esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Modeling, and Analysis of Piezoelectric-Actuated Device for Blood Sampling
## 2 Physical Modeling of the Device
### Biosensor  
The basic aim of the device under consideration is to analyze the glucose level in the blood sample collected, which is being used for diagnosis of several diseases. A biosensor is located in the pump chamber at a radial distance of 1 mm from the center of the microneedle so that the collected blood can react with the biosensor and glucose level in the blood can be measured. The surface area of biosensor is considered as 0.314 mm\({}^{2}\)[27; 28].
## 2 Background
### Security Analysis and Threat Modelling  
We aim to obtain a provably secure design that benefits from model-driven security approaches, as they allow us to identify and mitigate threats in the early phases of development for cyber-physical systems [27] or the Smart Home.  
According to the work by [PERSON] [28] in 2014, threat modelling is a process of finding threats to a system. The author proposes a framework of threat modelling consisting of the following steps:  
1. Modelling the system itself,
2. Finding threats to the afore-modelled system,  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Initiator** & **Target** & **Example** \\ \hline \multirow{3}{*}{G\(\mathrm{PCD}\)} & GTCD & PC in the local network syncs files with the local file server. \\ \cline{2-3}  & SHD & Smartphones control the lights with an application. \\ \cline{2-3}  & Internet & PC accesses a web page. \\ \hline \multirow{3}{*}{SHD} & GTCD & Motion sensor sends a status update to Smartphones. \\ \cline{2-3}  & SHD & Smart hub controls a device directly in the local network. \\ \cline{2-3}  & Internet & Smart thermostat uploads temperature profile to the cloud. \\ \hline \multirow{3}{*}{Internet} & GTCD & PC outside the network syncs files with the local file server. \\ \cline{2-3}  & SHD & Manufacturer sends updates to the device. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Communication patterns involving two parties.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Initiator** & **Relay** & **Target** & **Example** \\ \hline \multirow{3}{*}{G\(\mathrm{PCD}\)} & SHD & Smartphones controls lights indirectly over a smart hub. \\ \cline{2-3}  & Internet & Smartphones control devices that sync their state with the cloud. \\ \cline{2-3}  & Internet & SHD & Smartphones controls device indirectly over a server. \\ \hline SHD & Internet & SHD & A smart hub controls devices indirectly over a server. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Communication patterns involving three parties.  
Addressing the threats,
* Validation.  
[PERSON] defines three main focuses of threat modelling: asset-centric, attacker-centric, and system-centric [28]. Following the suggestion from the original work we will use the latter:  
_System-centric_ approaches try to elicit threats based on a model of the system itself. Focusing on the system, in contrast to the aforementioned alternatives, has the benefit of being able to perform abstract analysis and does not require thinking about the different types of attackers.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 8 Simulation
### Simulation Cases  
As described earlier, our case scenario belongs to the category of a Mobile Sink Static Node (MSSN) network where a sink is traversing the network to collect data while all sensor nodes are kept constant. If we are taking the sink as a mobile node then there are three possibilities:  
1. Direct data collection,
2. Cluster-based data collection with a controlled path,
3. Cluster-based data collection with an independent UAV path.  
In this scheme, the UAV can move freely and instruct the ground sensor nodes to form clusters and help them to select a cluster head. To evaluate this developed system, we studies three cases, one for each type:  
1. Direct Data Collection (DDC) by UAV, as described in [26],
2. Network Assisted Data Collection (NADC), as given in [27]
3. Our developed UAV Routing Protocol (URP).  
One hundred sensor nodes are deployed randomly with a uniform distribution in a 2 Km\({}^{2}\) area.
## 3 An Analysis of Application of Steam Injectors in a Boiler Regenerative Feed Water System
### Application of Steam Jet Injectors in Complex Regenerative Feed Water Systems
#### 3.2.1 Application of Single-Stage Injectors  
Figure 9 shows a thermal-flow diagram of the modified system in which two single stage steam injectors were used. The first injector supplies a vacuum heat exchanger in which the boiler feed water is heated to the temperature of 80 \({}^{\circ}\)C. The second injector feeds the deaerator in which, due to the achievable injector outlet steam pressure, the temperature of water heating is 100 \({}^{\circ}\)C. Further heating of the water, as in the reference system, takes place in an overpressure heat exchanger supplied directly from the 6.6 barA steam bleed up to a temperature of 140 \({}^{\circ}\)C. Table 4 presents the thermodynamic parameters of the medium in individual control planes of the cycle (1,2,3,... n), and control planes of injectors (C\({}_{i}\),\(X_{i}\),Li, where i stands for injector number).  
The mathematical model of the system is described by the system of the following balance Equations (10).  
\[\left\{\begin{array}{l}\dot{m}_{21}(i_{21}-i_{22})-\dot{m}_{13}(i_{13}-i_{12 })=0\\ \dot{m}_{21}i_{22}+\dot{m}_{19}i_{19}+\dot{m}_{16}i_{16}+\dot{m}_{10}i_{10}= \dot{m}_{11}i_{11}\\ \dot{m}_{21}+\dot{m}_{19}+\dot{m}_{16}+\dot{m}_{10}=\dot{m}_{11}=1\\ \dot{m}_{14}(i_{14}-i_{15})-\dot{m}_{10}(i_{10}-i_{9})=0\end{array}\right. \tag{10}\]  
The mass flow of the driven and drawn steam of the injectors can be determined based on the transformed definition of the ejection ratio (Figure 3):  
\[\dot{m}_{Ci}=\frac{\dot{m}_{Li}}{1+CH_{i}} \tag{11}\]  
\[\dot{m}_{Xi}=\frac{\dot{m}_{Li}}{1+\frac{1}{CH_{i}}} \tag{12}\]  
Therefore, the calculated efficiency of the system is:  
\[\eta_{CRreg1}=\frac{\dot{m}_{1}(i_{1}-i_{4})-\dot{m}_{2}(i_{2}-i_{4})-\dot{m}_ {3}(i_{3}-i_{4})}{\dot{m}_{1}(i_{1}-i_{13})}=0.374898 \tag{13}\]and as a result of the modification applied, the degree of system regeneration corresponds to  
\[\varepsilon_{CRreg1}=\frac{\eta_{CRreg1}-\eta_{CRref}}{\eta_{CRref}}=0.503\% \tag{14}\]  
The application of the modification resulted in the part of the waste heat that was previously lost in the condenser being usefully applied to heat the boiler feed water. The mass flows of the required bleed steam feeding heat exchangers also decreased, thus increasing the available enthalpy drop in the turbine.
# A Novel Dual-Permanent-Magnet Mechanical Antenna for Pipeline Robot Localization and Communication
## 3 Development of the Novel Mechanical Antenna
### Simulation and Experiments in Pipelines  
Due to the shielding effect of the metal pipe wall, when the antenna is placed inside a pipeline, the magnetic field signal will be weakened. To verify the effectiveness of the antenna in a pipeline, finite-element simulations were conducted. The simulation model is shown in Figure 8, wherein the mechanical antenna was placed at the center of the pipeline. The pipeline's outer radius \(r_{\text{p}}\) = 0.07 m, length \(l_{\text{p}}\) = 1 m, and thickness \(h_{\text{p}}\) = 0.005 m. The material of the pipeline was set to be air (no pipeline), aluminum, or iron. The aluminum's conductivity \(\sigma_{\text{Al}}\) = 3.77 \(\times\) 10\({}^{7}\) S/m. The iron's relative permeability \(\mu_{\text{Fe}}\) = 4000 and conductivity \(\sigma_{\text{Fe}}\) = 1.12 \(\times\) 10\({}^{7}\) S/m. The magnets had a diameter \(D\) = 0.025 m, height \(h\) = 0.008 m, distance \(2d\) = 0.065 m, and rotation frequency \(f\) = 30 Hz.  
The distribution of magnetic flux density \(B\) is shown in Figure 9 when \(y\) = 0.2 m. It can be seen that when the antenna was placed in the pipeline, \(B\) was about 10\({}^{-5}\) T and it was attenuated due to the shielding effect of the pipe wall. The shielding effect of the iron pipeline with high permeability was more obvious than that of the aluminum pipeline.  
Pipelines made of 20# steel are mainly used for boilers and heat exchangers to transport fluids, which are widely used in petrochemical, power stations, and large-scale equipment. In the experiment, the antenna was placed in a practical 20# steel pipeline with an outer radius of 180 mm, length of 1200 mm, thickness of 8 mm, and conductivity of about 4.22 MS/m, as shown in Figure 10. The measured results are shown in Table 2. It can be seen that when a 20# steel pipeline was used, the magnetic flux density was effectively received at 3 m from the carbon steel pipeline with a receiving antenna able to measure a  
Figure 8: The simulation model.  
Figure 9: The distributions of magnetic flux density: (**a**) air; (**b**) aluminum; (**c**) iron.  
magnetic flux density of as low as 0.15 pT. \(B_{x}\), \(B_{y}\), and \(B_{z}\) all had high attenuation which was consistent with the simulation results. When \(\alpha\) was changed, the amplitudes of \(B_{x}\), \(B_{y}\), and \(B_{z}\) changed significantly, indicating a good modulation.
## 4 Experiments and Results
### Experimental Environment  
Table 2 summarizes all the hyperparameters used in the three experiments. The autoencoder comprises an encoder and a decoder; the encoder consists of three convolutional layers utilizing the same kernels but with different channels. The Deformer hyperparameters include 196 patches, a patch size of 16 \(\times\) 16, a hidden size of 768, four intermediate multiplications, 12 hidden layers, and 12 attention heads.  
During the training of the three models, the resized Mel Spec \((W^{\prime}\times H^{\prime})\) size was set to 224 \(\times\) 224, which can be adjusted according to the hardware of the experimental environment. As mentioned before, noise injection was operated in pre-training. The noise injection ratio \(\beta\) was determined by experimental results, given that the highest classification performance was obtained when \(\beta\) was set to 0.75. Similarly, 0.75 was also used as the mask parameter in [22], which similarly achieved good results. As the input to the prior decoder is a patch, it allows for a higher batch size compared to others. The parameters of the AdamW optimizer were nearly similar. When setting the learning rate, it was considered that pre-training requires warmup. Unlike other approaches that use a fixed learning rate, pre-training employed a dynamically changing learning rate based on the WarmupDecayLR scheduler.  
Figure 6: Fine-tuning stage of Deformer.  
The experiments were conducted on a system running Windows 10 with 2 Xeon(R) Silver 4310 CPUs, 4 NVIDIA GeForce RTX 3090 GPUs, and 128 GB of DDR4 RAM. The proposed method was developed in Python 3.10.12 and implemented using the PyTorch 2.0.0 platform, complemented by the DeepSpeed acceleration engine for enhanced performance.  
In addition to conducting these experiments, a comparative assessment was performed with two baseline models. The first baseline model [13] employed a hybrid approach, combining ResNet18 and Bi-GRU. ResNet18 utilizes residual connections, comprising 18 weighted layers, including an initial convolutional layer, a max-pooling layer, 4 convolutional blocks (each with 2 convolutional layers), an average pooling layer, and a fully connected layer. Bi-GRU is a recurrent neural network designed for processing sequential data, consisting of a GRU layer and a fully connected layer.  
The second baseline model [16] adopted S3T, leveraging the Swin Transformer as a feature extractor in the time-frequency domain of music. It integrates a momentum-based MoCo paradigm for enhanced performance. The feature extractor follows the Swin-T configuration, using the compact version of the Swin Transformer with a hidden channel number of 96. Each block comprises 2, 2, 6, and 2 layers, ensuring increased efficiency.
## 3 Results and Discussion  
Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) and X-ray Photoelectron Spectroscopy (XPS)  
The surface chemical structures after the chemical surface modification were firstly characterized by ATR-FTIR. As shown in Figure 1, no infrared adsorption was observed on the pristine magnesium alloy surface, indicating that there were no chemical groups on the pristine magnesium alloy surface. After sodium hydroxide treatment, a sharp peak at 3700 cm\({}^{-1}\) can be found, which can be attributed to the adsorption of hydroxyls, suggesting that a significant amount of hydroxyls was introduced on the surface after NaOH treatment [25] and a chemical layer of Mg(OH)\({}_{2}\) was produced on the substrate. The hydroxyls make the following surface functionalization possible. In our recent study, we demonstrated that the self-assembly can be carried out on the NaOH-treated Mg surface, which can be used to immobilize the biomolecules for improving biocompatibility [26]. For the hydrogen fluoride treated sample, it can be clearly seen that no hydroxyls can be detected and there were two new peaks at 900 and 720 cm\({}^{-1}\), demonstrating that a magnesium fluoride layer was produced after HF treatment.  
XPS was utilized to further detect the surface atomic concentration of the different samples. Figure 2 shows the XPS survey spectra of the different samples. The element concentrations were listed in Table 1. It can be clearly seen that the peaks of C\({}_{18}\) and O\({}_{18}\) can be found on the curve of the pristine magnesium alloy expect Mg\({}_{18}\) and Al\({}_{2p}\), indicating that there was carbon contamination on the surface and the magnesium alloy was oxidized naturally. After sodium hydroxide treatment, the oxygen concentration increased significantly while the concentration of Mg\({}_{18}\) decreased obviously (Table 1), suggesting that more oxygen has been introduced on the magnesium surface. These oxygen
## 1 Introduction
In recent years, the combination of CFD and machine learning has promoted the development of CFD, brought new perspectives and research methods to this field, and made it gain new vitality and good development prospects. Machine learning requires a lot of data. Combining CFD with high-performance computers to perform a large number of calculations and then transforming it into a database for modeling is a relatively big impetus to the CFD technology. [PERSON] et al. [16] used polynomial regression, multivariate adaptive regression splines, random forests, and fully connected neural networks for the data-driven modeling of simulation digital twins. [PERSON] et al. [17] used the POD-Kriging method to build a digital twin furnace operating under flameless combustion conditions to predict the combustion data under new operating conditions. However, digital twins need to rely on means, including simulation, actual measurement, and data analysis, to sense the state of physical entities and monitor devices in real-time. This paper aims to build a digital twin framework based on the Internet of Things, CFD simulation modeling, and artificial intelligence for the real-time state monitoring of the push-plate kil. The framework is the integration of the geometric, rule, physical, and neural network models. In this paper, a data-driven model (POD-MCNN) embedded with POD and multiscale convolutional neural network is proposed for the first time to realize the fast off-line prediction of the flow field. We use POD to map the high-dimensional flow field data to low-dimensional features and to use the multiscale convolutional neural network for surrogate modeling. The multiscale convolution kernel can be used to extract features of different fineness and improve the feature learning ability of the model. Our model does not need the pooling operation, which allows the convolution layer to retain the extracted data features and Improve the accuracy of the network. The rest of the paper is organized as follows: Section 2 presents related work. Section 3 introduces the proposed digital twin framework and workflow for high-dimensional CFD data prediction, and emphatically introduced the neural network model POD-MCNN. Section 4 presents the push-plate kiln structure and gives the experimental details, dis cussing the performance of the proposed digital twin. Finally, Section 5 concludes the paper and suggests possible future directions.
## 6 Results and Discussion
### Results Depending on the Buffer Size  
Figures 8-10 present the results when increasing values of the buffer size. Regarding the delivery ratio, in all tested scenarios, the algorithms behave quite similarly, that is, the larger the buffer size,  
Figure 6: Average cost when increasing message TTL.  
Figure 7: Delivery delay when increasing message TTL.  
the higher the delivery ratio. The FSF algorithm outperformed the other algorithms in the Cambridge and Sassy scenarios, achieving ratios of up to 18% and 5%, respectively. The Friendship Routing algorithm, also based on social characteristics, gets the second-best delivery ratio in all scenarios. The PRoPHET routing strategy achieved the best delivery ratio in the NCCU scenario (up to 20%), followed by Epidemic routing.  
Again, the algorithms which delivered more messages took more average time to deliver these messages. For example, FSF in the Cambridge and Sassy scenarios, PRoPHET in the NCCU, and Friendship Routing in the Reality scenario. With respect to the metric average cost, FSF also outperformed the other algorithms in all scenarios tested. Moreover, it is worthwhile to emphasize that FSF also achieved the highest delivery ratio in the Cambridge, Sassy, and Reality scenarios. Altogether, the above results highlight the benefits of the FSF strategy for improving the delivery ratio in opportunistic networks, besides reducing the resource utilization.  
Figure 8: Delivery ratio when varying the buffer size.  
Figure 9: Delivery delay when varying the buffer size.
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
###### Abstract  
Automatic Dependent Surveillance-Broadcast (ADS-B) signals are very vital in air traffic control. However, the space-based ADS-B signals are easily overlapped and their message cannot be correctly received. It is challenge to separate overlapped signals especially for a single antenna. The existing methods have a low decoding accuracy for small power difference, carrier frequency difference and relative time delay between overlapped signals. In order to solve these problems, we apply the deep learning method to single antenna ADS-B signal separation. A multi-scale Conv-TasNet (MConv-TasNet) is proposed to capture long temporal information of the ADS-B signal. In MConv-TasNet, a multi-scale convolutional separation (MCS) network is proposed to fuse different scale temporal features extracted from overlapping ADS-B signals and generate an effective separation mask to separate signals. Moreover, a large dataset is created by using the real ADS-B data. In addition, the proposed method has been evaluated on the dataset. The average decoding accuracy on the test set is 90.34%. It has achieved the state-of-the-art results.  
ADS-B; signal separation; deep convolutional network; single antenna Article
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.1 XGBoost  
[PERSON] and [PERSON] [32] introduced the XGBoost algorithm in 2015 to solve classification and regression issues in different scopes [33; 34; 35; 36; 37]. This technique efficiently generates boosting trees of gradient boosting (GB) decision trees and machines in parallel [25; 38]. It optimises an objective function composed of two parts, training loss and regularisation, helping control overfitting. In the geotechnical context, XGBoost is applied to predict the PL values of fully grouted rock bolts by learning complex interactions between the input variables. Regularisation helps manage model complexity by pruning insignificant decision tree branches, particularly in smaller datasets like ours [32; 39]. Notably, due to the nonlinear problems of geotechnical concepts, XGBoost algorithm is being widely applied in different field scopes. For instance, [PERSON] et al. [40] evaluated slope stability by considering different influential parameters such as slope angle, cohesion, unit weight, pore water pressure, and height by applying different ML techniques, including the XGBoost algorithm. [PERSON] et al. [41] assessed the void ratio of soil based on shear-wave velocity by developing the XGBoost model. [PERSON] et al. [42] predicted tunnelling-induced ground settlements using an optimised XGBoost algorithm.
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
###### Abstract  
The Common Data Model (CDM) is being used to deal with problems caused by the various electronic medical record structures in the distributed hospital information system. The concept of CDM is emerging as a collaborative method of exchanging data from each hospital in the same format and conducting various clinical studies based on shared data. The baseline of a CDM system is centralized with an infrastructure typically controlled by a single entity with full authority. The characteristics of this centralized system can pose serious security issues. Therefore, the proposed SC-CDM system is designed as a platform for distributed ledger and provides data with a high level of confidentiality, security, and scalability. This framework provides a reference model that supports multiple channels, using secure CDM as an encryption method. The data confidentiality of CDM is guaranteed by asymmetric and symmetric protocols. Delivering CDM is protected by a symmetric key signed by the CDM creator and maintains lightweight distributed ledger transactions on Inter Planetary File System (IPFS), which acts as a file share. To deliver an encrypted CDM on the SC-CDM platform, the CDM is encrypted with a block cipher by a random symmetric key and Initialization Vector (IV). The symmetric key protocol is used for the fast encryption of large-capacity data. The SC-CDM is implemented the repository with IPFS for storing the encrypted CDM, in which symmetric key, two hash values, and IV are shared through blockchain. Data confidentiality of SC-CDM is guaranteed by only registered users accessing the data. In conclusion, the SC-CDM is the first approach to demultiplexing with the data confidentiality proof based on asymmetric key cryptography. We analyze and verify the security of SC-CDM by comparing qualitative factors and performance with existing CDM. Moreover, we adopt a byte-level processing method with encryption to ensure efficiency while handling a large CDM.  
common data model; distributed ledger; SC-CDM; IPFS; symmetric and asymmetric protocol; blockchain +
Footnote †: journal: Accepted: 12 October 2020; Accepted: 12 October 2020; Published: 13 October 2020