esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 State of the Art
### Exploration  
The ability of UAVs to reach inaccessible areas is a great advantage for exploratory missions, especially in land and marine environments. Nowadays, UAVs are commonly used for geological exploration and archaeological excavation missions. In [68], the authors applied model-free deep reinforcement learning on a UAV swarm to accomplish exploration missions. Each UAV is defined as an autonomous agent that interacts with its environment through some actions. For each movement and action, the agent receives a reward (bonus or malus). The objective is to maximize the reward of the agents' actions with the aim of better understanding the exploration policy of the environment.  
[PERSON] et al. [69] addressed the challenge of multi-UAV exploration by optimizing the paths of UAVs investigating a large and dynamic environment. The optimal environment exploration challenge is described as a pre-learned Gaussian process and is solved centrally. Each UAV agent evaluates the quality of its exploration process by using mutual information between both the explored and the rest of the environment.  
Furthermore, decentralized techniques provide well-known exploration benefits. In [70], the authors proposed a decentralized multi-agent version for environment exploration. They tackled the following three challenges: determining which information should be shared to enable unsupervised multi-agent coordination; (2) avoiding collisions; and (3) learning the environment's hyper-parameters while exploring.  
Following the previous work, a decentralized control approach for exploring unknown environments with a multi-Turtlebot system composed of three robots was presented in [17].  
For controlling the coordination between the uncrewed robots, they implemented a greedy decentralized method, allowing each robot to navigate and map an unknown environment without exceeding its boundaries.  
There are also other research projects [8; 9] that expand the exploration to archaeological excavation missions. For example, Efstathios and Fulvio [8] showed a state-of-the-art and deep analysis of archaeological remote sensing based on multi-UAV systems. Most existing methods are time-consuming, require a significant amount of hardware resources, and are computationally expensive. Using a multi-UAV system not only decreases the mapping time by distributing the coverage area, but it also enhances the map quality through information sharing [49]. It is possible to create a fast 3D map of archaeological sites using a collaborative mapping approach. This approach consists in partitioning the archaeological space and assigning portions to each UAV. Thus, each UAV performs the mapping task of its space and exchanges information on the boundaries of its space to the whole swarm for the achievement of the mapping mission in a distributed way [9].
## 2 Materials and Methods
A shuttle orbiter surveillance was one of the achievements recorded using a geostationary satellite [33]. Additionally, paper [34] describes an optimized system performance by implementing the DVB-S signal into FSR geometry to estimate aircraft velocity. These two (2) papers mark early work on satellite-based passive FSR system for air surveillance. To summarize, Table 2 presented the pros and cons of the DVB-S-based radars so far reported. Based on our awareness and, referencing Table 2, it is worth noting that the reported literature addressing quadcopter drone detection and its micro-Doppler features extraction by using the DVB-S-based passive FSR system was not yet explored or reported. To complement the knowledge, this paper implemented a passive FSR for drone detection and micro-Doppler feature extraction. It utilizes a signal of opportunity transmitted from a DVB-S (Malaysian satellite Measat3) with the satellite specification presented in Table 3. The proposed DVB-S-based passive FSR system geometry is illustrated in Figure 1. This paper only focuses on the detection of the drone by utilizing the extracted micro-Doppler feature as the stronghold for the detection based on the established signature of the drone from our findings, as published in [10]. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Aim and Features** & **Accomplishment** & **Limitations** \\ \hline
[27] & Bistatic Signal AF &
\begin{tabular}{c} Verified the effect of geometry on the AF using three rectangular pulses \\ \end{tabular} & Does not involve experimental validation \\
[28] & Bistatic Signal AF &
\begin{tabular}{c} Described effect of speed on processing gain of DVBS waveform ambiguities. \\ \end{tabular} & Lack range migration for small and fast-moving objects \\
[32] & Survey and analysis for airplane detection &
\begin{tabular}{c} Evaluated actual signal AF and analysis of the budgeted power \\ for better SNR \\ \end{tabular} & Relative low power yield due to long distance \\
[31] & System performance analysis &
\begin{tabular}{c} Power budget analysis and real detection of a truck, train and an airplane. \\ \end{tabular} & Experimental results do not validate theoretical estimates \\
[29] & APStar-5 Satellite for radar application & Signal waveform analysis & Addressed metal object with very high RCS \\
[30] & Verification of max. detection range & Signal analysis and proposed fast algorithm & No experimental detection \\
[35] & DVB-S2 signal analysis and its implication to SAR/ISAR & Verified multiple illumination effect on system performance. Experimental result for aircraft detection and velocity estimation & The SAR/ISAR does not involve micro-Doppler Analysis \\
[34] & DVB-S PFSR system &
\begin{tabular}{c} Experimental result for aircraft detection and Velocity \\ \end{tabular} & Does not involved micro-Doppler Analysis of a low-profile target \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DVB-S-based radars for airborne targets detection.
## 5 Results and Discussion
### Comparison of Results with Other Approaches
#### 5.4.2 Verification
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Recognition**} & \multicolumn{5}{c}{**Image Transformations**} \\ \cline{2-6}  & \multicolumn{1}{c}{**No**} & \multirow{2}{*}{**Brightness**} & \multicolumn{1}{c}{**Horizontal**} & \multirow{2}{*}{**Rotation**} & **Combined** \\
**MODEL** & **Transformation** & & & **Flip** & & **Transformations** \\ \hline BiMultNet & \multirow{2}{*}{0.44 \(\pm\) 0.55} & \multirow{2}{*}{0.97 \(\pm\) 0.57} & \multirow{2}{*}{1.34 \(\pm\) 1.09} & \multirow{2}{*}{2.53 \(\pm\) 0.66} & \multirow{2}{*}{3.06 \(\pm\) 0.96} \\ (Face modality) & & & & & \\ Face ResNet & 1.02 \(\pm\) 0.27 & 2.19 \(\pm\) 0.89 & 1.4 \(\pm\) 0.32 & 4.46 \(\pm\) 0.66 & 6.08 \(\pm\) 1.38 \\ Eigenfaces & 4.44 \(\pm\) 0.96 & 18.61 \(\pm\) 4.45 & 4.82 \(\pm\) 2.07 & 32.41 \(\pm\) 2.88 & 46.56 \(\pm\) 1.84 \\ Fisherfaces & 8.67 \(\pm\) 6.1 & 11.05 \(\pm\) 11.21 & 13.06 \(\pm\) 7.84 & 36.54 \(\pm\) 5.41 & 39.58 \(\pm\) 7.25 \\ \hline \hline \multicolumn{6}{c}{Signal to noise ratio (dB)} \\ \hline  & \multicolumn{1}{c}{Noiseless} & \multicolumn{1}{c}{0} & \multicolumn{1}{c}{5} & \multicolumn{1}{c}{10} & \multicolumn{1}{c}{15} \\ \hline BiMultNet & \multirow{2}{*}{0.74 \(\pm\) 0.65} & \multirow{2}{*}{0.93 \(\pm\) 0.98} & \multirow{2}{*}{0.83 \(\pm\) 0.98} & \multirow{2}{*}{1.01 \(\pm\) 0.68} & \multirow{2}{*}{0.44 \(\pm\) 0.55} \\ (Speech modality) & & & & & \\ SincNet & 3 \(\pm\) 0.62 & 14.97 \(\pm\) 1.18 & 7.15 \(\pm\) 1.36 & 4.27 \(\pm\) 0.88 & 3.28 \(\pm\) 0.7 \\ X-vectors & 2.22 \(\pm\) 0.39 & 5.8 \(\pm\) 0.75 & 3.16 \(\pm\) 0.55 & 2.5 \(\pm\) 0.37 & 2.27 \(\pm\) 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the verification results in terms of EER (%). prompted verification framework provided better results compared to unimodal biometric systems, regardless of the conditions presented by speech and facial data.
## 3 Five-Tier Architecture of Smart Grid Based on Blockchain
### Group Key Update for Intelligent Gateway Node Join  
In this section, the joining of the new intelligent gateway node is described. The group key is updated after the join to ensure backward secrecy. The process is as follows.  
(1) The new intelligent gateway node \(IG_{i}\) needs to broadcast its public key (\(upk_{IG_{i}},R_{IG_{i}}\)) in the group. Then, the new inserted node of logical key tree is inserted with the right-most leaf node in the subtree rooted at the insertion node. Furthermore, there are two possibilities according to whether the new inserted node has the sibling node, as shown on Figures 3 and 4:  
Figure 3: An example of the join of the new intelligent gateway node (the new inserted node does not have the sibling node and i = 3).  
(a) If the new inserted node does not have the sibling node, \(IG_{i}\) needs to compute \(K_{ii+1}=k_{i}(R_{IG_{i+1}}+H_{1}(ID_{IG_{i+1}}|\big{|}R_{IG_{i+1}})PK_{DC})\) and sends \(Sig_{IG_{i}}(K_{ii+1},ID_{IG_{i+1}},(upk_{IG_{i+1}},R_{IG_{i+1}}))\) to \(IG_{i+1}\). On the other hand, \(IG_{i+1}\) computes \(K_{i+1i}=k_{i+1}(R_{IG_{i}}+H_{1}(ID_{IG_{i}}|\big{|}R_{IG_{i}})PK_{DC})\), and sends \(Sig_{IG_{i+1}}(K_{i+1i})\) to \(IG_{i}\). Then, \(IG_{i}\) and \(IG_{i+1}\) computes \(M_{<0,0,>}\).  
(b) If the new inserted node has the sibling node, \(IG_{i}\) needs to compute \(K_{ii+1}=k_{i}(R_{IG_{i+1}}+H_{1}(ID_{IG_{i+1}}|\big{|}R_{IG_{i+1}})PK_{DC})\) and sends \(Sig_{IG_{i}}(K_{i+1},ID_{IG_{i+1}},M_{<0,0,>}G,R_{IG_{i+1}})\) to \(IG_{i+1}\). On the other hand, \(IG_{i+1}\) computes \(K_{i+1i}=k_{i+1}(R_{IG_{i}}+H_{1}(ID_{IG_{i}}|\big{|}R_{IG_{i}})PK_{DC})\), and sends \(Sig_{IG_{i+1}}(K_{i+1i})\) to \(IG_{i}\). Then, \(IG_{i}\) and \(IG_{i+1}\) computes \(M_{<0,0,>}\).  
(2) Irrespective of whether the new inserted node has a sibling node, the final group key \(M_{<0,0>}\) will change. The difference between the two cases is whether the group key is directly or indirectly affected.  
(3) The new group key is encrypted by the shared secret key broadcast to each node of the network.
## 3 Electromagnetic Security of Selected HF-Band RFID Card Readers and Access Control Systems
### Reader Systems
#### 3.1.2 NS106 Reader-Programmer  
An identical scope of tests was carried out in relation to the NS106 reader-programmer connected to a computer with a reduced level of electromagnetic emissions (TEMPEST). The relevant measurements were carried out in the layout presented in Figure 29.  
The measurements for this case were carried out in the frequency range from 1 MHz to 1 GHz. They were aimed (as in the previous example) to detect possible frequencies at which it would be possible to receive a signal from the identifier. Potentially dangerous frequencies may be, for example, the basic frequency of the reader's operation (13.56 MHz) and its harmonics. For the duration of the measurement, the monitor, keyboard and mouse were switched off to reduce electromagnetic disturbances in the measurement environment.  
A graph showing the signal level at individual frequencies is presented in Figure 30.  
Figure 30: Level of electromagnetic disturbances from the tested reader/programmer (harmonics observed up to 700 MHz).  
Figure 29: Measuring system for testing revealing emissions from the tested NFC reader–programmer: (**a**) diagram of the measuring system, (**b**) photo of the measuring system.  
In this case, a number of harmonic emissions (relative to 13.56 MHz) were observed, on which it was possible to receive data related to the transmission between the reader and the identifier.  
Figure 31 shows several selected waveforms recorded with an oscilloscope connected to the \"VIDEO\" output of the measurement receiver after tuning to selected frequencies.  
Figure 31: Sample images of the obtained waveforms for frequencies (**a,b**) 13.56 MHz, (**c,d**) 27.12 MHz, (**e,f**) 189.94 MHz, and (**g,h**) 691.56 MHz.
# Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
## 2 An Overview of the Techniques Used for Underwater Concrete Crack Detection  
Visual Inspection: Basic Method for Detecting Cracks in Underwater Concrete, Limited by Water Clarity and Visibility  
By quantifying the likelihood distribution of sea surface slopes, numerical approaches have enabled researchers to investigate both the refraction and reflection of light from the sky and the sun via roughened sea surfaces. In one study, the researchers explored various optical phenomena, such as the refracted sun's glitter, the brightness and reflectivity of an uneven sea surface owing to sky light, and the reflection of a harsh sea surface from direct sunlight [3]. While research on cracks in underwater surfaces is limited, most studies focus on the effects of water on various chemicals, their resistance to the environment, or their impact on the environment [4; 5; 6].  
Researchers have studied shade on underwater optical radiometers employing Monte Carlo computations of a light field with both the presence and absence of the sensors [7]. Optical sensors from space have been used to observe surface water, and various techniques have been developed to eliminate cloud or terrain shadows [8; 9; 10; 11; 12]. Additionally, flash photography directed vertically downward toward the water surface has been used to interpret wave slopes. Researchers have studied various optical effects of water, including optical absorption, temperature and humidity effects, dissolved organic materials, biological vegetation interaction, pressure effects, and optical propagation in turbulent water [13; 14; 15; 16; 17; 18; 19; 20].  
Acoustic Methods: Use of Sound Waves to Detect Cracks, Including Impact-Echo, Impulse Response, and Ultrasonic Methods: Fluorosensor  
In [21], the authors describe how optically and near-infrared wavelengths may be used to detect water depth and substrate type as two key factors in river physical habitats. The approach provided in [22] was used to remotely track chlorophyll content in fresh waterways and was shown to be strongly associated with real-world data, allowing for the detection of changes in surface water optical attenuation.  
Another research study investigated the association between acoustic emission cumulative energy and decreased cycling reversal loads in submerged concrete columns [23]. Ultrasonic surface waves were used to detect cracks in underwater concrete beams, with a root mean square deviation damage index used to analyze the wave data [24]. A method for assessing the concrete dams' blast resistance by investigating the induced vibration and crack penetration depth and use of a crack control agent to prevent failure of concrete structures was presented in [25; 26].
# Exploring the Feasibility of Autonomous Lighting Systems for Pedestrian Crossings in Off-Grid Areas
## 2 Materials and Methods
### Pedestrian Crossing Lighting Scenarios  
The demand for electric energy results from two factors, i.e., from requirements of the lighting levels at pedestrian crossing (carriageway and footway), determining the power of the luminaires (with well-known luminous efficacy), and the operating time of installation throughout the year.  
In order to confirm the feasibility of the assumed lighting conditions throughout the year, five variants of lighting scenarios were developed on the basis on computer simulation in Dialux software (DIALux 4.13). Luminaire light emission data are obtained from luminaire manufacturers, and simulations were performed for a typical road cross-section of 2 \(\times\) 3.5 m. Lighting and energy calculations were made for each scenario.  
In the first basic scenario (Figure 9), the lighting requirements presented in the guidelines [11] were assumed to be achieved by implementing class PC4 in the pedestrian crossing area, class C3 in the transition zone on the distance of 100 m from the pedestrian crossing exit, and for class P1 on the pavements.  
Scenario 2 (Figure 10) assumes a reduction in the lighting class at the pedestrian crossing to the PC5 level, according to the guidelines in [11]. The remaining lighting parameters remain unchanged.  
The authors carried out an additional variant analysis for the other pedestrian crossing lighting methods. The following scenarios were carried out in deviation from the requirements presented in [11]. They were simulated variant by variant to achieve the required power supply for lighting solutions.  
In scenario 3 (Figure 11), the transition zone is shortened to 35 m using a single luminaire on the street, retaining class C3 and lighting class PC4 at the pedestrian crossing and class P1 on the pavement.  
Figure 8: Basic lighting solution used outside the illuminated area.  
Scenario 4 (Figure 12) assumes that the transition zone can be abandoned, with pedestrian crossing lighting in class PC4 and pavement lighting being implemented for 25 m behind and in front of the crossing in class P1.  
Figure 10: Lighting scenario 2.  
Figure 9: Basic lighting scenario 1.  
The final scenario, Scenario 5 (Figure 13), entails the solution of reducing the lighting levels to the minimum requirements of the lighting classes described by the requirements in [38] and the guidelines in [11,36]. A similar scenario has been adopted, consisting of lighting the pedestrian crossing in class PC5 and pavements over a distance of 25 m behind and in front of the crossing in class P3, while abandoning the implementation of the transition zone.  
Figure 11: Lighting scenario 3.  
Figure 12: Lighting scenario 4.  
Table 3 presents the lighting classes and the total electrical power required for the described scenarios.
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## 3 Architecture and System Models
### System Models
#### 3.2.1 Air-to-Ground Path Loss Model  
The path loss model for the UAV in the sky and the user on the ground in this paper follows the model in [38]. In [38], the air-to-ground propagation path is first divided into the probability of a Line of Sight (LoS) environment and the probability of a Non-Line of Sight (NLoS) environment. The path loss, in this case, is as follows:  
\[L=P_{\text{LoS}}\;L_{\text{LoS}}+P_{\text{NLoS}}\;L_{\text{NLoS}} \tag{1}\]  
where \(L\) is the propagation loss, \(P_{\text{LoS}}\) and \(P_{\text{NLoS}}\) are the probabilities that the propagation path becomes LoS or NLoS, respectively, and \(L_{\text{LoS}}\) and \(L_{\text{NLoS}}\) are the corresponding propagation losses for these two cases. The probability that the propagation path becomes LoS is determined according to the environment of the corresponding area as follows:  
\[P_{\text{LoS}}=\frac{1}{1+a\exp(-b[\theta-a])} \tag{2}\]  
where \(a\) and \(b\) are constants determined by the environmental parameters of the relevant area [38] and \(\theta\) is the elevation angle from the user on the ground to the UAV. (\(P_{\text{LoS}}\) is a function based on the user's position, and the total integration of \(P_{\text{LoS}}\) over all solid angles does not necessarily yield a unit value.)  
Since the propagation path can be classified into two types, LoS and NLoS, the NLoS probability is as follows:  
\[P_{\text{NLoS}}=1-P_{\text{LoS}} \tag{3}\]  
The path loss of LoS and NLoS can be divided into propagation loss and additional loss (\(\eta\)) when the space is free, which can be expressed as follows:  
\[L_{\text{LoS/NLoS}}\;[\text{dB}]=L_{\text{free}}+\eta_{\text{LoS/NLoS}} \tag{4}\]  
where \(\eta\) represents the additional loss in LoS or NLoS condition, respectively, which is determined by the frequency band and is described in Section 4. \(L_{\text{free}}\;[\text{dB}]\) represents the free-space propagation loss, which can be expressed as follows:  
\[L_{\text{free}}\;[\text{dB}]=20\log d+20\log f+20\log\left(\frac{4\pi}{c}\right) \tag{5}\]  
where \(d\) [m] is the linear distance between the UAV and the user, \(f\) [Hz] is the carrier frequency, and \(c\) [m/s] is the speed of light.  
Substituting Equations (2)-(5) into Equation (1), we finally obtain the propagation loss as follows:  
\[L\;[\mathrm{dB}]=\frac{\eta_{\mathrm{LoS}}-\eta_{\mathrm{NLoS}}}{1+a\;\exp\Bigl{(} -b\Bigl{[}\arctan\Bigl{(}\frac{h}{R}\Bigr{)}-a\Bigr{]}\Bigr{)}}+10\log\Bigl{(} h^{2}+R^{2}\Bigr{)}+20\log f+20\log\Bigl{(}\frac{4\pi}{c}\Bigr{)}+\eta_{ \mathrm{NLoS}} \tag{6}\]  
where \(h\;[m]\) is the altitude of the UAV and \(R\;[m]\) is the horizontal distance between the UAV and the user, respectively. These are also shown in Figure 2.
## 3 Development of Software-Based Router and Traffic Generator
### Performance Analysis of The Software-Based Router Using the Designed Network Traffic Generator
These measurements were carried out in the conditions of the software router installation on different servers with different hardware characteristics (Celeron J1800|2 cores|2.41 GHz| RAM 4 GB; Intel Core i3-3230M (2.6 GHz)/RAM 4 GB; Intel Core i5-2410M (2.3 GHz)/RAM 6 GB; Core i7-7700|4 cores|3.6 GHz| RAM 8 GB). We proposed a scheme to measure the packet processing time of a router using a specialized packet-capture network interface card in a traffic generator (Ethernet  
Figure 6: The delay through router. port 1 and Ethernet port 2). This generator can send and receive traffic at the same time, and thus calculate the time difference between sending time--T1 (Ethernet port 1) and receiving time--T2 (Ethernet port 2) each packet, as a T2-T1. This allows the generator to calculate the time of packets transit over the network and processing on the router. Experimental scheme for determining the total nodal delay by the software router installed on the servers of different performance is depicted in Figure 7.  
The scheme does not require synchronization between the router network interface card and the packet-capture network interface card. Also the scheme does not require synchronization between the NICs of traffic generator (Ethernet port 1 and Ethernet port 2). The proposed basic model of the traffic generation system can be used to create test environments to study the real network using several separate physical computers to simulate clients. In studying the characteristics of the real network data transmission, the main and signaling traffic required for the test environment should be separated. At best, the signaling traffic should be transmitted over a separate physical network. Otherwise, it is necessary to create an additional virtual local area network at the test network. Such a scheme will allow to conduct experiments remotely, using a single device to control all other devices, significantly expands the capabilities of the researcher, simplifies the conduct of new experiments and saves time on their formulation. In the process of research to test the efficiency of the software router and traffic generator, an experiment was conducted. The investigation of the total nodal delay by the software router on the servers of different performance is shown in the Table 1.  
So, from the experimental results we can see that the same software router installed on different servers with different hardware characteristics can serve packets with different delays. Thus, it is established that the more productive server on which the software router is installed, the less total nodal delay will be. Figure 3.0 Experimental scheme for determining the total nodal delay by the software router installed on the servers of different performance.