esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Single Objective Modeling and Solving
### Algorithm Design
When no individual violates the constraints in the population, the penalty function is 0. Then, the individual with the best fitness value is the current global optimal solution. When all individuals in the population violate the constraints, the penalty functions of all individuals are greater than 0. Then, the individual with the least penalty function value is chosen as the current global optimal solution. Step 4: Mutation operation. Equation (21) is used to mutate the individual. \[ \begin{array}{l}(1)\ \mathrm{DE/rand}/1:\ v_{i}(G+1)=x_{r1}(G)+F\times(x_{r2}(G)-x_{r3 }(G))\\ (2)\ \mathrm{DE/best}/1:\ v_{i}(G+1)=X_{best}+F\times(x_{r1}(G)-x_{r2}(G))\\ (3)\ \mathrm{DE/rand}\mbox{-}\mbox{to-}\mbox{best}/1:\ v_{i}(G+1)=X_{i}+F \times(X_{best}-X_{i})+F\times(x_{r1}(G)-x_{r2}(G))\end{array}  \tag{21}\]  
where \(X_{best}\) is the best target individual; \(x_{r1}(G)\), \(x_{r2}(G)\), and \(x_{r3}(G)\) are random target individuals; \(v_{i}(G+1)\) is the mutant individual, and \(F\in[0,1]\) indicates the mutant scale factor, recommended as 0.5 [30]. Step 5: Crossover operation. To increase the diversity of the population, the crossover operation is performed between the temporary individual and the parent individual. Substitute the parent individuals for temporary individuals with a probability of \(CR=0.2\). Step 6: Cross-border processing. The classical DE does not consider how to address the constraints handling. The randomness of the general cross-border processing strategy is too strong to preserve the historical optimal information accumulated through evolution. We used the symmetric mapping processing strategy as previously reported [31]. As shown in Figure 7, \(range_{left}\) and \(range_{right}\) are the left and right boundaries of each variable in the individual, respectively. When the extent to which a variable violates the constraint is less than the absolute value of the variable range, the boundary point is taken as the center to map this variable equidistantly onto the range of value. Step 7: Competitive survival operation. We used the idea of competition survival to compare the fitness and penalty function of the corresponding individual in the parent population and temporary population, and selected the preferable individual to form a new population. The specific rules are: (1) if both individuals do not violate the constraints, we judge them by comparing their fitness values; (2) if a solution satisfies the constraints, whereas the other one does not, then the solution meeting the constraints is better; and (3) if both do not meet the constraints, the solution with the lower penalty function is better. Figure 7: Symmetric mapping processing strategy. Step 8: Evolution and iteration. We determined whether the scheduled iterations are reached. If so, then the operation is stopped; otherwise, proceed to step 3. If the desired iterations are reached, but the satisfactory solution has not been found, the population size and the iterations should be adjusted.
###### Abstract  
The interrelationships between the topological features, such as surface roughness deduced from atomic force microscopy (AFM), and wettability properties expressed by the contact angle of a water droplet on the surface of nanostructured wide bandgap oxide films prepared by spray pyrolysis are investigated for a wide range of compositions. A direct relationship between the surface roughness and the value of the contact angle was found for nanocomposite (In\({}_{2}\)O\({}_{3}\))\({}_{1-\mathrm{x}}\)(MgO)\({}_{\mathrm{x}}\), (In\({}_{1-\mathrm{x}}\)Ga\({}_{\mathrm{x}}\))\({}_{2}\)O\({}_{3}\), and Zn\({}_{1-\mathrm{x}}\)Mg\({}_{\mathrm{x}}\)O films, for which both the surface roughness and the contact angle increase with the increasing \(x\)-value. On the other hand, in ITO films doped with Ga, it was found that the surface roughness increases by increasing the Ga doping, while the contact angle decreases. Both the surface roughness and the contact angle proved to increase in Ga\({}_{2}\)O\({}_{3}\) films when they were alloyed with Al\({}_{2}\)O\({}_{3}\), similar to other nanocomposite films. An inverse relationship was revealed for a nanocomposite formed from Ga\({}_{2}\)O\({}_{3}\) and SnO\({}_{2}\). The contact angle for a (Ga\({}_{2}\)O\({}_{3}\))\({}_{0.75}\)(SnO\({}_{2}\))\({}_{0.25}\) film was larger as compared to that of the Ga\({}_{2}\)O\({}_{3}\) film, while the surface roughness was lower, similar to ITO films. The highest value of the contact angle equal to 128\({}^{\circ}\) was found for a (In\({}_{2}\)O\({}_{3}\))\({}_{1-\mathrm{x}}\)(MgO)\({}_{\mathrm{x}}\) film with an \(x\)-value of 0.8, and the largest RMS roughness of 20 nm was showed by a Ga\({}_{1.75}\)Al\({}_{0.25}\)O\({}_{3}\) film. The optical properties of the prepared films were also analyzed from optical absorption spectroscopy, demonstrating their bandgap variation in the range of (4 to 4.85) eV, corresponding to the middle ultraviolet spectral range.  
metal oxide films; AFM; EDX; roughness; hydrophobicity; hydrophilicity Article
# Two Low-Complexity Efficient Beamformers for an IRS- and UAV-Aided Directional Modulation Network
## 2 System Model
Similarly, the signal received at Eve can be given by  
\[y_{e}=\left(\sqrt{g_{ae}}\mathbf{h}_{ae}^{H}+\sqrt{g_{aib}} \mathbf{h}_{ie}^{H}\mathbf{\Theta}\mathbf{H}_{ai}\right)\mathbf{x}+n_{e}\] \[=\sqrt{g_{ae}\beta_{1}P_{1}}\mathbf{h}_{ae}^{H}\mathbf{v}_{CM} \mathbf{s}+\sqrt{g_{aib}\beta_{1}P_{1}}\mathbf{h}_{ib}^{H}\mathbf{\Theta} \mathbf{H}_{ai}\mathbf{v}_{CM}\mathbf{s}+\] Figure 1:0  
where \(\mathbf{h}_{ae}\in\mathbb{C}^{N_{x}\times 1}\) and \(\mathbf{h}_{ie}\in\mathbb{C}^{N_{x}\times 1}\) represent the Alice-to-Eve and the IRS-to-Eve channels, respectively. \(g_{ae}\) and \(g_{aiie}=g_{ai}g_{ia}\) are the path loss coefficients of the Alice-to-Eve direct channel and the cascade channel, respectively, and \(n_{e}\sim\mathcal{CN}\left(0,\sigma_{e}^{2}\right)\) is the AWGN at Eve. The signal received at the IRS can be indicated as  
Figure 1:1  
According to (2), the signal-to-interference plus noise ratio (SINR) of Bob is expressed as  
Figure 1:2  
In terms of (6), the SINR of [PERSON] is written as  
Figure 1:3  
The corresponding rates of Bob and Eve are expressed as  
Figure 1:4Figure 1:5  
respectively; hence, the calculation of the SR proceeds as follows:  
Figure 1:6  
where \([x]^{+}\triangleq\max\{0,x\}\).
## 2 Methods
### Simulation Example
#### 2.3.1 Estimation of Poles from Data  
To estimate the poles from the data, we propose using the Matrix Pencil Method (MPM) [58]. MPM approximates time series data by \(M\) complex exponentials. These estimated complex exponents represent the poles of the system of interest (in our case, the human sensorimotor system). The observed signal is represented as  
\[y(t)\approx\sum_{i=1}^{M}R_{i}\mathrm{e}^{S_{i}t}+n(t). \tag{1}\]  
Here, \(y(t)\) is the simulated response, \(S_{i}\) values are the estimates of poles, \(R_{i}\) values are coefficients, and \(n(k)\) is the noise in the simulated data. A brief overview of an algorithm for estimating poles from data is given in Appendix A.2.  
Figure 3 shows the real part of poles for simulated healthy and PPD individuals over a period of time, estimated from a few individuals in the synthetic data generated as described earlier. Further, it is to be noted that the synthetic data set consists of both individuals maintaining a regular frequency of visits and individuals maintaining an irregular frequency of visits; that is, the clinical tasks are not necessarily repeated at regular intervals but could be at irregular intervals.
## 3 Results and Discussion
### Interpretability Analysis  
To analyze model interpretability, CAMs were obtained as heatmaps for samples from each category based on a selected feature layer of the deep models. While each model responded differently to the sample features, they successfully classified images. Figure 7 shows the micrograph and CAM heatmap for class 2, highlighting that the VGG focus  
Figure 6: ROC and PR curves for five models.  
for class 2 differs significantly from those of other classes. Early in training, the CAM concentrated on broad features of the micrograph at a shallow level, including non-relevant elements such as explanatory text and scale information at the bottom of the image as Figure 7B,C. As training progressed, the focus shifted away from these areas, gradually highlighting the most relevant regions, such as the particle shapes within the micrograph (Figure 7D,E). This shift culminated in final CAM result emphasizing important areas (Figure 7F). These findings suggest that the deep learning models effectively captured and classified particle shape differences, aligning with the study objective of particle shape classification.  
Even within the same deep learning model, each particle shape feature of the sample responds differently, contributing to effective image classification. However, when comparing the VGG model and ViT for class 1 in Figure 8, differences in defocusing are evident in the guided backpropagation and guided backpropagation-CAM images. This defocusing effect reduces CAM precision in the ViT, as the input image is divided into multiple patches during processing. When these patches are reassembled into the heatmap, the result is blurry. Unlike the four CNNs, which compute Grad-CAM by weighting feature maps across channels, ViT weighs feature maps across patches, which maintain uniform feature map alignment with the original image size. Nevertheless, Grad-CAM remains effective in interpreting and visualizing decision-making processes and outcomes in micrograph images. The ViT model demonstrated the highest classification accuracy; however, the class activation mapping (CAM) heatmap was less focused. While this suggests that the ViT model is well-suited for classification tasks, it may not be the most optimal choice for studies requiring high interpretability.  
Figure 7: CAM heatmap progression for class 2 during training of VGG model: (**(A)**: Original image, **(B,C)**: CAM heatmap focusing on broad features of the micrograph, **(D,E)**: CAM heatmap highlighting relevant regions such as particle shape, **(F)**: Final CAM result image).
# Real-Time Monitoring of Passenger's Psychological Stress
## Materials and Methods
### Linear Regression Model  
The final stress model has been designed as a weighted sum on each of the previous statistical features. The predicted stress level \(s\) over one time window \(t\) is expressed in Equation (3), where \(F\) is the number of features, each of which \(f_{k}\) is associated with a weight \(\beta_{k}\) that quantifies its linear relationship with \(s\).  
\[s(t)=\beta_{0}+\sum_{k=1}^{F}\beta_{k}\cdot f_{k}(t) \tag{3}\]  
To estimate the full weight vector \(\beta\), a linear regression has been performed using the least-squares method on the feature matrix extracted from a learning database: the one recorded during participant's first trip.
## 2 Background and Literature Review
### Routing in SDVN  
In SDVNs, the traditional routing protocols can fail, as the connection between the SDN controller and the nodes can be lost due to the high mobility of the nodes, so a hierarchical SDVN architecture has been proposed to overcome the routing problem [42]. A network resource scheduling scheme with the objective of minimizing communication costs and a greedy routing algorithm was applied for heterogeneous SDVNs in [43]. An on-demand routing protocol having a two-level design with a Road Side Unit (RSU) as the local controller for selecting vehicles, to forward a packet within a road segment, and a global controller, to select a road segment having higher performance under large networks and high-speed vehicles, was presented in [44]. In [45], a spray-and-pray multiple copy routing system for SDVNs has been provided. It uses graph-based least communication steps to determine the utility of carriers and two-copy cooperative techniques to minimize delivery latency. A routing algorithm to find a globally optimized routing path for vehiclesswitching between a multihop forwarding model and a carry-and-forward model for SDVN is presented in [46]. Work in [47] not only found the shortest path for packet forwarding in SDVNs, but also considered the effect of link stability in finding optimum paths as vehicular networks are inherently dynamic and connections between vehicles can exist only for a short amount of time. The routing model in [48; 49] for SDVN prevents packet crashes, concurrent transmissions, and circular routing by prioritizing the traffic type to minimize service latency and jointly leveraging diverse interfaces to maximize data packet delivery. A routing protocol comprising both centralized and distributed, which is tribrid as it uses multicast, unicast, and store, carry, and forward concepts appropriately to find stable enough shortest routes satisfying QoS parameters in terms of latency, was depicted in [50]. In a hierarchically controlled SDVN, a cognitive routing protocol called CR-SDVN, which senses the spectrum to improve link stability and find a stable path for routing, was investigated in [51]. A clustering algorithm for long-distance routing along with a control communication overhead reduction approach has been proposed along with backup VANET routing in a fog computing-based SDVN environment [52]. Hidden Markov models have been used to predict the location of the destination vehicle in a highway mobility scenario of an SDVN to compute optimal routing paths at the controller [53].
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 4 Theoretical Analysis  
### Repair DT  
The randomly distributed nodes in the 100 m \(\times\) 100 m scene can construct DT. According to the properties of Delaunay, we know that DT is convex as shown in Figure 2. The coverage holes in the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(S_{i}\), \(M_{j}\) & Static sensor and mobile sensor \\ \(\theta_{i}\) & Angle of three Delaunay triangle \\ \(R_{s}\), \(R_{c}\) & Sensing radius and communication radius \\ \(P_{i}\) & Position of mobile assisted sensor \\ \(C_{0}\) & The circumcenter of the \(\Delta S_{1}S_{2}S_{3}\) \\ \(S_{uncovered}\) & Uncovered area of the triangle \\ \(d(s_{i},s_{j})\) & Euclidean distance between \(s_{i}\) and \(s_{j}\) \\ \(\mu\) & Input parameter to round the number of assisted sensors \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main Notations.  
Figure 2: Delaunay Triangulation (DT).  
boundary cannot be detected due to the triangle cannot be formed in marginal area. In order to solve this question, we need to deploy some nodes in boundary and corner of the area, which can construct the DT to fill the monitoring area as shown in Figure 3.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 4 Challenges and Development Trends in Reflector Antenna-Based SAR Technology
### Future Development Directions for Reflector Antenna-Based SAR  
Although phased array antennas currently dominate spaceborne SAR applications, reflector antennas offer substantial potential for future development. Key research directions include the following:  
(1) HRWS Imaging: Overcoming the fundamental trade-off between resolution and swath width remains a major challenge in spaceborne SAR design. A promising direction involves integrating a two-dimensional PAF with a reflector antenna, combined with range-direction DBF, multi-channel azimuth processing, and the SweepSAR technique to achieve flexible HRWS imaging.  
(2) Multi-Mode Imaging Capability: To accommodate diverse observation tasks and enhance system flexibility, next-generation spaceborne SAR systems must support multiple imaging modes. For instance, Israel's TecSAR supports stripmap, scan, spotlight, and mosaic imaging modes, while the U.S. Capella Space's Sequoia series offers stripmap, spotlight, and sliding spotlight imaging modes. Developing multi-mode-capable reflector antenna-based SAR systems is a crucial research objective.  
(3) Lightweight Design and Novel Deployment Mechanisms: The large size of reflector antennas necessitates deployable structures to meet launch constraints. Future research should focus on novel antenna configurations, advanced deployment mechanisms, improved shape error analysis methods, precision shape optimization, and active surface control techniques. The development of larger reflector antennas imposes stringent requirements on material selection and deployment mechanisms, including truss structures and high-strength reflective mesh materials.  
These perspectives indicate that the future of reflector antenna-based SARs will focus on HRWS imaging, multi-mode support, structural innovation, and precision control, further extending spaceborne SARs' application scope.
## 2 Mathematical Formulation  
As stated in the introduction, the objectives used for the optimization problem were the energy consumption, holding cost, and risk of shortage. These objectives were selected based on previous work on the topic (namely, the components of total holding cost and total shortage cost presented in [19]), and sustainability considerations required by the pillars of Industry 5.0. The specific objectives considered are shown below.  
* \(E\equiv\) energy consumption : total daily consumption of energy derived from acclimatizing the raw materials.
* \(C_{Hold}\equiv\) holding cost: total daily cost of holding the stocks at the different storage locations.
* \(R_{Sh}\equiv\) risk of shortage: measure of risk of not having sufficient raw materials for production, given the suppliers' lead time and the near-future raw material requirements.  
While the holding costs are linearly dependant on the occupation degree of each storage location, the formulation of the energy consumption and risk of shortage are more complex and requires novel implementations with the specific conditions of the problem in mind. The main rationale for these changes stems from the opportunities provided by a many-objective formulation--as the objectives are independent of each other, they do not have to be in the same units, nor be weighed to evaluate their impact on a single solution. This justifies, for instance, why the risk of shortage is directly measured in lead times of the materials and their standard deviations.
## 9 Prototype Testing and Results
### Evaluation of the Architecture Design  
A cost-benefit analysis offers essential insights into the potential expenses and advantages of the proposed system, assisting in informed decision-making regarding its development and deployment. Although the cost-benefit analysis (Table 5) is not inclusive to all, as some restrictions apply to the organization sharing confidential information, it covers the maximum knowledge and estimations based on the information provided by the National Bureau of Census of Pakistan. It is essential to highlight that Pakistan completed its inaugural digital national population census in March 2023, incurring an estimated cost of USD 3.5 million [63]. The digital census still stands on central (non-distributed) infrastructure, and news media broadcasted its same limitations and drawbacks, as identified in the literature review and case study conducted.  
Estimating an approximate cost is challenging due to several dependencies, including development, infrastructure, operations, monitoring, and training services. Typically, in response to government calls for project bids, software and information technology companies submit proposals. These proposals include the costs associated with development, operations, infrastructure, features/functionality, and various services offered, making it difficult to determine an exact cost. Nevertheless, we have attempted to outline the cost dimensions within the confines of IT systems and infrastructure.  
Figure 8: Various living arrangements: (**a**) in an open space; and (**b**) on a footpath and dumpster.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Costs** & **Benefits** \\ \hline The monthly hosting cost of Hyperledger Fabric & \\ could be between \$4200 and \$6800, considering & Data records are resistant to tampering \\ cloud-based services with moderate traffic & and can be verified \\ and data needs & \\ \hline Development and implementation expenses & Enhanced security and confidentiality \\  & of the data \\ \hline One-time resources training cost on blockchain & Smart contracts facilitate the automation of data \\ infrastructure/mechanism & verification and aggregation \\ \hline Costs for migrating from current systems & An unchangeable audit trail could minimize fraud \\ \hline Integration of fingerprint devices FS88 OEM & A synchronized system could boost the efficiency \\  & and precision of census operations \\ \hline SMS notifications & Offers a prospective framework for various \\  & governmental record-keeping systems \\ \hline Additional time/effort for consensus and governance & An open ecosystem could permit external \\  & applications to harness the data \\ \hline \hline \end{tabular}
\end{table}
Table 5: Costs vs. benefits.
## 2 Theory and Meta-Atom Design
The peak efficiency of 86.9% is achieved with \(L=165\) nm and \(W=45\) nm, and the adjacent geometries also exhibit efficiencies over 80%, thus resulting in a relatively large tolerance to fabrication error. Figure 1c shows that the transmission (T), co-polarized, and cross-polarized efficiencies remain uniform as the Nb\({}_{2}\)O\({}_{5}\) nanobar is rotated from 0 to \(\pi\). Figure 1d illustrates the simulated broadband analysis of an optimized nanobar in the range of 320 nm to 500 nm. The efficiency of the cross-polarized component is over 80% around the working wavelength of 355 nm. Moreover, there is a gradual increase in the transmission of the meta-atom, which implies that this Nb\({}_{2}\)O\({}_{5}\)-based nanostructure with an optimized geometry could gain high efficiency in the visible range. The high conversion efficiency of the meta-atom requires two conditions: a high transmission of both electric fields and a phase delay of \(\pi\) between the orthogonal electric field components, in other words, the meta-atom should operate as a high-performance miniature half-wave plate. It has been reported that in dielectric metasurfaces, the displacement current and its induced magnetic field produced both electric and magnetic resonance in the nanostructure, resulting in a high transmission efficiency [34]. Besides, the complex refractive index of Nb\({}_{2}\)O\({}_{5}\) has a sufficiently larger real part with a low extinction coefficient. This also contributes to the high efficiency of transmission. The phase delay of \(\pi\) can be attributed to the existing antiferromagnetic resonance mode in the structure [35]. For a better understanding of the physical mechanism behind the phase delay of \(\pi\), the vector field orientations and cross-sectional intensities of electric and magnetic fields for an optimized nanobar are plotted in Figure 2 under the incidence of a plane wave at 355 nm. As is demonstrated in Figure 2a,b, for the \(x\)-polarized incident wave, four circle displacement currents with the alternate direction of rotation induce four AMDs vertically located along the \(z\)-axis. The induced AMDs are staggered in anti-parallel orientations. This even number of displacement currents keeps the electric vectors of the incident and transmitted wave in the same direction. Figure 2c shows the simplified sketches of this antiferromagnetic mode under an \(x\)-polarized wave incidence. In contrast, the \(y\)-polarized incident wave induces three AMDs vertically located along the \(z\)-axis, resulting in the electric vector of the transmitted light being inverse to that of incident light (see Figure 2d-f). Thus, the transmitted electric vectors of \(x\)- and \(y\)-polarized components are in the opposite direction, introducing a phase delay of \(\pi\)