esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network
## 2 Methods
### Design of Neural Network Model  
#### 2.1.1 Overall Architecture of CNN-DCNN Framework  
Figure 1 presents the overall architecture of the CNN-DCNN framework, which aims to establish a deep learning-based neural network to regress a nonlinear function as follows:  
\[\left(\hat{P},\ \hat{U}\right)=f(S,Re;W,b) \tag{1}\]  
\[Re=cV_{ref}/\mu \tag{2}\]  
where \(P\) and \(U\) are the pressure and velocity field (specifically, \(U\) represents the velocity magnitude), while the \(\hat{}\)hat are placed on fields predicted by CNN-DCNN model, \(S\) is the input SDF matrix containing the information of AOA and airfoil shape, \(W\) and \(b\) are the network parameters to learn, whose updating rules will be introduced in Section 2.2. \(W\) is the weight parameter matrix, and \(b\) is the bias parameter matrix. \(Re\) is the Reynolds number defined by far field incoming velocity \(V_{ref}\), chord length \(c\) of airfoil, and dynamic viscosity \(\mu\). The main prediction process of CNN-DCNN is accomplished by the nonlinear function \(f\), which is expected to approximate, under different design parameters expressed by SDF matrix, the steady-state \(\hat{P}\) and \(\hat{U}\) as closely as possible to the \(P\) and \(U\) given by CFD simulations. In fact, the functional expression \(f\) The working procedure of the CNN-DCNN framework shall be summarized into three steps, namely data generation, training, and testing:  
1. Data generation: Firstly, a geometry library of airfoil is built up, including AOA, maximum camber, and its position along the chord. Those parameters are then formulated into input \(S\) by SDF, while corresponding CFD meshes are generated through gmsh, so that the flow field \((P,U)\) could be obtained by CFD calculation under given operating parameters, i.e., Reynolds number. As the result, the input \((S,\ Re)\) and the label \((P,\ U)\) compose the dataset \((S,\ Re,\ P,\ U)\).
2. Training: Saving a small portion as further \"testing dataset\", the dataset \((S,\ Re,\ P,\ U)\) acquired in step I is then split into \"training dataset\" and \"validation dataset\". (Validation dataset is used to judge whether the model is overfitting [44]) Through the combination of training dataset and operating parameters, the CNN-DCNN model is trained towards the hypothesis of \((P,U)=f(S,\ Re;W,b)\) as in Equation (1).  
Figure 1: Overall architecture of CNN-DCNN framework.  
* Testing: The trained model is tested by predicting \((\hat{P},\ \hat{U})\) of input \(S\) from the unseen airfoils, provided by the testing dataset. Accuracy of the CNN-DCNN model is then measured by comparing \((\hat{P},\ \hat{U})\) with \((P,U)\).
## 4 Conclusions  
In summary, we designed mixed perovskites to optimize bandgap and stability towards photovoltaic applications based on the understanding of the structural and electronic properties of halide-oxide perovskite compounds mixed in various configurations.  
Upon stoichiometrically combining two different types of perovskites, all cubic perovskites generally exhibited dominant contributions on the CBM and VBM bands. The B-p and X-p states tended to be positioned at the top of the VBM and the bottom of the CBM respectively, producing B-x bonds in which the bonding intensity was dependent on the atomic radius. The hybridization between p-p bonds was generally weak. We next found that bandgaps could be readily adjusted via different configurations of unit cells. The compound Cs\({}_{2}\)Sn\({}_{2}\)Cl\({}_{3}\)I\({}_{3}\)Sr\({}_{2}\)TiRhO\({}_{6}\) showed promise for photovoltaic applications, with bandgaps appearing at the \(\Gamma\) point and within the ideal ranges of 1.0-1.7 eV solar cell energy applications. The compound Cs\({}_{4}\)Sn\({}_{4}\)Cl\({}_{6}\)I\({}_{6}\) possessed an indirect bandgap of 1.9 eV as can be seen in Figure 5.  
Negative formation energy value, according to the equation that we used to calculate it, indicates that the material is stable. Although Cs\({}_{4}\)Sn\({}_{4}\)Cl\({}_{6}\)I\({}_{6}\) did not show a desirable bandgap value, its formation energy of 0.023 eV indicated high structural stability, further indicating advantages for use under harsh conditions. The two combinations can be applied under disparate conditions.  
Figure 5: \(Cont.\)  
Theoretical calculations do provide insight into the probable real-world trends to be found in the manipulation of these materials. However, it must be remembered that this theoretical calculation does not consider real-life factors. As such, experimental trial data and results may not be equivalent to those of theoretical calculations, even as they remain similar.  
Conceptualization, X.L. and H.W.; methodology, H.W.; software, X.L.; validation, H.W.; formal analysis, H.W.; investigation, H.W.; resources, H.W.; data curation, X.L.; writing--original draft preparation, H.W.; writing--review and editing, X.L.; visualization, H.W.; supervision, X.L.; project administration, X.L.; funding acquisition, H.W. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare that they have no conflict of interest.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 3 Overview of IEEE 802.15.7  
To evaluate the proposed handover strategy, we need to consider the impact of its properties in the VLC communication. For instance, the channel access and other mechanisms on the Medium Access Control layer (MAC) can cause latency on the protocol resolution, as a consequence, sometimes the user may not be able to complete the handover. On the other hand, the transmission properties described on the Physical layer (PHY) such as modulation scheme, codification, among other things dictate the link capability and range. For these reasons, this work includes the system's assessment of several processes and specs from the IEEE 802.15.7 MAC and PHY layers [22]. In what follows, we will describe the most relevant protocols in the MAC layer and the main aspects to consider about the PHY layer.
## 4 Model Results
### Comparative Evaluation of the Newly Formulated MSP Model and Various Other Machine Learning Models  
In an effort to assess the predictive power of the developed M5P model, it is systematically compared against two other prevalent machine learning models. The first one is a black-box model, specifically the Random Forest (RF) model, and the second is a white-box model, namely Linear Regression (LR). This comparative analysis is conducted within the context of predicting the punching shear resistance of flat slabs, using a comprehensive experimental database for a robust evaluation. Figure 7 illustrates a graphical representation showcasing the correlation between the observed experimental shear strength and the predicted shear strength. These correlations are derived from data present in the testing evaluation database. The graph incorporates a tolerance of \(\pm\)20%. Generally, it can be observed that the capacity predicted with the different methods diverges from the 'perfect line'. This 'perfect line' is conceptually defined as the line where the actual values align perfectly with the predicted values. The plot illustrates that the predictions generated with the M5P model are generally less scattered when compared to other machine learning models. A majority of these predictions comfortably fit within the \(\pm\)20% boundaries of the line of equality, also known as the perfect line.  
Table 4 and Figure 8 offer a comprehensive statistical analysis comparing the proposed machine-learning (ML)-based models for shear strength. This comparison is conducted using the testing database. Additionally, Figure 8 visually represents a comparison between the M5P model and other machine learning models, focusing on RMSE and MAE results. Upon reviewing the data from both Table 4 and Figure 8, it is clear that the M5P model outperforms the rest in terms of having the highest R\({}^{2}\) values and the lowest values for both RMSE and MAE, amongst all models examined.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **M5P** & **RF** & **LR** \\ \hline
**RMSE** & 71.9993 & 92.6412 & 121.9088 \\
**R\({}^{2}\)** & 0.9616 & 0.9339 & 0.8930 \\
**MAE** & 48.3483 & 58.9602 & 95.4274 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical characteristics of both M5P and other machine learning models (analyzed using the testing dataset).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Folds**} & \multicolumn{3}{c}{**Performance Measures**} \\ \cline{2-4}  & **MAE (MPa)** & **R\({}^{2}\)** & **RMSE (MPa)** \\ \hline
66.2896 & 0.9430 & 100.1055 & Fold 1 \\
51.2254 & 0.9581 & 83.0453 & Fold 2 \\
38.7523 & 0.9465 & 56.0006 & Fold 3 \\
57.7699 & 0.9410 & 89.9521 & Fold 4 \\
62.5797 & 0.9697 & 125.1674 & Fold 5 \\
55.3234 & 0.9517 & 90.8542 & Average \\
10.8431 & 0.0121 & 25.1970 & SD \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results derived from a five-fold cross validation.
# Re-Keying Scheme Revisited: Security Model and Instantiations
## 3 SCA on the Previous Re-Keying Schemes
### The Attacks  
The previous works supposed that the re-keying schemes are secure if the randomness \(r\) is fresh, and they discussed the security against the SCAs insufficiently. In fact, as they supposed, _the key recovery attack_ on both the master secret key \(mk\) and the session key \(tk\) seems infeasible with the SCA if the randomness is fresh, because of the property (assumption) of \(g\).  
Let us discuss another attack, a message recovery attack, with the SCAs. Assume an adversary who, monitoring a ciphertext \((r,c)\), aims to recover the plaintext \(m\) corresponding to \(c\). Against [PERSON] et al.'s re-keying scheme and [PERSON] et al.'s first scheme, such an attack is feasible if the decryption device accepts decrypting an arbitrary input without checking the freshness of \(r\). The attack proceeds as follows. The adversary repeats sending a decryption query \((r,c^{\prime})\) for randomly-chosen \(c^{\prime}\). The attacker measures the side channel information. For the fixed randomness \(r\), the session key \(tk\) is also fixed; and therefore, the adversary can determine \(tk\) with the SCA. Once the session key \(tk\) is determined, the adversary can recover \(m\) by decrypting \(c\) with \(tk\). The assumption, where the decryption device does not check the freshness of \(r\), is realistic because [PERSON] et al. assumed stateless devices as the lightweight devices.
# Motion Deblurring for Single-Pixel Spatial Frequency Domain Imaging
## 2 Materials and Methods
### Motion Degradation Model for Single-Pixel SFDI
Thereafter, two deblurred images \(\hat{I}_{\mathrm{DC}}(x,y)\) and \(\hat{I}_{\mathrm{AC}}(x,y)\) are obtained by performing inverse Fourier transforms to \(\hat{F}_{v}(u,v)\). The DC and AC amplitude images are then obtained using the single-snapshot demodulation [31]. In this work, only the low-frequency Fourier coefficients (dominated by signals) of the reflected image are measured, which prevents the inverse filtering from amplifying the noise spectrum. When high-frequency Fourier coefficients are measured, wiener filtering can be adopted as the alternative to inverse filtering to inhibit the noise amplifications.
# Influence of Phosphogypsum Dump on the Soil Ecosystem in the Sumy region (Ukraine)
###### Abstract  
This paper investigates the influence of a phosphogypsum dump on the surrounding environment (soil ecosystem) in the Sumy region (Ukraine). Analysis of the surrounding soils was performed to study the possible presence of compounds leaching from the dump. For physical chemical analysis of samples, X-ray fluorescence (XRF) analysis, X-ray diffraction (XRD) analysis, and Fourier transform infrared spectrophotometry (FTIR) were used. XRF analysis did not confirm the contamination of soils around the phosphogypsum dump, and the soil fluoride levels measured in this study were comparable to the average soil fluoride concentration of soils globally. The colonization of the surface of the phosphogypsum dump by living organisms after the reclamation process was also analyzed. Field research was carried out on the routes, which specify the boundaries of the contours of plant communities, for description of photocenoses in the territory of the dump, where the pH value ranged from 2.5 to 5.3, depending on the age of phosphogypsum terraces. Annual and perennial herbaceous plants are dominant on the reclaimed dump slopes. On the third and fourth phosphogypsum terraces (20 and 25 years old, respectively), tree forms have begun to settle, represented mainly by _Populus tgemula, Populus alba, Betula pendula_, and _Robinia pseudoacacia_. The studied patterns of ecological groups of plants growing under natural change of species can be used for the complete reclamation of the dump.  
Keywords:phosphogypsum dump; soil ecosystem; plant communities; reclamation +
Footnote †: journal: Applied Ecology, Faculty of Technical Systems and Energy Efficient Technologies, Sumy State University, 2, Rymskogo-Korsakova st., 40007 Sumy, Ukraine; [EMAIL_ADDRESS] (L.P.); [EMAIL_ADDRESS] (Y.C.); [EMAIL_ADDRESS] (E.Y.)
## 3 Experiments and Results
### Network Parameters and Experiment Configurations  
#### 3.2.1 Network Configurations  
Network parameters are the critical factors that affect the performance of the model. Here are some reference settings. The encoder utilizes Conv1d with a size of 256 and the ReLU activation function. The kernel size is 8, and the step string is 4. The dimension of the channel in the separation network is 128, the size of the block \(K\) is chosen as 250, the sliding step of the block is \(K//2\), the SuperFormer Block cycle \(T=2\), the forward adaptive module is repeated four times, the Intra/Inter-Transformer is repeated \(M=8\) times, and the speaker enhancement process is executed once. The configuration of decoder convolution layer is consistent with that of the encoder.  
The training objective for the network is scale-invariant signal-to-noise ratio (SI-SNR). Utterance-level permutation invariant training (uPIT) is applied during training to address the source permutation problem [11]. SI-SNR is defined as Formula (23):  
\[\begin{split} S_{T}=\frac{\hat{S}\cdot\hat{S}}{\|\hat{S}\|^{2}}S \\ S_{E}=\hat{S}-S_{T}\\ \text{SI-SNR}=10\ \log_{10}\frac{\|S_{T}\|^{2}}{\|S_{E}\|^{2}} \end{split} \tag{23}\]  
where \(\hat{S}\in\mathbb{R}^{1\times T}\) and \(S\in\mathbb{R}^{1\times T}\) are the model output and original clean sources, respectively.
## 3 Results and Discussion
### Effect of TP and TS on Salmonella Populations and Wax Gourd Firmness
Combining green tea extracts with atmospheric radio-frequency plasma has been shown to enhance protection against pathogens on fresh-cut dragon fruit [18]. An edible coating made of fish gelatin and TP has been developed and was found to be more effective in preventing spoilage of fish fillets than either treatment alone [39]. To our knowledge, this is the first report on the combined inactivation effect of TP and TS on _S. enterica_ in fresh-cut Wax Gourd. The best result was achieved with a combination of 1.5% TP and 3 min TS at 60 \({}^{\circ}\)C for _S. enterica_ inactivation. Considering that higher temperatures and concentrations of TP may result in increased energy and material costs under real marketing conditions, the combination of TP (1.0%) and TS (50 \({}^{\circ}\)C, 1 min) was selected for the wax gourd storage experiment and ANFIS modeling. Fluorescence microscopy analyses were performed using Hoechst 33,342 and PI staining to evaluate the inactivation effect of the combination of TP (1.5%) and TS (60 \({}^{\circ}\)C, 3 min). Hoechst 33,342 is a cell-permeable DNA dye that can stain both living and dead cells, emitting blue fluorescence upon excitation by ultraviolet light. PI, on the other hand, is a nucleic acid dye that cannot penetrate living cells with an intact cell membrane. Upon excitation by ultraviolet light, it emits red fluorescence. As shown in Figure 2, the untreated bacterial cells were only stained with Hoechst 33,342 (blue). However, after the combined treatment with TP and TS, most bacterial cells were stained with both Hoechst 33,342 (blue) and PI (red), and their morphology became blurred. This indicates that the combination of TP and TS can destroy the cell membrane, allowing PI to penetrate the membrane and stain the nucleus. The leakage of cytoplasm, ultimately, leads to cell death. These findings are consistent with previous studies, which have suggested that ultrasound or TS can disrupt cells and enhance sanitization efficiency by facilitating the penetration of chemical sanitizers through the cellular membrane [40; 41; 42].
# Evaluation of a New-Concept Secure File Server Solution
## 6 Summary  
In this article, the performance of a new-concept secure file server solution designed to protect against ransomware without causing significant performance degradation was evaluated. The article introduced the ARDS (Anti-Ransomware Defense System) model, which integrates various security features such as IP-less local backup, scheduled snapshots, and encrypted remote backups. During the performance tests, this solution was compared with a traditional file server architecture, revealing that the ARDS system generally performed better, particularly in reading speed, though there was a performance drop in writing operations due to encryption and snapshotting. In more detail, the results showed that ARDS TestServer performed better or equal in 66.7% of the read tests compared to the Windows 2019 Server environment at the maximum throughput limit of 1 Gbit/s, while in the write tests, the figure was 58.3%. At the 10 Gbit/s maximum throughput limit, the same tests showed that the ARDS TestServer read performance was better than the Windows 2019 environment in 83.3% of the tests, but in the write tests, Windows 2019 Server performed better in 87.5% of the tests. The research findings confirm that the ARDS solution offers effective protection without significantly impacting user experience or data transfer speeds by reading. The transfer speed drops that occurred during some writing test cases can be effectively eliminated according to best practice solutions and official documentation.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/fi16090306/s1](https://www.mdpi.com/article/10.3390/fi16090306/s1)]([https://www.mdpi.com/article/10.3390/fi16090306/s1](https://www.mdpi.com/article/10.3390/fi16090306/s1)).  
Conceptualization, G.A. and V.S.; methodology, G.A., V.S. and A.V.-F.; software, G.A; validation, G.A.; writing--original draft preparation, G.A., V.S. and A.V.-F.; writing--review and editing, G.A, V.S. and A.V.-F.; visualization, G.A. and V.S.; supervision, A.V.-F. and V.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the TKP2021-NVA-10 project with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the 2021 Thematic Excellence Programme funding scheme.  
Data Availability StatementData is contained within the article or Supplementary Materials.  
We are grateful to our colleague, [PERSON], who helped us by providing the hardware infrastructure and shared many useful pieces of advice to help us achieve our goals. In addition, we would like to give special thanks to [PERSON] for the technical support.  
The authors declare no conflicts of interest.
# Study About the Performance of Ascon in Arduino Devices
## 2 The Ascon Cipher Suite  
The Ascon cipher suite consists of a family of several algorithms: three versions of an authenticated encryption algorithm with associated data (AEAD) and two hash functions. The key properties, parameter sets, and their respective sizes (in bits) for each Ascon algorithm are summarized below. A more detailed description can be found in the Ascon document submitted to NIST [10].
## 4 Results and Discussion
### Blow-Off Dynamics
The hydrogen ratio has an obvious effect on the flame structure and the flame height. After comparing the blow-off processes of different conditions, one may conclude that the heat release area is significantly shorter at high-hydrogen-ratio conditions. It is worth noting that the HRR shape of 90H2 was different from the other three operating conditions. For a high hydrogen ratio case for which the heat release area is small, the heat release rate suddenly decreases when the lean fresh mixture (\(\phi\) = 0.2) reaches the flame. In contrast, when the hydrogen ratio is low, the flame is high and the flame surface area is larger. Therefore, the effect of the suddenly decreased equivalence ratio on the entire flame is delayed. This results in a slower process HRR reduction and a longer period of the blow-off process. Figure 11 shows scatter plots of HRR and temperature on a plane sliced along the axis. The different colored dots in Figure 11 correspond to the four instances in the blow-off sequence. Meanwhile, the first two instances (indicated by the black and red dots) were in the first stage of the blow-off process, and the remaining instances were in the second stage of the blow-off process. During the blow-off process, the heat release rate in the first and the second instance did not change significantly, which means that the heat release rate at the first stage of blow-off remained at the same level as a stable flame. After entering the second stage of blow-off, indicated by the blue dots and green dots in Figure 11, the heat release rate dropped rapidly, and the temperature also decreased. It can be concluded that a reduction in HRR influences the temperature and is one of the factors causing blow-off. Figure 11: Scatter plots of HRR and temperature on a plane sliced along the axis.
## 3 AMCTD Protocol  
The AMCTD protocol for UWSNs environments addresses the issues of short periods of stability, rapid energy consumption by nodes with low depth, and slow throughput during these unstable periods. By leveraging courier nodes to optimize movement under the network's sparse conditions, AMCTD promotes global load balancing. The protocol enables efficient consumption of energy by the sensor nodes in the UWSNs environment. Figure 2a,b show the AMCTD flowchart and data transmission, respectively.  
Hello packets are broadcasted by the sensor nodes in the first phase. Communication between sink, sensor and courier nodes starts up the network. Equation (1) is used by each sensor node to compute its weight.  
\[\text{Wi}=(\text{Priority value}\times\text{Ri})/(\text{Depth of water}-\text{Di}) \tag{1}\]  
where Wi represents weight of node i; Di represents depth of node i; Ri represents residual energy of node i; and priority value is constant.  
Weight and depth of each sensor node are shared with the nearby sensor nodes during the setup phase. In this step, the neighbors within the transmission range are identified and placed in a separate queue in order to aid in finding the optimal forwarder. The sensor node transmits data towards the sink using the CSMA/CA technique in the next phase.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
The source node chooses the optimal forwarder by comparing their weights. The best forwarder node is the sensor node in the immediate vicinity with the highest weight.  
Once the number of dead nodes in the UWSNs has increased by 2% during the weight update phase, each node then calculates its weight, utilizing Equation (2).  
\[\text{Wi}=(\text{priority value}\times\text{Di})/\text{Ri} \tag{2}\]  
The last step includes the motion of the courier sensor nodes, along with the variation in depth threshold of the nodes, designed to deal with the network sparsity.  
\[\text{Wi}=\text{Ri}/(\text{priority value}\times\text{Di}) \tag{3}\]  
The AMCTD protocol in UWSNs is affected by noise, attenuation, path loss and signal to noise ratio.