esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Application of Metamaterials
### Water and Air Purification  
Metamaterials can be used to create more efficient water and air filtration and assessment systems. In water systems, they can be an aspect of controlling the way that light passes through the water. This can make it easier to remove pollutants from the water, making it safer to drink. In air systems, they can be a part of process, removing pollutants from the air. By using MM to control the way air flows through the purifier, it is possible to remove more pollutants from the air.  
[PERSON] at el. [39] combined the cell structure design of metamaterials with ordinary materials, which leads to the creation of many new materials of different characteristics. This provides more possibilities for material research. He pointed out that water and air are usually polluted by various substances, such as organic matter, inorganic matter, and various microorganisms. Usually, filters with small critical sizes have good filtering effects, but their filtration resistance is large, and their flux is low. On the other hand, a large critical size filter will have a poor filtering effect. To overcome it, [PERSON] pointed out that using a plate structure to make the filter screen can lead to an adjustable critical size filter. Using such a filter screen will greatly improve the controllability of the filtration process.
## 3 Results and Discussion
### Rebinding and Selectivity-Non-Fluorescent MIPs  
Rebinding tests were carried out by HPLC (Table 2), first on the non-fluorescent MIPs; the best performing polymer for TY was 4 VP-TY with about 10 nmol/mg ligand captured and an imprinting factor (IF, measured in comparison to the corresponding non-imprinted polymers, NIPs) of 17.2 after 24 h, while for HT, MIP 4 VP-HT gave 22 nmol/mg rebinding although with an IF of 1.9 after 24 h. 4 VP was identified as the best performing monomer both considering the rebinding capability and the specificity measured by the IF. Actually, these results reflect the interactions observed by \({}^{1}\)H-NMR titrations where the major variations in the chemical shift for tyros were observed with 4 VP, while for hydroxytrosol the best results were obtained with 2 VP as the monomer. Differences in the rebinding properties have been observed during time, such as in the case of 4 VP-TY which triples up the rebinding capacity between 210 min and 24 h. Additionally, 4 VP-HT and IMD-HT doubled up the value in the same time as can be observed from Table 2. In Figure 4a,c, the decay of phenols concentration in samples treated with the imprinted polymers are reported, while Figure 4b,d show the decay of phenols concentration in samples treated with the corresponding non imprinted polymers (NIP). The residual concentrations of phenols were calculated from the HPLC results. The amount of rebound phenols listed in Table 2 were calculated by the variation from the initial amount in the test solutions. Despite the fact that in these three cases the rebinding properties enhanced over time, the IF changed differently. With 4 VP-TY and 4 VP-HT the IF also increased over time, indicating the formation of specific sites in the polymer which contributed significantly to the rebinding properties in a relatively slow process. In the case of IMD-HT, the IF value decreased over time, indicating that non-specific interactions occur in prevalence and an equal rebinding capacity is observed for MIP and NIP (IF = 1). OXO MIPs showed an IF < 1, with a rebinding capacity of the corresponding NIP major than the one for the MIP itself. This first series of evaluation led us to select the 4 VP monomer for further developments.  
Besides rebinding and specificity, 4 VP-TY and 4 VP-HT were evaluated also for selectivity. 4 VP-TY was evaluated in rebinding of HT, and proved to be selective for its template as rebinding of TY was about 4-times higher than that of HT (4.0 nmol/mg vs. 1.0 nmol/mg). Conversely, 4 VP-HT was not selective when the experiment was carried out at 50 \(\upmu\)M ligands (2.3 nmol/mg for both), but became selective towards HT when the measure was repeated at 100 \(\upmu\)M phenols (3.2 nmol/mg vs. 2.3 nmol/mg). The higher selectivity of the 4 VP-TY MIP could be probably explained by the smaller size of TY compared to HT, which bears an additional hydroxyl group at the aromatic ring. The selectivity of 4 VP-HT towards its template is higher (~8 times) than the one of 4 VP-TY towards its template (~5 times) (Table 2). This is probably due to the formation of a hydrogen bonds network around HT.
## 2 Factors Influencing the Risk of Road Subsidence around Sewer Pipes
### Analysis of Sewer Pipe Diameter and Road Subsidence Probability  
Based on the collected sewer pipe data, 450 mm was the most common pipe diameter (about 38%). This was followed by 600 mm (30%) and 300 mm (12%). In relation to the 21 types of sewer pipes found near road cave-ins, the predominant pipe diameters were 600 mm (42%), followed by 450 mm (26%), and 800 mm (7%) (Figure 2).  
Figure 1: Road subsidence with regard to the buried period of the sewer.  
Figure 2: Sewer pipe diameter ratio near road subsidence accidents.  
Figure 3 shows the ratio of the length of sewer pipes to the diameter across the collected data and the ratio of the length of sewer pipes to the diameter in the sections where road subsidence occurred. As a result of examining the frequency of road subsidence accidents according to the sewer pipe diameter, it was observed that the section experiencing road subsidence had a higher occurrence of sewer pipes with larger diameters. In addition, the Pearson correlation coefficient between pipe diameter and road subsidence was about 0.7, indicating a high correlation between the pipe diameter and ground subsidence. The results demonstrate that the larger the pipe diameter, the higher the risk of subsidence.
# Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications
## 3 Experiments
### Experiments in Outdoor Environment  
Within the scope of this study, the final tests were carried out in the outdoor environment, and the resulting maps of the LOAM, A-LOAM and HDL Graph SLAM algorithms are given in Figure 4.  
When the LOAM results given in Figure 4a,b are examined, it can be seen that there are significant drifts in the odometry and Z axis in regions with dense trees. According to the A-LOAM results given in Figure 4c,d, there is a curve in the long building walls due to the odometry drift. In the HDL Graph SLAM results given in Figure 4e,f, deformation is observed on the long building wall because of the odometry drift.  
When all the results are examined, it is seen that the LOAM algorithm achieves partially satisfactory results. However, with an insufficient processing capacity of the computer, the data loss occurs in point clouds, causing odometry drifts. Because the processing capacity of the computer used in this study was not high enough, and because of the optimization problems of the LOAM algorithm, the odometry drifts have occurred in all point clouds in this study. More accurate results are obtained with the A-LOAM and HDL Graph SLAM algorithms than with the LOAM.  
Figure 3: Results of the SLAM algorithms for staircase: (**a**) top view of LOAM results; (**b**) side view of LOAM results; (**c**) top view of A-LOAM results; (**d**) side view of A-LOAM results; (**e**) top view of HDL Graph SLAM results; (**f**) side view of HDL Graph SLAM results.
## 3 Results and Discussion
### ATR-FT-IR Analysis of Donkey Milk Samples  
Several aliquots of the samples were subjected to ATR-FT-IR analysis as described in Section 2.4. The mean spectra collected on FT- and R-milks at the two sampling times are shown in Figures 3A and 3B, respectively. In the average spectra, it is possible to observe some common peaks representative of the main constituents of milk. The main ones are the broad band at approximately 3327 cm\({}^{-1}\) ascribable to hydroxy group, OH, or NH stretching of water, carbohydrates, and proteins. This latter family of constitutes can also be attributed to the peaks at 1638 cm\({}^{-1}\) and 1548 cm\({}^{-1}\), associated with the absorbance of C=O (amide I), NH (amide I and II), and CN (amide II) groups. The presence of long-chain fatty acids can be spotted in the range of 2900 cm\({}^{-1}\)-1700 cm\({}^{-1}\) where the stretching of CH in methyl and methylene takes place. Eventually, peaks in the fingerprint region can be associated with the CO stretching of carbohydrates.  
Comparing the two spectra associated to FT-milk, at T\({}_{1}\), it is possible to appreciate an increase of the intensities of the peaks at 2924 cm\({}^{-1}\), 2854 cm\({}^{-1}\), and 1743 cm\({}^{-1}\), which can be attributed to fats. This observation is completely in line with the metabolic factors; in fact, the lipid component of milk decreases in quantity in the first weeks of lactation (T\({}_{0}\)) until it reaches a minimum. Afterwards, the amount of milk decreases, while the concentration of fats increases (T\({}_{1}\)). To a lesser extent, the same trend can be also noticed in R-milks.  
Figure 2: Biplot associated to the PCA model calculated on ICP-OES data. Legend: Blue Diamonds: FT-Milk at T\({}_{0}\); Yellow Diamonds: FT-Milk at T\({}_{1}\); Red Dots: R-Milk at T\({}_{0}\); Purple Dots: R-Milk at T\({}_{1}\).  
A slight difference among the spectra collected on FT-milk at T\({}_{0}\) and T\({}_{1}\) can be observed also on the peak at around 1638 cm\({}^{-1}\), ascribable to proteins. In order to further investigate this aspect, proteins quantification has been carried out using the Bradford method [10]. In FT-milk, total proteins constitute the 1.26% \(\pm\) 0.07 of the milk; in particular, lysozyme is 0.81 \(\pm\) 0.15 mg/mL, \(\alpha\)-lactalbumin is 3.34 \(\pm\) 0.08 mg/mL, and \(\beta\)-lactalbumin is 2.04 \(\pm\) 0.18 mg/mL. In R-milk, total proteins constitute the 1.15% \(\pm\) 0.04 of the milk; lysozyme is 0.90 \(\pm\) 0.14 mg/mL, \(\alpha\)-lactalbumin is 2.08 \(\pm\) 0.14 mg/mL, and \(\beta\)-lactalbumin is 1.86 \(\pm\) 0.08 mg/mL.  
IR signals were also analyzed by PCA. However, this did not show clear differences neither for the technological process nor for sampling. ASCA was also applied to confirm this outcome, and it did. Both sampling time and technological process were investigated as factors, and neither of them were considered significant. In light of this, it is possible to conclude that, from the IR point of view, no statistically significant differences can be observed on FT- and R-milks.
## 4 Results and Discussion  
First, the free vibration of FGM square plates is analyzed to validate the present research. The square plates are made of aluminum oxide (ceramic) and Ti-6 Al-4V (metal), with two opposite edges simply supported. The material properties, the ceramic variation and the plate size can be found in References [31; 32; 33; 34]. The results of the classical plate theory [32], semi-analytical [33] and analytical results [34] based on the higher-order shear deformation plate theory are also included for comparison. Since the validation analysis has been performed in our former work [31], the numerical examples are omitted here for the sake of simplicity. As Table 1 in reference [31] shows, the results are in good agreement with those of other theories, which validates our formulation and programing. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & \(E\) & \(\mathbf{\
u}\) & \(\rho\) \\ \hline PMMA & 2.5 GPa & 0.34 & 1190 kg/m\({}^{3}\) \\ GPLs & 1.01 TPa & 0.186 & 1062.5 kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Material properties of the polymer [35] and GPLs [6]. Then we study the free vibration problem in the FG-GPLs-reinforced plates. In what follows, the FG-GPLs reinforced plates are made from a mixture of the polymer PMMA and GPLs with a length of \(a_{\text{G}}=2.5\) um, width of \(b_{\text{G}}=1.5\) um, and thickness of \(t_{\text{G}}=1.5\) nm. The material properties of PMMA and GPLs are shown in Table 1. The effective material properties of the composite plates are determined by Equations (1)-(4). Unless declared otherwise, \(s=0.1\), \(W_{\text{G}}=1\%\), \(a_{\text{G}}=2.5\) um, \(b_{\text{G}}=1.5\) um, \(t_{\text{G}}=1.5\) nm, the plate is simply supported at two opposite ends with FGM pattern UD used in Tables 2-4. The dimensionless frequency is  
\[\Omega=\sqrt{\rho_{M}\omega^{2}a^{2}/E_{M}} \tag{26}\]  
Table 2 compares the first five natural frequencies of SSSS square plates for \(m=1\)-\(5\) with different GPLs weight fractions (\(W_{\text{G}}=0\), \(0.1\), \(0.5\), \(1\%\)). For every \(m\), the frequencies increase as the weight fraction rises. This can be expected as the increased GPLs content improves the stiffness of the plates substantially. Tables 3 and 4 are focused on the effect of GPLs shape on the natural frequencies of the SSSS square plates for \(m=1\)-\(5\) under free vibration. The influence of different shape ratios is investigated, including GPLs aspect ratio \(a_{G}/b_{\text{G}}\) and width-to-thickness ratio \(b_{\text{G}}/t_{\text{G}}\). It can be observed from Tables 3 and 4 that increasing \(a_{\text{G}}/b_{\text{G}}\) with a fixed \(b_{\text{G}}/t_{\text{G}}\) or raising \(b_{\text{G}}/t_{\text{G}}\) under a constant \(a_{G}/b_{\text{G}}\), grows the natural frequencies of the square composite plates. A higher value of \(a_{G}/b_{\text{G}}\) represents a larger surface area, and a larger \(b_{\text{G}}/t_{\text{G}}\) indicates a wider graphene sheet. Hence, the bigger or wider GPLs can provide the better stiffening effect for the composite plates.
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 4 Experimental Results and Analysis
### Performance Evaluation  
In this paper, recall and precision rate which commonly used in the field of information retrieval, are selected as the metrics to evaluate the performance of the algorithm. They are defined as follows:  
Recall rate = the number of correct targets detected from the retrieval source/the number of targets that should have been retrieved \(\times\) 100%  
Precision rate = the number of correct targets detected from the retrieval source/the number of targets actually retrieved \(\times\) 100%
###### Abstract  
The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future.  
deep learning; convolutional neural network; tree species classification; random forest; OHS-1 hyperspectral image +
Footnote †: journal: Journal of Forest
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 6 Simulation--Basic Topologies
### Influence of Number of Branches on Throughput  
The topology configuration with multiple branches and the NAYY150 SE cable was considered. The distance between Tx and Rx varied from 150 to 1150 m and the branch length was 20 m (terminated by the 10 k\(\Omega\) load). The number of branches varied from 1 to 3 branches. Figure 6 shows the throughput for different numbers of branches. It can be seen from the trend in this figure that the capacity decreases approximately by 4 Mbps from 22 Mbps to 18 Mbps for a 150 m cable between transmitter and receiver  
Figure 4: Channel capacity for NAYY150 SE cable with varying length from transmitter to receiver.  
Figure 5: Channel capacity for NAYY150 SE and CYKY 3 \(\times\) 2.5 cable with varying length from transmitter to receiver.  
With further increase in the number of branches (two and three), approximately the same capacity drop of 2 Mbps can be observed. This capacity drop is less significant for longer distances between transmitter and receiver.
# A Time-Frequency Image Quality Evaluation Method Based on Improved LIME
## 2 Materials and Methods
### Technical Route
#### 2.3.4 Quality Evaluation  
The quantitative result of the quality evaluation in this paper is a quality score \(Q\), and the higher the score, the higher the quality of the corresponding time-frequency image in the target task. It consists of accuracy \(A\) and consistency \(C\):  
\[Q=A-C \tag{19}\]  
where accuracy \(A\) is the performance of the trained network on the test set. After the neural network is trained on the training set with preset hyperparameters, the predictive classification of the network is compared with the test set label using the test set data as the input. Accuracy is recorded as the proportion of the number of predictions consistent with the label to the total sample. The interval of \(A\) is \([0,1]\), and the higher the value, the higher the probability.  
Consistency \(C\) is used to assess the consistency of the model's interpretation results with prior knowledge. The interpretation result of the model is represented as the coefficient matrix of the interpretable model \(g\), and the prior knowledge is also represented in matrix form. Considering that consistency measures are more concerned with the consistency of trends than the consistency of values, consistency \(C\) is obtained by calculating the cosine distance between the absolute value of the coefficient matrix \(W\) of the interpretable model \(g\) and the prior matrix \(P\), as follows:  
\[C=1-\frac{|W|\times P}{\|W\|\|P\|}=1-\frac{\sum_{i=1}^{n}|W_{i}|\times P_{i}}{ \sqrt[2]{\sum_{i=1}^{n}W_{i}^{2}}\times\sqrt[2]{\sum_{i=1}^{n}P_{i}^{2}}} \tag{20}\]  
The prior matrix \(P\) is related to the feature matrix \(X\). When \(X=[F_{ASM}\ F_{Con}\ F_{Man}\ F_{Var}]\), \(P=[1\ 1\ 0\ 0]^{\prime}\). The interval of \(C\) is \([0,1]\), and the smaller the value, the smaller the difference, that is, the better the consistency.
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 5 Topology and Silhouette-Preserving Simplification  
With vertex-clustering-based simplification, which is popular in tandem with dual contouring [17; 55; 61], the resulting mesh is both simplified and requires less computational resources during extraction.  
We use the probabilistic extension of quadratic error functions introduced by [PERSON] and [PERSON] [68] for our bottom-up simplification due to better robustness and expose a single standard deviation \(\sigma_{n}\) to configure the normal variance \(\Sigma_{n}=\sigma_{n}^{2}I\). The plane position variance \(\Sigma_{p}\) is set to 0 because it does not influence the position of the representative vertex and only increases the expectancy value of the simplification error.  
The formulation for the QEF components \(A\in\mathbb{R}^{3\times 3},b\in\mathbb{R}^{3}\), and \(c\in\mathbb{R}\) for a single input plane with position \(\bar{q}\) and normal \(\bar{n}\) as introduced more generally in [68] is therefore  
\[\begin{split} A&=\bar{n}\bar{n}^{T}+\sigma_{n}^{2}I \\ b&=\bar{q}^{T}\bar{n}+\sigma_{n}^{2}\bar{q}\\ c&=(\bar{q}^{T}\bar{n})^{2}+\sigma_{n}^{2}\bar{q} ^{T}\bar{q}\end{split} \tag{2}\]  
In our implementation, calculating the QEF solution \(x^{*}=A^{-1}b\) was sufficiently stable with single-precision floating-point numbers and pivoted LU decomposition with nonzero \(\sigma_{n}\), which is also equivalent to solving the generalized Tikhonov regularization of the original problem [68].  
Figure 11: Mesh Simplification Example. Close-up mesh of an unsimplified floor in one of our reconstruction frames with and without merging vertices. We used a voxel size of 2 cm and PCA normal reconstruction with a neighborhood radius of 1 cm. The center of the octree is placed on the floor; thus, the two layers of voxels are generated between the sign-changing split plane.  
Figure 10: Interior Mesh Generation Causes. Two examples of layers of voxels being generated: (**a**) shows two close-by opposite surfaces, and (**b**) visualizes an unfortunate voxel-grid placement for the surface.
# River Water Quality Monitoring Using LoRa-Based IoT
## 4 System Design
### Gateway  
For Gateway, an HAT is designed for the Raspberry Pi 3 board, and its electrical schematic is shown in Figure 23.  
Figure 21: IoT Node subsystem prototype, practical assembly.  
Figure 22: IoT Node program flowchart. After the peripherals are initialized (setup), it periodically sends LoRa messages with sensor data (loop).  
The PCB (Pi HAT) developed for the Gateway subsystem is shown in Figure 24.  
The final prototype of the Gateway subsystem, with the display, the Raspberry Pi and the HAT assembled, is shown in Figure 25.  
For the Gateway, a program is developed in Python [31] where the data are received with another LoRa transceiver. Figure 26 shows the program flowchart.  
In the Main section of the program, only the LoRa module is initialized, and the connection from the MQTT client to the MQTT Broker is carried out.  
Figure 23: Electrical schematic of Gateway subsystem.  
Figure 24: PCB developed for Gateway subsystem (Pi HAT).  
When the LoRa module receives a message, it will cause an interruption in the program, through the DIC0 pin, and then in the interrupt handler (on_rx_done()), the data are processed and published in a local MQTT Broker (Mosquito) [32] by an MQTT Client (Paho) [33; 34]. The data are subscribed by another MQTT Client in a local Node-RED [22] and finally displayed on the dashboard; see Figure 27.  
Figure 26: Gateway program flowchart, initialization and receive interrupt handler.  
Figure 25: Gateway subsystem prototype, practical assembly: (**a**) front view; (**b**) rear view.  
In Node-RED, a flow is developed to allow us to present the data on a dashboard. In the Real Time page, the sensor data (Figure 28a) and the radio LoRa reception parameters (RSSI, SNR, packet counter and Packet Error Rate (PER)) (Figure 28b) are shown.  
Also, in this Node-RED flow, the data are saved in Comma-Separated Values (CSV) format, for historic view and future external processing. These files can be visualized and downloaded on the Historical page of the dashboard; see Figure 29.  
Figure 27: MQTT architecture flowchart in Gateway subsystem.  
Figure 28: Dashboard, real time data page: (**a**) water data; (**b**) radio LoRa data.