esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 3 Sensing Matrix Optimization of Compression Acquisition
### Complexity Analysis  
The complexity of the proposed algorithm consists of three parts: compressed measurement, sensing matrix optimization, and signal reconstruction. Since Equation (5) only contains matrix multiplication operation, we can obtain the complexity of the compressed measurement is \(O(MN)\). In addition, since measurement matrix \(\mathbf{\Phi}\) and code matrix \(\mathbf{C}\) are known, the optimization of the sensing matrix \(\mathbf{\Theta}\) can be carried out offline, and its complexity can be ignored. In this paper, we adopt the orthogonal matching pursuit (OMP) algorithm to recover the signal, whose complexity is \(O(N(\log_{2}(N))^{2})\)[36]. Consequently, the complexity of the proposed algorithm is \(O(MN+N(\log_{2}(N))^{2})\). Table 1 gives the complexities of the Serial acquisition algorithm [37], ELAD acquisition algorithm [38], gaussian process (GP) acquisition algorithm [30], and the proposed conjugate gradient (CG) acquisition algorithm. Figure 2 shows the complexity change of the four algorithms with different code length \(N\).  
As shown in Figure 2, the complexity of the proposed algorithm is the same as the existing compression acquisition algorithms, which is lower than the serial acquisition algorithm. Meanwhile, with the increase of code length \(N\), the complexity difference becomes larger and larger.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acquisition Algorithm** & **Complexity** \\ \hline Serial acquisition algorithm & \(O(N^{2})\) \\ ELAD acquisition algorithm (ELAD) & \(O(MN+N(\log_{2}(N))^{2})\) \\ Gaussian process acquisition algorithm (GP) & \(O(MN+N(\log_{2}(N))^{2})\) \\ Conjugate gradient acquisition algorithm (CG) & \(O(MN+N(\log_{2}(N))^{2})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of algorithm complexity.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 5 Experimental Tests
### Experimental Setup
For the training process, the MATLAB Neural Network Toolbox (2011 version) [58] was utilized. This library furnishes several functions to create, train and test MLPs. This process takes place in the server, which was run by an ASUS Quad-Core Intel Core i7 2.4 GHz with 4 GB RAM. Regarding the MLP architecture, a hidden layer with 16 neurons was chosen for every configuration. This parameter was obtained from trial and error, observing the minimization of the generalization error for the worst configuration, _i.e._, when all 17 SRs are selected, which corresponds to the case with the highest number of entries to be processed by the ANN. The calibration process must be performed for each one of the chosen SR configurations. To do so, for each selected configuration with \(N^{\prime}\) antennas (\(N^{\prime}<N\)), an ANN was trained taking the corresponding subset of \(N^{\prime}\) RSS features from the radio map corresponding to the selected \(N^{\prime}\) SRs employed to infer the tag location. During the ANN training process, the generated radio map was divided randomly into three sets: a training set of 766 samples (70% of the radio map) to update the network weights iteratively by the training algorithm, a test set of 220 samples (20%) to assess the generalization error of the MLP during the training process and a verification set of 110 samples (10%) in order to evaluate the stop training criterion [57; 58]. Then, the ANN training process randomly initializes the numerical weights associated with each neuron. Each training step will try to find the most suitable weights to associate the input data (RSS vectors) with the desired output (room ID). For this purpose, the Levenberg-Marquardt algorithm was chosen for its memory and time efficiency [57]. Approximately, the training process reaches the convergence criterion for any SR configuration in less than 200 epochs (less than 2 min). This process was repeated several \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \(\mathbf{R_{1}}\) & \(\mathbf{R_{2}}\) & \(\mathbf{R_{3}}\) & \(\mathbf{R_{4}}\) & \(\mathbf{R_{5}}\) & **Total** \\ \hline \# Samples & 86 & 137 & 264 & 438 & 170 & 1095 \\ Samples/m\({}^{2}\) & 3.5 & 5.6 & 2.7 & 1.9 & 3.0 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of gathered fingerprints inside the scenario (431.8 m\({}^{2}\)). times, reinitializing MLP initial weights and the training, verification and test sets. Then, the trained network with the least generalization error (best performance) achieved was chosen. Later, the weights of the chosen trained neural network were stored in the localization engine database, to be accessed in the future during the online stage by the localization engine.
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 3 Operator's Dilemma in the Unlicensed Spectrum
### One-Shot Game in the Unlicensed Spectrum  
Our goal is to find the best strategies of two LTE operators in the unlicensed spectrum one-shot game. Suppose that LTE operator 1 thinks that LTE operator 2 will use the LBT scheme. At this time, we  
Figure 3: The payoff matrix of wireless systems in the unlicensed spectrum: Two LTE operators and two WiFi networks coexist.  
Figure 2: The payoff matrix of wireless systems in the unlicensed spectrum: two LTE operators coexist.  
notice that LTE operator 1 will get \(U_{s}\) of the temptation payoff by using LTE-itself (_Case III_). On the other hand, operator 1 can also use the LBT scheme, achieving \(U_{sl}^{LTE}\) of the reward for mutual cooperation (_Case II_). It results in better outcomes for WiFi networks too. However, since we assume that LTE operators are rational decision-makers, LTE operator 1 will choose LTE-itself because \(U_{s}>U_{sl}^{LTE}\) always holds. Now suppose that LTE operator 1 guesses that LTE operator 2 will be LTE-itself. LTE operator 1 has a choice between LTE-itself and LTE with LBT; consequently, it will get either \(U_{f}\) or \(U_{w}\). Thus, when LTE operator 1 is sure that LTE operator 2 will choose LTE-itself, LTE-itself will be the best strategy for LTE operator 1 because \(U_{f}>U_{w}\) holds (_Case I_).  
Consequently, no matter what the other operator does, choosing LTE-itself is obviously better for LTE operator 1, and vice versa for LTE operator 2. Both LTE operators are expected to take LTE-itself and will eventually get \(U_{f}\) which is worse than \(U_{sl}^{LTE}\) from the point of view of harmonious coexistence. To summarize the one-shot game in the unlicensed spectrum, we conclude that operators' rationality leads to a worse result for themselves as well as WiFi networks--called the operator's dilemma. Although exact values of payoffs are not explicitly described here, the operator's dilemma follows a similar logic to the prisoner's dilemma but with additional WiFi networks. The prisoner's dilemma is a well-known example in game theory that shows why two completely rational individuals might not cooperate, even if it is in their best interests to do so [25]. Next, we focus on how this dilemma can be resolved in several repeated games.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods  
According to the initial research results, image fusion methods can be roughly divided into the following two categories: spatial domain fusion methods and transform domain fusion methods. In spatial domain fusion, we directly process the image's pixels to produce the desired image outcome. In transform domain fusion, the image will first be transferred to the frequency domain, where we need to carry out Fourier transform image processing and then inverse Fourier transform to get the image result we want. The initial image classification techniques can no longer fulfill the requirements due to the advancements in image fusion technology in recent years. According to the existing theoretical technologies, there are several different types of image fusion techniques that can be classified, including multi-scale decomposition, sparse representation, neural networks, sub-space, significance, hybrid, compressed sensing, and deep learning. The infrared and visible picture fusion technique introduced in this section is depicted in Figure 4. The procedures in the figure are briefly described in the paragraphs that follow.
## 4 Experiments
### Parameter Study
#### 4.3.1 Effectiveness of the Trimming Parameter  
In this subsection, we shall have the parameter study of TC-PCA, including the parameter of trimming set, \(p\) and the number of testing images selected for each individual in the standard part, \(r\), to the classification performance, as shown in Table 8. Here, we choose \(p\) = 75, 80, 85, 90, 95 and \(r\) = 1, 2, 3, 4. There are two important points that can be observed from Table 8. First, the classification performance is not sensitive to \(r\) for the JAFFE face database whereas the AMCR increases with \(r\) for the Yale and AR face databases at the same value of \(p\), which are consistent with our previous results in Section 4.1 (see also discussions in the same section). Second, the classification performance for the JAFFE face database is stable for any value of \(p\) whereas the AMCR improves from \(p\) = 95 to \(p\) = 75 for Yale and AR face databases at the same value of \(r\). These results further justify that the AMCR for the databases with both standard and non-standard images increases gradually by removing a portion of outlier images in the training set, which in turn, reveals the effectiveness of trimming set in the TC-PCA model. It is important to note that we fix the parameter \(D\) from 1 to 70 for image classification and clustering but our experimental results show that increasing the value of \(D\) to more than 70 does  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**All Dim.**} & \multicolumn{6}{c}{**NAUC**} \\ \cline{3-8}  & & **PCA** & **HQ-PCA** & \(\mathbf{I_{1}}\)-PCA** & **RPCA** & **OM-RPCA** & **AM-PCA** & **TC-PCA** \\ \hline Synthetic Data 3 & 0.354 & 0.596 & 0.605 & 0.653 & 0.409 & 0.652 & 0.650 & **0.654** \\ JAFFE & 0.676 & 0.673 & 0.686 & 0.680 & 0.684 & 0.677 & 0.674 & **0.691** \\ Yale & 0.545 & 0.519 & 0.538 & 0.521 & 0.525 & 0.528 & 0.520 & **0.547** \\ AR (Normal + Lighting) & 0.476 & 0.491 & 0.495 & 0.490 & 0.498 & 0.487 & 0.489 & **0.509** \\ AR (Normal + Sunglasses) & 0.491 & 0.464 & 0.467 & 0.469 & 0.465 & 0.467 & 0.465 & **0.509** \\ AR (Normal + Scaves) & 0.464 & 0.434 & 0.441 & 0.437 & 0.440 & 0.436 & 0.436 & **0.484** \\ \hline \hline \end{tabular}  
* For the synthetic data, the range of D is from 1 to 3.  
\end{table}
Table 7: K-Means Clustering Accuracy.  
not affect the AMCR and NAUC. Other than the above analysis, we also find that the parameter setting \(p=75\) is the best for these face databases. We compare the performance of this setting with parameter estimation using the holdout method as described in Section 4.1, which has been used to estimate the optimal number of projection vectors. The result is shown in Table 9. We can see that other than \(r=2\) for AR (Normal + Sunglasses) and \(r=4\) for AR (Normal + Scarves), the parameter setting \(p=75\) performs better than automatic parameterization. This shows that the parameter setting \(p=75\) is the best for these face databases.
# A Low-Cost CPW-Fed Multiband Frequency Reconfigurable Antenna for Wireless Applications
###### Abstract  
A novel, cedar-shaped, coplanar waveguide-fed frequency reconfigurable antenna is proposed. The presented antenna uses low-cost FR4 substrate with a thickness of 1.6 mm. Four PIN diodes are inserted on the antenna surface to variate the current distribution and alter the resonant frequencies with different combinations of switches. The proposed antenna is fabricated and measured for all states, and a good agreement is seen between measured and simulated results. This antenna resonates within the range of 2 GHz to 10 GHz, covering the major wireless applications of aviation service, wireless local area network (WLAN), worldwide interoperability for microwave access (WiMAX), long distance radio telecommunications, and X-band satellite communication. The proposed antenna works resourcefully with reasonable gain, significant bandwidth, directivity, and reflection coefficient. The proposed multiband reconfigurable antenna will pave the way for future wireless communications including WLAN, WiMAX, and possibly fifth-generation (5G) communication.  
reconfigurable antennas; PIN diodes; wireless applications; fifth-generation (5G) communication +
Footnote †: journal: Accepted: 13 August 2019; Published: 14 August 2019
# Construction and Application of an Ecological Quality Evaluation System Based on a PIE-Engine +
## 2 Materials
### Construction of the Remote Sensing Index of Ecological Quality
#### 2.2.1 Ecological Function Index  
Currently, several common indicators have been used to reflect vegetation growth status in remote sensing, such as the Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR), Net Primary Productivity (NPP), and Gross Primary Production (GPP). Considering the physical significance and accessibility of these indicators, the annual average values of FVC, LAI, and GPP were selected to represent vegetation growth in horizontal and vertical directions and the strength of photosynthesis. The calculation formula was detailed in Equation (1) [7].  
\[EFI=\frac{1}{3}\frac{FVC}{FVC_{ref}}+\frac{1}{3}\frac{LAI}{LAI_{ref}}+\frac{1}{3 }\frac{GPP}{GPP_{ref}} \tag{1}\]  
_EFI_ is the Ecosystem Function Index. \(FVC\), \(LAI\), and \(GPP\) are, respectively, the annual average FVC, LAI, and GPP. \(FVC_{ref}\), \(LAI_{nf}\), and \(GPP_{ref}\) serve as the reference values for each ecosystem index, with the reference value defined as the top 5% of the corresponding index. FVC was obtained from the China regional fractional vegetation cover dataset available on the PIE-Engine, LAI was sourced from the MOD15A2H dataset, and GPP was extracted from the MOD17A2H dataset.
# Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image
## 3 Result and Discussion  
### NDVI Analyzed by UAV-Based Multispectral Image  
Using the RGB and near-infrared images taken by UAV, an NDVI map was created as shown in Figure 10, and the statistical characteristics of the NDVI vegetation index are shown in Table 5. In order to classify vegetation areas from the NDVI map, the threshold for NDVI must be determined.  
In this study, vegetation areas were analyzed while changing the NDVI threshold value from 0.2 to 0.5 as shown in Figure 11, and the vegetation area according to the threshold value of NDVI is shown in Table 6. Suppose the NDVI threshold is set to 0.2. In that case, the vegetation area is 49,344 m\({}^{2}\), and the NDVI threshold is 0.3, the vegetation area is 37,201 m\({}^{2}\), and the NDVI threshold is 0.4, the vegetation area is 25,586 m\({}^{2}\), and if the NDVI threshold is 0.5, the vegetation area is 14,320 m\({}^{2}\) classified. When the threshold value of NDVI is set high, it can be seen that only areas with good plant growth are classified as vegetation areas.  
In this study, NDVI was analyzed using UAV images taken with a Canon S110 NIR camera mounted on 21 July 2020, and vegetation areas were classified by applying various threshold values of NDVI. The weather conditions at the time of UAV image capture were  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threshold Value of** & **Vegetation (m\({}^{2}\))** & **Non-Vegetation (m\({}^{2}\))** & **Sum (m\({}^{2}\))** \\ \hline \(>\)0.2 & 49,344 & 60,669 & \\ \hline \(>\)0.3 & 37,201 & 72,812 & \\ \hline \(>\)0.4 & 25,586 & 84,427 & \\ \hline \(>\)0.5 & 14,320 & 95,693 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Vegetation area according to the threshold value of NDVI.  
Figure 11: Vegetation map according to the threshold value of NDVI.  
average cloudiness of 8.1, a maximum temperature of 30 \({}^{\circ}\)C, a minimum temperature of 21.5 \({}^{\circ}\)C, and an average temperature of 24.9 \({}^{\circ}\)C.  
July, when UAV photographing was conducted, is the season when the growth vitality of vegetation is very good, and as a result of confirming the vegetation area through field survey, the vegetation map when the threshold value of NDVI is 0.2 or more shows the vegetation area of the target area most accurately.  
Figure 12 shows the location of grass and trees in the vegetation map analyzed with the threshold value set to 0.2 or higher, and it was confirmed that the vegetation condition of the region is consistent with the vegetation map analyzed using UAV images.  
However, during the field investigation, it was confirmed that the basketball court and the waterproof coating roof were classified as vegetation as shown in Figure 13.  
The NDVI analyzes the vitality of vegetation using the wavelength characteristics of near-infrared and red band, so green artificial structures should not be classified as vegetation. The problem of vegetation misclassification to the basketball court and the waterproof coating roof covered with urethane coatings could not be found in previous studies.
## 5 Conclusions  
This research proposed an inverted L-shaped patch antenna with a corner-truncated partial ground plane diagonally adjoined to a square branch for the L-band spectrum (1-2 GHz). The inverted L-shaped radiating patch generated an LP field, and the adjoining square branch was used to perturb the LP field to generate a CP field. The corner-truncated partial ground plane was utilized to enhance the ARBW. Simulations were performed, an antenna prototype was fabricated, and experiments were carried out. The simulated and measured RLBWs (\(\,\mid\!S_{11}\!\mid\!\leq-10\) dB) were 53.12% (1.106-1.906 GHz) and 62.37% (1.07-2.04 GHz), and the corresponding ARBWs were 70.88% (1.02-2.14 GHz) and 77.87% (1.09-2.48 GHz). The simulated and measured maximum gains were 3.0 dBi at 1.7 GHz and 2.76 dBi at 1.6 GHz, and the proposed antenna achieved RHCP in a clockwise direction. Overall, the simulation and measured results were in good agreement. In essence, the proposed inverted L-shaped patch antenna with a corner-truncated partial ground plane diagonally adjoined with a square branch for the L-band spectrum could achieve an ARBW of 77.87% (1.09-2.48 GHz), rendering the technology suitable for satellite communication and navigation applications. Subsequent research will experiment with a stacked patch antenna by incorporating a metasurface structure into the antenna scheme to enhance the maximum gain and utilize the principle of equivalent circuit to investigate the proposed antenna in different evolutionary stages. In addition, the miniaturization of the proposed antenna could be achieved by using a higher-permittivity substrate or a magneto-dielectric metamaterial.  
Conceptualization, P.B., T.L. and C.P.; methodology, P.B., T.L. and C.P.; investigation, P.B.; writing--original draft, P.B.; writing--review and editing, T.L. and C.P.; supervision, C.P.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by King Mongkut's University of Technology North Bangkok. Contract No. KMUTNB-64-KNOW-29.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
## 3 Results and Discussion
### Atmospheric Weather Variations
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