esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System and Channel Models  
A diagram of an FSO communication system with modulation and demodulation schemes is shown in Figure 1. The electrical signal is first modulated using the modulation scheme, and then converted into an optical signal by a laser driver. Following this, the optical signal is transmitted to the receiver through atmospheric turbulence. Refraction, reflection, scattering and other influences arising from the variation in the refractive index of the turbulence environment result in fluctuations in the phase and amplitude, referred to as scintillation. The received signal is then converted into an electrical signal by a PIN photodetector and finally demodulated using the corresponding demodulation model. The signal after demodulation can be written as  
\[y=IRA\zeta m(t)+n(t), \tag{1}\]  
where \(R\) is the responsivity of the photodetector, \(I\) is the half peak of the light intensity, and \(\zeta\) is the modulation index. In addition, \(A\) is the amplitude of the subcarrier, \(m(t)\) and \(n(t)\) are the electrical signal and the additive white Gaussian noise subject to a distribution mboxemphN(0, \(\sigma^{2}\)), respectively, and \(\sigma^{2}\) is the noise variance.  
In order to give a comprehensive analysis of the influence of different types of noise on the communication system, we consider background noise, thermal noise and quantum noise in this study. Hence, the total noise is expressed as  
\[\sigma^{2}=\sigma_{bg}^{2}+\sigma_{T_{e}}^{2}+\sigma_{Quantum}^{2}, \tag{2}\]  
where \(\sigma_{bg}^{2}\), \(\sigma_{T_{e}}^{2}\), \(\sigma_{Quantum}^{2}\) denote the background noise, thermal noise, and quantum noise, respectively. Note that the above-mentioned forms of noise can also be expressed as, \(\sigma_{bg}^{2}=\dfrac{2 qI_{bg}R_{b}}{RI_{o}^{2}}\), \(\sigma_{T_{e}}^{2}=\dfrac{4k_{1}T_{e}R_{b}}{RR_{L}I_{o}^{2}}\), \(\sigma_{Quantum}^{2}=\dfrac{2 qR_{b}}{RI_{o}}\)[28], where \(q\) is the elementary charge, \(I_{bg}\) is the background irradiance, \(R_{b}\) is the symbol rate, \(I_{o}\) is average received irradiance, \(k_{1}\) is the Boltzmann constant, \(T_{e}\) is the temperature and \(R_{L}\) is the load resistance of the receiver circuit. Based on the signal intensity and noise, the signal-to-noise ratio (SNR) can be given as \(SNR=\dfrac{(IRA\zeta)^{2}P_{m}}{\sigma^{2}}\), where \(P_{m}\) is the subcarrier signal power [11].  
As mentioned above, the optical signal will be scattered as it passes through atmospheric turbulence. For the case of different weather conditions in the transmission link, a lognormal distribution model is employed to characterize the atmospheric turbulence under the weak regime in this study. The Gamma-Gamma distribution model is also introduced to characterize the turbulent channel, as it has been shown to give high accuracy under moderate-to-strong conditions.
## 5 Deployment Challenges
### Secured Communications Between Virtual Client and Surrogate  
In our proposed architecture and unlike the walled-garden model used to implement virtual services in enterprises [29], the surrogates are located at the edge of the public Internet and not necessarily administrated by the same ISP that is providing Internet access to the user. Security becomes a more critical concern and a scalable design should provide communication encryption as required and also avoid multiple authentication (e.g., by the ISP and the CP) through a suitable Single Sign-On (SSO) service [30]. Similarly, the user's profile, authorization and billing information could be shared between these two parties.
# Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
## 5 CDS Implementation Case Study: OFDM Long-Haul Fiber Optic Communications
### Simulation Results and Discussions  
#### 5.2.1 Pre-Adaptive Actions  
The BER of a conventional fiber optic communications system (i.e., without CDS, see Figure 3) is shown in Figure 9. We consider \(1.03\times 10^{-2}\) (with 14.3% OH) as the FEC threshold or _Threshold_ (\(m=0\)) for HD [25] (see Table 3). Thus, we can see from Figure 9 that the maximum achievable data rate is 208 Gb/s. The CDS uses 208 Gb/s for \(j=1\) (i.e., first data rate, \(R_{1}^{1}=208\) Gb/s). Also, we assume data rate variation by discretization step \(d=4\) Gb/sand a step of 1 dB for the launch power. Therefore, the data  
\begin{table}
\begin{tabular}{c c} \hline \hline Simulation Parameters & Value \\ \hline Fiber dispersion coefficient (\(\beta_{2}\)) & \(-22.1\) ps\({}^{2}\)/km \\ Nonlinear coefficient (\(\gamma\)) & 1.1 W\({}^{-1}\)km\({}^{-1}\) \\ Fiber loss coefficient (\(\alpha\)) & 0.2 dB/km \\ Number of fiber spans (\(N\)) & 20 \\ Span length (\(L_{span}\)) & 80 km (1600 km total length) \\ Noise figure (\(NF\)) & 4.77 dB \\ Number of OFDM subcarriers per frame & 256 \\ Subcarrier modulation & QAM-16 \\ Number of data frames & 128,000 \\ Number of frames for modeling & 100,000 \\ Number of frames for validation & 28,000 \\ Discretization step \(d\) & 4 Gb/s \\ \(PF_{i=0}^{k}\) & 2 \\ \(PF_{i=1}^{k}\) & 5 \\ Complexity threshold & \(10^{6}\) discretized cells \\ \(\max(F_{i=0}^{total,k})\), \(k=1,2,3,4\) & 3600 discretized cells \\ \(\max(F_{i=1}^{total,k})\), \(k=5,6,7,8,9\) & 518,400 discretized cells \\ \(\underset{m=2}{\text{total,}k}\), \(k=10\) & 74,649,600 discretized cells \\ \(\underset{m=0}{\text{Threshold}^{m=0}}\) or FEC threshold & \(1.03\times 10^{-2}\) \\ \(\underset{m=1}{\text{Threshold}^{m=1}}\) or FEC threshold & \(4.7\times 10^{-3}\) \\ Fiber type & Standard single mode fiber (SMF) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical simulation parameters of the OFDM fiber optic communications system and CDS.  
rate for PAC \(k=0\) is \(R_{r\!\!\!rf}=204\) Gb/s (see Equation (26)). The optimum launch power can be also found using the pre-adaptive method this is already used in the conventional system.
# Peak Energy Reduction in Flow Shop including Switch-Off Policies and Battery Storage
## 3 Reference Context and Proposed Approach
The tasks performed by the machine controller when an item entered into the system have been represented in Figure 1.  
When a part leaves the machine, the machine controller updates the _Plot_ reducing the value _Pi_, and verifies if the _Plot_ is lower than the _Pmax_ to start the battery charger with the speed _Sb_ computed as in the Expression 1. To improve the efficiency of the energy storage system, the introduction of switch-off policies is considered. In the literature, there are three main switch-off policies proposed: the Upstream, Downstream, and Upstream and Downstream policies [34]. The Upstream policy works on the upstream buffer; when the buffer tends to empty, the machine switches off. The machine switches on when the upstream buffer exceeds a defined level. This policy needs two parameters to set: the low and high levels of the buffer to switch off and on the machine. The Downstream policy works on the downstream buffer. The machine switches off when the downstream buffer reaches a defined level, and it switches on when the level is under a low level; moreover, this policy needs to set two parameters. In this paper, the Downstream policy is not considered, because this policy does not take into account the final machine of the flow line reducing significantly the potential reduction of power used. The Upstream and Downstream policy combines the upstream and downstream policies and needs to set four parameters.
###### Abstract  
Insulin resistance is closely associated with metabolic diseases such as type 2 diabetes and cardiovascular disease, and the resting heart rate (RHR) is an important marker of the autonomic nervous system's status. This study aimed to investigate the association between the RHR and the homeostatic model assessment of insulin resistance (HOMA-IR) in Korean adults. Using data from the 2019 Korea National Health and Nutrition Examination Survey (KNHANES 2019), we analyzed 6118 adults and classified the participants into four groups based on their RHR (\(\leq\)67, 68-71, 72-79, and \(\geq\)80 beats/min [bpm]). Logistic regression analysis considering demographic, lifestyle, and biochemical factors revealed that the odds of having a HOMA-IR of 2.5 or higher were significantly increased in the higher RHR groups. Specifically, compared to the \(\leq\)67 bpm group, the 68-71, 72-79, and \(\geq\)80 bpm groups had increased odds of 1.277, 1.599, and 1.919 times, respectively. These results are expected to contribute to the development of strategies for the early diagnosis and management of metabolic diseases through the RHR, and further research is needed to deepen the understanding of the physiological mechanisms of this relationship, including the management of the RHR through lifestyle modification and regular exercise and the effect of reducing insulin resistance.  
resing heart rate; insulin resistance; HOMA-IR; metabolic disease; KNHANES +
Footnote †: journal: Medical  
HOMA-IR in individuals with a high RHR using data from the 2019 Korea National Health and Nutrition Examination Survey [13]. Our intention was to analyze the feasibility of using the RHR as an early marker of insulin resistance and provide practical evidence contributing to the early diagnosis and management of metabolic diseases. Given this background, this study utilized KNHANES 2019 data to investigate whether the HOMA-IR is associated with RHR levels.
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 4 Experiments and Result Analysis
### Evaluation of the Impact of the Attention Mechanism on the Model
We calculated the precision, recall and F1-Score of the two models on the test set, as shown in Table 2.  
The table more clearly reflects the improvement in model performance by the attention mechanism SE module compared with the confusion matrix, with 3D-SE-Net achieving an average precision and recall of 95% for the recognition of these four driving behaviors. In the 3D-CNN model, the F1-Score value for the behavior 'Talking' was only 74%, while in the 3D-SE-Net model it increased to 92%, and the F1-Score values for 'Sleepy-nodding' and 'Yawning' also increased by more than 10 percentage points, suggesting that the attention mechanism can be effective in the recognition of various driving behaviors. To show more intuitively the effect of the attention mechanism on the optimization of the model, we visualized the feature maps using Grad-CAM [33]. Instead of segmenting the face image, we retooled the network to feed the whole face image into both models, which was done to show our visualized results more clearly. As can be seen in Figure 9, the 3D-SE-Net with the attention mechanism module added maintains good detection in both bright and dark lighting conditions, while the 3D-CNN without the attention mechanism performs relatively poorly in dark conditions. In addition, the heat map shows that the facial detection under the condition of wearing sunglasses is not satisfactory with or without the attention mechanism module. We analyze that this is due to the lack of training data for the model and the small number of sunglasses in the FDF dataset makes the 3D convolutional neural network overfitting. The model cannot accurately identify where the eyes are located, which is detrimental to the use of EAR. For this phenomenon using infrared camera to collect data may be a better solution. In the control group with and without glasses, the recognition effect of both models decreases to some extent, but 3D-SE-Net still outperforms 3D-CNN. Thus, we conclude that both lighting conditions and facial occlusion affect the recognition effect of the models to different degrees. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**3D-CNN (Without Attention)**} & \multicolumn{3}{c}{**3D-SE-Net (With Attention)**} \\ \hline  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Normal & 0.94 & 0.93 & 0.95 & 0.99 & 0.99 & 0.99 \\ Talking & 0.74 & 0.74 & 0.74 & 0.91 & 0.94 & 0.92 \\ Sleepy-nodding & 0.75 & 0.79 & 0.77 & 0.92 & 0.96 & 0.94 \\ Yawning & 0.83 & 0.82 & 0.82 & 0.97 & 0.91 & 0.94 \\ Average & 0.82 & 0.82 & **0.82** & 0.95 & 0.95 & **0.95** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation results on the FDF test set. Figure 9: Feature map processing results for different drivers and driving environments using Grad-CAM.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 4 Modelling of Quadcopters  
Modelling quadcopters is a crucial aspect of designing them effectively. The better the quadcopter model is, the closer it will be to its real-world implementation. Therefore, it is essential to explore quadcopters' configurations, aspects, and their mathematical motion equations.
## 5 Results and Discussion  
The NIST test suite is a statistical package that includes 17 tests designed to evaluate the randomness of arbitrarily long binary sequences created by cryptographic random or pseudorandom number generators based on hardware or software [33]. These tests look for several sorts of non-randomness present in sequences. The probability value (\(p\)-value) is used in these tests to determine the randomness of the sequence. The sequence is non-random if the estimated \(p\)-value is less than 0.01; otherwise, it is inferred as random [34; 35]. To pass the probability test, the probability value should be between 0.99 and 0.0094392. To check the randomness of the data, the minimum criterion is 100 binary sequences for 1,000,000 bits. Thus, we use 100 binary sequences of length 73,465,856 bits for each sequence evaluated and discussed. The proposed key generation algorithm results are transformed into binary because the NIST statistical test suite can consider the data only in binary form.  
Secondly, according to NIST [32], the test for randomness is to be performed in two ways: first, as a test of the proportion of sequences passing a statistical test; and, second, as a test of the distribution of the \(p\)-values. In addition, according to the standardization of the NIST test, if the proportion value is equal to 96 sequences out of 100 passing sequences, the test will be considered a pass, otherwise as a fail. The proposed design result is presented in Table 6. The minimum pass rate for the Random Excursions (Variant) test is approximately 50 for a sample of 100 binary sequences. The minimum pass rate for the random excursion is approximately 51 for a sample of 100 binary sequences. We can see in Table 6 that the number of sequences that have managed to pass each test is greater than the minimum rate. Therefore, the proposed Omega network-based pseudorandom key generation algorithm passed all NIST statistical tests.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Name** & **Probability Test (\(p\)-Value)** & **Proportion** & **Result** \\ \hline Frequency & 0.699313 & 98/100 & Pass \\ Block-frequency & 0.971699 & 99/100 & Pass \\ Cumulative SUM (1) & 0.759756 & 97/100 & Pass \\ Cumulative SUM (2) & 0.851383 & 98/100 & Pass \\ Runs & 0.534146 & 100/100 & Pass \\ Longest runs & 0.554420 & 99/100 & Pass \\ Rank & 0.275709 & 99/100 & Pass \\ FFT & 0.924076 & 100/100 & Pass \\ Non-overlapping & 0.262249 & 98/100 & Pass \\ Overlapping & 0.401199 & 100/100 & Pass \\ Universal & 0.334538 & 99/100 & Pass \\ Approximate Entropy & 0.494392 & 99/100 & Pass \\ Random Extrusions & 0.946308 & 51/51 & Pass \\ Random Extrusions Variant & 0.383827 & 50/51 & Pass \\ Serial (1) & 0.383827 & 98/100 & Pass \\ Serial (2) & 0.867692 & 98/100 & Pass \\ Linear Complexity & 0.935716 & 99/100 & Pass \\ \hline \hline \end{tabular}
\end{table}
Table 6: NIST test results.
## 3 Environmental Applications of Organoclays
### Water Pollution
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Clay** & **Modifier** & **Methodology** & **Application** & **Efficiency** & **Reference** \\ \hline Montmorillonite (Nigeria) & Hexadecyltrimethylammonium bromide & Cation exchange & Phenol Removal & \(\begin{array}{c}5.55\pm 1.18\,\mathrm{mg}\,\mathrm{L}^{-1}\\ \text{to Below}\\ \text{Detectable level}\end{array}\) & [57] \\ \hline Bentonite (Iran) & Hexadecyltrimethylammonium & Cation exchange & Dye Removal & 50 mg mg\({}^{-1}\) & [58] \\ \hline  & & 2014 & & \\ \hline Montmorillonite (Sigma Aldrich) & Dodecyltrimethylammonium bromide & & & \\ \hline Bentonite (ISA) & Hexadecyltrimethylammonium bromide & Cation exchange & Phenol Removal & 96.3\% & \\ \hline Bentonite (ISA) & Dodecyldimethylammonium bromide & & & 99.5\% & \\ \hline  & & 2015 & & \\ \hline Bentonite (China) & dodecyltrimethylammonium bromide & Cation exchange & Phenol Removal & 585.8 mg g\({}^{-1}\) & [60] \\ \hline Bentonite (Western Australia) & Arquad® 2 HT-75 and Palmic acid & Grafting & Heavy metal removal & Cu 7 mg g\({}^{-1}\) & [61] \\ \hline Na-montmorillonite (China) & Bis-N,N,N,-hexadecyldimethyl-phenylenediammonium dibromide & Cation exchange & Phenol Removal & 124 mg g\({}^{-1}\) & [62] \\ \hline Bentonite Clay (Germany) & hexadecyltrimethylammonium chloride & Cation exchange & Dye Removal & 99.99\% & \\ \hline Bentonite Clay (Algeria) & Bis-imidazolium salts & Cation exchange & Dye Removal & 108 mg g\({}^{-1}\) & [64] \\ \hline Montmorillonite (China) & Dioctadecyldimethylammonium chloride & Cation exchange & Phenol and Heavy Metal Removal & 93.1\% & \\ \hline Saponite (Spain) & Hexadecyltrimethylammonium bromide Aminopropyltriethoxysilane & Cation exchange & Caffeine Removal & 33.39 mg g\({}^{-1}\) & [66] \\ \hline  & & 2016 & & \\ \hline Clay (Morocco) & HNO\({}_{3}\) acid treatment & Adsorption & Heavy metal removal & 1.076 mg g\({}^{-1}\) & [67] \\ \hline Na-montmorillonite (China) & 1,10-didecycl-4,40-trimethylene bispyridinium bromide & Cation exchange & Phenol Removal & 222.2 mg g\({}^{-1}\) & [68] \\ \hline  & & & & \\ \hline  & Cctadecyltrimethylammonium bromide (ODTMA, organic modifier) & & & \\ \hline  & & & & \\ \hline  & & & & \\ \hline Bentonite & & & & \\ \hline Chitosan (Egypt) & Trimethylammonium bromide & Cation exchange & Dye Removal & 78\% & [70] \\ \hline  & Tetradecyl trimethyl ammonium bromide (Italy) & & & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & & \\  & & & & \\  & & & \\  & & & & \\  & & & \\  & & & \\  & & &
## 2 Software-Defined Networking
### SDN, Active and Programmable Networks  
In the past, various technologies were developed to enable programmability in communication networks. Active networks (AN) [17] were developed in the 1990s. The basic idea of AN is to inject programs into data packets. Switches extract and execute programs from data packets. With this method, new routing mechanisms and network services can be implemented without the modification of the forwarding hardware. However, this approach has not prevailed, due to security concerns and performance issues that can occur on executing programs in the forwarding devices. For example, an attacker can inject malicious programs into network packets and forward them into the network.  
Programmable networks (PN) [18, 19] also provide a means for programmability in the network by executing programs on packets similar to AN. However, programs are not included in the data packets as with AN. The programs are installed inside the network nodes, which execute the programs on the packets. This clearly reduced security concerns that occur with AN, because a network node only accepts programs after a prior signaling and node setup. Various proposals for PN were made in the past. For example, xbind [20] was proposed for asynchronous transfer mode (ATM) networks that are tailored towards quality of service (QoS) and multimedia applications. The aim of xbind was to overcome the complexity associated with ATM and to allow easier service creation by separating the control algorithms from the ATM protocol, which was enabled by the programmability of the forwarding devices through programming languages.  
Both approaches, AN and PN, introduce new flexibility by allowing programs to be executed within the network. They are more flexible than an OpenFlow-based SDN approach, because they allow one to program the data plane in an extensible way. New data plane functionality can be implemented through programs that are either part of data packets with AN or installed inside network nodes with PN. An OpenFlow-based SDN approach cannot extend the data plane functionality without an upgrade of the switches, due to the fact that OpenFlow only provides a fixed set of network operations. The OpenFlow controller is only able to program the switch with its supported set of operations.
# An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications
## 1 Introduction
The use of a lookup-table (LUT) that implements the inverse function against the nonlinearity of the LED constitutes the simplest predistorter [11]. The weight required to compensate for distortion from the nonlinearity of the LED is estimated and stored in the LUT. The transmitted signal is then predistorted by multiplying the weight in the LUT closest to this signal. Based on the LUT predistorter, an adaptive algorithm was used in a previous study to dynamically learn the weights stored in the LUT by training the normalized least mean squares (NLMS) error between the transmitted signal and the distorted signal as criterion and train with a stochastic-gradient-based approach [12], and thereafter the NLMS-based predistorter was capable of tracking changes in LED characteristics. In another study, by focusing on the fact that the distortion resulting from the LED is nonlinear, a nonlinear transformation of the input was introduced by a Chebyshev polynomial expansion and the weights were also trained by a stochastic-gradient-based approach [13]. Fundamentally, using a predistorter to compensate for the LED nonlinear input power to the output illumination function \(\mathcal{I}(p,\mathbf{\Theta})\) consists of cascading a block that exhibits its inverse function \(\mathcal{I}^{-1}(v,\mathbf{\Theta})\) in front of the LED, as shown in Figure 1. The major issue that particularly needs to be addressed is how to accurately approximate and train the inverse function \(\mathcal{I}^{-1}(v,\mathbf{\Theta})\) when the LED time-variant nonlinearity function \(\mathcal{I}(p,\mathbf{\Theta})\) is changed due to temperature variation, aging, or other physical changes denoted by a time-variant state vector \(\mathbf{\Theta}(t_{i})\) at time instant \(t_{i}\). That is, the LED nonlinearity should be dynamically learned and compensated for by using its approximated inverse function with an adaptive predistorter. As for function approximation, artificial neural networks (ANNs) are considered to be a powerful tool according to the associated and well-known universal approximation theory [14]. It was the recognition of this power that motivated the consideration of an ANN-based predistortion algorithm in the present study. In addition, an adaptive training method was designed to make the proposed ANN-based predistorter capable of tracking the LED characteristics due to temperature variation and aging. Meanwhile, although the existing works reported in the literature were mainly based on simulations without experimental verifications [11; 12; 13], in the present study, laboratory experiments utilizing physical devices were conducted to verify the theoretical analysis of the predistortion algorithm. The remainder of this paper is organized as follows. Section 2 presents a system model of adaptive predistorters for OFDM-based VLC. The proposed ANN-based predistortion is then presented in Section 3. Section 4 describes the simulation results with experimental verifications. Finally, the conclusions of the study are presented in Section 5.