esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Data: Efficient and Secure Transmission
## 2 An Efficient and Secure Transmission Framework
### An Effective Implementation of the Proposed Framework
#### 2.1.1 Reversible Invisible Watermarking Scheme for Hyperspectral Images
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# Optimal Design of Switchable Wearable Antenna Array for Wireless Sensor Networks
## 1 Introduction
The goal of the optimization process is usually a single-objective improvement of array performance in terms of radiation pattern parameters (e.g., the side lobe level and the directivity). In [15] a single-objective design problem was reformulated as a multi-objective problem. It was possible to accomplish a set of non-dominated solutions and then indicate the best-compromise solution. Additionally, in [11] a multi-objective approach was presented to optimize the amplitude and phase of the excitation and the separation between the array elements. In our research, we also considered the optimization problem as initially single-objective and then bi-objective, showing that the latter approach is very effective in identifying a satisfactory solution. Figure 1: The principle of adaptive array operation for interference rejection: (a) single antenna realizes broad radiation pattern; (b) antenna array can direct the beam towards the signal. In the literature, circular arrays that were the subject to the optimization process consist of elementary antennas that have omnidirectional radiation (in [10] an array of dipoles is used). In our study, we investigate the optimization of the array that consists of wearable antennas, which, to our best knowledge, is our original concept and was neither investigated nor published by other researchers. In this paper, we consider the utilization of phased array antennas to improve the performance of the Narrow Band Internet of Things (NB-IoT) system that, due to the low power of transmitters, can be sensitive to interferences. In this case, the wearable phased antenna array is investigated that could be applied for the wireless system that monitors physiological parameters of people (Figure 1b). For simplicity, we assumed that this array consists of vertically oriented half-wave dipole antennas designed to operate in free space at 700 MHz frequency band which is allocated in Poland for the fifth generation of wireless communication systems that include NB-IoT. To simplify the feeding network of the wearable array, we assume that the phase shift of signal connected to the _l-th_ antenna can take on one of two possible values \(\alpha(l)\in\{0,\ \alpha_{const}\}\). In the prior studies, a wearable antenna array that was fed with no phasing was used to obtain the bi-directional radiation pattern [17]. It was shown that the radiation pattern of the array depends strongly on the placement of each antenna element. In the antenna array optimization process the position of individual radiators on the body is identified together with the value of phase shift \(\alpha_{const}\) to improve the antenna SIR. The paper is organized in the following way: after the Introduction, models of direct and inverse problems are formulated in Section 2, where the optimization approach is also presented; eventually, optimization results are presented and discussed in Sections 3 and 4, respectively.
## 1 Introduction
Nevertheless, the higher resolution of Level 3 products do not contain more physical information than the coarse resolution of native GRACE datasets, and downscaling at higher spatial resolution is necessary to acquire high-resolution TWSA estimates that are useful for small scale studies [15; 27]. Previous works have shown that assimilating information from auxiliary variables at higher spatial resolution can improve the coarse spatial resolution of remotely sensed products like soil moisture [28; 29; 30; 31; 32] or TWS changes, also known as TWS anomalies [15]. Currently, there are two main downscaling approaches, which differ in their nature but also in terms of input data requirements. Physically-based downscaling methods, also known as dynamical downscaling, establish a physical model between the coarse resolution variable to be downscaled and the fine resolution auxiliary variables. Dynamical downscaling relies on a sound physical basis; its implementation is, however, computationally demanding. On the other hand, statistical downscaling seeks a statistical relationship between the coarse and fine-scale variables. The statistical approach is more simple to implement compared to the dynamic downscaling, but the establishment of a statistical relationship requires a large and reliable dataset, and the result depends highly on the choice of auxiliary high-resolution variables [15; 28]. An example can be found in the downscaling of soil moisture that has received considerable attention. One of the methods is the use of thermal inertia and vegetation and surface temperature to downscale passive microwave radiometer estimates of soil moisture from AMSR-E (Advanced Microwave Scanning Radiometer) [33], AMSR2 [34] and SMAP (Soil Moisture Active Passive) [35; 36; 37]. Within the present work, a spatial statistical downscaling approach for GRACE TWS anomalies (TWSA), from the original 1\({}^{\circ}\) grid to 0.1\({}^{\circ}\), is presented. The methodology uses the finer resolution, i.e., 0.1\({}^{\circ}\), remotely sensed precipitation from GPM-IMERG, and a pixel-wise regression formula is computed, accounting for the time lags between precipitation and TWSA. While most previous downscaling methods of GRACE have relied on modeled fine-resolution information [15; 38], in the present work, we evaluated the downscaling ability of a sole remotely sensed product, i.e., GPM-IMERG, to produce a simple yet reliable downscaling framework for GRACE TWS changes.
# Small Infrared Target Detection via a Mexican-Hat Distribution
## 4 Experimental Results and Analysis
### Step-By-Step Results  
As shown in Figure 3, our proposed method consists of four major steps. This experiment aims to present the influence of each step on the detection. Figures 8 and 9 show some representative results. Generally speaking, the proposed method is able to well enhance the IR targets under different backgrounds and can suppress much of the background clutters. Specifically, we can see that by applying the modified top-hat transformation, dark sky backgrounds are darkened further, and cloud clutters become smoother (Figures 8b and 9b). Then, with DoG filtering, a small IR target is emphasized,  
Figure 5: The twenty-first image in Seq.2. (x,y) in each sub-caption is the coordinate of the target center, and the detected target is circled in yellow.  
Figure 6: The twenty-fifth image in Seq.2.  
while the background is greatly suppressed, and thus the target becomes more prominent (Figures 8c and 9c). Next, by using the Mexican-hat distribution, candidate targets including the real target as well as some cloud edges are enhanced (Figures 8d and 9d). And then by using _Rule 2_ and _Rule 3_, the real target becomes the most prominent one (Figures 8e,f and 9e,f). As a result, the target can be easily detected by locating the brightest pixel.  
Figure 8c shows that DoG filtering is good at dealing with a synthetic target whose intensity strictly meets the Gaussian distribution. Generally speaking in this case the DoG itself is enough to detect the real target. But Figure 9c indicates that the DoG cannot well deal with real targets which may not have a strict Gaussian distribution. In this case, background clutters like cloud edges may have a similar intensity to a small target after DoG, and thus the simple DoG cannot detect targets correctly. From Figure 9f we can see that in the case of real targets our method still performs well. This is due to the rules based on the Mexican-hat distribution play an important role in enhancing real targets.  
Figure 8: The thirty-seventh image in Seq.1.  
Figure 7: The thirty-second image in Seq.2.
## 5 Experiments
### Test Results
#### 5.3.3 Property 3  
An ACK packet from a node \(v_{i}\) will be sent to another node \(v_{j}\) only if both conditions are satisfied: (i) \(v_{i}\) receives a DATA packet from \(v_{j}\); and (ii) \(v_{i}\) (currently) has a smaller metric than \(v_{j}\). This property can be formalized as:  
\[\forall_{x}(response(x)\wedge x.type=\textbf{ACK}\rightarrow\] \[\exists_{y<x}(request(y,x)\wedge y.type=\textbf{DATA}\wedge i. metric<j.metric))\]  
We still test this property through the traces collected and used for previous properties. The results are shown in Tables 4 and 5.  
Figure 3: Test results for Property 2 (Day 2).  
Different from no 'fail' verdicts in Property 2, we can observe a large number of fail verdicts in Nodes 101, 103, 107 and 110 from Table 4 and Figure 4. When we analyze these fail verdicts, they are all caused by the violation of '\(v_{i}\) has a smaller metric than \(v_{j}\)'. These 'ACK' packets are transmitted even when \(v_{i}\) has a greater metric than \(v_{j}\).  
This draws our attention to the implementation of these nodes. We went through the codes for Nodes 101, 103, 107 and 110 and found that there exists a configuration error in these nodes, which leads to this phenomenon.  
After we fixed this implementation error, there is no such fail verdict reported again on the second day testing results, as shown in Table 5 and Figure 5. Meanwhile, the 'inconclusive' verdicts are caused by the same reason as we mentioned in Property 2. These testing results sufficiently prove that our approach can detect errors and can help developers to find and fix existing bugs in the implementation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Traces** & **Total Messages** & **Pass** & **Fail** & **Inconclusive** \\ \hline
101 & 67,409 & 11,105 & 11,341 & 0 \\
102 & 95,206 & 29,552 & 0 & 0 \\
103 & 81,709 & 16,171 & 16,072 & 0 \\
104 & 84,590 & 29,128 & 0 & 1 \\
105 & 69,274 & 23,981 & 0 & 0 \\
106 & 70,113 & 23,983 & 0 & 2 \\
107 & 79,807 & 13,252 & 13,365 & 0 \\
108 & 78,483 & 29,330 & 0 & 1 \\
109 & 87,196 & 26,117 & 0 & 0 \\
110 & 62,235 & 10,016 & 9821 & 1 \\ Gateway 500 & 60,805 & 17,639 & 0 & 0 \\ Gateway 501 & 52,444 & 14,659 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results for Property 3 (Day 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Traces** & **Total Messages** & **Pass** & **Fail** & **Inconclusive** \\ \hline
101 & 13,203 & 2105 & 0 & 0 \\
102 & 18,342 & 4277 & 0 & 0 \\
103 & 15,744 & 1322 & 0 & 1 \\
104 & 18,852 & 3651 & 0 & 0 \\
105 & 14,711 & 431 & 0 & 0 \\
106 & 12,851 & 80 & 0 &
## 4 CFD Analysis of a Supercritical CO2 Heat Exchanger
### Flow Analysis
For the actual SCO\({}_{2}\) heat exchanger, the number of heat exchanger tubes was 16, as shown in Figure 1. Uniform mass flow distributions on each heat exchanger tube were assumed: the mass flow rate of the SCO\({}_{2}\) in each tube was 0.0625 kg/s. For the flue gas in the scaled SCO\({}_{2}\) heat exchanger, the mass flow rate was calculated as 0.1062 kg/s. The fluid properties were implemented in the CFD code with a real gas properties table format, using NIST REFPROP Database 23, Version 9.1 [25]. For the convergence criteria, the SCO\({}_{2}\) outlet temperature was monitored in each step as well as solution imbalances (mass, momentum, and energy) less than 1%. Figure 6 shows the CFD results for the center position of the SCO\({}_{2}\) heat exchanger: Figure 6a,b show the temperature and velocity distributions, respectively. High-velocity regions were focused on the left and right sides of the SCO\({}_{2}\) heat exchange chamber because there was empty space for installing the SCO\({}_{2}\) heat exchanger and tube displacement margin due to the thermal stress. However, the in-house code did not consider the empty space at the corner of the SCO\({}_{2}\) heat exchange chamber. The empty space could have resulted in different simulation results between the in-house code and the CFD analysis. However, the outlet temperature of the SCO\({}_{2}\) showed a similar performance (602 \({}^{\circ}\)C) compared to the in-house code result. The pressure drop of the SCO\({}_{2}\) flow path was calculated as 0.374 bar, which is lower than the design constraint. Further pressure drops should be considered in the heat exchanger headers and nozzles. Figure 6: _Cont._  
The temperature and heat transfer coefficient distributions on the heat exchanger are important because these parameters can influence the tube's integrity. Figure 7 shows the temperature and heat transfer coefficient distributions on the inner and outer heat exchanger tubes. Figure 7a,b are the results of the tube's outer surface and Figure 7c,d are the results of the tube's inner surface. The tube's maximum inner and outer surface temperatures were 647 \({}^{\circ}\)C and 637 \({}^{\circ}\)C, respectively. The tube's maximum surface temperature was lower than the temperature assumption value (650 \({}^{\circ}\)C) in the heat exchanger material selection. For the tube's inner area, the maximum heat transfer coefficient was about 4000 W/m\({}^{2}\)K, which was located at the tube's bending location. For the outer tube area, the maximum heat transfer coefficient was located near the outlet position of the flue gas and was 70 W/m\({}^{2}\)K. Based on the temperature and heat transfer coefficient distributions on the heat exchanger tube, thermal stress analysis was performed. Figure 6: CFD results at the centerline of the scaled SCO\({}_{2}\) heat exchanger: (**a**) temperature and (**b**) velocity distributions. Figure 7: \(Cont.\)
# Optical Lensless-Camera Communications Aided by Neural Network
###### Abstract  
Currently, the optical components of a camera embedded in the device constrain its overall thickness. Moreover, if the camera is strongly shaken, the lens and sensor may be misaligned, resulting in a defocusing effect. In this paper, we propose a novel lensless-camera communication model, which removes the lens of camera, therefore decreasing the overall thickness of the device without affecting communications. To decode the images captured by the lensless camera, a decoding algorithm aided by back propagation (BP) neural network was designed, which recognizes the blurred image patterns efficiently. To adapt to time-varying environments, an adaptive training sequence adjustment mechanism was designed. Simulation results show that the proposed image decoding algorithm presents a good bit-error-rate (BER) performance. The proposed system has robust movements and provides resilience to interference, benefiting from the neural network and the designed algorithm.  
optical camera communications; lensless-camera; BP neural network; light-emitting diode
## 4 Intelligent Computing Methods: Brief Descriptions and Implementations
### M5 Model Tree (M5 MT)  
The M5 technique, as an efficient classification system, is frequently used to train models that estimate values. M5 generally consists of tree-like models, which are mathematically similar to multivariate-linear equations. It has the capability to deal with high dimensionality of datasets efficiently. Furthermore, the main merit of M5 technique is that model trees are much smaller and more accurate in comparison with the Regression Tree (RT). Tree models are basically built by means of divide-and-conquer technique ([PERSON] [38]). The initial stage in constructing a model tree is to calculate the standard deviation (SD), as an error criterion, for the observed values in a training dataset. For a given sample of training dataset, a multivariate linear technique is fitted at each node of the model tree. M5 is limited to the input variables, which are referenced by linear equations somewhere in the node sub-tree. As each linear equation is constructed at each node, the mathematical shape of the linear equation can be eliminated to reach the optimum predicted error. Generally, M5 applies the Greedy Search (GS) algorithm in order to remove input variables (or attributes) that have marginal contributions to the development of the linear equation. Furthermore, processing the smoothing stage can enhance the accuracy level of each linear equation at the node.  
M5 MT model provides a collection of rules leading to linear regression equations. In the case of 3D-scour rate modeling, Weka3.9, as data-mining software, was considered. The linear equation being driven from M5 rules is expressed as:  
\[V_{H,R,L}^{*}=a_{0}+a_{1}\cdot(1+sin\alpha)+a_{2}\cdot\Big{(}1-\frac{e}{D} \Big{)}+a_{3}\cdot KC+a_{4}\cdot\theta_{w} \tag{12}\]  
with \(a_{0}\), \(a_{1}\), \(a_{2}\), \(a_{3}\), and \(a_{4}\) as constant coefficients of the linear equation.  
In the process of M5 development, Tables A4 and A5 in the Appendix B summarize, respectively, the M5 rules and the associated linear equations returned by the Model Tree for the prediction of \(V_{H}^{*}\). It can be seen from these tables that 12 rules were provided and, additionally, all 4 input variables were used as splitting parameters. In other words, all the inputs were important in setting the 12 formulations. In the case of \(V_{L}^{*}\) estimation, as it can be seen in Tables A6 and A7 in Appendix B, 12 rules were obtained from M5 analysis with splitting of the parameters \(1+\sin\alpha\), \(1-e/D\), and \(KC\). Interestingly, the 12 relationships presented in Table A7 indicated that the Shields' parameter, \(\theta_{w}\), due to regular waves, has no role in predicting the scour rates at the left hand of the pipeline. Finally, as indicated in Tables A8 and A9 in Appendix B, 13 rules in terms of linear regression equations were extracted from M5 to predict \(V_{R}^{*}\). Similarly, here too it was ascertained that the Shields' parameter does not control, at least significantly, the scour rate propagation at the right hand of the pipeline.
## 3 Results  
Trueness was evaluated based on 90 digital measurements obtained using three different scanning systems: 3 Shape Trios 3 (n = 30), Medit i500 (n = 30), and PIC Dental Photogrammetry (n = 30).  
As Table 2 depicts, statistically significant differences (\(p<0.001\)) were found across all scanners when comparing the three implant distribution configurations.  
For the 3 Shape scanner, the CCI C distribution yielded the highest trueness values, which corresponds to the lowest RMS scores (as trueness increases, RMS values decrease). No significant differences were found between CCI Options A and B (\(p=0.142\)).  
Regarding the Medit scanner, the CCI A distribution presented the lowest trueness. However, no significant differences were identified between CCI Options B and C (\(p=0.119\)).  
In the case of the PIC Dental system, statistically significant differences were observed among the three implant distributions evaluated. Nevertheless, post hoc pairwise comparisons revealed no statistically significant differences between CCI Options A and B (\(p=0.058\)), nor between Options B and C (\(p=0.105\)).  
When comparing the three implant distributions (Table 3), statistically significant differences were observed (\(p<0.001\)). Among them, the CCI C distribution demonstrated the highest trueness values while no statistically significant differences were found between the CCI Options A and B (\(p=0.722\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scanner** &
\begin{tabular}{c} **Number and Position of** \\ **Implants** \\ \end{tabular} & **Group** \\ \hline \multirow{3}{*}{3 Shape Trios 3} & 6 (CCI Option A) & TA \\ \cline{2-3}  & 4 (CCI Option B) & TB \\ \cline{2-3}  & 4 (CCI Option C) & TC \\ \hline \multirow{3}{*}{Medit i500} & 6 (CCI Option A) & MA \\ \cline{2-3}  & 4 (CCI Option B) & MB \\ \cline{2-3}  & 4 (CCI Option C) & MC \\ \hline \multirow{3}{*}{PIC Dental Photogrammetry} & 6 (CCI Option A) & PA \\ \cline{2-3}  & 4 (CCI Option B) & PB \\ \cline{2-3}  & 4 (CCI Option C) & PC \\ \hline \hline \end{tabular}  
* CCI Option A with 6 implants: 2 implants in the position of the lateral incisors, 2 anterior to the mental foramen and 2 in the region of the first molars; CCI Option B with 4 implants: 2 implants in the canine position and 2 implants in the first molar region; CCI Option C with 4 implants: 2 implants in the position of the lateral incisors and 2 implants anterior to the mental foramen.  
\end{table}
Table 1: Description of the study groups according to image acquisition device and implant distribution.  
A comparison of the scanning systems (Table 4) revealed significant differences in trueness among the three devices assessed (\(p<0.001\)). The Medit IOS outperformed the others in terms of trueness, whereas the PIC Dental system demonstrated the least favorable results.