esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents research on the physical layer security performance of a multi-user mixed RF/FSO system based on optimal user interference. In this system model, the RF link experiences Rayleigh fading, and the FSO link follows the Fischer-Snedecor \(\mathcal{F}\) distribution. The system adopts a double-hop-decode-and-forward (DF) relay scheme. We also consider the effect of directivity errors in the FSO link and assume the presence of an illegal eavesdropper with a single antenna near the RF link. The source node controls the energy collection and information forwarding using a multi-user structure based on simultaneous wireless information and power transfer (SWIPT). We select the optimal user to jam the eavesdropper's communication. We derive closed-form expressions for the mixed RF/FSO communication system's secrecy outage probability (SOP) and average secrecy capacity (ASC). Monte Carlo simulations are performed to verify the accuracy of these expressions. By formulating and simulating the simulation system, the impact of various important factors on the mixed system's physical layer security (PLS) is analyzed. The analysis indicates that increasing the number of antennas and interference signal-to-noise ratio (SNR) of the optimal user, the time allocation factor and energy conversion efficiency, and the improvement in the quality of atmospheric channels with improved weather will significantly enhance this system's PLS.  
RF/FSO; physical layer security; Fischer-Snedecor \(\mathcal{F}\) distribution; multi-user; secrecy outage probability; average secrecy capacity +
Footnote †: journal:  
00 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
Footnote †: journal:  
00 2023, 23, 6523. [[https://doi.org/10.3390/s23146523](https://doi.org/10.3390/s23146523)]([https://doi.org/10.3390/s23146523](https://doi.org/10.3390/s23146523))
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 4 Standards Evaluation Results
### Standards Selected for Analysis
#### 4.1.4 Real-Time Inter-Network Defense  
RID is an IETF initiative created to facilitate the process of sharing data about security incidents mainly structured under the IODEF format. It outlines a proactive internal communication network, capable of integration with mechanisms for detecting, identifying, mitigating and responding to incidents, aiming to compose a complete solution in the treatment of security incidents [53]. RID messages are transported under the HTTPS protocol and, in order to provide more security, the protocol adds another layer of security to manage sessions.
## 4 PMT&T Board Measurements
### Optimization of the Input Signal Conditioning Circuit  
A series of measurements were performed to find the minimum value of the input signal attenuation that ensures a good input dynamic range for the CITIROCs.  
In Figure 6a, it is possible to see that with a 3x attenuation, a compression for signals greater than 3 V is present.  
This compression is caused by the protection diode inside the CITIROC's inputs and would reduce the available input dynamic range since PMTs' signals can extend up to 8 V.  
The channel with the 6x attenuation, instead, shows a good linearity over the amplitude range of the input signal.  
From these considerations, a good value for the attenuators would be 6x, but from the calibration curves that will be presented in Section 4.2, it has been observed that doubling the attenuation and using higher gains for the preamplifiers allows the CITIROCs to work in a more linear region.  
Figure 6: Output of the input signal attenuators: (a) 3x attenuator, (b) 6x attenuator.  
For these reasons, the final values of the attenuations have been chosen considering an attenuation of 12x for signals produced by almost all the scintillators except for those of T1. These scintillators, being thinner than all the others, produce smaller signals that would be wiped by the 12x attenuation, and therefore, the value 6x is used instead.  
Table 3 shows the attenuation values for all the PMT&T channels.
# A Metamaterial Solution for Soundproofing on Board Ship
## 3 Metamaterial Solution: Materials, Methods, and Design
### FEM-Simulated STL of Metasolution  
The numerical tool described in the previous section was used to perform an extensive numerical analysis of the Metasolution, in order to efficiently design the geometry before prototyping.  
A series of simulations was carried out by varying the characteristic dimensions of the unit cell alternately, while keeping the others constant. The dimensions chosen for the optimization are reported in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **a\({}_{\text{cell}}\) [mm]** & **tck\({}_{\text{panel}}\) [mm]** & **h\({}_{\text{cell}}\) [mm]** & **Rw [dB]** \\ \hline \hline \multirow{3}{*}{h\({}_{\text{cell}}\)} & \multirow{3}{*}{150} & \multirow{3}{*}{1.0} & 35 & +4 \\  & & & 40 & +6 \\  & & & 45 & +5 \\  & & & 50 & +5 \\ \hline \multirow{3}{*}{a\({}_{\text{cell}}\)} & 100 & \multirow{3}{*}{1.0} & +5 \\  & 120 & & 40 & +5 \\  & 150 & & & +6 \\ \hline \multirow{3}{*}{tck\({}_{\text{panel}}\)} & \multirow{3}{*}{150} & 0.7 & & \(-1\) \\  & & 0.8 & & +3 \\ \cline{1-1}  & & 1.0 & & +6 \\ \cline{1-1}  & & 1.2 & & +7 \\ \hline \hline \end{tabular} a\({}_{\text{cell}}\): cell width; tck\({}_{\text{panel}}\): panel thickness; h\({}_{\text{cell}}\): cell height.  
\end{table}
Table 2: Values chosen for primitive unit cell characteristic dimensions (with reference to Figure 4b) used for the optimization process, with corresponding Rw.  
The results in terms of STL for each of the three groups of simulations are represented in Figures 5-7, and the \(\Delta\)Rw values calculated with respect to the benchmark solution value for each configuration are reported in Table 2. Since the results cannot be explicitly reported for confidentiality reasons, only the 10 dB increase is reported on the ordinate axis, so that a comparison between the curves is still possible.  
Figure 5: STL numerical results keeping the nominal dimensions for \(\mathrm{a_{cell}}\) and \(\mathrm{tck_{panel}}\) and varying \(\mathrm{h_{cell}}\).  
Figure 6: STL numerical results keeping the nominal dimensions for \(\mathrm{tck_{panel}}\) and \(\mathrm{h_{cell}}\) and varying \(\mathrm{a_{cell}}\).  
The configuration reported in Table 3 was chosen as the best one in terms of both STL and \(\Delta\)Rw.
# Rio Madeira HVDC System: Commissioning of the Ground Electrode for Bipole 2 at Porto Velho
## 5 Electrode Commissioning  
A 37 km long extension to the original 21 km electrode line was built for connecting the new electrode to the converter substation. The energization of the new electrode was performed in steps of the nominal current--10%, 20%, 30%, 40%, 50%, and 90%. The following measurements were then collected:  
* At the converter substation: DC currents in the neutrals of the 500 kV transformers and the potential difference between the substation grounding grid and the DC neutral bar (connected to the electrode line);
* At the electrode site: step potentials close to the wells' perimeters, and touch potentials on metallic structures (towers of the electrode line and fences at the switching house and the site perimeter);
* Outside the electrode site: touch potentials on metallic fences, DC currents in the neutrals of distribution transformers, and soil surface potential profiles (in two directions, West and South).  
For each measurement type, a spreadsheet was filled out, with general information (date, weather data etc.), specific data (electrode current during the measurement), and the required data: time and location of the measuring point (UTM coordinates) and measured parameter.  
At the converter substation, the highest measured neutral current was on one of the bipole 1 converter transformers, with 7.8 A (extrapolated to the nominal current). ABB, the manufacturer of this transformer, reported that the pickup DC current of the saturation protection at the neutral of its transformers is set to 32.5 A. The maximum neutral currents for the bipole 2 transformers are also of this order of magnitude; therefore, the measured neutral currents were significantly lower than the allowed maximum values, indicating a good safety margin and no risk of saturation of the converter transformers.  
The next items describe the main findings of the commissioning test. All the parameters here presented were referenced to the electrode nominal current of 2625 A.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 4 A Delay-Constrained Duty-Cycle Scheduling Algorithm
### Group-Based Forwarding Algorithm
#### 4.1.2 Data Transmission Phase  
In this phase, data packets are transmitted to the sink. When an event is detected near a node, this node packs information into a data packet and transmits the packet to the first awake node among its PFCs.  
Generally, sensor nodes wake up according to a Poisson process or when an event is detected. With Poisson wake-up scheduling, we divide the half-line \([0,\infty)\) of the working timeline of nodes into subintervals of length \(T\). Each node independently wakes up once at a random time during a duty cycle interval and broadcasts a beacon message to announce that it is available to relay packets. Thus, the wake-up rate for nodes is \(\dfrac{1}{T}\). The time that nodes wake up in a duty cycle interval follows a uniform distribution. The duty cycle interval \(T\) is divided into \(\epsilon\) equal time slots, where \(\epsilon\) is the number of subintervals, \(\dfrac{T}{\epsilon}\). The wake-up time for each node in the \(l^{\text{th}}\) duty cycle interval (i.e., \([(l-1)\times T,l\times T)\), \(l=1,2,3,\ldots\)) is calculated as follows:  
\[t_{l^{\text{th}}}=(l-1)T+\dfrac{rand(0,\epsilon-1)}{\epsilon}T \tag{2}\]  
where \(rand(0,\epsilon-1)\) returns a uniformly distributed random integer number in the range \([0,\epsilon-1]\).  
When detecting an event, a node becomes a transmitter and waits for beacon messages from its PFCs. Then, this transmitter relays data packets to the first awake node among its potential forwarders and waits for an acknowledge (ACK) message from the receiver. When the ACK is successfully received, the transmission ends. Otherwise, the transmitter backs off for a random period, and then retransmits the packet to the receiver. Packets are discarded when the number of retransmissions exceeds a given upper bound.  
Figure 2 shows an example of transmission between nodes A and B. Node B sends a beacon message after waking up and stays in the idle state, in which the node stays awake but does not transmit or receive packets. After receiving the beacon, node A checks whether or not node B is a PFC of node A. If so, node A forwards data packets to node B, which is the first awake node among node A's PFCs, and then waits for an ACK from node B.
## 2 Materials and Methods
#### 2.1.5 MOOSRA (Multi-Objective Optimization Based on Simple Ratio Analysis) Technique  
MOOSRA, developed by [PERSON] et al. in 2012, is preferred due to its short calculation time, few mathematical operations, high reliability, and simple applicability [68]. While the first two steps of the method are similar to the MOORA technique, it differs in the 3 rd step by comparing the maximization and minimization. The MOOSRA technique is implemented with four steps by using Equations (27)-(29) [68,69].  
**Step 1. Creating the decision matrix** The matrix X in Equation (1), which consists of m rows and n columns, is created.  
**Step 2. Normalizing the decision matrix** The normalized matrix is derived from decision matrix (1) using Equation (27)  
\[x_{ij}^{\star}=\frac{x_{ij}}{\sum_{i=1}^{m}x_{ij}^{2}} \tag{27}\]  
**Step 3. Weighting the normalized matrix**: The normalized matrix is weighted with Equation (28). In this context, the normalized matrix is multiplied by the weights (\(w_{j}\)) obtained by the PSI method.  
\[a_{ij}=x_{ij}^{\star}w_{j} \tag{28}\]  
**Step 4. Calculating the benefit scores of the alternatives:** The total benefit score (\(Y_{i}\)) of each alternative is calculated using Formula (29). The results are sorted from largest to smallest.  
\[Y_{i}=\frac{\sum_{j}^{g}x_{ij}^{\star}w_{j}}{\sum_{j=g+1}^{g}x_{ij}^{\star}w_{ j}} \tag{29}\]  
\(j=1,2,\ldots,\ g\) for maximization,  
\(j^{\prime}=g+1,\ g+2,\ \ldots,\ n\) for minimization  
The process is completed by sorting the \(Y_{i}\) values of the options from biggest to smallest.
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 3 Propagation Modeling
### Optimization  
Since there are multiple locations on each floor at which the RSSI is measured, a multi-objective min-max optimization is used to find the optimum model parameters. For the log-distance model, the overall fading (\(X_{g_{i}}\)) at each location (\(i\)) is optimized as an independent variable. Constant loss (\(L_{E}\)) is also added to the optimization functions, which compensates for any possible additional losses in the system such as cable loss, antenna mismatch, or polarization mismatch. Therefore, \(L_{E}\) and \(n/n_{A231}\) are not changing in any of the objective functions. The overall form of the objective functions and their constraints are formulated for the log-distance model in Equation (4). Where \(M_{i}\) is the summation of transmission power, antenna gains, and the measured \(RSSI_{i}\) at location \(i\)th (\(M_{i}<0\)). A similar optimization is also applied to COST231 and its adjusted variation, except \(F_{i}\) in the adjusted COST231 is a function \(n_{A231},\ L_{C}\) and \(\alpha_{j}\).  
\[\begin{split}\min_{n,X_{g_{i}};L_{E}}\max_{F_{i}}F_{i}(n,X_{g_{i }},L_{E})\left\{\begin{array}{c}1\leq n\leq 8\\ -20\leq L_{E}\leq 20\\ -20\leq X_{g_{i}}\leq 20\end{array}\right.\\ F_{i}(n,X_{g_{i}},L_{E})=-PL_{0}-M_{i}-L_{E}-10n\times\log\left(d\right)-X_{ g_{i}}\end{split} \tag{4}\]
## 6 Future Research Directions
### Big Data Analytics for 6G  
Future wireless communication will need higher data speeds and a significantly more dependable transmission link in order to keep up with the growth of multimedia services while maintaining a decent level of service quality. In order to increase data speeds, which are predicted to be 100-1000 times faster than 5G, 6G will likely utilize a higher frequency spectrum than prior generations. By stacking algorithms and identifying patterns in even the most complex and abstract data, deep learning in big data analysis imitates human learning processes [35].
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 7 Conclusions  
In this paper, we propose EE-LoRa, an algorithm for SF selection and power control in LoRaWAN networks with multiple gateways. Our algorithm includes two steps, where we first derive the optimal SF that maximizes the network's energy efficiency, defined as the ratio between the throughput and energy consumption. We make use of the Dinkelbach method to transform the fractional problem into a parameterized one. We solve this problem using the frmion solver after ensuring that we work in the convexity domain. After finding the optimal node distribution among different SFs, we use the adaptive SNR threshold method to deduce the new SF at each node. In the second stage, we apply a power control to configure the TPs. The simulation results showed that EE-LoRa enhances the energy efficiency of LoRaWAN networks compared to legacy LoRaWAN and relevant state-of-the-art algorithms.  
Conception and design of the study: A.L., S.L., A.E.S., and M.E.H.; acquisition of data and writing Matlab code: A.L.; analysis and interpretation of data: A.L., S.L., A.E.S., and M.E.H.; drafting the manuscript: A.L.; reviewing and editing: S.L., A.E.S., M.E.H. Approval of the version of the manuscript to be submitted: A.L., S.L., A.E.S., and M.E.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Saint Joseph University of Beirut and the Lebanese University.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Idle Current** & \(I_{id}\)**/2** & \(I_{id}\)**/4** & \(I_{id}\)**/8** & \(I_{id}\)**/16** \\ \hline \hline \% of energy efficiency improvement & 5.16 & 9.05 & 10.99 & 11.6 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Energy efficiency improvement by reducing the idle current when \(N_{t}=4000\) nodes.  
Figure 13: Energy efficiency variation with the minimum TP.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the reported results of this paper are available upon request.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Results and Discussion
### Proximate Composition  
Results regarding the chemical composition of the tested seeds and algae are presented in Table 2. The tested plant materials showed similar levels of humidity not exceeding 11%. Dry matter content in the seeds ranged from 91.17% (fenugreek) to 96.38% (white sesame). Across all analyzed seed samples, the protein content ranged from 16.33 g/100 g (milk thistle) to 24.58 g/100 g (almond). However, its content was almost three times lower compared to the studied algae, where it ranged from 55.40 g/100 g (chlorella) to 69.35 g/100 g (spirulina). The results of our study show the same range of protein content as in the report by [PERSON] et al. (2018), and [PERSON] et al. (2018) noted that almond presents similar overall nutrient profiles even when comparing different varieties, years of production and growing regions. Furthermore, the protein content in milk thistle seeds in this study was lower than the data obtained by [PERSON] et al. (2018). This varied protein content may be related to seed maturity stage and environmental conditions. The key ingredients plants need for protein production are glucose and nitrates, which are taken up from the soil by the roots. When glucose and nitrates are joined, they produce amino acids. During protein synthesis, multiple amino acids are bound together to make proteins ([PERSON] _et al._, 2018). The fat content in the analyzed samples differed, ranging between 5.13 g/100 g (fenugreek) and 52.25 g/100 g (almond).  
The chosen plant seeds and algae were characterized by very diverse carbohydrate contents, ranging from 13.94 g/100 g (almond) to 88.28 g/100 g (eggfruit). Similar carbohydrate contents were observed by [PERSON] et al. (2018), who analyzed fenugreek seeds. Moreover, based on data presented by [PERSON] and [PERSON] (2018), presoluble carbohydrates constitute a major part of the total carbohydrate content, whereas, in higher plants, insoluble carbohydrates are a major constituent of total carbohydrates.
# On Ad Hoc Communication in Industrial Environments
## 3 Comparing Methods for the Extraction of NEPs
### Average Duration of Encounters  
The average duration of encounters \(D_{\varnothing}^{XX}(r,v)\) is mostly dependent on the mobility of the nodes. [PERSON] et al. [28] assumed nodes stayed within the range of an access point for a prolonged duration. This assumption minimizes the influence of mobility on the encounter pattern, which is a valid assumption for the examined network traces. In the examined campus networks, students listen to lectures or visit libraries and similar locations for a duration of \(\geq 1\) h. This assumption, however, is not transferable to the examined use case of AGVs in an industrial environment; therefore, the effect of mobility on the two observation methods must be considered.  
The proposed models reduce the dependencies of the encounter duration of two nodes to the speed \(v\) of the nodes and the communication range \(r\). On average, a moving node passes the static communication range of an access point along a path of length \(\frac{\pi}{2}r\) (see Figure 11) and therefore for a time of \(D_{AP}=\frac{\pi}{2}\frac{r}{v}\). This average only accounts for a node passing an AP range. If the destination of the node is within the AP range, the average traveled distance within communication range (average destination is at the AP position; distance to reach the AP and subsequently leave range is \(2r\)) changes to \(2r\). It is assumed that \(\sqrt{A}\gg r\); therefore, this special case is subsequently not considered. An encounter between any two nodes persists as long as both are in range of the access point. At the point in time at which any node enters the range, every other node that already is connected to the AP will leave the range after \(\delta t\in]0,D_{AP}[\). Therefore, the average encounter duration of two nodes can be reduced to \(\frac{D_{AP}}{2}\):  
\[D_{\varnothing}^{NT}(r,v)=\begin{cases}\frac{\pi}{4}\frac{r}{v},&\text{if }N_{AP}A_{r}\leq A \\ \\ \frac{\pi}{4}\frac{\sqrt{\frac{A}{N_{AP}}}}{v},&\text{otherwise}\end{cases} \tag{3}\]  
Analogous to the model of \(N_{\varnothing}^{NT}\), the case of complete coverage has to be considered when calculating the duration. This is done with the scaled access point communication radius \(r_{AP}\).  
Determining the encounter duration of two mobile nodes is challenging. Therefore, an approximate for the average encounter duration was determined via a fit to data from extensive simulation:  
\[D_{\varnothing}^{TP}(r,v)=\frac{r^{\lambda}}{v} \tag{4}\]  
For typical values of \(A\) the best fit was generated with \(\lambda=1.11\). Both models are highly simplified. For the model of NEP acquisition via network trace, a dependence on \(N_{AP}\) can be observed. The duration of encounters in a real ad hoc network, however, does not depend on this parameter.
## 4 Experimental Results and Analysis
### Experimental Environment and Experimental Parameter Settings  
The hardware and software configurations used for the experiment are listed in Table 4, and the hyperparameter settings are listed in Table 5, where Optimizer denotes the optimizer, Learning rate denotes the learning rate, Batch size denotes the number of training samples per batch, and Epochs denotes the number of training rounds. In order to fully evaluate the effectiveness and efficiency of the proposed method, the relevant hyperparameters for adaptive reduction of learning rate are introduced: Patience is the automatic reduction of learning rate when the validation loss still does not decrease after 3 Epochs; Factor is the learning rate that is reduced by 0.1 times each time; and Min learning rate is the lower limit of the learning rate, i.e., the learning rate can be reduced to the Min learning rate is the lower limit of learning rate, i.e., the lowest value that learning rate can be reduced to.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Software and Hardware Type** & **Value** \\ \hline System & Windows10 \\ CPU & AMD Ryzen 7 5800H \\ GPU & NVIDIA GeForce RTX 3060 \\ Programming language & Python3.8 \\ Deep Learning Framework & Keras2.4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental environment configuration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Optimizer & Adam \\ Learning rate & 0.001 \\ Batch size & 64 \\ Epochs & 50 \\ Factor & 0.1 \\ Patience & 3 \\ Min learning rate & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameter settings for the base learner.