esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 5 Evaluation Results
### Effect of \(\theta_{C}\)  
Figure 4 shows the effect of the target utilization of the computational resource \(\theta_{C}\). From Figure 4, it can be found that RNA-OTS can achieve the lowest average delay \(\zeta_{D}\) while maintaining acceptable average utilizations of computational and storage resources,  
Figure 3: Convergence to optimal policies.  
and \(\xi_{S}\) (i.e., maintaining sufficiently low average utilization of computational and storage resources). This is because observation satellites in RNA-OTS efficiently exploit given computational and storage resources and decide their transmission policies by considering other observation satellites' policies. For example, if observation satellites in RNA-OTS are expected to have a connection with the ground station soon, observation satellites use their storage resources to store the observations. Then, after connecting to the ground station, observation satellites directly transmit the observation to the ground station. In doing so, observation satellites in RNA-OTS can effectively utilize their storage resource (i.e., the storage resource can be used only during a short time (when some observation satellites which are expected to have a connection with the ground station soon delay their transmission, the queuing delay at the relay satellite can naturally be maintained at a low level). Additionally, since observation satellites in RNA-OTS have more computational resources, they compress the observation (i.e., \(A_{i}=3\) or \(A_{i}=4\)) to reduce the delay \(\zeta_{D}\). Moreover, a certain observation satellite does not transmit its observation to the relay satellite when many other observation satellites transmit their observations to the relay satellite.  
Meanwhile, from Figure 4a, it can be found that the average delay of RNA-OTS decreases with the increase in the target utilization \(\theta_{C}\) of the computational resource. This is because observation satellites in RNA-OTS can aggressively compress the observation by using more computational resources under higher \(\theta_{C}\), which can be observed in Figure 4b. Therefore, the observation can have a smaller size, and thus be averagely delivered within a short duration. On the other hand, other comparison schemes do not change their policies regardless of \(\theta_{C}\), and therefore their average delay and average utilization of computational resources do not change (see Figure 4a,b).
## 4 Experimental Verification
The target position is \(0^{\circ}\) and disturbances consist of sinusoidal signals including  
Figure 8: Open-loop characteristic acceleration response of electro-optical system. \(0.8^{\circ}\) 2 Hz, \(0.19^{\circ}\) 5 Hz, \(0.07^{\circ}\) 15 Hz and \(0.0028^{\circ}\) 50 Hz. The root mean square (RMS) of the MFC, the MFC with DOB, and the MFC with MDOB is 12.3922\", 11.0351\" and 3.0112\", respectively. The DOB method has little improvement, because it can only enhance disturbance suppression ability at intermediate frequency. Moreover, the results prove that the MDOB method can largely improve disturbance suppression ability at low frequency, which is much more suitable for real conditions. After that, we can also compare the disturbance suppression abilities in the frequency domain. As shown in Figure 10, it is obvious that the disturbance suppression ability at low frequency is greatly improved by introducing the MDOB to the AFC, which is different from the improvement at intermediate frequency by using the DOB. The reason for the low enhancement of disturbance suppression at intermediate frequency with the MDOB is that the enhancement of disturbance suppression and the improvement of drift rejection are contradictory according to Equations (11) and (12). If constant \(K\) increases, more disturbance will be suppressed but less drift can be rejected, and the insufficient drift rejection ability will also lead to integral saturation. Therefore, there is a limit to the improvement of disturbance suppression, which is related to the specific characters of different types of MEMS accelerometers. The advantage of the MDOB is that although the improvement at intermediate frequency may not be as good as the DOB, the improvement at low frequency is significantly better than the DOB. As such, it is excellent for high-precision tracking systems mainly working at low frequency when the MEMS accelerometer is utilized. For instance, the bandwidth of the position loop of the electro-optical  
Figure 10: The disturbance suppression comparison in the frequency domain. Figure 9: The stabilization errors with given superimposed disturbances. system is about 10 Hz, hence the improvement of disturbance suppression ability at low frequency is significant, which means that we can obtain much higher tracking precision at low frequency. By recommending the MDOB method and adopting a corresponding MDOB controller, we successfully improve the disturbance suppression ability at low frequency and overcome the problem of massive drift caused by the insensitivity of the MEMS accelerometer. Finally, the experimental results have exactly proved the previous analyses.
# Detection of Tampering by Image Resizing Using Local Tchebichef Moments
## 4 Results
### Experimental Discussions  
#### 4.2.1 Tamper Forensics on a Single Resizing Method  
In this experiment, we test the detection performance of our proposed method for a single resizing method. The tampered images with scaling ratios from 10% to 50% are adopted to test the performance of our proposed approach. Table 1 shows the detection results. From Table 1, we can see that the detection accuracy is improved with the increment of the scaling ratio. When the scaling ratio is less than 20%, our proposed approach has a higher detection accuracy for the scale-and-stretch resizing method. Since the optimal local scaling ratio of each local block is calculated iteratively and the warped image is updated simultaneously to match the scaling ratios as much as possible, the entire image is resized in the process of scale-and-stretch re-targeting. However, the seam carving method resizes an image by deleting the seams with the lowest energy once. Therefore, the tampered images obtained by the seam carving method are difficult to be distinguished from the authentic images when the scaling ratio is low. With the increment of the scaling ratio, the algorithm will cause a global structure distortion. It can also be reflected in Table 1. Our proposed approach can get a higher accuracy rate for images obtained using the seam carving method than that using other resizing methods with the increment of the resizing ratio.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scaling Ratio (\%)**} & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \cline{2-4}  & **Scale-and-Stretch** & **Seam Carving** & **Scaling** \\ \hline
10 & 82.51 & 75.26 & 74.33 \\
20 & 88.45 & 91.67 & 87.37 \\
30 & 95.51 & 97.87 & 95.55 \\
40 & 98.95 & 99.77 & 98.87 \\
50 & 99.85 & 100 & 99.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons in terms of accuracy for tampered images with single re-targeting methods.
## 5 ESTCube-Luna Preliminary Mission Requirements and Platform Design Solutions
### Communication (COM)  
The distance and relative motion between the Moon and the Earth, as well as the E-sail spin motion, require the ESTCube-LuNa concept to include a combination of communication solutions, while also assuming that there can be extended periods without a direct line of sight between the lunar nanospacecraft and Earth-based ground stations. The operations must work with communication delays of more than 1 s and minutes-long periods of communication blackouts caused by the E-sail rotation and occultation by the Moon. For telemetry and command, we propose a turnstile antenna on the ultra-high frequency (UHF) band. This would cover a the whole sky and could be used for regular spacecraft monitoring, telemetry and control from the ground. For data downlink and ranging, we propose a set of patch antennas on the X-band placed on the four largest sides of the cubesat (the same sides as the body-mounted solar panels). On the 1 \(\times\) 4 U sides, there are transmit-only antennas for improved data downlink budget over the spacecraft's rotation. On the 2 \(\times\) 4 U sides, there is a pair of transmit (Tx) and receive (Rx) antennas for performing the ranging experiment. This can also be used for emergency firmware upload. The COM system comprises two subsystems: low-speed communication (LSCOM) and high-speed communication (HSCOM).
## 4 Experimental Results and Analysis
### Comparison with the Existing Method  
In order to verify the progressiveness of the proposed method, Efficientnet [36], Mobilenetv2 [37], Darknet19 [38], Googlenet [39], Resnet18 [40], Yolov5n, and Squeezenet [41] were selected for horizontal comparison.  
As shown in Table 12, the proposed LSDFnet has fewer parameters and lower FLOPs, achieving a high-speed processing performance with an acceptable detection performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Quantization Bits** & \begin{tabular}{c} **Memory Copy** \\ **Time/μs** \\ \end{tabular} & \begin{tabular}{c} **Hardware Computation** \\ **Time/μs** \\ \end{tabular} & \begin{tabular}{c} **Data Processing** \\ **Time/μs** \\ \end{tabular} & \
\begin{tabular}{c} **Total Time/μs** \\ \end{tabular} &
\begin{tabular}{c} **FPS** \\ \end{tabular} \\ \hline Int16 & 1594 & 1833 & 16,146 & 19,573 & 51.09 \\ Int8 & 971 & 1152 & 15,820 & 17,943 & 55.73 \\ Mixed & 1076 & 1299 & 16,110 & 18,485 & 54.10 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the total running time after deployment with mixed quantization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resource** & **Utilization** & **Available** & **Utilization Rate** \\ \hline LUT & 66,200 & 277,400 & 23.86\% \\ LUTRAM & 8843 & 108,200 & 8.17\% \\ FF & 97,745 & 554,800 & 17.62\% \\ BRAM & 171.50 & 755 & 22.72\% \\ DSP & 12 & 2020 & 0.59\% \\ IO & 133 & 362 & 36.74\% \\ GT & 1 & 16 & 6.25\% \\ BUFG & 16 & 32 & 50.00\% \\ MMCM & 7 & 8 & 87.50\% \\ PLL & 2 & 8 & 25.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: FPGA resource utilization.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Quantization Bits** & \begin{tabular}{c} **Dataset** \\ **Dataset** \\ \end{tabular} & \begin{tabular}{c} **Memory Copy** \\ **Time/μs** \\ \end{tabular} & \begin{tabular}{c} **Hardware** \\ **Computation Time/μs** \\ \end{tabular} & \
\begin{tabular}{c} **Hardware** \\ **Inference Time/μs** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Reduction Time** \\ \end{tabular} \\ \hline Mixed & 1 & 1030 & 1210 & 2240 & \\
2 & 1069 & 1389 & 2458 & \\
3 & 1102 & 1301 & 2403 & 30.65\% \\
4 & 1105 & 1297 & 2402 & \\ average & 1076 & 1299 & 2375 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 4 Discussion
### Experiments and Comparative Analysis
#### 4.3.2 Robustness Analysis  
In this part, the robustness experiments of post-processing, such as noise, blur and JPEG compression, are evaluated on the CASIA and NIST16 dataset. The post-processing parameters are set as follows: the mean value is zero, and variances are 0.001, 0.005, 0.01 and 0.02 in Gaussian noise; the window size is 3 \(\times\) 3, and variance are 0.01, 0.1, 0.3 and 0.5 in Gaussian blur; the quality factors are 80, 70, 60 and 50 in the JPEG recompression, respectively. The robustness results of \(F_{1}\) scores on noise, blur and JPEG are shown in Figure 5. Since the tampered trace features are hidden in JPEG compression and noise, the F1 score has slightly decrease. However, the proposed methods with GE and IIC are robust to JPEG compression and noise within a certain range. For Gaussian blur, our network framework maintains great robustness. The effects of different robustness experiments on the IIC and GE are shown in Figure 6, which can be seen that the proposed dual-streams framework has great robustness on noise, blur, and JPEG compression.
# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 4 Experimental Results
### Experimental Summary and Discussion  
To combine the advantages of two excellent algorithms and enhance the attention ability of the network, the proposed Swin-PANet integrates both attention guiding decoder and Swin Transformer into prior attention network for capturing both global contextual information and local features. Furthermore, an enhanced attention block is designed for better performing the coarse-to-fine strategy and enhancing attention ability of the network, where experiments and ablation studies on the GlaS, MoNuSeg, and ISIC 2016 datasets have been implemented in our model for overall evaluation. Since the proposed Swin-PANet can extract rich global contextual information with the shifted-window self-attention mechanism in prior attention network, and enhanced attention blocks are equipped in hybrid Transformer network to capture the long-range dependency along channel-wise for further enhancing the attention ability of the network, it can be seen in the quantitative comparison on GlaS and MoNuSeg datasets that Swin-PANet consistently achieves better performance than other state-of-the-art methods. Compared with the UCTransNet, the proposed Swin-PANet gains improvement range from 89.84% (82.24%) to 91.42% (84.88%) in terms of Dice and IoU metrics on GlaS dataset, and 79.87% (66.68%) to 81.59% (69.00%) in terms of Dice and IoU metrics on MoNuSeg dataset. On the other hand, ISIC 2016 dataset is selected for evaluating the contribution of each module in Swin-PANet. From the quantitative and visual comparisons of skin lesion segmentation, the results confirm the effectiveness of enhanced attention block and dual supervision strategy in Swin-PANet. Enhanced attention block is applied in hybrid Transformer network to achieve better feature fusion between global context and local contexts along channel-wise.  
Although Swin-PANet achieves better performance compared with some state-of-the-art methods, the proposed network still has limitation in the ability of transfer learning.  
Figure 6: Visual comparisons of skin lesion segmentation results produced by kinds of baselines on ISIC 2016 dataset. The baseline denotes the traditional network of U-Net. The Method A, Method B, and Method C denotes, respectively, the baseline with AGD, Swin-Trans, AGD + EAB. The regions highlighted by red boxes show that Swin-PANet has the capability of modeling global dependencies between boundary pixels and accurately segmenting the ambiguous boundary.  
As shown in Table 2, Swin-PANet is implemented on another dataset such as ISIC 2016, and achieves the performance of 90.68% and 84.06% in terms of Dice and IoU metrics. Compared with some special designed methods such as FAT-Net [42], Ms RED [43], and BAT [44], Swin-PANet has an immense gap in performance of skin lesion segmentation. How to make Swin-PANet perform well in different segmentation tasks is a challenging task, and we believe the backbone ability of Swin Transformer and the potential of the combination of Transformer and CNNs can make this task possible.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 4 Optimization Algorithms
### The Proposed Improved Equilibrium Optimizer Algorithm  
To address the limitations of the standard Equilibrium Optimizer (EO), such as slow convergence, tendency to fall into local optima, and Suboptimal Balance between Exploration and Exploitation, this study develops an Improved Equilibrium Optimizer (IEO) by introducing several targeted modifications. First, a Tent chaotic map is applied during the initialization phase to enhance population diversity and avoid premature convergence. Second, an elite non-dominated sorting mechanism is embedded to allow the optimizer to handle multiple objectives without requiring explicit scalarization, thereby preserving the integrity of each objective function. Additionally, a stochastic mutation strategy is adopted to increase the global exploration capability of the algorithm, and a binomial crossover operator is incorporated to promote information exchange and accelerate convergence. These improvements are seamlessly integrated into the EO framework, collectively contributing to enhanced search efficiency, solution diversity, and convergence stability. The following subsections describe the specific design and rationale of each enhancement in detail.
## 3 Results and Discussion
### Impregnation Methodology
#### 3.1.1 Cleaning of the Ceramic Material  
Industrial Leca production results in the presence of various impurities on the surface, in the form of powder, which needs to be removed to facilitate and guarantee the success of the impregnation process. For this purpose, the Leca before impregnation was washed as described in Section 2.  
In fact, the three initial washing cycles were very important to remove most of the mud and fine particles accumulated on the Leca surface and pores. After this, the sensitive and refined cleaning of the surface was made with tap water or acid solution. The results obtained in terms of TiO\({}_{2}\) impregnated weight reveal that 18.56 mg of TiO\({}_{2}\)/g of Leca were possible for the case where Leca was only washed with four cycles of water. The TiO\({}_{2}\) impregnation yield decreased to 8.98 mg/g of Leca when the Leca particles were washed in the 4 th cycle with a 5% HCl aqueous solution. Comparing both results, using acid to clean the Leca surface at the final step reduces about half the TiO\({}_{2}\) impregnated mass. This fact is a consequence of the HCl corrosive effect, which generates defects at the Leca surface sites, promoting the leaching of metal oxides such as iron from the Leca surface. This effect was also verified by [PERSON] et al. [33] during the cordierite synthesis from a natural zeolite. Considering these results, only water was selected for washing the Leca for subsequent parameters studies.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 8 Experimental Evaluation
### Lossless Data Reduction Algorithms  
The next subsections will present an explanation of the lossless algorithms, in addition to showing the tabulated metrics with the experimental results.
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
###### Abstract  
The Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) measures visible and near-infrared light extending over seven orders of magnitude of dynamic range. This makes radiometric calibration difficult. We have observed that DNB imagery has striping, banding and other nonuniformities--day or night. We identified the causes as stray light, nonlinearity, detector crosstalk, hysteresis and mirror-side variation. We found that these affect both Earth-view and calibration signals. These present an obstacle to interpretation by users of DNB products. Because of the nonlinearity we chose the histogram matching destriping technique which we found is successful for daytime, twilight and nighttime scenes. Because of the very large dynamic range of the DNB, we needed to add special processes to the histogram matching to destripe all scenes, especially imagery in the twilight regions where scene illumination changes rapidly over short distances. We show that destriping aids image analysts, and makes it possible for advanced automated cloud typing algorithms. Manual or automatic identification of other features, including polar ice and gravity waves in the upper atmosphere are also discussed. In consideration of the large volume of data produced 24 h a day by the VIIRS DNB, we present methods for reducing processing time.  
VIIRS; DNB; Suomi NPP; JPSS; imagery; striping; nonuniformity; destriping; calibration; low-light; remote sensing; stray light +
Footnote †: journal: Imaging
## 4 Data Results
### Correlation Analysis  
Correlation analysis was carried out on tourists' education mode preference, spatial element preference, environmental education perception and effectiveness data. Pearson correlation analysis shows that through pairwise comparison, there is significant positive correlation with positive influence (\(p<0.01\)). Therefore, H1 (r = 0.36), H2 (r = 0.47), H3 (r = 0.49), and H4 (r = 0.52) hypotheses are valid. The influencing path established by path analysis and regression coefficient is shown in Figure 3 It is found that the effect of educational mode preference on environmental education effectiveness and perception is small, of which the effect on the environmental education effectiveness (0.248) is slightly greater than that on the environmental education perception (0.113); the effect of spatial element preference on the environmental education perception is the largest (0.424), followed by its effect on the environmental education effectiveness (0.368).  
Regression analysis was used to further analyze the relationship among the four. The environmental education effectiveness was taken as the dependent variable, and the spatial element preference of education modes was controlled to observe the effect on it. As can be seen from Table 3, in education modes, guided and self-guided resource element interpretations have significant positive influence on the environmental education perception; guided and self-guided participatory interactive experiences, and self-guided resource element interpretation have significant positive effect on the environmental education effectiveness. The open space of spatial elements, and cobblestone paved road positively affect the environmental education perception. Open space, artificial road with interpretation facilities and wood paved road have significant positive influence on the environmental education effectiveness. Artificial roads without interpretation facilities has significant negative impact on the environmental education effectiveness.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Environmental Education Perception**} & \multicolumn{3}{c}{**Environmental Education Effectiveness**} \\ \hline  & B & SE & t & \(p\) & B & SE & t & \(p\) \\ Educational mode & 1.785 & 0.205 & 8.703 & 0.000 ** & 2.271 & 0.165 & 13.728 & 0.000 ** \\ preference constant & 1.785 & 0.205 & 8.703 & 0.000 ** & 2.271 & 0.165 & 13.728 & 0.000 ** \\ XD1 & 0.159 & 0.061 & 2.617 & 0.009 ** & 0.086 & 0.049 & 1.757 & 0.08 \\ XD2 & 0.025 & 0.062 & 0.395 & 0.693 & 0.113 & 0.05 & 2.253 & 0.025 * \\ ZD1 & 0.211 & 0.057 & 3.743 & 0.000 ** & 0.148 & 0.046 & 3.253 & 0.001 ** \\ ZD2 & 0.027 & 0.067 & 0.404 & 0.687 & 0.112 & 0.054 & 2.085 & 0.038 * \\ R\({}^{2}\) & & & 0.144 & & & & & & \\ Adjusting R\({}^{2}\) & & & 0.136 & & & & & 0.212 & \\ F & F (4,464) = 19.470, \(p\) = 0.000 & & & & & & & \\ D-W value & & & 1.608 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of spatial preference of environmental education on its perception and effectiveness.  
Figure 3: Influencing path (* \(p<0.05\)** \(p<0.01\)).