esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 7 Operational Phase of the Proposed Protocol  
The overall routing phases of EHCRP have been divided into three stages: i.e., data initialization and sensing phase, forwarder node selection phase and cooperative effort phase which are represented with the help of the flow chart shown in Figure 5.
## 3 Results
### Relationship Between SOC and NDVI Time Series Data  
Figure 8 presents the correlation analysis results of the SOC content and NDVI by Pearson correlation coefficient (\(r\)) 0. A significant positive correlation is observed between the SOC content and NDVI in July and August. By contrast, a significant negative correlation exists between the SOC  
Figure 6: Variation of NDVI (normalized different vegetation index) of the soil samples from January to December.  
Figure 7: Spatial distribution of NDVI (normalized different vegetation index) from January to December. The figures of (**a-l**) are corresponding the months of January to December.  
content and NDVI in January, February, March, April, November, and December. This finding could be explained by the mature rice, cotton, and lotus root from July to August, thereby presenting a significant positive correlation. In January, February, March, April, November, and December, the negative correlation may be caused by the incomplete maturity of crops and exposure of soil on the surface. The harvest of oilseed rape in May and June and rice and lotus root in September and October leads to the earth surface covered with soil. Thus, the correlation between the SOC content and NDVI in May, June, September, and October is insignificant. These results demonstrate that NDVI time series data are closely related to crop growth cycle, which may be deeply affected by the SOC content under vegetation in these months and could be used to predict the spatial distribution of SOC.
## 2 Materials and Methods
### Polyphenolic Content by HPLC  
* Polyphenolic extract preparation Freeze-dried apple powder (250 mg) was weighed into an eppendorf tube. A total of 1.5 mL of 60% of aqueous methanol with 1% (\(v/v\)) of formic acid was added, and the suspension was vortexed vigorously for 2 min. Tubes were left 60 min in a sonic bath. The extract was centrifuged for 20 min (20.878\(\times\) g), and supernatant was collected at 4 \({}^{\circ}\)C and transferred to a vial before the injection into the high-performance liquid chromatography (HPLC) system.
* High-performance liquid chromatography and mass spectrometry analysis Before analysis, 20 \(\mu\)L of each internal standard was added to the samples (genistein: 580 \(\mu\)g/mL; genistin: 380 \(\mu\)g/mL).  
HPLC separations were carried out by means of a SUNSHELL C18 (2.1 i.d. \(\times\) 100 mm) column, with a 2.6 \(\mu\)m particle size with the mobile phase, pumped at a flow-rate of 0.3 mL/min, and consistingof a mixture of acidified acetonitrile (0.1% formic acid) (solvent A) and 0.1% aqueous formic acid (solvent B). Following 0-2 min, 2% B; 2-13 min, 2% to 30% B; 13-20 min, 30% to 80% B; 20-22 min, 80% to 2% B; 22-30 min, 2% isocratic; this step was followed by the washing and reconditioning of the column.  
The identity of the phenolic compounds was confirmed using a triple quadrupole mass spectrometer (Thermo Scientific, TSQ Vantage) with a heated electrospray ionization (H-ESI II) operating in the negative ionization mode. The capillary temperature was 270 \({}^{\circ}\)C; the sheath gas and auxiliary gas were 40 and 5 arbitrary units, respectively; and the source voltage was 3 kV. The vaporizer temperature 200 \({}^{\circ}\)C argon was used for MS experiments with a collision pressure of 1.0.  
For the identification of phenolic compounds, a full-scan analysis was performed with scanning from \(m/z\) 100 to 950, while a product ion scan experiment was applied for ions not fully identified in the previous method. Identification was performed by comparing the mass spectra with data in literature, and the identification was confirmed by using pure standards of the components whenever possible.  
Quantification was achieved in Selected Ion Monitoring mode according to the concentrations of the corresponding internal standard, respectively; that is, genistin for glucoside and genistein for agilcone.
## 5 Simulation Results
### Convergence Analysis of the Distributed Estimation Consensus  
In order to perform a convergence analysis, it is worth mentioning that the estimation algorithm was carried out in a computer with the following specs: 7-th generation core-i7 microprocessor, 32 GB installed memory (RAM) and NVIDIA GEFORCE 940\(MX\). Several simulations were performed with the aim to know the algorithm performance under different parameter values. The principal parameter taken into account is the number of agents able to take a measurement, which in this case vary from 3 to 10. Additionally, in the interest to prove the robustness of the estimation algorithm, the measurement values are settled in a random way. In this way, the convergence of the algorithm depending on the number of agents is shown in Figure 13. Where it is appreciable how the convergence is affected by the number of agents. The greater number of agents giving measurements the more time it takes the algorithm to converge.  
As previously mentioned, several simulation scenarios were performed with the purpose of knowing how the variation of parameters can affect the algorithm convergence. In fact, 30 simulations were carried out for each variation in the number of agents (3, 5, 7 and 10). The results are summarized in Table 1, where is appreciable that by increasing the number of agents also increase the iterations number to converge in the same way the convergence time do. Taking this into account, the mean value \(\mu\) and standard deviation \(\sigma\) for each case were calculated. In which, the largest number of experiments converge between the mean value and the standard deviation, this corresponds to the approximately the 68 percent of the experiments converge within the interval \([\mu-\sigma,\mu+\sigma]\). The fact that the standard deviation are not large numbers shows that the algorithm is kind of robust under changes in its parameters.  
Figure 13: Convergence analysis with variation in the number of agents and random initial values.
# Interpolation-Filtering Method for Image Improvement in Digital Holography
## 2 Image Quality Enhancements in Digital Holography
### Noise Reduction Methods in Digital Holography
#### 2.3.4 Metrics for Assessing Image Quality  
To estimate the degree of improvement in the quality of the reconstructed image, various metrics are used that require the presence of a reference--ideal image. In numerical experiments, the image on the basis of which the hologram was generated serves as such an image. Such metrics include:  
1. Peak Signal to Noise Ratio (PSNR) [96] is the ratio of maximum signal-to-noise power in decibels: \[\text{PSNR}=20\times log_{10}\left(\frac{\text{M}_{\text{Ri}}}{\sqrt{\text{ MSE}}}\right),\] (4) where \(\text{M}_{\text{Ri}}\) is the maximum signal value in a pixel of the image, MSE is the mean squared error between original and reconstructed images. The higher the PSNR value, the closer the noisy (reconstructed) image is to the original one.
2. Correlation Coefficient (CC) [97]: \[\text{CC}=\frac{\sum_{i,j}\left(Ii_{ij}-\left\langle Ii_{ij}\right\rangle \right)\left(Ri_{ij}-\left\langle Ri_{ij}\right\rangle\right)}{\sqrt{\left( \sum_{i,j}\left(Ii_{ij}-\left\langle Ii_{ij}\right\rangle\right)^{2}\right) \left(\sum_{i,j}\left(Ri_{ij}-\left\langle Ri_{ij}\right\rangle\right)^{2} \right)}}\] (5)where \(I_{ij}\) and \(R_{ij}\)--original and reconstructed images; i,j--row and column numbers of images, \(\left\langle I_{ij}\right\rangle\), \(\left\langle Kl_{ij}\right\rangle\)--average values of original and reconstructed images. The closer CC to 1, the more similar the compared images.
3. Normalized standard deviation (NSTD) [98]: \[\text{NSTD}=\sqrt{\frac{\sum_{i,j}\left(I_{ii,j}\cdot R_{ii,j}\right)^{2}}{ \left(\sum_{i,j}I_{ii,j}\right)^{2}\left(\sum_{i,j}R_{ii,j}\right)}}\] (6) The closer the NSTD value is to 0 the more similar the original and reconstructed images are.
4. Structural similarity index (SSIM) [99]: \[\text{SSIM}=\frac{\left(2\left\langle I_{ij}\right\rangle\left\langle R_{ij} \right\rangle+C_{1}\right)\left(2\sigma_{IiRi}+C_{2}\right)}{\left(\left\langle I _{ij}\right\rangle^{2}+\left\langle R_{ij}\right\rangle^{2}+C_{1}\right) \left(\sigma_{1i}^{2}+\sigma_{Ri}^{2}+C_{2}\right)}\] (7) where \(\sigma_{li}\), \(\sigma_{Ri}\) are the variance on \(I_{ij}\) and \(Ri_{ij}\) respectively; \(\sigma_{IiRi}\) is the covariance of \(I_{ij}\) and \(R_{ij}\), \(C_{1}\), \(C_{2}\) are the coefficients. The higher the SSIM value, the less the images differ. SSIM estimates match better with human perception due to accounting for the spatial proximity of pixels.  
When analyzing the enhancement of an experimentally registered hologram, an ideal image is not available. In such cases, speckle contrast (SC) is used, which is calculated based on one reconstructed image [33; 35]:  
\[\text{SC}=\frac{\text{SD}}{\text{M}} \tag{8}\]  
where SD--standard deviation in the selected area; M--arithmetic mean of pixel signals of this area.
## 3 Results and Discussions
### Analysis of DE Optimization Parameters
* Optimal response factor comparison: effective strain rate
* Optimal response factor comparison: punch force The optimal response-punch force for optimal control factors obtained by the DE technique with varying reduction ratios is shown in Figure 17. The optimal punch force values have been observed to be considerably larger for AISI 1045 compared to AISI 1010 and 1018 with all cases of reduction ratios. However, slight variations are seen in the optimal punch values of AISI 1010 and 1018 because of differences in percentage carbon compositions in these alloys. An increase in billet volume beyond \(z\) = 0.6 marginally affects the optimal punch force, whereas an increasing trend was seen in the region \(z\) < 0.6. Figure 13: Comparison of optimal values of land height (\(h\)) of different steels. Figure 14: Comparison of optimal values of effective stress of different steels. Figure 15: Comparison of optimal values of the effective strain of different steels.
## 3 Results
### Findings
#### 3.2.5 Neurites  
Neurites refer to projections from the cell bodies of neurons, either dendrites or axons. Dendrites are branched protoplasmic extensions of nerve cells that propagate the electrochemical impulses received from other neural cells, while axons propagate electrical impulses to other neurons. We identified four papers evaluating the effects of seizures on dendritic and axonal structure. Specifically, spine loss, swelling, abnormal development, and vesicular release were analyzed.  
By visualizing dendrites of neocortical neurons in vivo, [PERSON] et al. (2016) demonstrated that minor seizures produced minimal effects on dendritic spines of neocortical neurons whereas severe seizures were associated with significant spine loss as well as dendritic swelling. In contrast, [PERSON] et al. (2016) observed that the degree of seizure-induced dendritic injury was related to seizure duration and that even brief seizures were capable of inducing dendritic beading and spine loss. Additionally, it was found that dendritic injury caused by minor seizures usually recovered within two weeks whereas dendritic injury caused by severe seizures recovered only partially.  
[PERSON] and [PERSON] (2016) investigated the properties of hilar basal dendrites (HBDs) using glutamate uncaging at dendrites of granule cells in rat hippocampal slices. In temporal lobe epilepsy, granule cells in the dentate gyrus can develop abnormally and can possess an HBD, promoting pathologically seizure propagation.  
Finally, [PERSON] et al. (2019) visualized the effects of seizures on presynaptic machinery, namely mossy fiber axons and vesicular release. The mossy fiber pathway consists of un-myclinated axons projecting from the hippocampal dentate gyrus. Following pilocarppine  
Figure 4: In vivo two-photon images of rat surface vasculature depicting vessel responses to 4-AP-induced ictal activity at different time points (**1: injection, 2: seizure onset, 3: maximum dilation, 4: restoration to baseline**). The upper inset images demonstrate vasodilation near the seizure focus at the time of seizure onset (**2**), subsequent propagation (**3**) and restoration to baseline (**4**). The lower inset images demonstrate vasoconstriction away from the seizure focus at onset (**2**), followed by dilation at a later point (**3**), before restoring to baseline (**4**). Scale bars: 500 \(\upmu\)m (main image) and 50 \(\upmu\)m (inset images). Adapted from “Practical and ictal neurovascular and metabolic coupling surrounding a seizure focus” ([PERSON] et al., _The Journal of Neuroscience_, 2011 ([PERSON] et al., 2011)). This figure has been modified. Creative Commons license: [[https://creativeccommons.org/licenses/by/4.0/](https://creativeccommons.org/licenses/by/4.0/)]([https://creativeccommons.org/licenses/by/4.0/](https://creativeccommons.org/licenses/by/4.0/)) (accessed on 20 November 2020).  
induced status epilepticus, significant increases in mossy fiber axon sizes and faster rates of vesicular release were observed, suggesting altered structural and functional properties due to seizure activity.
## 2 Related Work and Theoretical Foundation
### Existing Devices Used for Skin Disease Analysis  
Currently, manual or computer vision-based tools are used to diagnose skin diseases. Figure 1a shows a dermoscopy device widely used in advanced countries to diagnose melanoma cancer. It has demonstrated success in diagnosing other skin conditions such as psoriasis [4], lichen planus [4], and cicatricial alopecia. Dermoscopy visualizes the subtle clinical patterns of skin lesions and subsurface skin structures not typically visible to the unaided eye. However, when using this device, the health professional has to decide the severity and disease condition. Figure 1b presents the dermalite device, a user-friendly dermoscopy device attached to a smartphone. It enables capturing highly magnified dermatologist-grade photos of moles or other skin lesions that can be further shared with professional dermatologists for proper examination and diagnosis.  
Figure 1c shows a microscope device developed by Stanford scientists to spot the seeds of cancer, diagnose diseases that include skin cancer, and perform exact surgery (without cutting the skin). However, laser-based imaging tools are expensive, slower, bulkier, and less accurate, and they are usually not meant for public adoption [23].
### Brief of the Existing Technology and This Work  
Through the analysis of the existing methods, it can be found that the color discrimination systems based on imaging technology and spectral technology is relatively mature. But when the surface color difference is small or the surface condition is complex, such as the surface irregularity, colors too close to each other, or color differences caused by the optical structure, both the color discrimination systems based on imaging technology and spectral technology will appear to be in error. Among them, the color discrimination system based on imaging technology has difficulty detecting the color differences caused by the optical structure. When the light intensity of the lighting source is unstable, the result of color discrimination will be seriously affected. It is difficult to solve the error problem caused by surface irregularity and too close color by the color discrimination system based on spectral technology.  
Therefore, it is very promising to construct a modified color discrimination system that can solve complex problems. This paper proposes a color discrimination method based on laser detection system modified by ocular estimate method. The system can solve the problem of color discrimination of the surface irregularity, color too close to each other or color differences caused by optical structure, so as to achieve more accurate color discrimination which is widely used in industrial production and daily life and meets the needs.  
The color discrimination system proposed in this paper consists of three parts. Firstly, the sensitive wavelength and insensitive wavelength of the samples are calculated by statistical method according to the reflection spectrum. Then, the corresponding wavelength laser detection system is built according to the calculation results. Finally, the same color interval is demarcated for the tested color information according to the color categories classified by ocular estimate method.
## 2 Investigations of SB--General Overview and Statistics  
The survey covers the period of January 2019 to January 2023 and is a continuation of review paper [32]. During this period, along with established researchers and research groups [32], research centres in the following cities made a significant contribution to the development of SB: Rzeszow, Poland [36; 37; 38; 39]; Krakow, Poland [40; 41; 42; 43; 44]; Lublin, Poland [45; 46; 47]; Miskolc, Hungary [48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]; Surathkal, India [60; 61; 62; 63; 64]; Ural Federal University, Russia [65; 66]; and the Technical University of Gabrovo, Bulgaria [67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79]. Investigations by other authors have also been reported worldwide [80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99].  
The studies concerning SB are classified according to indicators, some of which were used in our previous review paper [32]. The general overview of the investigations of SB is systematised and classified in Table 1 on the basis of eight indicators:  
\begin{table}
\begin{tabular}{l l l} \hline
**Indictors** & **Subindicators** & **References** \\ \hline A. Deforming element & A.1. Material & \\  & A.1.1. Diamond & \\  & A.1.1. Artificial & [37; 38; 39; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 91; 92; 93; 94; 95; 96; 97; 98; 99] \\  & A.1.1.2. Natural & [66] \\  & A.1.2. Hardened steel & [67; 82; 98] \\  & A.1.3. Hard alloy & [36; 38; 38; 34; 89; 90; 97] \\  & A.2. Working surface & \\  & A.2.1. Spherical & [36; 37; 38; 39; 48; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 98] \\  & A.2.2. Cylindrical & [67; 82; 97] \\ \hline B. Materials processed by SB & B.1. Steeds & \\  & B.1.1. Unhardened & \\  & B.1.1.1. Carbon constructional & [47; 49; 50; 59; 58; 59; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 89; 90; 91; 92; 9
## 2 Experimental Section  
### Aphis glycines Colony  
Aphids used within greenhouse-based experiments were from a colony maintained at the Soybean Entomology Lab at Iowa State University (ISU), Ames, Iowa. _A. glycines_ were collected from soybean plants from multiple sites within Story County, Iowa. Aphids were kept on vegetation stage (V3-V6, 3-6 fully open, mature trifoliate leaves) soybean plants within multiple growth chambers (16:8 Light:Dark, 24 \({}^{\circ}\)C). Plants were replaced every 2-3 weeks, with foliage removed from infested plants and attached to non-infested plants.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Sampling** & **Sample** & **Analyses** & **Matrix and** \\  & & **Preparation** & & **\# identified compounds** \\ \hline This work & SPME (85 \(\upmu\)m Carboxen / PDMS); Extraction conditions: 25 \({}^{\circ}\)C, 24 h & & GC-MS & Soybean leaves (_A. glycines_-infested and non-infested), 62 compounds \\ \hline Zhu and Park [11] & Glass tube containing 100 mg & Solvent & GC-EAD & Soybean leaves (_A. glycines_ infested and non-infested), 25 compounds \\  & Super Q (80/100 mesh) & extraction. Direct injection & GC-EAD & Soybean leaves (_A. glycines_ infested and non-infested), 25 compounds \\  & & Direct injection & GC-MS & Soybeans, 49 compounds \\ \hline Boue & SPME (100 \(\upmu\)m PDMS, 50/30 \(\upmu\)m & SPME & GC-MS & Soybeans, 49 compounds \\ _et al._[10] & DVB/Carboxen/PDMS); Extraction conditions: 60 \({}^{\circ}\)C, 1 h & & \\ \hline Rosario & Dynamic headspace sampling; Tenax-GC adsorbent & Thermal desorption & GC-MS & Winged beans and soybeans, 116 compounds \\ \hline Damiani & 1. Tenax-GC adsorbent, & 1.Thermal & GC-MS & Soybean leaves, 9 compounds \\ _et al._[13] & 2. Purge and trap, & desorption & GC-FTIR & \\  & 3. SPME 100 \(\upmu\)m PDMS; Extraction & 2.SPME & & \\  & & & \\  & & & \\ \end{tabular}
\end{table}
Table 1: Comparison of sampling and analytical methods used to characterize volatile organic compounds (VOCs) in soybean leaves and beans.
# Optimization of Composite Material Repair Patch Shape Based on Strength Analysis
## 1 Introduction  
For the repair of aircraft composite structural components, the first step is to determine the location, type, and extent of the damage. Then, the repair procedure of retaining, repairing, or replacing the damaged part is followed according to the repair manual. Boeing and Airbus generally use inclined or stepped grinding repair methods for repairable damages on composite components. For example, the Boeing 787 aircraft's structural repair manual [1] specifies that the allowable repairable damage on the fuselage skin should be within 8.0 in \(\times\) 8.0 in. Typically circular or rounded rectangular patches are used for inclined or stepped grinding repair, as shown in Figure 1.  
Figure 1: Different shapes of composite material repair patches.  
[PERSON] et al. [2] have pointed out that the shape of the patch in repair will affect various physical properties of the repaired components. So, this study of selecting the shape of repair patches in maintenance can provide a more reliable basis for formulating composite component repair plans.
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
## 2 Related Works  
SD-WAN is recognized in OT networks since it is among the major enablers of IT infrastructures [39] (pp. 1-9). SD-WAN ensures safe, uninterrupted operations while providing protection against cyber threats [40]. As a result, it enhances the availability of ICS and CPS, which is essential for advancing along the Industry 4.0 path. A key advantage of SD-WAN is its ability to provide centralized security management, enabling consistent application and real-time monitoring of security policies across all network segments. This ensures that there is no extended exposure to potential threats [40, 41] (pp. 14-76). Further, SDN, a parent category of SD-WAN, eases network segmentation by isolating critical ICS/CPS components, helping contain lateral attackers' movement in line with industry standards [42]. This includes integrating security features into SD-WAN, such as next-generation firewalls, secure web gateways, and anomaly traffic detection for comprehensive security coverage [43]. SD-WAN also offers advanced threat detection and response capabilities, including IDS, IPS, and real-time threat intelligence, enabling effective cyber-attack detection and prevention. Additionally, SD-WAN platforms provide real-time analytics and access to threat intelligence feeds, facilitating proactive threat management [23, 24]. Data-in-transit are further secured with encrypted tunnels and a zero-trust model, ensuring only authenticated and authorized entities can access resources or network traffic. To safeguard against tampering, SD-WAN employs authentication certificates and mutual TLS encryption, strengthening communication security and ensuring network integrity. Furthermore, SD-WAN configurations are secured through profiles managed by a cloud controller, preventing unauthorized configuration changes on physical or edge devices. This ensures that only centrally authorized and managed configuration modifications are allowed, effectively mitigating the risk of local tampering [21, 22].