esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Velocity and Pressure Field in Symmetry Plane  
As results of the steady-state simulation using the \(k-\omega\) SST turbulence model, the pressure field (Figure 4) and velocity field (Figures 5 and 6) were received. The results from the simulation are compared with studies by other authors. The similarity criteria are characteristic structures generated behind an [PERSON] body (recirculation and edge vortices) and velocity profiles in the symmetry plane, as well as drag coefficients.  
Figure 7 schematically shows the vortex structures: the separation bubble above the rear part of the vehicle, two recirculation bubbles behind the geometry, and the c-pillar vortex generated on the rear side edge of the [PERSON] body. Similar structures were observed from the simulation in Figures 6 and 8. Additionally in Figure 8 the results are presented in isometric view. Area A represent the separation bubble, areas B1, B2 are recirculation bubbles, and area C is the c-pillar vortex.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Stage** & **I** & **II** & **III** \\ \hline Convergence criteria & Residuals & Residuals & Residuals \(10^{-4}\) + steady value of \\  & \(10^{-4}\) & \(10^{-4}\) & drag and lift coefficient with \\  & & & fluctuation around mean \\ \hline Number of & & & \\ iterations required & & & \\ to achieve the & & & \\ convergence criteria & & & \\ \hline Turbulence model & \(k-\omega\) SST & \(k-\omega\) SST & LES \\ \hline Pressure equation & Second Order & Second Order & Pressure Staggering Option \\  & & & (PRESTO) \\ \hline Momentum equation & First Order & Second Order & Bounded Central Differencing \\ \hline Relaxation & & & \\ Factor—Pressure & & & \\ \hline Relaxation & & & \\ Factor—Momentum & & & \\ \hline Drag Coefficient & 0.4409 & 0.3097 & 0.2979 (mean value) \\ \hline Lift Coefficient & 0.4177 & 0.2066 & 0.3637 (mean value) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solution methods and convergence criteria for each stage of the simulation process.  
Figure 5: Velocity distribution around the [PERSON] body at the symmetry plane.  
Figure 8: Vortex system in wake (simulation).  
Figure 6: Streamlines around the [PERSON] body at symmetry plane.  
Figure 7: Vortex system in wake (schematic) [9].
## 7 Methodology Applied in This Research
### Evaluation of PfSense (Open Access)
#### 7.4.2 Analysis and Findings  
In the observation, it was found that pfSense was able to give an extract to show the activities on the risk of application and protocols. Figure 16 below shows the variations in protocols observed by pfSense.  
Figure 16 above shows a difference in protocols compared to the other devices observed and the number of times this protocol was detected. In particular, Address Resolution Protocol (ARP) in identifying MAC addresses. Internet Control Message Protocol (ICMP) where the internet layer protocol is used by network devices to diagnose network communication issues. ICMP is mainly used to determine whether or not data are reaching their intended destination in a timely manner. Other matters that were familiar were TCP and TLSv1.2. VRRP and NTP refer to IP protocols and how the network is routed and timed.  
The second set of data shown shows the number of counts of different destinations that try to introduce themselves to the network via pfSense. It is through this detection that it was clear who was trying to access or tap into the SME business network. Figure 17 below shows the Destination Counts via pfSense Detection.  
Figure 16: Protocols from pfSense extracted from pfSense.  
Figure 17 above shows that the majority of the counts detected was from the engineering team in India using the Jump Server to gain access to the SME business data centre in the UK and vice versa. However, in this extract, it can be seen that some anomalies of others also trying to gain access into the data centre in the UK. These were coming from Taiwan and China. Some attacks are very smart and therefore pfSense is not able to identify its source. This shows that free OpenSource access tools can detect very low levels of network traffic and be able to prevent any anomalies from happening. The use of pfSense within this observation proves that these layers of filters enable these applications to work collectively to detect any patterns that are unpredictable using internal ML techniques very often not known to the end-user such as this SME. In order to take advantage of the varying levels of ML uses within these products requires yet again a level of knowledge and technical expertise plus a financial commitment in order to open these layers of code up to see what is happening within the algorithms.  
This concludes the three evaluations based on the experimentation's setup in both the office and home environments and discusses the concepts of costs versus ML knowledge in order to predict and capture zero-day attacks.  
The next section explains the general use of how Security Incidents and Events Management systems take a prominent stage in SMEs to manage these incidents and activities. Methods of reporting back into the business are explored in order for these SMEs to make informed choices on how to protect SME data and the SME business from both angles of office and home working.
life. Even though encryption effectively provides data privacy, monitoring indoor activities by relying on wireless IoT devices could disclose contextual information on data transmission [10; 11], not only posing risks to the privacy of individuals, but also compromising building security.  
This has motivated us to extend the research on our privacy-aware IoT and blockchain-based indoor location system to counter the spread of the COVID-19 disease. The presented indoor location system is particularly suitable for application in medical facilities, public buildings, and residential homes as a framework for privacy-aware indoor location monitoring. The proposed solution could be applied for structural health monitoring, studying behavioral patterns of a building's occupants and health-related issues such as locating lost patients with memory and orientation disorders, fall detection, and also identifying violations of social distancing, counting the number of persons in a room, and determining when and which room needs surface disinfection due to over-utilization, etc.  
The key contributions of the privacy-preserving framework are:  
* A novel privacy-preserving indoor location system with querying capabilities: The network of sensors is embedded in the floor and senses the local force applied over it. It is non-intrusive and does not require active user interaction. Moreover, the raw sensory data collected by sensors describe the force applied to the floor and can only lead to unique user identification via walking gait analysis. However, the walking gait analysis [12] requires large amounts of data from individual users, and in our privacy-aware framework, the raw data do not leave the source sensor, therefore inhibiting similar attempts.
* A secure WSN with anonymous source location and sensor network identity: We propose a new querying protocol for WSN, which uses multi-layer encryption to conceal the network identity of sensor nodes, obfuscating the computation described in [13]. The protocol relies on particular messages similar to those used in the onion routing [14] to convey edge data processing information to sensor nodes and privately retrieve data.
* A blockchain-based fault tolerant indoor location system with no single point of failure(SPOF): We address the fault tolerance shortcomings of sink nodes [15] in traditional WSNs by substituting it with a smart contract, which handles the processing of queries, and storing the results. A decentralized role-based access control (RBAC) contract provides user access authorization to monitor individual building spaces defining privacy boundaries and further improves the security over traditional centralized approaches.  
The remainder of the paper is structured as follows: In Section 2, we present the relevant literature. Section 3 highlights the core features of the proposed solution. In Sections 4 and 4, we present our onion route protocol and filtering. In Section 5, we detail how blockchain smart contracts can replace sink nodes. In Section 6, we provide the validation of the proposed framework, and finally give final remarks in Section 7.
## 7 Performance Evaluation and Results
### Classification Models  
We have evaluated our models based on the most related features to the target parameter that are extracted by using Boruta Feature Selection method [61]. RFR algorithm was adopted in this study due to its efficient performance and its capability of feature selection where this algorithm achieved outstanding results in our previous studies [59; 60]. After data preparation, the RFR algorithms are trained on 18,812,799 traffic flow records for 16 detectors separately where the target parameter is average speed and the input parameters are total volume, average occupancy, and average delay. Stacking ensemble learning approach was adopted to implement the DMLRF model where the RFR models are used as inputs to train the final model based on different hyper-parameter values. In this study, we implemented other regression base line models to compare it with our proposed model. The models are Linear Regression (LR), Logistic Regression (LogR), and K Nearest  
Figure 5: Proposed System Workflow.  
Neighbor (KNN). All the Models are trained validated on seven years of traffic data starting from 2008 to 2014 and one week was utilized for testing and is obtained from 2015, which is from 11 June 2015 to 17 June 2015. This week was selected for having more abnormal traffic flow cases.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 5 OpenFOAM FVTM Comparison  
In this section the test results are compared with the FVTM built using the OpenFOAM code. The code has been selected because it is able to deal with complex geometries and maintain the geometrical accuracy. Furthermore, the contact interfaces are directly extracted from the geometry which avoids any simplification in the contact area definition, leading to more accurate results [7]. Moreover, the contact interface setup is fully automated and the code has already been numerically tested and compared against ESATAN for such type of application [7; 42]. Thus, the comparison with experimental data is an added good proof for the use of the code in such scenarios and its future developments in the field [10; 11; 12; 13]. Since the hot control of the TVAC guarantees small temperature oscillations in steady-state  
Figure 17: Autocorrelation matrices at set points. If one TC influences others the color is blue, otherwise yellow.  
it has been decided to perform the comparison at set point 30 \({}^{\circ}\)C. This enabled to run the simulation of the FVTM with a steady-state solver. In the cold cases, given the oscillations at the BCs a transient solver would have been necessary thus increasing the computational effort and time needed to run simulations. Furthermore, data comparison requires many runs of the simulation with variables changes thus the steady-state solver being faster helps reducing the overall computational time. The team is working on the complete transient analysis and it is left as the next research outcome.
# Multi-Connectivity for Multicast Video Streaming in Cellular Networks
###### Abstract
MC multicasting allows users to potentially connect to and receive content from multiple base stations and over various Radio Access Technologies (RATs) simultaneously. It can address the demanding requirements of 5G, including high data rates, ultra-reliable low latency, and high mobility [9]. By enabling users to receive content from multiple base stations concurrently, it serves a larger user base and enhances the performance for cell-edge users. The procedures for establishing multi-connectivity within the Third Generation Partnership Project (3 GPP) multicast architecture and the associated control-signaling requirements were defined in [10].
## 4 Model Testing and Results Analysis
### Results Analysis and Comparison
Results confirm the superiority of the proposed GAHD-VAE approach in modeling long-term temporal dependencies and the attention mechanism's efficiency to highlight the internal correlation between elements. In summary, results in this study showed that the proposed model achieved an improved forecasting quality for both one-step and multi-step pedestrians and bicycle traffic flow forecasting. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Attention** & & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & **EV** \\ \hline Additive & \(\bigoplus\) & 2.827 & 2.108 & 0.961 & 0.969 \\ \hline Multiplicative & \(\bigotimes\) & 2.612 & 1.950 & 0.958 & 0.967 \\ \hline \end{tabular}
\end{table} 
Table 7: Averaged validation metrics per configurations of the attention mechanism. \(\bigoplus\) is the additive attention mode, while \(\bigotimes\) is the multiplicative attention mode. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **MODEL** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & **EV** \\ \hline \multirow{8}{*}{\(1\)} & ConvLSTM & 183.619 & 139.092 & 0.818 & 0.818 \\  & CNN & 210.837 & 162.627 & 0.818 & 0.877 \\  & LSTM & 98.575 & 79.5 & 0.796 & 0.902 \\  & GRU & 91.176 & 73.017 & 0.826 & 0.908 \\  & BiGRU & 86.176 & 68.847 & 0.844 & 0.915 \\  & BiLSTM & 74.93 & 59.556 & 0.882 & 0.938 \\  & GAHD-VAE & 60.945 & 47.493 & 0.922 & 0.925 \\ \hline \multirow{8}{*}{\(2\)} & ConvLSTM & 22.485 & 15.764 & 0.895 & 0.908 \\  & CNN & 25.639 & 12.826 & 0.899 & 0.914 \\  & LSTM & 34.633 & 18.455 & 0.747 & 0.747 \\  & GRU & 21.844 & 19.131 & 0.899 & 0.962 \\  & BiLSTM & 23.489 & 16.936 & 0.884 & 0.923 \\  & BiGRU & 22.144 & 18.488 & 0.896 & 0.95 \\  & GAHD-VAE & 11.755 & 7.326 & 0.971 & 0.977 \\ \hline \multirow{8}{*}{\(3\)} & ConvLSTM & 69.637 & 49.907 & 0.795 & 0.806 \\  & CNN & 90.285 & 56.004 & 0.602 & 0.602 \\  & LSTM & 65.821 & 46.566 & 0.854 & 0.864 \\  & GRU & 64.254 & 47.184 & 0.861 & 0.872 \\  & BiGRU & 67.314 & 49.322 & 0.848 & 0.86 \\  & BiLSTM & 66.488 & 48.488 & 0.851 & 0.861 \\  & GAHD-VAE & 60.603 & 41.953 & 0.877 & 0.878 \\ \hline \multirow{8}{*}{\(4\)} & ConvLSTM & 21.83 & 17.913 & 0.807 & 0.815 \\  & CNN & 19.137 & 15.519 & 0.84 & 0.86 \\ \cline{1-1}  & LSTM & 22.744 & 18.019 & 0.776 & 0.778 \\ \cline{1-1}  & GRU & 22.246 & 16.924 & 0.785 & 0.785 \\ \hline \end{tabular}
\end{table} 
Table 8: Performance evaluation of the seven models for daily forecasting of pedestrian and bicycle traffic flows. To summarize the assessments, the averaged metrics of effectiveness per model computed from Table 8 are listed in Table 9. The results support that the GAHD-VAE forecasting approach has higher accuracy overall than the other deep learning models (i.e., VAE, LSTM, GRU, BiLSTM, BiGRU, CNN, and ConvLSTM). Overall, the results indicate that the GAHD-VAE approach has high forecasting accuracy due to the robustness of variational inferences in approximating data probability distribution of traffic flow time-series, in addition to the promising capability of a self-attention mechanism to learn implicit information within data points of a given sequence.
## 2 Materials and Methods
### 2.3,2-diphenyl-1-picrylhydrazyl (DPPH) Radical and Hydrogen Peroxide Scavenging Activity of TVEE  
DPPH radical scavenging activity was measured using [PERSON] method [20]. TVEE (20 \(\mu\)L) diluted in each extract at 0.05, 0.1, 0.2, 0.5, 1, 2, and 4 mg/mL for each concentration and 180 \(\mu\)L of 0.15 mM DPPH solution were dispensed into a 96-well plate, left to react for 30 min at room temperature, and absorbance was measured at 515 nm.  
Hydrogen peroxide scavenging activity was measured by using [PERSON]'s method [21]. Viscera and muscle (20 \(\mu\)L each) were diluted in each extract at 0.05, 0.1, 0.2, 0.5, 1, 2, and 4 mg/mL for each concentration, and 20 \(\mu\)L of 10 mM hydrogen peroxide and 0.1 M sodium phosphate buffer were added and left to react in an incubator at 37 \({}^{\circ}\)C for 5 min. Then, 30 \(\mu\)L of 1.25 mM ABTS and peroxidase (1 unit/mL) was added, left to react in an incubator at 37 \({}^{\circ}\)C for 10 min, and absorbance was measured at 405 nm.  
The radical scavenging activity was calculated using the following equation:  
Radical scavenging activity (%) = [(Blank - (Sample - Control))/Blank] \(\times\) 100.
## 2 Materials and Methods
### Control Design  
At this point, the control problem statement is the following, provided the complexity of the coupled dynamical model composed of robotic base and the antenna subject to wind disturbances: Design a simple model-free control input \(\tau\) for dynamics (29) subject to unknown smooth friction \(\tau_{fr}\) and bounded wind disturbance \(\tau_{D}\), such that the closed-loop system enforces asymptotically practical tracking to point at a passive LEO satellite.  
Among several control schemes proposed for the Gough-Stewart platform, it remains to analyze objectively which one is convenient for the control regimes of the satellite pointing task. First, consider that civil access to satellite data is limited at discrete time instants, not continuously available, and that the passage time window of passive LEO satellite path lasts a few minutes. Then, only few data points are available to compute desired georeferenced trajectories; thus, the stability regime resembles to point-to-point regulation more than tracking, where polynomial approximations can be used to produce a smooth path along points.  
However, since the highly nonlinear plant is subject to time-varying and state-dependent disturbances, an apparent choice is a nonlinear tracking controller. Contrary to this, and recalling that robust linear actuators are available equipped with well-proven PID controllers, in addition to the fact that these actuators are closed architecture, we analyze model-free PID control structures.
## 2 System Model and Conventional Precoding Schemes
### Generalized Block Diagonalization
The set of the dominant channel components except right singular vectors corresponding to parallel vectors is defined as follows,  
\[\mathbf{D}_{k}=\underbrace{\left[\mathbf{v}_{k,1}\ \mathbf{v}_{k,2}\ \cdots\ \mathbf{v}_{k,N_{k}-l_{k}}\right]}_{L_{k}}. \tag{15}\]  
The channel component matrix of all devices except the \(k\)th device is constructed as follows,  
\[\mathbf{\bar{D}}_{k}=\left[\mathbf{D}_{1}\ \cdots\ \mathbf{D}_{k-1}\ \mathbf{D}_{k+1}\ \cdots\ \mathbf{D}_{K}\right]. \tag{16}\]  
The precoding matrix is obtained by the complementary projection as follows,  
\[\mathbf{P}_{k}^{\text{GBD}}=\left\{\mathbf{I}-\mathbf{\bar{D}}_{k}\Big{(} \mathbf{\bar{D}}_{k}^{H}\mathbf{\bar{D}}_{k}\Big{)}^{-1}\mathbf{\bar{D}}_{k}^ {H}\right\}\mathbf{D}_{k}. \tag{17}\]  
Since the remaining IUI needs to be suppressed, zero-forcing spatial filter as post-coding is used at the receiver. The post-coder of the \(k\)th device is given as follows,  
\[\mathbf{R}_{k}=\left[\mathbf{u}_{k,1}\ \mathbf{u}_{k,2}\ \cdots\ \mathbf{u}_{k,L_{k}} \right]. \tag{18}\]  
The received signal of the \(k\)th device after post-coding is written as follows,  
\[\mathbf{y}^{\prime}_{k}= \mathbf{R}_{k}^{H}\mathbf{y}_{k} \tag{19}\] \[= \mathbf{R}_{k}^{H}\mathbf{H}_{k}\mathbf{P}_{k}^{\text{GBD}} \mathbf{x}_{k}+\mathbf{R}_{k}^{H}\mathbf{n}_{k}\] \[= \mathbf{H}^{\prime}_{k}\mathbf{x}_{k}+\mathbf{n}^{\prime}_{k}.\]  
Since the column space of \(\Delta_{k,j}\) in (13) is belonged to the null space of \(\mathbf{R}_{k}\), the IUI is perfectly removed by post-coding. When the diversity gain cannot be obtained, the performance of the GBD scheme is poorer than RBD precoding. GBD scheme achieves performance enhancement by reducing the number of the data streams. Thus, GBD scheme is not proper to be used when the number of reduced data streams is not enough for most devices to achieve diversity gain. In other words, GBD scheme cannot avoid degradation of data rate which occurs by reducing the number of data streams to achieve better performance. Since GBD scheme is appropriate in the case that IUI remains, post-coding at the users is essential to eliminate remaining IUI. Post-coding of GBD scheme is enormous burden to receiver in that receiver must carry out the additional operation.
## 3 Results and Discussion
### Biosynthesis of AgNP
As seen in Table 1, all of the parameters evaluated (Z-average, PdI, and zeta potential) remained almost constant throughout the reaction time course. However, the intensity of the SPR band increased over time, indicating the continued production of AgNP. These results are in accordance with the literature and show that the reaction time is not the most critical parameter in AgNP optimization since it can be adjusted to obtain the best yield [33]. A higher temperature increases the rate of the reaction, but time is also crucial in developing stable AgNP [36]. Different pH values were evaluated using 10% (\(\
u/v\)) TE poured into 5 mM AgNO\({}_{3}\), as shown in Figure 3D. The reaction time was 2 h 30 in the absence of light at room temperature. The presence of the SPR peak was only visible when the reaction was performed at pH 11. The results of the previous experiments demonstrated that the biosynthesis of silver nanoparticles is favored by alkaline pH, which is in accordance with published data [37]. Given the above-reported optimization studies, for further characterization, AgNP were biosynthesized using 2% (\(\
u/v\)) TE in a 2 mM AgNO\({}_{3}\) solution at room temperature for a 150 min reaction time at pH 11 under constant stirring. The results obtained in a study using a tomato extract obtained from tomato fruits harvested at the light-red stage of ripeness are difficult to compare with our results, as the study employed different characterization techniques [26]. However, the reported average size of  
Figure 3: UV-Vis spectra of AgNP produced under different experimental conditions. (**A**) Effect of temperature (RT, 37 \({}^{\circ}\)C); (**B**) effect of concentration of AgNO\({}_{3}\) (1–10 mM); (C) effect of reaction time (15–180 min); (**D**) effect of pH (9, 11). synthesized AgNP was between 9.6 and 72.7 nm, with the maximum size being similar to that obtained in our study.