esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results Analysis
### Verification and Deduction  
To further investigate the universality and scientificity of the near-far field conversion method in obtaining the target RCS, the complex missile model shown in Figure 17a was selected as a scatterer to be excited by Bessel vortex waves (l = 1). Due to the limited experimental conditions, a missile scale model with a length of 842 mm and radius of 80 mm was simulated by FEKO to obtain the amplitude and phase distributions of the scattering near field, as shown in Figure 17b,c. The aperture field source was 10 wavelengths away from the top of the target, and the receiving plane was 5 wavelengths away from the bottom of the target.  
Figure 16: Experimental measurement and numerical calculation of the Bessel beam reflection on a conductor plate; results of far-field RCS. (**a**) _fai_ = 0\({}^{\circ}\), (**b**) _fai_ = 90\({}^{\circ}\).  
Figure 15: RCS of different targets by means of near-far field conversion and PO calculation. (**a**) Measurement and (**b**) theory.  
It can be seen that the amplitude of the forward scattered field along the axis of the missile model was a hollow ring, and the phase still had a helical wavefront in the central region. Some of the phase distortion in the larger radius region may have come from the diffraction effect of the target edge. Importantly, the near field results of complex targets will be used to verify the accuracy and robustness of the RCS obtained by the near-far field conversion method.  
Figure 18 shows the RCS of complex targets obtained by the FEKO simulation and near-far field conversion method. It can be seen that there was good agreement in the RCS main lobe region, which effectively validates the reliability of the near-far field conversion method in RCS measurement. The differences in the sidelobe may have been due to the sampling accuracy, which will be resolved by near-field diagnosis in subsequent RCS measurements. In general, the application of the near-far field method in RCS was verified and deduced.  
Figure 17: Complex target missile model (**a**) and its forward scattered field distribution (**b**,**c**).  
Figure 18: RCS of complex targets obtained by FEKO and near-far field conversion. (**a**) _fai_ = 90\({}^{\circ}\), (**b**) _fai_ = 0\({}^{\circ}\).
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 3 RPL Parent Selection Background
It uses a cost function that typically includes the ETX and a hysteresis component to prevent frequent changes in parent selection, which could destabilize the network. Other solutions to the parent selection problem include a variety of methods as was shown in Section 2. The next section will provide a comprehensive explanation of our approach to tackle this problem. It will outline how our method works and highlight the distinctive elements that make it unique to other implementations.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 1 Introduction  
Internet of Things (IoT) is emerging with an expectation of billions of connected devices to the Internet. These devices utilize low-power wireless communication technologies enabling long battery life and reliable end-to-end transmissions [1]. Capitalizing on this development, industries shift towards the Industrial Internet of Things (IIoT) through concepts such as Wireless Sensor Networks (WSN). Utilizing WSN decreases installation cost as compared to infrastructures for wired sensor networks [2]. According to [3], the cost of installing cables across an industrial plant can run from USD 100s/ft to USD 1000s/ft.  
In 2016 the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 low-rate wireless standard [4] was amended with a new Time-Slotted Channel Hopping (TSCH) media access control targeting industrial applications. The IPv6 over the TSCH mode of IEEE 802.15.4e (6 TiSCH) working group [5] was started by Internet Engineering Task Force (IETF) with an intent to enable IP traffic for IIoT utilizing TSCH. In addition, 6 TiSCH follows IETF Deterministic Network (DetNet) working group architecture [6], aiming to standardize layer three traffic in terms of networks requiring deterministic capabilities. Wireless networks have a higher probability of packet loss compared to wired due to interference and channel disturbance. Hence, 6 TiSCH recommends packet replication in its architecture in order to increase the reliability [5]. However, in WSN packet replication causes higher energy consumption [7] and, thus, reduced network lifetime. Industrial networks face tough lifetime requirements that WSN struggles to meet with packet replication.  
This paper proposes and investigates a novel technique utilizing a Reverse Packet Elimination (RPE) algorithm. Packet replication is used to increase the reliability of the network. In addition, RPE limits the negative effect of reduced network lifetime as the replicated packets are cancelled when the original packet successfully reached its destination. This is achieved as the RPE enables the sink to send a packet down the alternative path to eliminate the replicated packet from being transmitted upstream. The energy consumption is reduced as a RPE packet is smaller than an upstream packet, and hence utilizing less resources to traverse the path. In addition, this paper examines the effects of introducing an adjustable replication delay, where the transmission of the replicated packet is delayed. The motivation for this is to avoid transmitting the replication packet if the original packet has arrived at its destination (the sink).
## 3 Materials and Methods
### Optimization of the VOIs Selection Using Multi-Objective Optimization Evolutionary Algorithm (MOE)
To achieve this, a copy of the non-dominated individuals in \(P_{t}\) and \(ND_{t}\) is made to \(ND_{t+1}\)
5. During the execution of a new generation, operators of the evolutionary algorithm are applied to the individuals of the population. These include the binary tournament selection with replacement in \(ND_{t+1}\). As we have indicated before, there is an elitist process where the best solutions are copied from generation to generation, and MOE operators complete the population (until the 250 individuals used in this role are  
Figure 6: Binary coding used by the MOE algorithm, with the aim of selecting the most relevant VOI of the brain (both in gray and complete matter), and these regions being used for the construction of an accurate multi-class classifier. obtained). Binary crossover and mutation are also applied, and \(P_{t+1}\) is set on the resulting population. 6. Evaluation of fitness and Pareto functions from the construction. Our fitness function has two goals: Complexity of the obtained solution (measured as the number of VOIs selected) and accuracy of the classifier (using an SVM). 7. Repeat until Stopping Condition is reached (typically, a maximum number of generations is reached). The final solution of the multi-objective optimization is the so-called Pareto Front, which is a set of non-dominated solutions, chosen as being optimal, in which the two objectives were simultaneously optimized: the accuracy of the classifier (using SVM to perform a classification between SWEDD, PD, Prodromal, GenCohort and normal subjects) and the complexity of the system (measured as the number of needed VOIs). Figure 7 shows an example of coding for the solutions of the Pareto frontier. Assuming that there are M non-dominated solutions, each of the solutions have both a different number of selected VOIs (genes in our case), as well as a different accuracy. For each VOI, the number of features used is called N and is extracted using 3D-DWT. As can be seen in the bottom part of Figure 7, for a given patient K', using the individual-j of the Pareto Front, only a subset of the VOIs is used, each having N features. Therefore, each patient K' can be represented by a matrix of real numbers such as the one presented in Figure 7. This process must be carried out on all the patients in the database, as illustrated in Figure 8.  
As previously mentioned, the number of representative wavelet coefficients for each VOI is reduced thanks to the use of the mRMR algorithm. However, when using a high number of VOIs, the number of coefficients that are input to the SVM classifier is high. For this reason, a feature reduction is performed by means of PCA, reducing the dimensionality of the input space for the classifier of the fitness function. Therefore, for each patient, the matrix of real numbers is simplified, as represented in Figure 9.  
Figure 7: Binary coding used by the MOE algorithm for the selection of the most relevant VOIs in gray and whole matter.
## 3 Materials and Methods
### Method
#### 3.1.1 Stage 1 Training  
Figure 2 shows the general architecture for the proposed segmentation method. The first stage produces optic disc segmentation that is not perfect but has high dice score accuracy. The optic cup segmentation, however, is not proper, especially when using the trained model in a new dataset outside the distribution of the dataset used in the training.  
In the first stage, we use a Mask R-CNN network with initial weights pre-trained on the COCO dataset. We froze the backbone weights before the C3 block and allowed the rest of the weights to become tuned during the training. C3 refers to stage 3 downsampling block (Conv3_x) of the ResNet [12]. The resulted bounding box from this stage is used to crop the image around the ONH and feed the cropped image to the second stage. The first stage network investigates the entire image to locate and segment the optic disc/cup. With images coming from different distribution due to different acquisition devices, the model will not have the same performance on the new datasets. Most of the differences in the fundus image happen in the retinal background (background with respect to the ONH).
## 3 Results and Discussion
### Spatial Distribution of Organochlorine Pesticides  
By combining the possible contaminated areas on the ERT profile (Figure 5), the plant space distribution, and the water flow direction of the site, we delineated the contaminated areas at the site. The results are shown in Figure 6.  
We delimited a hexachlorocyclohexane-contaminated area in the 25-33 m area on the ML-I line, which could be caused by a leakage at the HCH production building.  
The 25-40 m area on the ML-II line was delineated along the direction of the groundwater flow near the acid tank. We suspected that materials in the acid tank were dripping or that there was another source that was leaking the HCH on the west side of the HCH production building.  
Figure 5: The ERT inversion results of the remote investigations along different measuring lines (Line I-IV).  
Figure 6: The ERT profiles of the pollution plume distribution.  
The entire ML-III line and the ranges of 20-40 m and 45-60 m above the ML-IV line had obvious highly anomalous resistivity. There were vinyl chloride buffer tanks and open unloading platforms. We delineated the whole area along the direction of the groundwater flow as a contaminated area.  
Both profile ML-III and profile ML-IV had high resistance. Near the two profiles, a high concentration of pollution was detected by ERT. According to the map of the factory, this was not possible to explain. We thought that it was caused by new pollution or another source of HCH leakage.  
In general, ERT can be used as a rapid detection method to determine the general underground conditions at the site. With the actual situation and other methods, it can help to find the source of pollution and the distribution of pollutants to a certain extent. However, due to the complexity of the underground situation in our study, the result was diversified and other methods were needed to assist in further verifying its correctness.
## 3 Results  
The microhardnesses at the selected depths for SDR, Filek Bulk Fill, SonicFill, Petri EvoCeram Bulk Fill, Xtra Base, and Venus Bulk Fill, polymerized by the four LCUs, are presented in Table 4. The ratio of hardness to the \"surface\" (0.5 mm) at the selected depths, for the bulk-fill composites polymerized by the four LCUs, are presented in Table 5.  
Our analysis revealed the following key findings regarding the microhardness of bulk-fill composites at a 4 mm depth across different light-curing units (LCUs):  
SDR: No statistically significant differences in microhardness were observed at the 4 mm depth compared to baseline for any of the LCUs tested. Additionally, there were no significant differences in microhardness among the various LCUs. Filtek Bulk Fill flowable restorative: Similarly, no statistically significant differences in microhardness were found at the 4 mm depth compared to baseline for any of the LCUs. However, a statistically significant difference was observed between the halogen (Ha) LCU, which demonstrated a lower microhardness, and the other LCUs. SonicFill: For most LCUs, there were no statistically significant differences in microhardness at the 4 mm depth compared to baseline, except for Va, which showed significantly lower microhardness. Additionally, significant differences were found between some of the LCUs, with the halogen (Ha) LCU showing a lower microhardness and the BS LCU exhibiting significantly higher microhardness values.
## 2 Materials and Methods
### Soil Preparation  
Samples of the fertile top layer of soil (0-20 cm) were collected independently from two different sites in Hubei Province, China, namely, a paddy field at the experimental fields of Huazhong Agricultural University in Wuhan city (Longitude: 114\({}^{\circ}\)20\({}^{\prime}\)49\({}^{\prime\prime}\) E, Latitude: 30\({}^{\circ}\)28\({}^{\prime}\)26\({}^{\prime\prime}\) N) and a sunflower field in Huangshi city (Longitude: 114\({}^{\circ}\)58\({}^{\prime}\)47\({}^{\prime\prime}\) E, Latitude: 30\({}^{\circ}\)12\({}^{\prime}\)02\({}^{\prime\prime}\) N). The texture types of the experimental soils were silty loam and sandy clay loam, respectively. The soil samples were left to dry in open air for about 2 weeks, after which the dry soil was pulverized with a wooden hammer, and the loose material was sieved with a 4 mm mesh sieve. Then, according to Equation (2), the amount of water required to provide the chosen experimental conditions was calculated and added to the sieved soil [24].  
\[V_{a}=\left(SMC_{req}\times W_{s}\right)-\left(SMC_{ex}\times W_{S}\right) \tag{2}\]  
Here, \(V_{a}\) is the amount of water that must be added to the soil to obtain the required soil moisture content (g), \(SMC_{req}\) is the required moisture content (g kg\({}^{-1}\)), \(SMC_{ex}\) is the moisture content of the sieved soil (g kg\({}^{-1}\)), and \(W_{s}\) is the weight of the soil (kg).  
The Afterberg constants of the experimental soils (plastic limit and liquid limit) were determined in the soil physics laboratories of Huazhong Agricultural University according to standard ASTM D 4318 [25] and are given in Table 2.
# Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R
## 8 Make Work Easier
R> wti <- crudeoil[-1, 1]  
R> ld.wti <- (diff(log(wti)))[-1, ]  
R> arch  
Engle's LM ARCH'Test  
data: as.vector(ld.wti)  
statistic = 58.349, lag = 10, p-value = 7.431e-09  
alternative hypothesis: ARCH effects of order 10 are present  
Another common procedure, used in various researches with time-series, is checking stationarity. However, because of Equation (7) time-series used in DMA do not have to be stationary. Actually, in this case variance updating by Equation (20) can perform better [157]. Nevertheless, DMA is not the only method implemented in this package. Secondly, stationarity checking is an often performed procedure. The function steel computes a few common stationarity tests. In particular, this function is a wrapper for three functions from tseries package by [PERSON] and [PERSON] [186]. Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests for stationarity are performed. The corresponding functions from tseries package are adf.test, pp.test and kpss.test. For ADF test the null hypothesis is that a unit root is present in the time-series. The alternative hypothesis is that the time-series is stationary. If \(n\) is the length of the tested time-series, then the lag order in the test statistic is the number obtained from discarding non-integer part of \((n-1)^{\frac{1}{3}}\). For PP test the null hypothesis is that a unit root is present in the time-series. The alternative hypothesis is that the time-series is stationary. The truncation parameter for the Newey-West estimator is the number obtained from discarding non-integer part of \(4\cdot\left(\frac{n}{100}\right)^{\frac{1}{3}}\). \(Z(\alpha)\) statistic is used. The PP test mainly differ from ADF test in a way to deal with serial correlation and heteroskedasticity in errors. Both ADF and PP tests are asymptotically equivalent. Contrary to most unit root tests in KPSS test the null hypothesis is that the time-series is stationary. The alternative hypothesis is that it contains a unit root. The truncation parameter for the Newey-West estimator is the number obtained from discarding non-integer part of \(3\cdot\frac{\sqrt{n}}{13}\). Unfortunately, KPSS test tends to reject the null hypothesis too often.
## 9 Artificial Neural Networks (ANNs) for Congestion Mitigation of WSNs
### DL for Congestion Mitigation of Wireless-Based IoT Networks
#### 9.1.5 Deep Learning for Cognitive Communication  
A radio-based system, referred to as a \"cognitive radio\", has the ability to learn, understand its environment, and change its behavior as needed [29]. User sensing requirements,  
Figure 9: ANN and DNN schematics to mitigate congestion in WSN.  
radio spectrum sensing, and environment sensing are three categories into which the concepts of adoption, learning, and sensing can be classified. There are numerous studies on this topic, one of which is [123], in which [PERSON] and [PERSON] give a concise introduction to Cognitive Radio (CR) and outline the design of the next-generation wireless network CR. Next-generation advanced network features, such as spectrum management, mobility, sharing, and congestion mitigation, are also addressed in this study. The study of congestion mitigation in wireless sensor networks (WSNs) is now limited to this particular congestion mitigation. [PERSON] and [PERSON] in [124] evaluated Cognitive Radio (CR) techniques and noted the drawbacks under real-world conditions. Exploring a framework or technique that can be used in a variety of situations has been referred to as an open challenge by other authors. According to the above viewpoint, there is enormous potential for a framework or approach that can be used for a variety of WSN congestion scenarios.  
The study of [125] proposed machine learning (ML)-based unsupervised and supervised learning methods for spectrum sensing in Cognitive Radio networks. To control the performance, a reinforcement-based deep learning system for spectrum sensing in cognitive radio networks was presented in [126]. Since secondary users can access an unused spectrum, thanks to dynamic spectrum sharing, the spectrum utilization efficiency is increased. This spectrum sensing technique mentioned above underutilizes the IoE spectrum, so a spectrum-sharing-based DL algorithm has a greater chance of reducing the congestion of the wireless IoE network.
# Machine Learning-Assisted Secure Random Communication System
## 5 Reception
### Legitimate Receiver 'LR'  
The legitimate receiver LR receives noise sequence **Y** as  
\[\textbf{Y}=\textbf{R}+\textbf{A} \tag{7}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Selected Values** \\ \hline \(\alpha\) & 0.6, 0.8, 1.1 \\ \(\beta_{0}\) & \(-\)0.8, \(-\)0.9, \(-\)1 \\ \(\beta_{1}\) & 0.8, 0.9, 1 \\ \(\Delta\beta\) & 1.6, 1.8, 2 \\ \(N\) & 15, 25, 35 \\ \hline \end{tabular}
\end{table}
Table 2: Parameter settings responsible for LR.  
\begin{table}
\begin{tabular}{c c c} \hline
**Symbol** & **Definition** & **Utilized Values** \\ \hline \(\gamma\) & \begin{tabular}{c} dispersion of noise generated to \\ represent ’0’ or ’1’ \\ \(\gamma_{A}\) \\ MSNR\({}_{\text{dB}}\) \\ \end{tabular} & \
\begin{tabular}{c} 1 \\ dispersion of channel noise \\ mixed signal-to-noise ratio \\ \end{tabular} &
\begin{tabular}{c} 1 \\ [0.316, 10] \\ \([-\)10, 5] \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 3: Channel parameters.  
which is accessible to both the LR and the ILR. As defined in Figure 4, the LR first performs a cart-based classification, followed by a pulse length-based classification, to retrieve a unit binary bit that is hidden in **Y**. The received noise sequence **Y** carrying the unit binary bit is defined as  
\[\textbf{Y}=\{y_{1},\;y_{2},\;\ldots,\;y_{N}\} \tag{8}\]
## 6 Conclusions  
Two remote sensing-based indices, the MODIS NIR BRDF_R index and the NDVI, are sensitive to the AR over grassland surfaces (\(R^{2}\): 0.5228 for NIR BRDF_R; \(R^{2}\): 0.579 for NDVI), and the combination of these indices can significantly increase the \(R^{2}\) to 0.721, which is consistent with the relationship between the wind speed and the field AR (\(R^{2}\): 0.7411). The proposed CI from remote sensing technology can accurately calculate AR values at a level comparable to considering the effects of wind speed, which is still difficult to acquire from remote sensing. The findings in this paper have powerful application potential for estimating temporal AR values over grassland surfaces during the growing season based on remote sensing data.  
Figure 6: Relationship between the average wind speed and the AR at A’rou station.  
**Acknowledgments:** This work was supported by Natural Science Foundation of China (NSFC, Grant No. 41271424) and the Advanced Science Foundation Research Project of the Chinese Academy of Sciences (Grant No.: QYZDY-SSW-DQC014) and NSFC (Grant No. 41501479). The authors express their gratitude to the HiWATER-MUSOEXE project and the Cold and Arid Regions Science Data Center in Lanzhou ([[http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn)]([http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn))) for providing data at A'rou station. Many thanks are extended to [PERSON] of Nanjing University for providing assistance with the field AR calculation program.  
**Author Contributions:** [PERSON] and [PERSON] designed and conducted the experiment; [PERSON], [PERSON], and [PERSON] provided suggestions for the analysis; [PERSON] wrote the manuscript and [PERSON] revised the manuscript.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest.