esa-sceva/satcom-chunk-collection huggingface dataset:
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
## 4 Outage Probability Analysis
### Uplink Outage Probability: An OMAC Link  
Let \(\gamma_{1}^{u}\), \(\gamma_{2}^{u}\), \(\gamma_{3}^{u}\), \(\gamma_{4}^{u}\) are the instantaneous uplink SNR of the received signals of node 1, node 2, node 3 and node 4 respectively at the AP and they can be written as  
\[\gamma_{1}^{u} = \frac{|h_{1}|^{2}\,P_{t}}{\sigma^{2}};\quad\gamma_{2}^{u}\,=\, \frac{|h_{2}|^{2}\,P_{t}}{\sigma^{2}}, \tag{26}\] \[\gamma_{3}^{u} = \frac{|h_{3}|^{2}\,P_{t}}{\sigma^{2}};\quad\gamma_{4}^{u}\,=\, \frac{|h_{4}|^{2}\,P_{t}}{\sigma^{2}}, \tag{27}\]  
Using (26) and (27), the outage probability of an OMAC link can be expressed as  
\[P_{out}^{OMAC}(R)=Pr(min[min(\gamma_{1}^{u},\gamma_{2}^{u})\,,min(\gamma_{3}^{ u},\gamma_{4}^{u})]\,<\gamma_{t}), \tag{28}\]  
where \(\gamma_{t}\,{=}\,2^{2R/W}-1\) is the threshold SNR, \(R\) is the threshold data rate, \(W\) is the channel bandwidth, for OMA-based wireless network, the channel bandwidth for each node is, \(W_{k}=\frac{W}{N}\), \(N\) is the number of nodes [32]. Let \(\gamma_{a}\,=\,min(\gamma_{1}^{u},\gamma_{2}^{u})\) and \(\gamma_{b}\,=\,min(\gamma_{3}^{u},\gamma_{4}^{u})\), then (28) can be rewritten as  
\[Pr_{out}^{OMAC}(R)=Pr(min(\gamma_{a}\,,\gamma_{b}\,)<\gamma_{t}), \tag{29}\]  
Using the probability theory of \(min(a,b)\), it can be further simplified as  
\[Pr_{out}^{OMAC}(R)=\underbrace{Pr(\gamma_{a}\,<\gamma_{t})}_{B1}+(1-Pr(\gamma _{a}\,<\gamma_{t}))\underbrace{Pr(\gamma_{b}\,<\gamma_{t})}_{B2}, \tag{30}\]  
First term in (30) can be computed by substituting \(\gamma_{a}\,=\,min(\gamma_{1}^{u},\gamma_{2}^{u})\) as follows,  
\[B_{1}\,=\,Pr(x<\frac{\gamma_{t}}{\rho})\,+\,(1-Pr(x<\frac{\gamma_{t}}{\rho})) Pr(y<\frac{\gamma_{t}}{\rho}), \tag{31}\]  
where \(\rho\,=\,P_{t}/\sigma^{2}\) is the SNR of each node, \(x=|h_{1}|^{2}\,\) and \(y=|h_{2}|^{2}\,\). By Applying probability density function (PDF) for the variables \(x\) and \(y\) and then integrating, \(B_{1}\) can be computed as  
\[B_{1}=1-\exp\frac{\gamma_{t}}{\rho}(\frac{\gamma_{1}+\gamma_{2}}{\eta_{1}\eta _{2}}), \tag{32}\]  
Similarly, the second term can be determined as  
\[B_{2}=1-\exp\frac{\gamma_{t}}{\rho}(\frac{\eta_{3}+\eta_{4}}{\eta_{3}\eta_{4}}), \tag{33}\]  
By substituting \(B_{1}\) and \(B_{2}\) in (30), the outage probability of an uplink can be determined as  
\[Pr_{out}^{ILL}(R)=1-\exp\frac{\gamma_{t}}{\rho}(\eta_{a}+\eta_{b}), \tag{34}\]  
where \(\eta_{a}=(\frac{\eta_{1}+\eta_{2}}{\eta_{1}\eta_{2}})\) and \(\eta_{b}=(\frac{\eta_{3}+\eta_{4}}{\eta_{3}\eta_{4}})\).
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 3 Datasets and Experiments
### Stability of ISBs  
In this part, we analyze the distribution of both code and phase ISBs in the short and long terms. By analyzing the short term, one day, we show the time-series of the ISB between the overlapping frequencies of BIC/L1/E1 and B2a/L5/E5a in BDS-3/GPS/Galileo. Then, the stability of the ISBs is assessed using the mean value and STD. By analyzing the long term, one year, we show the distribution, probability density function (PDF) and quantile against the quantile (QQ) plot of the ISBs between different overlapping frequencies. Then, the mean value by day, the STD by day, and box plot by month are displayed for a comprehensive assessment of the stability of the ISBs. All the ISBs were calibrated based on the identical type of receiver with a cutoff elevation angle of 15\({}^{\circ}\). The broadcast ephemeris was used to compute the position of satellites. Considering the relatively short length of the baseline GODN-GODS, we ignore the atmosphere delays when estimating ISBs. The noise of code and phase observations were set to 0.3 and 0.003 m, respectively.
# Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm
## 4 Experiments of Task Assignment for UAV Swarm Using MPPWPA
### Preparation for Simulation Experiments  
A Monte Carlo study, consisting of 20 independent runs, was used to compare the performance of the PSO, GA, ABC, WPA and MPPWPA with different parameters for the cost function of Equation (6) where \(\omega_{1}=\omega_{2}=0.5\).  
Three scenarios were: five UAVs against eight targets, 20 UAVs against 30 targets and 100 UAVs against 150 targets. The dimensions of the variable in the three scenarios were 8, 30 and 150 respectively. The initial locations of the UAVs and targets were generated randomly and projected onto a two-dimensional plane, as shown in Figure 15.  
The parameters of the WPA for different scenarios were set as shown in Table 2.  
Figure 14: Parallel propulsion for MPPWPA.  
Figure 15: Initial positions of UAV swarm and targets. (**a**) Scenario 1: 5 UAVs and 8 targets were randomly distributed in a space of 100 \(\times\) 100 m. (**b**) Scenario 2: 20 UAVs and 30 targets were randomly distributed in a space of 1000 \(\times\) 1000 m. (**c**) Scenario 3: 100 UAVs and 150 targets were randomly distributed in a space of 10,000 \(\times\) 10,000 m.  
The control parameters of the MPPWPA are shown in Table 3. This research mainly studied the influence of \(Num\) and \(P_{m}\) on the optimization performance. Other parameters related to WPA were the same with those in Table 2.
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 3 Serious Gaming Application Developed for LegUp
### Software  
The software architecture is developed in C# and uses the SDK (Software Development Kit) from Biometrics Ltd. [34] for acquiring and processing sensor data. The game for the HoloLens device is created using the Unity engine. Communication within the system is managed through two primary protocols: Bluetooth and TCP/IP.  
Real-time data are transferred via Bluetooth to a PC application from the electronic goniometers which measure two hip angles (abduction/adduction, flexion/extension), one knee angle (flexion/extension), and two ankle angles (flexion/extension, inversion/everson). After normalizing these values, the PC program uses the TCP/IP protocol to send the normalized data to the HoloLens application. In this setup, the HoloLens program acts as the server while the PC application serves as the client.  
After receiving the data, the Hololens program matches it with the corresponding joint motions and displays the appropriate holograms (Figure 6).  
Figure 5: Robot-Assisted Therapy setup integrating the LegUp rehabilitation robot (1), The control system (2), The User interface with the control’s commands for the parallel robot, streaming from HoloLens and graphics from sensors (3), Biometrics sensors placed on the patient’s leg (4), HoloLens 2 device (5).  
Figure 7 presents a pseudo-code structure that outlines the process of interaction in a HoloLens interface designed for rehabilitation games, specifically for two modes: \"Football\" and \"Color\". The flow starts with the selection of the game mode (Football or Color), and depending on the choice, different steps follow. For Football, the system loads a graphical interface, reads the patient's foot positions via goniometers, and prompts the patient to hit a ball. The program continuously monitors the foot's trajectory and corrects it if necessary, aiming for the foot to hit the ball. If the target is reached, a success message is displayed. An interrupt can stop the game based on patient events.  
In the Color mode, a similar process occurs, but the game asks the patient to touch a random colored target. The program monitors and corrects the foot's trajectory as needed. Upon completing the task, the system congratulates the user and asks if they want to play again. The game can also be stopped by a patient-triggered event. Both modes involve real-time monitoring, corrective feedback, and the possibility of stopping the session due to patient conditions.  
Figure 6: Schematic representation of the proposed system for serious gaming based on augmented reality.  
Figure 7: The pseudo-code for the HoloLens application.
# Impacts of Flight Operations on the Risk of Runway Excursions
## 4 Operational Analysis of Crucial Stages  
### Runway Threshold  
In the QAR data, different parameters exhibit varying frequencies of change. For instance, the control column records at a rate of eight times per second, while the airspeed records at a rate of four times per second. Therefore, in this section, we calculate the maximum and minimum positions of the control and attitude parameters at each stage based on the parameter changes. Based on the monitoring indicators, we conduct an analysis of the average and standard deviation for each style of results. During the selection of monitoring points, we integrated the guidelines outlined in the Flight Crew Operations Manual. Consequently, we opted for the runway threshold, the flare phase, and touchdown as key monitoring points. Additionally, we considered the actual deceleration operations performed during landing, which led us to include \"Speed brake on\" and \"reverse thrust on\" as additional monitoring points.  
This paper presents a basic statistical analysis of the recorded data from the Quick Access Recorder, including the position of the control column, aircraft attitude, speed, and other related parameters. The distribution of these parameters is displayed in this paper. Average is used to reflect the overall distribution of the data, while standard deviation is used to indicate the degree of variation within the dataset. Standard error is utilized to express the discrepancy between the sample average and the population average.  
At the runway threshold, as shown in Table 2, the mean maximum position of the Style 3 control column is 14.12 deg, with a mean minimum position of \(-\)14.23 deg and a standard deviation of 14.03 deg and 12.40 deg. This indicates that the range of operation for the Style 3 control column is greater than that of the other two styles, and Style 3 exhibits a higher level of dispersion, necessitating a more in-depth analysis. Furthermore, above the aircraft  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categorization** & **Touchdown Distance** & **Taxing Distance** & **Magnetic Heading Change Rate** \\ \hline Style 1 & 641.86 m & 1344.62 m & 0.36 deg \\ Style 2 & 797.21 m & 1871.47 m & 0.36 deg \\ Style 3 & 968.89 m & 1023.89 m & 1.05 deg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Center of clustering.  
Figure 1: Three-dimensional clustering effect diagram.  
at the runway threshold, the mean of the third-style wind speed is the highest at 9.28 knots, indicating significant influence from the wind. This suggests that within the third style, the variation in aircraft heading and the positional changes of the pilot operating the control column are influenced by wind. Under strong wind conditions, improper operation may pose a risk of lateral runway excursion.
## 4 VoLTE Scenario and Simulation Report
### Simulation Analysis
#### 4.4.4 MOS Analysis  
The QoE of a voice application is typically represented by the MOS, which shows the extent to which the end user accepts the voice service. The MOS as a QoE is calculated by several QoS parameters, such as packet loss, packet delay, jitter delay, and jitter buffer dimension [27]. The term MOS is scalar, ranging from 1 (worst case) to 5 (best case) [22]. Table 12 presents the single eNB MOS indicator values experienced in the 60 s simulation, while Figures 10 and 11 show the users' MOS indicator during the 60 s simulation. In the single eNB simulation, four out of ten UEs (UE[1], UE[2], UE[4], UE[9]) experience bad indicator values, while the other six UEs seem to be quite fair.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Jitter Delay (ms)/UE** & **UE[0]** & **UE[1]** & **UE[2]** & **UE[3]** & **UE[4]** & **UE[5]** & **UE[6]** & **UE[7]** & **UE[8]** & **UE[9]** \\ \hline
**Mean Value** & 5.28 & 6.00 & 6.00 & 6.00 & 6.00 & 5.94 & 9.71 & 12.77 & 6.34 & 5.98 \\
**Standard Deviation** & 2.28 & 0.00 & 0.00 & 0.00 & 0.00 & 1.94 & 16.00 & 48.31 & 1.72 & 1.62 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Dual eNB jitter delay values.  
Figure 8: UEs’ simulated jitter delay (single eNB).  
Table 13 presents the dual eNB MOS indicator values experienced in the 60 s simulation, while Figure 11 shows the MOS vector analysis. In the dual eNB scenario, five to ten UEs (UE[0], UE[3], UE[5], UE[6], UE[7]) experience fair MOS, while the other five experience good MOS. A significant improvement is acknowledged in the dual eNB scenario, which is also confirmed by the statistical analysis. The F-test showed that the F value (8.98) > F Critical one-tail (3.44); thus, we reject the null hypothesis, proving that there are significant differences in the variances of the \"MOS\" means across the two UE simulation groups.  
Moreover, the \(T\)-test showed that the t Stat \((-3.68)\) < t Critical two-tail (2.26); thus, we do not reject the null hypothesis, showing that the \"MOS\" means of the two UE simulation groups are significantly different from each other. The above statistical differences are illustrated in the vector analysis of Figures 10 and 11.
# Security Attack Behavioural Pattern Analysis for Critical Service Providers
## 5 Experiment and Result
### Security Attack Behavioural Pattern Analysis Using Asfalia Framework  
This section provides an analysis of the behavioural patterns of security attacks in CPSs using the Asfalia framework. The framework enables the monitoring of security attack events and covers all three realms of a CPS. In addition, the framework facilitates cross-realm analysis and monitoring, allowing for the detection of security events that occur across different realms. Our models specifically target adversaries that exist within specific realms, encompassing the three realms of a CPS (cyber, physical infrastructure, and social) as shown in Figure 4.  
Attack patterns are grouped into two categories: The first category of cyberattack is domain-based attack, which involves targeting specific domains or networks. The other category is mechanism-based attack, which focuses on exploiting vulnerabilities in various systems or mechanisms. For example,'social engineering' is a type of cyberattack categorised under the domain-based attack category, while 'collect and analyse' is a type of cyberattack categorised under the mechanism-based attack. There have been a total of 18 cyber security incidents classified as cyber attacks, based on the method of attack, and seven incidents classified as domain-based cyber attacks. The reports were categorised into two distinct classifications pertaining to cyber security and cyberattacks. An analysis of cyber security attack patterns was performed on five significant cyber incidents. The events were classified and categorised according to a domain-based attack. Out of the total of 254 cyber security incidents that have been reported, Asfalia has identified 7 vulnerabilities and 5 critical cyber security incidents. Moreover, the six targets that were targeted to cyberattacks were captured.
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 2 System Design
### System Establishment for Circuit Boards Test  
As it is analyzed for system design, an actual system is carried out for circuit boards tests. As far as it is concerned, the surface depth difference of circuit board is from 50 to 100 \(\upmu\)m in general. The camera and telecentric lens employed in the system are IGV-B2520M, Imperx, Boca Raton, Florida 33487, USA (resolution: 2456 \(\times\) 2058; sensor size: \(2/3^{\prime\prime}\); cell size: 3.45 \(\upmu\)m) and DTCM430-56-AL, COOLENS, Shenzhen 518000, Guangdong, China (magnification: 0.429), respectively. The laser module is EL405-20 GXLP, ELITE, Xi'an 710000, Shanxi, China (wavelength: 405 nm; line width: 20 \(\upmu\)m) while the 1D displacement platform is M-511.DG1 with C863.11, PI, Karlsruhe 76228, Germany (travel range: 102 mm; design resolution: 0.033 \(\upmu\)m).  
According to the equipment and application requirement, the intersection angle can be determined. On one hand, the error of measured surface depth is expected to be less than 10 \(\upmu\)m, which means \(h\) is set to be 10 \(\upmu\)m. It is obtained that the intersection angle is \(\alpha\geq 23.7^{\circ}\) from Equation (3) while \(\Delta p\) is set to 0.5 pixel. On the other hand, the width of the blind corner is expected to be as narrow as possible. In order to keep more details under satisfying the precondition of functional demands, the intersection angle \(\alpha\) designed is finally set to \(30^{\circ}\).
## 4 ML Applications for RIS-Assisted Communications
### ML-Based Applications for RIS-Assisted Communications Modeling  
The design of tools for simulating RIS-assisted communication systems provides a road map for researchers to understand how RIS surfaces interact with a network. For instance, open-source channel modeling solutions have been provided for indoor/outdoor scenarios, including the physical aspects of wireless propagation in the presence of an RIS [106].  
The authors in [107] use ML to configure RIS-assisted communication environments, modeling wireless as a custom interpretable backpropagation NN. The NN learns the propagation parameters and configures them to facilitate users' communication in the vicinity. Each RIS element is configured as a neuron or node whose weight corresponds to the power distribution. The proposed NN minimizes the number of tiles required to achieve efficient communication.
# Integrating Knowledge Graphs into Distribution Grid Decision Support Systems +
## 3 Knowledge Graphs  
Knowledge graphs are data management systems that combine different types of data and use graphs as representations of information [6]. Many KGs have been created using text-based extraction of content from the web, including Microsoft's Probase [23], Google Vault [24], and DBpedia [25]. Generic KGs have been proven to be useful in applications such as semantic search and data fusion.  
One way to represnt KGs is using a resource description framework (RDF). This data format [26] was introduced by the Semantic Web as a standard for linked open data. It is a popular graph-based data format in which each piece of data is stored as an RDF triple that contains two entities (two nodes in a graph), called subject and object, and a relation between them (an edge in a graph), called property. RDF-based KGs have received much attention and multiple studies have been published on their different aspects, ranging from their construction [27], through storage [28], query strategies [29] and information extraction [30] to applications [31]. The fact that subjects of triples could also be objects of other triples and vice versa means that we deal with a network of highly interconnected entities.  
A single RDF-triple \(<\)subject-property-object\(>\) can be considered a feature of an entity identified by the subject. In other words, each single triple is treated as a feature of its subject. Multiple triples with the same subject constitute a description of the given entity. A set of a few RDF triples with _Element_ as their subject is presented in Figure 1. Each triple provides a description of _Element_: its type--_primary transformer_, its connected voltage--_14 kV_, its service area location--_XYZ_, the feeder to which it is attached--_123_, and the connected phase--_A_.  
Quite often, the subject and object of one triple can be involved in multiple other triples, i.e., they can be subjects or objects of other triples. In such cases, multiple definitions can share features, or some features can be centers of other entity descriptions. All interconnected triples constitute a network of interleaving definitions of entities.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 2 Methodology  
The main design tasks are implemented using GNU Radio software, while the signal modulation and packet framing are carried out within GNU Radio's blocks. Data inputs and outputs are handled by the user datagram protocol (UTP) and transmission control protocol (TCP) network protocols.  
The design is implemented using a core-i7 laptop running an Ubuntu 18.04 LTS operating system. The tests discussed in detail in the \"Tests and Results\" section are run on the same core-i7 laptop to confirm its functionality and to measure its parameters. These parameters are obtained by repeating the test several times and calculating the mean value.  
Scripting is performed using the Python language. Bash script and Python are used for testing.  
This design could have been affected by the available hardware. If the design is to be implemented in another machine, it might need some adjustments. The version of the GNU Radio that is used is another important factor. When using a different version, the basic blocks and their dependencies might need to be updated.  
The next stage of this design is to use it in a Raspberry Pi device as a satellite subsystem.
# Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions
## 4 Case Study: Histological Image Analysis
### Experimental Results
#### 4.4.2 AI Training  
As depicted in Figure 5, each on-site client used different hardware resources for model training. As a result, whereas the client at institution A took around 60 s for 10 epochs on average, clients at institutions B and C trained each communication round for 540 and 534 s, respectively. All in all, the whole federation training took 46 min for the three communication rounds in this use case, as the central node waits for all three clients to finish training.  
To allow for an effective comparison of the prediction performance of models presented in this case study, several metrics of relevance were selected: sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and accuracy. A particularly important metric in pathology is sensitivity, which measures the ability of the model to correctly detect positive instances.  
Figure 4: Demonstration of the Notebook Federator method with component containerizer in a Jupyter Notebook: building reusable FL pipeline blocks, e.g., Client Training container.  
Table 2 shows the metrics on the test set after training for 30 epochs at each institution with its own local data under a centralized configuration, compared to the results when training as a federation with all three clients for three communication rounds of ten epochs each. It is noteworthy to highlight that the scope of this study is not to reach unprecedented classification results but rather to demonstrate the federation's improvement compared to that of individual institutions. In that sense, classification metrics on the test set clearly show an overall improvement of the model trained collaboratively (federation) in relation to those trained in a centralized manner. Although \(I_{B}\) reaches considerably high specificity and positive predictive value metrics on the test set when trained under a centralized setup, it is important to highlight that it is mostly due to its tendency to predict a sample as positive (i.e., tumorous) even when negative, as depicted in the negative predictive value results for that model.  
By and large, the classification results show the potential of the approach, taking into account that, the more institutions participating in model training, the more robust the resulting global model will be when exposed to unseen data.