esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Antenna with MTM Structure: Design Approach  
The geometric structure of the proposed antenna based on previously-designed, wide-band, zero-index, metamaterial unit cell is depicted in Figure 7. The antenna is printed on commonly-used, commercially-available, flame-retardant (FR4) material of 1.6 mm thickness with 4.6 dielectric constants, due to its low cost and convenience for making multilayer printed circuit boards (PCBs). The overall size of the MTM antenna is 22 \(\times\) 26 mm\({}^{2}\). The radiating patch was designed with a modified triangular stripe line and planar ground plane. The stripe line is not fully connected. There is a gap on the upper side of the triangle, which forms a capacitance and produces a coupling between the electric fields to create electric resonances. A metal-stripe, circular, ring slot with a 3.80 mm outer radius and 3.20 inner radius is placed inside the patch. The metal-stripe, circular ring have a strong effect on the upper frequency to keep the magnitude-of-reflection co-efficient less than \(-\)10 dB. By using the metal-stripe, circular, ring structure, the feedline current does not spread out and it helps to remain well-suppressed at a higher frequency. The values of the design parameters are given in Table 3. A planar MTM pattern (array of 2 \(\times\) 4) is mounted on both sides of the substrate to enhance the radiation and broaden the working bandwidth. The fabricated prototype of the proposed antenna is shown in Figure 7c,d. The antenna is fed by a 50 \(\Omega\) microstrip line situated at centre of the triangular patch. The patterned, radiating patch is coupled with a conducting ground plane back to the substrate. This coupling-patch ground pattern forms a capacitive-inductive equivalent circuit that induces backward wave travels and enhances the radiation in the plane [11; 14]. This edge-to-edge coupling between the patch and ground helps to create extra radiation over the bandwidth. The array of the MTM unit cell has a strong effect on increasing the electrical length of the antenna both in the lower and higher operating bands, as well as efficiency and gain.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Structure**} & \multicolumn{3}{c}{**Negative Index Frequency Zone (GHz)**} \\ \cline{2-4}  & **Permeability (\(\mu\))** & **Permititivity (\(\epsilon\))** & **Refractive Index (\(\eta\))** \\ \hline Unit cell & 7.9–12.3 & 2.85–4.84, 7.9–10.05 & 7.9–14.2 \\
1 \(\times\) 2 & 7.9–12.3 & 2.8–4.85, 7.985–9.986 & 7.950–14.50 GHz \\
2 \(\times\) 2 & 6.65–12.70 & 2.61–5.93,7.67–8.07, 8.49–10.10 & 6.68–8.09, 8.50–14.54 \\
2 \(\times\) 4 & 6.65–12.21 & 2.857–5.759, 8.264–9.974 & 8.258–14.533 \\ \hline \end{tabular}
\end{table}
Table 2: Metamaterial (MTM) effective parameters (\(\mu\), \(\varepsilon\), \(\eta\)) in the negative index frequency zone.  
Figure 7: _Cont._
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 6 Results and Evaluation
### Parameter Analysis
#### 6.4.1 Tag Density  
In the RFID system, the influence between the tags cannot be ignored, so the density of the tags has a certain influence on the localization error [36; 37]. We fix the antenna configuration and the plane of tags array remained unchanged, changing the space between the tags. Figure 18 shows the localization accuracy under different tag densities. The localization error is below 10 cm when the distance between tags is more than 15 cm. When it is about 25 cm or more, the influence between the tags is small, and the localization error is stable at about 7 cm. When it is 5 cm, the localization error is 14.24 cm. If the distance is smaller, the localization error is very large, and it will lose the practical significance of localization. ANTspin's fault tolerance for tag density is sufficient for most practical applications.  
Figure 17: Localization error in 3D. (**a**) Relative incident angle error in 3D; (**b**) Distance calculation error in 3D; and, (**c**) Localization error in 3D.
# Research on Airspace Conflict Detection Method Based on Spherical Discrete Grid Representation
## 2 Determination of the Airspace Grid Subdivision System
### Comparison of Airspace Grid Subdivision Systems  
The design of AG requires unique shape, accuracy and coding mode, and the four mainstream subdivision schemes have their own advantages and disadvantages. Therefore, based on the actual needs of airspace planning, this paper compares the \"Goodchild\" grid criterion [21] to make it meet the needs of AG and facilitate subsequent matrix operations.  
The drawbacks of the equal longitude and latitude grid and the variable longitude and latitude grid are that the area of the grid at high and low latitudes has a large deformation and degrades from rectangular to triangular at the poles, which does not meet the needs of AR.  
Although the adaptive grid is flexible, the irregular shape of the grid makes it difficult to perform recursive subdivision and encoding, which causes great computational difficulties and is not suitable for AR.  
Relatively speaking, the regular polyhedron grid can best meet the above requirements. First of all, the geometric shape of the regular polyhedron grid is approximately the same, and there will be no obvious change between high and low latitudes; secondly, the grid can carry out nesting and multi-level recursive division according to the same rules, so the grid is regular and easy to code; finally, there is a corresponding relationship between geographical coordinates and grid system and longitude and latitude coordinates can be converted into grid coding. The geometric structure of the diamond grid is simple and the direction is consistent [22], and the diamond grid can be regarded as a deformed square grid, which is suitable for matrix operation.  
In summary, this paper selects a positive icosahedral rhombic grid for global subdivision and constructs an AG.
## 3 Research Design
### Output Variable
#### 3.2.4 Forecasting Performance Indicators  
In the constructed RBF neural network model, the data set is reasonably divided into training set and testing set. Use the training data to train the network, and adjust the network parameters to make the output gradually close to the real performance index value. At the same time, the error changes during the train process are monitored. After the training is completed, the test set data are input into the trained RBF neural network to obtain the forecast values of the performance indicators. And compare the forecast values with the real values. Based on the coefficient of determination R\({}^{2}\) or other performance indicators in order to assess the forecast performance. Based on the evaluation results, it may be necessary to adjust the model parameters and retrain the model until a satisfactory forecast result is achieved. Throughout the process, attention also needs to be paid to the generalization ability of the model to ensure the accuracy and reliability of the forecast results.
## 2 Equipment and Method
### Satellite Remote Sensing Observations  
To trace the source of haze in Wuhan, the backward trajectory of the air mass was analyzed using the HYSPLIT model developed by the National Oceanic and Atmospheric Administration ([[http://ready.arl.noaa.gov/](http://ready.arl.noaa.gov/)]([http://ready.arl.noaa.gov/](http://ready.arl.noaa.gov/)) HYSPLIT.php). Wuhan was defined as the terminal point and the backward trajectories of the air mass at 500 m above sea level were calculated for the period 7-11 June 2012. The MODIS active fire detection technique, based on Advanced Spaceborne Thermal Emission and Reflection Radiometer imagery, has become relatively well developed [33; 34; 35; 36; 37; 38], and MODIS fire products were adopted in this study to both help establish the fires distribution diagram and investigate the source of the haze.  
The CALIPSO satellite was launched in April 2006 and it belongs to the A-train satellite constellation. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) system, loaded on CALIPSO, emits 532 and 1064 nm lasers vertically downward, receiving backscattered energy from both the atmosphere and the surface [39]. The CALIPSO satellite provides information on the vertical distribution and optical properties of aerosols and clouds at both regional and global scales, which is useful for improving the understanding of the effects of aerosols and clouds on radiation [39; 40; 41]. CALIPSO has been widely applied in research on dust and haze [42; 43], e.g., the radiation heating effect of dust plumes in Tibet, caused by long-distance transport of dust arising from sandstorms in the Taklimakan Desert, has been investigated [44]. Furthermore, the optical and microphysical properties and vertical distribution of aerosols in Shanghai have been measured by CALIPSO on haze days [45]. Therefore, to further the understanding of the vertical structure and characteristics of aerosols in Wuhan, CALIPSO was employed in this study.
## 4 Results and Discussion
### Exploratory Data Analysis  
#### 4.1.1 RTM Model Verification  
To verify the RTM model output, the results were compared with collected ground-based solar irradiance data. Due to limited ground-data availability, the model was verified with solar ground data from September 2005 to November 2008 only, at the city of Dhahran (26.30\({}^{\circ}\) N, 50.146\({}^{\circ}\) E) in eastern Saudi Arabia, Figure 7. The data were collected using 250-Watt polycrystalline (FU240-250P) PV panels. Dhahran is located in an arid/semiarid region with high-frequency dust outbreaks and low precipitation (about 80 mm y\({}^{-1}\)) during the winter and spring seasons [18]. The model showed good agreement with the in-situ  
Figure 6: Schematic diagram of the application of the solar radiation data format for the ResNet model from 504 locations in the period 2015-2018. (**a**) Original time series of the 504 locations resulting in 183,960 data points per year. (**b**) Proposed data format, where n is the location number and **d** is the day of the year. (**c**) The final format of the data, where 70% of n locations were picked randomly for training the model and the remaining 30% of the locations for its evaluation. (**d**) Input and output time series.  
measurements except during 2008, Figure 7. It can also be observed that most of the data mismatching takes place during the spring and the summer months. Anti-phase correlation between experimental and simulated solar radiation values during the winter months was also observed. This can be explained by the overestimation of dust loading in the atmosphere by the model during this period.
## 2 General Aspects on Micropiles
### Technology  
All stages of MP execution and materials used influence MP behavior; therefore, it is important to present these aspects in a synthetic manner hereafter. The main influence of the technology is on the surrounding soil, which is modified twice, firstly during the drilling process and secondly during grouting. From seismic behavior point of view, it has not been studied explicitly, yet the relationship between the execution technology and the seismic behavior gives no direct relation between a specific technological option and the behavior obtained afterward.  
The common drilling techniques for MPs are the overburden drilling techniques (single-tube advancement drive, single-tube advancement external flush, rotary duplex, rotary percussive, hollow stem auger, etc.) or open hole drilling techniques (rotary percussive, solid core continuous flight auger, etc.) [1]. These different techniques can influence the behavior of MPs due to the permanent modifications of the surrounding soil that they cause, e.g., softening the surrounding soil. The fact that type E MPs have a simultaneous grouting and drilling process makes them a good option for reducing the negative effects of drilling [5].  
Grouting techniques used for MPs are those noted Table 1 and Figure 4. They can be split into [1]: gravity fill techniques (primary treatment), pressure grouting technique (through the casing), and post-grouting techniques, which has a higher ground-to-ground bond strength and it can be completed in single-step post-grouting such as type C, or in a multistep post-grouting as type D MPs. Grouting materials include sand-cement mortars or neat cement grouts.  
The typical reinforcement elements are concrete reinforcing steel bars, continuous-thread steel bars, and continuous-thread hollow-core steel bars.  
Depending on the MP type (A, B, C, D, or E), soil type (cohesive/granular), and loading conditions (moderate to high lateral loads usually implies using a drill casing as a permanent resistance element), the drill casing can be temporary or used as a permanent  
Figure 4: Micropile classification system based on type of grouting (after [1]).  
reinforcing element, which can extend to the full or partial length of the MP. Furthermore, an additional smaller diameter pipe is occasionally used with the drill casing (inside the casing) [1; 2; 10].  
Grouting body and drilling casing together form the body of the MP owing to kinematic interaction; they can influence the seismic behavior of MPs and MPed structures. Usually, when studying their seismic behavior, the focus is not on these aspects, but rather on other criteria, which are usually considered when studying seismic behavior of other types of piles, such as length, inclination, pile-to-structure connection, etc. Refs. [3; 6; 7; 11; 12; 13].
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## Abbreviations  
The following abbreviations are used in this manuscript:  
LDPC  
Low-density parity check  
BIBD  
Balanced incomplete block design  
BIBD-LDPC codes constructed from BIBDs  
ML  
Maximum likelihood  
RC  
Row-column  
QC-LDPC  
N6  
Number of cycles of length 6  
N8  
Number of cycles of length 8  
AWGN  
Additive white Gaussian noise  
BPSK  
Binary phase-shift keying  
SPA  
Sum-product algorithm  
PEG  
Progressive edge growth  
PEG-LDPC  
LDPC code based on the progressive edge-growth algorithm  
BER  
Bit-error rate  
WER  
Word-error rate
## 3 Results  
### Automatic Classification of Hyperspectral Images Using Artificial Neural Networks  
The overall classification accuracy of the HSI of trees was 92.2% based on 10-fold stratified cross-validation, and 90.0% when the model was tested on the test dataset which was not used for training. The classification accuracy of the model decreased with more diffuse illumination conditions ranging from 96.1% at SKYL = 0%, to 88.0% at SKYL = 100%. There is a similar trend in classification accuracy for the shaded leaves (85.2% to 76.2% for SKYL = 1%, 100%, respectively) while the classification accuracy for the \"sunlit leaves\" increased from 70.1% to 81.0% in the same range of diffuse ratio. This result is likely due to the sharper differences between sunlit and shaded areas under less diffuse illumination conditions, making it easier to partition these regions during manual classification.  
Examples of low classification accuracy of the \"sunlit\" and \"shaded\" leaf classes are shown in Figure 3a,b, respectively. It should be noted that these do not necessarily reflect the discrepancies between the manually and automatically classified images. Firstly, the manual classification was based on three bands (605 nm, 850 nm, and 860 nm) of the hyperspectral image, while the automatic classification is based on a much broader range of data (i.e., all 50 bands) and the associated detailed texture and structure information, especially in the case of individual trees [53]. Furthermore, since the manual classification is based on the subjective decision of the operator, there is some room for human error and inconsistency. These errors might be inherited by the model since the manually classified images were used as targets in the training. However, due to the number of individual HSI used (n = 150), it is likely that the inconsistencies caused by human error were mostly averaged out. Therefore, upon visual inspection, many of the automatically classified images bear a closer resemblance to the original HSI than their manually classified counterparts, as shown in Figure 3c.  
In a few instances, especially at higher SKYL values, the model misclassified the \"shaded ground\" as \"sunlit ground\" as shown in Figure 3d. In other rare cases, also at higher SKYL values, areas belonging to the sunlit ground class were classified as shaded ground producing artefacts (see Figure 3e). However, since only shaded and sunlit leaves areas are considered in comparing fAPAR and fIPAR in this work, these artefacts did not affect the overall outcome.
## 3 Measurement Setup  
### Measurement System  
The measurement campaign aims to obtain the RF propagation channel parameters at the train tracks for T2I communications. The RF propagation campaign is interested in the 5.86-5.91 GHz frequency range with a direct-sequence spread spectrum (DSSS)  
\begin{table}
\begin{tabular}{c c c} \hline
**Reference Index** & **Interested Scenario** & **Frequency Band** \\ \hline
[7] & V2V & 5.6 GHz \\
[8] & V2V & 5.6 GHz \\
[9] & V2V & 5.9 GHz \\
[10] & V2V/V2I & 5.9 GHz \\
[26] & T2I & 5.8 GHz \\
[24] & T2I & THz \\
[25] & T2I & 2.6 GHz \\
[13] & V2I & 5.8 GHz \\
[14] & V2I & 5.9 GHz \\
[15] & V2V/V2I & 5.9 GHz \\
[16] & V2V & 5.9 GHz \\
[17] & V2I & 5.9 GHz \\
[18] & V2I & 5.8 GHz \\
[19] & T2T & 5.2 GHz \\
[21] & T2I & 2.1–2.6 GHz \\
[20] & T2T & 5.9 GHz \\
[22] & V2I & 2.4 GHz \\ \hline \end{tabular}
\end{table}
Table 1: Categorization of previous propagation channel evaluation studies.  
channel sounder. The detailed system setups are illustrated in Figure 1a, Table 2, and more thoroughly described in [26]. The sounder utilizes a 2047 maximal-length sequence to BPSK to modulate a continuous wave carrier. The used RF bandwidth is 30.72 MHz. Both the transmitter and receiver are software radio platforms running GNU Radio and using a National Instruments B210 universal software radio peripheral. The I/Q data and GPS location data are saved every 0.5 s. The PDP generation with the recorded data in the receiver is processed in the post-process stage. The detailed data analysis method is described in Section 3.2.
# Research on the Prediction of Operator Users' Number Portability Based on Community Detection
## 2 Modeling and Experiment Comparison
### Comparison of Prediction Models
The research mentioned above shows that the DNN model and the Light GBM model have higher accuracy in predicting the number portability of users, although the previous research did not take into account the influence of the network structure on the text matrix. Thus, the user community network was added to the data processing to compare the prediction accuracy of the DNN model and the Light GBM model to those without a community network. The results are shown in the last two rows of Table 3. After the community network was introduced, the accuracy of both the DNN model and the Light GBM model increased by 3 percent, and the other evaluation indicators also improved. It can be seen that after introducing the user community network, all prediction evaluation indicators of the model have improved. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline KNN & 0.8267 & 0.7923 & 0.7573 & 0.7587 & 0.8697 \\ SVM & 0.8373 & 0.8019 & 0.7802 & 0.7945 & 0.9037 \\ RF & 0.8659 & 0.8275 & 0.7913 & 0.8023 & 0.9353 \\ DNN & 0.9080 & 0.8864 & 0.8327 & 0.8409 & 0.9518 \\ LGB & 0.9134 & 0.8939 & 0.8535 & 0.8729 & 0.9605 \\ DNN WITH COMMUNITY DETECTION & 0.9394 & 0.9117 & 0.8876 & 0.8946 & 0.9747 \\ LGB WITH COMMUNITY DETECTION & 0.9449 & 0.9102 & 0.8953 & 0.9018 & 0.9873 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of advantages and disadvantages of three feature selection methods. Figure 7: Comparison of model accuracy.
## 2 Background Theory and Method
### Algorithm for Determination of Thermal Conductivity  
For the photothermal deflection method, the methods of finding the thermal conductivity with the minimum error from the experimental results by comparing the measured phase delay curve according to the relative position with the calculated phase delay curve according to the relative position for various thermal conductivities derived from theoretical analysis are commonly used [29; 36; 37]. These methods include the zero-crossing method [21], phase slope method [22], and phase curve method [29]. In this study, the thermal conductivity was determined using the phase curve method, and its algorithm is shown in Figure 5. This method directly compares the phase curve measured through the experiment (\(\chi_{exp}\)) with that derived from theoretical analysis (\(\chi_{cal}\)) through Equation (13) and determines the point at which the minimum root mean squared error (RMSE) occurs. This method has the highest accuracy.  
Figure 5: Algorithm for determination of thermal conductivity.
###### Abstract  
In this study, the degree of corrosion of steel reinforcement is compared to the reflected voltage of electromagnetic waves and the ASTM C876 specification. This study conducted some tests on steel bars with different degrees of corrosion by transmitting electromagnetic waves in the reinforced concrete. In the test, a corrosion potentiometer was used to analyze and compare the experimental results. The electromagnetic wave reflection signals from the image generated by the ground penetrating radar were used to capture the different media in the concrete components. The reflection coefficient method was used to analyze the calculated relative dielectric constant of the media and to obtain the reflection energy and phase changes on the medium interface. We compared and discussed the corrosion potential and the reflected voltage from the corroded steel bars in the reinforced concrete. The reflected voltage analysis of the ground penetrating radar showed that the average corrosion potential on the corroded plate on the 3 rd floor was \(-280\) mV (Area C). According to ASTM C876, the steel bars in the concrete were moderately corroded. The reflected voltage analysis of the electromagnetic waves concluded that areas A and B were moderately corroded, which is consistent with the conclusion from ASTM C876. Using the innovative method, this study has established a reference standard for the degree of corrosion of steel bars based on ASTM C876 and has calculated the quantitative state of corroded steel bars. The variations of the electromagnetic wave reflected voltage of the ground penetrating radar can mark the changes in the corrosion interface of concrete and steel reinforcement with different degrees of corrosion and different thicknesses of the protective layer.  
ground penetrating radar; electromagnetic wave; steel bars corrosion; reflected voltage