esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Statement  
Since imaging techniques can obtain the shape of a defect, they are more intuitive than ordinary A-scan and B-scan techniques. For the last few years, some eddy current testing was investigated and combined with imaging techniques to detect and evaluate defects, such as PEC C-scan imaging [5; 40], electromagnetic tomography (EMT) [41; 42], eddy current thermography [43], and magneto-optical imaging [44; 45; 46]. The C-scan imaging technique has several advantages such as a low cost, easy control, and automatization. A detailed application of the C-scan technique for aluminum alloy plates and a comparison with the A-scan technique is described in References [47; 48; 49]. Therefore, most defect shape mapping in this paper is completed by the PEC C-scan imaging technique.  
The rest of this article is organized as follows. First, the PEC rectangular excitation coil and \(By\) pickup coil are introduced in Section 3.1. Then, the experimental setup and specimens developed in our laboratory are shown in Section 3.2. Then, the curves of the A-scan's peak are analyzed at the beginning of Section 4. Defect shape mapping based on C-scan imaging is investigated in the rest of Section 4. Finally, conclusions are outlined in Section 5.
## 3 Synthesis and Analysis of Planar SALT Trajectories
### The \(L_{2}\) Lunar Gateway
For the planar motion, this expression can be rewritten in terms of the perilune distance \(r_{\pi}\) and the spacecraft velocity at perilune \(V_{\pi}\) as \(J=3\left(1-\mu\right)\pm 2\tau_{\pi}V_{\pi}-V_{\pi}^{2}+2\mu/r_{\pi}\) (the inertial spacecraft velocity relative to the Moon is assumed). The sign of the second term shows whether the  
Figure 8: Nested \(L_{2}\) lunar gateways for different Jacobi integral levels: \(J=3.06\), \(J=3.10\), \(J=3.15\). Figure 9: \(L_{2}\) lunar gateway for \(J=3.06\) with the \(r_{\pi}=3141\) km perilune distance contour line marked. Targeting point \(P\) results in the trajectory shown in Figure 6 (the argument of perilune \(\omega_{\pi}=83.5\) deg). orbit is direct or retrograde. Thus, the required LOI burn at perilune can be determined from the quadratic equation  
\[\Delta J=\pm 2r_{\pi}\Delta V_{\pi}-2V_{\pi}\Delta V_{\pi}-(\Delta V_{\pi})^{2}, \tag{11}\]  
where \(\Delta J=J_{\rm nom}-J_{\rm arr}\) is the Jacobi integral increment (the difference between its values for a nominal orbit and for the arriving leg). Among the two negative roots, we are interested in the one with the least magnitude \(\Delta V=|\Delta V_{\pi}|\). Such a magnitude is given by the formula  
\[\Delta V=V_{\pi}\mp r_{\pi}-\sqrt{(V_{\pi}\mp r_{\pi})^{2}-\Delta J}. \tag{12}\]  
This estimate of the LOI burn magnitude, though accurate enough, can be further improved when adapting the trajectory to more realistic dynamical models.
## 4 Tests and Results
### General Results  
Table 6 presents the general results for the analyzed scenarios. After running the optimization model with the first scenario, an objective function of 227278.4 [MW/h] was obtained in a time of 27.16 [s]. In the second scenario, that considers the effect of NCRES, the objective function decreased 10.37% compared to the first scenario. When all available resources are integrated (third scenario), the objective function decreases by 27.4% compared to the first scenario. Note that including all technologies (NCRES and BESS apart from conventional generation) require more computing time and a higher number of iterations; nonetheless a better objective function is obtained.  
Figure 8 shows the added generation profiles of the system for each of the three scenarios under consideration. As new types of resources are included, the total value of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Conventional** & **NCRES** & **All Technologies** \\ \hline Objective Function Value [MW/h] & 227,278.4 & 203,703.4 & 165,002.4 \\ \hline Execution time [s] & 27.16 & 52.03 & 88.17 \\ \hline Iterations & 145,447 & 154,646 & 327,239 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Objective function of the restoration process with and without NCRES and BESS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**b** & \(T_{\mathbf{b}\mathbf{c}p}\) & \(\mathbf{\eta_{b}}\) [MW/ut] & \(SOC_{B}\) & \(\mathbf{P_{bstart}^{t}}\) \\ \hline
1 & 1 & 0.5 & 30 & 0 \\ \hline
2 & 1 & 1 & 20 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of BESS.  
Figure 7: Renewable resources output power data.  
energy available in the system increases. Note that the greatest benefit is achieved when all technologies are involved in the restoration process.
## 3 PID Control of the Dynamical Model
### 3D Simulation  
[PERSON] et al. [14] showed the desired curve for angle \(\theta_{3}\) but did not display the actual angle values. Simscape is a powerful simulation tool that includes a mechanics explorer feature, enabling users to visualize and analyze the behavior of 3D Simscape models in real time. This feature provides an intuitive workspace for monitoring the simulation results at any point during the simulation. While watching the 3D simulation, at the instant when \(\alpha=30^{\circ}\), \(\beta=30^{\circ}\), and \(\Delta Z=0.6\) m were attained, it was observed that the moving plate was not stabilized correctly, as shown in Figure 5.  
Additionally, the simulation data inspector revealed that the actual angle, represented by \(\theta_{3}\), of the passive joint within the SRU limb is incapable of tracking its desired value in any given scenario, as depicted in Figure 6.  
Figure 5: Position of the upper moving plate of the 3-SBR/SRU when \(\alpha=30^{\circ}\), \(\beta=30^{\circ}\), and \(\Delta Z=0.6\) m.  
Figure 6: Comparison between the desired and actual values of \(\theta_{3}\) using the existing strategy.  
As a consequence of this issue, the dynamical model showcases sinusoidal fluctuations in the \(x\) and \(y\) coordinates of points \(A_{i}\), (\(i=1,\cdots,4\)). The positional variation of point \(A_{1}\) is illustrated in Figure 7. Although the figures illustrating the variations for \(A_{2}\), \(A_{3}\), and \(A_{4}\) were not included because of limited space, their results align consistently with those presented for \(A_{1}\).
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 3 Dynamic Multiple Junction Based Source Routing Protocol
### DMJSR Protocol Overview
#### 3.2.4 Forwarding between Junctions
As frequent use of carry and forward causes more delays due to lack of connectivity and also packet loss because of expiry of the time-to-live duration of the packet. In general, if our protocol during junction selection phase finds that there is no vehicular density around the next candidate junctions, it will simply employ carry and forward technique to move packet toward next destination junction.
# Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
## 2 Related Work
### Target Tracking Algorithms  
Giving the initial bounding box of the target in the first frame of the tracking video, a target tracking algorithm automatically estimates the bounding box of the target object in subsequent frames. The target tracking method is divided into two categories: the generative method and the discriminant method.  
The generative methods describe the target area as a generative models in the current frame, and search the area fit to the models as the predicted position in the next frame. The well-known generation methods include the Kalman filtering method and mean shift method. For example, the ASMS algorithm [13], using the mean shift method and color histogram features, has achieved good results.  
The discriminant method uses the target region as a positive sample and the background as a negative sample to train a classifier. It uses this classifier to find the optimal region as the position of target in the next frame. The most classical discriminant method is the correlation filtering method. MOSSE [14] uses the correlation filtering of single-channel grayscale features to achieve high-speed tracking of 615 FPS. CSK [15] adds dense sampling and kernel methods based on MOSSE, and it achieved higher accuracy. KCF extended the HOG feature using multi-channel gradients based on CSK method to further improve the accuracy. [PERSON] extended CSK with multi-channel color feature, which is known as Color Names, and achieved good results. The HOG feature extracts the gray gradient feature of the image, and the CN feature extracts the color feature of the image. Both of them have become the standard for feature extraction in the tracking algorithm. C-COT [16] used the depth feature of multi-layer convolution for the first time and won the championship in the VOT-2016 competition. Although the accuracy of C-COT is very good, its complexity is high and the operation cannot meet the real-time requirements. Based on C-COT, the ECO algorithm proposes acceleration measures from three aspects: model size, training set size and model update strategy. It can achieve high processing speed and accuracy using hand craft feature including HOG and CN features.  
C-COT and ECO use a pre-trained convolution neural network to obtain target features. The pre-trained content of the CNN does not match the actual application scenario.
## 3 Results and Discussion
### ABTS Radical scavenging Activity  
Figure 3 illustrated the ABTS radical scavenging activity of **1-14** in comparison with BHA. The findings presented in Figure 3 confirmed the DPPH scavenging effects presented in Figure 2 and demonstrated the ABTS radical scavenging effects of the parent compounds **1-3** (88.59 \(\pm\) 0.13, 62.00 \(\pm\) 0.24, and 29.98 \(\pm\) 0.13%, respectively) and the IC\({}_{50}\) values of **1** and **2** were 56.44 and 54.34 \(\upmu\)g/mL, respectively. It was observed that compound **1** exhibited the highest ABTS radical scavenging activity (88.59 \(\pm\) 0.13%) compared to standard BHA  
\begin{table}
\begin{tabular}{c c} \hline
**Compound** & **Reducing Power (Absorbance at 700 nm)** \\ \hline
**1** & 1.43 \(\pm\) 0.007 \\
**2** & 0.270 \(\pm\) 0.004 \\
**3** & 0.31 \(\pm\) 0.007 \\
**4** & 0.36 \(\pm\) 0.019 \\
**5** & 0.37 \(\pm\) 0.019 \\
**6** & 0.77 \(\pm\) 0.01  
(96.18 \(\pm\) 0.33%) at the same concentration. Chemical substitution on the structure of **2** with 2-hydroxy-5-methoxy to produce **11** resulted in a slight change in the ABTS radical scavenging activity to reach 53.42 \(\pm\) 0.14% (IC\({}_{50}\) = 93.59 \(\upmu\)g/mL). Based on all assay findings, the parent methylthio (**1**) demonstrated a significant antioxidant activity comparable to that of BHA. From the electronic chemistry point of view, the excellent activity of **1** relative to the other two parents (**2** and **3**) could be attributed to the electronic enriching of the parent structure and its pi-electron conjugation by the +R effect of the pair of lone-pair of electrons on _S_-atom of -SCH\({}_{3}\) group that is reinforced by the hyper conjugation effect of CH\({}_{3}\)-hydrogen atoms. The contrary was noticed by the drastic lowering in the antioxidant activity of **2** and **3**, where (-R)- and (-I)-effects of the phenoxy or sulfonyl caused the reverse effect occurred by the -SCH\({}_{3}\) group in **1**. Further theoretical studies were carried out by using the B3 LYP/6-311++g (2d,2p) level of theory to provide us with more insights about molecular antioxidant properties of the targets and to clarify the SAR of compounds **1**-**14**.
# Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes
## 3 Results
### Evaluation of SAM Segmentation Quality in NDT Slice Data-Sets
The few entities which exhibit lower segmentation quality in these data-sets and are located on the right edge are often due to insufficient quality in the reference segmentation data-set, as illustrated in Figures 11 and 12. A slightly lower segmentation quality can be observed for the corn data-set, which consists of a higher count of entities that are also not as homogeneous in colour compared to the marble data-set. Figure 9(c) demonstrates that the segmentation quality for the Me 163 data-set is notably lower compared to the previously mentioned data-sets. Figure 13 displays some typical error patterns in the original trained SAM images. Both under-segmentation and over-segmentation occur and segments are sometimes partially or not recognized at all. Figure 10: Graphs depicting the slice segmentation performance of the six evaluated SAM models on the three different testing data-sets. From left to right, the index of each segmented slice sorted by their loss value. In an ideal case, only a horizontal line close to the loss value of 0 would be visible. Among the different not fine-tuned SAM models, the smallest model vit_b showed the most promising results. While it was sometimes outperformed by the other two original SAM models, vit_1 and vit_h, in the well-segmented slices, it still had a higher segmentation quality in the moderately segmented slices. Therefore, we decided to use vit_b as the base model for fine-tuning and volumetric segmentation experiments. Among the subsequently trained networks, vit_bcvg exhibits the highest quality in Figure 10c. It is based on vit_b and uses _ConstantValueBackground_ (_CVB_) (see Section 2.2) for background examples. In simple cases, it matches the segmentation quality of non fine-tuned SAM variants. A considerable improvement in segmentation quality on the challenging entities could be achieved through training, although not to a satisfactory level. Figure 11: Error cases for the marble data-set. Here, the reference segmentation, which was generated by a connected component analysis, is erroneous. In (**b**), the point prompt (marked with a green cross) lies on the boundary of two marbles and vit_b segments the upper marble instead of the lower marble. In (**f**), the point prompt lies inside an artefact region. Figure 12: Error cases of the corn data-set. In the first case in (**b**), two kernels were erroneously segmented together in the reference segmentation. In contrast, in (**f**), the reference segmentation only appears erroneous as the current slice only depicts one voxel. The next slice in the input volume contains the kernel this voxel belongs to. The green crosses mark the position of the currently used point prompt. This model was chosen as the representative of our fine-tuned model for further tests on our data.
# Experimental Test and Prediction Model of Soil Thermal Conductivity in Pernafrost Regions
## 6 Discussion
### Prediction Effect and Influence Factors of RBF Neural Network Model  
As everyone knows, soil is a complex multiphase mixture and its thermal conductivity is influenced by many factors [7; 8]. Among various impact parameters, dry density and water content are the most important and analyzed factors. A generally accepted concept is that soil thermal conductivity increases as the dry density or water content increase. However, as shown in Figure 15, dry density as well as water content do not have strong positive correlations with soil thermal conductivity under specific natural conditions. The thermal conductivity of HWHCS distributes randomly and sparsely under different dry density and water content, and it seems hard to find a suitable function describing this phenomenon. Thus, the binary fitting formula can only take the form of a piecewise function to promote prediction accuracy. However, the \(R^{2}\) of fitting formulas are still very low (0.561 for unfrozen HWHCS). Herein, it can be concluded that the formula fitting method based on incomplete parameters may not be effective for soil thermal conductivity prediction, especially with multiple soil types and complex compositions.  
Figure 14: Mean absolute percent error (MAPE) distribution of HWHCS.  
Figure 15: Thermal conductivities of HWHCS with different dry density and water content.  
The RBF neural network method is capable of capturing and depicting features and sub-features embedded in a large amount of data, and of obtaining a definite output [39]. The output of RBF neural network is produced by mapping distances between input vectors and center vectors (hidden layer) to output through a non-linear kernel. The most attractive characteristic of a RBF neural network algorithm is that it can capture non-linear and complex relationship among parameters and build a feasible output prediction model using imperfect or incomplete data. Considering for these advantages, the RBF neural network method has been adopted for the prediction of soil thermal conductivity using dry density and water content as input parameters. It can be seen in Figure 13 and Table 6 that the RBF neural network method has achieved good prediction results. The \(R^{2}\) of unfrozen HWHCS, MWHCS, and LWHCS is 0.79, 0.82 and 0.81, which is much better than the binary fitting results.  
Moreover, in addition to developing and optimizing the algorithm itself, the neural network methods need a large amount of experimental data to obtain reasonable prediction results. Fortunately, nearly 1000 test data successively avoid the overfitting problem during training computation of a neural network. Therefore, despite input parameters being only part of the influencing factors, advanced algorithms and sufficient experimental data greatly improve the prediction accuracy of the RBF neural network model.
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 3 Proposed System Architecture
### Neural Network Architectures  
In total, seven base models were chosen for this work: EfficientFormerV2S2 [28], EfficientViT_B0 [29], EfficientViT_M4 [30], MobileViT_V2_050 [31], MobileViT_V2_100 [31], MobileViT_V2_175 [31], and RepViT_M11 [32], all made available in the Keras package [33]. Each model was quantized for on-edge classification using two different quantization techniques, further explained in Section 3.3. Experimentally, we devised the classification layers and tuned the hyper-parameters, as seen in Figure 3 below. Afterward, the feature extractor layers from the ViT-based CNN architecture, a global average pooling layer, a dense layer consisting of 1024 nodes, and a dropout layer with a value of 0.3 were applied. Finally, a dense layer for classification consisting of 9 nodes was applied, corresponding to the 9 classes. We used a learning rate of \(1\times 10^{-4}\). The rest of this section briefly explains and justifies the architectures chosen for classifying gastrointestinal diseases, along with a summary in Table 4.  
Figure 3: Model architecture designed for this work.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Class Name** & **Original Dataset** & **Training Dataset** \\ \hline Normal Clean Mucosa & 34,338 & 500 \\ Ileooccal Valve & 4189 & 500 \\ Reduced Mucosal View & 2906 & 500 \\ Pylorus & 1529 & 500 \\ Angiectasia & 866 & 500 \\ Ulcer & 854 & 500 \\ Foreign Body & 776 & 500 \\ Lymphangiectasia & 592 & 500 \\ Erosion & 506 & 500 \\ Blood—Fresh & 446 & 0 \\ Erythema & 159 & 0 \\ Polypy & 55 & 0 \\ Blood—Hematin & 12 & 0 \\ Ampulla of Vater & 10 & 0 \\ \hline Total & 47,238 & 4500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Class distribution of Kvasir-Capsule dataset before and after under-sampling for training.
## 3 Results
### Daily Scale Assessment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Entire Period**} \\ \hline
**Product** & **CC** & \
\begin{tabular}{c} **RMSE** \\ **(mm/day)** \\ \end{tabular} &
\begin{tabular}{c} **BIAS** \\ **(mm/day)** \\ \end{tabular} & **rBIAS (\%)** & **POD** & **FAR** & **CSI** \\ \hline APHRO & 0.76 & 3.42 & \(-0.51\) & \(-11.25\) & 0.61 & 0.17 & 0.69 \\ CHIRPS & 0.57 & 3.99 & \(-1.36\) & \(-25.76\) & 0.52 & 0.25 & 0.43 \\ ERA-5 & 0.81 & 3.38 & 0.82 & 26.43 & 0.71 & 0.15 & 0.70 \\ MSWEP & 0.86 & 2.29 & \(-0.30\) & \(-5.94\) & 0.75 & 0.14 & 0.72 \\ PGMFD & 0.68 & 3.85 & \(-0.76\) & \(-15.27\) & 0.60 & 0.19 & 0.51 \\ \hline  & & \multicolumn{6}{c}{Winter} \\ \hline APHRO & 0.74 & 3.60 & 0.17 & 4.95 & 0.59 & 0.11 & 0.57 \\ CHIRPS & 0.58 & 3.88 & 0.55 & 8.27 & 0.49 & 0.29 & 0.47 \\ ERA-5 & 0.81 & 3.32 & 0.89 & 32.72 & 0.64 & 0.10 & 0.60 \\ MSWEP & 0.90 & 1.80 & 0.10 & 3.03 & 0.86 & 0.06 & 0.83 \\ PGMFD & 0.69 & 3.63 & 0.45 & 6.76 & 0.55 & 0.18 & 0.50 \\ \hline  & & \multicolumn{6}{c}{Pre-Monsoon} \\ \hline APHRO & 0.77 & 3.64 & \(-1.15\) & \(-13.04\) & 0.74 & 0.13 & 0.70 \\ CHIRPS & 0.51 & 5.25 & \(-2.37\) & \(-24.67\) & 0.55 & 0.15 & 0.53 \\ ERA-5 & 0.79 & 3.55 & 0.97 & 28.36 & 0.75 & 0.11 & 0.70 \\ MSWEP & 0.88 & 2.09 & \(-1.02\) & \(-10.18\) & 0.75 & 0.10 & 0.73 \\ PGMFD & 0.67 & 3.76 & \(-1.44\) & \(-14.73\) & 0.65 & 0.14 & 0.64 \\ \hline  & & \multicolumn{6}{c}{Monsoon} \\ \hline APHRO & 0.78 & 3.35 & \(-0.33\) & \(-10.96\) & 0.77 & 0.10 & 0.76 \\ CHIRPS & 0.59 & 3.86 & \(-1.30\) & \(-23.71\) & 0.58 & 0.13 & 0.57 \\ ERA-5 & 0.85 & 3.24 & 0.58 & 24.18 & 0.81 & 0.09 & 0.78 \\ MSWEP & 0.86 & 3.11 & \(-0.14\) & \(-2.86\) & 0.83 & 0.08 & 0.80 \\ PGMFD & 0.71 & 3.61 & \(-0.74\) & \(-14.39\) & 0.66 & 0.11 & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The statistical metrics for daily performance evaluation of GPPs in contrast to in-situ gauges data during the entire study period 1981–2009 with different seasons. Figure 4: Spatial distribution of (**a**) CC, (**b**) Bias, and (**c**) RMSE from GPPs in contrast to in-situ gauge data during the entire study period. Figure 3: Taylor diagrams showing the statistical comparisons between GPPs and in-situ gauges data.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 4 Experimental Results  
The antennas were optimized using electromagnetic solver Ansys Electronics Desktop software and were manufactured by the photolithography process to validate the results obtained in the simulation. Figure 10a shows photographs of the fabricated antennas. Their reflection coefficients and radiation patterns were measured. The first was evaluated using  
Figure 8: Comparison between the return losses of the compared antennas (non-transparent and transparent).  
Figure 9: Comparison between the radiation patterns of the compared antennas: (**a**) XZ-plane and (**b**) YZ-plane.  
the Vector Network Analyzer. The latter was measured inside an anechoic chamber, as shown in Figure 10b.