esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate
## 2 Materials and Methods
### Luminance Coefficient  
In Europe, road brightness and visibility under artificial light are related to the distribution of luminance and illumination on the road surface. The illuminance on a road surface refers only to the amount of light reaching the surface, which does not indicate how bright the surface is. Illuminance (E) is the amount of incident light (luminous flux \(\Phi\)) per unit of area (A). The SI unit of illuminance is lux (lx). According to the SI system, illumination is expressed by Formula (1):  
\[\mathrm{E}=\frac{\mathrm{d}\Phi}{\mathrm{d}\mathrm{A}} \tag{1}\]  
Luminance, on the other hand, is defined as the luminous flux per unit of projected area and is a function of the illuminance on the road and the reflectance characteristics of the road surface [37] according to Formula (2):  
\[\mathrm{L}=\frac{\mathrm{d}\Phi}{\mathrm{d}\Omega\cdot\mathrm{d}\Lambda\cdot \cos\theta} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & **LA** & **PSV** & **\(\rho_{\mathrm{a}}\)** & **Mehylene** & **Grading of Filler** & **Flakiness** \\
**No.** & **Type of Aggregate** & **Grain Size** & **EN** & **1097-8** & **EN** & **1097-6** & **Blue Test** & **Aggregates** & **Index** \\  & & & & **1097-2** & **[31]** & **[32]** & **EN** & **933-9** & **EN** & **933-1** & **EN** 933-3** \\  & & & & **[30]** & **[31]** & **[32]** & **[33]** & **[34]** & **[35]** \\ \hline
1. & Amphibolite & 0/2, 5/8, 8/11 & max. 25 & min. 56 & 2.84 & & max. 16 */max. 2 ** & max. 20 ** \\
2. & Basalt & 2/5, 5/8, 8/11, 16/22 & max. 10 & min. 50 & 2.96 & & max. 1 ** & max. 18 ** \\
3. & Gabbro & 2/5, 5/8, 8/11 & max. 15 & min. 50 & 2.63 * 3.0 & 5 \(\div\) 7 * & max. 1 ** & max. 15 ** \\
4. & Granite & 2/8, 8/16, 8/11 & max. 15 & min. 50 & 2.65 & & max. 1 ** & max. 15 ** \\
5. & Quartzite & 0/2, 2/5, 5/8, 8/11, 8/16 & max. 25 & min. 56 & 2.64 & & max. 14 */max. 1 ** & max. 14 ** \\
6. & Melaphyre & 0/2, 2/5, 5/8, 8/11, 8/16 & max. 15 & min. 56 & 2.75 & & max. 14 */max. 1 ** & max. 18 ** \\
7. & Limestone & 0/2, 2/8, 4/11, 5/11, 5/8, & 25 \(\div\) 30 & 44 \(\div\) 56 & 2.69 \(\div\) 2.72 & - & max. 16 */max. 2 ** & max. 17 ** \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of aggregate set used to create a DM model.  
where: \(\Phi\)--luminous flux, A--surface area, \(\Omega\)--solid noun, \(\theta\)--angle between the direction of the solid noun \(\Omega\) and the normal of the emitter or reflecting surface A. The geometry required to determine the luminance coefficient is shown in Figure 1.  
In summary, the reflection of a surface area can be described by the luminance coefficient q, which is defined as the luminance of the surface area L, produced by illumination and reflection, divided by the illuminance of the surface plane of the are E according to Formula (3):  
\[\text{q}=\frac{\text{L}}{\text{E}} \tag{3}\]  
Luminance coefficient q is measured in \(\text{mcd}\cdot\text{m}^{-2}\text{lx}^{-1}\).
## 4 Discussion  
### Comparison of External Crack Widths from This Work and from the Literature  
The external crack width is an interesting value that would be very useful for determining the service life of a RC structure.  
From the results of our work, together with those of the existing literature previously considered [17; 19; 20], a linear band containing almost all the points is highlighted (Figure 12). It is very likely that the width of this band depends on various experimental parameters, such as the w/c ratio of the concrete, the tensile concrete strength, the addition of chloride ions to the concrete mix and other parameters. The results also clearly show that crack width increases with total charge. However, the influence of current density on the relationship between crack width and total charge is also evident. When considering experimental points (external crack widths) obtained for the same total charge in Figure 12, it can be noted that crack width can vary according to the impressed current density. Moreover, identical external crack width values could be obtained after the application of different total charges, sometimes using the same current density and sometimes not.  
Figure 11: Evolution of the maximum external crack width as a function of total charge, according to the current density.  
It must be underlined that in [20], a crack width of 0.7 mm was achieved with a total charge of 1536 A.h/m\({}^{2}\) and a current density of 200 \(\mu\)A/cm\({}^{2}\). This crack width value is smaller than that obtained by [20] (0.9 mm), with a total charge of 1344 A.h/m\({}^{2}\) and a current density of 350 \(\mu\)A/cm\({}^{2}\). Similar cases can also be observed in the present study. Such results highlight the importance of the intensity of the current density and the duration of the corrosion process in the degradation of RC prisms. It can be inferred that in order to predict the corrosion-induced internal degradation, it is crucial to consider the corrosion method and the test duration in the evolution of the crack widths.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 3 Proposed Method
### Watermark Extraction Procedure  
The watermark extraction process reconstructs the embedded message by replicating the core stages of the embedding pipeline, using only the watermarked video as input. The extraction relies on the spatial-domain computation of the DCT coefficient \(C(2,1)\)and on perceptual coherence to locate suitable blocks. Temporal redundancy is resolved by aggregating multiple extractions through majority voting. The complete watermark extraction involves the following steps:  
1. Divide each keyframe into non-overlapping \(8\times 8\) blocks.
2. Compute the perceptual score map \(P_{i,j}\) for each block using the same method as in Section 3.1.1. and select the top-ranked blocks as candidates for decoding.
3. For each selected block \(B_{i,j}\), compute the DCT coefficient \(C(2,1)\) directly from the spatial domain using Equation (4).
4. Recompute the quantization step using \(\Delta\ =Q\cdot J(i,j)\).
5. Decode the embedded bit from the quantized coefficient using the inverse QIM rule with Equation (13).
6. Repeat the decoding for the same bit across the \(r\) redundant keyframes.
7. Apply majority voting to recover the final bit by using Equation (15).  
This block-wise and coefficient-specific recovery scheme ensures reliable extraction even under heavy distortion. The majority voting mechanism provides robustness to isolated bit errors caused by compression, noise, or frame manipulation, making the scheme suitable for practical blind watermarking applications.  
Figure 2 shows a schematic summary of the complete embedding and extraction process. The upper section corresponds to watermark embedding, detailing the flow from perceptual map generation, block selection, coefficient computation, adaptive quantization, and temporal distribution. The lower section outlines the extraction stage, highlighting how watermark bits are retrieved from selected frames and decoded using majority voting. This flowchart reinforces the modular design of the method and clarifies the interaction between perceptual and temporal components.
###### Abstract  
This study investigated the impact of subcritical water extraction parameters on the polyphenols and flavonoids content in extracts made of the bark of walnut (_Juglans regia_ L.), as well as antioxidant activity measured using the DPPH and FRAP methods. The total polyphenols (TPC) and flavonoid (TFC) were determined using the spectrophotometric (UV-Vis) method. The response surface methodology (RSM) was employed to optimize the yield of bioactive compounds. On the basis of developed model, the highest polyphenols (192.2 mg (GAE)/100 g (dry mass)) (GAE--gallic acid equivalent), flavonoids (88.3 mg(QE)/100 g (dry mass)) (QE--quercetin equivalent) contents and antioxidant activity (21.3 \(\times\) 10\({}^{-6}\) MTE/1 g (dry mass)) (TE-Irolox equivalent) were obtained under the following extraction conditions: temperature 131.6 \({}^{\circ}\)C, raw material fraction size 0.9 mm and process time 10 min. The greatest impact on the extraction yield was observed in the case of the temperature, whereas the raw material fraction size and the process time had a secondary impact. Obtained results indicate that a smaller particle size and a shorter time may improve the process; however, a reduction in particle size can be challenging for a pressure cell filtration system and consequently increase the cost of the raw material pretreatment process. With the reduction in the process time, a lower cost of extract production can be achieved. This study confirmed the importance of a proper selection of extraction parameters to obtain the demanded extract composition.  
subcritical water; extraction; polyphenols; flavonoids; antioxidant activity; response surface methodology; Box-Behnken; _Juglans regia_ L.; optimization
# Effect of Soil Damping on the Soil-Pile-Structure Interaction Analyses in Cohesionless Soils
## 3 Analysis Results
### Verification Analysis
#### 3.1.1 Non-Liquefiable Soil (Case 1)  
The structure responses in Case 1 ([PERSON], 1991 [30] and Event K in [PERSON], 1998 [31]) were compared with the centrifuge test results through the acceleration response spectra (ARS), as shown in Figure 8. The MC model results were close to the test results in Gohl for both the peak and the spectral accelerations, whereas the hyperbolic model slightly overestimates the accelerations (Figure 8a). The hyperbolic and MC models slightly overestimate the spectral accelerations in the high-frequency domain and underestimate the low-frequency domain under Event K as shown in Figure 8b.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**PEER** & **Earthquake** & **Year** & **M\({}_{\mathbf{w}}\)** & **Station** & **Fault** & **R\({}_{\mathbf{up}}\)** & **(V\({}_{\mathbf{s}}\))\({}_{\mathbf{30}}\)** & **Pga** & **SF** \\
**Code** & & & & & & & **(km)** & **(m/s)** & **(g)** & \\ \hline RSN143 & Tabas, Iran & 1978 & 7.35 & Tabas & Reverse & 2.05 & 766 & 0.14 & 0.17 \\ RSN285 & Iripina, Italy-01 & 1980 & 6.90 & Bagnoli Iripino & Normal & 8.18 & 650 & 0.13 & 1.0 \\ RSN572 & Taiwan SMAKT1 (45) & 1986 & 7.30 & SMART1 E02 & Reverse & 51.35 & 672 & 0.14 & 1.0 \\ RSN1091 & Northridge-01 & 1994 & 6.69 & Vasquez Rocks Park & Reverse & 23.64 & 996 & 0.15 & 1.0 \\ RSN1206 & Chi-Chi, Taiwan & 1992 & 7.62 & CHY042 & Reverse & 28.17 & 665 & 0.15 & 1.5 \\ RSN1613 & Duzce, Turkey & 1999 & 7.14 & Lamont 1060 & Strike Slip & 25.88 & 782 & 0.16 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The earthquake records used in the soil–pile–structure interaction analyses.  
Figure 7: Time histories of the selected earthquakes.  
The model responses were compared through the maximum bending moments along the pile. Figure 9a shows the results of the study by [PERSON] [30], where the bending moments were captured by MC and were overestimated by the hyperbolic model. The bending moments obtained using both MC and hyperbolic models agreed with the centrifuge test results in Event K ([PERSON], [31]).  
The results showed that the hyperbolic and MC models could be used to simulate the soil behavior by applying a cut-off value of 0.05 to the \(G\)/\(G_{max}\) value. It should be noted that the assigned cut-off allows limiting the damping ratio. When the cut-off value was set to 0.02, the hyperbolic model yielded closer results than the MC model. Therefore, a 0.05 value was recommended for the \(G\)/\(G_{max}\) in the MC model to get more accurate responses.
## 6 Conclusions  
In this work, we developed an approach to map and update forest AGB by combining the multi-source remotely sensed data and a process-based ecological model, and concluded that:  
1. The process-based model driven by multi-source remotely sensed data could be used to dynamically forecast and update the forest AGB at the landscape level. The BEPS-based results explained 31% of variation in the field-based AGB and 85% of variation in the ALS-based AGB with the confidence level higher than 95%.
2. Both forest biotic (i.e., LAI, forest type etc.) and abiotic factors, such as soil type data and meteorological data, were considered in predicting the spatiotemporal distribution of forest AGB through the process-based ecological model.
3. Since the Landsat-based AGB predicted 99.0%, 93.8%, and 87.3% of variation in the wood biomass, coarse root biomass and leaf biomass, respectively, the initial forest AGB spatial distribution map can be used to parameterize the forest carbon pools quantitatively characterized in the process-based model.  
The dynamic and timely spatiotemporal distribution maps of updated forest AGB will be beneficial to the long-term monitoring for ecological observational studies and quantitatively assessing the carbon sequestration of forest ecosystem.  
This research was supported by the National Key R&D Program of China (2016 YFA0600202), National Science Foundation of China (NSFC) (41771374), and the high technology research and development program of China (863 program: 2012 AA12A306). The research was conducted at the International Institute for Earth System Science (ESSI), Nanjing University. We thank [PERSON], [PERSON], [PERSON], [PERSON] in ESSI and [PERSON] from the Chinese Academy of Forest for their help in field data collection and data processing. We thank Dr. [PERSON] for his help in the process of revising this paper. Four anonymous reviewers are also thanked for their insights and suggestions which greatly improved this paper. [PERSON] conceived and designed the experiments, and contributed reagents/materials/analysis tools; [PERSON] performed the experiments and analyzed the data; [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper together. The authors declare no conflict of interest.
# An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks
## 5 Experiment and Analysis
### Analysis of Network Throughput  
When the network topology is fixed, as the number of simulation rounds changes, the change in throughput is shown in Figure 4. The number of dead nodes increases and the link connectivity decreases, which has an impact on the reliable data transmission and the end-to-end throughput. The throughput obtained by the five algorithms all shows a downward trend. However, our algorithm reduces the energy consumption of the important nodes and considers the transmission cost of the interaction between links, which increases the data transmission reliability. This algorithm has higher network throughput.  
Then, we change the number of nodes and make them randomly distributed in the farm to obtain normalized throughput under different network topologies, as shown in Figure 5. Due to high energy utilization and slow node death rate, the algorithm has good performance in throughput. This means that our algorithm can monitor and transmit multi-source farm data.  
Figure 4: Throughput of sink.  
Figure 3: Network energy efficiency under a different number of nodes.
## 3 Methods
### Data Collection and Pre-Processing  
#### 3.2.1 Remote Sensing Data Preparation and Pre-Processing  
The preparation and preprocessing of remote sensing data consist of 4 parts: preparation of SLC (Single Looked Complex) data, image alignment and cropping combinations, selection of DEM data and alignment and selection of master images.  
Single Looked Complex (SLC) data are the basis of PS-InSAR. The data set in this study comes from the ESA Sentinel 1 Earth Observation Satellite. The satellite parameters are shown in Table 2.  
A total of 15 single-view complex images were selected in this study, and the period is between 22 September 2019 and 8 March 2020. The selected single-view images can cover the entire Shenzhen area.
# Dog Identification Method Based on Muzzle Pattern Image
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.4 Proposed Resize  
The resize stage was added to the proposed method 2. Before applying the proposed resize method, an experiment was performed in order to compare the proposed resize method with the general resize method with a fixed width and height. In addition, the proper resize values were chosen for the proposed resize from the experiment. In the experiment, the average number of good matches and the difference of the minimum good matches in genuine matching from maximum good matches in imposter matchings (i.e., GAP) are compared for all four feature extraction algorithms. The larger the GAP, the better the identification performance.  
Table 2 shows the results of the experiment. The GAP of the proposed resize was similar or larger than the fixed resize method. Especially, the proposed resize much improved the GAP for BRISK and ORB. In addition, 300 pixels were selected as the resize value of the proposed resize by considering both the average number of good matches and the GAP. The average number of good matches was larger with 350 pixels than with 300 pixels for all feature extraction algorithms, but the GAP decreased with 350 pixels, it especially decreased it more for ORB. With the experiment, it was verified that the proposed resize was better than the fixed resize, and, thus, the proper scale factor for the proposed resize was 300.  
The proposed resize was applied to the proposed method 2 and this was the final proposed method for dog identification based on the muzzle pattern images. The dog identification performance was dramatically improved for all feature extraction algorithms as shown in Table 1 (proposed method 3). The EER was 0% for SIFT and ORB, and 0.9% for SURF and BRISK. SIFT and ORB performed better than SURF and BRISK, and they identified all the images correctly without false matching and false nonmatching. The proposed resize improved the minimum number of good matches in the genuine matching, and it resulted in the reduction of the EER. This result showed that the proposed resize was effective for the improvement of the dog muzzle pattern recognition.
## 1 Structural Biology at XFEL Sources  
### X-ray Sources and Crystallography  
X-ray based methods have a rich history of contributing to biological structure determination. The first structural images of DNA, for example, were published in 1953, guided by X-ray crystallographic data [1, 2], a success that still captures the public's imagination.  
In more recent decades, large-scale, multi-user synchrotron facilities have been the driving technology behind X-ray structural biology investigation and have become the cornerstone for protein research [3]. Since experiment stations dedicated to macromolecular crystallography (MX) first appeared at synchrotron facilities in the 1970s, these facilities have been the go-to tool for crystallographers [4, 5]. Synchrotrons offered a fundamental improvement in the X-ray photon flux and enabled routine atomic structure determination of biological macromolecules as well as new, tunable wavelength-based experimental phasing methods, such as single- and multi-wavelength anomalous dispersion (SAD and MAD) [6, 7, 8]. Modern third-generation synchrotrons--such as the European Synchrotron Radiation Facility (ESRF) in France [9], the Advanced Photon Source (APS) at Argonne National Laboratory in the USA [10], the Super Photon Ring-8 GeV (SPring-8) in Japan [11], and PETRA III at Deutsches Elektronen-Synchrotron (DESY) in Germany [12]--can produce an average brilliance of up to \(10^{19}\)-\(10^{20}\) photons \(\,\mathrm{s}^{-1}\,\mathrm{m}\mathrm{a}\mathrm{r}^{-2}\mathrm{m}^{-2}\,\)\(0.1\%\,\mathrm{bandwidth}^{-1}\)[13] offering ample photons for traditional structure determination using relatively large crystals of macromolecules.  
X-ray free electron lasers (XFELs) are the next generation of brighter X-ray sources. With up to a 10\({}^{9}\) increase in brilliance over synchrotron sources, XFELs enable the development of new methods to study ultrafast processes, structural changes, and biological reactions at biologically relevant timescales [14; 15; 16], all from crystals that can be much smaller than those traditionally used at synchrotron sources.
## 3 Effective Thermal Conductivity Analysis
### Solid Conduction + Gas Conduction + Solid Conduction  
In this part, we consider the second type effective thermal conductivity caused by heat transfer through solid spheres and gas phase with stagnant flow. Point contact will be assumed and thermal radiation is neglected.  
The packed pebble bed consisting of mono-sized spheres with the presence of a stagnant fluid can be considered as a porous media filled with still working medium. The second type of effective thermal conductivity then can be considered as the prediction of the stagnant thermal conductivity of the porous media. [PERSON] and [PERSON] presented an empirical correlation for the stagnant thermal conductivity [15]. The accuracy of the correlation has been confirmed by tests done by [PERSON] et al. [16]:  
\[\frac{\lambda_{\epsilon}^{g}}{\lambda_{f}}=1-\sqrt{1-\epsilon}+\frac{2\sqrt{1 -\epsilon}}{1-\lambda B}\left[\frac{(1-\lambda)B}{\left(1-\lambda B\right)^{2 }}\ln(\frac{1}{\lambda B})-\frac{B+1}{2}-\frac{B-1}{1-\lambda B}\right] \tag{2}\]  
where \(\lambda_{\epsilon}^{g}\) is the second type effective thermal conductivity due to the solid conduction-gas conduction-solid conduction heat transfer process; \(\lambda_{f}\) is the heat conductivity of the stagnant gas, which is temperature dependent; \(\lambda\) is the ratio of the thermal conductivity of the solid phase and the surrounding fluid matrix.  
The second type of effective thermal conductivity calculated by Equation (2) is shown in Figure 4, using parameters of the SANA experiment under helium conditions. As we can see, this type of effective thermal conductivity only has a slight increase with rising temperature. The heat conduction ability of the helium increases with rising temperature, while the heat conduction ability of the graphite spheres declines. Hence, the total heat transfer ability increases slowly.  
Figure 3: Effective thermal conductivity due to the solid conduction-surface radiation-solid conduction heat transfer process.
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 4 MP-RDV: The Proposed Scheme
### Selection of Multiple Paths
The flowcharts of RDV and MP-RDV procedures in updating routing tables are shown in Figure 6. In RDV, when a 1-carry or multi-carry message arrives, a node applies the distance-vector next-hop selection algorithm per destination \(d\in D\), where \(D\) is a set of destinations included in the message. Specifically, for each \(d\), a node compares the distance of old and new routes and updates the routing-table entry with the shorter one. In contrast, MP-RDV maintains the best and the second-best routes in the routing table. Namely, when a new entry for destination \(d\) comes with a message, a node compares the new route with two old routes, and updates the routing table entries with the best and the second-best ones. In case of MP-RDV, after this routing-table update, the node tests the second-best route whether it meets the above conditions (A)(B) or not. The second-best route is activated if both of them meet, and is inactivated otherwise.