esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Relatability of SEM to Assess Cause-Effects on \(R_{\rm S}\)  
Structural equation models (SEM) provide a scientific framework to represent direct and indirect cause-effect relationships from highly related variables and test multivariate hypotheses about multiple ecological processes ([PERSON], 2017; [PERSON], 2018). The SEM presented in this study shows the multiple causal relationships that control the variability of R\({}_{\rm S}\) from a tropical dry forest (TDF) in northwestern Mexico and allowed us to discern the relative importance among biophysical controls at individual sites and across the succession gradient. These models underline how the paths change among the TDF successional stages, highlighting the greater complexity of interactions in an old-growth forest (OG) compared to early- (ES) and mid-secondary (MS) sites.  
Furthermore, the path model performance for the OG, MS and ES sites showed a good fit since models explained a high proportion of the variance of R\({}_{\rm S}\) (ES, R\({}^{2}\) = 0.76; OG, R\({}^{2}\) = 0.72; MS, R\({}^{2}\) = 0.68). These estimates are comparable with previous studies in temperate, tropical, subtropical, arid and semiarid ecosystems that tested significant cause-effect interactions among different biophysical controls and R\({}_{\rm S}\). For example, [PERSON] et al. (2018) observed R\({}^{2}\) = 0.79 in a steppe, and [PERSON] et al. (2018) explained R\({}^{2}\) = 0.17 in a Mediterranean forest; meanwhile, for a humid subtropical forest, [PERSON] et al. (2018) reported R\({}^{2}\) = 0.63 and [PERSON] et al. (2018) showed R\({}^{2}\) = 0.42 and R\({}^{2}\) = 0.39 for tropical and subtropical forests and grasslands, respectively. In addition, [PERSON] et al. (2018) reported R\({}^{2}\) = 0.50 in a semiarid shrubland. Therefore, SEM is a robust modeling tool to account for the effects of key controls of R\({}_{\rm S}\) variability and create a functional framework depicting the interactions, mechanisms and processes in TDF soils from northwestern Mexico (2018).
## 2 Experimental Section
### Characterization and Measurement  
The phase composition of as-prepared composites was analyzed by an X-ray diffractometer (XRD, PANalytical, Almelo, Holland). The carbon states in the samples were investigated from the Raman spectra through a JY HR-800 Raman microscope (Horiba Jobin Yvon, Paris, France) by irradiation with a 532 nm laser. The morphologies and microstructures of the composites were observed by the field-emission scanning electron microscopy (SEM, HELIOS NanoLab 600i, FEI Company, Hillsboro, OR, USA), transmission electron microscopy, and high-resolution transmission electron microscopy (TEM and HRTEM, Tecnai G2-F30, FEI Company, Hillsboro, OR, USA). The densities of samples with different PIP cycle numbers were measured by the Archimedes method using a high-accuracy electronic balance, and the details for the measurement were reported elsewhere [4; 5]. The compressive behavior and strength were evaluated using an Instron 5569 universal test machine to acquire the mechanical properties of as-prepared composites, and the tested specimens with the dimension of 10 \(\times\) 10 \(\times\) 12 mm\({}^{3}\) were uploaded at a constant rate of 0.5 mm/min. Notably, the average value was used to improve the accuracy and reliability of the data, which came from at least five samples for each kind of sample obtained in the same condition. The thermal stabilities of as-obtained composites both in air and Ar atmospheres were analyzed by the thermogravimetric analysis (TGA, SDT Q600, TA Instruments, Wilmington, DE, USA) with a heating rate of 5 \({}^{\circ}\)C/min up to the temperature of 1000 \({}^{\circ}\)C. To investigate the functional performance, namely the EMW absorption property of the composites, the electromagnetic parameters of samples with different PIP cycle numbers were measured at 2-18 GHz using a vector network analyzer (Agilent N5230A, Agilent, Palo Alto, CA, USA) in a wax matrix with 50% weight loading. The samples were obtained from the composites homogeneously mixed with wax at 85 \({}^{\circ}\)C and then the homogeneous mixture was pressed into a toroidal mold with an inner diameter of 3.00 mm and an outer diameter of 7.00 mm. After they were taken out of the mold,the measured samples were obtained, and the reflection loss (RL) was obtained using the relative complex permittivity (\(\varepsilon_{\mathrm{r}}=\varepsilon^{\prime}-j\varepsilon^{\prime\prime}\)) and complex permeability (\(\mu_{r}=\mu^{\prime}-j\mu^{\prime\prime}\)) by our self-programmed software, following equations according to the line theory [24; 25; 26]:  
\[RL(dB)=20\lg\left[\frac{Z_{in}-1}{Z_{in}+1}\right] \tag{1}\]  
\[Z_{in}=\sqrt{\frac{\mu_{r}}{\varepsilon_{r}}}\mathrm{tanh}\left[j\frac{2\pi fd} {c}\sqrt{\mu_{r}\varepsilon_{r}}\right] \tag{2}\]  
In which, \(Z_{in}\) refers to the normalized input impedance of the absorber, \(d\) stands for the thickness of the tested sample, \(c\) represents the light velocity in free space, and \(f\) is the frequency of the microwave frequency, respectively.
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 3 Materials and Methods
### Model Training and Backpropagation  
We train the ViTGNN model using supervised learning, where the pre-trained CNN and GNN are used as a feature extractor, and only the Vision Transformer is fine-tuned during the final training stage for classification. The CNN extracts spatial features from input images, which are then structured into a graph representation for processing by the GCN. The GCN aggregates information from immediate and second-order neighbors to refine spatial relationships before passing the processed features to the ViT classifier for final classification. The GCN is trained on the CNN output, where CNN-extracted feature maps serve as initial node attributes. The CNN and GCN are pre-trained in the feature extraction stage to ensure meaningful feature representations. The CNN extracts spatial features, while the GCN enhances them through neighborhood aggregation. The ViT, initialized with pre-trained weights, is fine-tuned during training to classify the refined feature representation. Unlike node-level supervision, where each graph node has a distinct label, ViTGNN employs image-level labels, assigning a single label to the entire image (COVID-19 or non-COVID-19). The cross-entropy loss is computed at the ViT output layer, providing global supervision that indirectly guides both the CNN and GCN. Since the GCN does not have explicit node-level labels, it learns representations based on the overall classification objective. Gradients are propagated backward, optimizing parameters at each stage, and trainable weight matrices \(W\) in the GCN layers are updated using gradient descent, with the AdamW optimizer improving convergence and weight decay regularization, preventing overfitting. Batch normalization and dropout regularization are applied across the CNN and GCN layers to maintain stability and avoid overfitting. Also, early stopping monitors validation loss and terminates training when no further improvement is observed. In summary, the ViTGNN Algorithm 1 integrates CNN-GNN-based feature extraction with a ViT-based classifier, providing an effective approach for COVID-19 detection in CT scans. Image preprocessing involves CLAHE and Gaussian blur, followed by resizing and normalization. The dataset is divided into training, validation, and test sets, with a custom transformation pipeline. Initially, a CNN is used to extract low-level visual features, which are then passed to a GNN for spatial relationship modeling. Subsequently, these features are fused into a ViT for classification. The model is trained using cross-entropy loss, the Adam optimizer, and early stopping. After training, it is evaluated on test data using accuracy, precision, recall, F1-score, and AUC. This hybrid model captures detailed local features and broader image context, enhancing both accuracy and interpretability compared to traditional models.
## 2 Related Work
### Data Security and Privacy Methods  
Data privacy has been a significant challenge in decentralized systems. Encryption and hashing have been pivotal in securing data within the blockchain and IPFS frameworks. The literature on various encryption algorithms and hashing techniques studied by [15] shows the advancement in these methods in maintaining data integrity and security. Furthermore, ref. [16] focuses on the role of these techniques in preserving data privacy in decentralized systems and on the importance of a balance between accessibility and security. In [17], the authors delve into privacy-preserving approaches such as zero-knowledge proofs and homomorphic encryption. These studies emphasize the development of techniques that secure data without compromising privacy. However, these techniques can provide solutions for data security and privacy but often fall short in terms of balancing data security with system efficiency, such as computation overhead, scalability, and dynamic data handling. In addition, relying on encryption inadvertently exposes private keys during decryption processes, making it a substantial security risk. The work in [18] proposes a blockchain-based framework for privacy preservation and secure access control in cloud storage. The proposed scheme combines the Ethereum blockchain and ciphertext-policy attribute-based encryption (CP-ABE) for user data security and privacy. However, due to privacy issues and data leakage, the cloud might not be a trustworthy source for data storage.
## 2 Description of Highway Vehicle Clustering  
### RSU Deployment Scheme Based on Vehicle Clustering  
Since highways have a simple road environment and the vehicles traveling on them exhibit low density and high speed due to their speed limits, vehicle speeds on highways typically range from 60 to 120 km/h, and their direction of travel is fixed. Due to the high speed of vehicle movement, the temporal nature of traffic flow, and the limited coverage range of RSUs, vehicles spend only a short time within the coverage of a single RSU. Therefore, studying vehicle mobility on an individual vehicle basis is usually unreliable. Instead, vehicles are clustered, and the highway vehicular sensor network is studied at the cluster level. Communication within a cluster can be achieved through single-hop or multi-hop methods, while communication between different clusters is performed between clusters. It is found that when the RSU deployment scenario based on vehicle clusters adopts the deployment scheme shown in Figure 1, i.e., the RSU interval distance (D\({}_{\text{RSU}}\)) is equal to the sum of the vehicle cluster length and twice the RSU communication radius, i.e., D\({}_{\text{RSU}}\) = E(c) + 2R\({}_{\text{RSU}}\), this ensures that the vehicle clusters can always maintain communication with one RSU during the driving process, which can solve the network interruption caused by the short vehicle-cluster length and also reduce the energy consumption and mutual interference of the vehicle self-organizing network when the vehicle cluster length is too long or the RSU deployment is somewhat dense.
## 3 VRIS Image Steganography Model
### Hybrid Loss Function
#### 3.4.3 Adam Optimizer Core Formula  
1. Momentum estimation: \[m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{t}\] (2) where \(m_{t}\) represents the first order estimate of the gradient (momentum), \(g_{t}\) is the current gradient and \(\beta_{1}\) represents the exponential decay rate of the momentum term.
2. Uncentred variance estimation: \[v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2}\] (3) where \(v_{t}\) represents the second order estimate of the gradient (uncentred variance) and \(\beta_{2}\) represents the exponential decay rate of the variance term.
3. Bias correction: \[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}}\] (4) \[\hat{v}_{t}=\frac{v_{t}}{1-\beta_{2}^{t}}\] (5)
4. Parameter update: \[\theta_{t}=\theta_{t-1}-\alpha\frac{\hat{m}_{t}}{\sqrt{\hat{v}_{t}}+\epsilon}\] (6) where \(\theta_{t}\) represents the parameter, \(\alpha\) is the learning rate, \(\hat{v}_{t}\) and \(\hat{m}_{t}\) are the bias-corrected versions of the first-order and second-order moment estimates of the gradient, respectively, and \(\epsilon\) is a constant used to prevent division by zero.
## 3 Results and Discussion
### Experimental Environment and Evaluation Indicators  
The hardware configuration and environment utilized for the experiments presented in this paper are detailed in Table 1.  
In order to provide a comprehensive evaluation of the performance of the enhanced CycleGAN model, we employ not only PSNR values as metrics for assessment but also LPIPS values. This enables a comparison of the impact of human visual perception. LPIPS assesses the degree of visual similarity between two images by simulating the human visual perception process. This approach focuses on evaluating the visual similarity of the images rather than solely on pixel-level similarity. LPIPS employs a trained convolutional  
Figure 7: Training and inference adapted to PSNR.  
neural network to discern image features that encapsulate both the visual content and structural aspects of an image. Consequently, it is a prevalent tool for assessing image quality, particularly in contexts where comparing the quality of images before and after operations such as image restoration, compression, or enhancement is of interest. The LPIPS values typically range from 0 to positive infinity. A value of 0 indicates that the two images are visually identical, whereas a positive value indicates the presence of a visual difference. The magnitude of the difference is reflected in the value, with higher values denoting a more pronounced discrepancy.
## 1 Introduction
For both co-swirler LDI combustor and counter-swirler combustor, the short strong central recirculation zones are produced. For the baseline arrays LDI combustor, the turbulent activity near the swirler exit was highest. In a self-sustaining combustion oscillations combustor, the thermoacoustic coupling was observed [37]. The oscillation amplitude was highest when the equivalence ratio was highest. The oscillation strength was affected slightly by the different fuel distributions, whereas the overall equivalence ratio was constant. The proper orthogonal decomposition (POD) was used to present the energetic spatial components that could represent OH* distribution and periodic variation. The fluctuation location and magnitude could be visualized by the POD modes. There were similar phases and locations between OH* variations and periodic changes. A multipoint lean direct injection combustor, which includes 36 fuel injectors and fuel-air mixers was investigated [38]. The construction consists of the injectors, swirlers and fuel distributer, where Jet-A was used as the fuel. The effects of inlet total temperature, inlet total pressure, and equivalence ratio on the NOx emission were investigated when the inlet temperatures and inlet pressures could reach up to 866 K and 4825 kPa. The spray combustion characteristics of a multi-point LDI combustor was investigated [39]. The RANS code was used to simulate the flow and combustion characteristics in the combustor. Using several spray sub-models for the liquid spray modeling, the spray properties were analyzed and discussed. The short flames from individual injectors, a uniform temperature profile at the chamber exit and the uniformly low temperature distribution inside the combustor were observed. The flow field at the injector exits becomes highly strained when the air flow velocity is increasing. The injector structure, rapid mixing, reacting, and operating cycle conditions would affect the performance, operability, and emission of the combustor. The swirl-venturi four-point lean direct injection combustor with hydrogen/air is explored and the influence of key geometric, swirl-venturi array and equivalence ratio on reacting flow and emission production characteristics is discussed. A swirl-venturi 2 \(\times\) 2 array four-point LDI combustor has been designed. The injector, swirler and venturi together govern the rapid mixing of hydrogen and air to form a combustible mixture. Using the clockwise swirler and anticlockwise swirler, the co-swirling and count-swirling swirler arrays of LDI combustors were achieved. With the Reynolds-averagedNavier-Stokes code for steady-state reacting computations, the four-point LDI combustors with hydrogen/air were simulated with a reduced 23-step reaction mechanism. The axial velocity, swirl number, velocity angle, effective area, total pressure drop coefficient, total temperature, mass fraction of OH and emission of pollutant NO of a hydrogen fuelled four-point LDI combustor, with different equivalence ratios, were achieved and discussed.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 3 Case Studies and Issues for Conventional CVR
### Case Studies: Non-Optimal Results
#### 3.1.3 Central Florida Electric Cooperative (CFEC)  
CFEC implemented voltage reduction as its first measure to reduce peak demand and lower demand charges. By adjusting in-line voltage regulation, the 125 V nominal voltage at selected distribution transformers was reduced by 1.5-3%. As a result, CFEC reduced monthly peak demand rates by up to 20%. Tests on the first substation to implement this voltage reduction revealed a 0.5% reduction in load for every 1% reduction in voltage (CVR factor of 0.5) during the summer and 0.75% load reduction in the winter. CFEC voltage reduction may be greater during winter peaks due to the prevalence of resistive heating in the service territory [19].
# The Influence of Filtration on the Results of Measurements Made with Optical Coordinate Systems
## 2 Materials and Methods
The entire analysis is intended to answer what size filters can be used in such cases and whether the selected filters reflect the actual image of the measured contour. In modern multisensory machines, all light sources can be controlled automatically using measuring software. The brightness can be controlled using light reflected from an object. This enables practical use in measuring the surfaces of different materials [6]. In the measurements performed, an optical magnification of 6.8 and transmitted illumination with an intensity of 50% were used. The use of a lower intensity causes underexposure of the image, while the use of a higher intensity causes overexposure of the image. In this study, measurements were taken in a single cross-section in order to observe the variability between the results for different filters. The position of the measurement window was determined based on the coordinate system built on the part, not the machine. This approach ensured the repeatability of the measurement, which is particularly important for parts of such small dimensions. Filtration is an increasingly popular tool enabling measurements from materials that leave flashes during the manufacturing process. This applies to plastic and composite products, which, due to their properties, are more often  
Figure 4: Views of the working distance of the Werth Zoom optical sensor used: (**a**) 220 mm; (**b**) 37 mm. used not only by car manufacturers but also in other material goods. These include all types of household appliances, electronics, and even sports equipment for both amateurs and professional athletes. During the tests, care was taken to minimize the impact of the environment. For this purpose, the temperature was kept within the acceptance limits, which were defined as 20 \(\pm\) 0.5 \({}^{\circ}\)C. The samples were prepared and left for 24 h. This was to ensure stability, which is extremely important for plastics.
## 6 Supervised and Unsupervised Machine Learning Model
### Unsupervised Machine Learning
#### 6.2.2 Long Short-Term Memory Model  
The long short-term memory (LSTM) model networks (see Figure 22) are well-suited to classifying, processing and making predictions based on time-series data that have lags of unknown duration between important events in a time series [67]. The LSTM architecture comprises three distinct gates, namely the forget gate (A), input gate (B) and output gate (C). Unlike the ANN model, the LSTM model has its specified embedded activation neural network layers where the number of neural network layers could be added repeatedly. The LSTM model is effective when knowledge about the previous values has a substantial effect on the present values [68]. Figure 21: Comparison of performance between GBM and BBM [29]. **Note**: 1. N-GBM: output of WBM is used as a new feature that the BBM can use for training the model. 2. A-GBM: regularization process is changed to include some a priori information. Figure 22: LSTM network architecture model [69]. For datasets with missing values or near zero data such as those shown in Figure 18, the ensembled method such as the LSTM is useful in regenerating the input data. The authors have considered the LSTM model that combines the time-series data collected from tugboat with classification data obtained from a K-mean clustering process. Their research outcome found that the combined model has a better capability in forecasting fuel consumption by using lesser historical data with a faster convergence duration, Table 6.  
An example of the utilization of LSTM model in predicting the fuel consumption is given in Figure 23. Figure 23 compares the accuracy of the LSTM method in terms of \(R2\) score and shows that an \(R2\) of up to 0.94 could be achieved when a four-month fuel data is used in training the ensembled system. It is to note here that the accuracy in predicting the fuel consumption drops when it is used to predict the consumption for larger future values. This accuracy could be further improved when a combined LSTM model that takes input from the time-series operational data, i.e., fuel rate, vessel speed and wind effect are used with the classification model (see Figure 24). The classification model is obtained from the K-mean clustering method [60] where the operational activity is clustered based on its various states. The utilization of the time-series operational data and classification model in the LSTM model allows a more accurate prediction of the fuel consumption where an \(R2\) close to 1.0 could be achieved as shown in Figure 24. The LSTM model, therefore,
# Adaptive Polling Medium Access Control Protocol for Optic Wireless Networks
## 4 The Adaptive Polling Medium Access Control (APMAC) Protocol
### Association Phase
For more clarification, the DFK-MTU algorithm is illustrated in Algorithm 1. The transmission time slot available in the AGF for each VLN is obtained by adding RTT and the processing time. After each consecutive data transmission time-slot, a guard time is added in order to avoid signal interference. ```
for each schedule durationdo  Establish a new Polling Table \((PT)\). Calculate \(MTU\) = Average of the current \(PT\). while\(PT\
eq Empty\)do for each node \(k\) : \(\{k=1,...,n\}\in PT\)do ifnode \(k\) buffer \(\geq\) current \(PT\)\(MTU\)then  The current \(PT\)\(MTU\) is the \(MTU\) for node \(k\). end if ifnode \(k\) buffer \(<\) current \(PT\)\(MTU\)then  Buffer size of \(k\) is the \(MTU\) \(k\). end if ifnode \(k\) buffer \(=\) current \(PT\)\(MTU\)then  The current \(PT\)\(MTU\) is the \(MTU\) for node \(k\). end if  end if  end for  end for  end for
```  
**Algorithm 1**Dynamic future knowledge maximum transmission unit (DFK-MTU) algorithm adopted from [27].