP represents negative impacts of human activities; whereas negative HNPP indicates thathuman activities promote NPP. The relative contributions of climate variation and human activities on NPP can be identified by comparing the slope of NPP, PNPP and HNPP [39; 40].  
\[\text{HNPP}=\text{PNPP}-\text{NPP}, \tag{4}\]
###### Abstract
For classification, machine learning models such as RF and K-NN are used, both of which are widely applied in the literature for failure detection in gearboxes [14; 20; 21; 22]. Although there are studies that use CIs and statistical models for failure diagnosis [10; 16], as well as investigations into optimal sensor placement [23; 24], no methodology has been reported that combines CIs ranking through a wrapper approach with the evaluation of the effect of sensor position and inclination on diagnostic accuracy. In this context, the objectives of this study are as follows: (a) To propose a feature selection methodology that establishes a CIs ranking based on time-domain vibration signals from a spur gearbox. (b) To evaluate the performance of the selected CIs using Random forest and K-nearest neighbours classification models. (c) To determine whether the sensor's position and inclination significantly influence feature extraction and the performance of the classification model. In summary, the main contributions of this work are:  
1. The design of a structured methodology for selecting relevant time-domain condition indicators from vibration signals. 2. The validation of this selection using two widely adopted classifiers in the literature (Random Forest and K-nearest neighbours)
3. The experimental evaluation of the effect of sensor position and inclination on fault classification performance in gear systems. These contributions aim to support condition-based monitoring strategies that are robust, interpretable, and applicable in real industrial environments.
# A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution
###### Abstract  
Knowledge graph conflict resolution is a method to solve the knowledge conflict problem in constructing knowledge graphs. The existing methods ignore the time attributes of facts and the dynamic changes of the relationships between entities in knowledge graphs, which is liable to cause high error rates in dynamic knowledge graph construction. In this article, we propose a knowledge graph conflict resolution method, knowledge graph evolution algorithm based on deep learning (Kgedl), which can resolve facts confliction with high precision by combing time attributes, semantic embedding representations, and graph structure features. Kgedl first trains the semantic embedding vector through the relationships between entities. Then, the path embedding vector is trained from the graph structures of knowledge graphs, and the time attributes of entities are combined with the semantic and path embedding vectors. Finally, Kgedl uses a recurrent neural network to make the inconsistent facts appear in the dynamic evolution of the knowledge graph consistent. A large number of experiments on real datasets show that Kgedl outperforms the state-of-the-art methods. Especially, Kgedl achieves 23% higher performance than the classical method numerical Probabilistic Soft Logic (nPSL).in the metric HITS@10. Also, extensive experiments verified that our proposal possess better robustness by adding noise data.  
knowledge graph; knowledge conflict resolution; neural recurrent network +
Footnote †: journal: Applied Sciences
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 4 Automated and Transparent Encryption and Decryption  
This section presents schemes for _single-user_ and _multi-user_ spatial files, an _automated_ encryption and decryption model, and a _key storage_ scheme, which are part of our proposed solution. Figure 2 outlines a schematic model of the proposed storage security solution for spatial files built around a DBFS. In a typical enterprise GIS environment, two types of files need protection during storage: single-user and multi-user files. The first set belongs to individual end-users and requires access by an end-user only. The second set requires sharing between more than one end-user at a project level and does not necessarily belong to any particular end-user. Our solution includes encryption schemes for both sets.  
Figure 2: Schematic model of the security solution for spatial files. As illustrated in the top region, GIS users are first authenticated against their DBFS account, and thereafter, upon session-creation, their single- and multi-user files are decrypted. Thereafter, access to files is controlled via the DBFS access control model. The bottom region illustrates the encryption process upon user logoff.
## 3 The Proposed Network Model  
Remote sensing images are quite different from natural images in that they require more sufficient reconstruction of edge details, since they contain extensive ground object information, such as forests, trees, buildings, mountains, and rivers. The current challenge for GAN in the field of remote sensing image super-resolution is that some models based on pixel level loss will cause the reconstructed image to become too smooth and not close to the quality perceived by human eyes; however, with the support of the GAN network,  
Figure 4: Discriminator structure.  
one can properly solve this problem by obtaining more abundant boundary information from its reconstructed image. In particular, our solution to combat this issue is a dual-path adversarial generation network model algorithm, the overall framework of which is shown in Figure 5. In this paper, we redesign the structure of the generator module, the parameter setting and activation function. We also revise and adjust the training parameters to obtain improved results from the loss function and the discriminator. The preliminary results show that the output image and discriminator are relatively optimized and balanced, and the super-resolution reconstruction of the remote sensing image is capable of achieving results as we expect.
## 4 Results and Discussion
### Results of the Laboratory Experiments
#### 4.1.2 Results of the Alkali-Silica Activation Test  
Figure 8 shows the increase in the ASR expansion with curing time. The expansion kept increasing with the rise in the WGP ratio. The 14-day ASR expansion of all mortars containing 300 \(\upmu\)m WGP was larger than the expansion of the control specimens. This demonstrates the negative effect of coarse glass powder, which is consistent with the previous investigations [69; 70]. In contrast, the mortar sample containing 10% 75 \(\upmu\)m WGP showed the lowest expansion value (0.0192% at 14 days), which was much less than the control sample and the ASTM threshold. This resulted from the pozzolanic reaction of WGP, which is prone to generating gels with nonswelling properties [71; 72]. However, the excessive ASR expansion was obvious when the WGP (75 \(\upmu\)m) ratio was increased to 30%. This phenomenon disagreed with the previous finding that fine glass particle was harmless to the ASR expansion. [PERSON] et al. [69] explained that this may be attributed to the microcracking of the glass powder during the grinding process. Although high WGP content can cause detrimental expansion, mechanical grinding's efficacy in mitigating ASR expansion has been verified.  
Figure 9 depicts the impact of chemical activation on 14-day ASR results of samples with different WGP particle sizes and contents. When the WGP ratios were 10% and 20%, the chemical activator showed adverse effects because a larger expansion appeared on chemically excited samples. However, this negative effect turned out to be positive when the WGP ratio was increased to 30%, as shown in Figure 9c. The ASR expansion was 0.0654% for 30% WGP samples activated by 2% Na2 SO4 and 2% alkali, which was 54% smaller than that of specimen 75G30. This might be because the alkaline activator increased the concentration of calcium ions, thus generating fewer swelling gels [66; 73]. Regarding the chemical activator's content, the relationship between it and ASR expansion was not obvious, and therefore, ML models were proposed, as discussed in the next section. Generally, chemical activation was feasible in mitigating the detrimental ASR expansion only for mortars with high WGP content. Specifically, the mitigation effect of chemical activator on ASR expansion mainly depended on the WGP content other than the activator's content. Combined with the FS results, mortars containing 30% 75 \(\upmu\)m WGP activated by 2% Na2 SO4 and 2% alkali can achieve improved strength and satisfactory potential for ASR expansion.  
Figure 8: The ASR expansion of mortar containing WGP (10%, 20%, and 30%) with mechanical activation.  
Figure 9: The 14-day ASR values of bar samples containing (**a**) 10%, (**b**) 20%, and (**c**) 30% chemically activated WGP.
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 5 Conclusions  
In this paper a complete Optimization Environment has been designed to improve the results of the design optimization for a reflectarray antenna capable of beam scanning. This problem is an interesting benchmark for the evolutionary optimization due to the large number of its design variables and because it is highly non-linear.  
The Optimization Environment has been proposed and all its components have been analyzed both theoretically and experimentally. Then, such designed environment has been applied with seven different Evolutionary Algorithms.  
The results show that the best performance is achieved by Differential Evolution and Social Network Optimization: the first algorithm has better optimal results, but the dispersion of the independent trials is much higher than for SNO. This second algorithm has been considered the most relevant in this kind of application. In fact, these problems have a high computational cost, thus the reliability is a fundamental aspect because it allows a reduction of the number of independent trials required. Additionally, the reliability is very important for the scalability of the problem, i.e., the design of larger reflectarray and other engineering problems with increased complexity.  
Figure 10: Comparison between radiation patterns of the aperture field method and of the full-wave. Each subfigure represents a different scan angle; the right diagram is the radiation pattern on the E-plane and the left one in the H-planes. (**a**) \(\theta_{scan}=10^{\circ}\); (**b**) \(\theta_{scan}=20^{\circ}\); (**c**) \(\theta_{scan}=30^{\circ}\); (**d**) \(\theta_{scan}=40^{\circ}\).  
Finally, considering the addressed engineering problem, the proposed optimization environment and the related procedures have been validated by the significant improvements provided to the performance of beam-steering reflectarray antennas, as assessed by full-wave simulation of the optimized configurations.  
Conceptualization, A.N., F.G., M.M. and A.G.; Data curation, A.N. and F.G.; Formal analysis, R.Z.; Investigation, A.N. and F.G.; Methodology, A.N. and F.G.; Project administration, F.G., R.Z. and A.G.; Software, A.N. and M.M.; Supervision, R.Z. and A.G.; Validation, A.N. and M.M.; Visualization, A.N. and M.M.; Writing--original draft, A.N., F.G., M.M., R.Z. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.