esa-sceva/satcom-chunk-collection huggingface dataset:
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
###### Abstract  
Wireless sensor networks are expected to automatically monitor the ecological evolution and wildlife habits in forests. Low-power links (transceivers) are often adopted in wireless sensor network applications, in order to save the precious sensor energy and then achieve long-term, unattended monitoring. Recent research has presented some performance characteristics of such low-power wireless links under laboratory or outdoor scenarios with less obstacles, and they have found that low-power wireless links are unreliable and prone to be affected by the target environment. However, there is still less understanding about how well the low-power wireless link performs in real-world forests and to what extent the complex in-forest surrounding environments affect the link performances. In this paper, we empirically evaluate the low-power links of wireless sensors in three typical different forest environments. Our experiment investigates the performance of the link layer compatible with the IEEE 802.15.4 standard and analyzes the variation patterns of the packet reception ratio (PRR), the received signal strength indicator (RSSI) and the link quality indicator (LQI) under diverse experimental settings. Some observations of this study are inconsistent with or even contradict prior results that are achieved in open fields or relatively clean environments and thus, provide new insights both into effectively evaluating the low-power wireless links and into efficiently deploying wireless sensor network systems in forest environments.  
wireless sensor networks; low-power wireless link; link performance; forest monitoring +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Machine Learning Algorithms
#### 2.2.4 Gaussian Naive Bayes Classifier  
The naive Bayes classifier is a probabilistic classifier based on [PERSON] theorem. The probability that the sample belongs to class (label) \(c\) takes into account the feature values \(x_{1}\), \(x_{2}\),... \(x_{m}\) (i.e., the posterior probability), and can be calculated according to the following formula:  
\[P(c\lvert x_{1},\ x_{2},\ldots x_{m})=\frac{P(x_{1},\ x_{2},\ldots x_{m}\lvert c )P(c)}{P(x_{1},\  
practice. An assumption about the independence of feature values from within a class leads to simplification:  
\[P(x_{1},\ x_{2},\ldots x_{m}|c)\approx\prod_{k=1}^{m}P(x_{k}|c) \tag{4}\]  
A statistical probability distribution can be assumed for each feature in a database. The most commonly assumed types of distributions are normal (Gaussian), polynomial, and Bernoulli. In this article, it was assumed that the features had a Gaussian distribution. Hence, the used classifier was Gaussian naive Bayes (GNB). The distribution density function was in the form of the normal distribution:  
\[P(x_{k}|c)=\frac{1}{\sigma_{c}\sqrt{2\pi}}e^{-\frac{(x_{k}-\kappa c)^{2}}{2 \sigma_{c}^{2}}} \tag{5}\]  
where \(\mu_{c}\) is the mean value of the feature \(k\) for vectors belonging to the class \(c\), and \(\sigma_{c}\) is the standard deviation of this mean.  
The application of a naive Bayes classifier amounts to the calculation of the posterior probabilities for each class and finally classifying the sample to the class for which the value of the calculated probability is the highest:  
\[c_{NB}=\underset{c\in\mathbb{C}}{\text{argmax}}P(c)\prod_{k=1}^{m}P(x_{k}|c) \tag{6}\]  
In the applied Gaussian naive Bayes, the priors were adjusted according to the data.
## 6 Pesticide Degradation Pathways  
### Herbicides  
There are various types of herbicides with different compositions. According to the different structures and functional groups in herbicides, chlorine dioxide and ozone will undergo different reactions. For example, the reaction between triazine herbicides, ozone, and chlorine dioxide mainly involves the substitution of hydroxyl groups and dealkylation reactions (Figure 7(1)) [80,107]. In the degradation of phenylurea herbicides, processes are involved in which the hydroxyl group replaces the hydrogen on the nitrogen element of the benzene ring that is attached to the urea structure, the hydroxyl group complexes with the benzene ring, and the cleavage of the urea structure (Figure 7(2)) [108,109]. In addition, the thioether bond in the triazine herbicide ametyn reacts with chlorine dioxide to form a sulfonyl group (Figure 8(1)) [110]. The degradation pathway of chlorine dioxide with sulfonylurea herbicides involves the cleavage of the urea structure and the cleavage of the sulfonyl group (Figure 8(2)) [67]. Ozone degradation of the triazine herbicide metribuzin occurs as a process of triazine ring oxidation, forming an acylurea structure, which reacts with the urea structure and ultimately opens the ring to a small molecule (Figure 8(3)) [111].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Name** & **Pesticide Type** & **Reaction Conditions** & **Reaction Rate Constant** & **Reference** \\ \hline Thiamethoxam & Nicotine insecticides & tertiary butyl alcohol, pH = 7 & \(k_{\text{O}_{3}}=15.4\); \(k_{\text{OH}}=3.9\times 10^{9}\) & [100] \\ Isoprothiolane & Organic sulfur & tertiary butyl alcohol, pH = 7.5 & \(k_{\text{O}_{3}}=255.8\) & [101] \\ Clothianidin & Nicotine insecticides & tertiary butyl alcohol, pH = 7 & \(k_{\text{O}_{3}}=102.64\); \(k_{\text{OH}}=3.7\times 10^{9}\) & [102] \\ Fluopyram & Pyridinylethylbenzamide & pH = 6.5 & \(K\)= \(k_{\text{O}_{3}}+k_{\text{OH}}=1.002\times 10^{8}\) & [103] \\ 2-Chloro-N-(2,6-dimethylphenyl) & Chloroacetamide & herbicides & 60 \({}^{\circ}\)C & \(k=2.40\times 10^{4}\) & [104] \\   acetamide & Organophosphorus & tertiary butyl alcohol, pH = 7, 20 \({}^{\circ}\)C & \(k_{\text{O}_{3}}=590\) & [39] \\ Dichlorvos & Anide herbicides & pH = 9.75 & \(k_{\text{O}_{3}}\)= 2.8; \(k_{\text{OH}}=3.2\times 10^{10}\) & [105] \\   Alachlor & Triterpenoid herbicides & tertiary butyl alcohol, pH = 7 & \(k_{\text{O}_{3}}=8.9\times 10^{5}\) & [106] \\   Tembotrique & Triterpenoid herbicides & tertiary butyl alcohol, pH = 7 & \(k_{\text{O}_{3}}=6.7\times 10^{5}\) & [106] \\ \hline \end{tabular}
\end{table}
Table 2: Reaction rate constants of ozone and common pesticides in solution.
# An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features
## 2 Related Work
### Point Cloud Registration Method  
Point cloud registration is a vital component of the LO algorithm, with the objective of computing transformations by minimizing a distance function between adjacent point clouds [25]. Commonly used point cloud registration methods in LO can be primarily categorized into three types: point-based methods, distribution-based methods [26; 27], and feature-based methods [28; 29; 30]. The iterative closest point (ICP) algorithm [31] is perhaps the most widely applied point-based registration method.  
Also, the ICP algorithm is one of the most classical point cloud alignment algorithms for Lidar odometry. The ICP algorithms uses the nearest points as correspondence to optimize the transformation parameters through iteration, which stops when the precision satisfies the specified requirements. However, the ICP algorithm heavily depends on the initial parameters. As the number of iterations increases, the algorithm tends to settle into a locally optimal solution when the initial parameters are incorrectly set. To address these limitations, a variety of improvements to the ICP algorithm have been developed, such as point-to-line ICP [32], which employs the distance metric from the source to the target point cloud as the error function. It reduces the number of iterations and errors but is less robust for large initial displacements. Furthermore, point-to-plane or plane-to-plane distance can also be utilized as error functions [33]. However, these countermeasures depend on the geometric feature level. It is easy to introduce errors because they only sample the data from the simulated curvature of the nearby areas to determine the geometric features.
# Functionally Graded Plate Fracture Analysis Using the Field Boundary Element Method
## 2 Materials and Methods
### Mesh and Constraints  
The described method was applied to a quarter of a rectangular plate due to symmetry. The plate has length \(w\) and height \(h=2w\), with a crack at the center aligned to the short plate side. The crack length is \(a\). Figure 1, shows the mesh and boundary conditions of the analyzed structure. The plate mesh used for FEM analysis was constituted by iso-parametric eight nodes domain elements where the Gauss point quadrature was employed. The same domain elements have been used in FBEM analysis together with three nodes quadratic boundary elements. The plate had null displacement on the nodes with \(y=0\), outside the crack. The x = 0 side had symmetry constraints. The structure was loaded by a traction \(\sigma=1\) along y-direction on the top side. Three different crack lengths were investigated, namely \(\frac{a}{w}=0.11\); \(\frac{a}{w}=0.33\); and \(\frac{a}{w}=0.55\).
## 3 Dynamic Analysis
### Result of Dynamic Analyses
#### 3.3.1 Effect of Train Speed  
As a representative example, Figure 9 shows the resulting vertical peak values of the acceleration and dynamic stress observed at the center of the top surface layer. As expected, the acceleration and dynamic stress both tend to increase with an increase in \(v\). A linear amplification is observed in the considered range of \(v\). The slope of the linear fitting line of the dynamic stress is gentler than that of the acceleration, thereby indicating that the increment of the vertical peak value of the dynamic stress is slower than that of the acceleration. For example, as \(v\) increases from 60 to 160 km/h, the acceleration increases by 271%, whereas the dynamic stress increases by 18%. The amplification of the vertical peak of the acceleration with \(v\) is more evident than that for the vertical peak of the dynamic stress. The vertical peak values of both the acceleration and dynamic stress increase with \(v\) in a linear manner for the range of tested speeds, and the respective relations can be expressed as follows:  
\[A_{rs,\max}=0.083v-2.1\ R^{2}=0.999 \tag{7}\]  
\[\sigma_{rs,\max}=0.26v+110.6\ R^{2}=0.934 \tag{8}\]  
where \(A_{rs,max}\) and \(\sigma_{rs,max}\), respectively, denote the vertical peak values of the acceleration (unit: m/s\({}^{2}\)) and dynamic stress (unit: kPa) on the top layer surface; \(v\) represents the train speed (unit: km/h); and \(R^{2}\) is the correlation coefficient.  
Figure 8: Comparison of peak accelerations values of the subgrade with the distance from the track centerline.
## 3 Results and Discussion
### Interface between PA6 and Glass Fibers  
The interfacial characteristics were measured using a single fiber pullout test, which indirectly predicted the mechanical properties of the composites. Figure 5a shows the scheme of the single fiber pullout test, in which a single glass fiber composite was constructed on a slide glass substrate. A force was applied to the glass fiber under unidirectional tension until the glass fiber was pulled out of the adhesive resin. This phenomenon was observed using an OM capable of capturing images in real time (Figure 5b). Figure 6 shows a graph of the embedded fiber length (\(L_{e}\)) versus maximum force (\(F_{max}\)). The embedded fiber length is nearly proportional to the maximum force within its range (100-200 \(\upmu\)m). The IFSS between 1011 BRT and the glass fibers was higher than that between GP 1100A (W) and the glass fibers (Table 4). Thus, these results aid the selection of appropriate resins for the manufacturing processes, as they indicate that a lower IFSS could ease the initiation of microcracks when the composite is stressed, leading to a degradation in the mechanical properties of the composite.  
Figure 5: Single-fiber pullout test: (**a**) specimen composition and (**b**) OM images before and after the experiment.  
Figure 6: Results of the single fiber pullout test.
## 2 Materials and Methods
### Measurement of Greenhouse Gas Emissions  
The measurements of CO\({}_{2}\), N\({}_{2}\)O, CH\({}_{4}\) were conducted by static chamber gas chromatography technique [29]. For this purpose, the 50 \(\times\) 50 cm stainless-steel chamber was introduced into the 10 cm deep compost. The 50 cm height chamber was fully wrapped by an insulating sheet to reduce the chances of temperature changes during gas sampling from inside to outside the chamber. The samples were measured at daily intervals for the first week, and then frequency was changed to one day gap, later twice per week for the remaining weeks. Five samples were taken in the field after every seven minutes with 60 mL syringes. These syringes were attached with closed chambers by 3-way stopcocks through the Teflon tube. During gas sampling, the temperature inside the chamber and a manual thermocouple thermometer also determined compost temperature. The samples collected were carried to the laboratory, where a gas chromatograph (HP-5890 Series II, Hewlett- Packard Alto, GC, California, USA) was fitted to an electron capture detector (ECD) for analysis. CO\({}_{2}\), CH\({}_{4}\), and N\({}_{2}\)O fluxes were detected from linear or nonlinear values as increased, selected based on r\({}^{2}\) values in their headspaces with time, and considering, along other parameters, the headspace height of the chamber, temperature, and pressure of the air. The method of [PERSON] et al. [34] was used to measure greenhouse gas emissions on sampling days. The cumulative gases emissions were determined by the following equation [35]:  
\[\mathrm{C}=\frac{\sum\mathrm{F}_{\mathrm{i}+1}+\mathrm{F}_{\mathrm{i}}}{2} \tag{2}\]  
where C represents cumulative emission, F is flux, i expressed as the initial day of sampling, and t is time duration (h), sample collection the experiment. The sum of all fluxes was taken according to the sampling frequency for all cumulative emission of gases.  
Calculation of global warming potential (GWP) was also made to evaluate total global warming effects for CO\({}_{2}\), CH\({}_{4}\), and N\({}_{2}\)O emissions. During the whole experimental period, the GHG emissions were transformed to CO\({}_{2}\)-equivalent and summed to get total GHG discharges as warming potential (for 1 mol CH\({}_{4}\) = 34 mol CO\({}_{2}\)-equivalent) and (for N\({}_{2}\)O = 298 mol CO\({}_{2}\)-equivalent).
# MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes
## 3 Algorithm Description
### The Multi-Level ICP Matching Method
In particular, in large-scale and sparse environments, the accumulative error cannot be completely eliminated when the robot travels a long distance if only the above two matching processes are considered. So the following method matches the current frame \(z_{i}\) with the area-neighbor keyframe  
Figure 3: An example of the matching process. Figure 2: The matching process of the current frame and map. \(z_{\tau}\) based on a priori of pose estimation. If the matching score is less than a certain threshold, the pose constraint between \(x_{i}\) and \(x_{\tau}\) will be established. The process is listed as Algorithm 2. The initial parameter is: \(L=\{(0,x_{0},z_{0})\}\), \(d=0\). ```
Input: Current pose and observation: \(x_{i},z_{i}\) Output: Transformation \(T_{i\tau}\) which aligns \(z_{i}\) and \(z_{\tau}\)
1: calculate \(d=d+\|x_{i}-x_{i-1}\|\)
2:if\(d<d_{thr}\)then
3:return null
4:end
5:foreach\((k_{\tau},x_{\tau},z_{\tau})\) in \(L\)do
6:if\(k-k_{\tau}>k_{thr}\)then
7:if\(\|x_{t}-x_{\tau}\|<x_{thr}\)then
8: put \(z_{t}\) and \(z_{\tau}\) into Algorithm 1, get \(T\) and \(Score_{fitness}\)
9:if\(Score_{fitness}<S_{thr}\)then
10:\(T_{i\tau}\gets T\)
11: return \(T_{i\tau}\)
12:end
13:end
14:end
15:end
16:\(L=L\cup(k_{i},x_{i},z_{i}),d=0,k=k+1\)
17:return null
```  
**Algorithm 2**Matching the current frame with an area-neighbor keyframe.
## 2 Materials and Methods
### SNR Calculation  
There are several imperative tools used for classifying the various patterns of mobility in wireless communications. These patterns are known as mobility models and include the Vehicular Mobility Model, the High-Speed Train Mobility Model, the Human Mobility Model, and the Ship Mobility Model. Through the extraction of extensive datasets, such mobility models provide researchers with the ability to scrutinize and approximate the influence of several mobility factors such as vehicular speed, congestion levels, ambiguity, social interactions, location preferences, and more. To perform an extensive experiment in this field, it is critically important to assess both the Human Mobility Model (HMM) and the Vehicular Mobility Model (VMM) as they play a vital role in apprehending and speculating the dynamics of wireless communications. By concentrating on such specific mobility models, researchers can attain valuable insights into the complexities of mobility patterns and enhance the overall efficiency and reliability of wireless communication systems [5].  
Using a simulated channel model, an assessment has been conducted to determine the received signal power and noise power at the user equipment. The received signal power is calculated as the product of the transmitted power, the antenna gain, and the path loss between the base station and the User Equipment (UE). The noise power is calculated as the product of the noise figure, the bandwidth, and the Boltzmann constant. Then, the Signal-to-Noise Ratio (SNR) is calculated as the ratio of the power of the received signal to the power of the noise, expressed in decibels (dB) [34].  
The Signal-to-Noise Ratio (SNR) is calculated as the ratio of the power of the received signal to the power of the noise, expressed in decibels (dB). In the context of this problem, the SNR can be calculated as  
\[SNR=10 log_{10}(P_{signal}/P_{noise})\]  
where \(P_{signal}\) is the power of the received signal and \(P_{noise}\) is the power of the noise.  
The parameters required to calculate the SNR for this problem are as follows. \(P_{signal}\): The power of the received signal, which is calculated as the product of the channel gain and the transmitted power. \(P_{noise}\): The power of the noise, which is calculated as the product of the noise figure and the receiver bandwidth [5].  
The distance between the transmitter and receiver, the carrier frequency, and the properties of the propagation environment determine the channel gain. The transmitter power and receiver bandwidth are also specified in the problem. The noise figure represents the noise added by the receiver and is also specified in the problem [5].  
Subsequently, an analysis has been conducted to establish the SNR by comparing the power of the received signal to the power of the noise, resulting in creating a dataset comprising SNR values recorded at 20 millisecond intervals. These recorded SNR values offer significant insights into the operational efficiency of the communication system within an indoor microcellular setting in urban areas [33].
## 3 Results
### Scoring Method
#### 3.1.3 Comparison of Whole-Word and Phonemic Scoring Methods  
The comparative accuracy scores for all five speakers across the three word lists are shown in Figure 1. Statistical analysis to compare differences between the two scoring methods indicated that phonemic scoring yielded a significant difference in the overall accuracy scores only for the W&B list (F = 8.741, d.f. = 1, 14, \(p\). \(<\) 0.05) with nonsignificant differences for the NIT (F = 2.18, d.f. = 1, 14, ns) and the CRT (F = 2.10, d.f. = 1, 14, ns). Inspection of the range of scores obtained indicated that the variability of speaker scores was narrower for the phonemic scoring method than for the whole-word method. Finally, Speaker E demonstrated the highest variability of accuracy scores for all three lists and both scoring methods. He also was rated as being the least intelligible speaker for all lists except the CRT, in which Speaker D received the lowest overall accuracy score.
# Comparison of Crack Detection Performance of Low and High Contact Ratio Spur Gears
## 2 Gear Design and Determination of Crack Paths
The second region demonstrates a linear relationship in crack growth on a logarithmic scale, while it grows exponentially with the stress intensity factor. This area is where Paris' law is applicable, and analyses of crack progression are based on it. The third region represents predominantly unstable crack propagation, where the equivalent stress intensity factor range is utilized for fatigue crack propagation. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Gear Parameters** & **Case I** & **Case II** & **Case III** & **Case IV** & **Case V** \\ \hline Module m\({}_{\text{n}}\) (mm) & & & 3.175 & & \\ \hline Pinion teeth number (z\({}_{\text{p}}\)) & & & 40 & & \\ \hline Gear ratio (i) & & & 1 & & \\ \hline Pressure angle (\(\alpha\)) (deg) & & & 20 & & \\ \hline Addendum (h\({}_{\text{n}}\)) & 1 & 1.25 & 1.3 & 1.4 & 1.5 \\ \hline Dedendum (h\({}_{\text{f}}\)) & 1.25 & 1.50 & 1.55 & 1.65 & 1.75 \\ \hline Rack cutter tip radius (\(\rho_{\text{f}}\)) & & & 0.1 & & \\ \hline Contact ratio (\(\epsilon_{\text{ex}}\)) & 1.714 (LCR) & 2.085 (HCR) & 2.157 (HCR) & 2.229 (HCR) & 2.440 (HCR) \\ \hline Face width (mm) & & & 25.4 & & \\ \hline Material & & & AISI-9310 & & \\ \hline Backup ratio & & & 3.3 & \\ \hline \end{tabular}
\end{table} 
Table 1: Design parameters of LCR and HCR gears. Figure 3: Determination of crack initiation point: (**a**) FE bending stress result; (**b**) artificial initial crack. the body. As the backup ratio increases, the direction of the crack changes from the body to the direction of breaking the tooth. This is because, with the increase in the backup ratio, the weakest area where the crack can propagate has shifted from the body region to the tooth root region. Since these types of cracks directly break a single tooth, they appear to be benign cracks rather than rim cracks [13]. Since the backup ratio of the LCR and HCR gear wheels examined in this study was determined as 3.3, the cracks are expected to be in the direction of breaking the tooth. The results of the crack propagation analyses for the Case I LCR and Case V HCR gears are seen in Figure 5. Crack propagation analysis results obtained for other cases are shown in Figures 11-13 in Appendix A. When the results were examined, it was determined that the crack developed in the direction of breaking the tooth, as expected in all cases. The root cracks are defined by crack length as 1.5, 3, and 4.5 mm, respectively. CAD files were exported in stl. format from ANSYS 19.1 for each crack level. CAD files in stl. format were converted to step. format using FreeCAD 0.20 software to be used in stiffness calculations. In this way, the stiffness of cracked gears will be calculated directly using the paths obtained from the crack propagation analysis without making any assumptions for LCR and HCR gears.
## 3 Simulation Results
### Time-Domain Current Response
#### 3.1.3 Influence of the Pipeline Magnetic Permeability  
As can be noticed in Figure 2a, the different values of the pipeline relative magnetic permeability \(\mu_{r}\) have a negligible influence on the phase conductor current. Conversely, higher values of \(\mu_{r}\) lead to lower induced current magnitudes on the pipeline, shown in Figure 2b. This is because the pipeline penetration depth for the current density decreases with \(\mu_{r}\), increasing the pipeline impedance. Finally, an opposite trend can be noticed for the unit step and unit ramp current response of the soil in Figure 2c, where the soil current magnitude increases with \(\mu_{r}\). The soil current is caused by the opposite inductive contributions generated by the power line and the pipeline currents. Hence, since the power line current is substantially unaffected by \(\mu_{r}\), when the pipeline current magnitude decreases, a larger current magnitude is induced in the soil.