esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### The Benefit of Modelling Forest Carbon Fluxes  
The ability to simulate carbon allocation and pools of a heterogeneous forest using an individual- and process-based forest model with a species-specific parametrisation allows a broad range of analyses and virtual experiments. Such an analysis cannot be done with EC or inventory data alone, especially with regard to mixed forests.  
While field measurements are often limited in space or time, forest models can be applied to multiple sites as well as across larger areas and allow for a variety of applications, such as the analysis of intra- and inter-annual carbon cycles of forest ecosystems. In particular, individual-based forest models are relevant tools as they enable the investigation of stand attributes or processes such as carbon dynamics, from local (tree, plot) and short temporal scales (day) up to stand and long temporal scales (years to centuries). This can help to examine the effect of inter- and intra-species competition, species mixing and individual tree growth as well as the effect on stand level. For example, one can analyse the influence of microclimate in a forest on carbon dynamics or the influence of species-specific water use strategies and how this might affect the water cycle due to changes in interception, precipitation or soil water content [27; 67] Individual-based models based on forest inventories including tree size as well as species composition with a species-specific parametrisation enables a transfer to other sites. This approach could also facilitate large-scale analysis of carbon flux dynamics on tree level by linking model simulations with remote sensing (e.g., [PERSON] et al. [68]).  
Simulation studies using forest models could help to develop new mitigation strategies based on site specific management plans, e.g., by promoting highly-productive or better-adapted tree species for more resistant forests under extreme events such as droughs [10; 48; 69]. Virtual experiments with different climates facilitate research on the reactions of different forest compositions and species (mixtures) and how this might change stability, biodiversity or other ecosystem services. These simulation experiments can help to identify important effects on different temporal and spatial scales [27]. The main restriction of forest simulations is the available data for validation and parametrisation, especially for mixed forest stands. This situation might change in the future with new long-term experiments and monitoring sites as well as more available inventory data of mixed forest [70].
## 2 Materials and Methods
### System Design Requirements
The requirement is that the system must ensure minimal delay in data transmission and processing, targeting response times in the millisecond range to support time-sensitive analysis. The real-time EAP delays impact the effectiveness of alerts and data accuracy. Low latency compromises the EAP's ability to respond promptly to changes in sensor data, enabling timely analysis and prediction. The system uses HTTP POST requests from the MSStack modules to achieve low latency to an ASPNET API hosted on Azure App Service. This API relays sensor data to the Azure SQL database. The local network configuration, internet bandwidth, and Azure's processing are optimized to reduce transmission delays, ensuring near-instantaneous data availability for analysis. 4. Cost-Efficient Operation with Serverless Configurations. The requirement is that the system employs serverless configurations within Azure to optimize costs, particularly during periods of low data load. Continuous operation of cloud resources is costly, especially if the system experiences long periods of inactivity or low activity. Serverless configurations allow resources to be paused automatically during inactivity and resumed upon receiving data, reducing operational costs. Azure's serverless SQL database configuration automatically pauses compute resources when there is no activity, avoiding costs when the system is idle. When new data arrive, resources are resumed without manual intervention, providing seamless and cost-effective scaling. This setup is ideal for IoT-based applications with fluctuating data loads, ensuring that resources are only used (and billed) when needed.
## 4 Experiments
### Ablation Study  
In this section, we conduct ablation experiments to verify the effectiveness of different blocks within the network. As shown in Table 4 and Figure 9, the removal of the TFM block from the full model restricts the network's receptive field to a portion of the image, leading to a deterioration in the perception of reconstructed details in overexposed regions. The generated results exhibit distorted details in these regions, with a decrease  
Figure 8: Qualitative results of our method comparison with state-of-the-art methods on HDR-Real testset [36]. The first and the last images are LDR input and the corresponding ground truth HDR images. The remaining images are the result images from existing methods, HDRCNN [38], ExpandNet [42], FHDR [41], DeepHDR [39], SingleHDR [36], Input, HDRUNet [23], KUNet [44], CEVR [34] and our RLUNet, respectively. In addition, the red box magnifies the artifact suppression effect in overexposed areas, The green box magnifies the color and detail reconstruction of the overexposed area.  
in the model's objective index of 1.96 dB in PSNR-N. Similarly, the removal of the CAAM block results in a significant drop in network performance due to the absence of an artifact suppression mechanism, causing numerous artifacts to appear in the image, which significantly impacts visual quality. The PSNR-N decreases by 9.15 dB, and the PSNR-\(\mu\) decreases by 2.24 dB. Without the RLM block, the network lacks linearized mapping, leading to noticeable chromaticity deviations between the generated results and ground truth, particularly in the objective index HDR-VDP-2, which is closely related to human eye perception. This decreases by 6.1 points compared to the full model. The DQ module addresses quantization artifacts, and its removal leads to the appearance of such artifacts in overexposed regions, resulting in a drop in the objective index PSNR-N by 2.19 dB. The ROR module recovers overexposed region detail information through partial convolution, and the results generated without this module show a lack of significant reconstruction effect in overexposed regions. It is noteworthy that there is a significant color deviation compared to the full model, with a decrease in PSNR-N by 7.02 dB. Through these ablation experiments, the effectiveness of each module in reconstructing highly perceptual HDR images is demonstrated.
## 3 Results
### Possible Improvement of the OEE  
During data collection, it was observed that small vans often do not use LUZ zones to unload goods but rather as parking spaces while they carry out their services at homes (plumbing, carpentry, cleaning, etc.). A simulation was carried out in which the use of LUZs was prohibited for small vans and these same activities were assigned to delivery vans exclusively in LUZs. OEE in the area would only increase by 3%. A comparative t-test was therefore carried out between the occupancy times of small vans and large vans, taking into account unloading in the LUZ, double parking and illegal parking, i.e., all unloading recorded during the time slot.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **A** & **E** & **Q** & **OEE** \\ \hline Monday & 0.60 & 0.87 & 0.71 & 0.37 \\ Tuesday & 0.69 & 0.85 & 0.67 & 0.39 \\ Wednesday & 0.41 & 0.88 & 0.89 & 0.32 \\ Thursday & 0.77 & 0.86 & 0.64 & 0.43 \\ Friday & 0.49 & 0.90 & 0.77 & 0.34 \\ \hline \end{tabular}
\end{table}
Table 18: OEE by day of the week.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **A** & **E** & **Q** & **OEE** \\ \hline
7 & 0.61 & 0.88 & 0.66 & 0.36 \\
8 & 0.52 & 0.92 & 0.82 & 0.39 \\ \hline
9 & 0.84 & 0.89 & 0.56 & 0.43 \\
10 & 0.43 & 0.89 & 0.88 & 0.33 \\
11 & 0.20 & 0.96 & 0.93 & 0.18 \\ \hline
14 & 1.29 & 0.80 & 0.40 & 0.42 \\
15 & 0.43 & 0.85 & 0.92 & 0.34 \\
16 & 0.21 & 0,96 & 0.92 & 0.18 \\ \hline \end{tabular}
\end{table}
Table 17: OEE by time slot.  
Figure 7: Hourly evolution of the OEE indicator.  
The results of the \(t\)-test showed a t-statistic of \(-3.44\), indicating that, on average, the occupancy times of delivery vans are lower than those of small vans. The \(p\)-value obtained was 0.00059, which is considerably lower than the significance threshold of 0.05, allowing us to conclude that there is a statistically significant difference between the two groups. These findings suggest that delivery vans tend to have lower occupancy times in loading and unloading areas than small vans, which may have implications for the management and optimisation of urban logistics operations. This data reinforces what was observed in the data capture in the sense that small vans are mainly used for general services (carpentry, plumbing, cleaning, etc.) and not for unloading per se, and they could have restricted access to the LUZ.
# CFD-DEM Simulation of Dust Deposition on Solar Panels for Desert Railways
## 3 Numerical Simulation Results and Discussion
### Light Transmittance  
The deposition of dust on a solar panel will lead to the attenuation of light transmittance. According to Lambert-Beer law, the light transmittance through a certain medium can be given by the following formula [32]:  
\[T=\exp[-(1-\gamma)N_{s}A] \tag{13}\]  
where \(\gamma\) is the transparency of particles. For sand particles, it can be taken as \(\gamma=0.45\). \(N_{s}\) is the number of sand particles deposited per unit area and \(A\) is the cross-sectional area of sand dust particles. Figure 15 shows the change in the light transmittance with  
Figure 13: Cloud chart of particles deposited on solar panel in a day with a particle size of 100 um when \(u_{*}=0.40\) m/s and \(K=3.5\times 10^{6}\) Jm\({}^{-3}\).  
time due to the deposition of dust and fine sand on the solar panel. It can be seen that the light transmittance gradually decreased with the development of time, which was basically consistent with the field measurement results reported by [PERSON] et al. [14].
## 3 Proposed Methods
### Model Configuration Strategies
#### 3.2.2 Optimizer  
When choosing an optimizer, the momentum-based SGD [33] algorithm is used in this paper instead of the original SGD algorithm because the update direction of the original SGD algorithm entirely depends on the gradient calculated by the current batch, which in turn leads to a less stable model. In contrast, the momentum-based SGD algorithm simulates the inertia of an object in motion by borrowing the concept of momentum from physics, preserves the direction of the previous update to some extent during the update, and fine-tunes the final update direction using the gradient of the current batch, i.e., replaces the real gradient with the momentum before accumulation.  
The SGD algorithm based on momentum observes the historical gradient. It reduces the gradient in the current direction if the current gradient is not consistent with the historical gradient direction; conversely, if the current gradient is consistent with the historical gradient direction, the gradient in the current direction is increased. In general, at the beginning of model iteration, the current gradient of the model is consistent with the direction of the historical gradient, so momentum will play an important role in helping the model reach the optimal point more quickly. In contrast, at the later stage of model iterations, the model's current gradient is inconsistent with the direction of the historical gradient. It oscillates around the convergence value, so momentum will play a decelerating role in increasing the stability of the model and thus stop the model from falling into a local optimum.  
Assuming that SGD depends on the gradient of the current batch, the current gradient, \(m_{t}\), is calculated as shown in Equation (8), where \(g_{t}\) denotes the gradient and \(\eta\) denotes the learning rate.  
\[m_{t}=\mu*m_{t-1}+g_{t} \tag{8}\]  
where \(\mu\) is the momentum factor. In addition to this, the warmup strategy is also used as the method of learning the rate decay. The variation in learning rate with epoch value is shown in Figure 12. It can be observed that the learning rate gradually increases at the early stage of network model training, i.e., when \(epoch<10\), when the model will correct the data distribution, which helps to slow down the overfitting phenomenon that may occur in the initial stage of the model. When \(epoch>10\), the learning rate gradually decreases, when the model will converge better, which helps to maintain the stability of the model at a deeper level.  
Figure 12: Trends in learning rate.
## 2 System Model and Channel Model
### Outage Probability of the FSO Link  
The outage probability is an important factor in wireless communication. It is defined as the probability that the SNR falls below a certain threshold of quality of service and the FSO link is unable to transmit data. Thus, the FSO outage probability can be expressed as  
\[P_{op}(\gamma_{TH})=P(\gamma_{F}<\gamma_{TH})=F(\gamma_{TH}) \tag{7}\]  
where F(\(\cdot\)) is the cumulative distribution function (CDF) given by  
\[F(\gamma_{TH})=\int_{0}^{\gamma_{TH}}f_{\gamma_{F}}(\gamma_{F})d\gamma_{F} \tag{8}\]  
where \(\gamma_{TH}\) denotes the threshold of the FSO link.  
Therefore, the outage probability of the FSO link is derived by substituting (6) into (8). In order to simplify (8), using the integral formula of the Meijer g function [34], the CDF of \(\gamma_{F}\) is given as follows.  
\[P_{op}(\gamma_{TH})=\frac{2^{\alpha+\beta-3}\varsigma^{2}}{\pi\Gamma(\alpha) \Gamma(\beta)}\times G_{3,7}^{6,1}\Bigg{[}\bigg{(}\frac{\alpha\beta\varsigma}{ 4}\bigg{)}^{2}\frac{\gamma_{TH}}{\gamma_{F}}\Bigg{|}\begin{array}{c}1,\frac {\varsigma^{2}+1}{2},\frac{\varsigma^{2}+2}{2},\\ \frac{\varsigma^{2}}{2},\frac{\varsigma^{2}+1}{2},\frac{\varsigma}{2},\frac{ \kappa+1}{2},\frac{\kappa}{2},\frac{\kappa+1}{2},\frac{\kappa}{2},0\end{array} \Bigg{|}\Bigg{]} \tag{9}\]
## 5 Enhancements to ShinyAnonymizer Data Encryption Techniques
### Technical Details of Encryption Techniques
#### 5.1.3 Blowfish  
Description: Blowfish is a symmetric-key block cipher. Data is encrypted in blocks of 64 bits under keys ranging from 32 to 448 bits. It was generated to replace DES, as it is faster and more reliable; moreover, it exudes flexibility and efficiency.  
Application: Blowfish allows a user to conduct the encryption of data in numerous application types. Among other important uses, these include virtual private networks, CDs, and files. Its many uses and strong safety features make it pretty suitable for most such applications.
## 2 Materials and Methods
### Research Methods  
#### 2.3.1 Random Forest Model and Accuracy Metrics  
This study employs the Random Forest classifier from the ensemble learning library in Python (part of the Scikit-learn package) ([[https://colab.research.google.com/](https://colab.research.google.com/)]([https://colab.research.google.com/](https://colab.research.google.com/)) (accessed on 5 January 2025)) as the core algorithm tool to simulate the response relationships between disaster-inducing environmental factors and geological hazard points, quantifying the contribution of each factor to landslide, collapse, and debris flow disasters [27; 28; 29] (Equation (1)). A random selection of 80% of the samples is designated as the training set, while 20% are used as the validation set to simulate and calculate related targets based on the associative conditions between disaster-inducing environmental factors and hazards. According to previous studies, once the number of feature trees exceeds 500, improvements in the model tend to stabilize, yet the computational costs increase significantly [30; 31; 32]. Therefore, this study limits the range of feature trees to 0-500, striking a good balance between computational efficiency and predictive performance.  
\[f(m)_{ij}=\omega_{T}\ T+\omega_{Gs}\ Gs+\omega_{Pr}\ Pr+\omega_{Eg}\ E_{g}+\in \tag{1}\]  
In the formula, \(f(m)_{ij}\) denotes the geohazard prediction result at point/pixel \(i\) in zone \(j\), determined by the random forest model; \(T\), \(Gs\), \(Pr\), and \(Eg\) represent the geomorphology, geological structure, precipitation, and engineering geology factors at point/pixel \(i\) in zone \(j\); \(\omega_{T}\), \(\omega_{Gs}\), \(\omega_{Pr}\), and \(\omega_{Eg}\) are the model weights assigned to these factors in zone \(j\); and \(e\) is the model residual.  
To ensure the accuracy and reliability of the Random Forest model in simulating and calculating disaster-inducing environmental factors for geological hazards, this study introduces Root Mean Squared Error (RMSE, Equation (2)) as the evaluation metric. By calculating the error values of the Random Forest model under different numbers of random feature trees, a quantitative assessment of the model's precision is achieved. In the analysis of disaster-inducing environmental factors, RMSE can accurately quantify the predictive accuracy of each factor for disaster-inducing scenarios. Based on this, the importance distribution of each environmental factor influencing geological disasters is obtained. Guided by zoning methods, the disaster-inducing environmental zoning of landslides, collapses, and debris flows on the Qinghai-Tibet Plateau is completed, which has also benefited from lattice-based conductivity modeling [33], along with the contribution degree of environmental factors in each zone.  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}} \tag{2}\]  
In the formula, \(RMSE\) represents the degree of deviation between predicted and actual values, \(n\) is the number of samples, \(y_{i}\) denotes the true value of the \(i\)-th sample, and \(\hat{y}_{i}\) denotes the predicted value of the \(i\)-th sample.
## 4 Simulation Results and Analyses
### Simulation Conditions  
In order to better emulate real situations, assuming that all EVs have the same battery characteristics, the arrival time of each EV is random and different and the initial \(SOC_{EV}\) is randomly generated in the simulation. Different drivers will take the charging price and charging time into account to choose the charging mode they need, and to emulate this action, the charging mode is also randomly generated. The parameters of the PV, EV, battery storage, public grid, and DC bus are detailed in Tables 4-8, respectively.  
voltage of the DC microgrid storage and \(C_{REF}\) is its capacity. The rated DC bus voltage was chosen as 400 V according to [31].
## 1 Introduction
At present, most studies on the traceability of wine-producing areas in China focus on the identification [21; 22] of large producing areas, such as provincial classification (such as in Ningxia and Xinjiang [13]), and the research on subdivided producing areas is relatively insufficient. In this study, a unified brewing process was employed to exclude the influence of the winemaker style. Then, GC\(\times\)GC-TOFMS technology was utilized to analyze the aroma characteristics of wines from five core sub-producing areas at the eastern foothills of Helan Mountain in Ningxia, and OPLS-DA technology was applied to identify the producing areas. Furthermore, the relationship between climatic conditions and flavor characteristics of the producing areas was explored using a Pearson correlation analysis. This study aims to reveal the relationship between climatic factors and flavor characteristics and provide a theoretical foundation for addressing the homogenization of wine in small producing areas, clarifying typical style characteristics and establishing a traceability technology system based on characteristic aroma. Furthermore, this research method can offer a reference for the segmentation of other regions, facilitating a profound understanding of the diverse flavors of producing regions, supporting precise cultivation and brewing management, enhancing the quality of product style, and promoting the sustainable development of the global wine industry.
## 2 Experimental Measurements
### Mesh Independence Study  
A comprehensive mesh independence study was conducted to ensure the reliability of numerical results. Five different mesh densities were evaluated to determine the optimal mesh configuration that balances computational efficiency and solution accuracy. Table 4 presents the results of this mesh independence analysis. The mesh independence study demonstrates that the medium mesh density provides an optimal balance between computational efficiency and accuracy. The deviation in the average \(Nu\) between the medium and fine mesh was less than 1%, indicating that further mesh refinement would not significantly improve solution accuracy while substantially increasing computational cost. Therefore, the medium mesh configuration was selected for all simulations in this study. This mesh structure consists primarily of tetrahedral elements with boundary layer refinement near the fluid-solid interfaces to accurately capture thermal and velocity gradients in these critical regions.
# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 4 Fuzzy Logic in User Experience Assessment
### The Model Development Process  
The development of a typical inference system based on fuzzy logic involves:  
1. Fuzzification. This step entails transforming crisp input and output values into fuzzy sets that describe the variable states. The grouping of the values into the fuzzy sets allows for handling uncertainty and vagueness in the data;
2. Defining a rule-based system to operate with the fuzzy states. These rules are typically in the form of \"IF [condition] THEN [conclusion]\" and use linguistic variables to express relationships between inputs and outputs. For instance, an example rule could be \"IF [video fps IS low] AND [video compression IS high] THEN [quality IS bad]\";
3. Defuzzification. The final step involves converting the fuzzy output (e.g., quality IS bad) back into a crisp result. This process produces a clear and quantitative assessment based on the inference system of the model.