esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 6 Evaluation
### Network Load  
In the case where the number of network nodes remained fixed at 16,000 and the block size stayed at 0.5 MB, we conducted simulation experiments based on the existing bandwidth utilization data of NDN multicast and IP unicast [14]. Due to the characteristic of ICN networks supporting network caching, the data flow (output traffic) from blockchain nodes in sending blocks to the entire network is much smaller than the data flow (input traffic) received by the entire network of blockchain nodes. Hence, we selected the data flow received by blockchain nodes as the evaluation metric. The variation in block broadcast  
Figure 19: Broadcast time with different numbers of nodes.  
Figure 20: Block broadcast time of BLOCK-ICN under 100,000 nodes varies with changes in broadcast coverage.  
bandwidth to the entire network for BLOCK-ICN and the bitcoin network with respect to network size is shown in Figure 22. The horizontal axis represents the network size, while the vertical axis represents the average flow of a single block being broadcast to the entire network. Based on experimental results with other parameters controlled, it can be observed that as the blockchain network size increased, the block network bandwidth consumption for both protocols gradually increased. However, for the same network size, the block broadcast bandwidth consumption for BLOCK-ICN was smaller than that of the bitcoin network. For instance, when the network size was 16,000, the block broadcast bandwidth consumption for the bitcoin network was 63,996 MB, while for BLOCK-ICN, it was 19,998 MB, resulting in a reduction in block broadcast traffic by 68.8%. In conclusion, BLOCK-ICN had a better network load performance compared to the bitcoin network.  
Finally, we used the SimBlock visualization tool simblock-visualizer to demonstrate the propagation process of blocks in the experimental data. Figures 23 and 24, respectively, show the communication between nodes in the Bitcoin network and the BLOCK-ICN network with 10% ICN nodes using simblock-visualizer. In these screenshots, blockchain nodes are represented in blue, while the messages between nodes are represented in green. From the figures, it can be clearly seen that, at the same timestamp, the BLOCK-ICN network with deployed ICN nodes demonstrated significantly improved block broadcasting efficiency compared to the Bitcoin network.
# VanityX: An Agile 3D Rendering Platform Supporting Mixed Reality
## 6 Test Cases
### Rendering Medical Imaging  
Direct volume rendering (DVR) is best suited for medical and other scanned images. A medical image represents a thin slice of a human body taken from a scanning device such as computed tomography (CT) or magnetic resonance imaging (MRI). Each slice is composed of individual pixels arranged into a rectangular grid--combining individual 2D images into a 3D grid, we obtain clouds of points. An element in the 3D grid is called a voxel (volume element).  
Many techniques including deep and machine learning [44] have been developed for the segmentation [45], reconstruction [46], and registration [47] of images, including medical ones. VanityX supports various volume rendering techniques such as texture-based rendering, ray-cast rendering, and rendering using transfer functions. With reference to Figure 15, we render a slice of a human abdomen (liver) and a human skull. The slice of a human abdomen is anonymized and rendered using texture-based rendering, where each axial slice is represented as a texture mapped (sampled) onto a quad. The human skull is rendered using transfer functions that map scalar data onto color and opacity to depict relevant features of the model being rendered.
## 3 Mathematical Modeling and Design Methodology
### Characteristics of a PV Panel  
In order to accurately size hybrid renewable energy systems (HRESs), including a solar PV array combined with pumped hydro storage, the proper design of the PV system is important to determine the realistic number of PV modules [17]. A \"KC200 GT\" polycrystalline PV panel was considered in this paper. Table 1 presents the characteristics of this PV panel as provided by the manufacturer. Further, the I-V and P-V characteristic curves of the mentioned PV panel for the ISD, SD, and TD models were simulated as shown in Figure 8.  
Figure 7: Equivalent circuit of a PV array using the TD model.  
Here, 25 \({}^{\circ}\)C is considered as a special case in Figure 8 in order to depict the I-V and P-V characteristics under standard test conditions (STCs), and to show the differences between the ISD, SD, and TD photovoltaic models. Moreover, Figure 8 shows that for a single PV panel, the power that can be extracted is 189.149 W in the case of the TD model. This power increases by 8.43% with the ISD model. So, the TD model gives more realistic and accurate results because it takes into account the realistic phenomenon of recombination losses. This will lead to a true size of the HRES, especially with the large-scale power system, whose load is shown in Figure 3.  
In summary, many RES studies use the ISD model for PV system design [32; 33]. However, the TD model (shown in Figure 6) is more accurate, because it takes into account both the diffusion and the recombination effects. This results in a more precise mathematical description of the solar panel, leading to a true size of the PV array and true reliability and environmental estimates of the entire system [17].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters (KC200 GT Datasheet)** & **Value** \\ \hline Open circuit voltage (\(V_{\alpha c}\)) & 32.9 V \\ Short circuit current (\(I_{sc}\)) & 8.21 A \\ Maximum power at STC (\(P_{max}\)) & 200.143 W \\ No. of Cells in series & 54 \\ Temperature coefficient of \(V_{\alpha c}\) & \(-\)0.35502 V\({}^{\circ}\)C \\ Temperature coefficient of \(I_{sc}\) & 0.06 A/\({}^{\circ}\)C \\ Maximum Power Voltage (\(V_{mpp}\)) & 26.3 V \\ Maximum Power Current (\(I_{mpp}\)) & 7.61 A \\ Diode ideality factor (\(\alpha_{1}\)) & 0.97736 \\ Diode ideality factor (\(\alpha_{2}\)) & 1.2 \\ Light-generated current (\(I_{L}\)) & 8.2288 A \\ Shunt resistance R\({}_{\text{sh}}\) (ohms) & 150.6921 \(\Omega\) \\ Series resistance Rs (ohms) & 0.34483 \(\Omega\) \\ Efficiency & 16\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasheet of the selected PV module.  
Figure 8: Current-Voltage (I–V) and Power-Voltage (P–V) characteristic curves of the solar PV selected module at standard test conditions (STC).
## 3 Results and Discussion
### Fourier Spectrum Analysis of Acceleration  
In addition to the previously mentioned seismic inertial force, the frequency change that takes place during the seismic wave transmission process also affects the slope's dynamic response characteristics, as can be observed by looking at the Fourier spectrum of the slope's acceleration [34]. Using the Fast Fourier Transform for the signals acquired at different monitoring points, we may accomplish this by transferring the acceleration signals from the time domain to the frequency domain for Fourier spectrum frequency analysis. Meanwhile, [PERSON] et al. [35] showed that when the seismic wave amplitude is more than 0.2 g, the top of the slope tends to produce substantial strains, which lowers the high-frequency response of the seismic wave. In order to fully examine the variable features of the Fourier spectrum under earthquake action, this study focuses on the Fourier spectrum of slope acceleration when the amplitude of the seismic waves is less than 0.2 g.  
The spectrum features of the slope surface monitoring locations (M1, M7, M12, M16, and M19) under the action of 0.1 g seismic waves were examined as an example based on the numerical simulation results with superior time continuity. It is evident from the findings in Figure 7 that the slope surface monitoring point's acceleration rises with elevation. The Fourier spectrum gradually changes from having a single peak (\(f_{1}\)) to having two peaks (\(f_{1}\) and \(f_{2}\)). The amplitude of the low-frequency peak (\(f_{1}\)), which is near the main frequency of  
Figure 6: \(AAF\) response law: (**a**) \(AAF\) response law along elevations at different elevation monitoring points in the slope; (**b**) \(AAF\) response law along slope directions at the same elevation monitoring points in the slope.  
the input seismic wave (\(f_{0}\)) in Figure 5b, does not change much as the elevation rises, while the amplitude of the high-frequency peak (\(f_{2}\)) gradually increases as the elevation rises. This suggests that the Fourier spectrum of the slope acceleration's amplification impact is selective, which is in line with the findings of earlier research [36].
# Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction
## 3 Case Study
### Comparative Analysis of Different Interval Prediction Models
#### 3.3.2 Comparison of Machine Learning (ML) and Deep Learning (DL) Models  
In this section, four commonly utilized AI regression algorithms will be chosen to construct SCQP-based models for comparison with the Bi-SCQLSTM. These algorithms will be used. These models are split conformal extreme learning machine (SCQELM), split conformal random forest (SCQRF), split conformal recurrent neural network (SCQRNN), and split conformal long-short term memory (SCQLSTM). Figure 11 compares the visualization results of the Bi-SCQLSTM with other SCQP-based models on _PICP_, _MPIW_, and _CWC_.  
As can be seen from Figure 11, SCQELM and SCQRF perform the worst of the five models. The effective coverage of the PIs constructed by SCQELM deviates significantly from _PINC_, with a _PICP_ of only 0.856, while the corresponding _MPIW_ and _CWC_ are 7.908 and 28.151. The performance of SCQRF slightly outperforms, with _PICP_ reaching 0.871, while _MPIW_ and _CWC_ are 7.422 mm and 23.775 mm, respectively. On the contrary, SCQRNN, SCQLSTM, and the Bi-SCQLSTM based on DL algorithms all achieve satisfactory results. The average _PICPs_ of three models all than 0.9, with the proposed Bi-SCQLSTM being the highest to 0.952. Correspondingly, its _MPIW_ and _CWC_ are both only 5.801 mm.  
It is clear that the advantages of DL techniques in constructing dynamic regression models extend to interval prediction as well. Additionally, compared with the unidirectional deep neural network, the Bi-LSTM with different time-dimensional hidden layers can help the proposed model to explain more possible trends in dam deformation by integrating past and future information. As a result, the Bi-SQLSTM still has the best generalization ability and robustness among many SCQP-based models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Deformation Interval Prediction Models** & **Computation Time (s)** \\ \hline Bi-SCQLSTM & 32.261 \\ Bi-QLSTM & 32.094 \\ Bi-SCLSTM & 28.513 \\ CIE & 28.024 \\ GPR & 20.932 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computation time of different interval prediction results.
## 5 Discussion
### Impact of Learning Optimizers  
The choice of optimizer significantly influences the training dynamics and performance of deep learning models. Optimizers such as SGD, Adam, and AdamW each employ distinct strategies for navigating the loss landscape, impacting metrics. SGD (Figure 7c) consistently delivers superior performance across models like ResNet101 and EfficientNet-B2, leveraging momentum to achieve high accuracy and robust generalization. In contrast, Adam and AdamW, which use adaptive learning rates, often underperform with certain architectures (e.g., ViT-B-16 and ConvNeXt) under the given hyperparameters, resulting in lower accuracy and poor convergence. The radar graphs (Figure 7a-c) generated for each optimizer visually illustrate these effects, comparing the performance of the best models across the evaluated metrics, and highlighting the strengths and limitations of each optimizer in optimizing diverse model architectures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Optimizer** & **Learning Rate** & **Scheduler** & **Loss Function** & **AUC** \\ \hline ResNet50 & SGD & 0.01 & CosineAnnealingWarmRestarts & CrossEntropy & 0.996138 \\ ResNet101 & SGD & 0.01 & StepLR & CrossEntropy & 0.995715 \\ EfficientNet-B2 & AdamW & 0.0001 & StepLR & LabelSmoothing & 0.995261 \\ DenseNet121 & SGD & 0.01 & CosineAnnealingLR & CrossEntropy & 0.995232 \\ ConvNeXt & SGD & 0.001 & CosineAnnealingLR & LabelSmoothing & 0.99426 \\ ViT-B-16 & SGD & 0.001 & StepLR & CrossEntropy & 0.993844 \\ MobileNetV3 & SGD & 0.01 & StepLR & CrossEntropy & 0.993534 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimal hyperparameter configurations for maximizing area under the ROC curve (AUC) across deep learning models.
## 3 Specimens and Experimental Techniques
### Electronic Speckle Pattern Interferometry (ESPI)  
We input the material constants derived from measurements and calculations using RM and the material parameters provided by manufacturers or literature into finite element software to analyze vibration characteristics and obtain resonant frequency values and mode shapes. Next, we used the vibration characteristics obtained using optical non-destructive testing to verify the accuracy of RM.  
Derived from holography, ESPI involves the casting of two coherent light beams on the target object and then the differences and changes in the optical path lengths are used to obtain the deformation data of the target object. The measurement method commonly used when ESPI is applied to vibrations is the time-averaging method. Within the exposure time of the CCD (charge-coupled device), images of the vibrating object are captured at different times, and a zero-order Bessel function is used to modulate the interference fringes. The brightest region in the interference images is the nodal line, where the displacement is zero, and the light and dark fringes show the contours of displacement. The resolution of the measurement system depends on the light source wavelength. We set up a sub-micron level ESPI system so as to obtain the full-field distributions of micro-vibration displacements in the interference patterns [25, 26]. We then compared the ESPI measurement results of the low-frequency out-of-plane vibrations and the high-frequency in-plane vibrations in the piezoelectric specimens with the FEM analysis results derived from the piezoelectric material constant inputs.
## 3 Methodological Approach
### Evaluation Metrics  
Both for the sake of comparability with similar studies and to adequately assess the performance of the classifier, it was vital to employ descriptive evaluation metrics that were capable of identifying and capturing the ability of the classifier. Apart from providing visual test results, the performance of all experimental set-ups conducted in this study were assessed with widely used evaluation criteria: the F1 score, precision, recall, IoU, and confusion matrix. This subsection aims to describe these criteria briefly.
# Stabilization of Transverse Modes for a High Finesse Near-Unstable Cavity
## 4 Experimental Setup
The red lines show the direction of laser beams, and the black lines denote the BNC cable. The Matisse 2 DX dye laser was pumped by a Millennia eV 10 W (a diode-pumped solid-state laser from Spectra-Physics), and finely tuned to the frequency 494892 GHz. EOM was an electro-optic phase modulator. Lens was used for mode-matching. M11 and M12 were two mirrors to change the direction and position of the incident laser light. PBS: polarization beam splitter. QWP: quarter-wave plate. The FP cavity was formed by three parts, a plane mirror M1, a temperature-controlled platform, and a concave mirror M2 with a PZT on its back side. CCD: charge coupled device. PDH-1000-5D is an apparatus which integrates photon detector and frequency mixer. PI servo: high-speed proportion integration differentiation controller. \[\delta\
u=\frac{\Delta\
u_{FSR}}{F}\,, \tag{4}\]  
where \(n_{0}\) is the refractive index of air. For the experimental parameters in our case, the free spectral range \(\Delta\
u_{FSR}\) and the linewidth \(\delta\
u\) were calculated to be 2.78 GHz and 379 kHz, respectively. The reflection beam from the FP cavity was selected and fed into a photo-detector (Hamamatsu S5821) using a quarter-wave plate (QWP) and a polarization beam splitter (PBS), which formed an opto-isolator. The photo-detector was a part of the stable laser system PDH-1000-5D (Stable Laser Systems), which also included a function generator (sine wave with a frequency of 5 MHz) and a frequency mixer. Both the electric signals from the photo-detector and the function generator were fed into the frequency mixer, and the product of the two was the error signal. The error signal was fed into a high-speed servo controller (LB1005, Newport). By choosing a proper PI Corner and Gain, one can get the feedback signal that was amplified by 60 times with a low noise through the High Voltage Amplifier (Waviclelaser), and then the feedback signal was loaded onto the ring-shaped piezoelectric transducer to form a closed loop.
## 3 Results and Discussion
### Skin Parameter Estimation
#### 3.4.3 Analysis of the Skin Macrorelief Parameters  
Analysis of the skin macrorelief parameters permits the evaluation of the skin structure and, in particular, the verification of the anti-aging effect of a given cosmetic product [41]. The desired effect was assumed to be a reduction in the values of the total wrinkle area and the wrinkles' mean length and depth, determined on the basis of a special way of lighting the skin replicas [44; 46].  
Analysis of the changes in the skin macrorelief parameters after the 4-week application of the macroemulsions tested, Figure 5, showed their positive effect; however, the effect of macroemulsion 2 was more positive, in which the _Solidago virgaurea_ extract was more compatible with its other components. The 4-week application of macroemulsion 2 resulted in a nearly 2% (\(p<0.05\)) greater reduction in the total wrinkle area and an over 12% greater reduction (\(p<0.05\)) in the mean wrinkle length than that observed for macroemulsion 1. Only the mean wrinkle depth was reduced to a greater degree by macroemulsion 1  
Figure 4: Percentage changes \(\Delta\%\) in the selected parameters of the skin topography determined in vivo as a result of the application of macroemulsions 1 and 2.  
application than the application of macroemulsion 2; however, the difference was statistically insignificant (of 1%), indicating no differences in this effect between the two cosmetic products. The results of the macroemulsions' effects on the skin macrorelief are not fully consistent with those concerning the skin topography. Nevertheless, the results definitely point to the intense anti-aging and moisturizing effects of macroemulsion 2. The desirable changes in the total wrinkle area and the wrinkles' mean length are fully consistent with the reduction in the values of the skin topography parameters SEr, SESm and SESc.
## 1 Introduction  
The generation of femtosecond laser-induced periodic surface structures (fs-LIPSS) is a high-throughput, parallel surface-structuring method with subwavelength or deep-subwavelength resolution [1; 2; 3; 4; 5; 6; 7]. The modified surfaces with LIPSS patterns have important applications in the fields of structural color [8; 9; 10; 11], super-hydrophobicity [12; 13], self-cleaning [12; 14], and anti-bacterial [15; 16], among others. One commonly accepted formation mechanism of fs-LIPSSs is the interference between the incident femtosecond laser and the laser-induced surface plasmon polaritons (SPPs) [17; 18; 19]. Therefore, compared with the scalar femtosecond laser, the vectorial femtosecond laser can locally manipulate the propagation direction of SPPs and produce more versatile surface patterns [20; 21; 22; 23; 24; 25; 26; 27; 28]. In addition, the employment of the vectorial femtosecond laser to produce LIPSSs on a target surface is also an effective method used to investigate the unique excitation/propagation modes of SPPs by freezing the SPPs into the surface patterns.  
In this paper, a non-cylindrical femtosecond vector beam was generated by two phase gratings used correspondingly as the beam splitter and combiner. The polarization of the femtosecond vector beam at the beam cross-section (_xoy_ plane) varied only along the vertical (y) direction and was kept constant along the horizontal (x) direction. Periodiccurved ripples were produced on the tungsten surface after it was irradiated by the vectorial femtosecond laser. By increasing the polarization variation speed along the vertical direction, we found that uniformly distributed periodic nanoholes with an average diameter of 406 nm were formed at the vertexes of the curved ripples. The vertexes of the curved ripples were actually the converging points of the SPPs induced by the vectorial femtosecond laser. Therefore, we deduced that the converging SPPs induced by the non-cylindrical vectorial femtosecond laser interfered with the incident laser, leading to the generation of the semi-circular curved ripples and the nanoholes at the vertexes of the ripples. The results in this paper demonstrated a high-throughput nanohole fabrication method and exhibited the SPPs' propagation dynamics by freezing the SPPs into the final LIPSS patterns.
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Evaluation Methodology
#### 3.1.4 Image Fusion Evaluation Index--\(CQ_{max}\)  
All the fusion results were evaluated using the \(CQ_{max}\) index (see Figure 7). As shown in Figure 7, larger values of \(CQ_{max}\) indicated better fusion results, in which the fused image was more similar to the original multispectral image. The figure shows fusion results produced by three different methods applied to GF-1 data: PCA (0.0281), NNDiffuse (0.0257), and GS (0.0154). The \(CQ_{max}\) values for PCA and NNDiffuse results are similar, indicating the two techniques are superior, with PCA slightly outperforming NNDiffuse. These panchromatic and multispectral fusion methods were also applied to GF-2 data, producing the following \(CQ_{max}\) values: NNDiffuse (0.0322), PCA (0.0312), and GS (0.0307). These effects are similar, with NNDiffuse producing the best results. Results using WV-2 data were as follows: NNDiffuse (0.0427), GS (0.0177), and PCA (0.0176). In this case, the NNDiffuse technique produced the best results, indicating the fused image was closest to the original multispectral data.  
The \(CQ_{max}\) index was used to evaluate fused enclosure seine images for comparison with the initial multispectral images. As shown in Figure 7, PCA produced the best fusion results for GF-1 data, while NNDiffuse produced the best results for GF-2 and WV-2 images. Among these, NNDiffuse applied to WV-2 data produced the best results. However, this result is not completely consistent with the previous ones.
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 3 State-of-the-Art Privacy Measures
### Privacy-Preserving Time-Series Data
Weaknesses:  
When applying the privacy techniques, there must be knowledge about the intended use of the data. An incorrect privacy filter would completely destroy the utility of the data. For some data protection techniques, the applications that process the data must be adapted accordingly. Information emphasizing, for instance, provides only maximums and minimums instead of a continuous data stream.