esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Image Enhancement Results
#### 5.1.2 Band Ratio (BR)  
The reflectance spectrum of a rock depends on the mineralogical composition of its surface, which is usually a mixture of the whole rock mineralogy and weathering minerals. Accordingly, the selected bands in this technique depend on their spectral reflectance and the positions of the absorption bands of the mineral being detected. Therefore, the presence of ferrous iron (Fe\({}^{2+}\)) on weathered surfaces produces absorptions centered at about 0.45 \(\upmu\)m, 1.0-1.1 \(\upmu\)m, 1.8-1.9 \(\upmu\)m, and 2.2-2.3 \(\upmu\)m, depending on its lattice environment. The ferric iron (Fe\({}^{3+}\)) produces absorptions at about 0.65 \(\upmu\)m and 0.87 \(\upmu\)m [61]. Based on the spectral absorption and reflection characteristics of harzburgite, this study selected the b5/b4 and b4/b7 band ratios. The operation process is to divide the grayscale values of corresponding pixels into two different band images, which can effectively suppress the topographic changes in the Tibetan mountain area and highlight the information of exposed rocks. The  
Figure 5: WV-2 images of metamorphic rocks: (**a**) regional image, RGB: 864; (**b**) slate, RGB: 864; (**c**) serpentinization, RGB: 532; (**d**) marble, RGB: 532.  
Figure 6: WV-2 images of peridotite: (**a**) peridotite; (**b**) serpentinized peridotite.  
differences between objects with significantly different spectral characteristics are easily recognizable in false color composite images [29; 60; 98; 99; 100; 101]. The final results of the b5/b4, PC4, and b4/b7 RGB images showed exquisite contrasts between different rock units in the study area (Figure 7). This contrast enabled better detection of the mantle peridotite as a dark blue color, sandstone presented as green, and slate presented as light blue. We believe that band-ratio combinations proved their effectiveness in lithological mapping due to their shown ability to highlight spectral properties of specific rocks and minerals, while eliminating or reducing other types of information.
## 3 Results and Discussion  
### Hydrolytic Stability Test and Integrity Test  
The boiling water test demonstrated the AA-WBA binders' resistance to hydrolytic degradation. The specimen mass loss percentage was lower than 3% in all formulations. The AA-WBA binders also remain consolidated after the integrity test. Figure 3a shows the pH results of AA-WBA binders. There was a significant pH increase at the beginning of the integrity test due to the alkalinity of all samples. The pH values were stabilized after 120 min around a range between 10.75 and 11.25. The conductivity values (Figure 3b) increased gradually during the test because of the contribution of Na\({}^{+}\) and OH\({}^{-}\) ions found in unreacted NaOH and Na\({}_{2}\)SiO\({}_{3}\). The presence of Cl\({}^{-}\) ions in WBA also led to an increase of conductivity. As expected in both cases, the higher the concentration used to formulate the AA-WBA binder, the higher the pH and conductivity values obtained. These results demonstrate the chemical stability of AA-WBA binders.  
Figure 3: Alkali-activated weathered bottom ash binders’ (AA-WBAs) values of (**a**) pH (**b**) conductivity during the integrity test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**WBA** & **Na\({}_{2}\)SiO\({}_{3}\)** & **NaOH (Pearls)** & **Water** \\ \hline
0.6 & 566 [48] & 884 [48] & 1.9 [48] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Raw materials cost (\(\epsilon\)t\({}^{-1}\)).
## 3 Validation of Numerical Simulation Method
### Validation of Pressure Distribution  
Referring to reference [35], the numerical simulation of the NACA0012 airfoil at Reynolds number of 250,000 is carried out. The FSTI in experiment is \(Tu=0.58\%\). The angle of attack (AoA) of the airfoil is \(\alpha=4^{\circ}\), and the chord length is \(c=0.3048\) m. The calculation condition is consistent with the experiment. The quantity of computational mesh is about 100,000 cells, and the first layer grid element to wall distance is \(d_{1}=2.03\times 10^{-5}c\), \(y^{+}=0.25\). The inflow velocity is \(V=11.982\) m/s.  
Solutions are considered sufficiently converged when the order of magnitude of the residuals is low and remains constant. Figure 2 shows the assessment between the experiments and the results of the CFD, where the CFD by Counsil represents numerical solution calculated in reference [36].  
Figure 2 shows that, the numerical simulation of the pressure distribution fits well with the experiment result, and describes the separation and reattachment process well. In addition, the CFD results are consistent with those given by Counsil. It shows that the calculation method is accurate to predict the transition process.
## 4 NMPC Controller Design for Aircraft Engines with HGWO
### Controller Obtaining  
In the NMPC controller design, in order to guarantee the instruction tracking and reduce fuel consumption in the control process, the objective function is  
\[\min J=(Y_{h}-W)^{T}Q(Y_{h}-W)+\Delta U(k)^{T}R\Delta U(k) \tag{26}\]\[s.t.\ u_{\min}\leq u(k+i)\leq u_{\max}\]  
\[\Delta u_{\min}\leq\Delta u(k+i)\leq\Delta u_{\max}\]  
where \(Q=qI_{P\times P}\) is the weighted matrix of the output error, \(R=rI_{M\times M}\) is the weighted matrix of the control increment, \(I_{P\times P}\) is the identity matrix with \(P\) dimension, \(I_{M\times M}\) is the identity matrix with \(M\) dimension, \(M\) is the control horizon, \(W(k)=[w(k+1),w(k+1),\ldots,w(k+P)]\) is the reference trajectory output vector, and \(\Delta U(k)=[\Delta u(k),\Delta u(k+1),\ldots,\Delta u(k+M-1)]\) is the control increment vector with \(\Delta u(k)=u(k)-u(k-1)\).  
In each solution process, the receding horizon optimization will obtain an optimal control incremental sequence \(\Delta U(k)\) and take the first element of the sequence, and we can then obtain the optimal control input of the aircraft engines at the current time:  
\[u(k)=\Delta u(k)+u(k-1).\]  
The steps of applying the HGWO algorithm into the receding horizon optimization to solve the controller are as follows:  
Step 1: Determine the population size and the maximum number of iterations, set the constraints of the aircraft engines, set the prediction horizon and the control horizon, and set the crossover probability.  
Step 2: According to the constraints, use the logistic chaotic sequence to generate the individual location of the grey wolf population.  
Step 3: According to the grey wolf position \(X\) to calculate the grey wolf individual fitness value, i.e., the reciprocal of the performance index, select \(\alpha\), \(\beta\), and \(\delta\).  
Step 4: Based on the current positions of \(\alpha\), \(\beta\), and \(\delta\), update the position of the other individuals in the population using Equation (2).  
Step 5: After the crossover operation to obtain new individuals, use the greedy rule to select superior individuals.  
Step 6: When either the optimal condition is satisfied or the number of iterations is enough, the iteration is ended and the optimal solution is output as the control input for the current time; otherwise, return to Step 3.
## 7 Discussion
### Open Challenges and Potential Solutions  
#### 7.1.1 Data Privacy Problem--Federated Learning  
One of the most significant challenges is the privacy issue caused by accessibility. Deep learning requires a large amount of data to achieve adequate performance. However, in the process of securing a large amount of data, problems may arise that include sensitive personal information or information that should not be exposed to unidentified persons.  
Indoor localization systems collect real-time location information of users connected to the Internet through various means such as smartphones, cars, and IoT devices. If this personal location information is used without consent, it can cause many problems. Furthermore, if location information, such as a radio map of a country's military facility and infrastructure, is misused, it can lead to national security issues. However, if accessibility to information is reduced to solve these problems, there is a problem that the performance of AI may decrease due to the insufficient amount of data necessary for learning. Therefore, a solution for sensitive information and privacy is needed while securing a large amount of data. In order to address privacy issues caused by accessibility, federated learning has been recently studied. Unlike the conventional method of gathering data in a central server for training, federated learning means leaving the data on each individual device and training on each device to obtain parameters, which are then aggregated and weighted to update the model as one [44, 45]. In this case, private data are not transmitted to the server, but only model parameters are sent, which can help alleviate privacy issues.
# Estimating Weibull Parameters Using Mabchour's Method (MMab) for Wind Power at RAWA City, Iraq
###### Abstract  
Wind power is one of the most important sources of renewable energy. In this research paper, we developed an approach to select the optimum site among four different locations in Iraq (Talafar, Nasiiyah, Baghdad and RAWA) according to wind power density. Based on the optimization process, it was found that the RAWA city is the optimal site. We adopted Mabchour's Method (MMab) to estimate the Weibull distribution parameters (\(c\), \(k\)) for RAWA city at two heights (10 m and 50 m) for the period (2017-2019). It was found that the Mabchour technique (MMab) produced accurate results with minimum consumed time and effort. This was because the values of \(k\) and \(c\) were close to each other. Additionally, the coefficient values of the results of the Weibull measurements were very close to the average wind speeds that we measured. The values of the correlation coefficients between the Weibull scale parameters and the form were calculated and were equal to \(R^{2}=0.9971\). The minimum value of the coefficient of variation (COV) for turbulence intensity was found to be 26% in July 2018, when the wind speeds reached their maximum. The highest error of wind power density between measured data (\(P_{M}\)) and Weibull distribution (\(P_{W}\)) was found to be 4.48%, at a height of 50 m.  
wind energy; wind power density; Weibull distribution; Mabchour's Method (MMab) 2022  
[PERSON], [PERSON]; [PERSON] Estimating Weibull Parameters Using Mabchour's Method (MMab) for Wind Power at RAWA City, Iraq.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 1 Introduction
### Paper Organization  
The rest of this paper is organized as follows. The system model and the problem formulation for the UAV-assisted DFRC data collection in the ocean monitoring system are described in Section 2. Section 3 provides the proposed method (CRMOP) to jointly optimize the UAV location and the transmit power of the buoy, followed by the CRBC as a baseline. The simulation results and discussion are shown in Section 4. Finally, we conclude this paper in Section 5. All abbreviations have been presented in Table 1.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Acronym** & **Definitions** \\ \hline UAV & Unmanned Aerial Vehicle \\ \hline LoS & Line-of-sight \\ \hline NOMA & Non-orthogonal Multiple Access \\ \hline BS & Base Station \\ \hline DFRC & Dual-functional Radar-communication \\ \hline NSGA-II & Non-dominated Sorting Genetic Algorithm II \\ \hline MOEA/DD & Many-objective Optimization Algorithm \\ \hline CRB & Cramer–Rao Bound \\ \hline CRBC & CRB Constrained \\ \hline CRMOP & Communication and Radar-Sensing Many-objective Optimization \\ \hline AP & Access Point \\ \hline
3D & Three-dimensional \\ \hline CSI & Channel State Information \\ \hline AWGN & Additive White Gaussian Noise \\ \hline RCS & Radar Cross-section \\ \hline MSE & Mean Square Error \\ \hline PBI & Penalty-based Boundary Intersection \\ \hline SBX & Simulated Binary Crossover \\ \hline PF & Pareto Front \\ \hline OMA & Orthogonal Multiple Access \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of abbreviations.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 3 Energy Storage System for the Marine Power System
### Lithium Cobalt Oxide (LiCoO\({}_{2}\), Loc)  
First commercialized by Sony in 1991, LiCoO2 is known for its straightforward synthesis route and high specific energy, ranging from 150-240 Wh.g\({}^{-1}\)[45]. Despite these advantages, LiCoO\({}_{2}\) has drawbacks, including a shorter cycle life, toxicity, high cost, and the limitation of utilizing only about 50 percent of its theoretical capacity [111; 116]. Furthermore, LiCoO\({}_{2}\) is prone to rapid capacity fading when subjected to high voltage values (>4.4 V) and high current densities. In response to these challenges, researchers have developed LiM\({}_{y}\)Co\({}_{1-y}\)O\({}_{2}\) (M: metals), a modified cathode formulation that incorporates various metals such as Ni, Al, Mg, Fe, Mn, Ti, Zn, Cr, B, Rh, and other transition metals. This synthesis aims to create cathode materials that are cost-effective, high-energy, longer-lasting, and safer [110]. When evaluated for marine vessels, although it has high energy density (it is widely used in older consumer electronics), it is appropriate for some maritime applications because of its short life cycle, limited power rates, and safety concerns [27; 107].
## 2 Materials and Methods  
### Experimental Design  
This study was conducted over six consecutive almond growing seasons, from 2017 to 2022. We monitored two commercial drip-irrigated almond orchards located in the province of Albacete (SE Spain) and separated by a distance of 25 km. The first study site was located at \"Las Tiesas\" research facility (39.0424\({}^{\circ}\) N, \(-\)2.0890\({}^{\circ}\) W). An 11 ha almond (_Prunus dulcis_ (Mill.) [PERSON]) orchard was planted in 2015 with cv. Lauranne grafted onto the GF-677 rootstock. Tree spacing was 6 m (within row) and 7 m (inter-row), resulting in 238 trees per ha\({}^{-1}\). A second almond orchard of about 10 ha was planted in Tarazona de la Mancha (Albacete, Spain) (39.2660\({}^{\circ}\) N, \(-\)1.9397\({}^{\circ}\) W) in 2017. The cultivar used in this case was Penta grafted onto the GXN (Garnem) rootstock, with spacing between plants and between rows of 4.5 and 6.5 m, respectively (342 trees ha\({}^{-1}\)) (Figure 1).  
The climate in this region is semi-arid temperate Mediterranean, characterized by dry and warm summers, resulting in high evaporative demand. Long-term average annual rainfall is about 320 mm, and the annual cumulative grass reference evapotranspiration (ETo) is around 1250 mm. Meteorological data for the six years of the study (2017-2022) were collected from the \"La Gineta\" weather station situated at 8.5 linear km from \"Las Tiessa\" and 18 linear km from \"Tarazona\", belonging to the Spanish Agro-climatic Information System for Irrigation [61]. Climate data were gathered from the official meteorological station rather than from the locations to ensure coherence in spatializing the results. Moreover, this weather station is the most representative of the atmospheric evaporative demand in the study area (LTs-TzM). Crop management practices were adopted to minimize water, nitrogen, weeds, and biotic stresses throughout the cropping season.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Next-Hop Selection
#### 3.8.2 Location-Based  
As explained previously in this survey, these routing protocols use geographic information in the selection of the next-hop in order to relay data to the destination, which eliminates the number of transmissions significantly.
## 3 Results and Discussion
### Antidiabetic Activity of the Natural Products Synthesis by Endophytic Bacteria
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experimental Group**} & \multirow{2}{*}{**Concentration**} & **Relative Melanin** & **Relative Tyrosinase** \\  & & **Content (\%)** & **Activity (\%)** \\ \hline NC & 50 \(\upmu\)M & 100.0 \(\pm\) 5.193 & 100.0 \(\pm\) 5.193 \\
8-MOP & 50 \(\upmu\)M & 132.0 \(\pm\) 2.818 & 124.1 \(\pm\) 3.172 \\ \hline \multirow{2}{*}{_B. haltolerans_ XJB-35} & 1 \(\upmu\)g/mL & 138.6 \(\pm\) 7.638 & 123.1 \(\pm\) 3.761 \\  & 10 \(\upmu\)g/mL & 161.5 \(\pm\) 3.751 & 130.0 \(\pm\) 5.653 \\  & 50 \(\upmu\)g/mL & 229.9 \(\pm\) 6.737 & 154.7 \(\pm\) 3.266 \\ \hline \multirow{2}{*}{_B. haynesii_ XJB-5} & 1 \(\upmu\)g/mL & 185.6 \(\pm\) 12.51 & 125.0 \(\pm\) 4.56 \\  & 10 \(\upmu\)g/mL & 191.5 \(\pm\) 76.61 & 131.4 \(\pm\) 3.94 \\  & 50 \(\upmu\)g/mL & 198.2 \(\pm\) 18.65 & 138.7 \(\pm\) 2382 \\ \hline \multirow{2}{*}{_L. fusiformis_ XJB-17} & 1 \(\upmu\)g/mL & 129.3 \(\pm\) 10.02 & 115.3 \(\pm\) 7.194 \\  & 10 \(\upmu\)g/mL & 139.8 \(\pm\) 1.822 & 118.3 \(\pm\) 6.790 \\  & 50 \(\upmu\)g/mL & 183.8 \(\pm\) 7.762 & 121.9 \(\pm\) 2.753 \\ \hline \multirow{2}{*}{_B. proteolytic_ XJB-16} & 1 \(\upmu\)g/mL & 118.8 \(\pm\) 11.50 & 116.9 \(\pm\) 5.334 \\  & 10 \(\upmu\)g/mL & 138.1 \(\pm\) 7.450 & 103.7 \(\pm\) 6.514 \\ \cline{1-1}  & 50 \(\upmu\)g/mL & 179.0 \(\pm\) 2.288 & 146.6 \(\pm\) 5.417 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effects of melanin synthesis and tyrosinase activity of secondary metabolites in B16 cells treated with various concentrations (n = 3).
# Novel Chitosan Derivatives and Their Multifaceted Biological Applications
## 2 Novel ChDs
Chitosan derivatives containing Schiff-bases are formed by the conjugation of a primary amine on chitosan with an active carbonyl group [44]. The substitution reactions are comparatively easier and produce mine-containing Schiff bases [45]. The relation between chitosan Schiff-bases and pharmacological activities are well known [11; 45; 46]. Carboxymethyl chitosan (CMCS) is one of the most important ChDs that are widely reported for their varied biological properties [47; 48]. CMCS are prepared by reacting chitosan with chloroacetic acid in water under microwave irradiation [49; 50]. Compared to chitosan, CMCS possesses good water solubility, which greatly expands the applications of chitosan [51]. CMCS is well established for its enhanced antibacterial ability against _Staphylococcus aureus_ and _Escherichia coli_[52]. It also has been reported for its anti-tumor activity because it has stronger electrostatic interaction with negatively charged tumor cells, thus inhibiting the growth of tumor cells [49; 51]. Antifungal and antioxidant properties are represented by this compound. Synthesis of mono-\(N\)-carboxymethyl chitosan (MCC) is obtained via chemical modification with glyoxylic acid in the presence of sodium borohydride, decreasing the transepithelial electrical resistance of Caco-2 cell monolayers [31]. Chemical grafting of chitosan is a process by which one or more species are connected as a side chain to the main chitosan chain [16]. Chitosan can be grafted via radiation, free radical, or enzymatic and cationic copolymerization. Chitosan-poly(ethylene glycol) diacrylate and chitosan derivatives with galactose groups are notable examples. Figure 1 gives an overview of the chitosan derivatives predominantly known for their bio-applications. As much as the biological properties of chitosan are dependent on the degree of deacetylation (DD), the degree of substitution (DS) depends on the percentage of newly added groups into chitosan. These influence the biological functionality of ChDs. The DS of ChDs have a profound impact on their solubility and biological activity at neutral pH [53]. Poor dissolution of chitosan and reduced solubility limit the application of chitosan in drug delivery [54]. Chitosan is chemically modified to overcome these barriers. The outcome of these chemical modification in the form of various ChDs helps overcome the limitations of chitosan for biological applications.
## 3 Case Study  
### Synthetic Case Study  
We first illustrate our method on a synthetic case study with three categories (Figure 2). There are 606 locations that have been randomly sampled from this image. The prediction is conducted over a 64 \(\times\) 64 grid covering the square. The marginal probability of the occurrence vector is (0.2756, 0.4653, 0.2591). The categories may represent geological facies, soil types, land uses or any other categorical variable [20].  
As a first task, the data set is used to estimate the two-point input transition probabilities. Experimental transition probabilities can be directly estimated from paired sampling data by counting the transition frequencies of classes with different lags [21]. A continuous transiogram [22] can be obtained via interpolation [23] or fitting the transition probabilities cloud with various models. Following [6], the exponential model is employed for transiogram fitting. After all these preparations, the input vectors of the ANNs can be determined.  
We consider the four nearest sampling points as the neighbors of the unknown location. To use Equation (3), we must train three compositional ANNs to compute the pre-posterior (multi-point) probabilities. The problem here is the choice of the input nodes. By analyzing the correlation plot (Figure 3) of these four transition probabilities, no significant correlation is found among the input vectors; therefore, we choose \(P\left(D_{i}|A\right)\) as the input of \(P\left(D_{i}|AD_{1}\cdots D_{i-1}\right)\), \(i=2,\,3,\,4\), respectively.  
As a second task, the Gaussian-distributed interaction noises with different means and variances are simulated. For the 606 training data set, the output nodes \(P\left(D_{i}|AD_{1}\cdots D_{i-1}\right)\) can be computed via Equation (2). For comparison purposes, we use 10 groups of parameters for estimating and simulating  
Figure 2: Reference map with dimension 64 \(\times\) 64 and three categories.
# An Ultra-Thin Composite Metasurface with Hybrid-Damping Modes for Broadband Sound Absorption
## 2 Broadband Absorption of the Composite Metasurface  
Figure 1a demonstrates the unit of the composite metasurface, which is composed of three parts: the hybrid-damping HR structure, porous cells and the porous layer. The unit has an incident area of \(S_{0}=L\times W=70\) mm \(\times\) 44 mm, in which the thickness of the porous layer and the HR structure are \(H_{1}=10\) mm and \(H_{2}=21\) mm, respectively. The HR structure is composed of 16 cells, in which cells 1-8 are compound HRs composed of two cavities, and cells 9-16 are single-cavity ones. The longer compound HR cells are folded and extended beneath the shorter single-cavity cells, effectively reducing the overall thickness of the structure. It is worth noting that the cell absorption performance mainly depends on the cavity volumes, so it can be flexibly arranged, such as horizontally or vertically. The porous cells 17 and 18 with length \(l=25\) mm and width \(w=10\) mm are arranged in parallel with the HR structures, of which the heights are \(h_{1}=20\) mm and \(h_{2}=12\) mm. The melamine foam is selected as the base material for the porous cells and the porous layer, but the basic parameters are not completely consistent and will be given in later discussions. The absorption coefficient of the metasurface is presented in Figure 1b and compared with that of pure melamine foam with the same thickness. It can be observed that the metasurface obtains an ultra-broadband absorption spectrum over 750-10,000 Hz with an average absorption above 85%. Compared with pure melamine foam, the low-frequency average absorption coefficient is significantly enhanced from 60% to nearly 83% below 1600 Hz. The broadband HR structure plays a dominant role in the spectrum below 3000 Hz, and with the incorporation of porous cells, the absorption range is expanded to 6000 Hz. Finally, it extends further to 10,000 Hz by adding the porous layer, effectively achieving broadband absorption spanning from a low- to high-frequency range with an ultra-thin structure. However, it is unfortunate that owing to the utilization of fewer porous materials, the metasurface cannot achieve the same excellent absorption performance above 1.5 kHz as the melamine foam with the same thickness.