esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Single-Input Multi-Output Inverter  
### Topology Structure and Its Evolution Process  
The basic extension method for inverters to generate multiple outputs is shown in Figure 1. Based on the traditional half-bridge inverter, the power switch \(S_{a}\) was selected for expansion. In Figure 1a, \(S_{a}\) was expanded to form two series-connected power switches in Figure 1b. Furthermore, the first power switch in Figure 1b was expanded to form the two series-connected power switches in Figure 1c. By following the above method for step-by-step expansion, \(S_{a}\) can be expanded into a series connection of multiple power switches, and the voltage at both ends of each power switch can be used as the output voltage of the inverter, achieving multi-output expansion. It should be pointed out that this extension method is general and can be applied to any power switch in bridge inverters. Based on the above extension method, the extension process of the multi-output inverter proposed in this paper is shown in Figure 2a. Firstly, to ensure the voltage output capability of the proposed inverter, a synchronous boost circuit was constructed by connecting one inductor and capacitor based on a half-bridge inverter. Then, based on the extension method, \(S_{a}\) and \(S_{b}\) were, respectively, extended once to construct a multi-output inverter, as shown in Figure 2b. The proposed inverter consists of four power switches \(S_{1}\)-\(S_{4}\) and their internal anti-parallel diodes \(D_{S1}\)-\(D_{S4}\), one inductor \(L\), one capacitor C, and one DC source. In the proposed inverter, the input voltage is \(U_{dc}\), the voltage of capacitor C is \(U_{C}\), and the current of inductor \(L\) is \(i_{L}\). The four output voltages of the proposed inverter are \(u_{1}\)-\(u_{4}\). Compared with traditional half-bridge inverters, the proposed inverter can generate four outputs by controlling the switching states of each power switch and can also boost the output capacity of the proposed inverter through an inductor. Each output of the proposed inverter was connected to a power transmitting and receiving circuit as load. Due to the fact that the outputs of the proposed inverter are voltage sources, they can be connected to multiple different resonant compensation networks. Therefore, Figure 2b does not provide a specific topology of the resonant compensation network, but only illustrates it with a block diagram. In Figure 2b, \(L_{pi}\) (\(i\) = 1, 2, 3, 4), \(L_{si}\), \(M_{i}\), and \(R_{i}\) are the transmitting coil, receiving coil, mutual inductance, and load resistor, respectively. T-RCN\(i\) and R-RCN\(i\) are the resonant compensation networks in the transmitting and receiving sides, respectively.
# Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
## 4 Post-Processing of IR Signals: Proposed Supervised Learning Model  
The study proposes a supervised learning model implemented to classify and differentiate bioactive edible oils--olive, sunflower, tomato seed, and pumpkin seed--based on their thermal profiles. The approach utilises a combination of CNN with Long Short-Term Memory (LSTM) units to capture spatial and temporal dynamic features inherent in IR thermographic sequences. The model is specifically designed to assess the key quality attributes of the oils, including thermal stability (consistency over time), thermal reactivity (response to temperature variations), and biochemical composition linked to nutritional benefits. The dataset used for model training and validation consisted of 200 thermal video sequences (50 for each oil type). Each video included 50 frames, leading to a total of 10,000 images. Data augmentation techniques (rotations, flipping, and Gaussian noise) were applied to enhance dataset robustness. All frames were resized to 128 \(\times\) 128 pixels and normalised to a [0, 1] scale. A stratified split divided the dataset into 80% training and 20% testing sets, with a 5-fold cross-validation across the dataset. Model performance on the test set was summarised using a confusion matrix, which confirmed the generalisability of the classification system across oil types.  
The proposed CNN-LSTM assesses the evolution of thermal patterns across time; the model aims to identify distinct thermal \"fingerprints\" that reflect the internal structure and degradation behaviour of each oil. The resulting framework enables the development of a non-invasive, intelligent system capable of acting as a digital assay for nutritional quality assessment and traceability in bioactive oils. The primary objective of the CNN-LSTM model developed in this study is to classify four types of bioactive edible oils--olive oil, sunflower seed oil, tomato seed oil, and pumpkin seed oil--based on their unique thermographic profiles. The classification is driven by three key thermal characteristics: thermal stability (uniformity of heat distribution over time), thermal reactivity (the responsiveness to heating and cooling cycles), and nutritional implications, which are inferred from thermal patterns linked to biochemical composition (e.g., unsaturated fatty acids, tocopherols, and polyphenols).  
Figure 3: Integrated infrared (IR) data acquisition and transmission system. (**a**) Front section housing the thermal camera (a\({}_{1}\)); (**b**) internal section containing the oil sample (b\({}_{1}\)) and the Peltier cell for thermal modulation (b\({}_{2}\)); and (**c**) rear section encompassing the acquisition and transmission control unit, composed of: (c\({}_{1}\)) bus cable connected to the thermal camera, (c\({}_{2}\)) data transmission module, (c\({}_{3}\)) Raspberry Pi for processing and control, (c\({}_{4}\)) acquisition board with integrated ATMEGA328P microcontroller, and (c\({}_{5}\)) power supply system.
## Materials and Methods
### Data and Pre-Processing
#### 2.2.2 Forest Survey Data  
The field surveys were conducted from June to July 2017. The distribution of sampling plots was randomly generated, and a total of 117 \(50\times 50\) m plots were sampled. Location of each sampling plot was measured by Global Positioning System (GPS) and real-time kinematic (RTK), accurate within 1 m. The sample parameters recorded were species names, tree heights, crown cover areas, basal area (tree diameters over 10 cm), and Differential Global Positioning System (DGPS) coordinates in the universal transverse mercator (UTM) system. Based on the basal area factor (BAF) [29], any tree species with a basal area frequency above 50% in each plot was selected as the dominant tree species [30]. According to the field surveys, seven dominant tree species classes were identified, comprising Amur linden, Chinese pine, Dahurian larch, aspen, white birch, Manchurian walnut, and Manchurian ash.
# Improving Transaction Speed and Scalability of Blockchain Systems via Parallel Proof of Work +
## 1 Introduction
### Decentralized System  
A decentralized protocol empowers saving assets in a platform that can be found on the Internet. Through a decentralized protocol, the owners have absolute authority over their resources and have the right to exchange assets with anyone at any time [9]. The innovative nature of blockchain has found a way to form a decentralized system in the web. This system will allow owners to process their property any time they want without the participation of a third party. Individuals can specifically enjoy the exchange for a minimum charge. Moreover, a decentralized system such as this has no single point of failure, unlike a centralized system.  
To this end, in this paper we present a method for improving the transaction speed and scalability of blockchain systems by extending our previous work in [10] and results from [11]. In the proposed method, all miners will use the same transaction data except for the nonce for a certain block, thus ensuring that no multiple miners perform the same work, accomplished through a manager. This model differs from traditional Proof of Work or the Bitcoin pool mining [12] in several aspects, such as the responsibilities of the manager, contribution of active miners, and the reward system.  
The remainder of this paper is organized as follows: discussion of related work is presented in Section 2, while Section 3 presents details of the proposed method, along with an explanation of its features. Section 4 discusses the implementation and evaluation results of the proposed system. Challenges and solutions are discussed in in Section 5. Finally, conclusions and ideas for future work are presented in Section 6.
# Recent Progress in On-Chip Erbium-Based Light Sources
## 2 Electronic Energy Level Structure and Amplification in Er-Based Medium  
The neutral form of the rare earth element Er is [Xe]4f\({}^{2}\)6s\({}^{2}\), which has a similar electronic structure to xenon (Xe). The 6s\({}^{2}\) electrons and the 4f electron, which are in a weakly bound state, are usually removed when bound into the host materials and becomes Er\({}^{3+}\). On this account, the partially filled 4f electron shell is shielded by the larger-radius 5s and 5p orbitals and forms a localized electronic environment. Therefore, the luminescence properties of Er\({}^{3+}\) are almost unaffected by the host materials.  
The energy-level model of Er\({}^{3+}\) and the corresponding electronic transitions are shown in Figure 1, which contains ground state absorption (GSA), excited state absorption (ESA), spontaneous emission, stimulated emission, energy-transfer-upconversion (ETU) process and cross-relaxation (CR) process [38]. For Er-based amplifiers and lasers operating around C-band, the transition process is mainly based on \({}^{4}\)I\({}_{13/2}\)-\({}^{4}\)I\({}_{15/2}\) stimulated emission, which can be pumped by 980 nm or 1480 nm lasers. Meanwhile, the spontaneous emission based on the \({}^{4}\)I\({}_{13/2}\)-\({}^{4}\)I\({}_{15/2}\) transition would radiate non-coherent photons in a broad spectrum around the signal wavelength, which is known as amplified spontaneous emission (ASE) noise.  
For Er-based optical amplifier, the expression for the gain coefficient \(g\) can be simplified as follows:  
\[g(\mathrm{h}v)=\sigma_{em}(\mathrm{h}v)N_{2}-\sigma_{abs}(\mathrm{h}v)N_{1}, \tag{1}\]  
where h is Planck constant, \(v\) is the optical frequency, \(\sigma_{em}\) is the emission cross section, \(\sigma_{abs}\) is the absorption cross section, \(N_{2}\) and \(N_{1}\) are the concentration of Er\({}^{3+}\) in the excited(\({}^{4}\)I\({}_{13/2}\)) and ground (\({}^{4}\)I\({}_{15/2}\)) energy levels, respectively. \(\sigma_{em}\) and \(\sigma_{abs}\) are usually on the same order of magnitude. Therefore, to achieve optical net gain, \(N_{2}\) need to be much higher than \(N_{1}\), which can be achieved by pump absorption. The concentration of Er\({}^{3+}\) in each energy level can be derived by rate equations [42].  
When signal light propagates in the gain medium, the internal signal-optical gain \(G\) can be expressed as:  
\[G=\exp[(\Gamma g\text{-}\alpha)L], \tag{2}\]  
where \(\Gamma\) is the optical mode confinement factor, \(\alpha\) is the propagation loss and \(L\) is propagation length.  
Small size and high gain are important performance parameters of silicon-based on-chip erbium-based light sources. From the above equations, it can be seen that in order to improve the signal gain (\(G\)) of the amplifier at a compact size, it is necessary to increase the Er\({}^{3+}\) concentration (\(N\)) in the gain material, improve the optical mode confinement factor (\(\Gamma\)), and reduce the propagation loss (\(\alpha\)). For on-chip Er-based light sources, the enhancement of Er\({}^{3+}\) concentration is the most crucial, which is determined by the host material.
# Dual-Band Single-Layer Fractal Frequency Selective Surface for 5G Applications
## 5 Angle of Incidence  
All previous simulations use incident waves perpendicular to the FSS surface. This section checks the design robustness under different angles of incidence. Figure 10 shows the simulation results for different angles, where \(\phi\) and \(\theta\) are angles in the \(dy\) and \(dx\) directions. When \(\theta\) changes, the angle of incidence changes, but the incident electric field is still parallel to the FSS. When \(\phi\) changes, the angle of incidence changes, and the incident electric field is no longer parallel to the FSS. The bow tie has a decent invariance against changes in the \(\theta\) direction. This means that the resistance against changing angles in this direction is strong. Some extra variations above 4.5 GHz are introduced, but the behavior around 700 MHz and 3.5 GHz stays more or less the same as long as the size of \(\theta\) does not exceed 45\({}^{\circ}\). The situation for \(\phi\) is entirely different; the slightest change (more than 10\({}^{\circ}\)) in angle and the lower frequency behavior will begin to disappear, which means that the design is not strong against polarization of the incident wave. The FSS might still be usable because the notch is still wide at \(-10\) dB. If a wave is tilted in both directions, a similar result is visible as only \(\phi\) changes. This is likely since the unit cell is not uniformly symmetric and presents better behavior with waves impinging in one of the directions.  
Figure 9: The effects of \(dx\) and \(dy\) variation. (**a**) Around 800 MHz. (**b**) Around 3.5 GHz.
# Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model
## 3 Principle of SVR Prediction Model
### Kernel Function Selection  
Since different types of kernel functions and parameters have a large impact on the generalization ability of predictive models, the comparison optimization of kernel functions is necessary to ensure the prediction accuracy of SVR prediction models [35].  
Common kernel functions are as follows:  
The linear kernel function is  
\[K\big{(}x_{i},x_{j}\big{)}=x_{i}x_{j} \tag{17}\]  
The polynomial kernel function of order \(d\) is  
\[K(x_{i},x_{j})=\big{(}x_{i}x_{j}+1\big{)}^{d} \tag{18}\]  
The radial basis kernel function is  
\[K(x_{i},x_{j})=exp\Bigg{(}-\frac{\big{\|}x_{j}-x_{i}\big{\|}^{2}}{2\sigma^{2} }\Bigg{)} \tag{19}\]  
The Sigmoid kernel with arguments \(k\) and \(\theta\) is  
\[K\big{(}x_{i},x_{j}\big{)}=tanh\big{(}k\big{(}x_{i}x_{j}\big{)}+\theta\big{)} \tag{20}\]  
In the formula, the related parameters of \(C\), \(d\), \(\sigma\), \(k\), \(\theta\), and other kernel functions involved can be selected and determined by the optimization algorithm.
# Green Project Planning with Realistic Multi-Objective Consideration in Developing Sustainable Port
## 2 Literature Review
### Multi-Objective Optimization Techniques for Engineering Optimization Problems  
To solve multi-objective engineering optimization problems in practice, usually, multiple objectives need to be considered and achieved simultaneously. The weighted sum method [22; 23] and the multi-objective evolutionary algorithm [24; 25; 26; 27] are commonly used. However, the weighted sum method needs to determine the weight value of different objectives in advance, but the weight value is usually difficult to determine. There is no a single solution which can simultaneously optimize all objectives when multiple objectives are conflicting. To handle this problem, the concept of Pareto optimality is used to find Pareto optimal solutions [24; 28; 29]. Genetic algorithm is the most popular meta-heuristic technique for multi-objective problems [26; 30]. The fast elitist non-dominated sorting GA (NSGA-II) has attracted more and more attention since NSGA-II was proposed by [PERSON] et al. [31]. NSGA-II has attracted the attention of many researchers and is widely used to solve optimization problems. A multi-objective evolutionary algorithm based on decomposition was proposed by [PERSON] [32], which is another commonly used method that performs similarly to NSGA-II on multi-objective 0-1 knapsack problems and continuous multi-objective optimization problems. [PERSON] et al. proposed a MOEA/D-ACO algorithm which combine ant colony optimization (ACO) and the multi-objective evolutionary algorithm (EA) based on decomposition (MOEA/D). This algorithm decomposes a multi-objective optimization problem into a number of single-objective optimization problems [33]. However, these methods cannot be directly used to handle the GPP problem investigated in this paper because different chromosome representations and genetic operators are required for different optimization problems. These multi-objective evolutionary algorithms avoid the determination of weights, and instead generate a set of Pareto optimal solutions. However, how to choose the appropriate one from these solutions as the final solution is a major difficulty in practical application.  
For real-world multi-objective problems, the precisions of the weights of different objectives are usually not high. There exist limited weight sets in practice. Initiated by this, this paper proposes a novel multi-objective optimization method to handle realistic multi-objective optimization problems, a weight sets-based multi-objective evolutionary optimization approach.
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
###### Abstract  
Inverse halftoning is an ill-posed problem that refers to the problem of restoring continuous-tone images from their halftone versions. Although much progress has been achieved over the last decades, the restored images still suffer from detail loss and visual artifacts. Recent studies show that inverse halftoning methods based on deep learning are superior to other traditional methods, and thus this paper aimed to systematically review the inverse halftone methods based on deep learning, so as to provide a reference for the development of inverse halftoning. In this paper, we firstly proposed a classification method for inverse halftoning methods on the basis of the source of halftone images. Then, two types of inverse halftoning methods for digital halftone images and scanned halftone images were investigated in terms of network architecture, loss functions, and training strategies. Furthermore, we studied existing image quality evaluation including subjective and objective evaluation by experiments. The evaluation results demonstrated that methods based on multiple subnetworks and methods based on multi-stage strategies are superior to other methods. In addition, the perceptual loss and the gradient loss are helpful for improving the quality of restored images. Finally, we gave the future research directions by analyzing the shortcomings of existing inverse halftoning methods.  
deep learning; inverse halftoning; digital halftone image; scanned halftone image; image quality evaluation +
Footnote †: journal: Applied Sciences
## 6 Discussion of the Control Effects of the Collapse Control System
### Fluid Viscous Damper Control  
#### 6.2.1 Parameter Setting  
Due to the differences in bridge type, site, control target, and so on, the design parameters of the FVD used in each engineering case are different. The main design parameters are damping coefficient C and velocity index \(\alpha\). According to the characteristics of the object studied in this paper, it is determined that the optimal range of damper parameters are as follows: C increases from 1 kN(s/m)\({}^{\alpha}\) to 12 kN(s/m)\({}^{\alpha}\), and \(\alpha\) varies from 0.2 to 1. Thus, 42 parameter schemes are obtained. After that, the isolation effect of the CHDR system is taken as the optimization objective. In order to make the CFVD system comparable with the existing CHDR system and have certain advantages, the relative displacement between the side pier and the girder and the bending moment at the bottom of the middle pier should be smaller than those in the CHDR system. Eight of 42 schemes meet those conditions, so the scope of schemes is reduced to 8. Therefore, the range of design parameters is reduced as C ranging from 8-12 kN-(s/m)\({}^{\alpha}\) and \(\alpha\) between 0.2 and 0.4. The control of the structure by the FVD is reflected in the control of force and deformation. Considering the characteristics of seismic damage and collapse failure of the continuous girder bridge, displacement control is important. Sometimes the large displacement response of the substructure leads to serious consequences of girder lowering, so the damping coefficient C should not be too small. In the condition of the same damping coefficient C, the smaller the speed index \(\alpha\), the better the effect of controlling the bending moment of the middle pier. Then, from the consideration of economy and non-linearity, the final compromised design parameters of the FVD are as follows: C is 10 kN(s/m)\({}^{0.4}\) and \(\alpha\) is equal to 0.4. These parameters were used to set the damping material in the FEA model for simulation of the FVD.
## 8 Routing Protocols of Multi-UAV Systems
### Topology-Based Routing Protocols
#### 8.1.2 Proactive  
Proactive protocols [83; 84] use dynamic routing tables. Each UAV has a routing table that stores, periodically for each UAV, the routes to broadcast messages to the destination UAVs. The delay between sending and receiving the message is very short because the paths are already known beforehand. However, when the swarm reaches a certain level of scalability, a network bottleneck ensues, as changes in the routing table are bandwidth- and  
Figure 16: Paradigm of topology-based protocols: (**a**) static; (**b**) proactive; (**c**) reactive; (**d**) hybrid.  
energy-intensive. For this reason, this protocol is not used for applications requiring a large number of highly mobile UAVs. The most well-known proactive protocols are Optimized Link State Routing (OLSR) [83], Destination Sequence Distance Vector (DSDV) [84], and Better Approach to Mobile Ad-Hoc Network (BATMAN) [89].  
The OLSR protocol uses [PERSON]'s algorithm [62] to calculate the routes for sending messages between the different UAVs, while the DSDV protocol uses the Bellman-Ford algorithm [61]. On the other hand, the BATMAN protocol optimizes the calculation of routes between UAVs. Unlike the OLSR and DSDV protocols, which calculate routes for all possible sending combinations, the BATMAN protocol only calculates routes for UAVs that are directly or indirectly (via the gateway UAVs) connected to each other.
## 2 Numerical Methods
### Turbulence Model Independence Study  
Four turbulence models (i.e., Standard k-\(\epsilon\), RNG k-\(\epsilon\), Realizable k-\(\epsilon\), and SST-\(\omega\)) and three near-wall functions (i.e., standard wall functions, non-equilibrium wall functions, and enhanced wall treatment) are selected for comparison with the experimental results [20]. The resultant film cooling effectiveness values are given in Figure 4. The RNG k-\(\epsilon\) turbulence model has been selected due to its superior performance in simulating turbulent flows characterized by complex vortical structures. This model is particularly effective in capturing the impacts of small-scale turbulence, which is critical for accurately predicting the flow and heat transfer characteristics associated with film cooling applications. Furthermore, the non-equilibrium wall function enhances the accuracy of near-wall flow predictions, particularly in regions where the coolant jet interacts with the surface of the plate. This methodological combination has proven effective in a previous study [3] focused on similar flow phenomena. The RNG k-\(\epsilon\) turbulence model with non-equilibrium wall function shows the best performance and is chosen for numerical simulations in this paper.  
Figure 4: Comparison of cooling efficiency along the centerline for different turbulence models with experimental results [40].  
Figure 3: Comparison of cooling efficiency of various grid sets along the centerline with experimental results [40].