esa-sceva/satcom-chunk-collection huggingface dataset:
# Siamese Tracking with Adaptive Template-Updating Strategy
## 3 Our Approach
### Adaptive Template-Updating Strategy
#### 3.2.2 Target-Detection Network  
Target detection is another hotspot issue in computer vision. For our real-time system, we only focused on one-stage target-detection algorithms, which have a higher speed. As one of the most advanced target-detection methods, YOLOv3 [37] can detect targets of different scales with high accuracy. Tiny YOLOv3 sacrifices detection accuracy, especially for small targets through channel pruning, in order to improve detection speed. However, in our task, most targets were small. To avoid loss of contextual information, we utilized a \"dilated module\" inspired by dilated convolution [38] to expand the receptive fields. The comparison of the receptive fields between a convolution network and a dilated convolution network is summarized in Table 2.  
The \"passthrough module\" was added to combine target location information in the lower layers and semantic information in the higher layers. To be specific, the features of the lower layers contained more fine-grained information, which helps to locate small targets. Therefore, we used a passthrough layer with stride 2 that transformed the feature map from \(2\text{N}\times 2\text{N}\times\text{C}\) to \(\text{N}\times\text{N}\times 4\text{C}\). The function of the passthrough layer is explained in Figure 6.  
Finally, we obtained similar accuracy with YOLOv3, and similar speed with tiny YOLOv3. The details and experiment results are available in previous work [39] from our team.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Convolutional Network Layer** & **0** & **1** & **2** & **Dilated Convolutional Network Layer** & **0** & **1** & **2** \\ \hline RF size & 3 & 5 & 7 & RF size & 3 & 7 & 15 \\ Kernel size & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) & Kernel size & \(3\times 3\) & \(3\times 3\) \\ Stride & 1 & 1 & 1 & Stride & 1 & 1 & 1 \\  & & & & Dilated rate & 1 & 2 & 4 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of receptive fields, kernel sizes, and strides between convolutional network and dilated convolutional network of each layer.  
Figure 5: Shape-adaptive template generating process: (**a**) original image; (**b**) contour map; (**c**) target mask; (**d**) target template.  
Figure 6: Passthrough-layer function: black circles with numbers indicate pixel in feature map before and after going through passthrough layer.
# Machine Learning Reveals Migmatic Fertility of Skarn-Type Tungsten Deposits
## 2 Dataset and Data Preprocessing  
### Dataset  
In this study, fertile magmas are those that exhibit a spatial association with documented skarn-type tungsten deposits, whereas barren magmas represent the opposite condition. Apatite geochemical data related to skarn tungsten deposits were compiled from published literature (Supplementary SI). After data screening, a total of 1047 observations from intrusions related to skarn-type tungsten deposits (Supplementary SI) were labeled as \"fertile apatite\", while 4729 observations from intermediate-felsic intrusions without mineralization or with other types of mineralization were labeled as \"barren apatite\". The selected trace elements for modeling included Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Mn, Nd, Pr, Sm, Sr, Tb, Tm, V, Y, and Yb. The rationale behind choosing these trace elements was based on three criteria: (1) significant compositional variation during petrogenetic processes, (2) proven effectiveness in discriminating skarn-type mineralization systems by prior studies, and (3) extensive documentation in the literature. We conducted an exploratory data analysis (EDA) and visualized the trace element profiles of apatite from fertile and barren intrusions using box plots (Figure 2). The results reveal pronounced contrasts in trace element concentrations between apatite derived from fertile versus barren intrusions. Moreover, the dataset was standardized prior to model training.  
Figure 1: Workflow chart of ML algorithms to generate the magmatic fertility models in this study.  
Besides the global apatite dataset, additional trace element data (204 samples) from apatite within the Nanling skarn W-Sn metallogenic province (Middle-Lower Yangtze River metallogenic belt, Supplementary SII) were used. These apatite samples were subjected to identical screening criteria applied to the global dataset and subsequently employed to validate the constructed ML models and for visualization analysis.
# A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention
## 2 Preliminary Work
### Human Visual System (HVS)  
Image quality evaluation based on HVS is one of the most reasonable methods when assessing pictures, because the results obtained are consistent with the subjective results [15; 28]. Therefore, it is of great significance to study how the human visual system works. Human awareness of images is affected by the physiological structure of the eyeballs and the psychological function of the brain shows different visual discrimination and attention in different conditions. For example, visual masking refers to the visibility reduction when one stimulus is in a similar background [29]. The limited human visual ability will ignore some details hidden in the background in time [30]. Besides, the perceived intensity is proportional to physical stimuli on a logarithmic scale according the Weber-Fechner law [31]. The law indicates that human eyes are insensitive to the change of gray value when the brightness of objects in the pictures is at a high level [32]. In addition, psychophysical studies show that the perception of HVS for an image is selective. Different regions or objects have diverse levels of visual saliency [33; 34]. The HVS will detect the stimulus with high saliency by the distinctive size, intensity, color, or orientation contrasting with the surroundings [33]. In other words, these stimuli attract almost all the attention of the human eyes.  
The above theories of HVS indicate that if the discontinuous edges located at the high saliency regions, the quality of mosaic images will be reduced, while the discontinuous edges will result in little detectable effect when they are in low-saliency regions or masking backgrounds. Therefore, using HVS to determine the location of the seamline can effectively reduce the discontinuities, thereby improving the quality of stitching.
## 5 Results and Discussion
### Discussion
Furthermore, more measured signals (e.g., anode and cathode outflow relative humidity, species concentrations, recirculation flow) and more \"exciting\" system inputs would significantly affect the obtainable results by increasing the Fisher information of the parameters, leading to better parametrization results. Compared to the usual \"single-step\" approach, the two-step method simplifies the parametrization process significantly by reducing the solution space's dimension. As depicted in Figure 2e, the parameters resulting from a single-step parametrization of the entire model (without any further considerations) have a much larger spread, which  
Figure 4: Plots (**a,e**): Cathode supply manifold pressure \(p_{\text{ca,sm}}\) (blue, yellow and red), and air mass flow \(ht_{\text{ca,in}}\) (purple). Plots (**b,f**): Cathode exit manifold pressure \(p_{\text{ca,em}}\) (blue, yellow and red), and FC temperature \(T\) (purple). Plots (**c,g**): Anode exit manifold pressure \(p_{\text{an,em}}\) (blue, yellow and red), and anode supply manifold pressure \(p_{\text{an,sm}}\) (purple). Plots (**d,h**): Stack voltage \(U\) (blue, yellow and red), and stack current \(I\) (purple). The plots depict parameterization and validation data, respectively, and **y** denotes output and **u** input. implicitly means a worse fit of the simulation data with the measurements. Figure 4 additionally shows the simulation results with parameters obtained from the single-step approach. The simulations achieve an average \(R^{2}\) value of 33% for the parametrization data and \(-62\%\) for the validation data, which is much worse than the proposed two-step method (93% and 91%, respectively). However, increasing the optimizer's population size and the number of generations the single-step approach would yield similar results as the two-step method. By doing so, the single-step approach searches more thoroughly through the bigger solution space of the entire model for the optima, but at the expense of exponentially growing computation time. Therefore, considering limited resources and the same optimizer options, the two-step method delivers more accurate results than the single-step approach, making the former superior in this case. A comparison of the proposed two-step method with methods consisting of more than two steps is, in this case, not meaningful because this work utilizes pre-existing data. Multiple-step methods [14; 18] require appropriately designed experiments, which is usually not the case for pre-existing data. If the data allows the utilization of a method with more than two steps, then the multiple-step method will likely deliver better results than the two-step method considering similar resources. The fact that the two-step method does not need appropriately designed experiments compensates for this drawback.
# Analysis and Assessment of a Brushless DC Outrunner Motor for Agriculture Drones Using JMAG
## 2 Propulsion System Design in Agriculture Multi-Rotor UAVs
### Propeller
#### 2.1.2 Boundary Conditions  
The computational domain consists of two cell zones: a static region and moving region (Figure 3). It is in the inner, moving domain that the propeller is located and the rotational speed assigned. As for the outer region, the inlet, outlet, and wall enclose a stationary cylinder. The general characteristics and dimensions are defined as follows:  
* Static domain: A cylinder of 11D (11 times the propeller's diameter) in diameter and 9.6D in height.
* Moving domain: A cylinder of 1.1D in diameter and with a height of 0.14D.
* Boundary conditions: The inlet and outlet correspond to the atmospheric pressure (1 atm) in an absolute frame, reproducing hover conditions. Similarly, the no-slip condition was set to the outer wall and propeller (Table 3).
* Interface treatment: Absolute formulation.  
A CFD analysis of the propeller (Figure 4) defines the optimum velocity and torque in a range of 2400-3000 RPM and torque of 1.75-2.88 Nm with an efficiency of 9.66-7.56 g/W. These values were compared and confirmed with T-motor online data.  
The thrust-speed and torque-speed curves for the propeller from the CFD analysis show their characteristic quadratic dependence on speed (Figures 5 and 6). The propeller is expected to reach about 3000 rpm to comply with the maximum thrust and thrust-to-weight ratio previously defined. However, nominal operation is enclosed in a 2200 to 2600 rpm range. The propeller efficiency is commonly delivered in grams per watt to denote the  
Figure 3: Geometric characteristics of CFD domain.  
With the optimal torque and velocity range with this propeller for this application, the task now is to select the BLDC motor capable of efficiently providing that mechanical power.
## 4 Experimental Results
### Determination of the Compressive Strength of Concrete via the SonReb Method  
The probabilistic compressive strength was determined using Equation 8 based on the correlation of the data obtained from destructive and non-destructive tests, and multiple regression statistical analysis was used to determine the constants a, b and c, which were 0.465, 1.055 and 0.340 for the samples aged from 28 to 1126 days, respectively, and for those ages 1926 days they were 0.5003, 0.390 and 1.9723, respectively. The results for the compressive strength are given in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Age (Days)**} & \multirow{2}{*}{**Test**} & \multicolumn{3}{c}{**Cube Dimensions**} & \multirow{2}{*}{**Mass**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**F\({}_{\text{ccube}}\)**} \\  & & **a** & **b** & **h** & **(kg)** & **(kN)** & **(N/mm\({}^{2}\))** \\ \hline \multirow{3}{*}{28 th} & Cube 1 & 149.8 & 149.9 & 150.2 & 7.77 & 747.6 & 33.3 \\  & Cube 2 & 149.9 & 149.9 & 150.3 & 7.78 & 776.3 & 34.5 \\  & Cube 3 & 149.9 & 150 & 150 & 7.78 & 785.1 & 34.9 \\ \hline \multirow{3}{*}{244 th} & Cube 4 & 150 & 149.9 & 150 & 7.76 & 863.1 & 38.4 \\  & Cube 5 & 149 & 150.5 & 150.3 & 7.73 & 882.2 & 39.3 \\  & Cube 6 & 150.5 & 149.6 & 150 & 7.76 & 883.8 & 39.3 \\ \hline \multirow{3}{*}{280 th} & Cube 7 & 149.8 & 149.5 & 150 & 7.70 & 872.1 & 38.9 \\  & Cube 8 & 150 & 149 & 150.2 & 7.73 & 875.0 & 39.1 \\  & Cube 9 & 149.7 & 149.9 & 150 & 7.72 & 903.8 & 40.3 \\ \hline \multirow{3}{*}{293 rd} & Cube 10 & 149.7 & 151 & 150 & 7.77 & 893.7 & 39.5 \\  & Cube 11 & 149.6 & 150.3 & 150.2 & 7.75 & 910.7 & 40.5 \\  & Cube 12 & 149.6 & 149.4 & 150 & 7.69 & 904.3 & 40.5 \\ \hline \multirow{3}{*}{342 nd} & Cube 13 & 149.7 & 149.8 & 150 & 7.66 & 891.7 & 39.8 \\  & Cube 14 & 149.3 & 149.7 & 149.5 & 7.66 & 920.3 & 41.2 \\  & Cube 15 & 149.5 & 149.7 & 150.3 & 7.76 & 926.9 & 41.4 \\ \hline \multirow{3}{*}{1126 th} & Cube 16 & 148.6 & 147.7 & 149.8 & 7.60 & 1034.1 & 47.1 \\  & Cube 17 & 148.8 & 150.0 & 150 & 7.65 & 997.6 & 44.7 \\  & Cube 18 & 148.5 & 149.5 & 150 & 7.62 & 976.4 & 44.0 \\  & Cube 19 & 150.9 & 148.6 & 149.6 & 7.69 & 969.0 & 43.2 \\  & Cube 20 & 149 & 149.6 & 149.4 & 7.67 & 970.6 & 43.6 \\  & Cube 21 & 146.5 & 149.5 & 150 & 7.55 & 1025.0 & 46.8 \\  & Cube 22 & 150.5 & 149.7 & 149.4 & 7.69 & 900.6 & 40.0 \\  & Cube 23 & 147.2 & 149.5 & 149.7 & 7.56 & 1008.8 & 45.8 \\  & Cube 24 & 149.5 & 149.9 & 150.3 & 7.74 & 1009.9 & 45.1 \\  & Cube 25 & 151.2 & 149.4 & 149.8 & 7.73 & 954.7 & 42.3 \\  & Cube 26 & 148.1 & 149.5 & 149.9 & 7.61 & 983.3 & 44.4 \\  & Cube 27 & 150.6 & 149.5 & 149.8 & 7.66 & 956.9 & 42.5 \\ \hline \multirow{3}{*}{1926 th} & Cored specimen N\({}^{\text{e}}\) & D (mm) & L (mm) & Mass(kg) & F(kN) & \(\rm f_{c,cube}(N/mm^{2})\) \\ \hline \multirow{3}{*}{1926 th} & Cored 1 & 99 & 99 & 1.792 & 298.9 & 44.7 \\  & Core 2 & 99 & 99 & 1.796 & 303.3 & 45.3 \\  & Core 3 & 99 & 94 & 1.746 & 331.9 & 49.6 \\  & Core 4 & 99 & 100 & 1.867 & 351.5 & 52.5 \\  & Core 5 & 99 & 100 & 1.890 & 322.3 & 48.2 \\  & Core 6 & 99 & 9
## 3 Discussion
### Multi-Target Directed Ligand (MTDL) Definition  
MTDLs combine pharmacophores from different inhibitors to treat multifactorial diseases, here, targeting several enterovirus (EV) proteins with one compound. They are drugs containing two or more pharmacophores structurally overlapping or separated by a spacer in a single molecule. The linked molecules comprised an enzyme-cleavable linker binding the active inhibitors, with minimal alteration to their initial structure. Their design can include the fusion of compounds that happens when one chemical group is identified in two drugs, which are fused by this group to obtain one only compound, while merging has the same concept, not focusing on chemical structure but pharmacophores' interactions. These concepts are illustrated in Figure 7.  
To ensure that affinity with the various targets is preserved, in silico evaluation is required. Fused or merged MTDL must be supported with de novo and crystallographic data while compounds bound by a labile linker can be designed with an experimental approach only as they are released with their initial form. In the design-in approach, the fusion or merge must preserve interactions of inhibitors with each target. It enables us to determine whether scaffold hopping is workable and whether substituents are feasible. Design-out begins with a compound acting simultaneously on all targets, which is not our case here.  
Fusion of several chemical functions or merging of numerous pharmacophores requires adapting synthesis pathways for inhibitors. In this case, the maintenance of the affinity of the new compound for each considered target is challenging. The use of a labile linker allows compounds to be released in vivo and could limit the loss of affinity for the respective targets. Here, the key parameter is the release of each compound. Enzymatic cleavable binding agents are well known, including their structure [Figure 8] and degradation rate [151]. New linkers have recently been developed to perform simultaneous versions, while traditional ones run them sequentially.  
Figure 8: Enzyme degradable linkers.  
Figure 7: Illustration of concept or MTDL. Multitarget-directed ligands (MTDLs) can be classified as “linked” (A) or “fused” (B) or “merged” (C) based on the degree of overlap of the pharmacophores.
## 4 Conclusions  
This paper presented a numerical study using the inverse Finite Element Method (iFEM) to reconstruct the two-dimensional displacement and strain fields of a cracked plate undergoing membrane deformations. The study's main goal was damage detection. The plate was instrumented using fiber-optic strain sensors that measure only uniaxial strains. Several fiber arrangements were investigated, and strain-interpolation strategies were used to improve the quality of strain data used for the iFEM analysis. Various damage scenarios were investigated by varying the damage size, position, and orientation. The effects of measurement noise were also investigated. The location of the damage occurred as a region of strain concentration in the reconstructed strain field, and the iFEM results were successful in detecting and localizing the damage. It was also shown that information regarding the damage orientation is represented in the iFEM results. The addition of measurement noise led to difficulties in detecting small-size cracks because the noise overshadows the strain perturbations due to the actual damage. The use of a fiber pattern resembling a strain rosette produced superior results because it provided greater information regarding the strain field within an element. These results highlight the potential of strain measurements based on fiber optic sensors for practical SHM applications. This research has also revealed that additional post-processing procedures can be investigated to obtain requisite information regarding the position and orientation of damage.  
Figure 23: Noise study considering Unitcell-2 results for Damage Case-1; contour plots of \(I_{D}\) for different noise levels (threshold = 0.5): (**a**) 2.5%, (**b**) 5%, (**c**) 7.5%, (**d**) 10%, (**e**) 12.5%, (**f**) 15%, (**g**) 17.5%, and (**h**) 20%.  
**Author Contributions:** Methodology, R.R.; software, R.R.; review, A.T., M.G. and C.S.; editing, R.R.; supervision, A.T., M.G. and C.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 6 Blockchain Potential in V2X Networks
### Blockchain as a Security Mechanism in V2X  
The existing vehicular networks face several potential security attack surfaces and threats in inter-vehicle and in-vehicle communications, such as cyber-attacks through internet and edge-cloud based connections by using malicious software, passive attacks, tampering the sensor devices, etc. In this paper, we will focus on inter-vehicle communication security. The conventional security and privacy mechanisms are not strong and are inefficient at protecting intelligent and autonomous vehicles because of the differences in their vulnerability to cyber-attacks. In vehicular networks, a large number of vehicles are connected with each other for information exchange, such as accidents, event reports, traffic information, weather information, infotainment messages, etc. It is extremely challenging to realize a centralized vehicular system for managing and processing such a huge amount of data when considering the timely dissemination of information and, at the same time, ensure network scalability [63]. The V2X communications are vulnerable due to the sensitive data sharing between the intelligent vehicles [64; 65]. Some of the security requirements of intelligent vehicular systems are as follows:  
* Traceability: each participating vehicle should show data provenance to identify the data flow tracing i.e., the source of the information and any changes in ownership during transmission through the transaction.
* Transparency: the information exchanged between participating vehicles, such as critical accident information, traffic-related messages, etc., should be transparent among the member nodes in the blockchain network, except for the private information. This helps legitimate vehicular nodes obtain information regarding traffic situations and lets them respond accordingly.  
* Resilience: if a transaction is erroneous or was corrupted, the corruption is immediately evident, and all the vehicles are kept informed of it. A new transaction can be used to resolve an error, as well as the corrupted transactions can be recovered.
* Immutability: this prevents the event or critical messages from being tampered with after they have been registered in the database.