esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Nowadays, Unmanned Aircraft Systems (UAS) have become more and more popular for both civilian and commercial applications. They have been used for environmental monitoring and measurements, emergency response, homeland security, precision agriculture, land management, infrastructure monitoring, and commercial applications such as aerial photography and transportation of goods and post [1, 2]. However, as no human pilots are on Unmanned Aircraft (UA), they generally lack the critical ability to Detect and Avoid (DAA) other aircraft during their flight operations [3]. In addition, the other significant issue for operating UAS is the fact that the UA pilots control the UA in a remote manner, typically relying on radio frequency (RF) and/or satellite communication to establish a Command and Control (C2) link between the Ground Control Station (GCS) and the UA. For safe operations of UAS, the C2 link has to be robustly, reliably, and redundantly established. When the C2 link does fail, the UA should perform predictable and acceptable \"lost link\" procedures similar to manned aircraft autopilot systems [4].  
For these safety concerns, civil aviation authorities like the Federal Aviation Administration (FAA) in the US and Transport Canada in Canada have not allowed UAS to be integrated into non-segregated airspace (airspace shared with manned aviation) [5, 6]. Currently, they only allow UAS to operate within Visual Line of Sight (VLOS) in limited areas under Certificates of Waiver or Authorization (COA) in the US and Special Flight Operations Certificates (SFOC) in Canada [7, 8]. As to commercial demands of UAS Beyond Visual Line of Sight (BVLOS) operations in civil airspace, both FAA and TransportCanada have required UAS to be equipped with DAA systems to prevent potential loss of separation or mid-air collisions from oncoming traffic during flight operations. Large UAS can have the same DAA systems installed as manned General Aviation (GA) aircraft, such as airborne radar, the Traffic Collision Avoidance System (TCAS), and the Automatic Dependent Surveillance-Broadcast (ADS-B), to achieve a level of safety equivalent to manned GA aircraft [4; 9]. However, due to limitations of Size, Weight, and Power (SWaP), small UAS require newly-developed small, light, and low-power versions of airborne sensors or ground-based sensors for safe operations in civil airspace. To reflect this reality, a small UAS DAA system based on light-weight airborne ADS-B and ground portable radar systems is proposed by the RAVEN project (the RAVEN project is based at Memorial University, St. John's, NL, Canada, and their main research objective is to develop intelligent DAA systems to allow small UAS to share the same airspace with manned aviation) to assist small UAS in achieving the equivalent level of safety as manned GA aircraft. This is important for the future integration of small UAS into civil airspace.
## 3 Numerical Results
### Case (B): Optimal Location by Evolution Algorithm  
Case (B) used SADDE and APSO to find the optimal transmitter location. To save computing time, both the transmitter and the receiver used an array of two antennas. The height of the transmitter was 2 m, and that of the receiver was 1.5 m. The outage probability was calculated as defined in Equation (7).  
Initially, the optimal transmitter location was set at axis (2.5, 5.0, 2.0) m, the center of the office. The parameter values are shown in Table 1. As shown in Figure 5, when the SNR was 56 dB, there were 11 outage points. To improve the communication quality and reduce the outage probability [26], SADDE and APSO were used to find the optimal location of the transmitter. In Figure 6a, after using the SADDE to adjust the optimal transmitter location to axis (2.81, 5.08, 2.0) m, the outage points dropped from 11 to 10. After using the APSO to adjust the optimal transmitter location to axis (2.61,  
Figure 4: (a) Average bit error rate (BER) vs. signal-to-noise ratio (SNR) for three different antenna arrays; (b) Outage probability vs. SNR for three different antenna arrays.  
Figure 3: Three different shapes of arrays for the transmitter and receiver. (a) L array; (b) circular array; (c) Y array.  
3.95, 2.0) m, the outage points dropped from 11 to 7, as shown in Figure 6b. When the SNR increases from 56 dB to 64 dB, the number of the outage points for SADDE was reduced from 10 to 1, whereas the number for APSO dropped from 7 to 0. This result demonstrated that APSO was better than SADDE in finding an optimal transmitter location, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline search target (m) = (x, y, z) & location of the transmitter (x, y, 2.0) \\ fitness function & outage probability of the receivers \\ frequency range & 3.1–10.6 GHz \\ SNR & 40 dB to 80 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation setup.  
Figure 5: The distribution of the outage points for the transmitter at (2.5, 5.0, 2.0) m. (SNR = 56 dB).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SNR** & **Algorithm** & **SADDE** & **APSO** \\ \hline SNR = 56 dB & Number of outage points = 10 & Number of outage points = 7 \\ SNR = 70 dB & Number of outage points = 1 & Number of outage points = 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison for SADDE and APSO.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Tests** & **Mean** & **Std. Deviation** & **Std. Error Mean** & **Z Wilcoxon 1 ** & **Sig. (2-Tailed)** \\ \hline \multirow{2}{*}{Pair 1} & forefoot\_left\_surface & 72.8333 & 9.64192 & 3.93630 & & \\ \cline{2-6}  & rearfoot\_left\_surface & 54.6667 & 11.27239 & 4.60193 & & \\ \hline \multirow{2}{*}{Pair 2} & forefoot\_right\_surface & 76.1667 & 12.76584 & 5.21163 & & \\ \cline{2-6}  & rearfoot\_right\_surface & 58.1667 & 15.05213 & 6.14501 & & \\ \hline \multirow{2}{*}{Pair 3} & forefoot\_left\_load\_percent & 23.6667 & 6.86052 & 2.80079 & & \\ \cline{2-6}  & rearfoot\_left\_load\_percent & 22.0000 & 5.44059 & 2.22111 & & \\ \hline \multirow{2}{*}{Pair 4} & forefoot\_right\_load\_percent & 25.6667 & 4.45720 & 1.81965 & & \\ \cline{2-6}  & rearfoot\_left\_weight\_ratio\_percent & 25.6667 & 12.80104 & 5.22600 & & \\ \hline \multirow{2}{*}{Pair 5} & forefoot\_left\_weight\_ratio\_percent & 51.3333 & 11.87715 & 4.84883 & & \\ \cline{2-6}  & rearfoot\_left\_weight\_ratio\_percent & 48.6667 & 11.87715 & 4.84883 & & \\ \cline{2-6}  & forefoot\_right\_weight\_ratio\_percent & 47.6667 & 9.77070 & 3.98887 & & \\ \cline{2-6}  & rearfoot\_right\_weight\_ratio\_percent & 52.3333 & 9.77070 & 3.98887 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Forefoot-reafoot differences (Wilcoxon test)—age group 13–15 years/pairs (\(N=6\)). For the group over 15 years old, in the case of the first two pairs of tests, the differences between the rearfoot and forefoot areas are similar to those of the other groups, so these are significant. Even if insignificant values of \(\mid\)Z\(\mid\) are obtained for the rest of the data pairs, the results contrast sharply with those of the other age stages, especially with the group under 13 years old. If, at the level of the left leg, there is a relative balance in the distribution of body weight between the two areas, at the level of the dominant/right foot, the load on the front (forefoot_right_load_percent) has a higher value than the load on the back (rearfoot_right_load_percent). The situation is the same for the right foot in the pair (forefoot_right_weight_ratio_percent vs. rearfoot_right_weight_ratio_percent), where the average load on the forefoot area is excessively high and contrasts sharply with average values for ordinary adults, presented in the introduction. At the level of the differentiated load for each left/right leg of forefoot vs. rearfoot areas (forefoot_weight_ratio_percent and rearfoot_weight_ratio_percent), equal-load values (50-50%) or higher values of the forefoot area are not considered to belong to the normality standard (Figure 17). A higher value of this rapport (maximum 59%) for the rearfoot area is considered normal by the software. Only one subject (out of the whole group of 20) has a distribution according to this standard for both legs, seven subjects for only one of the legs (Figure 18), and the remaining 12 have large variations/load imbalances for both legs, both in the variant of overloading
# Failure Assessment of Embankment Dam Elements: Case Study of the Pirot Reservoir System
## 4 Numerical Model of the Zavoj Dam
### Functional Assessment of Dam Elements  
The presented analysis is a part of broader research conducted within the consideration of the integral function ability of a multipurpose reservoir system. Numerical assessment of partial stability of the dam elements and the surrounding rock mass identified key elements of the structure whose potential failure or loss of stability could affect the normal functioning of this system. The analysis identified the following scenarios and their failure mechanisms:  
* Scenario 1: Damage to the grout curtain--loss of water resistance function due to effect of seismic loads;
* Scenario 2: Damage to the overflow section with sluice gates--functionality can be reduced due to occurrence of seismic loads;
* Scenario 3: Damage to the slope above the spillway chute--slope may lose stability as a result of seismic loads, reduce the capacity of the spillway, and prevent evacuation of water at high levels in the reservoir.  
As noted above, the load scenario used in all conducted analyses is the effect of seismic loads. Seismic load was applied using seismic acceleration in two directions, independently: in the water flow direction and in the dam axis direction.  
For all conducted analyses of stability, the remaining load-bearing capacity was calculated according to the theory presented in the previous section. In the conducted analysis, it was adopted that in all finite elements in which the bearing capacity is less than 2.5%, there is a high possibility of local loss of stability and collapse. This is especially important in areas where the loss of local stability would jeopardize the functioning of other elements of the system. One of such elements is the spillway. The spillway was divided lengthwise into ten parts, so that the length of one segment approximately corresponds to the width of the spillway section, as shown in Figure 7. The volume of the slope above the spillway was divided in the same way: one segment of the rock mass above the spillway corresponds to one zone of the spillway. The volume of rock mass elements in which the remaining load-bearing capacity is below the adopted limit is compared with the volume of the corresponding spillway segment. The ratio of the volume of the spillway segment and the volume of the finite elements of the slope that have lost load-bearing capacity is presented as the degree of functionality reduction of the spillway segment, accordingly, the spillway functionality of the segment was calculated as:  
\[C_{i}=\left(1-\frac{V_{i,sl}}{V_{i,sw}}\right)100\%, \tag{16}\]  
where \(C_{i}\) represents capacity of the \(i\)th segment of the spillway, \(V_{i,sl}\) is the volume of finite elements of the \(i\)th segment with remaining load-bearing capacity less than 2.5%, and is the volume of the \(i\)th segment of the spillway. The effective capacity of the entire spillway \(C\) represents the minimum value of the capacity of any spillway segment.
## 7 Results
Figure 5a shows the accuracy trend for different values of SNR in dB. It can be seen that the use of the Adam solver algorithm has a similar performance to the RMSProp and the SGDM, but it is slightly more robust for lower values of SNR in dB. Figure 5b shows the accuracy trend for different values of the window size used in the first Convolutional Layer. It can be seen that a value of \(S_{w}\) equal to 10 provides the optimal accuracy at any value of SNR. A potential reason for this is that such a relatively small window supports a more effective extraction of the discriminating feature by the CNN algorithm. On the other hand, even smaller values of the window size may increase the computing time of the CNN. Figure 5c shows the accuracy trends for different values of the initial learning rate used for training, where it can be seen that a value of 0.001 provides the optimal accuracy trend, in particular for its robustness to noise. As in the previous case, a smaller value of the initial learning rate may increase the computing time of the CNN. Figure 5d shows the accuracy trends for the different numbers of convolutional layers used in the CNN architecture. It can be seen that three convolutional layers are optimal both at low values of SNR and at higher values of SNR. In fact, it is not necessary to increase \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Time Frequency Transform**} & \multirow{2}{*}{**Values Range**} & \multicolumn{2}{c}{**Optimal Values for CWT**} \\  & & **Sombrero Wavelet (Weather Radar Data Set)** \\ \hline Spectrogram & Window size (10, 20, 30, 40) & 20 \\ \hline Smoothed WVD & Window size (10, 20, 30, 40) & 30 \\ \hline CWT (all wavelets) & Voices per octave (4, 8, 12, 16) & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the optimal values for the time-frequency parameters. Figure 5: Optimization results of the CNN for the CWT (Sombrero) TFT for the weather radar data set. (**a**) Optimization of the CNN solver algorithm, (**b**) Optimization of the CNN filter size (first layer), (**c**) Optimization of the CNN initial learn rate, (**d**) Optimization of the CNN number of convolutional layers. the number of convolutional layers because four convolutional layers perform less than three convolutional layers.
# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 4 Proposed Algorithm for Feature Selection  
### The Proposed Framework  
A meta-heuristic algorithm having a balance between exploration and exploitation ability has a great impact on its performance. For an algorithm with good exploration ability, we can enhance its exploitation ability by introducing operators with strong exploitation ability so as to regain the balance between exploration and exploitation abilities. Based on the analysis in Section 3, this paper presents a novel ABC framework. There are three points in the description of the framework:  
1. The employed bee phase of the ABC algorithm is retained so that it can explore the search space widely and avoid reaching the local optimum;
2. The updating mode of the ABC algorithm's onlooker bee phase is changed to the new updating strategy, as inspired by other algorithms with more powerful exploitation capacities. The searching scheme of these algorithms with powerful exploitation abilities is introduced as an operator. According to our observation, higher diversity in the bee swarm can help the algorithm to find more potential search space, but after a certain period, the solutions should converge and approach optimal solutions with reductions in colony diversity. We believe that applying operators with strong exploitation abilities to the optimization process can reduce the diversity of the algorithm in the late stage, and bring about a higher convergence speed. Therefore, the introduction of operators with powerful exploitation abilities can help our novel ABC framework find better solutions;
3. The scout bee phase is removed, because the exploration ability of the scout bee phase will increase the diversity of the algorithm during the later period. Moreover, the scout bee phase will waste the execution time, and consume computational resources and memory during the calculation process.  
Figure 1 illustrates our proposed framework and its differences from the processes of the original ABC algorithm. Overall, the two methods utilize the same updating mechanism in the employed bee phase. However, without the scout bee phase, our method does not need to compute the value of the counter throughout the algorithm. Since the onlooker bee phase in our method is updated by the operators of an algorithm with strong exploitation abilities, we do not use roulette-wheel selection, so we do not need to calculate the selection probability of each individual.  
FS is, in essence, an optimization problem in a binary searching space. The value of each element of the solutions is limited to 0 or 1 [36]. However, the ABC algorithm originally proposed is used in continuous space. To adapt our proposed ABC framework to FS, we need to transform the continuous values to binary values. This transformation is fulfilled by Equation (4).  
\[xb_{id}=\begin{cases}1&if\ r<\text{sigmoid}(x_{id})\\ 0&otherwise\end{cases} \tag{4}\]  
where \(r\) is a random value in [0, 1]. The function of \(sigmoid(x)\) is formulated as in Equation (5):  
\[sigmoid(x)=\frac{1}{1+exp(-10*(x-0.5))} \tag{5}\]
# Single Trace Side Channel Analysis on NTRU Implementation
## 3 Proposed Single Trace Side Channel Analysis on NTRU Implementation
### NTRUEncrypt  
Representing PolynomialsIn the NTRUEncrypt, the polynomial is represented as the coefficients in order. For example, \(F(x)=x^{3}+x-1\) stored as \(F\)={\(-1\), \(1\), \(0\), \(1\)}. Before the polynomial multiplication of cipher-text and private key, there are steps to compute \(f=pF+1\).  
Polynomial MultiplicationThe the Equation (3) operates using the grade school multiplication. Unlike NTRU Open Source, the polynomial multiplication operates separately. These steps are described in Algorithm 3.  
```
0: Primary polynomial \(F\in\mathcal{L}_{f}\), cipher-text \(e\in\mathcal{R}\)
0: message \(m=f\cdot e\pmod{q}\)
1:for\(0\leq i<N\)do
2:\(f_{i}\gets F_{i}\times p\)
3:endfor
4:\(f_{0}\gets f_{0}+1\)
5:for\(0\leq j<N\)do
6:\(t_{j}\gets e_{0}\times f_{j}\)
7:endfor
8:for\(1\leq i<N\)do
9:\(t_{i+N-1}\gets 0\)
10:for\(0\leq j<N\)do
11:\(t_{i+j}\gets t_{i+j}+e_{i}\times f_{j}\)
12:endfor
13:endfor
14:\(t_{2N-1}\gets 0\)
15:for\(0\leq i<N\)do
16:\(m_{i}\leftarrow(t_{i}+t_{i+N})\pmod{q}\)
17:endfor
18:return\(m\)
```  
**Algorithm 3** Decryption in NTRUEncrypt
### The Service Interface  
The service interface allows one to interact with CVMan by asking questions, such as the aforementioned PQI-PQ4. For each question, a request is accompanied with a number of parameters. The service interface may re-formulate the question according to its interface with the clone or vulnerability management component and then determines which component or sub-component should be given the re-formulated question, collects the answer from the clone or vulnerability management component, and translates the answer to a format that may be easier for the requester to understand. Moreover, the service interface may be extended to incorporate appropriate visualization tools for presenting the answer to the requester. Specifically, the interface can offer the following services.  
In order to answer PQ1, the interface takes the input provided by the requester, which includes a set of programs. Then, the interface feeds the input to the clone management component, which detects clones, as discussed above, and formulates the corresponding clone landscape, represented by a SCG. The clone management component reports back the metrics that are measured from the SCG, namely the clone surface of each program and the clone prevalence of each cloned code fragment in the set of programs.  
In order to answer PQ2, the interface takes the input provided by the requester, which includes a set of \(n\) versions of some program(s). Then, the interface feeds the input to the clone management component, which detects clones and constructs the corresponding clone landscape TCG. The clone management component would report back the metrics that are measured from the TCG, namely the clone surface of each program, the clone prevalence of each cloned code fragment in the set of programs, and the clone lifetime. These metrics can be leveraged to conduct further analyses.  
In order to answer PQ3, the interface takes the input provided by the requester, which includes a set of \(n\) versions of some program(s). The interface feeds the input to the clone management component, which detects clones and constructs the corresponding clone landscape TCG. Then, CVMan extracts vulnerabilities from vulnerability sources, annotates the code fragments that have at least one line in common with a vulnerable code fragment, and, finally, outputs these vulnerable code fragments, as well as the programs that belong to the requester.  
In order to answer PQ4, the requester is given a specific vulnerability (for example) by a trusted third party. The requester would want CVMan to identify all of the vulnerable programs that contain clones of the given vulnerable code. The interface takes the new vulnerability and feeds it to the vulnerability management component. Then, CVMan annotates the code fragments in the existing clone landscape that have at least one line in common with a vulnerable code and, finally, outputs these vulnerable code fragments (programs) and TCG with annotated vulnerability.
# SIMECK-T: An Ultra-Lightweight Encryption Scheme for Resource-Constrained Devices
### Contributions  
The proposed SIMECK-T encryption scheme is a combination of SIMECK [29] with internal TEA rounds [30]. The algorithm produces random output at high speeds, making it suitable for low-powered devices.  
Without having any lookup tables, the proposed encryption scheme is immune to side-channel attacks.  
Performance discontinuities that occur when substitution boxes are dynamically interchanged in SPECK-R [19] are completely avoided.  
The proposed algorithm was evaluated using Hash XOR Permutation (HXP) metrics, an encryption scheme proposed by [PERSON] et al. [31], and it was shown that ultra-lightweight encryption schemes can be produced without compromising security levels and without having to rely on substitution boxes. The same metrics have been used by [PERSON] et al. for the evaluation of IRC6 [12].  
TErestrial Trunked RAdio (TETRA) TEA3 [32] was investigated for potential use in a resource-constrained environment with reduced rounds. The security level of TEA3 was shown to be unable to be preserved by reducing the number of rounds due to performance issues. The cipher uses a single substitution box as lightweight DES [33] does, but it was shown to be very slow.  
The augmented version of SIMECK [29], named SIMECK-R, is shown to be a viable encryption scheme comparable to SPECK-R [19], using the same number of rounds.  
The implementation of SIMECK-T is very fast, produces high-quality random output, and is available as free software [34].
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 3 Underlay D2D Network Based on ProSe Service  
### Introduction  
During the first generations of cellular networks, only one air interface was defined and used to connect UE to the network infrastructure by supporting uplink and downlink operations. With the 3 GPP release 12 [11], a sidelink has been introduced, allowing UE to connect directly with other UEs. This sidelink defines a set of physical, transport, and logical channels that can be deployed for synchronization, discovery, and direct communication operations within a D2D communication underlying cellular system. The newly defined physical layer channels are as follows:  
* PSBCH: Physical SL broadcast channel, which carries system information and synchronization signals.
* PSCCH: Physical SL control channel, carries UE-to-UE control plane data.
* PSDCH: Physical SL discovery channel, which supports UE direct discovery transmissions.
* PSSCH: Physical SL shared channel, which is used for user plane data transmission.