esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Procedures for BVLOS Flight Operations
### Procedure Training and System Tests on Hardware-in-Loop (HIL) Testing Platform  
To improve the efficiency of BVLOS flight operations, an HIL testing platform was designed and constructed in the RAVEN project lab to help the PIC to carry out mission preparation, procedure training, and DAA system tests before field tests. As shown in Figure 14, real-time aircraft telemetry data from two separate HIL simulators (i.e., host UA and traffic UA) are fed into the ADS-B message encoder and converted into real-time ADS-B messages. ADS-B units receive the ADS-B messages from the RS232 serial communication link instead of the 978/1090 MHz RF link. The PWM signals or RS232 data are the outputs from the ADS-B units that command the autopilot to control the host UA to make appropriate maneuvers if needed.  
In Figure 15, the two laptops on the left are HIL systems for the host UA and the traffic UA, where the GCS software communicates with the autopilot and the simulated UA in FlightGear [19], a visualized aircraft model and flight environment simulator software. The ADS-B message encoder software is running on the laptop on the right, and this laptop is also used to display received ADS-B messages on the map where two UA are flying. With this HIL testing platform, the PIC can design, test and verify flight plans, and learn and practise emergency procedures for BVLOS flights in the field.
## 3 Our System Model and Primer
### Path Loss Model for Channel Attenuation (\(\gamma\))  
As the communication signal propagates in the wireless medium in the air, it attenuates and loses power when reaching the receiver. We use the standard path loss model for channel signal attenuation, which has also been empirically validated [22; 23]. In the path loss model, the received power is \(P_{i}\cdot\alpha\cdot d^{-\gamma}\), where \(P_{i}\) is the transmitted power magnitude from the transmitter, \(i\) where \(i\) is the flying base station or the stationary base station, \(\alpha\) is a constant characterizing the channel condition and the antenna gains, \(d\) is the distance between the transmitter and receiver, and \(\gamma\) is the path loss exponent. \(\gamma\) typically ranges between 2 and 5, with greater fading and attenuation increasing its value (e.g., the urban environment) [22; 23].
## 2 Prediction of Wind-Power probability Density Based on Quantile Regression LSTM
### Summary  
LSTM has a good fitting ability for nonlinear time series and has a good prediction effect in medical protein structure prediction, road traffic flow rate prediction, etc. Probability density is mainly used to obtain the probability density curve, provide the fluctuation range of wind power, and obtain more useful information. Quantile regression is used to combine the two, and regression prediction is conducted under different quantiles. Finally, nuclear density estimation is used to calculate the probability density function, which provides wind power information for the subsequent optimal regulation model of closing angle.  
Optimal-Control Model of the Parallel Closing Angle of Ring Network Considering Wind-Power Uncertainty  
Compared with the radiative grid structure with poor resistance to source and load fluctuations, the ring network can not only improve network security and enhance system transmission capacity but can also withstand the power fluctuations of new energy and better support the recovery of important loads. Through the analysis of the power grid structure and the trend of power flow, the voltage phase angle of the nodes at both ends of the line to be paralleled is studied. Generally, the power grid is divided into the sending end and the receiving end according to the voltage phase angle of the nodes at both ends. By reducing the active power from the sending end to the receiving end, the phase angle difference of the node voltages at both ends can be effectively reduced. Generally, the phase angle difference at both ends is reduced by inputting the load at the sending end and increasing the output of the unit at the receiving end.  
Due to the uncertainty of wind-power output, the fluctuation of active power output after grid connection will cause the power flow fluctuation of the access point, thus causing the phase angle fluctuation of the point to be paralleled. Through the probability density prediction of wind-power output, this paper not only further improves the prediction accuracy, but also integrates various output probability scenarios of wind power, and fully considers the impact of the uncertainty of wind-power output on the closing angle and the adjustment scheme.  
Based on considering the uncertainty of new energy output, this paper establishes a control model to reduce the phase angle difference at both ends of the parallel point by adjusting the active output of the conventional unit of the system and putting the load to be restored after power failure as the control measure. In the process of regulation and control, multiobjective collaborative optimization is carried out considering that the sum of output adjustment of conventional units is the smallest and the most important loads to be restored are input.
# Key Agreement and Authentication Protocols in the Internet of Things: A Survey
## 2 Materials and Methods
### Cyberattacks on IoT Systems  
Communication in IoT environments is exposed to various attacks. The attacks' results also can be multiple, and everything depends on the attacker's knowledge, intentions, and imagination. For example, the attacks' effects may be data loss, interception, or modification. We can indicate many kinds of attacks in IoT. We summarised common attacks in IoT in Table 1.  
An essential aspect of security that must be considered in IoT systems is the implementation of the CIA triad (confidentiality, integrity, availability). This is a model aimed at the steering of security policy. Confidentiality protects us from unauthorized attempts to access confidential information. Integrity ensures data consistency, accuracy, and reliability. Availability ensures that access to data will be easy for authorized parties [78]. The security protocols are one of the methods that implement mentioned rules. The protocols are short programs that describe the communications course and rules. They can secure communication and ensure security aspects like mutual authentication, user anonymity, perfect forward or backward secrecy, or untraceability [26; 79]. The security protocols play a significant security function during the users' communication. They define a sequence of steps during which users execute activities like authentication, authorization, key agreement, and exchange. We can highlight that security protocols ensure secure authentication and authorization processes so that they can prevent unauthorized access by dishonest users. Moreover, they support key agreement and exchange processes, so the users should be sure that a secure channel provides their communication. Unfortunately, many protocols can be broken because they do not provide adequate security. Users' information can be stolen and used by attackers. The following section will overview existing security protocols for the IoT or WSNs. We will focus on protocols for authentication and agreement of the session key. In addition, we will discuss the security levels offered by these protocols.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 6 Discussion
### End-to-End Delay  
We evaluated the end-to-end delay performance of AODV, DSDV, and OLSR in reconnaissance scenarios. To evaluate the end-to-end delay performance, we measured the end-to-end delay values from 5 nodes to 30 nodes in increments of 5 nodes and averaged the values. Figure 7 is the averaged end-to-end delay of AODV, DSDV, and OLSR. In Figure 7, OLSR yields the lowest end-to-end delay performance, while DSDV yields the highest  
Figure 6: Performance analysis according to the GCS location: (**a**) GCS location in the simulation area, (**b**) left top, (**c**) top, (**d**) middle, and (**e**) bottom.  
end-to-end delay performance. AODV yields reasonable end-to-end delay performance. In this end-to-end delay performance, the smallest value represents the best performance, so OLSR has the best performance, followed by AODV and DSDV. Interestingly, DSDV and OLSR are proactive routing protocols, and AODV is a reactive routing protocol. In the reconnaissance scenario, because the topology of nodes frequently changes, many control messages are generated to update the routing table. OLSR selects MPR nodes to manage the routing table, so the control messages do not make much overhead. In contrast, DSDV makes all nodes manage their own routing tables, so the control messages create a great deal of overhead compared to OLSR. AODV updates the routing table only when the updates are needed. As AODV takes time to search the path among nodes, the end-to-end delay performance of AODV is worse than that of OLSR.
# Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI
## 3 Results
### Training
No difference in accuracy was detected between learners trained with AR or by the surgeon, showing that AR is a feasible and valuable training tool in addition to the traditional guidance of more experienced surgeons in the operating room. [PERSON] et al. [77] investigated the feasibility of constructing a patient-specific hybrid simulator to perform THA surgery in order to train resident surgeons and trainees in a safe and comfortable environment. The system allows surgeons wearing a Microsoft HoloLens 1 (Microsoft, Redmond, WA, USA) head-mounted display (HMD) to perform a THA surgery procedure on five patients using patient-derived 3D models, interacting with them using voice commands, gestures, and movements. Virtual and physical content was recorded using Vuforia SDK. That simulator received positive feedback from medical staff involved in the study to evaluate visual/auditory perceptions, and gesture/voice interactions. [PERSON] et al. have conducted a similar work [84], using Vuforia SDK functionalities as a real-time registration tool to detect and track the virtual content onto the real scene. In order to ensure the smooth running of the sensing and tracking functions, the support for a Vuforia Image Target was anchored to the mannequin. The same research group [27] also proposed a method to build a hybrid simulator for the fixation of pedicle screws based on 3D-printed patient-specific spine models and AR and virtual fluoroscopy visualization. A patient-specific spine phantom and surgical tools, both equipped with markers for optical tracking, and a calibration cube were used. The scene is rendered on a PC with a traditional monitor, where the torso mannequin with the overlaid virtual content is seen to assist the implantation of the screws at the proper anatomical targets. The accuracy of the tracking method and consistent visualization of the vertebral model and surgical instruments were evaluated in both AR and virtual fluoroscopic modes by quantitative tests. From the results obtained, it is clear that further research and clinical validation are needed to make the simulator a valuable training tool for the insertion of pedicle screws in lumbar vertebrae. [PERSON] et al. [42] worked on a method that uses Dynamics 365 Remote Assistance and Microsoft HoloLens 2 to provide the surgical team with all the training and information needed to properly implant prostheses and medical devices remotely from a manufacturer's support center, without a technician's presence. Collected data show that HoloLens could enable the avoidance of the manufacturer's technicians in the operating room to improve the experience of both patients and physicians, and thus reduce the duration of the surgeries while reducing some economic and environmental costs.
# Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?
## 3 Methods
### Dimensionality Reduction with Principal Components  
The inclusion of explanatory variables in both ARIMA and ES introduces estimation challenges when the number of variables is high. In retailing, the number of parameters can even exceed the available sample size [37], making it infeasible to obtain model estimates. The challenge can also be exacerbated by the presence of multicollinearity, since most of the promotional variables change at the same time when a particular event occurs. Principal Component Analysis (PCA) is a widely used method to overcome both problems [1; 11].  
It creates linear combinations of the original variables, where the combination weights result in new orthogonal variables [62]. By construction, these variables are ordered by decreasing variance, which can enable us to eliminate low variance components, achieving compression with typically only a few components being sufficient to capture most of the information in the original set of variables. In the literature there are various alternatives to select which principal components to eliminate. For example, ref. [1] use information criteria, while [11] use a variance threshold to eliminate components with low information content. Here we rely on the second approach, using 70% of the mean variance in the components as the cut-off point. Note that this approach is fairly robust and does not require fine tuning of the threshold.
## Materials and Methods
### Laboratory Treatments with Phyto-Derivatives  
Two phyto-derivatives were tested for their biocide activity: liquorice alcoholic leaf extract (_Glycyrrhiza glabra_ L) (LIQ) and lavender essential oil (_Lavandula angustifolia_ Mill). (LAV). The liquorice alcoholic leaf extract at 50% (_v/v_) was provided by Trifolio-M GmbH (Lahnau, Germany), while the lavender essential oil was supplied by Sarandrea Marco & CO. S.r.l. (Collepardo, Italy).  
In the first set of experiments the collected biofilms (5 g) were homogenised prior to inoculation on to agarized BG11 growth medium. Once extensive growth of the biofilm was observed, they were treated with LAV (5% _v/v_), LIQ (10 and 30% _v/v_) and sterile saline solution (NaCl 0.9% _w/v_) as control (CTRL). Each treatment was tested in triplicate. The extracts were prepared by diluting with distilled water to the desired concentration. In the preparation of the LAV 5%, the solution was emulsified using 1% TWEEN 20. Two applications of 100 \(\mu\)L of each treatment were performed at time 0 and 5 days.  
Photosynthesis, the most fundamental and intricate physiological process in phototrophic organisms, is highly sensitive to the majority of stresses (biotic or abiotic) [32]. Therefore, a measure of chlorophyll fluorescence could be considered as a good indicator of the effect of plant extracts on the vitality of the biofilm phototrophic component. A mini-PAM portable fluorometer was used to measure the maximum quantum yield immediately before each treatment (days 1 and 5) and then at 5, 30 and 90 min, 1 and 5 days after each treatment (10 days in total).  
The second test involved the remaining 15 g of biofilm sampled from the selected area on the wall, which was then homogenized and transferred into 24 well plates. In each well, 250 mg of biofilm was placed on a sterile nitrocellulose filter and treated with either LIQ (10%), LAV (5%) or a mixture ofLIQ10% and LAV5% (LIQLAV). As a positive control the chemical biocide benzalkonium chloride 50% (Antichita Belsito, Rome, Italy) was applied at 0.6% (v/v) (BENZ). As negative control, the biofilm was treated with sterile saline solution (CTRL). All the tests were done in triplicate. Measures of maximum quantum yield were conducted immediately before the application of each treatment (100 \(\mu\)L) and then after 5 days. In order to avoid the cross contamination of treatments, one well plate was used for each substance.  
To evaluate the biocide effect on the heterotrophic microorganisms, the nitrocellulose filters supporting the biofilm were weighed and suspended 1:10 w/v in a solution of sodium pyrophosphate (0.05%) and Tween 80 (0.001%). The suspensions were incubated in an orbital shaker at 28 \({}^{\circ}\)C and 150 rpm for 1 h. The number of cultivable heterotrophic microorganisms was obtained by plating 100 \(\mu\)L of suspension and following decimal dilutions in TSA, MM and PDA agar media. The plates were then incubated at room temperature until the complete growth of colonies. Counts of viable microorganisms were referred to as colony forming units per gram of wet biofilm (CFU/g wet biofilm).
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 3 System Overview and Problem Formulation
### System Overview
#### 3.1.2 Failure Detection  
Failure detection mechanism is used in the system to detect link failure, which further makes the cluster aware of node failures. To detect failure each node implements a failure detector. They use heartbeat or other mechanisms to keep monitoring a number of remote nodes selected by a specific principle. If a failure is detected, the monitor node will immediately mark that node by setting the reachability status of it to be Unreachable and use gossip protocol to ensure the entire cluster finally noticing this issue. Unlike the \(\phi\) FD adopted by Akka, the failure detection mechanism in our work has an extra feature of estimating the severity of stochastic packet loss.
## 4 Agent Training with Markov Decision Process (MDP)
### DRL-Based Solution
#### 4.2.4 Once Training Process  
The single training process of the DDQN-EW agent is illustrated in Figure 7. The process begins with the agent observing the current state \(s\) from the AWR-PF. An action \(a\) is then selected according to the \(\varepsilon-\text{greedy policy}\). Upon executing action \(a\), the weight for the current epoch is determined, and the weighted RLS algorithm performs a single iterative positioning update using this weight combined with epoch-specific information. The positioning result is compared with the true position to compute the immediate reward \(r\). The next state \(s^{\prime}\) is then observed, and the terminal flag \(d\) is determined based on \(s^{\prime}\). The experience tuple \((s,a,r,s^{\prime},d)\) generated from this interaction is stored in the replay buffer \(\mathcal{D}\).  
Figure 7: Single training process of Double Deep Q-Network-based Epoch Weighting (DDQN-EW) algorithm. Boxes of the same color indicate the same structure.  
During training, a mini-batch \(B\) is sampled uniformly from the replay buffer \(\mathcal{D}\). The online network and target network are then used to compute the estimated Q-values and target Q-values, respectively. The optimizer calculates the loss \(L(\theta)\) based on these values and performs backpropagation to update the online network parameters \(\theta\). Finally, the target network parameters \(\theta^{\prime}\) are softly updated to complete one training iteration.  
This iterative process ensures stable and efficient learning through the \(\varepsilon-\)greedy policy, soft update, and experience replay mechanism.
## 1 Introduction
[37] used a numerical visualization method to investigate whether appropriate micro VGs can suppress the pressure wave. The flow around the VGs was characterized using numerical schlieren visualization. [PERSON] et al. [38] performed wind tunnel experiments on the DU97-W-300 airfoil, where the oil flow visualization method was used to verify the effect of VGs on airfoil stall separation suppression. The results demonstrated that VGs can delay stall, while under a stall condition, the presence of VGs increases the load fluctuation. In 2019, [PERSON] et al. [39] used the numerical simulation method to study the performance of the S1210 airfoil with VGs under improved trailing edge conditions. The results showed that by adding counter-rotating VGs near the airfoil trailing edge, the lift coefficient of the wing can be increased by 17% and the stall angle can be delayed from 10\({}^{\circ}\) to 12\({}^{\circ}\). Moreover, a rounder and thicker trailing edge can increase the lift coefficient by 13.5%, thus improving the performance. Nowadays, VGs are becoming an important part of wind turbine blade design. However, the challenges involved in calculating the flow field around VGs have not yet been satisfactorily addressed. [PERSON] et al. [40] used the scale decomposition method to simulate the stall behavior of the DU97-W-300 airfoil at high Reynolds numbers. In addition, a detailed sensitivity analysis was performed to evaluate the effects of time integration, grid width resolution, and region width on the simulation accuracy. Using this method, the flow on the airfoil surface with VGs was numerically simulated. [PERSON] et al. [41] proposed a numerical model of VGs for RANS simulations. The performance and application of the VG numerical model were also investigated. A wind turbine impeller was taken as an example to study the reliability of the model. Without considering the mesh-induced errors, the results showed that the vortices generated in the numerical simulation were weaker than those in the fully analytical calculation. The technical development of VGs as a means of flow control has been analyzed since the 1990s. Although many scholars have investigated the flow control mechanism of VGs, in the practical application of VGs, there are still some problems to be solved. When VGs are used to control the flow of wind turbine blades, an optimal VG spacing needs to be determined since there is flow interference between VGs and the spacing between VGs may affect the development and dissipation of concentrated vortices. The effect of VG spacing on vortex characteristics and wind turbine airfoil aerodynamic characteristics has not yet been reported. Therefore, in this paper, the effect of VG spacing on vortex characteristics and the aerodynamic characteristics of special airfoils for wind turbines is investigated using numerical simulations and wind tunnel experiments.
# Analysis of Security Issues and Countermeasures for the Industrial Internet of Things
## 3 Security Issues in IIoT Systems
### Security Issue 7--Privacy  
The massive scale of IIoT and the dynamics in communications, make the system more vulnerable from the privacy point of view for protecting personally identifiable information (PII) [102]. The device's privacy protection (e.g., information related to a device's daily tasks, frequency of interactions with other devices, etc.) requires dynamic security protections to make IIoT systems more secure from potential threats and attacks.  
An attacker may try to break a communication path and make a replay of interactions to perform transactions under a falsified identity. Further, the attacker can manipulate unauthorized logging data of a system and can generate trust-related attacks, e.g., self-promoting attack (a malicious node provides a positive reference for itself), bad-mounting attack (a malicious node provides a negative reference against an exemplary, i.e., good node), and good-mounting attack (where a malicious node giving good recommendations for themselves) [68].  
Another dimension of privacy is important for IIoT systems, i.e., context-aware privacy mechanisms. This will help to implement security protection by taking into account the current environmental requirements (e.g., location, device, user, etc.) [69] and privacy-by-design principles [103]. Further, an attacker can disclose a user's privacy by taking explicit control over devices via device capturing and tracking and tag tracking (e.g., RFID tag tracking). One considerable issue is the theft of credentials (e.g., logging information and password) in such cases. This can be done by the device's identity spoofing, in which an attacker can enforce non-authorized IIoT device's control (e.g., medical equipment) by violating the elevation of privileges and bringing a malicious action (e.g., changing the dose of an insulin pump) into an authorized session [104].