esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Microstructural Properties  
Through the optical microscopy it was possible to analyze the microstructure of the samples, specifically the PCL domains' dispersion inside the PA6 matrix. Figure 4a-e shows the micrographs of the cross-section of the prepared blends obtained through light microscopy. From these images, it can be observed how different PCL domain dimensions can be found in the blends, especially at elevated PCL levels. Lower levels of PCL in the blends (namely 5 and 10 wt%) generally generate domains of reduced dimensions compared to blends with higher PCL contents (namely 20 and 30 wt%). An important fraction of small domains is present alongside larger domains, indicating the presence of coalescence phenomena, especially in blend compositions with higher PCL levels, as already shown by [PERSON] et al. in a poly-L-lactic acid/polycaprolactone blend [52]. Moreover, the size distribution of PCL domains was fitted through a lognormal distribution, and the average domain size was reported in Figure 4f. This plot confirms that the dimension of PCL  
Figure 2: Dynamic rheological behavior of the produced PA6/PCL blends. Trends of (**a**) storage and loss modulus and of (**b**) complex viscosity as a function of the angular frequency.  
concentrations of PCL could be attributed to coalescence phenomena [54]. Furthermore, a rather poor interfacial adhesion can be observed between the two constituents at all the relative concentrations. In the recent work of [PERSON] et al. [55], the investigation of PA6/PCL blends filled with multiwalled carbon nanotubes was carried out, and in that case a lack of adhesion between the constituents was also observed. This lack of interfacial interactions could probably lead to a worsening of the mechanical properties of the prepared blends.
## 4 Analysis of Single Objective Algorithms Overview
### Multi-Objective Applications
These studies utilize diverse MOO methods, including NSGA-II, MOEA, and SPEA-2, which have been customized to address the individual demands and intricacies of each issue area. Table 4 provides readers with a comprehensive view of the various applications of MOO and the distinct approaches employed to tackle intricate decision-making problems in diverse domains. Furthermore, it emphasizes the significance of utilizing advanced optimization approaches to better decision-making processes, enhance system performance, and stimulate innovation in many fields. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Application Domain** & **Description** & **References** & **Method/Algorithm Used** \\ \hline \multirow{6}{*}{Engineering design} & Optimal design of mechanical, electrical, and structural systems considering multiple objectives such as cost, performance, and reliability. & & NSGA-II, MOEA, multi-objective differential evolution, MOFDO, MOLPB, MOPSO, MOEO \\  & Design and optimization of renewable energy systems (solar, wind, hydro) considering cost, efficiency, and environmental impact simultaneously. & [27, 28, 29, 39, 48, 54, 58, 86] & MOEO \\  & & & multi-objective ant lion optimizer (MOALO), MODA \\ \hline \multirow{6}{*}{Renewable energy systems} & Design and optimization of renewable energy systems (solar, wind, hydro) considering cost, efficiency, and environmental impact simultaneously. & [5, 61, 62, 63, 65, 66, 70, 72, 87, 88, 89, 90, 91, 92, 93, 94] & MOPSO, NSGA-II, SPEA2, I-MODE \\ \hline \multirow{6}{*}{Supply chain management} & Optimization of supply chain networks to minimize cost, lead time, and inventory while maximizing customer satisfaction and resilience to disruptions. & [95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112] & NSGA-II, MOGA, Multi-Objective Genetic Algorithm (MOGA), MOPSO, SPEA2 \\ \hline \multirow{6}{*}{Healthcare} & Patient treatment planning, scheduling, and resource allocation in healthcare systems consider objectives like cost, patient outcomes, and resource utilization. & & NSGA-II, NSGA-III, Multi-Objective Simulated Annealing (MOSA), -\(\varepsilon\)-constraint approach, MOPSO, SPEA2, \\  & Conservation planning, land use optimization, and biodiversity conservation consider conflicting objectives like habitat preservation, economic development, and ecosystem services. & & MOEA, NSGA-II, Multi-Objective Genetic Algorithm (MOGA) \\ \hline \multirow{6}{*}{Finance and investment} & Portfolio optimization, risk management, and asset allocation considering objectives such as return on investment, risk exposure, and liquidity. & [9, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 19
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Resin-Based Material**} & \multicolumn{3}{c}{**Ceramic Thickness**} & **0.5 mm vs. 0.8 mm** \\ \cline{2-4}  & **0.5 mm** & **0.5 mm** & **0.5 mm (\(p\)-Value)** \\ \cline{2-4}  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & \\ \hline Variolink Light & 1.81 \(\times\) 10\({}^{6}\)\(\pm\) (3 \(\times\) 10\({}^{5}\))\({}^{\rm\,B}\) & 1.71 \(\times\) 10\({}^{6}\)\(\pm\) (1 \(\times\) 10\({}^{5}\))\({}^{\rm\,E}\) & 0.361 \\ Variolink Neutral & 2.06 \(\times\) 10\({}^{6}\)\(\pm\) (2 \(\times\) 10\({}^{5}\))\({}^{\rm\,BC}\) & 1.73 \(\times\) 10\({}^{6}\)\(\pm\) (1 \(\times\) 10\({}^{5}\))\({}^{\rm\,E}\) & 0.005 \\ Variolink Warm & 1.88 \(\times\) 10\({}^{6}\)\(\pm\) (2 \(\times\) 10\({}^{5}\))\({}^{\rm\,B}\) & 1.38 \(\times\) 10\({}^{6}\)\(\pm\) (1 \(\times\) 10\({}^{5}\))\({}^{\rm\,CD}\) & \textless{}0.001 \\ RelyX Veneer B0.5 & 8.01 \(\times\) 10\({}^{5}\)\(\pm\) (2 \(\times\) 10\({}^{5}\))\({}^{\rm\,A}\) & 8.99 \(\times\) 10\({}^{5}\)\(\pm\) (1 \(\times\) 10\({}^{5}\))\({}^{\rm\,AB}\) & 0.296 \\ RelyX Veneer & 9.78 \(\times\) 10\({}^{5}\)\(\pm\) (2 \(\times\) 10\({}^{5}\))\({}^{\rm\,A}\) & 1.15 \(\times\) 10\({}^{6}\)\(\pm\) (1 \(\times\) 10\({}^{5}\))\({}^{\rm\,BC}\) & 0.072 \\ ReyX Veneer A3 & 6.53 \(\times\) 10\({}^{5}\)\(\pm\) (4 \(\times\) 10\({}^{5}\))\({}^{\rm\,A}\) & 7.78 \(\times\) 10\({}^{5}\)\(\pm\) (1 \(\times\) 10\({}^{5}\))\({}^{\rm\,A}\) & 0.442 \\ G-aenial Flo A2 & 2.04 \(\times\) 10\({}^{6}\)\(\pm\) (3 \(\times\) 10\({}^{5}\))\({}^{\rm\,AC}\) & 1.58 \(\times\) 10\({}^{6}\)\(\pm\) (2 \(\times\) 10\({}^{5}\))\({}^{\rm\,DE}\) & 0.003 \\ G-aenial Flo A3 & 2.47 \(\times\) 10\({}^{6}\)\(\pm\) (6 \(\times\) 10\({}^{5}\))\({}^{\rm\,C}\) & 2.36 \(\times\) 10\({}^{6}\)\(\pm\) (2 \(\times\) 10\({}^{5}\))\({}^{\rm\,F}\) & 0.630 \\ F Supreme XTE A3 & 2.02 \(\times\) 10\({}^{6}\)\(\pm\) (2 \(\times\) 10\({}^{5}\))\({}^{\rm\,BC}\) & 1.65 \(\times\) 10\({}^{6}\)\(\pm\) (2 \(\times\) 10\({}^{5}\))\({}^{\rm\,DE}\) & 0.05 \\ (preheated) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean fluorescence values (arbitrary unit, a.u.) and standard deviation (SD) obtained for bonded ceramic samples according to the resin-based luting agent and the feldspathic ceramic thickness (n = 10). For each column, different letters indicate significantly different fluorescence mean values among luting agents used for each feldspathic ceramic thickness. Figure 2: Fluorescence emission spectra different groups according to resin-based material used for luting feldspathic veneers (0.8 mm thickness). Graph values are in millions of a.u.
###### Abstract  
This study optimizes furfural production from pentose released in the liquid hydrolysate of hardwood using an aqueous biphasic system. Dilute acid pretreatment with 4% sulfuric acid was conducted to extract pentose from liquid _Quercus mongolica_ hydrolysate. To produce furfural from xylose, a xylose standard solution with the same acid concentration of the liquid hydrolysate and extracting solvent (tetrahydrofuran) were applied to the aqueous biphasic system. A response surface methodology was adopted to optimize furfural production in the aqueous biphasic system. A maximum furfural yield of 72.39% was achieved at optimal conditions as per the RSM; a reaction temperature of 170 \({}^{\circ}\)C, reaction time of 120 min, and a xylose concentration of 10 g/L. Tetrahydrofuran, toluene, and dimethyl sulfoxide were evaluated to understand the effects of the solvent on furfural production. Tetrahydrofuran generated the highest furfural yield, while DMSO gave the lowest yield. A furfural yield of 68.20% from pentose was achieved in the liquid hydrolysate of _Quercus mongolica_ under optimal conditions using tetrahydrofuran as the extracting solvent. The aqueous and tetrahydrofuran fractions were separated from the aqueous biphasic solvent by salting out using sodium chloride, and 94.63% of the furfural produced was drawn out through two extractions using tetrahydrofuran.  
Keywords:aqueous biphasic system; dilute acid hydrolysate; furfural production; solvent extraction; response surface methodology +
Footnote †: journal: Applied Sciences
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 3 Iterative Positioning Algorithm for the Target Node Based on Distance Correction  
An iterative positioning algorithm for the target node based on distance correction is proposed to help users overcome the influence of noise on positioning accuracy. The main issues presented in this paper for building an iterative positioning algorithm based on distance correction are threefold. First, a log-normal distribution mathematical model should be constructed to measure RSSI, based upon which the impact factor of noise on distance detecting can be derived. Second, a triangle centroid positioning algorithm should be built to determine the initial positioning coordinates of the unknown target node, and lastly, the distance deviation coefficient and its median can be determined. Therefore, the iterative positioning algorithm for the target node based on distance correction is constructed according to the median of the distance deviation coefficient.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 4 Discussion  
The results of this study highlight the potential of the PPI in monitoring vegetation phenology in mountainous regions. The comparison of PPI and NDVI time-series depicts a less noisy signal for PPI, particularly during the transition seasons when snow could be present and for coniferous trees. For the retrieval of seasonality parameters, we found close concordance between the two indices for the SOS, EOS, and length of season for deciduous trees, grasslands, and shrublands. However, this is not the case for POS and for all seasonality parameters for coniferous trees. In the following section, we will discuss these results relying on existing literature.
## 2 Materials and Methods
### Methods
#### 2.2.3 Modification of the CASA Model  
In the CASA model, NPP was determined by the absorbed photosynthetically active radiation (APAR) and the light use efficiency (\(\epsilon\)) [24]. Here, APAR was obtained from the PAR and FAPAR.  
\[NPP(\chi,t)=APAR(\chi,t)\times\epsilon(\chi,t) \tag{6}\]  
\[APAR(\chi,t)=PAR(\chi,t)\times FPAR(\chi,t) \tag{7}\]  
where \(\chi\) denotes the location; \(t\) represents time; and \(NPP(\chi,t)\) is the NPP (g C/m\({}^{2}\)) at location \(\chi\) and time \(t\).  
\(\epsilon\) is constrained by both temperature and moisture and is calculated as follows:  
\[\epsilon(\chi,t)=T_{\epsilon 1}(\chi,t)\times T_{\epsilon 2}(\chi,t)\times W _{\epsilon}(\chi,t)\times\epsilon_{max} \tag{8}\]  
where \(T_{\epsilon 1}(\chi,t)\) and \(T_{\epsilon 2}(\chi,t)\) represent the effects of low and high temperatures on light use efficiency, respectively; \(W_{\epsilon}(\chi,t)\) is the moisture stress; and \(\epsilon_{max}\) is the maximum light use efficiency under ideal conditions. Detailed calculation methods and values for \(T_{\epsilon 1}\), \(T_{\epsilon 2}\), and \(\epsilon_{max}\) can be found in [PERSON] et al. (2022) and [PERSON] et al. (2024) [5; 25].  
The NPP calculated by the CASA model was interpolated to a 1-day resolution using a cubic spline function, and the CASA results were constrained by the actual vegetation phenological dates and temperature [11; 26].  
\[NPP_{mod}=f\times NPP_{org} \tag{9}\]\[f=\begin{cases}\frac{1}{\left(\frac{5-t}{5}\right)+1}\times\frac{1}{\left(\frac{5 0S-4}{50S^{2}}\right)+1},\text{ if }T\leq 5\text{ and }d\leq SOS\\ \frac{1}{\left(\frac{50S-2}{50S^{2}}\right)+1},\text{ if }T>5\text{ and }d\leq SOS\\ \frac{1}{\left(\frac{4-tOS}{tOS}\right)+1},\text{ if }T>5\text{ and }d\geq EOS\\ \frac{1}{\left(\frac{4-tOS}{tOS}\right)+1}\times\frac{1}{\left(\frac{5-t}{5} \right)+1},\text{ if }T\leq 5\text{ and }d\geq EOS\\ \end{cases} \tag{10}\]  
where \(NPP_{org}\) is the monthly NPP simulated by the original algorithm in the CASA model, and \(NPP_{mad}\) is the result after being constrained with \(f\). \(d\) is the day of year, and \(T\) is daily average temperature. SOS was satellite-derived, while EOS was derived from the CDD\({}_{\text{SG}}\) model.
###### Abstract  
Automatic modulation classification (AMC) is a fundamental technique in wireless communication systems, as it enables the identification of modulation schemes at the receiver without prior knowledge, thereby promoting efficient spectrum utilization. Recent advancements in deep learning (DL) have significantly enhanced classification performance by enabling neural networks (NNs) to learn complex decision boundaries directly from raw signal data. However, many existing NN-based AMC methods employ deep or specialized network architectures, which, while effective, tend to involve substantial structural complexity. To address this issue, we present a simple NN architecture that utilizes features derived from Hankelized matrices to extract informative signal representations. In the proposed approach, received signals are first transformed into Hankelized matrices, from which informative features are extracted using singular value decomposition (SVD). These features are then fed into a compact, fully connected (FC) NN for modulation classification across a wide range of signal-to-noise ratio (SNR) levels. Despite its architectural simplicity, the proposed method achieves competitive performance, offering a practical and scalable solution for AMC tasks at the receiver in diverse wireless environments.  
automatic modulation classification; neural network; Hankelization; singular values 2025  
15 June 2025  
6 July 2025  
Accepted: 11 July 2025  
Published: 14 July 2025  
###### Abstract  
Automatic modulation classification (AMC) is a fundamental technique in wireless communication systems, as it enables the identification of modulation schemes at the receiver without prior knowledge, thereby promoting efficient spectrum utilization. Recent advancements in deep learning (DL) have significantly enhanced classification performance by enabling neural networks (NNs) to learn complex decision boundaries directly from raw signal data. However, many existing NN-based AMC methods employ deep or specialized network architectures, which, while effective, tend to involve substantial structural complexity. To address this issue, we present a simple NN architecture that utilizes features derived from Hankelized matrices to extract informative signal representations. In the proposed approach, received signals are first transformed into Hankelized matrices, from which informative features are extracted using singular value decomposition (SVD). These features are then fed into a compact, fully connected (FC) NN for modulation classification across a wide range of signal-to-noise ratio (SNR) levels. Despite its architectural simplicity, the proposed method achieves competitive performance, offering a practical and scalable solution for AMC tasks at the receiver in diverse wireless environments.
# Electromagnetic Power Harvester Using Wide-Angle and Polarization-Insensitive Metasurfaces
###### Abstract  
A new wide-angle and polarization-insensitive metasurface (MS) instead of traditional antenna is built as the primary ambient energy harvester in this paper. The MS is a two-dimensional energy harvesting array that is composed of subwavelength electrical small ring resonator that is working at 2.5 GHz (LTE/WiFi). In the case of different polarization and incidence angles, we demonstrate the metasurface can achieve high harvesting efficiency of 90%. The fabricated prototype of 9 \(\times\) 9 MS energy harvesting array is measured, and the experimental results validate that the proposed MS has a good performance more than 80% of energy harvesting efficiency for arbitrary polarization and wide-angle incident waves. The good agreement of the simulation with the experiment results verifies the practicability and effectiveness of the proposed MS structure, which will provide a new source of supply in wireless sensor networks (WSN).  
metasurface (MS); energy harvesting; wide-angle; polarization-insensitive Article
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 3 Azimuth Bias
### Range Error Composition  
The final range bias is the addition on the one hand of the propagation bias due to the atmosphere and on the other hand to the constant bias produced by the transponder delay and the error of reference time:  
\[\Delta\rho=\Delta\rho_{0}+\Big{(}\alpha_{1}\rho+\alpha_{2}\rho^{2}\Big{)} \bigg{[}1+\alpha_{3}\bigg{(}1-\frac{h_{t}}{h_{r}}\bigg{)}\bigg{]} \tag{48}\]
# UAV Application for Typhoon Damage Assessment in Construction Sites
## 3 Research Methods and Procedures
### Analytic Hierarchy Process
#### 3.1.2 Hierarchy and the Consistence Check  
The priority weights of hierarchy elements can be determined, and the consistency check of the comparison matrix can be performed by using AHP. After determining the relative importance of the index of the next layer relative to the index of the previous layer and then solving the eigenvector with the constructed matrix, the applied formula is as follows in Equation (4).  
\[\text{AW}=\lambda_{\text{max}}\cdot W \tag{4}\]  
where \(\lambda_{\text{max}}\) is the largest eigenvalue of matrix A; \(W\) is the relative weight of each index layer element.  
Then, the judgment matrix constructed is ordered in a consistent check and rank shown in Equation (5).  
\[\text{CI}=\frac{\lambda_{\text{max}}-n}{n-1} \tag{5}\]  
where the smaller the value of CI, the higher the consistency of the judgment matrix. When CI = 0, the matrix has complete consistency; the larger the value of CI, the worse the consistency. In order to make the constructed matrix have satisfactory consistency, we need to introduce the random consistency index RI shown in Table 4.  
When CR meets the condition CR < 0.10 by calculated in Equation (6), it shows that the judgment matrix we constructed conforms to the consistency check.  
\[\text{CR}= \frac{\text{CI}}{\text{RI}} \tag{6}\]
# Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
## 3 Threats to Digital Agriculture
### Research and Intellectual Property  
In agriculture, years of collaboration and research work among academics, researchers, students, industry partners, funding organisations, and government produce novel solutions to improve the yield and quality of crops in many kinds of environments. Malicious users and state actors are highly interested in this research and IP, which contribute to the national economy and people's livelihood. Threats to IP can come from an insider, social engineering, technological vulnerabilities/misconfiguration, and data leakage.
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 3 Network Model and Problem Statements
### Problem Statements  
In this subsection, the problem is formally defined for developing a routing protocol based on EG for WMSNs with multiple sinks. The main objective of the proposed protocol lies in electing CHs using EG. The first problem statement describes this phenomenon mathematically for the convenience of readers. Another contribution of EGR is the data redundancy avoidance method to efficiently manage the large amount of data in WMSNs. The second problem statement describes this objective:  
Problem statement 1: Given a set of sensor nodes in the network, \(S=\{s_{1},s_{2},\ldots,s_{n}\}\), our objective is to determine the following:  
* The number of clusters \(k\).
* A set of CHs determined using EG, \(H=\{h_{1},h_{2},\ldots,h_{k}\}\), and \(k\) clusters \(C_{1}=\{h_{1}\}\), \(C_{2}=\{h_{2}\}\),..., \(C_{k}=\{h_{k}\}\). Here, H is the set of CHs and \(h_{1},h_{2},\ldots,h_{k}\) are the CH nodes in cluster \(H\). \(C_{1}\), \(C_{2}\),..., \(C_{k}\) present different clusters in the network.
* A set of CMs determined using EG, \(M=\{m_{1},m_{2},\ldots,m_{k}\}\). Here, \(M\) is the set of CMs and \(m_{1}\), \(m_{2}\),..., \(m_{k}\) are the CM nodes in a cluster.  
Problem statement 2: Given that \(M=\{m_{1},m_{2},\ldots,m_{k}\}\) and number of sensor nodes, \(n\leq 100\), our objective is to determine the following:  
* The overlapped region between two nearby sensor nodes (for example \(m_{1}\) and \(m_{2}\)) to choose one from the two based on the average residual energy if the overlapped area \(A\), crosses a certain threshold \(T\).
* If the data captured (still images for our case) by one sensor node is identical to another based on their overlapped FoVs to avoid transmission of same type of data.
###### Abstract  
With the ability to capture daily imagery of Earth at very high spatial resolutions, commercial smallsats are emerging as a key resource for the remote sensing community. Planet (Planet Labs, Inc., San Francisco, CA, USA) operates the largest constellation of Earth imaging smallsats, which have been capturing multispectral imagery for consumer use since 2016. Use of these images is growing in the remote sensing community, but the variation in radiometric and geometric quality compared to traditional platforms (i.e., Landsat, MODIS, etc.) means the images are not always 'analysis ready' upon download. Neglecting these variations can impact derived products and analyses. Users also must contend with constantly evolving technology, which improves products but can create discrepancies across sensor generations. This communication provides a technical review of Planet's PlanetScope smallest data streams and extant literature to provide practical considerations to the remote sensing community for utilizing these images in remote sensing research. Radiometric and geometric issues for researchers to consider are highlighted alongside a review of processing completed by Planet and innovations being developed by the user community to foster the adoption and use of these images for scientific applications.  
cubest; smallsat; planet; constellation; calibration; radiometric normalization; geometric correction 1  
A Technical Review of Planet Smallsat Data: Practical Considerations for Processing and Using PlanetScope Imagery. _Remote Sens._**2021**, _13_, 3930. [[https://doi.org/10.3390/rs13193930](https://doi.org/10.3390/rs13193930)]([https://doi.org/10.3390/rs13193930](https://doi.org/10.3390/rs13193930))