esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data and Processing
#### 2.2.2 LAI Data  
The MODIS LAI products MCD15A2 (version 5.0) at 1 km and MCD15A2H (version 6.0) at 500 m, were selected to describe the vegetation density in this study. The 8-day MODIS LAI product is available at [[http://wist.echo.nasa.gov](http://wist.echo.nasa.gov)]([http://wist.echo.nasa.gov](http://wist.echo.nasa.gov)) in the sinusoidal projection. According to the quality control layer (FparLai-QC) involved in this product, the MODIS LAI consists of a main look-up-table (LUT) algorithm (QC < 64) and a back-up algorithm (64 \(\leq\) QC < 128). The LUT algorithm exploits surface reflectance information in the red and near-infrared bands based on a 3-D radiative transfer equation. When the LUT algorithm fails, the back-up algorithm adopts NDVI-LAI empirical relationships to estimate the LAI [26]. The LUT algorithm usually has a higher reliability than that of the back-up algorithm [26]; the clumping effects at the canopy scale are considered in the LUT algorithm [27].
## 2 Proposed Method
### Data Pre-Processing  
In this section, we use the capacity data provided by [PERSON] et al. [21] to show the procedure of our model. The RUL of historical batteries is obtained by accelerated aging experiments. The lithium batteries are cycled to failure under fast-charging conditions. These lithium-ion batteries (LIBs) were tested in Arbin LBT potentiostat at 30 \({}^{\circ}\)C, and they all have a nominal capacity of 1.1 Ah. The dataset consists of 124 commercial lithium-ion batteries with lithium-ion phosphate (LFP)/graphite cells manufactured by A123 Systems (APR18650M1A). The charging policy of these LIBs has the format \"C1(Q1)-C2\", where C1 and C2 are the first and second (constant current) CC charging steps, respectively, and Q1 is the state-of-charge (SOC, %) when the currents switch. The charging parameters of these batteries are shown in Table 1[21]. The paper has provided 124 groups of datasets. However, due to the limitation of article length, we provide the experiment result of 6 groups of data that have the most obvious individual differences so that they can demonstrate our method's outstanding performance in dealing with individual differences.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **C1** & **Q1** & **C2** \\ \hline Case 1 & 6C & 40\% & 3C \\ Case 2 & 5.3C & 54\% & 4C \\ Case 3 & 5.9C & 60\% & 3.1C \\ Case 4 & 5.6C & 36\% & 4.3C \\ \hline \hline \end{tabular} The data of the capacity of case 1 to case 4 are shown in Figures 2–5.  
\end{table}
Table 1: Charging policies of LIBs from [PERSON] et al.  
Figure 2: Batteries 1 and 2.  
Figure 4: Batteries 5 and 6.  
Figure 5: Batteries 7 and 8.  
Figure 3: Batteries 3 and 4.  
Although the manufacturing process, the materials, and the charging policy are identical according to Table 1, we can clearly observe in Figures 2-5 that the maximum capacity, the degradation rate, the changing trend of degradation rate, and the failure time are different. All the above differences can be concluded as individual differences.  
After obtaining the capacity data, the degradation rate of the batteries is calculated from the capacity data. Assume the capacity data for the historical sample and the target battery are, respectively, expressed as [\(C_{1}^{i}\)] (\(i\) = 1, 2, 3...) and [\(C_{2}^{i}\)] (\(i\) = 1, 2, 3...). The degradation rate can be expressed as:  
\[D_{r1}=\frac{C_{1}^{i}-C_{1}^{i+1}}{C_{1}^{i+1}},\ D_{r2}=\frac{C_{2}^{i}-C_{2} ^{i+1}}{C_{2}^{i+1}} \tag{1}\]  
where \(D_{r1}\) is the degradation rate of the historical sample and \(D_{r2}\) is the degradation rate of the target battery.  
Repeat the calculation in Formula (1) and obtain the degradation rate of all corresponding points. Then, the RUL of historical batteries and the degradation rate of both historical and target batteries are extracted.
# Texture Analysis is a Useful Tool to Assess the Complexity Profile of Microcirculatory Blood Flow
###### Abstract  
The quantitative assessment of cardiovascular functions is particularly complicated, especially during any physiological challenge (e.g., exercise), with physiological signals showing intricate oscillatory properties. Signal complexity is one of such properties, and reflects the adaptability of the physiological systems that generated them. However, it is still underexplored in vascular physiology. In the present study, we calculate the complexity of photoplethysmography (PPG) signals and their frequency components obtained with the wavelet transform (WT), with two analytical tools--(i) texture analysis (TA) of WT scalograms, and (ii) multiscale entropy (MSE) analysis. PPG signals were collected from twelve healthy young subjects (\(26.0\pm 5.0\) y.o.) during a unilateral leg lowering maneuver to evoke the venoarterioral reflex (VAR) while lying supine, with the contralateral leg remaining stationary. Results showed that TA was able to detect a decrease in complexity, viewed as an increase in texture entropy (TE), of the PPG scalograms during VAR, similarly to MSE, suggesting that a decrease in the competence of vascular regulation mechanisms might be present during VAR. Nonetheless, TA showed lower sensitivity than MSE for low frequency spectral regions. TA seems to be a promising and straightforward analytical tool for the assessment of the complexity of PPG perfusion signals.  
Keywords:signal complexity; texture analysis; multiscale entropy analysis; wavelet transform; photoplethysmography +
Footnote †: journal: Applied Sciences
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 3 System Design
### Tree Construction Algorithm
#### 3.5.4 Grafting Process
When a non-tree node receives a GRAFT message, it calculates the accumulated delay using Formula (9) based on the neighbor delay recorded in NSIT (Neighborhood State Information Table) and the Previous Delay recorded in the GRAFT message:  
\[D_{accum}=PreviousDelay+NSIT[RequestNode]\cdot delay \tag{9}\]  
This \(D_{accum}\) is then added to the local accumulate delay table. The Relay node selection process mentioned earlier is executed to obtain the NA of Gateway. Add Gateway to the forwarding node list of MFT[MID], and set the NA recorded in the Request Node field \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline \(LS_{i}\) & The i-th entry in LS Table. \\ \(LDT[NA]\) & Least delay routing entry with NA as destination. \\ \(NSIB[NA]\) & Neighborhood state information base entry with NA as destination. \\ \(ProbeDelay\) & Latency estimated based on inaccurate information. \\ \(CalibratedDelay\) & Delay calibrated by neighborhood information. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the notations. Figure 10: Delay estimation during the grafting process. as the predecessor node. Finally, modify the Message Type field to GRAFT, change the Request Node field to the NA of the non-tree node, update the Previous Delay to \(D_{accum}\), and forward the message to the Gateway. Lastly, register the mapping of MID to its own NA in the corresponding ENRS. As the Graft message is transmitted to the receiver node hop by hop, the construction of the path from the multicast tree to the receiver is completed, concluding the grafting process.
## 2 Results and Discussion
### Desorption and Reusability Study  
For the potential application of an adsorbent in wastewater treatment plant, the regeneration for reuse is very important. In order to investigate the reuse value of EDA-MPMs, adsorption-desorption process was repeated for four times. As can be shown from Figure 10, the relationship between the times for reuse and the adsorption efficiency of EDA-MPMs for Cr(VI) was illustrated. It can be seen that the adsorption capacity of the magnetic microbeads for Cr(VI) could still be maintained at over 90% level after the consecutive three-time adsorption-desorption processes. This suggests that the EDA-MPMs possess the potential of regeneration and reuse.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Temperature (K)** & \(\Delta G^{\circ}\) **(kJ/mol)** & \(\Delta H^{\circ}\) **(kJ/mol)** & \(\Delta S^{\circ}\) **(J/mol/K)** & \(R^{2}\) \\ \hline
298 & \(-20.492\) & & & \\
308 & \(-22.522\) & 35.085 & 186.69 & 0.996 \\
318 & \(-24.219\) & & & \\ \hline \end{tabular}
\end{table}
Table 4: Thermodynamic parameters for Cr(VI) adsorption onto EDA-MPMs.  
Figure 9: Linear plots of \(\ln b\)_versus_\(1/T\) for the adsorption of Cr(VI) onto EDA-MPMs.
## 5 Simulation Setting and Results
### Total Network Consumed Energy  
The comparison of EERD and CoEERD with the CoDBR and ODBR is shown in Figure 10. The total network consumed energy of the EERD is minimum as compared to all the schemes. The lowest distance parameter in EERD scheme reduces the energy consumption. Node nearest to the sink with the maximum energy and minimum BER is selected as a DSN. In EERD, minimum number of nodes are contributed in information forwarding. The ODBR scheme considers only the depth criteria for the selection of best RLN, which does not guarantee low consumption of energy. The proposed CoEERD scheme is a cooperative-based scheme, where the cooperation of single RLN with the DSN increases the node contribution during the packet broadcasting and results in high energy consumption as compared to the EERD. Moreover, the CoDBR scheme also uses cooperation and considers only the depth information for the selection of data advancer node. The cooperation of two RLNs with the DSN in CoDBR scheme, increases the energy consumption. At round 400, the CoDBR, CoEERD, and ODBR schemes consume high energy and have the values of consumed energy approximately 3500 J and 3400 J, respectively. At the same number of rounds, the proposed EERD scheme consumes 3100 J energy, which shows that the EERD scheme saves the network energy.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Total sensors nodes & 400 \\ Total sink nodes & 4 \\ Deployment area width & 500 m \\ Deployment area depth & 500 m \\ Data packet size & 1000 bits \\ Wind speed & 10 m/s \\ Control packet size & 48 bits \\ Idle mode exhaust power & 10 mW \\ Transmission power & 2 W \\ Reception power & 0.5 W \\ Initial energy available for 1000 rounds & 10 J \\ Transmission range & 100 m \\ Bandwidth & 30,000 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used for simulation.
# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
## 3 Proposed Method
### Edge-Aware Secret Hiding Module
#### 3.2.4 Discriminator  
To further enhance the watermark imperceptibility, we employ a discriminator network \(D\) that takes both the watermarked image \(I_{vm}\) and cover image \(I_{cov}\) as input. This design follows the principle of the conditional-GAN [47], where the generation process is guided by specific conditional inputs. The discriminator architecture consists of four convolution layers, where the channel dimension first increases and then decreases, followed  
Figure 3: Visual examples illustrating the edge-aware secret hiding module’s performance, displaying the cover image (\(I_{cov}\)), watermarked image (\(I_{vm}\)), edge detection teacher output (\(E_{touch}\)), edge detection output (\(E_{pred}\)), and watermark embedding output (\(I_{hide}\)) from top to bottom.  
by an average pooling layer to output the image authenticity score. During training, the discriminator parameters \(\theta_{a}\) are updated by minimizing the following discrimination loss:  
\[\mathcal{L}_{\text{Dis}}=\log(1-D(\theta_{a},I_{cov}))+\log(D(\theta_{a},I_{wm})), \tag{8}\]  
where \(D(\cdot)\) represents the discrimination process, outputting an image authenticity score in the range of (0,1). Here, \(D(\theta_{a},I_{wm})\) represents the discriminator's score for the watermarked image, which should be close to 0 as \(I_{wm}\) is a generated (fake) image, while \(D(\theta_{a},I_{cov})\) represents the score for the original cover image, which should be close to 1 as it is a real image. This loss function encourages the discriminator to distinguish between real and watermarked images effectively. Correspondingly, the watermark embedding network parameters are updated by minimizing the following adversarial loss to generate more natural watermarked images:  
\[\mathcal{L}_{Gen}=\log(1-D(\theta_{a},I_{wm})). \tag{9}\]  
This loss term encourages the embedding network to generate watermarked images that can fool the discriminator into producing high authenticity scores (close to 1) for \(I_{wm}\).  
Through this adversarial training process, the watermark embedding network learns to generate watermarked images that become increasingly difficult for the discriminator to distinguish from the original cover images, thereby ensuring optimal watermark imperceptibility while maintaining the natural characteristics of the cover images.
## 2 Materials and Methods
### In Vitro Cell Viability Assay  
Cell culture method: The human breast cancer cell line MCF-7 from ATCC(r) was used to perform the cell viability assay. The cells were stored under liquid nitrogen at the MSRC until experiments were conducted. For the experiments, MCF-7 cells were thawed and grown in Dulbecco's Modified Eagle's Medium (DMEM) employing 5 % FBS and 1% penicillin-streptomycin (Pen-Strep) and incubated at 37 \({}^{\circ}\)C in 5% carbon dioxide (CO\({}_{2}\)). Cell passages were conducted twice weekly every 7 days. The cells were left to rise by 80% of confluency and appropriated to perform the cell viability experiments. This cell culture method was adapted from the ATCC product information and other previous works [26,27].  
Cell viability test: Two-fold serial dilutions of compounds gracilioether M (**6**), 11,12-dihydro progenitor M (**7**), and the mixture of 9,10-dihydroxforteo G (**8**) and plakortone G (**9**) in a concentration range of 0-400 uM were previously prepared to determine the IC\({}_{50}\). Subsequently, MCF-7 cells were seeded in 96-well plates at a 5.0 \(\times\) 103 cells/mL density. Cells were incubated for 24 h at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\), allowing for cell adherence to the 96-well plate. After 24 h of seeding, MCF-7 cells were treated with 100 uL of compounds **6** and **7** and the mixture of compounds **8** and **9** (0-400 uM). The control group was treated with just media (DMEM, 1% Pen-Strep). The cell viability was determined after 72 h of treatment. AlamarBlue(r) assay was employed to determine the cell viability; the media was exchanged with 100 uL of AlamarBlue solution (10%). After exchanging the media, the 96-well plates were incubated for 4 h. The fluorescence (TECAN Microplate Reader Infinite M200) was measured at 560.0 nm of excitation and \(\lambda_{\text{max}}\) = 590.0 nm of emission. The MCF-7 metabolic active cells (cells under proliferation) were analyzed, taking into consideration the viability of the control group (100%) compared with the MCF-7 cells treated with compounds **6** and **7**, as well as the mixture of compounds **8** and **9**. The IC\({}_{50}\) was determined for these compounds by generating dose-response curves (% live cells vs. concentration), employing GraphPad Prism vs. 9.3.0 and a nonlinear regression method. The experiments were performed in triplicate, and the mean, standard deviation, and coefficient of variation (%CV) were reported. This cell viability assay was adapted from previous studies [26,27].
###### Abstract  
(1) Background: Research examining the combined influence of anthropometric characteristics, chronological age, and training age on motor performance in handball is limited. Given the sport's demands and the participation of both adolescent and adult athletes, understanding these relationships is essential for talent identification, personalized training, and long-term athlete development. This study aimed to explore how these variables affect motor performance indicators such as speed, agility, and explosive power. (2) Methods: A cross-sectional study was conducted involving 29 male handball players. Anthropometric data (height and body weight), chronological age, and training age were collected. Motor performance was assessed using a 30 m sprint, a vertical jump test, and an agility test. (3) Results: Chronological age showed a strong positive correlation with training age (r = 0.819), and moderate correlations with height, body weight, vertical jump, agility, and sprint time. Training age was moderately correlated with vertical jump (r = 0.465) and agility (r = 0.439). Height and body weight were positively associated with sprint time. BMI exhibited low but consistent correlations with all motor tests. Regression analysis revealed that height significantly predicted sprint performance (\(\beta\) = 0.401, \(p\) = 0.033), while BMI was not a significant predictor. No significant regression models were found for agility or vertical jump performance. (4) Conclusions: The results suggest that both chronological and training age influence certain aspects of motor performance in handball players. Height may serve as a useful predictor of sprint ability, but anthropometric indicators such as BMI appear insufficient for explaining performance in agility or explosive power tasks. These findings support the use of multidimensional and individualized approaches in athletic assessment and training design.  
Keywords:biological maturation; motor performance; training history; performance parameters +
Footnote †: journal: Applied sciences
## 4 Implementation and Results
### Implementation and Discussion
#### 4.2.1 column Detection Results and Discussion
On the other hand, taking images from the side view of structural elements can aggravate tilting of distant objects in images, leading to the failure detection of distant columns: the most distant column 1 in Figure 4b failed to be detected because the aggravated inclination of its boundary lines exceeds \(5^{\circ}\). To avoid such deficiency, images should be taken from the front view of structural elements. Figure 3: column recognition of the No. 1 image in Table 1 (sub-region 3, 7, 12, 16, and 19 are columns): (**a**) variance of gray-level histograms (GLHs); (**b**) skewness of GLHs; (**c**) kurtosis of GLHs; (**d**) the GLH in sub-region 6 (non-structural element); (**e**) the GLH in sub-region 7 (a column); (**f**) the GLH in sub-region 8 (non-structural element).
## 2 Results and Discussion  
A total of 79 headspace and volatile compounds of the organic residue found at the bottom of an ancient Roman amphora were identified by direct GC and GC-MS analyses (without any derivatization procedure). The obtained chemical profiles of the headspace and dissolved residue are remarkably different (Table 1), but complementary for understanding the sample complexity. The headspace composition, besides valuable evidence on the monoterpene traces present, provided additionally a snapshot of trapped wine aroma traces (in comparison to fresh pine resin headspace composition). On the other hand, the chemical profile of the dissolved residue afforded essential information for understanding the ancient procedures of water-proof coating preparation. However we must keep in mind that the identified compounds probably underwent some degree of hydrolysis, oxidation, or microbial breakdown over the period of archaeological deposition. The dark brown colour of the organic residue indicates a probable and ubiquitous heating of pine resin/wood during preparation of the amphorae coating which can cause three probable effects: (1) terpene volatilization, (2) thermal dehydrogenation and (3) decarboxylation of part of the abiotic acid. The residue was insoluble in water and soluble in acetone, methanol and dichloromethane. It has a resinous smell when heated and burns when in contact with a flame. \begin{table}
\begin{tabular}{l l c c c} \hline
**No.** & **Compound** & **RI** & **A** & **B** & **C** \\ \hline
1. & Acetone & \(<900\) & 0.3 & 2.8 & - \\
2. & Acetic acid & \(<900\) & 0.1 & 2.0 & - \\
3. & Butan-2-one & \(<900\) & - & 0.6 & - \\
4. & Ethyl acetate & \(<900\) & - & 0.4 & - \\
5. & Benzene & \(<900\) & - & 0.2 & - \\
6. & Propionic acid & \(<900\) & 0.1 & 2.1 & - \\
7. & Heptane & \(<900\) & - & 0.7 & - \\
8. & Ethyl propionate & \(<900\) & - & 0.4 & - \\
9. & Isoamyl alcohol & \(<900\) & 0.3 & 0.9 & - \\
10. & Toluene & \(<900\) & - & 0.5 & - \\
11. & Butanoic acid & \(<900\) & - & 1.6 & - \\
12. & Octane & \(<900\) & - & 1.6 & - \\
13. & Furfural & \(<900\) & - & 0.5 & - \\
14. & Isovaleric acid & \(<900\) & - & 0.4 & - \\
15. & Ethylbenzene & \(<900\) & - & 0.4 & - \\
16. & Isoamyl acetate & \(<900\) & 0.1 & 0.8 & - \\
17. & \(p\)-Xylene & \(<900\) & - & 0.8 & - \\
18. & Valeric acid & \(<900\) & - & 1.2 & - \\
19. & Heptan-2-one & \(<900\) & - & 1.0 & - \\
20. & Nonane & 900 & - & 0.6 & - \\
21. & Heptanal & 905 & - & 0.4 & - \\
22. & Isopropylbenzene (cumene) & 932 & - & 0.4 & - \\
23. & \(\alpha\)-Pinene & 941 & 0.1 & 0.2 & - \\ \hline \end{tabular}
\end{table} 
Table 1: The organic residue VOC composition obtained by HS-SPME and from the residue CH\({}_{2}\)Cl\({}_{2}\) solution after direct GC and GC-MS analysis. **Table 1.**_Cont._
## 2 RS Systems  
RS systems can be generally divided into two groups of passive and active. Passive RS systems record reflected electromagnetic energy in the visible, Near-Infrared (NIR), and Shortwave Infrared (SWIR) bands, as well as emitted electromagnetic energy in the Thermal Infrared (TIR) bands. On the other hand, most active RS systems (e.g., microwave systems) measure the backscattering radiation from different objects on the Earth at higher wavelengths compared to passive systems. In the following subsection, a brief description of different RS systems that are mainly used for oceanographic applications is provided.
# Modeling and Solving Method for Supporting Vehicle-to-Anything' EV Charging Mode
## 2 V2A Charging Mode
### V2H Charging Scenario  
When an EV needs power refilling, the most convenient approach for EV users to recharge their cars is to connect EVs to home private charging piles. Meanwhile, it is assumed that EV and the other household appliances (e.g., air conditioner, washing machine, etc.) are all managed and controlled through a HEMS (Home Energy Management System). Under this condition, V2H can be operated. V2H refers to the power compensation from the EV battery to other home loads, e.g., air conditioner, washing machine, water heater, and so on, as shown in Figure 2. EV owners can choose to perform V2H in response to price variations; for instance, they can charge their cars when the power price is low and discharge their cars during high-price time slots. In this manner, EV users can achieve the goal of paying less for energy bills.  
Figure 1: Overview of V2A paradigm.  
Figure 2: Paradigm of V2H.  
Generally, V2H endows EV with the following features: (1) V2H typically involves only one EV for a single house; (2) V2H can help reduce household power consumption bills; (3) V2H can smooth the daily load profile of a single family; (4) V2H can interact with the utilization of household distributed power resources, thus improving the energy efficiency of a house; (5) since a household is a basic unit for a gird, V2H can also enhance the development of the smart grid.