esa-sceva/satcom-chunk-collection huggingface dataset:
# Field-Based Optimal Placement of Antennas for Body-Worn Wireless Sensors
## 2 Methods
### Statement of the Optimal Design Problem  
The optimal design problem can be posed as follows:  
_Having fixed the position of both a head sensor (antenna 2) and an arm sensor (antenna 3), find the position of the chest sensor (antenna 1) minimizing both \(S_{21}\) and \(S_{31}\) scattering parameters in the frequency band \(B\) between \(f_{L}\) and \(f_{H}\) (for 2.4 GHz ISM band we assumed that \(f_{L}=2.4\) GHz and \(f_{H}=2.5\) GHz while for 5 GHz ISM band \(f_{L}=5.7\) GHz and \(f_{H}=5.9\) GHz)._  
The position of antenna 1 is defined by distance \(d\), height \(z_{p}\) and azimuthal angle \(\varphi_{p}\). Therefore, for a given frequency \(f\), the scattering parameters depend on the triplet of design variables (\(d\), \(z_{p}\), \(\varphi_{p}\)); the formal statement follows:  
_Starting from a guess solution \(x_{0}\), find_  
\[\inf_{x\in\Omega}\sup_{f\in B}\left[S_{21}\left(x,f\right),S_{31}\left(x,f \right)\right]\,\ x=\left(d,z_{p},\varphi_{p}\right)\,\ \Omega\subset\Re^{3}\,\ B=[f_{L},f_{H}] \tag{2}\]  
subject to the solution of the field analysis problem; in Equation (2) \(\Omega\) is the feasible region _i.e._, the set of admissible positions for antenna 1 on the chest surface (Figure 5). Apparently, a min-max problem is originated: given a feasible solution \(x\), the field analysis is performed for a discrete set of frequencies \(f_{k}\), \(k=1\), \(n_{f}\) (where \(n_{f}\) is equal to 227 for given FFT parameter of FDTD method) within the bandwidth \(B\) assuming the six-cylinder human body model. Subsequently, \(S_{21}\) and \(S_{31}\) scattering parameters are computed for each frequency \(f_{k}\) in the set. Eventually, the objective function value is the highest between \(S_{21}\) and \(S_{31}\) values within the bandwidth \(B\).  
A numerical approximation to the solution of Equation (2) is found by means of the optimization algorithm presented in the following paragraph.  
Figure 5: Six-cylinder model and feasible region (chest node region).
# Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators
## 6 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Silicon on Insulator**} & \multicolumn{3}{c}{**Silicon Nitride**} \\ \cline{2-7}  & **This work** & [5] & [8] & [48] & **This Work** & [53] \\ \hline Architecture & Triangle & Triangle & Triangle & Triangle & Rectangle & Exagonal \\ Central Wavelength & 1550 nm & 1310 nm & 1570 nm & 1550 nm & 1550 nm & 1550 nm \\ Footprint & \(5\times 1.5\) mm\({}^{2}\) & - & 4.9 \(\times\)2.4 mm\({}^{2}\) & - & \(16\times 8\) mm\({}^{2}\) & \(11\times 5.5\) mm\({}^{2}\) \\ Average MZI IL & 0.7 dB & 1.5 dB & \textless{}0.1 dB & \textless{}0.4 dB & 1.5 dB & 2 dB \\ Optical I/O Loss & 6.5 dB/GC & - & 3.5 dB/SSC & 5.8 dB/GC & 1.5 dB/SSC & 3.5 dB/EC \\ MZI ER & 26.8 dB, \(\sigma=4.2\) dB & \textless{}43 dB & 66 dB & 27 dB & 12.8 dB & \textless{}0.7 dB & \textgreater{}24 dB \\ P\({}_{\pi}\) & 55 mW, \(\sigma=2.1\) mW & 21 mW & - & 3 mW & 296 mW, \(\sigma=2.7\) mW & 290 mW \\ B\({}_{3 dB}\) & 35 nm & - & - & - & 40 nm & \textgreater{}20 nm \\ Expressivity & \textgreater{}97\% & \textgreater{}97\% & \textgreater{}100\% & \textgreater{}97\% & 75\% & \textgreater{}97\% \\ ENOB Reduction & 2.3–3.3 & \textgreater{} 1.5 & 1.32 & 2.5 & 1.3–2.4 & 4.5 \\ Energy/Operation (9 In) & 2.6 pJ & - & - & - & 13.9 pJ & - \\ Scalability & 17 & 8 & \textgreater{}100 & 30 & 8 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the realized linear optical processors with MZI-based reconfigurable chips in the literature.
## 4 Results  
### On-Body Access Point Network Architecture  
#### 4.1.1 Balancing Energy Consumption  
Figure 5 presents results from a 5 minutes real-time run using the reference system (\(M=0\)) as depicted in Section 3.2. Figure 6 presents the same real-time run using the proposed cost function with \(M=100\). The accumulated energy of each ED is presented as a function of time. Note that at the end of the 5 minute run of the reference system, the device which consumed the most power was ED7 with approximately 1.06 \(\upmu\)J and would be depleted of its power source before all other EDs. On the other hand, with \(M=1  
Figure 5: Accumulated energy spent by each end device over time for reference system (\(M=0\)). Note that ED7 would eventually deplete its battery while energy still remains at other nodes of the network.  
Figure 6: Accumulated energy spent by each end device over time for the proposed algorithm using \(M=100\). Note that energy consumption is balanced and all nodes consume the same amount of accumulated energy over time.  
While more energy is consumed overall, no single ED consumes more energy than others and it is expected that all EDs would deplete their battery at the same time. In the reference system, when ED7 depletes its battery, there is a significant amount of energy left unused in the network. However, when an ED depletes its power source in the proposed system, there would be almost no energy left in the network. This results in a NL improvement. For example, assuming all EDs have a battery capable of supplying an accumulated energy of 0.625 uJ, the reference system (\(M=0\)) would last for 175 seconds (until 0.625 uJ are spent by ED7), while the proposed system (\(M=100\)) would last for 240 seconds (until 0.625 uJ are spent by all EDs almost simultaneously). Another advantage of the proposed system (\(M=100\)) relates to maintenance of the network. Since all EDs deplete their batteries simultaneously, the network can be serviced once to replace/recharge batteries, instead of multiple times in the reference system (\(M=0\)).
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
## 2 Background
### Intrusion Detection Systems
#### 2.1.2 Why Do We Need Machine Learning?  
There are a number of reasons for researchers to consider the use of ML in network intrusion detection. One such reason is the ability of ML to find similarities within a large amount of data. The main assumption of ML-based approaches is that an intrusion creates distinguishable patterns within the network traffic and that these patterns can be efficiently detected using ML approaches [15]. These approaches promise automated data-driven detection that infers information about malicious network traffic from the vast amount of available traffic traces. Furthermore, ML can be used to discover anomalies in data without the need for prior knowledge about those data. By combining the characteristics of ML techniques and capable computing units, we can create a powerful weapon with which to respond to network intrusion threats. Moreover, we have a great deal of data nowadays, but human expertise is limited and expensive. Therefore, using ML, we can automatically discover patterns which humans may not be able to recognize due to the scale of the data. In the absence of ML, human experts would need to define manually crafted rules, which would not be scalable.
## 4 Results and Discussion
### Groundwater Protection Strategy in Coastal Area  
#### 4.3.1 Groundwater Protection Objectives Based on Groundwater Vulnerability  
According to the results of the vulnerability assessment of shallow groundwater in Tangshan City, the following medium- and long-term objectives for groundwater protection have been formulated in view of the current water quality and water environment.  
1. To guarantee water safety and prevent groundwater pollution. Groundwater is one of the important sources of drinking water, and protecting groundwater means guaranteeing people's drinking water safety and basic usage of water.
2. To maintain ecological balance on this basis. Protecting groundwater can maintain the water supply of wetlands, keep the stability of the ecological environment, and promote the maintenance of species diversity and the balance of the natural ecosystem.
3. To pay attention to the protection of water sources for agricultural irrigation to ensure the sustainability of agricultural irrigation and maintain the normal growth of crops, paying particular attention to reducing the adverse effects on soil salinization in the coastal area.
4. To prevent the decline in groundwater level and ground subsidence. Over-exploitation of groundwater will lead to the decline in groundwater level and ground subsidence, which will in turn lead to geological disasters and ground subsidence. One of the objectives of groundwater protection is to avoid these problems and maintain the stability of the groundwater system.  
To realize the sustainable management and utilization of groundwater and to support the sound economic development of Tangshan City, groundwater resources are non-renewable resources, and the protection and rational utilization of groundwater is an important part of realizing the sustainable development of the region, which is also an important goal of this study.
# Wavelet-Based Multi-Class Seizure Type Classification System
## 1 Introduction
### Review of Related Work
However, in [8], the authors chose to utilize all seizure types in the dataset regardless of the number of patients. Table 3 presents the investigated seizure types for each study. It can be observed from Table 1 that in TUSZ, there are six specific types of seizures and two non-specific general types. From a pathological point of view, these types are not completely disjoint but form a hierarchical sub-grouping [4; 26]. It has been stated in [7] that when there is inadequate evidence to label the type of seizure confidently, the corpus team tends to label an event as either focal non-specific or generalized non-specific based on the seizure's focality and locality [25]. Both of these types are not medically distinct from one another, whereas SPNS and CPSZ are more specific types of FNSZ, and ABSZ, TNSZ, TNSZ, and MYSZ are more specific types of GNSZ [4; 26]. Thus, considering the label FNSZ as a unique type of seizure against the specific focal types CPSZ and SPSZ might cause the classifier not to perform well, and similarly for the classification of GNSZ. Therefore, in this study, we are considering two different classification problems. In the first problem, each label is considered in the dataset as a unique seizure type, and results are compared with the existing state-of-the-art results. On the other hand, the second problem is the introduction of a new challenge, which is more important pathologically, that deals with the specific seizure type classification to investigate the effect of the non-specific labels in TUSZ (five-class classification). In order to solve the above mentioned problems, we propose a novel technique that focuses on wavelet-based machine learning methods for automatic seizure type classification in multi-channel EEG recordings. We only utilized EEG data and decomposed the EEG signals into different levels of components using DTCWT to extract specific features from these decomposed components. We used shift-invariant DTCWT for feature extraction from a biomedical signal and its classification, which is done for the first time in the literature for seizure type classification. Moreover, we tested our proposed technique on the largest available seizure EEG database, containing various types of epileptic seizures. In order to ensure the effectiveness and generalization of our technique, we thoroughly tested our proposed technique across subjects in addition to normal testing. The experimental results show that our proposed novel technique performs well for both problems of seizure-type classification. The rest of this paper is organized as follows: Table 2 discusses information about the data utilized in this research and the details of our proposed technique. Table 3 presents the evaluation methodology and the analyses of the obtained results. A thorough discussion is provided in Table 4. Table 5 concludes the article with a future research plan.
## Materials and Methods
### Orientation Control
#### 2.6.1 Solar Position and Heliostat Angles  
To guide the heliostat to the desired position, it is necessary to obtain the sun position at any time during the day. A reference system of horizontal coordinates describes the position of the sun in the sky by using the azimuth (\(A_{s}\)) and elevation angles (\(E_{s}\)). The azimuth angle is measured in regards to the South (positive to the West and negative to the East), whereas the elevation angle ranges from the horizon to the zenith, as shown in Figure 10. The azimuth and elevation angles of the sun position are given by a solar position algorithm with a maximal error of 0.0027\({}^{\circ}\)[21], which is sufficient for high concentration thermal systems, with a lower computational cost than fast algorithms. The algorithm takes the current date and time of the day and the geographical coordinates of the heliostat as input data. The algorithm also uses the local weather record to calculate the atmospheric refraction correction of the elevation angle of the sun, using the annual average local pressure and temperature.  
In order to reflect the solar radiation on the desired target, the azimuth and elevation angles of the heliostat (\(A_{h}\) and \(E_{h}\)) are given by the angles of the bisectrix of the angle formed by the solar vector (\(\overset{\rightarrow}{S}\)) and the fixed vector between the heliostat and the target (\(\overset{\rightarrow}{T}\)) [1], as shown in Figure 11. The bisector  
Figure 10: Solar vector.  
Figure 9: Block diagram of the orientation control algorithm.  
between \(\stackrel{{\rightarrow}}{{S}}\) and \(\stackrel{{\rightarrow}}{{T}}\) must be equal to the normal vector of the heliostat reflecting surface (\(\stackrel{{\rightarrow}}{{N}}\)), which is given by the addition of the unit vectors of \(\stackrel{{\rightarrow}}{{S}}\) and \(\stackrel{{\rightarrow}}{{T}}\) as follows:  
\[\stackrel{{\rightarrow}}{{N}}=\ \ \ \hat{S}_{x}+\hat{T}_{x}\ \ \ \hat{S}_{y}+\hat{T}_{y}\ \ \ \hat{S}_{z}+\hat{T}_{z} \tag{1}\]  
where \(\hat{S}\) and \(\hat{T}\) are the unit vectors of \(\stackrel{{\rightarrow}}{{S}}\) and \(\stackrel{{\rightarrow}}{{T}}\), and are given by Equations (2) and (3).  
\[\hat{S}=\left[\ \sin(A_{s})\cos(E_{s})\ \ \ \cos(A_{s})\cos(E_{s})\ \ \ \sin(E_{s})\ \ \right] \tag{2}\]  
\[\hat{T}=\left[\ \sin(A_{t})\cos(E_{t})\ \ \ \cos(A_{t})\cos(E_{t})\ \ \ \sin(E_{t})\ \ \right] \tag{3}\]  
Finally, the azimuth and elevation angles of the heliostat are given by Equations (4) and (5), respectively.  
\[A_{h}=\tan^{-1}\left(\begin{array}{c}\stackrel{{\rightarrow}}{{N }}_{y}\\ \stackrel{{\rightarrow}}{{N}}_{x}\end{array}\right) \tag{4}\]  
\[E_{h}=\tan^{-1}\left(\begin{array}{c}\stackrel{{\rightarrow}}{{N }}_{z}\\ \sqrt{\stackrel{{\rightarrow}}{{N}}_{x}+\stackrel{{ \rightarrow}}{{N}}_{y}^{2}}\end{array}\right) \tag{5}\]
## 2 Background
### Characteristics of a Space DGNSS Scenario
#### 2.2.1 Measurements Errors
Therefore, as a worst-case scenario, satellite orbital errors with a norm of 400 m of which the direction is randomly generated and follows a normal distribution are considered in this contribution. The objective of considering this worst-case scenario is to assess the potential of DGNSS techniques to cancel the effect of GNSS orbital errors despite the large ISR values, resulting in the decorrelation of the Signal In Space Ranging Error between the two users. In Section 2.1.2, the observables alignment technique was presented assuming that the receiver timestamps after clock bias correction, \(t_{i,k}\) and \(t_{j,k}\), are perfectly known. However, in real GNSS scenarios, \(\hat{t}_{*,k}\) is estimated based on the prior estimation of the receiver clock bias \(\hat{b}_{*,k}\). The quality of these estimates hence depends on the satellite visibility and the GDOP [14; 49; 50; 59]. And as already shown in [42], in lunar GNSS scenarios, the visibility of the satellites is low and the DOP values are high. As a consequence, in a lunar DGNSS scenario, the inter-user measurements time-offset estimates \(\Delta\hat{t}_{ijk}\) might be affected by significant residual errors. Given the high user velocities characteristic of lunar orbiters, a small error in timestamp offset estimation can result in a large error in ISR estimation as the pseudoranges of the aiding user would be interpolated at a slightly erroneous point in time, using Equation (4). Additionally, as space GNSS users are characterized by larger Doppler shifts when compared to terrestrial users, a small estimation residual error left on \(\Delta\hat{t}_{ijk}\) would result in a larger time-compensation error when compared to a terrestrial scenario.
## 3 Results  
### Impact of Compaction on the Substrate's Hydrodynamic Properties  
Figure 3a-e show the water retention curves of the five substrates using the van Genuchten [30] model for different bulk densities, and Table 2 presents the parameters of the water retention models for the uncompacted and compacted states of the substrates. In general, as compaction occurs, the size of the pores decreased, and capillary forces increased.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Substrate**} & \multirow{2}{*}{**State**} & \(\rho\) & \(\theta_{s}\)1  & \(\alpha\)1  & \(n\)1  & \(K_{s}\) & \(\xi\)2  & \(\mu\)2  & \(\eta\)3  & \(\varepsilon\)3  & \(\zeta\)3  \\ \hline S1 & UC & 0.85 & 0.19 & 0.64 & 0.1170 & 1.44 & 348 & 0.032 & 0.762 & 1.025 & 1.329 & 0.072 \\  & C & 0.99 & 0.22 & 0.57 & 0.0190 & 1.90 & 239 & 0.056 & 1.035 & 1.996 & 1.382 & 0.962 & 0.051 \\ S2 & UC & 0.23 & 0.18 & 0.76 & 0.0590 & 1.65 & 3690 & 0.086 & 1.445 & 1.120 & 1.132 & 0.094 \\  & C & 0.40 & 0.31 & 0.69 & 0.0019 & 4.18 & 410 & 0.252 & 2.461 & 1.850 & 1.781 & 0.666 & 0.067 \\ S3 & UC & 1.00 & 0.00 & 0.52 & 0.1600 & 1.33 & 1850 & 0.034 & 0.701 & & 0.928 & 1.458 & 0.094 \\  & C & 1.20 & 0.00 & 0.44 & 0.0175 & 1.85 & 1540 & 0.067 & 1.000 & 1.949 & 1.344 & 0.994 & 0.067 \\ S4 & UC & 0.85 & 0.00 & 0.61 & 0.0270 & 1.35 & 172 & 0.114 & 0.709 & 0.946 & 1.432 & 0.210 \\  & C & 0.95 & 0.00 & 0.50 & 0.0028 & 2.27 & 115 & 0.328 & 1.258 & 2.011 & 1.214 & 1.118 & 0.246 \\ S5 & UC & 0.51 & 0.13 & 0.70 & 0.0510 & 1.75 & 1380 & 0.029 & 0.946 & & 1.277 & 1.056 & 0.035 \\  & C & 0.70 & 0.18 & 0.63 & 0.0041 & 3.07 & 442 & 0.098 & 1.749 & 1.882 & 1.912 & 0.579 & 0.017 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hydrodynamic properties of the substrates used in this study. The first row of each substrate refers to the uncompacted (UC) values and the second row corresponds to the compacted (C) values obtained with the parametric analysis.
## 3 Experimental Results and Discussion
### Training and Evaluation of DCNNs
#### 3.2.2 VGG-16 Classification Results  
The training performance of VGG-16 for 30 epochs, with and without noise, is shown in Figure 11. Without the noise dataset, VGG-16 achieved maximum training and validation accuracies of 96.20% and 93.50%, respectively, with 0.140% training and 0.190% validation losses. For 20 dB noise, the training and validation accuracies decreased to 93.50% and 92.30%, respectively. The confusion matrices for the noise and noiseless datasets are shown in Figure 12, which demonstrate that 99.48% is the highest testing accuracy achieved for the noiseless dataset followed by 99.39% for 30 dB noise, 99.25% for random noise and 99.13% for 25 dB noise. Furthermore, for the VGG-16 model, the precision, recall, and F1-score are also calculated. The results indicate that VGG-16 achieves superior performance on the noiseless test dataset, with (1) an average value of accuracy (99.48%), (2) precision (96.17%), (3) recall (95.88%), and (4) F1-score (95.90%), whereas the lowest performance is recorded with the 20 dB noise test dataset with the following average values: (1) accuracy (99.13%), (2) precision (93.50%), (3) recall (93%), and (4) F1-score (93.07%), as shown in Table 4.  
Figure 11: VGG-16 training performance for noisy and noiseless datasets.
###### Abstract  
The simple and robust log-likelihood ratio (LLR) computation of coded Multiple Phase Shift Keying (MPSK) signals in Wireless Sensor Networks (WSNs) is considered under both phase noncoherent and Rayleigh fading channels for healthcare applications. We first simplify the optimal LLR for phase noncoherent channel, the estimation of the instantaneous channel state information (CSI) for both the fading amplitude and the additive white Gaussian noise (AWGN) is successfully avoided, and the complexity-intensive process for zero-order Bessel function of the first kind is also perfectly eliminated. Furthermore, we also develop the simplified LLR under Rayleigh fading channel. Correspondingly, the variance estimation for both AWGN and the statistical characteristic of the fading amplitude is no longer required, and the complicated process for implementation of the exponential function is also successfully avoided. Compared to the calculation of optimal LLR with full complexity, the proposed method is implementation-friendly, which is practically desired for energy-limited WSNs. The simulations are developed in the context of low-density parity-check (LDPC) codes, and the corresponding results show that the detection performance is extremely close to that of the full-complexity LLR metrics. That is, the performance degradation is efficiently prevented, whereas complexity reduction is also successfully achieved.  
IEEE 802.15.4c; coded multiple phase shift keying; noncoherent detection; channel state information +
Footnote †: journal: Applied Sciences
# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
## 4 Data Set Preparation
### No. of Stripes per LED Region (\(S_{t}\))  
Since LED modulated data results in dark and bright stripes in the captured image, the number of stripes and the width plays an important role in recovering the data stream from the image frame. The number of stripes is varied proportionally with the size of LED, mark, and space frequency of LED, and also the distance between the camera and LED [16]. It is noticed that high-speed cameras with high read-out capability outperform in terms of communication speed. The width of the bright and dark stripes can be theoretically determined as [19]:  
\[S_{T}=\frac{1}{ft_{r}}, \tag{11}\]  
where \(f\) represents mark or space frequency, and \(t_{r}\) is the time needed to read-out a single pixel of the image.