esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Cross-Dataset Generalization  
To assess the generalization capability of our model, we conducted a cross-dataset evaluation using images from the DICM dataset ([[https://paperswithcode.com/dataset/dicm](https://paperswithcode.com/dataset/dicm)]([https://paperswithcode.com/dataset/dicm](https://paperswithcode.com/dataset/dicm)) (accessed on 27 May 2025) and [[https://github.com/baidut/BIMEF](https://github.com/baidut/BIMEF)]([https://github.com/baidut/BIMEF](https://github.com/baidut/BIMEF)) (accessed on 27 May 2025)), which was not included in training. This dataset features challenging low-light scenarios distinct from those found in LOL or SID, including real-world urban scenes, varied lighting conditions, and high noise levels.  
We compare our model against several state-of-the-art approaches, including Zero-DCE, Retinex-Net, and SID. Since ground-truth references are not available for this dataset, we use no-reference image quality metrics, specifically NIQE and BRISQUE, along with qualitative visual comparisons. Table 4 presents the results in terms of NIQE and BRISQUE scores, where lower values indicate higher perceptual quality and more natural image statistics, while Figure 6 presents a comparison on an unseen low-light image from a DICM-style dataset. Note that PSNR and SSIM could not be computed on DICM images due to the lack of reference images. We therefore rely on widely used no-reference metrics (NIQE and BRISQUE) to evaluate perceptual quality.  
Figure 5: Runtime vs. model complexity for various low-light image enhancement methods. Our proposed model achieves a favorable trade-off, delivering the highest frame rate with a compact parameter count, thereby demonstrating its suitability for efficient deployment on edge devices. Here, the following acronyms/abbreviations are used to link with the corresponding references: SID [11], Retinex-Net [8], RetinexDIP [10], Zero-DCE [9] and [PERSON] et al. [13].
# Triangular Sierpinski Microwave Band-Stop Resonators for K-Band Filtering
## 4 Design of the 20 GHz Coupled Hexagonal Resonators  
From the good coupling conditions predicted for the single resonators, coupled structures were designed concerning the symmetry criterion for the entire configuration. Three  
Figure 8: Configuration C0C1 (**a**), made using a C0 triangle interfaced with a C1 one, and its predicted performance (**b**) compared to the pure C0 configuration. Both transmission (S\({}_{21}\)) and reflection (S\({}_{11}\)) parameters are drawn to give evidence for the expected electrical matching of the resonator.  
coupled resonators, from C0 to C3, were considered, mirroring the structure with respect to the central conductor of the CPW, obtaining four hexagons with different levels of complexity in this way. The simulated structures and their transmission response are shown in Figures 9 and 10.  
Understanding the spectrum of the hexagonal resonators presented in Figure 10 is not a trivial task, because it is evident that the original response of the individual structures has been significantly modified by the coupling among them, and even C0 does not reflect the single resonator response. At this stage, we left the boundaries and the CPW design unaltered in order to make a direct comparison with the previously predicted results. The combination of resonators based on the C1 and C3 geometries is favored for good electrical coupling and, in principle, for the possible choice between two possible bandwidth values. The above results confirm that understanding the coupling mechanisms is very important, analogously to the single resonator design, to obtain a structure potentially useful for improving the electrical matching of the entire structure; i.e., to obtain a deeper notch or specific bandwidths. This result was achieved without further optimizations only in the case of the C1 and C3 structures, with absorption peaks at least 10 dB deeper than those shown in Figure 7 for the individual resonators.  
Figure 9: Hexagonal resonators obtained by combining three coupled triangles mirrored with respect to the central conductor of the CPW. All the possible internal complexity levels were designed up to the C3 configuration.
# An Accelerating Approach for Blockchain Information Transmission Based on NDN
## 2 Theoretical Support
There are usually two naming methods, hierarchical and flattened, and forwarding is based on the longest name prefix matching. According to the forwarding model of NDN, we can see that NDN supports multicast delivery and content distribution. The forwarding model of NDN is shown in Figure 1.  
There are several advantages to choose the NDN network to support blockchain-based applications. Firstly, the NDN network is a distributed network with the key of supporting content distribution naturally, and the blockchain-based applications will also generate a large amount of data distribution and has a distributed structure Secondly, the NDN network has a content caching mechanism, and each node could distribute the information through CS to reduce overhead. Finally, the state of the NDN network node is also updated at all times. This feature coincides with the content of the network node required by the blockchain network, and could reduce the redundant data synchronization overhead, which accelerates the speed of information synchronization between blocks. However, there are some differences between the NDN and the blockchain network. The core communication mechanism of NDN belongs to the pull type, which means the consumers send interest packets to the network proactively, and then the producers send back the data packets to the consumers. While in the blockchain network, the miners actively push information to other nodes in the network. In response to this problem, we refer to our previous research. [PERSON] [3] introduced the concept of PPIT, and improved the transmission architecture of the NDN network, so that we can deploy the blockchain-based applications on the NDN network.
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 4 Simulation Results  
In this section, we use Monte Carlo Simulation to verify the correctness of our derived closed-form expressions of the system outage probability and throughput. By fixing some experimental parameters, changing the time assignment factor, the total transmit power of the source node, the Rice factor, the minimum required transmission rate of the system, and the energy conversion efficiency, we discuss the effects of various parameters on the outage probability and throughput of the system. In the simulations, the first hop distance \(d_{\text{SR}}\) and the second hop distance \(d_{\text{RD}}\) are expressed as \(d_{1}\) and \(d_{2}\). We simulate two cases, one is with the equal distance, i.e., \(d_{1}=5\) m, \(d_{2}=5\) m, another is with the unequal distance, i.e., \(d_{1}=4\) m, \(d_{2}=7\) m. The experimental results show that the two cases of distance have different effects on the system performance under different parameters.  
The icon \"Ana\" devotes the result of theoretical analysis and is represented by discrete points in the figure. The icon \"Sim\" represents the results obtained by the Monte Carlo Simulation, which is represented by a solid line in the figure. The Monte Carlo method is usually used to randomly generate the channel coefficients to account for the outage probability. For example, we realize \(10^{6}\) times and then count the times of system outage to obtain outage probability. If the numerical result derived by our simulations is consistent with the result obtained by the Monte Carlo simulation, it means that the closed solution is accurate.
## 3 Results and Discussion
### Characterization of Nanogold Composite NGC
#### 3.1.3 Transmission Electron Microscopy (TEM)  
The utilization of TEM measurement provides advantages in visualizing the morphology and structure of individual nanocomposites resulting from the combination of Au\({}^{\text{o}}\)@PPh\({}_{2}\)-PIILP and PET-NF. Figure 4 serves as evidence confirming the successful formation of the composite. NGC composites exhibit distinct morphologies and decrease in particle size for each specimen due to an increased quantity of gold particles (Figure 4a); this characteristic may also be linked to the polyphenolic composition of PPh\({}_{2}\)-PIILP [42]. A higher quantity of MB absorbed onto NGC reveals heterogeneous, uneven particles of varying sizes and shapes. (Figure 4b).  
Figure 3: FE-SEM of (**a**) Au\({}^{\text{o}}\)@PPh\({}_{2}\)-PIILP; (**b**) PET-NF; and (**c**) NGC.  
Figure 2: FTIR spectra obtained both before and after adsorption of MB by NGC.
## 5 Conclusions  
The results from this three-year study indicate that high-resolution multispectral satellite imagery (WV2 and WV3) coupled with targeted field sampling can be used to accurately determine the spatial variability of yield (kg\(\cdot\)tree\({}^{-1}\)) and average fruit size (g) per tree across an avocado orchard. Although, the relationship between canopy reflectance properties (VIs) with total fruit yield (kg\(\cdot\)tree\({}^{-1}\)) was largely variable, the positive relationship to fruit size (g) per tree remained consistent. This result, offers great benefit to avocado growers as the largest fruit can be better located and then selectively harvested at the start of the season when prices are high. This offers efficiencies in time, fuel costs and labour. In terms of yield, the estimation accuracies achieved from satellite imagery for all five blocks in 2016 and 2017 far exceeded those derived from the 'eyeball' method of yield estimation. Although RENDVI1 was identified as the best algorithm for yield estimation across all blocks, the relatively low coefficient of determination indicated growing location and season were influencing the relationship. To further assess the robustness of this methodology, additional growing locations and growing years with different varieties are warranted.  
The authors of this paper would like to acknowledge the Federal Government-funded Rural Research and Development for Profit Scheme and Horticulture Innovation Australia Ltd. for funding this research, as well as Avocado Australia Ltd. for their support. Also, [PERSON] (Simpson Farms), [PERSON] (Auschilli), and [PERSON] for their collaboration with this research. [PERSON], [PERSON], [PERSON], and [PERSON] (University of New England) and [PERSON] (Department of Agriculture and Fisheries Queensland) for assistance with in-field sampling.  
[PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] analysed the data and wrote the paper.  
The authors declare no conflict of interest.
## 5 Optimization of the Three-Layer One-Segment Tube
### Classical FEM-Based Optimization Procedure  
The average time necessary for FEM computations to find the first ten natural frequencies for a six-meter-long, three-layer non-symmetric laminate tube fixed at one end reaches approximately 13.9 s on a 4-core system with Intel(r) Core(r) i5-4570 [EMAIL_ADDRESS] GHz and 8 GB RAM. This, of course, depends very much on the adopted FE mesh and the applied numerical procedure. However, it can be accepted as a basis for further consideration. The problem of maximization of the fundamental natural frequency \(f_{1}\) needs the objective function, that is, \(g(\boldsymbol{\Lambda})=-f_{1}(\boldsymbol{\Lambda})\), to be solved about 180 times on average. The parameters of the applied interior-point optimization algorithm (see [34; 42]) are as follows: no constraints, no gradient nor Hessian supplied, maximum number of iterations equal to 100, 50 repetitions with a unique random starting point, and each calculation of the objective function needs an FE call. The exact number of objective function evaluations varies from 4 to 592, and the time necessary to perform \(f_{1}\) maximization reaches an average of 25 min. However, for a particular case, it varies from 33 to 85 min (the time covers the entire optimization procedure, including all necessary FE simulations).  
Each random initiation of the procedure leads to a different solution (and probably to a different local minimum). For 50 multi-start random initiations, the best-obtained result is \(f_{1}=37.786\) Hz for lamination angles \(\boldsymbol{\Lambda}^{*}=[50.9/-4.5/77.7]\). The overall time necessary to repeat the procedure 50 times and the number of FE calls are 21 h and 9061, respectively. The result of calculations involving classical, derivative-based optimization methods and FEM to obtain the objective function value is a reference point for the proposed GA+ANN optimization procedure. The genetic algorithm needs the objective function to be computed thousands of times, and for the maximization of \(f_{1}\), the time necessary to obtain the results using FEM to calculate the objective function value would be 28 days. Obviously, this is an unacceptable time investment.  
In order to make the GA procedure applicable with limited time constraints and typical computer hardware, the evaluation of natural frequencies was not done through FEM. Instead, neural networks were applied since, in contrast to FE simulations, a trained neural network is capable of giving its prediction almost instantly.
## 2 Literature Review
### Anns  
Neural network technology was developed by [PERSON] in 1993 [28; 29]. The main features of an ANN are that it can mimic the self-learning and organization ability of the human brain, it is capable of handling incomplete data, and can solve complex and ill-defined problems. The basic computational units of the neural network are referred to as nodes, and nodes are connected in three layers: the input, output and hidden layers. The hidden layer extracts important features contained in the input data [28]. In Kansei engineering, ANNs have been used to determine relationships between product design attributes, such as size, shape and color, and also identify brand and consumer emotional responses in order to make Kansei predictions.  
A few studies have illustrated the use of ANNs in the product design field. [PERSON], [PERSON] and [PERSON] proposed a conceptual design method that used fuzzy ANN-based algorithms, integrating product form and color to predict consumers' evaluation of the product's image [30]. Recent study regarding sports-shoe appearances was published by the author of this study, PLS partial least squares and ANN were used to developing a NN-based Kansei prediction system [31]. [PERSON] used ANNs to determine the optimal combination of product form and product color [32]. An ANN model was also used to examine the complex relationship between web page design elements and users' feelings, ultimately building a web page design support database [33]. [PERSON] et al. established an ANN model to relate the design parameters of a new product to its perceptual value, and used a mobile phone design as a case study [34].  
Once trained, an ANN can perform predictions and generalizations at high speeds, and the root-mean-square error (RMSE) is used to analyze model prediction accuracies [35]. Compared with linear modeling techniques, such as multilinear regression and PLS, ANNs are superior as a modeling technique for molecular descriptor data sets showing nonlinear conjunction, and thus for both data fitting and prediction strengths [36]. Among the different learning algorithms, backpropagation uses the concept from the method of steepest descent, a gradient method, to minimize the error function; this makes backpropagation the most common way of training ANNs. The industrial design applications of ANNs typically focus on feature selection, classification and product image prediction. They can manage large-scale investigations of nonlinear data, such as product design attributes or consumer emotional responses to provide consumer Kansei reaction predictions, sort and categorize product attributes, and perform grouping and regression [37].  
However, ANNs have a number of shortcomings, such as their low learning rate [38] and difficulty determining the structure of hidden layers when devising a suitable network structure [39]. It is also not easy to achieve the global optimal solution [40], initialize the network's weights, or explain the training results.
## 4 Emerging Lightweight Security Approaches
### Symmetric Encryption--Lightweight AES Approaches
#### 4.1.5 Lightweight Masked AES Implementation  
Hardware-level attacks on IoT devices happen when a malicious user obtains access to the physical device, i.e., the embedded system with its FPGA modules and microcontrollers. The malicious user then performs a side channel attack (SCA) in order to obtain the encryption key or the plaintext. SCA exploits the physical properties of the device, such as power dissipation, delays, temperature, or electromagnetic emissions. When power dissipation is exploited, this attack is called a correlation power analysis (CPA). For AES encryption, CPA attacks may target the S-Box circuitry used in the SubBytes transformation. The lightweight masked AES approach proposed in [80] aims to prevent the power dissipation analysis by a malicious user. This scheme is illustrated in Figure 7C.  
In general, CPA attacks can be prevented using the idea of a masked AES. It means that some random data are added during the encryption process. This hides the real values of the plaintext blocks and the AES state matrix as they go through the substitution box (S-Box). The traditional masked AES approach is to add random data to the plaintext before the first round of encryption. However, [PERSON] et al. [80] proposed to add fixed random data to each round key, so that every round will have a false key. These random data are called intermediate data I\({}_{\text{c}}\) and the false round keys are called K\({}_{\text{f0}}\), K\({}_{\text{f1}}\),... K\({}_{\text{fm}}\), where \(m\) is the number of rounds. Usually, \(m\) is ten rounds for a 128-bit AES key. Thus, in Table 7, this scheme is classified as a change in the round key schedule. Although the number of rounds is the same, they adapted the key by adding the intermediate data I\({}_{\text{c}}\) to the key in each round.  
In addition, when the last encryption round ends, there is a reconstruction circuit to remove the masked data and recover the correct ciphertext. The proposal in [80] created a reconstruction circuit using a new type of XOR gate that is based on a technology calledWave Dynamic Differential Logic (WDDL) [90], which helps to prevent a CPA attack. WDDL gates can be implemented using standard gates, so they can be used in either FPGAs or dedicated ASICs. With WDDL, the signal transitions consume the same amount of power; therefore, it becomes harder for attacks to inspect the signals and the operations they go through by looking at the device's power consumption.
# Material Removal Model for Lapping Process Based on Spiral Groove Density
## 2 Materials and Methods
### Material Removal Model according to Indentation Theory
During lapping, abrasives are dispersed in a slurry and supplied directly over the platen to remove the workpiece material between the platen and wafer; only the abrasive particles in the land area where the actual wafer comes into contact with the platen participate in lapping. Therefore, it is assumed in the model that abrasives are uniformly distributed over the land of the platen, and the number of active abrasives participating in  
Figure 3: Abrasive indentation model. lapping, \(N_{a}\) [ea], is proportional to the land area of the platen in contact with the wafer, as expressed in Equation (7):  
\[N_{a}=k_{a}\cdot C_{a}\cdot A_{c}, \tag{7}\]  
where \(k_{a}\) [mm], \(C_{a}\) [ea/mm\({}^{3}\)], and \(A_{c}\) [mm\({}^{2}\)] represent the participation coefficient of the abrasives, concentration of abrasive, and contact area, respectively. The contact area in this model is proportional to the groove ratio changing with pitch, as expressed in Equation (8):  
\[A_{c}=A_{w}\cdot d_{g}, \tag{8}\]  
where \(A_{w}\) [mm\({}^{2}\)] is the area of the wafer and \(d_{g}\) is the groove density. Therefore, the volume of material removed per unit time, \(MRR_{V}\) [mm\({}^{3}\)/min], as a function of the groove density in the lapping process is expressed as shown in Equation (9):  
Figure 2:0  
The thickness of the material removed per unit time, \(MRR\) [um/min], can be obtained by dividing \(MRR_{V}\) by the area of the processed wafer. Figure 4a,b shows the number of active abrasives and the material removal rate as a function of the groove density, respectively, calculated using the material removal model. As the groove density increases, the land area in contact with the wafer decreases, and the number of active abrasives participating in the lapping process decreases linearly. The material removal rate of a single abrasive is found to increase proportionally with the groove density because the load on a single abrasive increases with the decreasing number of active abrasives. As seen in Figure 5, the material removal rate of all abrasives increases proportionally with the groove density because the increase in the material removal rate of a single abrasive with groove density is more significant than the decrease in the number of active abrasives in lapping. The simulation results were verified experimentally by measuring the material removal rate based on the changes in the groove density, and the contact interface condition between the wafer and platen was observed by monitoring various signals. Figure 2:1 (**a**) Theoretical analysis result of number of active abrasives; (**b**) theoretical analysis result of material removal rate by one abrasive as a function of groove density.