esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
A three-dimensional CFD model incorporating particle transport and electro-hydrodynamics in an electrostatic precipitator is constructed to investigate the flow field and the characteristics of the electrostatic field in the ESP. The present CFD model is validated with the experimental data, and the current results show very good agreement with the available experimental data. The 3D CFD model presented in this study could be used for design and detailed analysis of the ESP considering geometric, electrostatic, and particle transport parameters. In addition to that, an improved analytical model was proposed to calculate the collection efficiencies for the wire-plate type electrostatic precipitator. In all cases considered in this study, a stable fluid flow exists in the ESP channel. The main conclusions could be given as follows:  
* With the decrease in the wire-plate distance, the electric field strength and the current density between the corona electrodes tends to increase. It is shown that increasing the potential difference, decreasing the gas flow velocity, and reducing the distance between the charging and collecting electrodes results in better collection efficiencies.
* Corona charge wire electrode diameter has a strong effect on the collection efficiencies. Collection efficiencies for all particle diameters are increasing with larger wire diameters. However, the energy usage of ESP increases with rising wire diameter.
* Collection efficiencies generally have a minimum of about 0.1-0.2 \(\upmu\)m particle size for all cases. Although the collection efficiency values change with the operating conditions, the most penetrating particle size (MPPS) appears between 0.1 and 0.2 \(\upmu\)m. This is resulted from the increase of diffusion charging effect for smaller size particles.
* The improved analytical model could be confidently used for the collection efficiency estimation of the wire-plate type ESPs.  
Conceptualization, M.K., M.M. and A.F.A.; Data curation, M.K., M.M. and A.F.A.; Formal analysis, M.K. and M.M.; Investigation, M.K., M.M. and A.F.A.; Methodology, M.K., M.M. and A.F.A.; Project administration, M.K.; Supervision, M.K.; Writing--original draft, M.K., M.M. and A.F.A.; Writing--review & editing, M.K., M.M. and A.F.A. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the Scientific and Technology Research Council of Turkey (TUBITAK), under project number 218M604. The authors would like to thank TUBITAK for his support.  
Figure 18: Relative power consumption comparisons based on the corona wire diameter and the applied voltage.
# A Metawindow with Optimised Acoustic and
## 3 Results
### Integrated Optimisation of Acoustics and Ventilation  
The proposed configurations' ventilation performance is finally numerically investigated through a FEM computational fluid dynamic (CFD) model. In the numerical analysis, the air is set to flow through each configuration, and the corresponding responses are analysed for different inlet velocities and different ventilation opening percentages. Figure 5a shows the pressure drop against inlet air velocities for different AMW unit configurations, while Figure 5b shows the same parameter results for Configuration B for different airflows and different percentages of ventilation opening. An increase in the required pressure differential across the AMW unit is observed proportionally to the increasing flow rate. Moreover, a higher pressure drop results from an AMW unit with a 13% ventilation opening, demonstrating that the smaller ventilation openings offer high airflow resistance due to the static airflow resistance. This is because the static airflow resistance, \(\sigma_{f}\), is indirectly proportional to the ventilation opening (\(\sigma_{f}=8\eta/\psi{(0.5d_{p})}^{2}\), where \(\eta\) is the dynamic viscosity of the fluid, \(\psi\) is the percentage of the opening, and \(d_{p}\) is the opening diameter) [11]. It is evident that as \(d_{p}\) becomes smaller, \(\sigma_{f}\) increases rapidly, and due to buoyancy forces results in a high pressure drop (\(\Delta P=-\sigma_{f}v_{a}\), where \(v_{a}\) is the airflow velocity) across the AMW unit [41]. Consequently, most of the AMW unit configurations are suitable for natural ventilation, and a dynamic tuned ventilation capacity can be achieved for particular ranges by adjusting the window's ventilation opening.  
It is worth mentioning that this study is intended to present a preliminary evaluation of the ventilation and acoustic performance of the AMW unit, where the effect of flow-induced noise is neglected. As the AMW unit structure comprises cavities along the flow path, Rossiter tones may occur aerodynamically. Previous studies have investigated the effect of low-Mach-number flow (below 0.1) on a similar system comprising cavities and perforations and have found that the maximum deviation between no flow and Mach 0.1 is around 10 dB, mainly at the attenuation peak [42]. Therefore, as ventilation only requires a very low flow speed, the current simplification by neglecting aerodynamic noise is considered to be reasonable. The results in the absence of flow could also set a baseline for future comparisons with the flow.
## 3 Theoretical Method for Determining the WFZDH
### Method for Determining the WFZDH  
By analyzing the maximum bending subsidence deformation within the key stratum mechanical model and the critical span at the initial fracture of the key stratum, a WFZDH determination method is proposed:  
Figure 4: Bending moment distribution of four-edge clamped thin plate.  
Step 1: According to the rock stratum assignment conditions, combined with the key stratum discrimination method [15], the key strata positions above the coal seam can be obtained. Meanwhile, the load borne by each key stratum and their respective stratigraphic positions were also calculated. The key strata were then sequentially numbered from bottom to top as key stratum \(j\) (\(j\) = 1, 2, \(\ldots\)).  
Step 2: By considering the information on the working face dimensions and advance distance, in combination with the parameters of the key stratum, such as tensile strength, thickness, and the load it bears, the relationship between the suspended span and its initial fracture distance was determined. If the suspended span of a key stratum exceeds its initial fracture distance, the key stratum can undergo fracturing, enabling the next step of the evaluation to proceed.  
Step 3: By sequentially substituting the bearing load, thickness, and Poisson's ratio of each key stratum into Equation (14), the maximum bending subsidence deformation of each key stratum was calculated. Meanwhile, the height of the free space below each key stratum was calculated using Equation (1). If the free space height below a key stratum is greater than its maximum deformation, it indicates that the key stratum has not fractured, and the WFZ will continue to develop upwards. Conversely, if the free space height is less than the maximum deformation, the key stratum will not fracture, and the WFZDH will be at the bottom of that key stratum.  
There are two applicable conditions for the application of this discriminative method: Firstly, this method is primarily intended for determining the WFZDH in the working face with boundary conditions of four-sided fixed support. Secondly, it requires a comprehensive understanding of the thickness, lithology, and mechanical parameters of the strata. The WFZDH determination procedure is shown in Figure 5.
## 3 Results and Discussion
### Insulator String Condition Based on UV Emission Area and Level Status in Field Test  
Some results of the insulator string condition assessment using UV quantitative parameters and visual inspection are shown in Table 6. The video name represented the insulator string assessed. In general, the insulator string with level three had an average value that was greater than level two, while the insulator string with level two had an average value that was greater than level one.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Video Name** & **Average Value of UV Emission Area** & **Condition Level** \\ \hline
06\_19\_21\_37\_58 & 0.001599738 & 2 \\
06\_20\_04\_01\_56 & 0.002146720 & 2 \\
06\_21\_21\_52\_54 & 0.019572314 & 3 \\
06\_21\_21\_53\_23 & 0.024267247 & 3 \\
06\_21\_22\_13\_16 & 0.000029556 & 1 \\
06\_21\_22\_15\_15 & 0.000022527 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: UV quantitative parameter and visual-based assessment results in representing the insulator string condition in the GT and GIS areas.  
Figure 5: UV quantitative parameters for insulator string: phase suspension string at the GIS area.
## 4 The WBANs Communication Stack
### Routing Protocols
#### 4.3.2 QoS-Aware Routing Protocols
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocols**} & \multirow{2}{*}{**Objective**} & \multicolumn{6}{c}{**Performance Metrics**} \\ \cline{3-8}  & & **Network** & **Network** & **Mobility** & **Delay** & **PDR** & **Energy Consumption** \\ \hline Routing Service & To provide priority- & & & & & & \\ Service & based routing with user & Small & N/A & Yes & N/A & Medium & N/A \\ FW [88] & specific QoS & & & & & & \\ \hline RL-QRP & To Achieve high packet & & & & & & \\
[89] & delivery ratio and low & Small & Low & Yes & High & High & N/A \\  & delay & & & & & & \\ \hline LOCAL & To provide Data & & & & & & \\ MOR [90] & dependent QoS & Medium & N/A & Yes & Low & High & High \\ \hline DMQoS & & & & & & & \\
[91] & Data-dependent QoS & Large & Very Low & Yes & Low & High & Medium \\ \hline QPRD & To reduce end-to-end & Very & High & Yes & Very & High & Low \\
[92] & delay & Small & High & Yes & Low & High & Low \\ \hline QPRR & To improve end-to-end & & & & & & \\
[93] & reliability & Small & High & Yes & N/A & High & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of QoS-Aware Routing Protocols. and energy consumption. In terms of high packet delivery and low delay, RL-QRP [89] provides best performance. Concerning priority-based routing and user specific QoS Routing Service Framework [88] achieves best performance. However, neither approach considers energy efficiency. For large-scale networks and with very low network throughput, DMQoS [91] performs better to reduce delay and improve reliability. For small-scale networks with high network throughput, QPRD [92] results into less packet delivery delay while same authors have improves the reliability of data delivery in QPRR [93].
# Multilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry
## 3 Simulation Analysis
### Unchanging Number of Layers
Additionally, the fraction of electromagnetic wave transmission was decreased, and the power to reach the second layer was decreased, according to Equation (6). As a result, the peak power was drastically decreased. Figure 4: The SNR waveform of the reflected signal after decoupling the direct reflected signal. Figure 5: The power reflectivity results for the two modes with different moisture intervals. We set the wave impedance of the air and the first and second layers of soil as \(\eta_{a}\), \(\eta_{s1}\), and \(\eta_{s2}\), respectively. Then the reflected signal power of the second layer of soil can be written as:  
\[p_{s2}=p_{a}\frac{2\eta_{s1}}{\eta_{s1}+\eta_{a}}\frac{\eta_{s2}-\eta_{s1}}{\eta _{s1}+\eta_{s2}}\frac{2\eta_{a}}{\eta_{s1}+\eta_{a}}e^{-2\pi l_{s}} \tag{15}\]  
Figure 7 displays the outcomes of a theoretical Equation (15) computation. The sink model is in the panel on the left. In the top layer of soil, we change the SMC. Reverse osmosis is modeled in the right panel, and the bottom SMC is changed. Three layers total were created through simulation in both models. According to the sink model, when SMC in the first layer rises, the strength of the signal transmitted into the second layer falls and the reflectivity of the second layer also decreases. In the reverse osmosis model, when the bottom SMC rises, the second layer of the soil's SMC rises as well, increasing reflectivity. Furthermore, the second layer of the soil's reflectivity tends to stabilize as the bottom SMC rises. Because the energy of the electromagnetic wave is already low when it penetrates the soil to a depth of 21 cm, the penetration depth used in remote sensing is 10.5 cm [15]; therefore, the increase in the underlying soil moisture has no impact on the power reflectivity for the reverse osmosis mode. Figure 6: Spectrum of the two models with different soil moisture changes without noise. Figure 7: The reflectivity of the second layer changes with SMC.
## 3 Orthogonal Experiments and Grey Relational Analysis
### Selection of Process Parameters
#### 3.1.2 Stamping Speed  
In numerical simulations of sheet metal forming, the actual drawing speed is typically increased by a factor of 100 to serve as the stamping speed (i.e., virtual stamping speed) [12]. If the stamping speed is too high, the workpiece is prone to uneven plastic deformation; conversely, if it is too low, production efficiency decreases. Based on numerous numerical simulation trials and practical production experience, the average stamping speed is set at 3000 mm\(\cdot\)s\({}^{-1}\). Four levels of stamping speed are considered: 1000 mm\(\cdot\)s\({}^{-1}\), 3000 mm\(\cdot\)s\({}^{-1}\), 5000 mm\(\cdot\)s\({}^{-1}\), and 7000 mm\(\cdot\)s\({}^{-1}\).
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
## 2 Mathematical Background
### Review of Membership-Mappings-Based Privacy-Preserving Transferable Learning
#### 2.5.1 Optimal Noise Adding Mechanism for Differentially Private Classifiers  
The approach suggested in [22] relies on a tailored noise adding mechanism to achieve a given level of differential privacy loss bound with the minimum perturbation of the data. In particularly, Algorithm 5 is suggested for a differentially private approximation of data samples and Algorithm 6 is suggested for building a differentially private classifier.  
```
0: Dataset \(\mathbf{Y}=\left\{y^{i}\in\mathbb{R}^{p}\mid i\in\{1,\cdots,N\}\right\}\); differential privacy parameters: \(d\in\mathbb{R}_{+}\), \(\epsilon\in\mathbb{R}_{+}\), \(\delta\in(0,1)\).
1: A differentially private approximation of data samples is provided as \[y_{j}^{+i}=y_{j}^{i}+F_{v_{j}^{i}}^{-1}(r_{j}^{i};\epsilon,\delta,d),\ r_{j}^{i} \in(0,1)\] (37) \[F_{v_{j}^{i}}^{-1}(r_{j}^{i};\epsilon,\delta,d)=\left\{\begin{array}{ll} \frac{d}{\epsilon}\log(\frac{2r_{j}^{i}}{1-\delta}),&r_{j}^{i}<\frac{1-\delta }{2}\\ 0,&r_{j}^{i}\in\left[\frac{1-\delta}{2},\frac{1+\delta}{2}\right]\\ -\frac{d}{\epsilon}\log(\frac{2(1-r_{j}^{i})}{1-\delta}),&r_{j}^{i}>\frac{1+ \delta}{2}\end{array}\right.,\ r_{j}^{i}\in(0,1).\] (38) where \(y_{j}^{+i}\) is the \(j\)-th element of \(y^{+i}\in\mathbb{R}^{p}\).
2:return\(\mathbf{Y}^{+}=\left\{y^{+i}\in\mathbb{R}^{p}\mid i\in\{1,\cdots,N\}\right\}\).
```  
**Algorithm 5** Differentially private approximation of data samples [22]
# Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered
## 2 System Architecture
### The Edge-Integrated RIS Control Mechanism
Since the TCP feedback time \(t_{d}\) is extremely short (3\(\sim\)4 \(\upmu\)s), the system can rapidly adjust to the optimal beam mode, improving real-time performance. This optimized approach reduces the total feedback latency from 3\(\sim\)4 s in conventional methods to 1\(\sim\)2 s, achieving an improvement in efficiency of 50\(\sim\)70% [28]. Figure 1: Edge-integrated RIS control mechanism application scenarios.
# The Spotting Distribution of Wildfires
## 2 A Transport Model for Firebrand Transport and Combustion
### The Impulse Release IBVP  
The distribution of firebrands at time \(t\geq 0\), location \(x\), height \(z\) and mass \(m\) may be described by \(p(t,x,z,m)\in\mathbb{R}^{+}\). Since flying and burning are deterministic processes, we model the whole process with a transport equation  
\[\frac{\partial p}{\partial t}+\frac{\partial(wp)}{\partial x}+\frac{\partial( vp)}{\partial z}+\frac{\partial(fp)}{\partial m}=0 \tag{5}\]  
on the domain  
\[(t,x,z,m)\in\Omega:=[0,\infty)\times[0,\infty)\times[H,\infty)\times[0,\vec{m}]. \tag{6}\]  
The functions \(w(z),v(t,m),f(t,z,m)\) describe the physical processes of wind transport, falling, and burning, respectively. We will use the appendix to give detailed, physically based, description of these terms.  
To obtain a well defined model, we need to specify boundary and initial conditions. The above Model in Equation (5) is hyperbolic and the spatial characteristics are pointing downwards (falling) and to the right (wind). Hence we only need boundary conditions on the left boundary at \(x=0\). Like in a confetti problem, where you throw confetti into the wind and see where the pieces land, we assume a one-time release of fire brands and we are interested to see where they land. When the fire releases fire brands over a period of time, then a simple integration of our model will account for all the landed brands. Hence we set an initial condition of  
\[p(0,x,z,m)=\left\{\begin{array}{ll}N\phi(z,m)&\quad\mbox{for}\quad x=0\\ 0&\quad\mbox{else.}\end{array}\right. \tag{7}\]  
where \(N\) is the total number of launched brands, and a boundary condition as  
\[p(t,0,z,m)=0,\quad\mbox{for}\quad t>0. \tag{8}\]
## 3 UAV Computing
### SAR Missions  
The primary objective of SAR operations is to locate and rescue persons who may be lost or trapped with injuries due to a natural catastrophe. The first 72 h following a disaster are essential [36], since SAR efforts are at their peak and rescue personnel must safely locate and help survivors in disaster scenarios. As a result, SAR's purpose is to assist in taking rapid steps to save lives.  
Different technologies, including WSN, autonomous UAVs, satellite observations, data processing, and social networks, must be deployed at the same time to boost the effectiveness of SAR operations [36]. As a result, UAVs and fogs can extensively assist SAR operations since they can give quick and real-time data about the surrounding area. In addition, in catastrophe circumstances where continuous updates are required or if rescue crews cannot reach the target area readily and safely owing to debris or other barriers, UAVs and fog-connected WSNs are deployed.  
UAVs are an excellent surveillance tool since they can fly over the intended location and communicate information to rescue personnel, such as photographs and video for a specified target. In this case, UAV-2-station, UAV-2-UAV, and UAV-2-fog networks can help maintain the connection of UAVs, stations, and fog nodes. However, the videos or photos captured by UAVs differ significantly from images gathered on the ground, necessitating the need for assisted intelligent edge computing [46]. Therefore, this factor should be considered while developing an image processing system for UAVs used in SAR missions. Figure 3 illustrates how UAV computing helps to monitor disaster areas, take action in real-time, and efficiently save lives.
## 3 Test Results and Analysis
### Amount of Steel Used  
To better assess the economic feasibility of the reinforcement design guided by different optimal STMs, a reinforcement design evaluation index, namely, reinforcement efficiency, was introduced. The calculation equation is as follows:  
\[\rho_{s}=\frac{P_{u}-P_{0}}{W_{s}} \tag{1}\]  
where \(P_{0}\) is the ultimate load-carrying capacity of a plain concrete specimen (kN); and \(W_{s}\) is the amount of steel used for the specimen (kg).  
Figure 17 shows the amount of steel used and the reinforcement efficiency of each irregular concrete deep beam specimen in the group. It can be seen from Figure 18 that for the irregular concrete deep beams in Group A specimen A-04 used the least amount of steel (4.74 kg), which was 7% and 26% less than that of specimens A-02 and A-03, respectively. Accordingly, specimen A-04 had the highest reinforcement efficiency (11.29 kN/kg), which was 16% and 83% higher than that of specimens A-02 and A-03, respectively. For the irregular concrete deep beam specimens in Group B, specimen B-04 used the least amount of steel (4.36 kg), which was 22% and 41% less than that of specimens B-02 and B-03, respectively. Accordingly, specimen B-04 had the highest reinforcement efficiency (32.33 kN/kg), which was 84% and 111% higher than that of specimens B-02 and B-03, respectively. That is, the rebars were most efficiently used in specimens A-04 and B-04, which were thus the most economically feasible.
## 3 Molecular Mechanisms of Omega-3 Fatty Acids in Cancer
### Inflammation and Immunomodulation  
As previously mentioned, oxylipins formed from the metabolization of n-3 PUFAs interfere with the conversion of AA to inflammatory PGs and LTs [105]. Specifically, DHA is a precursor to the D-series resolvins, protectins, maresin MaR1 and the E-series resolvins.  
On the other hand, EPA and DHA are direct precursors of anti-inflammatory 3-series prostanoids (PGE3 and thromokane A3), 5-series LTs (LTB5, LTC5, LTD5 and LTE5) and the E-series resolvins [106].  
In addition, n-3 PUFAs have been shown to reduce inflammation by downregulating NF-xB and NLRP3 pathways. DHA has been shown to prevent NLRP3 and NLRP1b inflammasome activation and inhibit caspase-1 activation and IL-1b maturation against various damage-associated molecular patterns in a dose-dependent manner via G protein-coupled receptors (GPR) 40 and GPR120. The 2013 study by [PERSON] et al. demonstrated that the scaffolding protein \(\beta\)-arrestin-2 (ARRB2) binds to GPR120 and GPR40 and is a major contributor to the inhibition of DHA in NLRP3 or NLRP1. However, the study suggests that there is another mechanism of inhibition independent of ARRB2, as DHA retains some of its anti-inflammatory properties through the same pathway in the absence of ARRB2 [107].  
Moreover, EPA and DHA cause immunomodulation by increasing circulating IL-10 and IL-22, diminishing the inflammation in the cancer response [108]. This point has been demonstrated in cells cultured in high concentrations of EPA and DHA in human nonmelanoma skin cells [109].  
Referring to other mechanisms, it has been found in prospective cohort epidemiological studies that high intake of n-3 PUFAs decreases the risk of colorectal cancer with a high density of FOXP3+ T cells. Subsequent in vitro investigations have elucidated that DHA exerts a suppressive effect on regulatory T cells, thereby alleviating the inhibition on the proliferation of effector T cells. This immunomodulatory mechanism facilitates the mitigation of certain forms of colorectal cancer, thus indicating a potential avenue for risk reduction through DHA-mediated processes [110]. Although the experiment was performed with colorectal cancer cells, it will be necessary to test whether this mechanism that enhances the antitumor response is shared in other types of cancer.
# NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters
## 4 Experimental Results  
This section aims to verify the effectiveness of the proposed non-uniform deployment framework (NUDIF) in heterogeneous edge clusters through systematic experiments. The experiment focuses on evaluating the throughput performance of NUDIF under different device scales and conducts a comparative analysis with traditional single-pipeline optimization methods.