esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments  
### Datasets  
To verify the effectiveness and benefit of the proposed AGCHK for attributed graph clustering, we conduct experiments on four benchmark datasets. The dataset details are demonstrated in Table 1. Cora, Citeseer, and Pubmed [4] are citation networks whose nodes represent documents and edges are citation links. Wiki [17] is a webpage network, whose nodes are webpages and edges are link relations. The node features of Cora and Citeseer are binary word vectors, and the node features of Pubmed and Wiki are computed by the term frequency-inverse document frequency.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### Performance Metrics Analysis
#### 4.3.1 Throughput (TH) Optimization  
* Bulk service (A) allows multiple energy demands to be processed at once, maximizing throughput;
* TH ranges between [1.36, 16.015], with peak values achieved when bulk service is dominant;
* The highest throughput is observed in Cost Set III, as solar-powered BTS can operate continuously without fuel constraints.
# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
## 2 Channel Model
### Neural Network Model  
A neural network, as a tool with self-adaptive processing capability, forms internal rules by training a large amount of data. It can achieve goals of classification, identification, and prediction of new data with the signal processing time shortened. Then, the overall time latency of the system is reduced. As the most commonly used network, the convolutional neural network (CNN) plays an important role in image processing, object recognition, emotion recognition, and so on [22; 23]. It also serves as an auxiliary tool to assist research in other fields, such as the metamaterial [24].  
The CNN network used in this work contains one input layer, seven convolution kernels, one discarding layer, two fully connected layers, one activation layer, and one output layer as in Figure 1a. Each convolution kernel consists of one convolutional layer, one normalization layer, one nonlinear activation layer, and one pooling layer. There are 64 filters used in each convolutional layer. The input and pooling layers are designed with size matrices of \(2\times 1024\times 1\) and \(1\times 2\), respectively. A NVIDIA Geforce RTX 2060 GPU is used to train the CNN network with an Adam optimizer employed. The regularization parameter and the initial learning rate are both set to be 0.0001. To weaken the overfitting phenomenon, the fading period and fading factor are set and listed in Table 2.  
The long short-term memory network (LSTM) network is a new type of network based on the evolution of time series models. It has a better memory mechanism and can make a better usage of past and future data. It is more suitable for object recognition based on time series data instead of image data by CNN. The LSTM architecture consists of one sequence input layer, six bidirectional LSTM layers, six normalization layers, six nonlinear activation layers, one discarding layer, one fully connected layer, one activation layer, and one output layer as shown in Figure 1b. Since the input of the LSTM network is a data sequence, the multidimensional array should be transformed to be 3-dimensional before inputting. The construction and training of neural networks are carried out on MATLAB R2021b.  
For the network training of LSTM, the same GPU environment and optimizer are used. Due to the different allocation strategies of CNN and LSTM to data weights, different parameter settings are adopted to weaken overfitting as shown in Table 2. In terms of learning parameter settings, the initial learning rate of LSTM is 0.0001. In particular, due to their difference in network construction and total number of parameters, the overfitting cannot be mitigated with an identical regularization parameter. It is set to be 0.00001 in LSTM network with one order of reduction compared to CNN, which helps to avoid overfitting. In addition, since the LSTM does not need such a large number of training rounds as CNN, no fading factor is set in the training process.
## 4 Discussion
### Land Degradation Due to Human Activities or Effects of Rainfall: RESTREND Method  
The RESTREND of the NDVI revealed that LD in the GSDM was mainly due to rainfall. [PERSON], [PERSON] and [PERSON] [29] identified the RESTREND method as an effective technique for determining the drivers of vegetation degradation. The result of this study agrees with other studies that report that vegetation dynamics in arid or semi-arid regions are very sensitive to changes in rainfall [27; 58]. Furthermore, the district was affected by severe droughts in 2002-2004, 1992, and 2015 [59; 60], making rainfall the key climate variable affecting vegetation productivity.  
The areas where the RESTREND recorded a significant decline are in the central to western parts of the district (Ephrain Mogale and Makhuduthamaga local municipalities). This implies that the degradation process that occurred mainly in Ephrain Mogale and Makhuduthamaga local municipalities is the result of human activities, as described in the GSDM Integrated Development Plan [12]; such as livestock overgrazing, rural settlement, agriculture, unsustainable fuel wood harvesting and land tenure conflicts. The RESTREND magnitude showed a significant increase of 9.52% in the northern to eastern part of the district in the Fetakgomo Tabatse local municipality, implying that vegetation has improved for other reasons besides rainfall.  
Insignificant positive RESTREND values in Fetakgomo Tabatse local municipality and some southern parts of Elias Motsoaledi local municipality mean that vegetation production in these areas is improving due to rainfall. Although rainfall has declined in these areas, the reason for the improvement in vegetation can be attributed to bush encroachment and alien invasive species, which are indicators of LD [13; 55]. When [PERSON] et al. [15] analysed the occurrence of bush encroachment in South Africa, increasing significant NDVI trends were observed. [PERSON] et al. [15] showed that rainfall had the greatest influence on four of the significant variables (i.e., cattle density, carbon dioxide, soil moisture and fire occurrence) identified to explain bush encroachment in South Africa. [PERSON] et al. [14] also reported that communal rangelands experienced the greatest increase in bush encroachment, which doubled in low-rainfall areas.  
The RESTREND results show that majority of the district (53%) is facing LD. However, the 41.41% of the district that had an insignificant negative RESTREND could mean that LD is due to a combination of human and climatic factors. Other studies have reportedthat the RESTREND method has shown that LD is the result of an equivalent combination of climate variability and human activities [61; 62]. Therefore, it was important in the study to use a systems analysis to effectively identify the human and climatic factors driving LD and their interconnectedness, and to propose intervention measures.
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric
### Structural Similarity  
The FR metric SSIM [1] compares luminance, contrast and structure of distorted image \(x\) and pristine reference image \(y\), i.e.,  
\[SSIM(x,y)=[l(x,y)]^{\mathbf{\alpha}}\left[c(x,y)\right]^{\mathbf{\beta}}[s(x,y)]^\mathbf{{\gamma}} \tag{1}\]  
where \(l(x,y)\), \(c(x,y)\) and \(s(x,y)\) respectively represent luminance, contrast and structure comparison functions. \(\mathbf{\alpha}>0\), \(\mathbf{\beta}>0\) and \(\mathbf{\gamma}>0\), which are parameters to adjust the relative weight of the three components.  
Weber's Law [21] indicates that the magnitude of a just-noticeable luminance change \(\triangle I\) is approximately proportional to the background luminance \(I\) for a wide range of luminance values. Thus the luminance comparison function is defined as  
\[l(x,y)=l\left(\mu_{x},\mu_{y}\right)=\frac{2\mu_{x}\mu_{y}+C_{1}}{\mu_{x}^{2}+ \mu_{y}^{2}+C_{1}} \tag{2}\]  
where \(\mu_{x}=\frac{1}{N}\sum_{i=1}^{N}x_{i}\) and \(\mu_{y}=\frac{1}{N}\sum_{i=1}^{N}y_{i}\) (\(N\) is the total number of image pixels. \(x_{i}\) and \(y_{i}\) are single pixels in \(x\) and \(y\)) represent the mean intensity of image \(x\) and image \(y\) respectively. \(C_{1}\) is a positive constant to avoid instability when \(\mu_{x}^{2}+\mu_{y}^{2}\) is very close to zero. \(C_{1}=(K_{1}L)^{2}\), \(K_{1}\ll 1\) is a small constant, and \(L\) is the dynamic range of the pixel values (e.g., 255 for 8-bit grayscale images).  
Similarly, the contrast comparison function is  
\[c(x,y)=c\left(\sigma_{x},\sigma_{y}\right)=\frac{2\sigma_{x}\sigma_{y}+C_{2}}{ \sigma_{x}^{2}+\sigma_{y}^{2}+C_{2}} \tag{3}\]  
where \(C_{2}=(K_{2}L)^{2}\), \(K_{2}\ll 1\), and \(\sigma_{x}=\left(\frac{1}{N-1}\sum_{i=1}^{N}\left(x_{i}-\mu_{x}\right)^{2} \right)^{1/2}\) and \(\sigma_{y}=\left(\frac{1}{N-1}\sum_{i=1}^{N}\left(y_{i}-\mu_{y}\right)^{2} \right)^{1/2}\) are standard deviations of \(x\) and \(y\), respectively.  
The definition of structure correlation function is  
\[s(x,y)=s\left(\frac{x-\mu_{x}}{\sigma_{x}},\frac{y-\mu_{y}}{\sigma_{y}}\right) =\frac{\sigma_{xy}+C_{3}}{\sigma_{x}\sigma_{y}+C_{3}} \tag{4}\]  
where \(C_{3}\) is a small positive constant to avoid instability when \(\sigma_{x}\sigma_{y}\) closes to zero, and \(\sigma_{xy}=\frac{1}{N-1}\sum_{i=1}^{N}\left(x_{i}-\mu_{x}\right)\left(y_{i}- \mu_{y}\right)\).
## 2 Materials and Methods
### Finite Element Method (FEM)  
The accuracy of results proved to be strongly dependent on the convergence testing process and the mesh quality [33; 34; 35]; thus, the mesh element size chosen for this research was established at 0.5 mm of tetrahedral elements in the part of the screw and bone. Contrarily, on the lower surface of the abutment, a mesh refinement was performed in order to achieve a more accurate distribution of Von Mises stress. This area is considered critical when the implant is subjected to inclined loads, as supported by existing literature [35]. Mesh refinement can be useful in solving problems that require more precise details of the model or involve high stress gradients, such as analyzing complex structural components or simulating fluid flows. However, it is important to note that excessive mesh refinement can lead to longer computation times and increased complexity in managing the results. Therefore, it is essential to balance the mesh refinement based on the problem specifications and available computational resources to obtain reliable and efficient results. In this case, a mesh refinement was applied, resulting in a reduction of the element size to 0.3 mm in the abutment portion (Figure 4).  
The friction generated between the abutment and the dental implant is an important factor to consider when designing and installing a dental implant. Indeed, the final goal is obtaining adequate stability and tightness between the abutment and the implant, in order to guarantee the absence of micromovements at the implant-abutment connection zone and a long-term implant stability. An appropriate level of friction between the abutment and the implant is desirable to avoid those micro-movements that could compromise the integrity of the interface, prevent the loss of the prosthetic material and the abutment mobility, as well as reduce the risk of bacterial infiltration by creating a suitable bacterial seal. A frictional coefficient of 0.3 was set as the implant-abutment contact condition in the present FEA study (Figure 5). Between the implant and the bone, on the other hand, a fixed type of contact was considered, without any type of micromovement. This hypothesis is validated from the moment in which the osseointegration between the two surfaces occurs. The software used for the FEA simulation was Ansys Workbench (Canonsburg, PA, USA). The application of FEA to dental implants raises important ethical considerations related to scientific validity, informed consent, respect for the limits of the method sharing of results, and benefit to patients.  
Figure 4: Mesh size: 0.5 mm and 0.3 mm.
# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
###### Abstract  
Recently, end-to-end light field image compression methods have been explored to improve compression efficiency. However, these methods have difficulty in efficiently utilizing multi-domain features and their correlation, resulting in limited improvement in compression performance. To address this problem, a novel multi-domain feature learning-based light field image compression network (MFLFIC-Net) is proposed to improve compression efficiency. Specifically, an EPI-based angle completion module (E-ACM) is developed to obtain a complete angle feature by fully exploring the angle information with a large disparity contained in the epipolar plane image (EPI) domain. Furthermore, in order to effectively reduce redundant information in the light field image, a spatial-angle joint transform module (SAJTM) is proposed to reduce redundancy by modeling the intrinsic correlation between spatial and angle features. Experimental results demonstrate that MFLFIC-Net achieves superior performance on MS-SSIM and PSNR metrics compared to public state-of-the-art methods.  
light field image; end-to-end compression; multi-domain feature learning +
Footnote †: journal: Applied sciences
## 6 Open Research Issues and Perspectives
Although plasma assisted-ALD allows a superior control on the uniformity of SiN\({}_{x}\)[63] and AlN [64] thin films, the main issue is the plasma-induced damage or doping in graphene. Optimized ALD approaches for the growth of these materials on graphene are envisaged for the next years. Hexagonal boron nitride (h-BN) is an insulating layered material with lattice structure similar to graphite. Due to the atomically sharp interface formed between h-BN and graphene, it is considered as the ideal insulator to achieve the intrinsic graphene mobility [65]. However, due to its low dielectric constant (k \(\approx\) 4), the h-BN interfacial layer must be combined with a high-k dielectric overlayer in order to reduce the effective oxide thickness (EOT) for realistic integration in graphene field effect transistors. Recently, the direct ALD growth of a high-k insulator (Y\({}_{2}\)O\({}_{3}\)) on the sp\({}^{2}\) h-BN surface has been demonstrated, and the deposition mechanism was explained by enhanced adsorption of the Y precursor on h-BN due to the polarization [66]. The challenge in this research field is the large area growth of h-BN on graphene by scalable approaches. The growth of MoS\({}_{2}\) and other TMDs on EG is currently explored for the realization of 2D materials Van-der Waals heterojunctions on large area [67]. In this context, ALD can represent a valid alternative to the most employed CVD approaches. Dedicated studies on the selection of the precursors and on ALD deposition conditions are expected in the forthcoming years in this emerging research field.
# Local Differential Privacy-Based Federated Learning under Personalized Settings
## 5 Experiments
### Experiments on Independent Identically Distributed Data
#### 5.2.2 Results Discussion  
The experiment results are shown in Figures 2-4. We denote the arithmetic average method as 'A'. We denote the weighted average method as 'B'. We denote the probability-based selection method as 'C'. In the non-personalized scenario, as we can see from Figures 2a, 3a, and 4a, when all clients set the same privacy budget, the larger the privacy budget, the higher accuracy of the federated model. The accuracy is highest when Gaussian noise is not added, because the local models from clients are not perturbed. When clients do not add Gaussian noise to the local model, the federated model accuracies for those three datasets are 88.7%, 73.1%, and 49%, respectively, and this shows that for IID data, the LDP-based federated framework is more applicable and effective for the MNIST and Fashion-MNIST datasets, than for the forest cover-types dataset.  
In the personalized scenario, as can be seen from Figures 2b,c, 3b,c, and 4b,c, when more clients set large privacy budgets, the accuracy of the federated model increases. In addition, it is worth noting that under the same setting of privacy budgets, the accuracy of the probability-based selection method performs best among the three model aggregation methods, and both the probability-based selection method and the weighted average method perform better than the arithmetic average method. Compared with the arithmetic average method, those three datasets obtain accuracy gains in the weighted average method and the probability-based selection method. For the MNIST dataset, the accuracy gains obtained by the weighted average method and the probability-based selection method are 5.57% and 11.28%, respectively. For the Fashion-MNIST dataset, the accuracy gains obtained by the weighted average method and the probability-based selection method are 3.04% and 3.93%, respectively. For the forest cover-types dataset, the accuracy gains obtained by the weighted average method and the probability-based selection method are 4.07% and 10.90%, respectively. In general, both the weighted average method and the probability-based selection method perform best on the MNIST dataset.
# CFD Analysis and Validation of a Foreign Material Winnowing Machine for Pepper Harvester
###### Abstract  
The winnowing machine of chili pepper harvesters was developed to reduce the potential problem of low pepper stem and fruit separation. The developed winnowing machine was combined with two impellers and a center bearing to prevent a strain on the drive shaft and to ensure durability. The terminal velocity of chili pepper was measured, and an aerodynamic analysis was conducted based on this winnowing machine. A CFD (Computational Fluid Dynamics, Ansys Fluent 2020 R1) analysis was conducted for three levels of discharge port guide form (0, 3, and 5 guides) and three levels of rotating speed (1600, 1800, and 2000 RPM) of a winnowing machine designed utilizing aerodynamic analysis results. A validation test was conducted by fabricating a winnower test device. As for aerodynamic analysis conducted using measured values of terminal velocity, chili pepper fruits were collected at an outlet wind speed lower than 17.5 m/s and chili pepper branches were separated at a speed higher than 12.5 m/s. As a result of CFD analysis, the wind speed deviation at outlets of the 0-, 3-, and 5-guide depending on the rotating speed appeared to be 15.8, 1.4, and 1.0 m/s on average, respectively. The result of the CFD analysis showed values higher than wind speeds of the actual winnower test device by a minimum of 0 and a maximum of 2.4 m/s. Through the CFD analysis and the wind speed validation test of the winnower test device, optimal conditions to separate foreign materials were found to be a winnowing machine at a rotating speed of 1800 RPM with a discharge port having three guides or a winnowing machine at a rotating speed of 2000 RPM with a discharge port having five guides.  
CFD (Computational Fluid Dynamics); terminal velocity; winnowing machine separation; chili pepper harvester +
Footnote †: journal: _sciences_  
_Applied_  
_Acticle_  
CFD Analysis and Validation of a Foreign Material Winnowing Machine for Pepper Harvester
## 4 Verification Test Results  
### Test Materials and Devices  
The verification test bench was built in September 2022. The main instruments used were as follows: a sensor (Beijing Xinyuhang Measurement and CT Co., Ltd., model JN338-A); a torque speed measuring instrument (Beijing Xinyuhang Measurement and CT Co., Ltd., model JN338); a transport motor (Beijing Times Superior Electrical Technology Co., Ltd., model SD110 AEA12020-SH3, rated power 1.2 kW, rated speed 2000 rad/min, rated moment 6 N/m); a cutting motor (Beijing Times Superior Electrical Technology Co., Ltd., model SD110 AEA12020-SH3, rated power 1.5 kW, rated speed 1500 rad/min, rated moment 10 N/m); and a driver (Beijing Times Superior Electrical Technology Co., Ltd., model SD300-30 AL-GBN).  
As shown in Figure 8, the verification test was carried out on the test bench. A 10A single-sided double-hole bending plate chain was selected as the stalk sample conveyor chain. The disc cutter on one side was coaxial with the cutting motor and torque sensor through the detachable flange. The disc cutter on the other side was coaxial with the dial teeth chain and the cutting motor. The torque measuring instrument was connected to the sensor and communicated with the computer in real-time through the RS232 serial port. During the test, the drivers were firstly used to drive the cutting motors to the designed rotation speed, and then the other driver was used to drive the feed motor to clamp and advance the samples at the set speed until the cutting process was completed. With this test device, the disc cutter could be changed and the feeding and cutting rotation speeds could be adjusted and measured. The maximum cutting torque of the disc cutter was obtained by using the torque measuring instrument to study the maximum force in the cutting process.  
The castor cultivar was Israel Kaifeng No.5. After the cutting test, the cut part of the stalk was used as for the determination of the physical properties. The average diameter of the castor stalk at 150 mm from the ground was 21.6 mm.
## 2 Modelling
### Interactions with the Boundaries and Other Elements  
The interaction of the ray with boundaries is governed by specific equations which depend on the type of boundaries. The most common and relevant for this work are walls, material discontinuities, mirrors, inlets and ray detectors. Walls have different possibilities. The ray may freeze, stick, disappear, have a specular reflection, have a diffuse scattering, pass through, etc. The disappear function is used to remove from the simulation the rays which touch the walls. The freeze function retains the ray position and direction (since they no longer change over time), while stick retains the ray position but resets all other variables to zero. The other wall functions are general cases of the following more specific boundary conditions. Material discontinuities are used to simulate [PERSON]'s law when a ray passes from one material to another. In this case, it is possible to require that both the refracted and reflected rays are generated (the incident ray becomes the refracted while the reflected is a new ray, called secondary). Mirrors specularly reflect the rays at the boundary. Inlets allow releasing rays with different properties. It is possible to set any ray feature, such as release time, the number of rays, initial direction, ray frequency and wavelength, initial phase, initial intensity and polarisation, and the initial values of the auxiliary variables. The ray detector returns a Boolean variable which describes if the ray has reached the ray detector, mostly used for post-processing. The boundary condition which involves a continuation of the ray propagation, such as material discontinuities and mirrors, allows defining the value of auxiliary variables after the interaction with the boundary condition. For example, the reduced Stokes vector components after the interaction with a mirror are defined by the incident Stokes vector components and the Mueller matrix of the mirror.