esa-sceva/satcom-chunk-collection huggingface dataset:
# A GNN-Based False Data Detection Scheme for Smart Grids
## 5 Performance Evaluation
### Experimental Settings
Secondly, the anomaly detection methodsBiGCN, TGNF, and STGNN based on graph neural networks perform better than the traditional deep learning technique RvNN. This is because convolutional neural networks and recurrent neural networks cannot handle non-Euclidean data such as graph data, which makes it difficult for these methods to fully learn the graph structure information during data transmission. Therefore, there is still room for improvement in their results. Our raised STGNN performed better than BiGCN because, although BiGCN considered bidirectional features during data transmission, it does not take into account its dynamics, resulting in its inability to learn temporal features. The STGNN has achieved better performance compared to TGNF because TGNF only focuses on the dynamics of data transmission while ignoring the bottom-up backward diffusion characteristics, whereas the SRGNN can obtain more structural information from bidirectional transmission graphs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Event Type** & **Precision** & **Recall** & **F1** \\ \hline \multirow{3}{*}{DTC} & \multirow{3}{*}{0.473} & F & 0.826 & 0.814 & 0.821 \\ \cline{3-6}  & & N & 0.813 & 0.824 & 0.847 \\ \hline \multirow{3}{*}{SVM-TS} & \multirow{3}{*}{0.574} & F & 0.801 & 0.887 & 0.894 \\ \cline{3-6}  & & N & 0.875 & 0.824 & 0.847 \\ \hline \multirow{3}{*}{RvNN} & \multirow{3}{*}{0.737} & F & 0.908 & 0.89 & 0.899 \\ \cline{3-6}  & & N & 0.897 & 0.914 & 0.905 \\ \hline \multirow{3}{*}{BiGGCN} & \multirow{3}{*}{0.861} & F & 0.921 & 0.92 & 0.915 \\ \cline{3-6}  & & N & 0.92 & 0.919 & 0.914 \\ \hline \multirow{3}{*}{TGNF} & \multirow{3}{*}{0.897} & F & 0.948 & 0.937 & 0.942 \\ \cline{3-6}  & & N & 0.938 & 0.949 & 0.944 \\ \hline \multirow{3}{*}{STGNN} & \multirow{3}{*}{0.892} & F & 0.951 & 0.96 & 0.955 \\ \cline{3-6}  & & N & 0.96 & 0.951 & 0.955 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of various indicators in IEEE 118-bus. Figure 4: Result of early anomaly detection in IEEE 118-bus.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 2 Implemented System
### Controller  
The controller, depicted in Figure 1 in green, is responsible for the digital control of the whole system. It is composed of a controller board, a pulse generator, and an arbitrary waveform generator (AWG).  
The board establishes a telnet connection with the host PC via Ethernet (ETH) where the user can program the radar using a command line interface (CLI). The CLI can be used to define a great range of radar parameters such as the pulse length, shape, time between pulses, number of pulses, and a lot of other internal parameters such as gains and delays. It is also possible to display several system status parameters such as pulse configurations and temperatures. Table 5 shows all the variables that can be monitored and configured.  
The pulse generator is responsible for timing all the components of the system. It enables/disables the transmitting and receiving chains and triggers the arbitrary wave generator as well as the acquisition board. This enables the system to be fully coherent and synchronized from signal generation to acquisition.  
Finally, the AWG enables the design of arbitrary amplitude modulated waveforms in the digital domain that can be uploaded to the system.
# On the Impact of Particulate Matter Distribution on Pressure Drop of Wall-Flow Particulate Filters
###### Abstract  
Wall-flow particulate filters are a required exhaust aftertreatment system to abate particulate matter emissions and meet current and incoming regulations applying worldwide to new generations of diesel and gasoline internal combustion engines. Despite the high filtration efficiency covering the whole range of emitted particle sizes, the porous substrate constitutes a flow restriction especially relevant as particulate matter, both soot and ash, is collected. The dependence of the resulting pressure drop, and hence the fuel consumption penalty, on the particulate matter distribution along the inlet channels is discussed in this paper taking as reference experimental data obtained in water injection tests before the particulate filter. This technique is demonstrated to reduce the particulate filter pressure drop without negative effects on filtration performance. In order to justify these experimental data, the characteristics of the particulate layer are diagnosed applying modeling techniques. Different soot mass distributions along the inlet channels are analyzed combined with porosity change to assess the new properties after water injection. Their influence on the subsequent soot loading process and regeneration is assessed. The results evidence the main mechanisms of the water injection at the filter inlet to reduce pressure drop and boost the interest for control strategies able to force the re-entrainment of most of the particulate matter towards the inlet channels' end.  
Keywords:internal combustion engines; emissions; particulate matter; wall-flow particulate filter; pressure drop; soot distribution; particulate layer +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Comparison of Batch and Fed-Batch Cultures in Molasses Media  
The addition of five times the dose of the molasses hydrolysate in portions in the M2-fb culture compared to the M1-b culture resulted in twice the biomass yield--11.82 gDCW/L after 62 h. In a study by [PERSON] et al. [21], batch culture of _Y. lipolytica_ yeast JMY5578 in YNB medium without amino acids and ammonium sulfate with molasses and ammonium chloride lasting 72 h, resulted in a biomass yield of 14.2 gDCW/L. To be mentioned, the C/N molar ratio of the molasses hydrolysate used in the current study was 18.3, while in the cited research the C/N value for the culture medium was 35. There is an assumption that, hydrolysate may have contained insufficient amounts of sugars available to the cells. The biomass yield from the M1-b batch culture was more than half the result of the batch culture in the study by [PERSON] et al. [21] In comparison, the value of biomass yield after 62 h of the M2-fb culture was similar to the batch culture of the JMY5578 strain. What's important, the auxotrophic JMY5578 strain was unable to degrade fatty acids and remobilize triacylglyceredons from cellular reserves. Despite this, the content of lipids accumulated in the cells was finally at 6.7% (L = 0.95 g/L). After 62 h of batch and fed-batch culture with molasses hydrolysate, cellular lipid content was negligible. Despite the unsatisfactory final result, wild yeast strain KKP 379 generally accumulated higher amounts of intracellular lipids reaching maximum yields for M1-b and M2-fb after 24 and 48 h in both cases above more than 35%. After this time, due to the depletion of the necessary carbon source in the M1-b culture, there was a significant decrease in cellular lipid content. Nevertheless, the decrease in this case may have been due to the osmotic pressure of the molasses solution and the inadequate C/N ratio of the hydrolysate. Moreover, the M2-fb culture feeding strategy contributed to a gradual decrease in the C/N ratio as a result of medium supplementation with molasses hydrolysate as a source of nitrogen and a shift in the stage of cellular lipid accumulation as a consequence, compared to M1-b. According to the literature, in order to effectively stimulate lipid accumulation in the cells of oleaginous microorganisms, the C/N molar ratio should be above 20. Higher C/N molar ratios, as a consequence of nitrogen limitation, favor the accumulation of SCOs [9,22]. It should be noted that, cell physiology in waste substrates can be disrupted due to reactions with their various components. An example is furfural, which is formed by acidic and thermal treatment of sugars, considered as one of the inhibitors of growth and the process of cellular lipid biosynthesis [23].
# A Framework of Wearable Sensor-System Development for Urban 3D Modeling
## 3 Calibration Framework for Wearable Sensor Systems
### Point-Plane Correspondences Based System Calibration for Sensor Bundles
#### 3.2.2 Application of the Proposed System Calibration Method  
In the proposed system calibration methodology, three or more planes installed perpendicular to each other are required to accurately adjust the coordinates of GCPs while securing BA accuracy. Figure 5 shows three patches arranged perpendicular to each other, and the white and black dots show the positions of the initial GCPs and the actual GCPs, respectively. The position (plane direction coordinates) of each GCP in the plane direction is limited by the constraint that \"GCPs must be located on the corresponding plane\" for GCPs on different patches. For example, the GCPs in patches 2 and 3 cannot be adjusted in directions perpendicular to the plane (\(z_{p2}\) and \(z_{p3}\) respectively), as each of them must be located on a plane, and the vertical orientations of patches 2 and 3 coincide with the plane directions (\(x_{p1}\) and \(y_{p1}\)) in patch 1, respectively. Hence the constraint that the GCPs of patches 2 and 3 must be on each patch serves as a condition for positioning the GCPs of patch 1 on the plane.  
The proposed system calibration method was applied to each sensor bundle as shown in Figure 6; where the green dots are LiDAR point clouds in Epoch m, and the black dots are the GCPs. When the proposed system calibration was performed using the entire image (n images) and the point cloud obtained from Epoch m, the EOPs of each image acquired  
Figure 4: Point-plane correspondences in proposed system calibration.  
Figure 5: Constraints for positioning GCPs.  
in n epochs were calculated based on the LiDAR coordinate system of Epoch m. Moreover, as the relative position between the camera and LiDAR was fixed, EOPs of the image m (EOPs\({}_{l}^{m}\)) are the same as the ROPs (ROPs\({}_{l}^{\text{C}}\)) between the LiDAR and the camera. Therefore, through the point-plane correspondence-based approach, system calibration between LiDAR and camera can be performed and the ROPs between sensors can be estimated.
## 4 Residual Properties Analysis
### Energy Dissipation  
Energy dissipation factors (\(\eta=S_{\text{OUYC}}/S_{\text{OABC}}\)) are calculated according to the load-deformation curve. As showed in the Figure 18, the \(S_{\text{OUYC}}\) is equal to the shadow area surrounded by the load deflection curve. The \(S_{\text{OABC}}\) is equal to the rectangle area, which is passes through the peak point (U) and the limit deformation point (Y).  
Figure 16: The sketch of energy equivalent method.  
Figure 17: Ductility. (**a**) rectangular prisms specimens; (**b**) columns; (**c**) bending beams; (**d**) shear beams; (**e**) The influence of temperature; (**f**) The influence of replacement percentage.  
Figure 19 shows the energy dissipation factors of each component after exposure to different elevated temperature. As seen from Figure 19a-e, the law of energy dissipation factors of each component is similar to that of ductility factor. Energy dissipation factor decreases with the increase of temperature when \(T\) is lower than or equal to 400 degrees centigrade. When \(T\) is equal to 400 degrees centigrade, the energy dissipation factor of test block, column, bending beam and shear beam decrease by 13%, 11%, 7% and 10%, respectively. The energy dissipation factor increases slightly when \(T\) is from 400 degrees centigrade to 600 degrees centigrade. The energy dissipation factor decreases steeply when \(T\) is above 600 degrees centigrade. When \(T\) is equal to 800 degrees centigrade, the energy dissipation factor of test block and column decreases 36% and 10%, respectively. As seen from Figure 19f, the energy dissipation factor of each component is not obviously affected by the replacement percentage. The energy dissipation factor varies from \(-10\)% to 16% with replacement percentage increases.
# Validating the Adoption of Heterogeneous Internet of Things with Blockchain
## 3 Conceptual Model and Research Hypotheses
### Behaviour Factors
#### 3.1.2 Social Related Factors  
Social factors refer to the factors in the social environment that are important to the adoption of new technology. A network of social relationships is an important source of support and appears to be an important factor influencing behavior and decision-making. In addition, social factors depend on the ability of people to form and enhance a reputation with others as regards adopting specific technologies. Thus, we divided the social impact into two specific factors: the social network and reputation.  
**Social Network.** Social networks trace a significant amount of user behavioral data. Therefore, analyzing this behavioral data can provide great advantages. The Social Internet of Things (SIoT) is one of the concepts in which social networks meet IoT systems to support networking services in more effective and efficient ways [35]. According to [PERSON] et al. [36], communication between people is the core of forging social connections. In opportunistic IoT systems, contacts between nodes are fundamentally linked with user social behaviors and social features. Opportunistic IoT architecture allows the deployment and use of smart objects for collaboration between users who influence each other. Thus, peer influence becomes more important than ever, offering a tremendous opportunity for new business [36]. Blockchain technology plays an important role in user behavior management for network media and ensuring transaction security [37; 38]. There are many models designed with blockchain technology that apply decentralized contracts to motivate trust networks, and therefore, to secure information exchange contracts [39; 40; 41]. Blockchain, as an effective technology for decentralized distributed storage, supports trusted social networks, and different studies have shown the consistent relationship between social networks and behavioral intention regarding the adoption of new technology [42; 43; 44]. Thus, the following is hypothesized:  
**Hypothesis 2A (H2A).**_Social networks have a positive effect on IoT blockchain technology adoption._  
**Reputation.** Reputation refers to the general belief in or estimation of something. When the technology is trustworthy and brings benefits, it has a good reputation. In previous literature [45], reputation is used to assess the quality of the system where IoT devices for data trading are centralized and lack a reputation system. As a result, the IoT system was integrated with the Ethereum blockchain to ensure data quality and improve user behavior. [PERSON] et al., in 2017 [46], discussed the most commonly used blockchains in the financial domain that allow one to store the user's reputation on the blockchain to improve performance. Therefore, we can conclude that there is a positive relationship between reputation and behavioral intention towards the adoption of IoT systems integrated with blockchain:  
**Hypothesis 2B (H2B).**_Reputation has a significantly positive social effect on IoT blockchain technology adoption._
# Multi-Topology Routing Algorithms in SDN-Based Space Information Networks
## 4 Multi Topology Routing Algorithm  
In the SIN, different spatial tasks have different QoS requirements. Spatial tasks can be divided into three categories:  
1. Delay sensitive service. Such services mainly include language services, real-time video interactive services, which allow a certain packet loss rate, but have certain requirements for transmission delay and delay jitter.
2. Throughput sensitive service. Such services mainly include a large number of data file transfer services. They do not require real-time performance, but they require a certain bandwidth guarantee. Usually, the packet loss rate is required to maintain a constant transmission rate.
3. Best effort service. Such services have no special requirements for delay, bandwidth, packet loss rate and so on [24].
## 7 Conclusions  
In recent years, frequent typhoon disasters have caused great damage to houses. The manner in which to quickly detect damaged roofs has become a challenge. This study considered the residential roof in the southern part of Chiba Prefecture after typhoon Faxai in 2019 as the research area using aerial photos, and the sequence and principle of detection methods were described in detail. Specifically, it included the sample making, the parameter adjustment, the model training and validation. Subsequently, the detection results obtained using the model were compared with the results of the field investigation after typhoon Faxai. It proves the feasibility of this method. In addition, it also summarizes the advantages and disadvantages of this method in the field investigation.  
Compared with the traditional field investigation and other image analysis methods, the deep learning method in this study can greatly shorten the damage detection time, and the F value of the damage classification is more than 0.8. Furthermore, this study provides a research template for further research on damage detection of roofs after a typhoon. In the future, this method is expected to be extended to many fields, such as to detect damage in walls and other parts of the house following the occurrence of a typhoon.  
Note, (1) batch size: the number of data contained in each subset of training is called batch size. For example, when the dataset of 2000 roofs is divided into 100 subsets, the batch size is 20.  
Note, (2) backbone: the omitted form of the backbone network, and its scale and type vary with the network. The backbone of the model in this paper is RESNET 50 and RESNET 101, which show the number of the convolution layer.  
Note, (3) learning rate: learning rate is a parameter that adjusts the size of parameter changes in the optimization of deep learning. An excessive learning rate will lead to good results, and too small a learning rate will prolong the training time.  
Conceptualization, J.X.; methodology, T.T.; validation, F.Z.; formal analysis, W.L.; investigation, J.X. and T.T.; resources, W.L. and T.T.; data curation, T.T.; writing--original draft preparation, J.X.; writing--review and editing, J.X.; funding acquisition, T.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by JSPS Grant-in-Aid for Special Purposes (2019) G19K24677, a field investigation on the effects of prolonged power outages caused by Typhoon Faxai ([PERSON]).  
Not applicable.  
Part of this paper was supported by a field investigation after Typhoon Faxai ([PERSON]). Thank to everyone who accompanied us in the field investigation after Typhoon Faxai in 2019.  
The authors declare no conflict of interest.
# Global Power Dynamics in the Contemporary Space System
## 4 System Actors--Space Powers
### Major Space Actors  
Currently, there are 78 states with space agencies around the world, 16 of which have the capability to launch spacecraft, and 6 of these agencies (The National Aeronautics and Space Administration (NASA), the China National Space Administration (CNSA), the European Space Agency (ESA), the Indian Space Research Organisation (ISRO), the Russian State Corporation for Space Activities (ROSCOSMOS), and the Japan Aerospace Exploration Agency (JAXA)) have the additional capability to land spacecraft on other planets [41]. These agencies possess the capacity to launch and recover multiple satellites, develop and deploy cryogenic rocket engines, and operate space probes. Each of these space powers has different capabilities, interests, influence, and power. The analysis will focus on the most relevant actors and describe their key characteristics, power, objectives, and strategies.
## 2 Materials and Methods
### Drop Height Calculation  
One method used to determine the free-fall drop height is called the '1 g shift method'. This method measures the duration between the 1 g state where the package is not in motion, the 0 g state where the package is free falling, and when the impact happens with several gs [20]. This method can be used to effectively determine the drop height when a pure free-fall drop event occurs, but it cannot be used to determine different types of impacts. However, packages experience various kinds of shocks and impacts, such as free-fall drops, tumbles, side impacts, tosses, and kicks, when they go through the parcel delivery system [20]. Therefore, a method was employed to analyze and convert different types of shocks and impacts into the 'equivalent free-fall drop heights' (EFFDH) by reversing the relationship introduced in Equation (1). It can significantly ease the process of laboratory-based package testing by simplifying the different types of shocks into simple free-fall drops while keeping the same stress applied to the packages. The EFFDH for each shock event can be calculated using predetermined e values from the calibration process, resultant velocity change collected from the field, and Equation (2) [30]:  
\[EFFDH=\frac{1}{2\text{g}}\bigg{(}\frac{\Delta v}{1+e}\bigg{)}^{2} \tag{2}\]  
where:  
\(EFFDH\) = equivalent free-fall drop height (m or in.)  
\(e\) = coefficient of restitution  
\(\Delta v\) = resultant velocity change (m/s or in./s)  
g = acceleration due to Earth's gravity (9.81 m/s\({}^{2}\) or 386.4 in./s\({}^{2}\))
## Nomenclature  
\(W=\) Watt  
\(m=\) Metre  
\(g=\) Gram  
\(U=\) Cubesat Specification Unit of Volume  
\(K=\) Kelvin  
\(Pa=\) Pascal  
\(Mol=\) Molar Mass  
\(V=\) Volt  
\(N=\) Newton  
\(=\) Degrees Celcius  
\(A=\) Ampere  
\(S=\) Second  
\(F=\) Force (N)  
\(\dot{m}=\) Mass Flow Rate (\(\mathrm{kgs}^{-1}\))  
\(q=\) Electric Charge (C)  
\(B=\) Strength of Magnetic Field \(\left(\mathrm{A}\;\mathrm{m}^{-1}\right)\)  
\(Hz=\) Hertz  
\(B=\) Byte  
\(g=\) Acceleration due to Gravity \(\left(9.81\;\mathrm{ms}^{-2}\right)\)  
\(\Delta V=\) Rate of Change of Velocity \(\left(\mathrm{ms}^{-2}\right)\)  
\(F_{drag}=\) Force due to Drag \(\left(\mathrm{N}\right)\)  
\(\rho=\) Density \(\left(\mathrm{kgm}^{-3}\right)\)  
\(C_{D}=\) Coefficient of Drag  
\(A_{sat}=\) Area of Satellite incident with flow \(\left(\mathrm{m}^{2}\right)\)  
\(G=\) Gravitational Constant \(\left(6.67408*10^{-11}\;\mathrm{m}^{3}\mathrm{kg}^{-1}\mathrm{s}^{-2}\right)\)  
\(M_{earth}=\) Mass of Earth \(\left(5.9723*10^{24}\;\mathrm{kg}\right)\)  
\(r_{earth}=\) Radius of Earth \(\left(6.378137*10^{6}\;\mathrm{m}\right)\)  
\(V_{sat}=\) Velocity of the Satellite \(\left(\mathrm{ms}^{-1}\right)\)  
\(M_{propellant}=\) Mass of Propellant \(\left(\mathrm{kg}\right)\)  
\(N_{pulses}=\) Number of Pulses Lifetime  
\(M_{Bit}=\) Mass of one pulse \(\left(\mathrm{kg}\right)\)  
\(C=\) Capacitance \(\left(\mathrm{F}\right)\)  
\(E=\) Energy Stored (J)  
\(P=\) Power \(\left(\mathrm{W}\right)\)  
\(J_{s}=\) Solar Constant Flux \(\left(1369\;\mathrm{Wm}^{-2}\right)\)\(J_{Ia}=\)Albedo Flux (470 Wm\({}^{-2}\))  
\(J_{IR}=\)Earths Infra Red Flux (230 Wm\({}^{-2}\))  
\(r=\)radius (m)  
\(\dot{\rm Q}=\)Rate of Heat Transfer (Wm\({}^{-2}\))  
\(\epsilon=\)Emissivity  
\(\alpha=\)Absorptivity  
\(\beta=\)Is the angle between the local vertical and the sun's rays  
\(\sigma=\)[PERSON] Constant \(\left(5.67*10^{-8}\)WmK\({}^{-4}\right)\)  
\(A_{p}=\)Projected area of the CubeSat (m\({}^{2}\))