esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion  
### Turbulence Model and Wall Functions for Steam Ejector Modeling  
The flow state during the entire working process involves both high pressure and high-velocity flows and shockwaves. Therefore, a suitable turbulence model is essential for accurate numerical  
Figure 6: Comparison of static pressure distribution along the ejector wall between wet steam model and experimental data.  
Figure 7: Static pressure distribution along ejector wall between ideal gas model and wet steam model against the experimental data.  
predictions. Figure 8 illustrates the static pressure results from three turbulence models against the experimental data. Three different turbulence models (i.e., standard \(k-\varepsilon\), realizable \(k-\varepsilon\) and the \(k-\omega\)\(SST\) model) were applied in this study. As shown in Figure 8, the results of the realizable \(k-\varepsilon\) model were the most accurate compared to experimental results. The only section in which the \(k-\omega\)\(SST\) model showed slightly improved prediction over the realizable \(k-\varepsilon\) model was during first half of the throat section (i.e., axial position from 0.15 m to 0.2 m). However, after the shock, the \(k-\omega\)\(SST\) model shows a bigger deviation from experimental data than the \(k-\varepsilon\) models. In addition to turbulence models, wall functions also need to be carefully considered because the turbulent flows near the wall region have large gradients, and the experimental data was collected from the wall of the steam ejector. Therefore, implementing wall functions should improve the accuracy of the wall-bounded turbulent flows. Figure 9 illustrates the axial pressure distribution for the realizable \(k-\varepsilon\) model with different wall functions. As can be seen in Figure 9, the enhanced wall function showed a better performance compared to the non-equilibrium wall function. Taking into consideration the aforementioned results, the realizable \(k-\varepsilon\) model with the enhanced wall functions was chosen to further investigate the condensation effect on the performance of the steam ejector.  
Figure 8: Axial static pressure distribution of different turbulence models against the experimental data.  
Figure 9: Static pressure distribution of different wall functions against the experimental data.
## 3 Results and Discussion  
### pH Values of Infusions  
Figure 1 shows the effect of the addition of chokeberry pomace to green tea leaves and hibiscus flowers on the pH value of the obtained infusions.  
The pH values of GTL, WHF and RHF infusions were 4.9, 3.2 and 3.2, respectively. The addition of dried chokeberry pomaces did not cause statistical differences in pH values. However, the pH value of infusions for dried green tea leaves (both with and without the addition of CP) was about 40% higher than that of hibiscus flower infusions. The values of the tested parameters of infusions from green tea leaves and hibiscus flowers in commercial mixtures were at a similar level, 4.9 and 2.7, respectively [33]. [PERSON] et al. [34] also found that the pH values of green tea leaf infusions ranged from 5.03 to 6.10 and depended on the initial pH and mineral content of the water. The research of [PERSON] et al. [35] shows that the pH value of infusions obtained from chokeberry pomace ranges from 4.01 to 4.13. In our case, a slight decrease in the pH value with increasing content in the mixture of dried chokeberry pomace was observed for the green tea infusion. However, these changes were  
Figure 1: Influence of dried pomace addition to green tea leaves or hibiscus flowers on the pH value of infusions. The different letters above the bars indicate a significant difference at \(p<0.05\) for the two—way ANOVA and Tukey post hoc test; GTL—dried green tea leaves (0.5 g); WHF—dried white hibiscus flowers (0.5 g); RHF—dried red hibiscus flowers (0.5 g); CP—dried chokeberry pomace; 2.0, 2.5 and 3.0—indicate pomace addition in g.  
not statistically significant. Initially, the low pH of hibiscus flower infusions remained at a similar level of 3.3 after the addition of dried chokeberry pomace.
# An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments
## 3 Proposed System  
We proposed architecture using an adversarial DBN-LSTM framework to detect and defend against DDoS attacks to protect the controllers.  
As shown in Figure 1, we proposed a system that consisted of four independent modules in the application layer to protect the SDN controllers in the control plane (the source codes cannot be shared due to privacy and the development of our application). The system can be summarized as follows:  
1. Data Collection: the SDN controller collects data from the physical and virtual switches in the data layer.
2. Data Processing: non-numerical features collected from the switch will be converted into numerical features.
3. Adversarial Deep Learning Anomaly Detection: this module will detect and mark the DDoS attack through the trained deep learning model.
4. Abnormal Defensing: this module will perform corresponding operations on DDoS attacks to avoid the harm of DDoS attacks.
## 3 Results
### Fourier Transform Infrared  
The FTIR analysis of HDTMA-VMT\({}_{0.2\text{M}}\) before and after defluoridation showed the functional groups of the adsorbent and identified regions of fluoride adsorption. Figure 3 shows the IR spectra of both stages.  
The spectra of HDTMA-VMT\({}_{0.2\text{M}}\) before and after defluoridation showed the major functional groups and established that fluoride sorption took place. Before defluoridation, the spectra showed a peak at 952.93 cm\({}^{-1}\), which corresponds to Si-O stretching vibrations of Si-O-Si silicate bonding [17]. The band at 1652.17 cm\({}^{-1}\) represents O-H bending vibration of hydrating water molecules [27], while the bands at 669.85 and 436.69 cm\({}^{-1}\) represent the Si-O bending modes [28]. More specifically, the spectrum of the HDTMA-VMT\({}_{0.2\text{M}}\) showed peculiar peaks at 2916.85 (CH\({}_{3}\)), 2849.66 (CH\({}_{2}\)), and 1469.14 (CH\({}_{3}\)) cm\({}^{-1}\), which indicates the modification of vermiculite with HDTMA as observed in [PERSON] et al. [17] and [PERSON] et al. [29].  
A careful examination of the transmittance axis of HDTMA-VMT\({}_{0.2\text{M}}\) after defluoridation reveals a decrease in intensity of specific bands at 2917.91, 2850.15, 1469.89 cm\({}^{-1}\), and 951.69 cm\({}^{-1}\). The reduction to their new transmittance values suggested that the available sites created by the HDTMA (CH\({}_{3}\), CH\({}_{2}\)) had been occupied by fluoride and is an indication of fluoride sorption [20].
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Energy-Harvesting Techniques
#### 3.2.2 Wireless Power Transfer  
Recent developments in wireless power transfer (WPT) are expected to increase the lifetime of WSNs significantly and make them continuously operational, given that WPT techniques can be utilized to transmit electromagnetic energy between transmitter and receiver devices without any contact between the two. WPT is expected to overcome the constraint of the supply of WSNs. Consequently, some researchers highlighted the utilization of mobile node that can deliver power to deployed sensor nodes such as [121; 122]. WPT technology poses a challenge to the energy cooperation between neighboring nodes in WSNs. Therefore, in the future, sensor nodes could harvest energy from the environment and transfer this energy to other nodes in the network; in this manner, a self-sustaining network can be achieved [123]. Accordingly, recent research has investigated multi-hop energy transfer [124; 125], which paved the way for new energy cooperative schemes and WPT charging protocols.  
WPT can be classified into three main subcategories: (i) electromagnetic (EM) radiation, (ii) magnetic resonator coupling, and (iii) inductive coupling, as shown in Figure 2. Wireless charging in WSNs can be performed via EM radiation and magnetic resonant coupling. EM signals suffer from attenuation over distance, and active radiation may pose a threat to the human body. Meanwhile, magnetic resonant coupling can address the power requirements of agriculture based on WSNs because of its efficiency within a distance of several meters. Many studies used WPT to charge sensor nodes for different applications and fields. WPT-based inductive coupling [121] has been used to charge the battery of sensor nodes scattered in agriculture fields. The energy is transferred from the source coil (mounted on a UAV) to the receiver coil (attached with sensor nodes). A 2.4 W transfer power is transferred to charge a single sensor node. The results addressed a number of limitations and challenges related to the charging of sensor nodes via a UAV. In addition, the results revealed that the battery lifetime of the deployed sensors can be significantly extended through the UAV. A similar approach was adopted by [PERSON] et al. [122] to charge a WSN in an agricultural field through the harvesting of electromagnetic radiation.
## 1 Introduction
### Applications
Additionally, three calibrated test parts (a sphere, cylinder, and gauge block) were utilized by [PERSON] and [PERSON] (2011) to compare the accuracy of five 3D scanners [18]. On the basis of their study, they concluded that the scanners that used white light (Comet, Atos) provided the highest accuracy. A structured methodology developed by [PERSON], et al. (2015) was used to compare different RE programs applicable to inspection activities [19]. Six RE software programs, including PointMaster v.5.3.3, Gom Inspect v.7.5, Geomagic Qualify v.12, 3D Reshaper v.7.1, Rapidform XOV2, and Polyworks Inspector v.12 were compared to evaluate their performances. In recent times, [PERSON] et al. (2017) [20] have combined the data from Electronic Speckle Pattern Interferometry (ESPI) with laser scanning data for 3D characterization of racing tyres sections. A comparative evaluation of different systems is highly valuable when several options are available for a particular application. To conduct a reliable assessment, a robust tool or technique such as design of experiment must be employed. Therefore, in this work, handheld laser and white light scanners have been evaluated (for an automotive application) using a design of experiment approach.
## 4 Semantic Interoperability Provision in Heterogeneous Healthcare Service Environments
### Semantic Interoperability in Healthcare Data Models
#### 4.2.5 Common Data Model  
The Common-Data-Model (CDM) transforms the heterogeneous types of data collected from diverse sources, having different structure into coherent, unique data formats. The process of transforming the data into the CDM model involves standard vocabularies, core schema, meta-data schema, and the common data format. To satisfy the requirement of semantic interoperability for healthcare data, CDM provides a key role. A single source of data cannot provide an overall status, and it is insufficient to be analyzed where applications are dependent on many data sources with different semantics and formats. To comply with the semantic interoperability requirement, a common and standard data model is highly necessary. To fill the gap of existing approaches for a commonly approved data model, we propose our CDM realized as Interoperable Shared Data Model (ISDM).  
The ISDM provides data enhancements using a common schema and Meta schema and standard vocabulary with data transformation procedures to map the data from heterogeneous domains into an interoperable shared data resource to provide sharing and integration among applications at different data levels (as presented in Figure 12a).  
Figure 12: _Cont._Transformation to the interoperable shared data model is done by extracting the entities from the domain-specific data model with their metadata description and their translation from the concepts of a domain to the generic entities of ISDM. An example of this transformation is shown in Figure 12b, given below. Such translation involves a core schema to transform the concept of the domain and a Meta schema to transform the related metadata of the domain. Core Schema provides the collections of reference concepts and their intermediate relationships used to represent the data in subject domains. The meta schema provides a set of reference metadata for the transformation of domain metadata to a cross-domain interoperable metadata description. The major elements of CDM involve the management of core data and metadata, which promotes the improvement in the data interoperability provision process through the features such as the expressive description of data elements; commonly understood meaning of data across diverse domains; reusability of data among different applications; and the uniform transformation of data into standard formats.
###### Abstract  
High-precision rainfall field reconstruction and nowcasting play an important role in many aspects of social life. In recent years, the rain-induced signal attenuation of oblique earth-space links (OELs) has been presented to monitor regional rainfall. In this paper, we set up the first OEL in Nanjing, China, for the estimation of rain intensity. A year of observations from this link are also compared with the measurements from laser disdrometer OTT-Parsivel (OTT), between which the correlation is 0.86 and the determination coefficient is 0.73. Then, the simulation experiment is carried out: an OELs network is built, and the Kriging interpolation algorithm is employed to perform rainfall field reconstruction. The rainfall fields of plum rain season from 2016 to 2019 have been reconstructed by this network, which shows a good agreement with satellite remote sensing data. The resulting root-mean-square errors are lower than 3.46 mm/h and spatial correlations are higher than 0.80. Finally, we have achieved the nowcasting of rainfall field based on a machine-learning approach, especially deep learning. It can be seen from experiment results that the motion of rain cell and the position of peak rain intensity are predicted successfully, which is of great significant for taking concerted actions in case of emergency. Our experiment demonstrates that the densely distributed OELs are expected to become a futuristic rainfall monitoring system complementing existing weather radar and rain gauge observation networks.  
ra rainfall field; remote sensing; reconstruction; nowcasting; deep learning; oblique earth-space links (OELs) +
Footnote †: journal: Remote Sensing  
1
# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 2 Materials and Methods
### Deep Learning Models  
DL in medical imaging is mostly represented by a basic structure called a CNN [57; 75]. There are different DL techniques, such as GANs, deep autoencoders (DANs), restricted Boltzmann machine (RBM), stacked autoencoders (SAEs), convolutional autoencoders (CAEs), recurrent neural networks (RNNs), long short-term memory (LSTM), multiscale convolutional neural network (M-CNN), and multi-instance learning convolutional neural network (MIL-CNN) [3]. DL techniques have been implemented to train neural networks for breast lesion detection, including ensemble [75] and transfer learning [129; 157; 166] methods. The ensemble method combines several basic models in order to get an optimal model [167], and transfer learning is an effective DL method to pre-train models to deal with small datasets, as in the case of medical images.  
ANNs are composed of an input and output layer, plus one or more hidden layers, as shown in Figure 6. In the field of breast cancer, three types of ANN are frequently used: backpropagation, SOM, and hierarchical ANNs. To train an ANN with a backpropagation algorithm, the error function is given to calculate the gradient descent. This error propagates in the backward direction and the weights are adjusted for error reduction. This processing is repeated until the error becomes zero or is a minimum [3].  
Convolutional Neural Networks  
CNNs are the most widely used Neural Networks when it comes to DL and medical image analysis. The CNN structure has three types of layers: (i) convolution, (ii) pooling, and (iii) full-connection layers, which are stacked in multiple layers [74]. Thus, a CNN's structure is determined by different parameters, such as the number of hidden layers, the learning rate, the activation function (rectified linear unit (ReLU)), pooling layer for feature map extraction, loss function (softmax), and the fully connected layers for classification, as shown in Figure 7.  
Furthermore, there are several methods for improving a CNN's performance, such as dropout and batch normalization. Dropout is a regularization method that is used to prevent a CNN model from overfitting. A batch normalization layer speeds up the training of CNNs and reduces the sensitivity to network initialization.
## 3 Results  
Figure 1a-c show the TOF mass spectra of an IBr molecule exposed to an 800 nm femtosecond laser pulse with a pulse duration of 70 femtoseconds (FWHM). The laser intensities are (a) 40 TW/cm\({}^{2}\), (b) 90 TW/cm\({}^{2}\), and (c) 300 TW/cm\({}^{2}\), respectively. In our work, dissociative ionization dominates the dissociation and ionization process due to the femtosecond laser field. A series of ions, Br\({}^{m+}\) (m = 1-3) and I\({}^{m+}\) (n = 1-4), are observed, implying that the parent molecule ions are highly unstable and easily break down into fragment ions. At a low laser intensity, the ions Br\({}^{+}\), I\({}^{+}\), and I\({}^{2+}\) are the main products. As the laser intensity increases, the generation of multiple-charged ions Br\({}^{m+}\) (m = 2-3) and I\({}^{m+}\) (n = 3-4) shows the involvement of Coulomb explosion processes (CE) in this experiment. In addition, the raw DC-sliced ion images (512 \(\times\) 512 pixel) and the normalized velocity distributions of the ions P\({}^{n+}\) (n = 1-4) and Br\({}^{m+}\) (m = 1-3) are shown in Figure 2.  
Figure 2: The raw DC-sliced images (512 \(\times\) 512 pixel) of the fragment ions (**a**) I\({}^{+}\), (**b**) I\({}^{2+}\), (**c**) I\({}^{3+}\), (**d**) I\({}^{1+}\), (**e**) Br\({}^{+}\), (**f**) Br\({}^{2+}\), and (**g**) Br\({}^{3+}\) at the intensity of 300 TW/cm\({}^{2}\). The laser polarization direction  
Figure 1: TOF mass spectra of the molecule IBr at (**a**) 40 TW/cm\({}^{2}\), (**b**) 90 TW/cm\({}^{2}\), and (**c**) 300 TW/cm\({}^{2}\), respectively.  
is represented by the double-headed arrow. The estimated dissociation process, \(\mathrm{IBr^{(n+m)+}\to\mathrm{I^{n+}+Br^{m+}}}\), is represented by the channel \(\mathrm{(p,q)}\). The corresponding velocity distributions of the fragment ions \(\mathrm{(a^{\prime})\,I^{+},(b^{\prime})\,I^{2+},(c^{\prime})\,I^{3+},(d^{\prime} )\,I^{4+},(e^{\prime})\,Br^{+},(f^{\prime})\,Br^{2+}\), and \(\mathrm{(g^{\prime})\,Br^{3+}}\) are also shown, in which the intensities have been normalized. The KER in eV is labeled in \(\mathrm{(a^{\prime}-g^{\prime})}\). The experimental data are represented by the circles, while the fitting peaks are represented by the green dashed lines and the sum of the fitting peaks are presented by the red solid lines.
## 2 State of the Art
### TDMA-based Protocols
That is why heuristics are used. Among them, those based on node degree achieve the best performance as shown in [19, 20, 21]. SERENA [22] takes advantage of this property by means of node priority computation. The closest work to our is TDMA-ASAP [23], where this protocol, designed for data gathering applications, can be classified as an adaptive TDMA. It uses node coloring to provide spatial reuse, save energy and decrease the end-to-end delays to collect data at the sink node. Moreover, this protocol can adapt to various traffic conditions by slot stealing: a node can take the unused slot of its brother in the data gathering tree. The solution we propose in this paper differs from TDMA-ASAP because it supports general applications, where the communication is not limited to a tree. Moreover, broadcast transmissions are allowed as well as immediate acknowledgement of unicast transmissions. Furthermore, we show how to detect and solve color conflicts when they occur due to the wireless environment.
## 3 Evaluation
### Comparative Analysis of Stereo-Matching Networks  
The results from raw Holoscopic images (EIs) were also compared against two state-of-the-art deep learning stereo-matching algorithms: the methods by [PERSON] et al. [22] and [PERSON] and [PERSON] [23]. [PERSON] et al. [22] proposed a technique to enhance the generalisation abilities of stereo-matching networks. Their main objective was to maintain the consistency of features between corresponding pixels. Their methodology combines pixel-level contrastive learning with a stereo-selective whitening loss to enhance the consistency of features across various domains. This technique is highly versatile and may be easily integrated into pre-existing networks without any disruptions.  
[PERSON] and [PERSON] [23] employed supervised learning and convolutional neural networks (CNNs) to address the task of estimating disparities from stereo image pairs. They proposed a Pyramid Stereo-Matching Network (PSMNet) as an alternative to the patch-based Siamese networks commonly employed in current architectures. The PSMNet overcomes the limitation of incorporating contextual information in uncertain regions by incorporating spatial pyramid pooling and a 3D CNN.  
Both of the pre-trained models were used to extract disparity from all 24 raw Holoscopic images in the dataset, and an \(80\times 80\) resolution was chosen based on the accuracy level from the previous section. These results were then compared with those obtained from this paper's method applied to the same dataset. The disparity outcome of a basic EI of both deep learning models was highly blurred and undefined, as seen in Figure 23. These outcomes can be attributed to various factors, including the dissimilar characteristics of the higher-resolution stereo images utilised for training the models developed by [PERSON] et al. [22] and [PERSON] and [PERSON] [23] compared to the low-resolution and low-texture EIs. Therefore, when these models are employed on the EIs, they struggle with accurately capturing intricate details. Furthermore, the efficacy of these models is greatly influenced by their specific architecture, particularly [PERSON] and [PERSON] [23]'s PSMNet, which further reduces the resolution of low-resolution EIs, resulting in unsatisfactory outcomes.  
As depicted in Figure 24, MAE values for this paper's method ranged from 0.419 to 0.625, which were considerably lower than the MAE values reported by [PERSON] et al. [22], ranging from 0.637 to 0.801, and [PERSON] and [PERSON] [23], ranging from 0.686 to 0.798. Regarding the PBP, this paper's method achieved percentages ranging from 39.6% to 64.1%, which indicates a superior performance. In comparison, [PERSON] et al. [22] and [PERSON] and [PERSON] [23] obtained greater percentages, with ranges of 63.3% to 78.7% and 65.7% to 77.9%, respectively.  
Figure 23: Both deep learning algorithms result in blurry and undefined EIs compared to our result [22; 23].