esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Liquid Medium Growth Assay  
Based on the visual observation of the growth of _P. ostreatus_ and _P. erygii_ in the absence or presence of TCE at 14 and 140 mg L\({}^{-1}\), both fungi were able to grow normally in the presence of TCE at the two concentrations tested, forming round pellets similar in shape and size to those of the control. Therefore, the presence of TCE at concentrations of 14 and 140 mg L\({}^{-1}\) in the liquid broth did not seem to be toxic to either _P. ostreatus_ or _P. erygii_ at a macrostructural level (Figure 1).  
Figure 2 shows the SEM images of _P. ostreatus_ and _P. erygii_ in the absence and presence of TCE in the liquid medium. These images illustrate that the two fungi exhibited different effects at micro structural level in the presence of TCE. A morphological analysis of the scanning electron micrographs of _P. ostreatus_ showed that their hyphae had been structurally altered to some extent when cultured in the highest concentration (140 mg L\({}^{-1}\)) of TCE (Figure 2C). The ends of the hyphae appeared to be split and frayed and the hyphae did not maintain the typical tubular structure that can be seen in the control (Figure 2A). In the sample that had been cultured in TCE at 14 mg L\({}^{-1}\) the hyphae retained the tubular structure, but the hyphal ends also appeared to be broken (Figure 2B). Therefore, although _P. ostreatus_ tolerated the presence of TCE up to 140 mg L\({}^{-1}\) and produced morphologically  
similar mycelial pellets at macroscopic level, the structure of hyphae had been damaged by TCE, especially at the highest concentration.  
Figure 1: Pellets of _Pleutotus ostratus_ (**A**) and _Pleutotus erggii_ (**B**) growing in liquid broth without (control) and with trichloroethylene (TCE) at 14 and 140 mg L\({}^{-1}\).  
Figure 2: SEM images of _P. ostratus_ (**A**–**C**) and _P. erggii_ (**D**–**F**) grown in malt extract liquid broth for 14 days without (**A**,**D**) and with TCE at 14 mg L\({}^{-1}\) (**B**,**E**) and 140 mg L\({}^{-1}\) (**C**,**F**).  
On the contrary, the hyphae of _P. erygii_ seemed to maintain their morphological structure and appeared unaffected by the presence of the pollutant at any concentration (Figure 2E,F) exhibiting the same the tubular structure both in the presence or absence of TCE.  
The secretion of the extracellular enzymes laccase and MnP are another indication of fungal functioning [40]. Although _P. ostreatus_ did not express laccase in the absence of TCE in liquid broth, low levels were detected in the presence of the contaminant (Figure 3A), despite the structural alteration of its hyphae. In addition, MnP was detected in both the absence (from day 9) and presence of TCE (14 mg L\({}^{-1}\)) (Figure 3A). However, _P. erygii_ expressed laccase and MnP in the absence and presence of TCE (14 and 140 mg L\({}^{-1}\)) (Figure 3B).
## 3 Results and Discussion  
This section focuses on the experimental verification of the proposed method. Firstly, the experiment without turbidity interference was designed. FDPE_PLS model was used to predict COD, and the results were compared with the PE_PLS model. The experiment of turbidity compensation was designed, and the turbidity compensation model was established and compared with MSC. Finally, the method proposed in this paper is tested on actual water samples and compared with other common methods.  
Figure 7: The flow chart of FDPE_PLS with turbidity interference.
## 3 System Model
### Hop-by-Hop Routing Scheme  
Routing is conducted within the defined MHR between the established source and destination. In the grid-mesh topology, the minimum-hop path between the source and destination satellites is formed along the edge of the defined MHR. The shortest path follows this edge. In this case, the criterion for selecting the optimal direction is the length of the ISL between the two satellites. The length of the intra-plane ISL \(L_{v}\) is fixed and calculated as follows [12]:  
\[L_{intra}=\sqrt{2}R\sqrt{1-\cos(\frac{2\pi}{M})}, \tag{1}\]  
Figure 2: Minimum hop region in LEO satellite network.  
where \(R\) is the orbital radius. The length of the inter-plane ISL, denoted as \(L_{inter}\), varies according to the latitudes of the two neighboring satellites, calculated by  
\[L_{inter}=\alpha\times\cos(\mathrm{lat}), \tag{2}\]  
where lat is the satellite latitude the inter-plane ISL is connected to (the constellation phase factor is zero). \(\alpha\) is calculated as follows:  
\[\alpha=\sqrt{2}R\sqrt{1-\cos\left(\frac{2\pi}{2\times N}\right)}. \tag{3}\]  
Considering the geographical characteristics of the Earth, the lengths of the inter-ISLs connected at higher latitudes are shorter than those near the equator. However, the intra-ISLs connecting satellites in the same orbit have the same length for all satellites. Therefore, if the latitude of the current satellite is higher than that of the destination satellite, the shorter-length inter-ISL is selected as the primary direction. However, if the latitude of the current satellite is lower than that of the destination satellite, the intra-ISLs are selected for the primary direction. This process continues until the satellite reaches the same latitude as that of the destination. Thus, the next hop is selected to minimize the total length of all the ISLs constituting the path without exchanging information between the satellites.  
When the routing path is through the polar region, inter-ISL connections are not possible in polar regions. To utilize a shorter inter-ISL, the intra-ISL is primarily used up to just before the polar boundary to transmit up to the same plane as that of the destination. Once the satellite reaches the polar boundary, the shortest available inter-ISL is selected as the primary direction. Therefore, the hop-by-hop routing scheme utilizes only the geographic location information of the two satellites. The path with the shortest ISL is chosen as the next hop. The pair consisting of the destination and direction of the next hop is matched and stored in the routing table for further use. In the case of link failure, a strategy is required needed to reduce the number of dropped packets. To handle potential link failures, both primary and secondary next-hop directions are precalculated. If the primary direction is unavailable, the forwarding direction is determined using a precalculated secondary direction. This provides a backup plan for routing against unexpected network problems.
# Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles
## 1 Introduction
In the broader context of machine learning, the importance of setting random seeds for reproducibility is well-recognized, although not specific to aerodynamic applications; for example, the fine-tuning of large language models [17], the general usage of seeding in coding ML projects [18], and the study of black swans, which are seeds that produce radically different results [19]. While these studies provide valuable insights into the impact of random seeds across various ML applications, there remains a gap in the literature specifically addressing their role in aerodynamic performance prediction. A key novelty of this present study lies in the implementation of ensemble modeling structured around three distinct pseudo-random seed values, a strategy that was not previously applied in aerodynamic ML contexts. Given the inherent sensitivity of aerodynamic predictions to model initialization and stochastic training dynamics, addressing seed-induced variability is critical. In addition to machine learning ensemble models designed for performance prediction, ensemble methods have also been applied in the field of aerodynamic state estimation, as shown by [PERSON] and [PERSON] [20] in a state-of-the-art review on AI and machine learning in aerodynamics. For instance, [PERSON] and [PERSON] [21] proposed an ensemble-based Kalman filter to estimate aerodynamic flow states around airfoils and cylinders. Their method demonstrates how ensemble averaging can be effectively utilized to capture system uncertainty and improve robustness in flow reconstruction using limited pressure or velocity measurements. Similarly to this paper's approach, [PERSON] and [PERSON] [22] applied a bagging deep ensemble approach by training multiple neural networks with varied initializations in order to predict aerodynamic forces from simulated and experimental data. Furthermore, [PERSON] et al. [23] explored stacking-based ensembles for multi-objective aerodynamic shape optimization, stacking diverse base learners to boost both predictive accuracy and optimization efficiency. In addition to these, a growing number of studies have adopted ensemble strategies in aerodynamic machine learning tasks to enhance predictive performance and robustness; e.g., Refs. [24; 25; 26; 27; 28]. While all these studies explore the performance of ensembles composed of different models, none explicitly investigate the impact of random seed initialization on a fixed machine learning architecture. The novelty of this present study lies in shifting the ensemble modeling focus toward internal calibration, demonstrating that prediction performance can vary by as much as 250% solely due to seed selection. This highlights the substantial influence of initialization on model reliability, even when architecture and data remain unchanged. By systematically analyzing and averaging across multiple seeds, this approach mitigates the influence of unfavorable initializations and enhances the stability and reproducibility of predictions.
## 1 Introduction
In this example, the centre of rotation is located between B and C point. It is therefore critical for the surgeon to carefully decide the movements in space of this triangle, both in the sagittal and rotational directions, especially in cases with asymmetry in chin and mandibular angles. In order to decide on these important parameters, the surgeon traditionally has evaluated the so-named \"model surgery\" technique, that is the simulation of the movements of bone pieces made on plaster models mounted in an articulator. Nowadays, however, the possibility to simulate bone movements on appropriate computer programs based on 3D reconstruction offers numerous advantages. MODEL SURGERY--In the traditional analogical method, the impressions of the dental arches are developed in plaster and mounted on an articulator using face bow. Between the bases of the articulator and the models is interposed a properly calibrated plaster base, with reference notches allowing a precise repositioning of the models themselves, simulating the desired movements of the bone pieces. The movement of the mandible is analysed for both the anterior median region and the two lateral retro-molar regions. As previously stated, the anterior median region of the lower model represents the repositioning of the chin, i.e., the symmetry of the chin with respect to the midline of the face. The right and left lateral retro molar regions of the plaster model represent the repositioning of the mandibular angles, i.e., the symmetry of the lateral position of the mandibular angles from the midline of the face. CT PROGRAMMING--In the modern digital method [16], optical impressions of the arches are superimposed on the CT reconstruction of the maxilla and mandible, thus obtaining the virtual 3D simulation of the patient. This technology is in line with the current quest for ever greater customisation of treatment and pursuit of patient satisfaction, a concept now established in other branches of dentistry [17; 18; 19]. The osteotomy sections are then simulated on the virtual 3D reconstruction and bone pieces are repositioned according to the established treatment plan. In the CT reconstruction, the best possible repositioning of both the chin symmetry and the symmetry of the mandibular angle regions is directly evident and/or may be related to 3D cephalometric planes. The Authors believe that programming mandibular angles, if done with the traditional method of articulator-mounted model surgery, fatally leads to an incorrect estimation of the skeletal movements and demonstrates the anatomical reasons for this inaccuracy. The Authors believe that virtual articulator programming is much more efficient in this regard and avoids the mentioned error. The Authors show this concept with a clinical case that illustrates the differences between the two types of programming compared.
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 3 Evacuation Planning through Reinforcement Learning
The latter becomes available to the ESCAPE service through sensors established at the evacuation routes, which send the information to the Emergency Control Center that, in turn, broadcasts that information to the evacuees. At the initial stage of evacuation planning, evacuees have no prior knowledge of reward probability \(\hat{r}_{m,e}(t=0),\forall m\in M,\forall e\in E\), and action probability \(Pr_{m}(t)=[Pr_{m,1}(t),\ldots Pr_{m,e}(t),\ldots Pr_{m,1|\mathds{E}|}(t)]\). Thus, the initial selection of an evacuation route by evacuees is made with equal probability, i.e., \(Pr_{m,e}(t=0)=\frac{1}{|E|}\). It is also highlighted that, based on the principle that each evacuee tries to improve their perceived satisfaction from the evacuation process, a convergence toward the evacuation route that each evacuee wants to join is finally obtained. The algorithmic description of autonomous evacuation planning based on the reinforcement-learning approach of stochastic learning automata is provided in Section 5.
## 2 Z-P Yield Criterion  
The Zienkiewicz-Pande yield criterion, also known as the Z-P yield criterion, was proposed by [PERSON] and [PERSON] as an alternative to the Mohr-Coulomb yield criterion [29]. It was developed to address the limitations of the Mohr-Coulomb criterion, particularly when dealing with edges and sharp corners. The exact mathematical expression of the Z-P yield criterion can be described as follows:  
\[f=\beta_{2}p^{2}+\beta_{1}p-\gamma+\left[\frac{q}{g(\theta)}\right]^{n}=0 \tag{1}\]  
where \(p=\frac{\sigma_{li}}{3}\), \(q=\sqrt{\frac{1}{2}s_{ij}s_{ij}}\), \(s_{ij}=\sigma_{ij}-\frac{1}{3}\sigma_{kk}\delta_{ij}\), \(\sigma_{ij}\) is the Cauchy stress and \(s_{ij}\) means the deviatoric Cauchy stress; \(\beta_{1}\) and \(\beta_{2}\) refer to coefficients; \(n\) is an exponent, generally 0, 1 or 2; \(\gamma\) is the yield parameter; \(g(\theta)=\frac{2k}{(1+k)-(1-k)\sin 3\theta}\) is the shape function, \(k=\frac{3+\sin\varphi}{3-\sin\varphi}\) represents the ratio of triaxial tensile strength to triaxial compression strength; \(\theta\) indicates the stress lode angle, and when \(\theta=\pm\frac{\pi}{\theta}\), the Z-P yield criterion is fitted with the M-C yield criterion.  
This paper uses a branch of the hyperbola as the yield curve on the \(\overline{\epsilon}^{p}=\frac{\sqrt{2}}{3}\sqrt{\left(\bar{\epsilon}_{p1}- \bar{\epsilon}_{p2}\right)^{2}+\left(\bar{\epsilon}_{p1}-\bar{\epsilon}_{p3} \right)^{2}+\left(\bar{\epsilon}_{p2}-\bar{\epsilon}_{p3}\right)^{2}}\) meridian plane. The Z-P yield criterion can therefore be depicted in the following form [30]:  
\[f=\tan\overline{\varphi}p+\sqrt{\frac{q^{2}}{g^{2}(\theta)}+a^{2}\tan \overline{\varphi}}-\overline{c}=0 \tag{2}\]  
where  
\[\tan\overline{\varphi}=\frac{6\sin\varphi}{3-\sin\varphi},\overline{c}=\frac{ 6c\sin\varphi}{3-\sin\varphi},\sin 3\theta=-\frac{3\sqrt{3}}{2}\frac{J_{3}}{ \sqrt{j_{2}^{3}}} \tag{3}\]  
and \(\varphi\) represents the internal friction angle of the material, while \(c\) represents its cohesion. \(J_{2}\) and \(J_{3}\) correspond to the second and third invariants of the deviatoric stress tensor, respectively; a is a coefficient, and when it approaches 0, the yield curve is infinitely close to the M\(-\)C envelope.  
The \(Z-\)P yield criterion addresses the issue of sharp corners and edges on the \(\pi\) plane and the p\(-\)q meridian plane encountered in the M\(-\)C yield criterion. Additionally, it takes into account the effects of hydrostatic pressure and intermediate principal stress, as well as the difference between the yield curve and the hydrostatic pressure (Figure 1).  
Figure 1: _Cont._
## 3 Analysis of Experimental Results  
### Setting of Relevant Mechanical Parameters  
As the soil materials underwent reciprocating push and pull shears on the testing machine, hysteresis loops were formed on the shear stress-shear displacement curve [31], representing the energy loss from overcoming the frictional resistance of the soil grains during the reciprocating shear. The shear stress-shear displacement curves of coarse-grained soil under the reciprocating shear are shown in Figure 4. To facilitate the analysis, the A\(\rightarrow\)B shearing process is denoted as a forward shear, and the shear strength is expressed as \(\tau_{T}\); The B\(\rightarrow\)A shear process is denoted as a reverse shear, and the shear strength is expressed as \(-\tau_{L}\) (the negative sign denotes the direction); the area of the hysteresis loop is denoted by \(S\).  
Figure 4: The schematic diagram of mechanical parameters related to reciprocating shear hysteresis loop of soil samples.  
Figure 3: Reciprocating shear test path.  
According to the relevant theories of geotechnics [31], the shear stiffness, \(K\), and damping ratio, \(\lambda\), of the tested argillaceous siltstone rockfill materials can be calculated using Equations (2) and (3).  
\[K=\frac{K_{1}+K_{2}}{2} \tag{2}\]  
\[\lambda=\frac{\lambda_{1}+\lambda_{2}}{2}=\frac{1}{2}(\frac{S}{4\pi S_{\Delta O \Sigma}}+\frac{S}{4\pi S_{\Delta O\Delta}}) \tag{3}\]  
where \(K_{1}\) is the forward shear stiffness (kPa); \(K_{2}\) is the reverse shear stiffness (kPa); \(S\) is the hysteresis loop area (kPa\(\cdot\)mm); \(\lambda\) is the damping ratio. \(\Lambda_{1}\) is the damping ratio under the forward shear strength, and \(\lambda_{2}\) is the damping ratio under the reverse shear strength.
## 3 Results
### Case Study B
#### 3.2.3 Results  
From the analysis, the only data of note that are applicable to pure and balance of system zero bid models are shown in Tables 11 and 12. The data in these tables are colour dependent based on LCOE values.  
* Red donates a scenario at which a loss would occur
* Green indicates a range at which a zero bid CfD is applicable  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Windfarm** \\ \hline Nameplate Generational Capacity (MW) & 800 \\ Net Capacity Factor (\%) & 0–60 \\ Discount Rate (\%) & 8.9 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Control Parameters.  
Figure 4: (**a**) 25 Year Case Scenarios; (**b**) Scenario distribution; (**c**) Enhanced distribution; and (**d**) Generational capacity.  
* Blue highlights the LCOE when a pure subsidy free bid is valid  
From the input parameters, under very-low-cost reduction, with high-capacity factors is a zero-subsidy model achievable in these circumstances. From the observation of data, Figure 5 shows the output data for the BEIS calculator under variable cost scenarios over 25 years. The effect of the net capacity factor upon LCOE calculations have a minor effect on the outcome. In net capacity ranges of 10%, average LCOE adjustment per percentage change for a very low case scenario is shown below in Table 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**25 Years** & \multicolumn{3}{c}{**V. Low**} \\ \hline
**Capacity Factor (\%)** & **BEIS** & **Fixed** & **Floating** \\ \hline
46 & 89.91 & 85.688 & 95.388 \\
48 & 86.814 & 83.218 & 91.849 \\
50 & 84.253 & 80.687 & 88.171 \\
52 & 82.160 & 88.538 & 84.780 \\
54 & 84.244 & 86.199 & 81.840 \\
56 & 88.440 & 84.185 & 78.724 \\
58 & 46.770 & 82.310 & 76.010 \\
60 & 45.211 & 86.578 & 71.478 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Net Capacity (20 Years).  
Figure 5: Net Capacity Factor vs. LCOE.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**20 Years** & \multicolumn{3}{c}{**V. Low**} \\ \hline
**Capacity Factor (\%)** & **BEIS** & **Capacity Factor (\%)** & **BEIS** \\ \hline
46 & 88.971 & 85.688 & 95.388 \\
48 & 86.814 & 83.218 & 91.849 \\
50 & 84.253 & 80.687 & 88.171 \\
52 & 82.160 & 88.538 & 84.780 \\
54 & 84.244 & 86.199 & 81.840 \\
56 & 88.440 & 84.185 & 78.724 \\
58 & 46.770 & 8
# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
## 3 The Structure of DMCNet
### Signal Denoising Module  
The transmitted signal is subject to interference after passing through the channel, and the received signal contains varying levels of noise depending on the signal-to-noise ratio (SNR). A lower SNR corresponds to higher noise in the received signal, which significantly affects the overall signal detection performance. To mitigate the impact of channel noise, this paper introduces a denoising module in the first layer of DMCNet. The received signal is divided into data and pilot components, and each component undergoes denoising using their respective denoising networks. Two approaches were explored in the experiments. The first approach utilizes a two-layer fully connected network, where the input signal is initially expanded to twice its original size in the first layer and then compressed back to the original length in the second layer. The second approach employs a residual structure for the two-layer fully connected network, which incorporates a shortcut connection that subtracts the output of the fully connected layer from the original signal. This approach aligns better with the definition of the noise model. The structure of the denoising module is shown in Figure 3: