esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 3 Node Weight Allocation Strategy Based on Data Fusion
### Basic Idea  
In collaborative channel perception, non-differential data fusion can only be performed when each node participating in data fusion has the same importance or contribution value. However, non-differential data fusion may not be suitable for channel perception in data link networks with dynamically changing topologies and complex electromagnetic environments. Considering the influence of each node device's actual transmission and reception performance on the statistical capability of the channel traffic pulse, it is necessary to accurately measure the extent of difference in the traffic pulse statistical capabilities of each node device within the data link network. Based on this difference in capabilities, different weights need to be assigned to each node in order to improve the accuracy of channel state perception.  
The difference in the traffic pulse statistical capability of node devices is closely related to characteristic parameters such as receiving sensitivity, antenna VSWR, SNR, the number of operating months, and MTBF. The problem of how to use multiple characteristic parameters with different dimensions to measure the traffic pulse statistical capability of node devices can be seen as a multi-source data fusion problem.  
Data fusion is an information fusion algorithm that can effectively handle uncertainty issues arising from multiple characteristic parameters. This paper integrates the data fusion algorithm from the Dempster-Shafer Theory (DST) with a data link cooperative channel state perception method. It applies the advantages of data fusion from the evidence theory to evaluate the traffic pulse statistical capability of network node devices. The normalized characteristic parameters of node devices are considered as the evidence parameter set within the recognition framework. By calculating the credibility and falsity of the characteristic parameters, it measures the differences and conflicts between one-hop neighbor node devices to achieve a comprehensive evaluation of their traffic pulse statistical capability. Based on this, the geometric mean method is employed to calculate the channel state perception weights for each node within the one-hop range, and a reasonable weight allocation strategy is formulated.
## 3 Results
### Metabolic Changes in BR upon the Germination Process  
To have an overview of the metabolite profiles of all the BR and GBR samples investigated in the present study, a principal component analysis (PCA) was conducted. As shown in Figure 1A, the PCA score plot exhibited a distinct separation between BR and GBR along PC1, which explained 69.9% of the total variance. On the contrary, no clear discriminations were observed within each clustering. These results indicate significant metabolic changes in BR after the germination process, independent from the subspecies and cultivars of the investigated rice samples.  
To identify the metabolites contributing to the metabolic differentiation between the BR and the GBR, a supervised orthogonal partial least square discriminant analysis (OPLS-DA) was performed. Comparable discrimination between the BR and the GBR was observed along the predictive component (Figure 1B). The cross-validation parameters and the results of the permutation test demonstrated the robustness of the constructed OPLS-DA model without overfitting (Figure S4). Subsequently, the discriminating metabolites were selected on the basis of the VIP values (>1) and the FDR \(p\)-values (<0.05); a heat map was employed to visualize the variance of these discriminating metabolites (in order of the VIP values).  
Figure 1C depicts the detected metabolites exhibiting changed levels in response to the germination treatment. A broad spectrum of metabolites, including sugars (e.g., fructose and glucose), amino acids (e.g., alanine, valine, and serine), organic acids (e.g., succinic acid and citric acid), and lipids (e.g., sitosterol and stearic acid), all presented significantly increased levels after the germination process for both the _Japonic_ (Figure 1C: lanes 1, 2, and 3) and the _India_ (Figure 1C: lanes 4, 5, and 6) rice cultivars. In contrast, only a few metabolites, i.e., raffinose, sucrose, trehalose, glucosamine, sitosterol, stearic acid, oleic acid, and linolenic acid, showed lower levels in the GRB than in the BR. These results suggest that the germination process induced similar metabolic changes in all the investigated BR samples, regardless of the subspecies of the rice samples.  
Figure 1: (**A**) PCA score plot of the metabolomics data of brown rice (BR, open) and germinated brown rice (GBR, solid). (**B**) OPLS-DA score plot of the metabolomics data of BR (open) and GBR (solid); the boundaries of the clusters correspond to the 95% Hotelling’s T2 ellipses. (**C**) Heat map of the metabolites contributing to the metabolic discrimination between brown rice (BR) and germinated brown rice (GBR). Lanes 1–3: _Japonic rice_ cultivars NJ, LJ, and TH; lanes 4–6: _India rice_ cultivars ZZ, ZJZ, and LY.
## 1 Introduction
In any case, by using metal/silicon junctions, responsivities of only 5 mA/W [19] and 30 mA/W [14] were reported for vertically-illuminated and waveguide PDs, respectively. This low responsivity is mainly due to the fact that the carriers that are photo-excited in the metal have a low probability to be emitted over the Schottky barrier. However, it is well-known that the carrier emission probability can be increased by reducing the metal thickness, or, in other words, that the lower the metal thickness the higher the emission probability [20; 21]. This consideration leads to the idea of replacing the metals with 2D materials and, in this context, graphene is of course a good candidate. Si-based graphene PDs are typically based on field effect transistor (FET) structures and many review articles are reported about them in literature [22; 23]. However, graphene PDs based on FET structures are characterized by some drawbacks: a low photo-conversion area (low efficiency) due to the fact that the internal electric field is formed only in narrow region (200 nm) close to the source and drain edges [24] and a further high dark current generated in any case when a bias drops between source and drain. On the contrary, IPE-based graphene/Si PDs promise to overcome these limitations because the photo-conversion area is linked to the graphene area in contact with Si and because the dark current is that one flowing through a reversely biased Schottky diode. In this review article, the emerging field of the NIR IPE-based graphene/Si PDs, is presented. In the first section, IPE theory for junctions involving 2D materials will be elucidated and put in comparison with the classical theory used for metals. Then, the main structures reported in literature will be discussed and their performance will be compared. Finally, it will be highlighted as these PDs have the capabilities to compare with the Ge technology in the field of NIR detection, also offering new advantageous characteristics.
## 4 Calibration and Characterization
### Measuring Principle  
The goal of a radiometer is to measure the antenna temperature \(T_{A}\), which is defined as the radiometric noise power collected by the antenna. However, since all the antennas have some losses, the antenna temperature measured at its output terminals \(T_{A}^{\prime}\) is ([1], pp. 207-208)  
\[T_{A}^{\prime}=\eta_{A}\;T_{A}+\left(1-\eta_{A}\right)T_{pA}, \tag{10}\]  
where \(\eta_{A}\) is the antenna radiation efficiency, and \(T_{pA}\) is the physical temperature of the antenna. Approximate values for \(\eta_{A}\) are shown in Table 2. Signals received by the antennas are amplified and filtered by the front-end and back-end stages, which also introduce some additional noise. Therefore, the power level measured at the input of the detector \(P_{R}\) is ([1], pp. 350-352)  
\[P_{R}=k_{B}\;G_{r}\;T_{sys}\;B_{r}=k_{B}\;G_{r}\;\left(T_{A}^{\prime}+T_{r} \right)\;B_{r}, \tag{11}\]  
where \(k_{B}\) is the Boltzmann constant, \(G_{r}\) is the total gain of the receiving chain, \(T_{sys}\) is the total system temperature, \(T_{r}\) is the equivalent input noise temperature of the receiving chain, and \(B_{r}\) is the system bandwidth.  
In the case of MERITXELL, because of the high gain of the first stage (more than 60 dB), \(T_{r}\) can be approximated by the equivalent noise temperature introduced by the front-end stage, and the \(B_{r}\) is set by the bandwidth of the back-end stage (span parameter of the SA).  
After that, the noise signal reaches the detector, and the power level measured at its output \(P_{m}\) can be expressed after combining previous equations as  
Figure 9: Diagram of the digital downconversion and sample retrieval of the I/Q acquisition mode.  
\[P_{m}=G_{d}\,P_{R}+P_{d}=G_{d}\,G_{r}\,k_{B}\left(\eta_{A}\,T_{A}+(1-\eta_{A})\,T_ {pA}+T_{r}\right)\,B_{r}+P_{d}, \tag{12}\]  
where \(G_{d}\) is the conversion gain, and \(P_{d}\) accounts for a possible bias of the detector.  
In order to determine the number of unknown parameters in (12), and hence, to known the equivalence between the antenna temperature and the measured power after the detector, a calibration procedure must be applied.
## 2 System Reliability Evaluation Methods
### System Reliability Evaluation Method Based on the Modified AHP  
AHP is a decision-making method that combines qualitative and quantitative solutions to solve complex problems of multiple objectives. Considering the subjectivity of the traditional AHP method in constructing the judgment matrix, an objective judgment matrix construction method suitable for evaluating the bridge system reliability is proposed to modify the traditional AHP in this research. The modified hinge plate method (MHPM) was adopted to consider hinge joint damage in the system reliability evaluation. The specific system reliability evaluation process based on modified AHP is shown in Figure 1.
###### Abstract  
In this work, a two-stage methodology to design super-hydrophobic surfaces was proposed. The first step consists of creating a rough nano/micro-structure and the second step consists of reducing the surface energy using octadecyltrimethoxysilane. The surface roughening was realized by three different short-term pretreatments: (i) Boiling water, (ii) HNO\({}_{3}\)/HCl etching, or (iii) HF/HCl etching. Then, the surface energy was reduced by dip-coating in diluted solution of octadecyltrimethoxysilane to allow the formation of self-assembled silane monolayers on a 6082-T6 aluminum alloy surface. Super-hydrophobic aluminum surfaces were investigated by SEM-EDS, FTIR, profilometry, and contact and sliding angles measurements. The resulting surface morphologies by the three approaches were structured by a dual hierarchical nano/micro-roughness. The surface wettability varied with the applied roughening pretreatment. In particular, an extremely high water contact angle (around 180\({}^{\circ}\)) and low sliding angle (0\({}^{\circ}\)) were evidenced for the HF/HCl-etched silanized surface. The results of electrochemical tests demonstrate a remarkable enhancement of the aluminum alloy corrosion resistance through the proposed superhydrophobic surface modifications. Thus, the obtained results evidenced that the anti-wetting behavior of the aluminum surface can be optimized by coupling an appropriate roughening pretreatment with a self-assembled silane monolayer deposition (to reduce surface energy) for anticorrosion application.  
superhydrobicity; self-assembly; anticorrosion; aluminum +
Footnote †: journal: Applied Soft
## 2 Preliminaries
### The Weighted Aggregated Sum Product Assessment Approach  
[PERSON] et al. [27] introduced the WASPAS approach, a hybrid model of WSM and WPM for decision making. Renowned for its efficiency, mathematical simplicity, and consideration of expert preference flexibility, this approach has gained significant recognition. The associated calculation steps of the WASPAS approach are as follows [40]: Step 1: Create the initial decision matrix.  
Based on the different alternatives, create the initial decision matrix between the criterion and the alternatives, as shown in Equation (1).  
Step 2: Normalize the initial decision matrix \(N_{ij}\).  
Normalize the initial decision matrix using Equations (11) and (12).  
\[N_{ij}=\frac{a_{ij}}{\max\limits_{i}a_{ij}} \tag{11}\]  
\[N_{ij}=\frac{\min\limits_{i}a_{ij}}{a_{ij}} \tag{12}\]  
Equation (11) is used when the assessment criterion belongs to the benefit criterion. On the contrary, Equation (12) is used when the assessment criterion belongs to the non-benefit criterion.  
Step 3: Compute the values of WSM and WPM for different alternatives.  
Compute the value of WSM and WPM using Equations (13) and (14).  
\[Q_{i}^{1}=\sum\
olimits_{j=1}^{n}N_{ij}\cdot w_{j} \tag{13}\]  
\[Q_{i}^{2}=\prod\
olimits_{j=1}^{n}N_{ij}^{w_{j}} \tag{14}\]  
Step 4: Compute the total relative importance of different alternatives.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**n** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline RI & 0.00 & 0.00 & 0.58 & 0.90 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 & 1.49 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The RI values of different square matrices.  
The total relative importance of the \(i\)th alternative is expressed as \(Q_{i}\). The value of the preference parameter (\(\lambda\)) is usually determined subjectively by experts. When experts cannot determine a parameter, 0.5 is often used as a baseline.  
\[Q_{i}=\lambda Q_{i}^{1}+(1-\lambda)Q_{i}^{2} \tag{15}\]
## 2 Materials and Methods
### Abiotic Stress and Herbivory Treatment  
At 28 DAG, six hydroponic units continued to receive the same nutrient solution as previously described in growth conditions, while in two hydroponic units 5% (\(w/v\)) polyethylene glycol 8000 (Sigma PEG8000, Sigma Aldrich, St. Louis, MO, USA) and 1 mM nitrogen were added for simulating the drought stress and nitrogen deficiency, respectively (ABIO group). The final PEG concentration was gradually achieved by the addition of 2.5% (\(w/v\)) PEG8000 every two days. The osmotic potential of the solutions, measured by an osmometer (Freezing point osmometer, Osmomat 3000, Gonotec, Berlin, Germany), was \(-\)0.55 MPa for 5% PEG and \(-\)0.05 MPa for the control solution (0% PEG). To obtain 1 mM N for the nitrogen deficiency, the NH\({}_{4}\)NO\({}_{3}\) was not added and the KNO\({}_{3}\) and Ca(NO\({}_{3}\))\({}_{2}\) were reduced to 1 mM and 0.5 mM, respectively. In order to balance K and Ca, the K\({}_{2}\)SO\({}_{4}\) and CaSO\({}_{4}\) were increased to 3 mM and the 3.5 mM, respectively. Preliminary experiments were run to ascertain that the selected PEG8000 and N concentrations did not prejudice plants' survival.  
At 42 DAG, the hydroponic units were treated as follows for obtaining the whole set of treatments (Figure S1):  
1. two hydroponic units were renewed with the optimal nutrient solution (CTR group);
2. two hydroponic units were renewed with the nutrient solution with N deficiency and PEG (ABIO group);
3. two hydroponic units received the optimal nutrient solution but the plants were infested with _Tuta_ larvae to induce the biotic stress (BIO group);
4. two hydroponic units maintained the same nutrient solution with N deficiency and PEG and, in addition, the plants were infested by _Tuta_ larvae (COMB group).  
The plant infestation was obtained by placing two first instar larvae of _Tuta_ in the 1 st fully-developed leaf (with five leaflets) from the bottom and, to avoid larvae escaping, each infested leaf was then bagged with a nylon mesh of 4.7 cm diameter (Figure S2). We added herbivorous insects to plants after 14 days of abiotic stress treatment in order to simulate the effects of a pest outbreak which are predicted to become more frequent with climate change [34].
## 3 Methodology
### Room Segmentation
#### 3.1.2 Room-Space Segmentation
 \tag{3}\]  
where \(l_{v}\) is the label for the trajectory \(i\) belonging to trajectory segment \(\phi_{v};I_{v}\) is the ideal value for the label \(l_{v};r_{o}\) is the ratio of the overlapping area between trajectory \(i\) and each trajectory segment \(\phi_{v};o_{i}\) is the visual area of trajectory \(i\), and \(G_{\phi_{v}}\) is the visual area of trajectory segment \(\phi_{v};\left\{\phi_{1},\dots\phi_{v}\dots\phi_{g}\right\}\) are the set of initial trajectory segments, where \(\phi_{v}\), \(v\in\{1,\dots,\theta\}\). Lower \(D_{v}(l_{v})\) values mean less penalty when assigning the sample trajectory point \(i\) to the trajectory segment \(\phi_{v}\). The overlap between the visible area of two trajectory points is calculated using the number of the same index for visible cells. **Smoothness term. **\(E_{S}\) is the sum of binary functions \(B_{v,uv}(l_{v},l_{w})\) and is used to regularize label by penalizing the assignment of different labels to adjacent trajectories, as defined by Equation (4):  
\[ \begin{split}& B_{v,uv}(l_{v},l_{w})=\left\{\begin{array}{ll}( \frac{1}{2}e^{(-\frac{\theta is(i,j)}{\Delta t})}+\frac{1}{2}e^{-(1-o_{(i,j)})}) &if\ (l_{v}\
eq l_{w})\\ 0&otherwise\end{array}\right.\\ & o_{(i,j)}=\frac{1}{2}(\frac{o_{i}r_{o}}{o_{i}}+\frac{o_{i}r_{o}}{o_{j}}) \end{split}  \tag{4}\]  
where \(i\) is the sampled trajectory point; and let \(i\) be as center point and get its k-nearest neighbor (KNN) trajectory points, \(j\in K;o_{i},o_{j}\) are visible area of trajectory points \(i\) and \(j\), respectively; \(dis(i,j)\) is the distance between trajectory points \(i\) and \(j\), \(O_{(i,j)}\) is the overlapping ratio of visual area of trajectory points \(i\) and \(j\); \(\Delta t\) is a distance threshold. The smoothness term indicates the penalty between adjacent trajectory points. If a pair of neighboring trajectory points belong to the same space, the smooth cost between them is 0; otherwise, this value is closer to 1 as the overlapping ratio is greater and the distance of the adjacent trajectory points is smaller. The smooth term can reduce the number of redundant spaces, thus solving the over-segmentation problem in long corridors for complex indoor environment. Again, the global optimization of the indoor space is solved by an energy minimization function via multi-label graph cuts [(43; 44; 45)] to automatically cluster similar visible point clouds. The room segmentation results are shown in Figure 6b.
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 4 Healthcare Applications
### Orthopedics  
Implants play a key role in the management of patients with orthopedic injuries in order to restore alignment, structural integrity and motion. Most implants are available in standard sizes designed to fit most patients. However, patients on the extreme ends of anatomical variability or deformity might require specialized implants to ensure a proper fit [129]. Much like dentistry, CAD models of patient anatomy generated through radiological imaging allow for design and fabrication of custom-fit implants using AM. Orthopedic implants also need to integrate (or even regenerate) with the patient's own bone, which creates tissue support and prevents implant failure. There are a few factors relating to implant structure and composition that drive its ability to promote bone regeneration. Porosity is an important property that makes an implant penetrable to tissue and vessel ingrowth [130]. High-resolution AM techniques are well suited for creating highly porous implants that allow for an interwoven mesh of bone to secure the implant to the surrounding bony tissue [9]. Additionally, 3D printing of bioabsorbable and osteoinductive materials, such as calcium phosphate cement, has been shown to be very effective at inducing bone growth [131; 132]. Lastly, the ability of the implant to concentrate the effect of drugs and growth factors (such as bone morphogenetic protein (BMP) or VEGF) through local and sustained release at the site of implantation can allow for decreased systemic exposure to these compounds. Furthermore, lower quantities of therapeutics would be required, leading to increased cost-effectiveness [6; 133]. It is also important to point out the feasibility for using 3D printing for orthosis generation, such as patient-specific casts for fractures [134].
## 2 Materials and Methods
### System Components
#### 2.4.1 Photovoltaic (PV) Panel  
HOMER determines the PV array's real-time output using Equation (2), which accounts for the array's rated capacity under Standard Test Conditions (STCs), the incident solar irradiation relative to STCs, a derating factor (e.g., for dust or wiring losses), and the influence of temperature on PV performance [47].  
\[P_{PV}=P_{rated,STC}\times f_{PV}\times\left(\frac{G_{T}}{G_{T,STC}}\right) \times\left(1+\alpha_{temp}\times\left(T_{cell}-T_{cell,STC}\right)\right) \tag{2}\]  
where \(P_{PV}\) is the actual PV power (kW), \(P_{rated,STC}\) is the rated PV power at STC (kW), \(f_{PV}\) is the PV derating factor, \(G_{T}\) the GHI on the array (kW/m\({}^{2}\)), \(G_{T,STC}\) is the solar radiation at STC (1 kW/m\({}^{2}\)), \(\alpha_{temp}\) is the temperature power coefficient (\(\%/^{\circ}\)C), \(T_{cell}\) is the actual PV cell temperature (\({}^{\circ}\)C), and \(T_{cell,STC}\) is the cell temperature under STC (25 \({}^{\circ}\)C).  
Using these variables, HOMER simulates PV performance hour by hour. The software then iterates through different possible system configurations--adjusting PV capacity and other parameters--to find an optimal design. By factoring in real-time irradiance, temperature, and efficiency losses, HOMER provides a more accurate representation of  
Figure 6: Yearly load heatmap profile.  
the PV array's performance over an entire year, enabling better sizing and economic decision-making.  
The system modeled in this analysis is a generic flat plate PV system with a rated capacity of 1 kW. The panel operates with a temperature coefficient of \(-0.5\%/^{\circ}\)C and is designed for a typical operating temperature of 47 \({}^{\circ}\)C. Its efficiency is 13%, representing a standard commercially available PV module. The derating factor is 80%, accounting for potential losses such as dust, shading, and wiring inefficiencies. The ground reflectance is set to 20%, and the system uses a fixed configuration with no tracking. The panel slope is set to the default value of 39.92\({}^{\circ}\) based on the site location's geographical condition, and the azimuth is aligned at 0\({}^{\circ}\) south for optimal solar exposure.