esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Effect of the Fluorination Process on the Recovery of REMES by Vacuum Distillation
#### 3.3.2 Treatment for REMES by Fluorination and Vacuum Distillation  
To fluorinate the oxide impurities and rare earth compounds, 20 g of NH\({}_{4}\)HF\({}_{2}\) was utilized to react with 10 g of REMES. The materials were mixed in a graphite crucible and placed in an airtight vertical furnace with a cover. To fulfill the fluorination reaction, the fluorination experiment was conducted at 773 K for 6 h.  
As shown in Figure 11, the diffraction peaks of NdOF and Nd\({}_{2}\)O\({}_{3}\) disappeared in the XRD pattern of the fluorinated REMES. In addition, the fluorine content increased to 30.64 wt.%. Then, the fluorinated slag was compacted and then distilled at 1573 K and 0.1 Pa for 6 h. The recovery efficiencies of the REEs and Li were calculated by Equation (1), and the results are displayed in Table 3.  
According to the experimental results, the recovery efficiency of the REEs increased to 86.23%. Additionally, the XRD pattern also confirmed the high purity of the rare earth product. Although CaF\({}_{2}\) could still be found in the product by SEM-EDS, the purity of the rare earth product was adequate. Notably, Fe\({}_{2}\)O\({}_{3}\) could still be observed in the fluorinated slag, indicating that it was difficult to fluorinate Fe\({}_{2}\)O\({}_{3}\) in the slag and that the existing Fe\({}_{2}\)O\({}_{3}\) might be the main obstacle for the distillation of rare earth fluoride. Furthermore, as shown in Figure 5a, the peaks of Fe in the XRD pattern of the distillation residue indicated that Fe\({}_{2}\)O\({}_{3}\) would be reduced by carbon during the distillation process, which might decrease the effect of Fe\({}_{2}\)O\({}_{3}\) on the distillation of rare earth fluorides. Therefore, NH\({}_{4}\)HF\({}_{2}\) could indeed increase the recovery efficiency of REEs and reduce the influence of impurities on the product purity.  
Figure 10: **(a)** The standard Gibbs free energy change of the reaction between SiO\({}_{2}\) and NH\({}_{4}\)HF\({}_{2}\); **(b)** the standard Gibbs free energy change of the reactions between oxide impurities and NH\({}_{4}\)HF\({}_{2}\); **(c–e)** the saturated vapor pressure of AlF\({}_{3}\), CaF\({}_{2}\), and MgF\({}_{2}\).  
Additionally, a condenser was installed at the top of the furnace, and most of the gaseous fluorides could condense on this condenser, including SiF\({}_{4}\). Figure S1 shows that SiF\({}_{4}\) could be captured by NH\({}_{4}\)F and condensed as (NH\({}_{4}\))\({}_{2}\)SiF\({}_{6}\) instead of being released into the air. Therefore, the potential pollution can be addressed.  
Overall, NH\({}_{4}\)HF\({}_{2}\) could transfer the oxide impurities as well as rare earth oxides and oxyfluorides with most gaseous fluorides recovered in the condenser, but only a small amount of CaF\({}_{2}\) could affect the purity of the rare earth condensate.
# Multiscale Content-Independent Feature Fusion Network for Source Camera Identification
## 2 Methodology
### MCIFFN Structure
#### 2.1.3 Multiscale Fusion Analysis
The adaptive filter in the preprocessing module of the MCIFFN structure learns the effective features to suppress the useless features and adjusts the filter parameters adaptively through a large amount of sample learning to suppress the useless features to the greatest extent and retain the effective features as much as possible. In addition, inspired by the idea of feature fusion in ResNet [27], we fuse the features extracted by the two scale filters through identity mapping and send them to the CNN network. \(F_{3}(\bullet)\) is a filter with receptive field 3, \(F_{5}(\bullet)\) is a filter with receptive field 5, \(I\) is the input image, \(N_{1}\) is the input noise of CNN in the first branch, \(N_{2}\) is the input noise of CNN in the second branch, \(N_{3}\) is the input noise of CNN in the third branch, and the input characteristics of the CNN in the three branches can be expressed as Formulas (2). \[\left\{ \begin{aligned} & N_{1}=F_{5}(F_{3}(I))+F_{3}(I)\\ & N_{2}=F_{3}(F_{5}(I))+F_{5}(I)\\ &\qquad\qquad\qquad\qquad N_{3}=I\end{aligned} \right. \tag{2}\]  
As shown in Formulas (2), the first branch is that image \(I\) first passes through a \(3\times 3\) filter to get the output feature \(F_{3}(I)\), then \(F_{3}(I)\) is sent through a \(5\times 5\) filter to get the output feature \(F_{3}(F_{5}(\bullet))\), and finally, output features of the two filters are fused to get the input feature \(N_{1}\) of the CNN-1 network. Different from the first branch, the second branch is image \(I\), which first passes through a \(5\times 5\) filter to obtain the output feature \(F_{5}(I)\), then sends \(F_{5}(I)\) to a \(3\times 3\) filter to obtain the output feature \(F_{3}(F_{5}(\bullet))\), and finally fuses the output features of the two filters to obtain the input \(N_{2}\) of the CNN-2 network. Different from the first two branches, the third branch does not preprocess the input image, which can retain some color-related features. Therefore, the input of CNN-3 is the image \(I\). Finally, the MCIFFN fuses the multiscale features of CNN output from three streams and sends them to a softmax classifier for classification. The purpose of our proposed MCIFFN scheme is to provide a network structure suitable for source camera identification. Therefore, the CNN feature extraction network in the three branches of the MCIFFN can select the same or different convolutional neural networks according to the experimental task. In this experiment, the feature extraction network shown in Figure 3 is selected.
## 4 Results and Discussion
### Comparison of the CMs  
The values of CMs for 1000 system calls (Figure 12) reveal that the lowest intrusion misclassification values are obtained by static (AWSCTD-CNN-S) and dynamic (AWSCTD-CNN-D) models and are equal to 26 and 25, respectively. The FCN family models generate two times more misclassified results: 53, 55, and 57, respectively, for FCN, LSTM-FCN, and GRU-FCN. The AWSCTD-CNN-GRU model shows the best misclassification value for benign applications, where only 32 items have been identified as an intrusion.  
Figure 12: _Cont._  
Although AWSCTD-CNN-GRU demonstrated the best result for one specific task, AWSCTD-CNN-S can still be considered as the overall winner when taking into consideration all previously discussed metrics.
## 3 Analysis of Test Results
### Single-Factor Analysis
#### 3.2.6 Compressive Strength  
Based on Equation (7), the relationship curve among various factors and the slurry compressive strength is shown in Figure 11. The fly flash-cement ratio exerts a more significant influence on the slurry compressive strength than the water-binder ratio, soil-binder ratio, binder-sand ratio, and particle size. As the soil-binder ratio, or muck content, increases, so does the slurry compressive strength. On one hand, the increase in the slurry compressive strength can be attributed to the addition of residuum, which provides more fine aggregate for the slurry, thereby increasing the uniformity and segregation degree of the slurry. On the other hand, the silty clay in the muck can offer more cohesion to the slurry mixture than the bentonite [36]. Compared to the muck content, the muck particle size has a relatively minor impact on the compressive strength. Regarding other factors, the slurry compressive strength augments with an increase in the binder-sand ratio but diminishes with higher water-binder and fly flash-cement ratios. Notably, due to the pozzolanic reaction of cement, the increase in the cement ratio can significantly enhance the slurry compressive strength [51].
## 2 Materials and Methods
### Post Hoc Interpretability Methods  
During the last decade, the use of ML models has exponentially grown in multiple areas, achieving great acceptance and popularity due to their remarkable results in supervised tasks [50]. Despite their benefits, most ML models are characterized by a lack of interpretability (known as black-box models). Interpretability can be defined as the ability of a human to understand the cause of a decision in computational models [51]. This is essential to be considered and adopted in clinical practice, and several methods have been proposed for extracting knowledge and gaining interpretability [52; 53]. Many interpretability methods have been proposed, with the post hoc and model-agnostic approaches the most used [53]. These approaches that provide explanations through both inspection of learned features and feature importance could support the identification of risk factors for several diseases. In this work, the post hoc method called Shapley additive explanations (SHAP) [54] was considered.  
SHAP is a post hoc interpretability method based on the aggregation of local interpretations and allows one to explain the general behavior of a model by analyzing the prediction of several samples [54]. Each feature value of a sample is treated as a player in a coalitional game, (i.e., a game where several players cooperate towards the same objective). In this case, the objective is the prediction for a particular sample. In this way, SHAP divides the final payout (the output of the model) between all players (the feature values of the instance to be analyzed). The Shapley value is computed as the mean of the marginal contribution of the player in all the possible coalitions.
## 2 Materials and Methods
### Quantification of Hydroxyl Radical  
In numerous studies, various methods have been proposed to quantify the hydroxyl radical, one of the most reactive radicals. In this study, a quantitative method employing dimethyl sulfoxide (DMSO) was selected [49]. DMSO with a purity of 99.7% was purchased from Sigma-Aldrich (St. Louis, MO, USA). The initial concentration of DMSO was 10 mg/L, and the reactor's working volume was 3 L. The air flow rate was adjusted to 5 L/min using an air flow pump, and the plasma reaction was conducted.  
The literature indicates that DMSO reacts with hydroxyl radicals, resulting in the formation of methyl radicals and methane sulfonic acid as primary intermediate products [50]. The methyl radical is formed by the reaction of DMSO, and the hydroxyl radical reacts with oxygen to form the methyl peroxy radical, as shown in Equation (8). Equation (9) indicates that 2 moles of methyl peroxy radical yield 1 mole of formaldehyde. In summary, it is possible to quantify hydroxyl radicals using reaction stoichiometry and experimental evidence because 2 moles of hydroxyl radical yields 1 mole of formaldehyde according to Reactions (7)-(9).  
\[\text{(CH${}_{3}$)}_{2}\text{SO}_{3}+\text{-OH}\rightarrow\text{CH${}_{3}$COOH }+\text{-CH${}_{3}$} \tag{7}\]  
\[\text{-CH${}_{3}$}+\text{O${}_{2}$}\rightarrow\text{-CH${}_{3}$OO} \tag{8}\]  
\[\text{2-CH${}_{3}$OO}\rightarrow\text{HCHO}+\text{CH${}_{3}$OH}+\text{O${}_{2}$} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Unit** & **Properties** \\ \hline Test organism & species & _Daphnia magma_ \\ \hline Maintenance temperature & \({}^{\circ}\)C & 20 \(\pm\) 2 \\ \hline Light photoperiod & h & 16 \\ \hline Dark photoperiod & h & 8 \\ \hline Dilution ratio of PFOS solution & \% & Control (0), 6.25, 12.5, 25, 50, 100 \\ \hline Number of _Daphnia magma_ & EA & 20 \\ \hline Test repetition & times & 4 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental set-up of acute toxicity test using _Daphnia magma_.  
The formaldehyde was quantified using the Korean standard methods for the examination of water and wastewater (ES 04605.2) [51]. Table 3 lists the analytical conditions of the GC-ECD for the detection of formaldehyde.
# Evaluation of the Urban Weather Generator on the City of Toulouse (France)
## 4 Discussion
### Influence of the Rural Station  
The analysis of Section 2.3.1 highlights that the choice of the rural station has a significant impact on the simulation results and performance. This differs from [30] who tested UWG (2014 version) under the humid tropical climate of Singapore and concluded that the location of the rural station did not have a major impact on the simulation.  
Figure 11: **Simulated vs. measured air temperatures** (in °C) at Carmes for the two pairs of days used for validation. A specific color is applied to each point according to the UTC hour (from 0 to 23).  
Figure 12: **Standardized residuals vs. simulated air temperatures** (in °C) at Carmes station on the two pairs of days. A specific color according to the UTC hour (from 0 to 23) has been used. Red lines correspond to \(\pm 3\) and points below/above are considered outliersSeveral reasons may explain these opposite observations. First, from a **geographic** point of view, the city of Toulouse is characterized by a fairly urbanized center and a more pronounced green belt, whereas Singapore looks more like a continuum of urbanization. In this context, the localization of the rural station is less sensitive in Singapore because of the low urban gradient. Besides, Singapore is surrounded by the sea. Therefore, finding a rural station away from a body of water, as recommended in the rural station selection criteria, seems to be difficult. The second reason may be the different **climate** of Toulouse and Singapore that are completely different. Singapore has indeed an equatorial climate with two seasons, wet and dry, while the climate of Toulouse is generally characterized by four seasons having relatively mild winters and hot summers where the frequency of UHI is higher (and the difference with the rural station amplified). These points can explain our observation that, in our study, the choice of the rural station is significant, especially on days that are favorable to UHI, unlike observations made in [30].  
In practice, the fact that the Meteopole station is still inside the urbanized area of Toulouse, even if located in a green area, may explain its poor ability to provide a proper rural climate context. Even if Mondouzil is located further from Toulouse city center, Figure 1 shows that this station is close to the built-up area. Consequently, its measurements may be influenced by surrounding urbanization. Finally, the Centre equestre station, far away from the city and rather in green areas, appears as the rational choice. A more detailed analysis of the local climatic context would help to better understand these results.  
For a generalization of the use of UWG, it would be relevant to deepen this investigation on the impact of the location of the rural station.
## 4 Routing Protocol
### Distance Discovery  
1. The sink node broadcasts a packet named Startup in the network with the information including hop, type and nodeld. Here hop represents the value of hop distance between the sink node and receiving node and the initial value is set to 1. Type represents the type of messages and the nodeld means identity of the sending node.  
Figure 3: The procedure of Sensor Protocol for Information via Negotiation (SPIN).  
_Step2_: When a sensor node receives the startup packet, it stores this hop value in memory as its own hop distance from the sink node. After that, the node sets the hop value as 1 and then re-broadcasts the startup packet to its neighbors with the modified hop value. _Step3_: It is possible for a sensor node to receive multiple startup packets from different intermediate nodes. Whenever a node b receives startup packets from its neighbors \(a_{i}\), \(1\leq i\leq n\), it checks the hop distances and sets the distance to the minimum, _i.e._,  
\[\min\{\forall h(a_{i},b),i=1,n\} \tag{4}\]  
where \(h(a_{i},b)\) represents hop distances between the nodes \(a_{i}\) and \(b\) and \(n\) is the number of neighbor nodes of node \(b\) from which it receives the startup packets.  
_Step4_: This process is continued until all nodes in the network get the startup packets at least once within the steps above. The negotiation phase will be launched after successful completion of this phase.
# Fother Characterization Using a Novel Bubble Size Measurement Technique
## 3 Results
### Comparison of Novel and Conventional Bubble Size Measurement Techniques
In the case of the APBS probe, the image analysis field of view was approximately 63 cm\({}^{2}\), with each image containing about 1000 bubbles. In the case of the PVM probe, the field of view was only 1.17 mm\({}^{2}\). In such a small image area, the number of bubbles per image varied between 8-10 bubbles per image, to only a partially visible single bubble. Overall, 90 images were obtained using the APBS probe (30 images per test run), containing over 80,000 bubbles. On the other hand, the PVM technique analyzed 2450 images, which yielded the detection of under 3000 bubbles. Despite the 28-fold increase in the number of images processed, the time requirements for the use of the PVM probe technique were very similar to that of the APBS probe. The \begin{table}
\begin{tabular}{c c c} \hline
**D32 (mm)** & **PVM** & **APBS** \\ \hline Test 1 & 0.954 & 1.058 \\ \hline Test 2 & 0.945 & 1.125 \\ \hline Test 3 & 0.988 & 1.142 \\ \hline Average & 0.962 & 1.109 \\ \hline
95\% CI & 0.026 & 0.050 \\ \hline \end{tabular}
\end{table} 
Table 4: Sauter mean bubble diameter measured with PVM and APBS probes. \begin{table}
\begin{tabular}{c c c} \hline
**Comparison** & **PVM** & **APBS** \\ \hline Number of analyzed images & 2450 & 90 \\ \hline Number of detected bubbles & 2894 & 83,683 \\ \hline Test time (min) & 3 & 30 \\ \hline Image analysis time (min) & 90 & 6 \\ \hline Data post-processing time (min) & 0 & 30 \\ \hline \end{tabular}
\end{table} 
Table 5: Summary of the measurement parameters for PVM and APBS probes. Figure 9: Bubble size distributions obtained using the PVM and APBS probes (there are error bars for APBS, but they are small and not visible). time gains made in analyzing fewer images were lost in the labor-intensive process of probe installation and cleaning. In addition, the APBS probe required post-processing of the data to perform the bubble diameter correction due to the difference in hydrostatic pressure between the sampling point and the measurement point.
# Theoretical and Numerical Research on High-Speed Small Refrigeration Twin-Screw Compressor
## 4 Numerical Model  
### Control Volume and Grids of the Fluid Domain  
The CFD domain comprises the main and gate rotor fluid regions along with the suction and discharge ports. The numerical model incorporates critical geometric clearances, including the tip, interlobe meshing, and axial end clearances. Figure 5 illustrates the complete control volume configuration with detailed boundary definitions.  
The fluid dynamics within the computational domain are governed by the fundamental conservation laws of mass, momentum, and energy. The turbulence characteristics are modeled using the shear stress transport (SST) k-\(\omega\) formulation, which effectively combines the advantages of the k-\(\omega\) model's near-wall resolution with the k-\(\varepsilon\) model's far-field performance. The total energy model is employed to simulate thermal energy transport, providing a comprehensive approach to accurate heat transfer prediction in high-speed compressible flows.  
The thermodynamic properties of refrigerant R410A are determined using the Redlich-Kwong (RK) equation of state. The analysis assumes a negligible influence of the lubricating oil throughout the computational domain. The operational boundary conditions, including inlet/outlet pressure and temperature parameters, are specified in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Numerical Value** \\ \hline Refrigerant & R410A \\ Evaporating temperature (K) & 276.15 \\ Condensing temperature (K) & 323.15 \\ Inhalation superheat (K) & 5 \\ Clearance (mm) & 0.02 \\ Gas dryness (-) & 0.95 \\ Rotation speed (rpm) & 3000 \\ Internal volume ratio (-) & 2.85 \\ Specific heat capacity at constant pressure & 1880 \\ of lubricating oil (J/(kg\(\cdot\)K)) & 1880 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of the design condition.
# On Data Leakage Prevention Maturity: Adapting the C2M2 Framework
## 2 Materials and Methods
### Input Knowledge
#### 2.2.1 DLP Definition  
In alignment with the National Institute of Standards and Technology's (NIST) Special Publication 800-137 [18], DLP (also known as data loss prevention) in this article is defined as the detection and prevention of intentional and unintentional policy violations concerning data in transit, data at rest, and data in use. DLP encompasses a range of techniques and technologies designed to protect data across all stages, ensuring compliance with legal and regulatory standards. Primarily, it is a task within a security operations center (SOC), where outgoing data are scrutinized against specified rules to secure customer data and intellectual properties and to prevent reputational damage.
## Network Setting and Computational Model  
### Scenario Description  
We considered multi-access edge computing in 5G HetNet comprising a set of SBSs connected to one MBS (Figure 1). MDs with limited computational capacity are randomly situated, thereby allowing them to directly connect to the SBSs in order to offload their computational task(s), with the SBSs connected to the MBS through fiber-optic links [13; 16]. The SBSs and the MBS are integrated with MEC servers that can service the computational task(s) of the MDs. We defined the MEC servers co-located with the MBS and the SBSs as mMECs and sMECs, respectively. In this scenario, the MDs connect to the SBSs, which enable latency reduction and increases data rates. For this reason, we did not consider the MDs directly connected to the MBS [16]. We assume that the computing capability of MEC server attached to the SBS is limited and, so, when receiving many task offloading requests, may become overloaded; therefore, the computational task of MD can first be offloaded to the sMEC, but in the event that the computation resource is exhausted, the computational task will be offloaded to the mMEC, where powerful computing resources are available [38].  
We denote the set of MDs as \(\mathcal{M}=\{1,2,\ldots,M\}\) and the set of SBSs as \(\mathcal{N}=\{1,2,\ldots,N\}\). Each MD has a computing-intensive task to be accomplished, and here, we assume that each MD has one task, which is subsequently executed on either the MD, sMEC, or mMEC. Each MD can offload to an sMEC via wireless links or continue offloading to the mMEC via fiber-optic links. The computational task of each MD is atomic and cannot be divided. The computational task of each MD is represented as \(I_{m}=\{D_{m},C_{m}\}\), \(m\in\mathcal{M}\), where \(D_{m}\) is the data size of the computational task to be distributed to the edge cloud for computation and \(C_{m}\) is the total number of computational requirements required to complete the computational task, as measured in CPU cycles.