esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Comparison between GRASPSPP and Iterative Search Algorithm  
In this section, we compare the results between the GRASPSPP algorithm and the iterative search (IS) algorithm presented in 2019 [24] by [PERSON] et al. The authors performed 20 independent runs at 1000 iterations; thus, we configured our computational experimentation with the same runs and iterations.  
Figure 8 shows the comparison of the average errors (_errAvg_) for the 20 runs for each instance set, for which we also calculated the average and standard deviation among all the sets of instances for the GRASPSPP (1.13 average, 0.06 standard deviation) and IS (1.12 average, 0.02 standard deviation). These _errAvg_ are the average of the minimum error and the maximum error:  
\[errAvg=\frac{minError+maxError}{2} \tag{9}\]  
and the error is calculated as follows:  
\[error=\frac{obtainedHeight}{bestHeight} \tag{10}\]  
Additionally, this figure shows that GRASPSPP outperforms IS in six instances, and IS outperforms GRASPSPP in one instance. The Wilcoxon test result showed that GRASPSPP and IS produce statistically equivalent solutions with a significance value of 0.233.  
Figure 7: Waste performance comparison.  
Figure 9 shows the computational times between the GRASPSPP (2.70 average, 2.07 standard deviation) and IS (17.77 average, 9.51 standard deviation) algorithms. Here, the computational times are the average of the computational time of the 20 runs per set of instances. In all cases, the IS algorithm obtains higher computational times than GRASPSPP. Additionally, the Wilcoxon test result showed that GRASPSPP statistically outperforms IS with a 98.2% certainty.
## 6 Comparison of the Pseudo-Dynamic Test and Nonlinear Dynamic Analysis Results
### Nonlinear Dynamic Analysis Results before and after Seismic Strengthening
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Building**} & \multirow{2}{*}{**Floor**} & \multirow{2}{*}{**Max. Response Strength**} & \multicolumn{2}{c}{**Max. Response**} & \multirow{2}{*}{**Final**} & \multirow{2}{*}{**Degree of Earthquake**} \\  & & \(V_{max}\) (kN) & & **Displacement Ratio** & & \\  & & & \(\delta_{max}\) (mm) & & & \\ \hline \multirow{3}{*}{Non-strengthened} & 1 & 3584.2 & 49.1 & Shear collapse & Collapse \\ \cline{2-6}  & 2 & 2651.9 & 38.2 & Shear failure & Heavy \\ \cline{2-6}  & 3 & 1411.3 & 22.6 & Shear crack & Medium \\ \hline \multirow{3}{*}{
\begin{tabular}{c} CCSS- \\ strengthened \\ \end{tabular} } & 1 & 4244.3 & 13.6 [0.28] & Shear crack & Small \\ \cline{2-6}  & 2 & 3671.4 & 18.1 [0.47] & Shear crack & Small \\ \cline{2-6}  & 3 & 2269.2 & 15.0 [0.66] & Shear crack & Small \\ \hline \hline \end{tabular}  
* The degree of earthquake damage was evaluated based on [22] and [PERSON], [PERSON], and [PERSON] [23].  
\end{table} 
Table 10: Comparison of the maximum seismic response and extent of damage before and after seismic strengthening. Figure 23: Seismic response time–displacement history of non-strengthened buildings and buildings strengthened by CCSS (1F). As shown in Figure 24, shear failure occurred in 15 out of 24 columns on the first floor of the unreinforced building and three columns on the second floor. Shear cracking occurred in all columns on the third floor. In the case of the CCSS-reinforced building shown in Figure 24. However, shear cracking of \(\mu_{s}=2.5\) or less or no cracking (less than \(\mu_{s}=1\)) occurred in the columns on all floors, indicating that no column member exceeded the point of shear failure. According to [PERSON] [22] and [PERSON], [PERSON], and [PERSON] [23], the seismic damage to the unreinforced building is the level of collapse and the damage to the CCSS-reinforced building can be judged to be small. These results satisfy the level of performance that leads to less-than-moderate seismic damage (life safety), which is the target performance under a seismic load of 200 cm/s\({}^{2}\), as described in Section 7.1, indicating the commercializability of the CCSS method.
# Watermarking Protocols: A Short Guide for Beginners
## 4 Performance Issues
Therefore, the tests mentioned above show that the major contribution is \(T_{sel}^{1}\) (phases 4 and 5 in Figure 1) and is less than 2 min on an Intel Core 2 Quad CPU at 2.40 GHz, used as a single processor. The remaining contribution \(T_{buy}^{1}\) (phase 9 in Figure 1) is about 30 s on the same computing system. The results achieved by the QIM watermarking algorithm are essentially similar to those obtained by the spread spectrum watermarking algorithm since the two expressions (1) and (2) require a similar number of multiplication exponentiations in the case of the use of the same homomorphic cryptosystem. On the contrary, a scheme based on joint watermarking requires a reduced computational load. In the tests documented in [53; 60; 61], images of 1024 \(\times\) 1024 pixels were watermarked by applying the expressions (3) and (4). The watermarked features were extracted by choosing 4 frequency DCT coefficients, excluding the DC value, from the 8 \(\times\) 8 DCT blocks composing the images. This results in \(M=2^{16}\) content features to watermark. In all experiments, the LUT size was set to \(T=2^{16}\), and \(R=4\) LUT entries were added to encrypt each content feature. The embedded watermarks were 128-bit strings. Under the above assumptions, the total computation cost \(T_{total}^{2}\) can be approximated by the sum of two similar contributions, denoted as \(T_{sel}^{2}\) and \(T_{buy}^{2}\), respectively: the former corresponds to the evaluation of (3), whereas the latter corresponds to the evaluation of (3). This is because the remaining computation costs concern the generation of LUTs, which are long-term encryption/decryption tables used to protect a single content distributed to multiple buyers (see Section 3.4). Therefore, \(T_{total}^{2}\approx T_{sel}^{2}+T_{buy}^{2}\) is about 9 s on an Intel Core 2 Quad CPU at 2.40 GHz used as a single processor. Moreover, \(T_{total}^{2}\) is split between the buyer and seller. The experiences documented above show that the protection schemes based on homomorphic encryption perform worse than joint-watermarking-based schemes since they require heavy multiplication exponentiations. On the contrary, the schemes based on joint watermarking mostly require sums. Moreover, they make it possible to distribute the computational costs of the protocols between buyers and sellers, thus improving scalability.
# Determining the Quality of a Dataset in Clustering Terms
## 2 Materials and Methods
### Methodology
#### 2.2.7 Multilevel (Louvain) Algorithm [27]  
1. Initially, each node is assigned to a different cluster. The number of clusters is equal to the number of nodes. For each neighbour \(j\) of node \(i\), it is checked whether the total modularity increases by moving node \(i\) from its cluster to the cluster of node \(j\). Node \(i\) is moved to the cluster of node \(j\) for which the modularity increment is the largest (positive). If none of the observed increments is positive, node \(i\) remains in its cluster. Step two is repeated sequentially for all nodes. This is called a single iteration. The iteration is also repeated until modularity improvement cannot be achieved. Note that a node can be and usually is visited more than once.
2. The second step is to rebuild the new network by grouping the nodes labelled in the same community (merging individual nodes) in step 1.  
The edge weights between two new communities (containing more than 1 node) are determined by summing the edge weights from each node in one community to the other. From nodes in the same community, the edges form loops. The diagonal elements in the new neighbourhood matrix are nonzero.  
One iteration of steps 1 and 2 is called a transition. After the first transition, the algorithm attempts a second transition and so on. The number of communities decreases after each transition. This continues until no changes in the communities are observed and maximum modularity is reached.
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 3 Wireless Communication System Applied in Underwater Devices
### Application and Limitations of Single Mode Underwater Communication Technology
Electric field communication, inspired by weakly electric fish, has shown promise in short-range, low-detectability scenarios. Additionally, ongoing work on quantum-secured optical channels and covert acoustic protocols represents a new frontier for secure and invisible underwater data exchange. Overall, while single-mode underwater communication technologies such as acoustic and optical methods offer distinct advantages, their respective limitations in terms of transmission range, data rate, robustness, and adaptability underscore the necessity for future systems to explore hybrid or multi-modal solutions.
## 3 Flow Control Devices for Gust Load Alleviation
### Fluidic Actuators  
As a means for active aerodynamic flow control, fluidic actuators, including synthetic jets, circulation control using Coanda effects, jet blowing and suction, have been investigated extensively. These methods have been shown to be effective for aerodynamic improvements through boundary-layer modulation, such as drag reduction [73; 74], delay of transition [75; 76] and flow separation control [10; 77; 78], flow improvement for air intakes [79; 80], stall control and lift augmentation [81; 82].  
Flow control for load control is currently attractive for modern aircraft design, as it has the potential for flapless control with benefits including fewer moving parts, possibly less weight [31], less maintenance and enhanced stealth characteristics [32]. A few studies have been carried out to evaluate the capability of fluidic actuators known as jet flaps. The jet flap consists of a narrow slot extending across the wing surface, which ejects high momentum air [83]. For blowing tangentially near the trailing edge on an airfoil surface, the airflow will closely follow the profile of the surface due to the Coanda effect. The control method making use of the Coanda effect at the trailing edge can directly manipulate the airfoil circulation and is therefore called Circulation Control (CC).
# Design of a Compact Planar Magnetic Levitation System with Wrench-Current Decoupling Enhancement
## 5 Experimental Setup and Results
### Linear Interpolation Performance Verification  
The PID parameters in Table 5 were used to verify the performance improvement with and without the linear interpolations, on both combinations of \(x\)-\(y\) and \(\psi\)-\(\theta\) axes. In Figure 12, the \(\psi\)-\(\theta\) axes' linear interpolation was able to eliminate the sharp oscillation effect when switching wrench matrices in the lookup table. Because the grid size of the lookup table on the \(\psi\)-\(\theta\) axes was five degrees, larger error gaps were expected at the switching location. On the \(x\)-\(y\) axes, the step response at the wrench matrix switching location showed its performance with constant oscillations. There were smaller oscillations on the \(x\) axis than on the \(y\) axis, due to the coil layout configurations. The \(x\)-\(y\) axes' linear interpolation could reduce the additional source of oscillations caused by the uncontrollable yaw rotation effect on the \(x\)-\(y\) step responses and \(x\)-\(y\) steady-state errors shown in Figure 13 and Figure 14, respectively. To smooth out the overall errors, applying a linear interpolation is one of the approaches to achieving so. The results showed that the control performance improved with no further tuning needed.  
Figure 11: Overall system setup.  
Figure 12: \(x\)–\(y\) and \(\psi\)–\(\theta\) axes interpolation results: (**a**) step responses without linear interpolations; (**b**) step responses with 2-D linear interpolations.  
Figure 13: All axes step responses.
## 4 Performance Evaluation
### Affective Feedback Using Projected Avatars
#### 4.5.2 Results  
Based on the answers obtained from the questionnaire, we attempted to answer three questions related to this robotic device. The first question tried to determine whether both the LED and projected avatars could convey the intended affective expressions. The results presented on Table 2 show a clear difference for both types of stimuli. The stimulus for the happy state made with projected avatars obtained a score of 4.73 for the happy state compared to 1.46 for the sad state. In the case of the stimulus made with colored lights, the happy state obtained 3.62 compared to 2.19 for the sad state. The stimulus for the sad state made with projected avatars obtained a score of 4.35 for the sad state compared to 1.27 for the happy state. Colored lights obtained 3.69 for the sad state and 2.23 for the happy state. For all the combinations, the participants could perceive the represented affective state showed on the videos using both LEDs and avatars. Note that, to confirm the effect of the order of stimulus on participants' score, we applied a Kruskal-Wallis test for each stimuli, and no significant difference due to the order has been found for all stimulus (Avatar (Happy): \(p>0.5,\ \chi^{2}=1.65,\ \eta^{2}=0.066\), Avatar (Sad): \(p>0.5,\ \chi^{2}=2.30,\ \eta^{2}=0.092\), LED (Happy): \(p>0.5,\ \chi^{2}=1.63,\ \eta^{2}=0.065\), LED (Sad): \(p>0.5,\ \chi^{2}=1.91,\ \eta^{2}=0.076\)).  
To answer the second question, we compared the stimuli made with the projected avatars and LEDs to display the affective state. When presenting the stimuli for the happy state, participants' perception of a happy state obtained 4.73 with projected avatars compared to 3.62 with LEDs. In the case of the stimuli for the sad state, participants' perception of a sad state obtained 4.35 compared to 3.69 with LEDs.  
The third study was related to the question of embodiment, and the results showed that 20 participants perceived the projected avatar as part of the physical robot, five participants perceived that the avatar and robot were different entities, and one participant did not include this answer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(n\) = 26} & \multicolumn{2}{c}{**Projector**} & \multicolumn{2}{c}{**LED**} \\ \cline{2-5}  & **Happy** & **Sad** & **Happy** & **Sad** \\ \hline Score as “Happy” (mean\(\pm\) SD) & \(4.73\pm 0.59\) & \(1.46\pm 0.80\) & \(3.62\pm 0.88\) & \(2.19\pm 0.83\) \\ \hline Score as “Sad” (mean\(\pm\) SD) & \(1.27\pm 0.52\) & \(4.35\pm 0.92\) & \(2.23\pm 0.85\) & \(3.69\pm 0.72\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean and standard deviation of participant’s answer as “Happy” or “Sad” to each stimuli.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 5 Research Challenges and Future Research Directions
### Multimodal Domain Generalization  
Challenge: 1 Modality distribution differences: Data distribution varies significantly across modalities (e.g., RGB, depth, infrared), and differences in sensor quality and resolution can lead to unreliable multi-modal live/spoof feature extraction [213]. This may mislead other modalities through cross-modal fusion. 2 Imbalance and scarcity of training Samples: The imbalance between genuine and attack samples is more pronounced in multimodal settings. Particularly for new attack types that are scarce, models often excessively rely on the dominant modality with the fastest convergence (i.e., fast modality), which hampers the full utilization of other slower modalities [214]. 3 Insufficient unified multimodal datasets: There are a limited number of datasets available for cross-modal research, especially exceptionally high-quality multimodal datasets, which restricts in-depth exploration in this area.  
Future Research Directions: First, create richer multimodal datasets. Second, it is essential to further develop more effective multimodal domain generalization methods for addressing modality unreliability and imbalance. Finally, exploring more lightweight and efficient multimodal FAS algorithms (such as EfficientFormer-V2 [215]) aims to meet real-time detection and low-computation resource requirements, ensuring the safe and reliable application of cross-modal FAS technologies in practical scenarios.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## Materials and Methods
### Verification
#### 2.3.2 Levelling  
In the area of the Miechowice district of Bytom, the geodetic levelling network was established for mining deformation monitoring. The levelling cycles were measured every six months along the main streets of the neighbourhood. During the period of the processed SAR images, two levelling cycles were measured: April 2017 and October 2017. In the current study, we used six profiles (Figure 7 left) which crossed the range of deformation. Because of the time-consuming technique, levelling cannot be performed in one day. Figure 7 (right) shows that the classical geodetic measurements took much more time in October 2017. To have compatible values of the subsidence during the period from April to October 2017, we performed a linear interpolation on the levelling values in time according to the referenced dates for each cycle in such a way that they can be directly compared with the subsidence  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Drawn Line (Profile**} & \multicolumn{3}{c}{**Determined Subsidence Parameters from the**} & \multirow{2}{*}{**Std of Subsidence**} \\  & & **Knothe–Budryk Theory** & & **Parameters Matching** \\ \cline{2-4} \multirow{2}{*}{**Lines from Figure 7 in**} & **Subsidence** & **Rock Mass** & **Exploitation** & \(\sigma\)**, mm** \\  & **Factor \(a\)** & **Parameter tan\(\beta\)** & **Rim \(p\), m** & \\ \hline no 1 (2\({}^{\prime}\)-2\({}^{\prime\prime}\)) & 1.13 & 4.5 & 90 & 235 \\ no 2 (1\({}^{\prime}\)-1\({}^{\prime\prime}\)) & 1.00 & 3.1 & 90 & 91 \\ no 3 (4\({}^{\prime}\)-4\({}^{\prime\prime}\), 2\({}^{\prime}\)-2\({}^{\prime\prime}\), 6\({}^{\prime}\)-6\({}^{\prime\prime}\)) & 1.14 & 2.7 & 50 & 257 \\ no 4 (6\({}^{\prime}\)-6\({}^{\prime\prime}\)) & 1.25 & 3.3 & 90 & 86 \\ no 5 (5\({}^{\prime}\)-5\({}^{\prime\prime}\), 6\({}^{\prime}\)-6\({}^{\prime\prime}\), 4\({}^{\prime}\)-4\({}^{\prime\prime}\)) & 1.08 & 2.9 & 90 & 281 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset of determined parameters of the Knothe–Budryk theory and standard deviation (Std) values of matching for exploitation of the 503 seam with longwalls No. 5 and 6.  
values retrieved from DInSAR measurements. The subsidence for each benchmark from the levelling cycles was divided into the exact number of days between each pair of measuring dates, resulting in the subsidence per day for each period. Then, the cumulative subsidence that corresponds to the mismatched day-shift was supplemented or deducted in the neighbouring levelling cycle so that the total number of days corresponds to the six-month period defined by the Sentinel-1 acquisition dates in the end of March and beginning of October.
## 4 Simulation Studies and Performance Assessment
### Comparison with Traditional Mathematical Matching Methods  
A comparative analysis was conducted between the proposed methods and conventional mathematical matching approaches, with three distinct methods evaluated, as illustrated in Figure 15. Three methods are used for this comparison. The first method (Pload and proportional matching) achieves frequency regulation through proportional allocation based on fixed public load. This method adjusts the 1/3 distribution ratio of public load plus local load (20 kW), and uses the proportion coefficient of ideal total power output to match other DGs (DG1: 1, DG2: 0.7605, DG3: 0.6752), as shown by the blue line in the figure. The second method (constant ratio of common load) makes adjustments by setting coefficients (1.7 times) proportional to the public load. Results show that the frequency value is only affected when the public load has power output, as indicated by the black line in the figure. The third method (common load sharing) directly distributes the public load equally among each DG (1/3 each), without using compensation coefficients like the first method, as shown by the green line.  
Figure 14: Voltage waveform from 5 s to 6 s.  
Figure 15: Comparison with other mathematical matching methods.
# Research on Synthetic Aperture Radar Processing for the Spaceborne Sliding Spotlight Mode
## 6 Measured Data Results  
Furthermore, we validate the proposed PFA algorithm by the measured data of an airborne SAR. The slant range of the data is 25 km, and the theoretical resolution is 0.15 m \(\times\) 0.15 m. Under these parameters, the effective imaging scene radius of PFA is about 300 m, and the corresponding imaging area is about 3600 m and 1100 m in the azimuth and range domain respectively, which is far beyond the effective imaging scene of PFA. Therefore, it is necessary to divide the whole image into small sub-images and re-focus in order to improve the image quality.  
After LRD processing without precise focusing, a basic image with a two dimensional matrix of 8192 \(\times\) 24,000 points is obtained. According to the relationship between the effective radius of PFA imaging and the whole scene size, the whole image is divided into 11 \(\times\) 16 = 176 sub-images. Each sub-image contains 1024 \(\times\) 2048 pixels with overlapping 256 \(\times\) 512 pixels.  
Measured data results of modified PFA based on sub-images are shown in Figure 19, and a local area in the image is shown in Figure 20 specifically. It can be seen that the modified PFA algorithm can significantly improve the focusing effect of the image. Figure 21 is the profile of point target in the image, and Hamming window is used in RAW data process on order to reduce the sidelobe levels.  
Evaluation results of point targets in the SAR image are shown in Table 6.  
Figure 19: Measured data results of PFA based on sub-images.  
Figure 20: Detail of the local area.
## 3 Parameter Optimization and Performance Test
### Optimization Results and Verification Test of Drive Cam Installation Angle
#### 3.2.4 Testing Indexes  
The seedling dropping success rate \(Y_{1}\), the variation coefficient of the seedling dropping interval \(Y_{2}\), and matrix damage rate \(Y_{3}\) were selected as the test indexes. The seedling dropping success rate \(Y_{1}\) was used to measure the passing ability of the pot seedlings in the seedling cylinder and whether the pot seedlings were stuck during the dropping process. The variation coefficient of seedling dropping interval \(Y_{2}\) was used to measure the uniformity of the seedling dropping interval at the different positions, so it was an important index to judge the uniformity of the actual planting spacing in the later stages. The matrix damage rate \(Y_{3}\) calculates the average mass of the same batch of pot seedlings before and after dropping and determines the collision between the pot seedlings and the inner wall of the pot during the dropping process.  
The calculation method of each test index was as follows  
\[Y_{1}=\frac{n_{2}}{n_{1}}\times 100\% \tag{31}\]  
\[Y_{3}=1-\frac{\overline{m_{ai}}}{\overline{m_{bi}}}(i=1,2,3,4,5) \tag{32}\]