esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Analysis of Cyclone Separator for Separation of Cenospheres
## 3 Results and Discussion
### Influence of Upper Discharge Opening Radius Size on Separation Performance  
Under the condition of keeping the inlet velocity constant at 5 m/s, the specific effect on the performance of the cyclone separator in separating nanospheres was analyzed by adjusting the size of the radius of the upper discharge opening. This methodology aims to explore the mechanism and law of the change in the upper discharge opening size on the separation efficiency and other related performance parameters. As a result, the radius of the upper discharge opening was set to 60 mm, 70 mm, 80 mm, 90 mm, and 100 mm. Figure 3 shows the velocity vector diagrams of different upper discharge opening radii, and Figure 4 shows the particle trajectory diagrams of different upper discharge opening radii.  
As shown in Figure 3, the fly ash and cenospheres particles first enter the cyclone separator through the inlet and undergo an initial acceleration during the entry channel. Subsequently, the particles descend in a spiral along the inner wall of the separation chamber, during which the particles gradually slow down. Eventually, the particles undergo  
Figure 4: Particle trajectory plots for different radii of the upper discharge opening.  
another acceleration process at the discharge opening to help them be effectively discharged from the cyclone. The simulation results yielded the cenospheres separation efficiency \(\gamma_{A}\) (number of escaped cenosphere particles/total cenosphere particles) and fly ash separation efficiency \(\gamma_{B}\) (number of escaped fly ash particles/total fly ash particles) as shown in Table 3, and the Newton's efficiency \(\eta_{N}\) was calculated from Equation (5). Figure 5 shows the variation of the Newton's efficiency with different radii of the upper discharge opening. After synthesizing the data in Figures 3-5, it can be observed that the increase in the radius of the upper discharge opening has a relatively limited effect on the Newton's efficiency. Specifically, the magnitude of change in the Newton's efficiency does not show a significant increase during the expansion of the upper discharge opening radius, indicating that the upper discharge opening radius has a relatively weak regulatory effect on Newton's efficiency. However, when the radius of the upper discharge opening \(d_{3}=100\) mm, it was observed that a small number of particles escaped directly from the upper discharge opening without undergoing a sufficient separation process. This phenomenon indicates that the radius of the upper discharge opening should not be too large to avoid affecting the separation efficiency of the cyclone separator.
## 5 Experimental Evaluation
### Experimental Setup and Datasets  
We select three representative federated learning tasks covering image, text, and handwritten character classification under heterogeneous settings. This selection is motivated by the need to validate TAIM's effectiveness across diverse data modalities and types of heterogeneity that mirror real-world challenges. Specifically, the FEMNIST task, provided by the LEAF benchmark, involves handwritten character recognition with a highly non-IID user-based split that naturally models the statistical heterogeneity found in real-world user data. CIFAR-10 is a classical image classification dataset with 10 classes. We generate non-IID partitions using a Dirichlet distribution with \(\alpha=0.3\) to systematically control and evaluate the impact of statistical heterogeneity. Sent140 is a Twitter sentiment analysis task where each client reflects individual language styles, making it an ideal testbed for modeling the behavioral heterogeneity that TAIM is designed to manage. For all experiments, datasets are split into training, validation, and test sets with a ratio of 80:10:10, respectively, unless otherwise specified. This split ensures consistent evaluation across all baseline and proposed methods.  
Model Configuration and Training Details  
We adopt a CNN for CIFAR-10, a two-layer CNN for FEMNIST, and LSTM for Sent140 as local models. In each round, 10% of clients are randomly selected. Local training runs for five epochs using SGD with a learning rate of 0.01 and momentum 0.9. Trust parameters are set as \(\gamma=0.8\), \(\lambda_{1}=0.3\), \(\lambda_{2}=0.4\), \(\lambda_{3}=0.3\). The sigmoid suppression function uses \(k=10\), \(\mu=0.5\).
# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 5 Simulation and Performance Analysis
### Results and Discussion  
We discussed the framework proposed to augment OLSR routing protocol efficiency, which ultimately proved to balance QoS metrics. These performance metrics were studied to find the impact of different OLSR configurations on the proposed fitness function. The trade-off between accuracy of routes and cost in the proposed equation manages correctly the capacity of links between the vehicles, therefore, reducing the necessary number of packets exchanged, reducing link failure and help in avoiding congestion. Table 2 describe the configuration parameters of the proposed BOLSR-PSO with four routing protocols - OLSR RFC-3626, OLSR-PSO, OLSR-Gomez, and RAND algorithms.  
In our BOLSR protocol, whenever a vehicle aggregates the neighboring vehicles information at a specific point of time, it uses HELLO-interval and TC messages to estimate the quality of communication state in the routes discovering process. It is important to note that the parameters that are used in OLSR protocol manage this process at limited time. This time must be enough to collect, process, calculate, select routes, and broadcast route information. If the time interval for each parameter was not enough to received packet during this specific period, then, the probability of link failure increases. This may lead to packet loss and increase end to end delay.
# A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances
## 3 Treatment Techniques for Short-Chain PFASs
### Other Techniques
#### 3.3.2 Thermolytic and Sonochemical Degradation  
In the recent decade, thermolytic and sonochemical degradation have attracted wide attention. [PERSON] et al. [59] found that 99% of CF\({}_{4}\) could be removed under urban incineration conditions (about 850 \({}^{\circ}\)C) and assumed the pyrolysis of 800-900 \({}^{\circ}\)C could resolve long and short-chain PFASs efficiently. [PERSON] et al. [60] explored the gas phase decomposition of PFOA in quartz tubes at 355-385 \({}^{\circ}\)C, finding that PFOA retained thermal stability below 300 \({}^{\circ}\)C but completely degraded at 370 \({}^{\circ}\)C after 360 min. However, the by-products of small molecule substances were generated continuously, which demonstrated the greater resistance of short-chain PFASs. Subsequently, [PERSON] et al. explored pre-concentrating PFhxA on GAC, which could improve the thermal mineralization rate from 46% to 74% [61]. Furthermore, in order to investigate the effect of ultrasonic chemistry on the degradation of PFASs with different carbon chain lengths, [PERSON] et al. investigated the degradation efficiency on six kinds of PFASs at 358 kHz, the order was PFOA > PFhxA > PFBA and PFOS \(\approx\) PFhXS > PFBS [62]. Similarly, [PERSON] et al. found that the degradation efficiency of ultrasonic chemicals raised with carbon chain length increased [63]. For one thing, long-chain PFASs with strong hydrophobicity were inclined to be adsorbed on the gas-liquid interface for thermal decomposition or oxidation degradation. For another, short-chain PFASs were more difficult to be defluorinated than long-chain congeners. Generally, these techniques could mineralize short-chain PFASs, but nongreen environmental factors about the generous discharge of CO\({}_{2}\) and high energy consumption limited the development in actual application.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 2 System Model
### Data Rate Expression Based on Shannon Capacity Model  
The DL achievable data rate of a UE \(u\) attached with BS \(b\) with bandwidth \(\omega_{b}^{mm}\) over one PRB \(n\) per mini slot \(m\) can be calculated as,  
\[r_{bu}^{mm}=\omega_{b}^{mm}\log_{2}(1+\Gamma_{bu}^{mm}), \tag{5}\]  
We denote the total received rate of a UE \(u\), represented by \(R_{bu}\), calculated as follows,  
\[R_{bu}=\sum\
olimits_{b\in\mathds{B}}\sum\
olimits_{u,\hat{u}}\alpha_{bu}^{ mm}\ x_{u,u}^{mm}\ \ \mathcal{G}_{bu}^{n}\ \ \mathcal{G}_{bu}^{nm}\ \ \mathcal{G}_{bu}^{m}\ \mathcal{G}_{bu}^{m}\ \mathcal{G}_{bu}^{m}\ \mathcal{G}_{bu}^{m}\ \mathcal{G}_{bu}^{m}, \tag{6}\]  
\(\Omega_{bu}^{mm}\) is the allocation indicator of the PRB \(n\) to UE \(u\) in BS \(b\) per mini slot m, where \(\Omega_{bu}^{mm}=1\) if a PRB \(n\) is allocated to a UE \(u\) and \(\Omega_{bu}^{mm}=0\), otherwise. Furthermore, \(\gamma_{bu}^{m}\) is the fraction of the PRB allocated to UE \(u\) and is calculated as \(\gamma_{bu}^{m}=\left\lceil\frac{R_{bu}}{r_{bu}^{mm}}\right\rceil\), where \(\lceil\cdot\rceil\) is the ceiling function.  
\[\gamma_{bu}^{m}\geq\ f_{bu}^{m}, \tag{7}\]  
When a UE \(u\) is associated with a BS \(b\), \(\gamma_{bu}^{m}\) PRBs are allocated to a UE \(u\), and this BS is duty to achieve the following condition  
\[R_{bu}\geq\ R_{u}^{min}, \tag{8}\]
## 3 Results and Discussion
### Determination of Volatile Substances  
#### 3.2.1 Determination of Volatile Substances in Surimi Gel by Electronic Nose  
The total contribution rate of the first principal component (PC1) and second principal component (PC2) of the volatile components of hairtail surimi gel at different fermentation times was 99.58% (>85%), which indicates that the two principal components fully covered the main information characteristics of the sample and that the distribution characteristics of each group's data on PC1 were the main factor in determining the discrimination effect. The surimi samples with different Monascus fermentation times showed a certain change trend in PC1; meanwhile, the four groups could be separated, which indicates that the volatile flavor was markedly distinct with different Monascus fermentation times. Moreover, the unfermented group was similar to the 1 h fermented group but different from the 3 h and 5 h groups, which indicates that the characteristic volatile flavor substances of the Monascus-fermented hairtail surimi significantly changed from 1 h of fermentation (Figure 6a).  
The loading analysis results of the four groups of samples are shown in Figure 6b, which shows that the contribution rate of PC1 is 98.69%; the larger that the main response value of each sensor on PC1 is, the more effective it is for sample identification. Additionally, the sensor was distributed closer to the origin (0,0) and has a smaller ability to identify the sample. The results showed that the response value of W1W (inorganic sulfide) on PC1 was the largest, and it is the main sensor for identifying the MFHS gel. The response values of W1S (methane (methyl)) and W5S (nitrogen oxide) on PC2 were relatively large, and the contribution rates of other sensors (W3S, W6S, W5C) to the first and second principal components were almost zero, indicating that the three sensors could hardly identify the volatile flavor of hairtail surimi gel at different fermentation times. To clarify the changes in the volatile flavor components of hairtail surimi gel during Monascus fermentation, GC-MS was used to further analyze and identify the volatile flavor components of hairtail surimi gel.  
Figure 6: PCA, loadings analysis of the volatile flavor of hairtail surimi gel at different fermentation times ((**a**)—PCA analysis; (**b**)—Loading analysis).
###### Abstract  
A system that can acquire and share information about the environment from vehicles using various sensors and smartphones was developed to prevent accidents that may occur on mine haulage roads. A light detection and ranging sensor and an accelerometer in a smartphone were used to determine the road surface conditions at a mining site, and dust and gas sensors were used to measure the atmospheric environment. The GPS function of the smartphone was utilized to obtain the location data of the vehicle, and a smartphone application was developed to collect and share this information. A preliminary test was conducted at Samcheok Campus, Kangwon National University, and a field test at the Samdo mine. The data acquired from each vehicle could be shared in real time. Additionally, by analyzing the spatial distribution of each dataset, sections with rough road surfaces and those with poor atmospheric conditions could be identified. If the technology is further developed and big-data analysis is performed in the future, the developed technology could contribute to improving the mining environment.  
smartphone application; mine haulage; accelerometer; dust sensor; gas sensor Article
# Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis
## 2 Materials and Methods
### Utility Evaluation  
The process for utility evaluation is visualized in Figure 1. A synthetic version of the training data (2017 up to 2020) is generated with CBSDG. Using the synthetic data set, the analyses that [PERSON] et al. [26] performed on the real training data (i.e., the original data) are performed with the same hyperparameter choices (as used in the original analyses). Hb deferral predictions are made for donations from 2021 using SVMs based on the original data and based on the synthetic data. Utility is evaluated by comparing these predictions. The donations from 2021 were not used to train the SVMs, nor to construct the MSPN. The process is repeated 50 times (leading to 50 MSPNs, 50 synthetic data sets and 50 corresponding sets of SVMs). The objective is to asses the extent to which predictions made by SVMs are expected to differ when using synthetic instead of real, personal donor data.  
For both sets of predictions, predictive performance is quantified by computing precision and recall scores. Particularly the latter is important, because less than 2.5% of donors is deferred for low Hb. Because the analyses are performed 50 times (once for each synthetic data set), the distributions of precision and recall demonstrate the stability of predictive performance over different synthetic data sets.  
Figure 1: Visualization of utility. CBSDG generates synthetic data with a mixed sum-product network (MSPN). With both the synthetic and training data, a support vector machine (SVM) is trained, with which predictions are made for donors in the test data set (indicated with dashed line). These predictions are compared as a measure of utility. The process is repeated 50 times: 50 MPSNs are generated, resulting in 50 synthetic data sets and 100 corresponding SVMs (one per sex).  
To convey in greater detail to what extent the variable relations were represented by the synthetic data, we also evaluate how the separate predictors impacted the modeled probability of deferral. [PERSON] et al. (2022) [26] used SHAP values (SHapley Additive exPlanations [29]) for 100 donation records from 2021, to quantify and visualize the impact of every predictor, using the Python package shap. This analysis is repeated for the SVMs for all synthetic data sets. These SHAP values indicate for each of these 100 donors, how much the predictors impacted their probability of Hb deferral, on a scale of \(-1\) to 1. Specifically, the SHAP value pertaining to a particular predictor and donation record approximates how the probability of deferral would differ if the value on this predictor would have been missing, averaged over all possible scenarios in which the value could be missing [29].
## 3 The Optimization and Reconfiguration of UDNs Incorporating VPPs
### Objective Functions
#### 3.1.3 Maximize Operational Efficiency  
The intelligent operator of the UDN maximizes benefits by utilizing VPP resources to participate in demand response. The revenue mainly includes the profit from electricity sales \(\eta_{\text{OP}}\), the revenue from demand response \(\eta_{\text{DR}}\), and the subsidy cost for utilizing VPP resources \(\omega_{\text{VPP}}\). The objective function is as follows:  
\[\left\{\begin{array}{l}\max\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!  
From (23), (24), and (25), it can be seen that the optimization objectives of the UDN reconfiguration model incorporating VPPs are multi-objective. This paper converts the three optimization objectives into a single objective as follows:  
\[\text{min}\theta=w_{1}\times\frac{\theta_{1}}{\theta_{\text{1 max}}}+w_{2}\times \left(\frac{\theta_{2}}{\theta_{\text{2 max}}}+\frac{\theta_{3}}{\theta_{\text{ 3 max}}}\right) \tag{26}\]  
where \(\theta_{\text{1 max}}\), \(\theta_{\text{2 max}}\) and \(\theta_{\text{3 max}}\) are the voltage deviation, network losses, and operational benefits of the UDN reconfiguration incorporating VPPs, respectively; \(w_{1}\) and \(w_{2}\) are the weights for stability and economic indicators, respectively. Considering the equal importance of stability and economy after UDN reconfiguration, \(w_{1}=w_{2}=0.5\)[26].
# Dynamic Aeroelastic Response of Stall-Controlled Wind Turbine Rotors in Turbulent Wind Conditions
## 5 Energy-Transfer Characterization of the Pulse (Unstable Oscillatory Regime)
We shall start by combining Equations (1) and (2):  
\[U_{h}=A_{0}e^{-\lambda_{1}(t_{ref}-t_{d})}=A_{0}e^{-\lambda_{1}t_{mf}}e^{- \lambda_{1}t_{d}}=U_{h_{ref}}e^{-\lambda_{1}t_{d}} \tag{6}\]  
Figure 17: Time delay for each pulse, taking the curve for the pulse with the lowest energy as a time reference. The red lines indicate the enveloping curves for the exponential component of the different signals. \[\frac{U_{h}}{U_{h_{ref}}}=e^{\lambda_{1}t_{d}} \tag{7}\]  
From the findings about the energy-transfer mechanism in the previous section, we have that, when normalized by the kinetic energy of the pulse, the blade displacement curves collapse into a unique signal, which scales with the time evolution of the rotor's elastic vibrational energy. This has the units of flexibility or the inverse of stiffness \(1/K_{el}\), which depends exclusively on the mean value of the wind speed. Hence, for all of the pulses occurring at the same wind speed, we have:  
Figure 16:0  
Figure 16:1  
Equating expressions (7) and (9), we finally obtain:  
Figure 16:2  
Figure 16:3  
According to Equation (11), if the mechanism of energy transfer found earlier for the stable regime also holds for the unstable regime, the logarithm of the ratio between the kinetic energy of any pulse versus the energy of the reference pulse occurring at the same mean wind speed must show a linear relationship with the corresponding time delay for that same pulse, and the slope of that linear relation must be the value of \(\lambda_{1}\) at that wind speed. Figure 18 shows a plot of the logarithm of the kinetic energy ratio in Equation (11) versus the time delay for wind speeds covering the entire unstable regime. It can be seen that the values for all of the pulses show a very clear alignment with a linear relation, and least-squares fitting gives a value of \(\lambda_{1}\) for each wind speed, which is in very close agreement with the values obtained by the fitting of \(\lambda_{1}\) in the exponential expansion stage of \(U_{h_{ref}}\) from the semi-log plots. Figure 19 shows a plot of \(\lambda_{1}\) obtained with these two methods for the complete range of wind speeds in the unstable oscillatory regime. The coincidence of the value of \(\lambda_{1}\) obtained from these two different methods--one measuring the expansion as it progresses in time, and the other evaluating the time that would have been taken for the same expansion to build the same energy delivered by the pulse--proves that the time delay, \(t_{d}\), is indeed associated with the time required for the exponential expansion to build up the same amount of energy contained in the respective pulse. This also validates the hypothesis that the energy-transfer mechanism is exactly the same as in the stable regime.
# Magnetic Localization Method for Vehicles Based on Transformer
## 2 Related Work
### Traditional Methods  
Traditional methods, including DTW, MAGCOM, and filter-based approaches, are fingerprinting methods that work by comparing a measured magnetic field sequence with magnetic sequences stored in a constructed database. They then find the most similar matching sequence to determine the current position [14]; this process is demonstrated in Figure 1.  
Specifically, this process consists of two phases, the offline and online phases [15], as shown in Figure 2. In the offline phase, a magnetic database with a certain granularity is built in the region of interest through measurement and interpolation. During the online phase, the collected magnetic sequences are manually extracted to features and matched with the fingerprints in the database. One of the above algorithms is used to calculate the most likely location.  
Figure 1: Principle of fingerprinting methods.  
According to fingerprinting localization principles, it can be inferred that the errors in positioning results are attributed to two factors. On the one hand, the utilized database is usually quantized in a mesh grid form, and the magnetic field at each grid point comes from a single measurement [16], or the average of multiple measurements [17], or the interpolation of adjacent points [18]. Such a database consists of many isolated points, so it is not sufficient to fully describe the relationships between magnetic sequences and their positions. In addition, the magnetic field at each grid point is fixed, so it is insufficient to describe the noise distribution of the measurements because the measured magnetic field at a point is not a constant. On the other hand, feature-matching criteria are manually designed and cannot be used to precisely model the noise in measured data. Taking MAGCOM as an example, many comparison criteria are available, such as the PROD criterion, the Normalized PROD (NPROD) criterion, MAD criterion, and the Mean Standard Deviation (MSD) criterion [15]. These criteria are not comprehensive; they focus either on errors in a point-by-point manner or only on the overall trend, which easily causes mismatches. In a typical magnetic positioning application involving vehicles, the fingerprinting method can achieve an accuracy of 3.34 m (2\(\sigma\)).