esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Template-Based Watermarking for DIBR 3D Images
## 3 Proposed Method
### Proposed Watermark Embedding Method
#### 3.2.2 Template Embedding  
Since the curvelet coefficients contain spatial information, the template block position can be extracted from the curvelet coefficients. Therefore, the entire image is transformed into the curvelet domain without requiring a block-based curvelet transform. We then insert the peak point templates into the curvelet coefficients.  
As shown in Figure 7, four peak points are inserted into one template block. If the number of peak points is too small, the robustness drops, and if there are too many points, the visual quality drops. Experimentally, four points were appropriate.  
Figure 5: Detailed embedding process. (**a**) Template embedding process; (**b**) message watermark embedding process.  
Figure 6: Example of template and message watermark block placement in the spatial domain.  
The template embedding process is divided into three steps:  
1. Forward curvelet transform: The forward curvelet transform is applied to the whole image.
2. Peak points' insertion: Select two directions of a curvelet, \(l_{1}\) and \(l_{2}\), with scale value \(g\). \(l_{1}\) and \(l_{2}\) are selected such that they differ by \(90^{\circ}\), to increase the detection rate by corner detection in the template extraction step, and \(g\) should be selected to be a middle frequency, as a compromise between invisibility and robustness. As discussed above, four peak points are inserted into one template block, \[\begin{split} C_{m}(g,l_{1},k)&=C(g,l_{1},k) \cdot\alpha_{t}+\beta_{t}\\ C_{m}(g,l_{2},k)&=C(g,l_{2},k)\cdot\alpha_{t}+ \beta_{t}\end{split}\] (6) where \(C_{m}\) is the modified curvelet coefficient, \(C\) is the original curvelet coefficient, \(\alpha_{t}\) and \(\beta_{t}\) represent the strength of the inserted template and \(k\) is the location of the template point.
3. Inverse curvelet transform: The inverse curvelet transform is applied to the modified coefficients.
# Experimental and Numerical Analysis of an Innovative Combined String-Cable Bridge
## 4 Numerical Method and Comparison between Experimental Results
### Comparison of Experimental and Numerical Results
From the displacement graphs (Figure 13), we can see that the largest displacement differences are in the unloaded part of the structure. As we can see, the shape of the de-formation is the same in all cases, regardless of the size of the loads. Additionally, as previously mentioned, we can see that outside the places of maximum displacements, the displacement differences between the numerical model and the experimental model differ more. This is due to the reason mentioned earlier--large and heavy supports and due to friction in the nodes creating additional \"constraints\". This effect is most visible in the unloaded part of the structure (point 3R, see Figure 9). If we talk about asymmetrical loading and compare the results of only the loaded part, the difference in total displacements does not exceed 9 percent. In all cases, the location of the largest displacements coincides, regardless of whether the study was carried out numerically or experimentally. During the performed calculations, we can also notice the influence of the prestressing forces of the string on the displacements. While prestressing has little effect under symmet  
Figure 12: Numerically and experimentally obtained displacements when the loading is symmetrical and prestressing force in the upper element (string): \(T_{1}\) = 0.472 kN. rical loading, the effect becomes much greater under asymmetrical loading. Such results are revealed in Tables 11-13. As for the axial force in the string (Table 15) we see that the experimental and numerical results differ by no more than 14%. In almost all cases, numerically obtained axial forces are higher than those experimentally obtained. Additionally, we can see that increasing the axial force in the structure (if the load does not change) decreases the total displacements of the structure. This is shown by both experimental and numerical analysis data. In the general case, from the obtained results we can see that the differences between the numerically and experimentally obtained results, if we consider the loaded part of the structure, in terms of maximum displacements, do not exceed 9 percent (Table 14), and the differences in the axial forces of string do not exceed 14 percent (Table 15).
## 3 Results and Discussion
### Instrumental Colour Measurement
Instrumental colour evaluation of the cooked minced meat products revealed highly significant differences (\(p<0.01\)) in redness (a*) and colour saturation (Chroma) values (Table 9). The effect of heat treatment on minced meat products containing hemp products is variable. The addition of hemp products resulted in lower redness (a*) values in meatballs, meat loaves and sausages [17; 18; 20]. However, other researchers found that hemp products did not affect redness (a*) in pork meat patties [19]. Parameters related to hemp product type and added amount influence colour differences. Chicken meat sausage samples with 10-40% hempseed meal showed significant colour changes. This was attributed to chlorophyll destruction due to protein breakdown, causing magnesium loss and generating dull green molecules, increasing product yellowness, though this was not observed in our study [20]. [PERSON] et al. [19] found that chlorophyll removal during lipid extraction with supercritical CO\({}_{2}\) may explain why heat treatment did not affect the colour of meat products. It is evident that the chlorophyll content of hemp products should be considered when incorporating hemp ingredients into the batter of minced meat products. Total colour difference (\(\Delta E_{\text{Lab}}\)) exceeded the value of 5 between samples in treatments HF0 and HF4 and also between samples in treatments HF0 and HF6 (Figure 2), showing that the control and treated samples had different colours [19]. Figure 2: Total colour difference (\(\Delta E_{\text{Lab}}\)) between control (HF0) and samples containing hemp flour (HF4 and HF6).
## 3 Results
### Green Wave Control Scheme Generation  
The relevant parameters of the test road network, such as green splits, travel time, weighting factors of bandwidth, etc., were input into the presented model, and the corresponding MILP problem was solved by the optimization software LINGO 20.0. According to the LINGO solution report, the state of the solutions is globally optimum. The optimum common cycle time is about 100.39 s, which can be rounded to 100 s in the traffic signal optimization practice. The optimum bandwidths and relative offsets are shown in Table 8. The relative offset is the time difference between the beginning of the outbound green time at \((i,j)\) and that at \((i,j+1)\). The optimum phase sequences are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c} \hline
**Road Segments** & **Car Bandwidth Ratios** & **Bus Bandwidth Ratios** \\ \hline Between (1, 1) and (1, 2) & 1.062 & 0.848 \\ Between (1, 2) and (1, 3) & 0.972 & 1.242 \\ Between (2, 1) and (2, 2) & 0.946 & 1.490 \\ Between (2, 2) and (2, 3) & 1.043 & 1.543 \\ Between (3, 1) and (3, 2) & 0.955 & 0.746 \\ Between (3, 2) and (3, 3) & 1.073 & 0.820 \\ Between (4, 1) and (4, 2) & 1.016 & 1.295 \\ Between (4, 2) and (4, 3) & 0.876 & 1.195 \\ Between (5, 1) and (5, 2) & 1.101 & 1.689 \\ Between (5, 2) and (5, 3) & 0.905 & 0.529 \\ Between (6, 1)and (6, 2) & 1.108 & 0.779 \\ Between (6, 2) and (6, 3) & 1.019 & 0.804 \\ \hline \end{tabular}
\end{table}
Table 6: Bandwidth ratios.  
\begin{table}
\begin{tabular}{c c c} \hline
**Road Segments** & **Outbound Dwell Time/s** & **Inbound Dwell Time/s** \\ \hline Between (1, 1) and (1, 2) & 19 & 18 \\ Between (2, 2) and (2, 3) & 15 & 18 \\ Between (4, 2) and (4, 3) & 17 & 17 \\ Between (6, 1) and (6, 2) & 18 & 16 \\ \hline \end{tabular}
\end{table}
Table 7: Average dwell time at each bus stop.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Road Segments** & **Car Outbound** & **Car Inbound** & **Bus Outbound** & **Bus Inbound** & **Relative** \\  & **Bandwidths/s** & **Bandwidths/s** & **Bandwidths/s** & **Bandwidths/s** & **Offsets/s** \\ \hline Between (1, 1) and (1, 2) & 12.6 & 13.4 & 12.3 & 12.6 & 51 \\ Between (1, 2) and (1, 3) & 7.4 & 11.1 & 15.7 & 19.4 & 52 \\ Between (2, 1) and (2, 2) & 15.9 & 17.1 & 25.8 & 27.0 & 51 \\ Between (2, 2) and (2, 3) &
## 2 Related Work and Motivations
#### Problems Statement  
UANs are generally facing a series of problems like the network coverage [6; 34], surface sinks positioning [35; 36; 37], vulnerability and data security [7; 38; 39], data latency [33], and energy management of deployed nodes [6]. In relation to the battery-driven underwater sensors, many AUV-based schemes [21; 24; 29], cluster-based schemes [15; 16; 17; 19] and MS-based approaches [9; 18] have been proposed in the literature. Even though, these developed schemes are well-organized, but not as efficient as required for the harsh underwater environment due to these subsequent reasons:  
1. The previously designed schemes used the cooperative communication links to forward the data from the root FNs to the SS. However, the FNs closer to the SS always take part in relaying the data of their predecessors and also sense their NSF. This extra duty of relaying data consumes an additional amount of energy and become a root cause to deplete forward FN's battery earlier as compared to the distant nodes [28; 33].
2. These approaches [4; 15; 16; 18] utilized the clustering method for forwarding the data towards the SS. However, due to poor CH selection measure and cluster size control criterion increases the burden on the large size network. That makes the network unstable and may lead to end the network lifetime earlier.
3. The multi-AUVs based approaches perform persuasively for a large network, but not perform well on few-layer networks and also increase the overall cost of the network. Sometimes, the movements of multiple AUVs increase the water current movements and badly affect the communications of neighbor AUVs and FNs.
## 3 Multi-Link Channel Access Schemes and Coexistence Issue
### Coexistence Issue for Sync-FT Operation  
In practical WLANs, legacy SLDs may coexist with EHT MLDs. Therefore, a coexistence issue arises between them. An illustration of such a coexistence scenario is given in Figure 8. In the scenario depicted in this figure, a legacy device starts a BO with the same backoff count as an MLD under the Sync-FT scheme. During the illustrated operation period, the legacy device obtains a single TXOP on Link 1, while the MLD obtains two TXOPs. This is because the Sync-FT scheme enables the MLD to transmit on a link before backoff completion on that link; we call such a link a _free-riding link_ in this paper. A free-riding link resumes its ongoing BO after transmission is complete, using the count value frozen immediately before the transmission. This behavior prioritizes channel access for the MLD and provides it with more TXOPs, resulting in long and frequent wait times for the legacy device. This behavior can also be interpreted as the free-riding link starting a new BO with a relatively deflated backoff count.
## 3 Results and Discussion  
In this section, we evaluate the performances of our proposed cost computation measurement and cost aggregation strategy. The rectified stereo image pairs from the Middlebury benchmark dataset [32] are used as experimental data. The experimental parameters are given in Table 1, and they are kept constant for all tests. The percentage of bad pixels of the estimated disparities over the stereo pairs was served as evaluating criterion. And the disparity error threshold was set to 1.0 pixel.
## 4 Numerical Simulation
### Parametric Study Results and Discussions  
#### 4.3.1 Effect of Embankment Height  
To investigate the load transfer mechanism of the structure, GRPS embankments with embankment heights of 2 m, 3 m, 4 m, and 5 m were established, and the stress distribution curves obtained are shown in Figure 12. It can be seen that the stress at the top of the pile cap increases significantly with the increase in buried depth. As the load increases, the maximum stress of pile tops X1 and X3 is 181.7 kPa and 149.5 kPa, which is 409.68% and 319.35% larger than the maximum stress of 35.65 kPa of X2. The synergistic action of geogrid and gravel cushion makes the load transfer from the soil between piles to the top of piles, and pile bearing capacity is given full play.  
Figure 11: Comparisons between testing and numerical results; (**a**) Displacement obtained from numerical simulation. (**b**) Load-settlement curve. (**c**) Axial stress of P1 under 41 kPa. (**d**) Tension distribution of the geogrid.  
Figure 12: Stress distribution of GRPS embankment at different embankment heights; (**a**) The height of the embankment is 2 m. (**b**) The height of the embankment is 3 m. (**c**) The height of the embankment is 4 m. (**d**) The height of the embankment is 5 m.  
As presented in Figure 12, the stress distribution of X1 gradually increases with the increase in embankment height. The slope of the stress curve of the embankment changes with the increase in height. When the embankment height is higher than 3 m, the slope increases significantly to about 1.5-2 m. At this point, the gap between the soil stress at the top of the pile and the soil stress between the piles increases rapidly, and the soil arch is formed. The reason for this is the differential settlement of the top plane of the pile cap when subjected to pressure from the upper loads. Because the soil at the top of the pile cap is blocked by the pile with greater stiffness, the settlement is much smaller than the soil between piles. As the load continues to pass down, the soil at the top of the pile is constantly squeezed at the pile cap, and the soil will develop into a soil arch when squeezed. The soil arch effect will be influenced by the GRPS embankment under different embankment heights, while the influence of the soil arch effect is limited, and the influence of the soil arch effect on soil stress is small in a certain height range.
## 3 Experimental Imaging Results without Moving Elements
### Negative Magnifications  
We first examine cases where we obtain inverted images. Figure 4 presents the system configuration and the output image when the first SLM is encoded with a focal length of \(f_{1}=70.2\) cm and the second SLM is turned off. Therefore, we have a single lens imaging system, where the object distance is \(p\), and the image distance is \(d+q\). The output is inverted with a magnification of about \(m=-2.3\) in excellent agreement with the theoretical magnification obtained using Equation (5). The size of the image is larger than the detector. As we moved the position of the input slide, different areas of the letter were seen. We note that, in doing these experiments, we simply varied the focal length encoded onto the device until the image was focused. This procedure accounts for small deviations of the distances \(p\), \(d\) and \(q\).  
Figure 5 shows the zoom system configuration and the output image when the first SLM is turned off and the second SLM is encoded with a focal length of \(f_{2}=69.6\) cm. Again, we have a single lens imaging system, where now the image distance \(q\) is smaller than the object distance \(p\) + \(d\). The output is inverted with a magnification of about \(m=-0.43\) in excellent agreement with the theoretical magnification obtained by using Equation (5). The size of the image is smaller than the detector.  
Finally, Figure 6 shows the output image when both devices are encoded with focal lengths of \(f_{1}=f_{2}=100\) cm. Now the focal length of the composite lens system is approximately \(F=167\) cm, and the object is inverted with a magnification of \(m=-1\) in excellent agreement with theory. Note that this configuration places the input object plane in the front focal plane of the first lens and the final output image plane in the back focal plane of the second lens.  
Figure 4: Zoom system configuration with \(f_{1}=70.2\) cm and \(f_{2}=\infty\) and experimental image with magnification of \(m=-2.4\).  
Figure 3: Input object captured by camera. Here the slide with the word “PROFESOR” is placed directly to the detector. The size of the object slightly exceeds the size of the detector.  
Note that in these three cases shown here, the encoded lenses have focal lengths larger than the Nyquist limit of 38 cm. Therefore, the displayed lenses are well reproduced and the final image is well focused in all cases. There is, however, some low pass filtering caused by the limited size of the SLMs compared to the long distances required in the system.
# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 3 Materials and Methods
### Preprocessing  
Preprocessing is intended to filter out all the noise data that may be added while gathering the data from the sensors. EEG frequencies are primarily present in the 0.3 to 40 Hz bands, but higher frequencies can be present as the noise can occur due to blinking of eyes and muscle activities. Therefore, it is essential to extract meaningful data before further processing. Furthermore, it was required to amplify the signal-to-noise ratio to improve results obtained from feature extraction. EOG artifacts were removed using the tool EEGLAB version 2021.0, and a bandpass frequency filter of 0.3-45.0 Hz was applied. The data was averaged to the common reference. The data was segmented into 60-s trials, and a 3-s pre-trial baseline was removed [38]. We opted for a sampling rate of 128 Hz. This sampling rate allows us to capture the dominant rhythms of the brain, such as delta, theta, alpha, and beta waves, which are crucial for assessing various brain states. While a higher sampling rate could potentially provide even finer details, 128 Hz offers a good balance between capturing essential information and keeping the data size manageable for further analysis.  
We employed a median filter during the preprocessing stage. This technique functions by analyzing short segments of the signal around each data point. Within these segments, the median value, essentially the \"middle\" value, is calculated. The original data point is then replaced with this median value. By prioritizing the central tendency within a localized window, the median filter effectively cancels out impulsive noise spikes or dips that deviate significantly from the surrounding brain activity. This filtering process helps us extract a cleaner representation of the underlying neural processes, allowing for a more accurate analysis of stress-related changes reflected in the EEG recordings.
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 3 Polymer-Inorganic Coordinated Hybrids: Complexation-Induced Properties
### Pyridine-Based Polymers
#### 3.1.2 Electrical Conductivity of Coordination Complexes of PVPs with Metal Ions  
Complexation of P4 VP with metal ions is known to significantly enhance electrical conductivity [104; 105]. The overlap of the frontier molecular orbitals of the metal atoms with the neutral basic pyridine ligands in the polymer allows for network formation and enhanced interchain electron transfer [106]. [PERSON] et al. [107] explored the enhancement of ionic conductivity in poly(2-vinyl pyridine) (P2 VP) and P4 VP upon complexation with copper iodide (CuI). This increase in conductivity, especially pronounced in P2 VP/CuI complexes (from 4.6 \(\times\) 10\({}^{-15}\) to 4.2 \(\times\) 10\({}^{-5}\) S/cm), is likely a result of both cation and anion transport, considering that P2 VP has sterically hindered Cu-N sites. The effect of ion mobility is further highlighted by temperature-dependent conductivity data, which revealed that the conductivity of the P2 VP/CuI hybrids increases with temperature, particularly as the material nears its glass transition temperature (Tg). Saturating the CuI at 80% mol of P2 VP reverted conductivity values back to those of pure P2 VP, likely due to a lack of free volume.  
[PERSON] et al. [104]. provide an in-depth examination of the electrical conductivity of P2 VP complexes with different transition metals, copper (Cu), cobalt (Co), palladium (Pd), and platinum (Pt). While Cu complexation has a minimal effect on conductivity, Co increases conductivity by two orders of magnitude, Pd by six, and Pt by seven. The authors attribute this trend to several factors, including polarizability, ionization potential, metal ion size, intermetallic distances, and the rotational and vibrational dynamics of the metal ions. They highlight a clear correlation, where higher ionization potentials correspond to greater conductivity as the metal changes from Cu to Pt. The relatively lower conductivity of Cu- and Co-based complexes is attributed to ionic conductivity in agreement with their temperature dependence, wherein conductivity decreased with cooling. Surprisingly, Pd- and Pt-based P2 VP complexes exhibit almost constant conductivity in the low-temperature region, which then declines at higher temperatures. Collectively, evidence suggests that charge transport could be governed by delocalized electrons through metal-metal orbital overlap. The close packing of Pd and Pt atoms supports electron delocalization along overlapping dz\({}^{2}\) orbitals, forming a robust conduction pathway.
# Effect of Mixture Design Parameters of Stone Mastic Asphalt Pavement on Its Skid Resistance
###### Abstract  
Although it is well known that the stone mastic asphalt (SMA) pavement has good skid resistance, the skid resistance is not satisfactory and its durability is poor when the mixture design is unreasonable. In order to obtain excellent skid resistance for SMA pavement, this paper seeks to analyze the influence of mixture design parameters on the skid resistance of SMA pavement. The mixtures were designed with an orthogonal experiment. There were five factors, namely the percentage of aggregates passing the maximum size (P\({}_{\text{NMSA}}\)), the percentage of aggregates passing the sieve size which is only one smaller than the maximum size (P\({}_{\text{NMSA-1}}\)), the percentage of aggregates passing the control sieve size (P\({}_{\text{CS}}\)), the percentage of aggregates passing the sieve size which is only one smaller than the control sieve size (P\({}_{\text{CS-1}}\)), and asphalt content (AC), and each factor had four levels. The skid-resistance index (SI) obtained by the 3D measurement was used to evaluate skid resistance. The results show that the three parameters (P\({}_{\text{NMSA}}\), P\({}_{\text{CS}}\), and AC) are the key parameters to improve skid resistance. Among them, P\({}_{\text{NMSA}}\) has the greatest impact on the skid resistance, AC is the second, and the impact of P\({}_{\text{CS}}\) on skid resistance is the smallest. Moreover, the design parameters with best skid resistance are proposed.  
pavement; stone mastic asphalt; skid resistance; orthogonal experiment +
Footnote †: journal: Applied Physics Letters