esa-sceva/satcom-chunk-collection huggingface dataset:
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 3 Results
### Estimation of Crop Height, FW, and DW in Field Trial  
The crop height, FW, and DW regression models were validated with manual data that were collected at 100 and 140 DAS. The crop height of the wheat genotypes in the experiment ranged from 42 to 92 cm on 100 DAS and 64 to 102 cm on 140 DAS with a normal distribution. The mean crop heights were 80 and 84.6 cm on 100 DAS and 140 DAS, respectively. A correlation-based assessment was performed to evaluate the CBM sensor-derived height readings (havg) with respect to the manual plot height measurements (Figure 9a). The assessment achieved a strong linear relationship between the sensor height readings and the manual crop height with an \(R^{2}\) of 0.79, _RMSE_ of 6.09 cm, _MAE_ of 5.03 cm, and a ratio of _RMSE_ and _MAE_ of 1.21. Unlike the highest points that were measured during the ground-based surveys, the sensor height readings represent the complete relief of the crop surface; therefore, it was found to be about 17.5 cm lower than the average canopy height of the manual measurements.  
The crop volume measurement of wheat was earlier reported to correlate with FW and DW [17]. Therefore, linear regression modeling was applied to estimate the FW and DW from BV, producing an \(R^{2}\) of 0.70 and 0.84, _RMSE_ of 0.49 Kg and 0.14 Kg, and _MAE_ of 0.38 Kg and 0.11 Kg, respectively (Figure 9b,c). The ratio of _RMSE_ and _MAE_ was 1.29 and 1.27 for FW and DW, respectively.  
Figure 8: Correlation model between the sensor measured distance and the reference distance. The figure contains the measurement for each of the eight detector elements. Fitted regression models were generated individually for all the detector elements.  
A frequency-domain analysis of the residual absolute error showed that the 50% of the observations had an absolute error \(\leq\)4.28 cm for crop height, \(\leq\)0.28 Kg for FW, and \(\leq\)0.09 Kg for DW, when compared with the regression line that was established by the fitted linear model (Figure 9d-f). The accuracy in measuring BV in field conditions influenced the final estimated FW and DW biomass. For example, when estimating canopy BV with a LiDAR sensor, a few centimeters may have some relative effect on the estimation of FW and DW. Nevertheless, the stacked LiDAR unit that was used here had benefit for better capturing the canopy's spatial profile, which is otherwise challenging with one ultrasonic sensor. The biomass gain between the two time points is also visible in the plots (Figure 9b,c). The fitted linear correlation line follows through the sample distribution at each time point, demonstrating the applicability of the methods in progressively measuring the biomass estimates.
# Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method
## 2 Split-Spectrum Processing (SSP)
### Recombination Algorithms of SSP  
There are numerous SSP recombination algorithms that could be employed for use in GWT to reduce the effect of coherent noise which have been described in the literature [16; 22]. In this paper, the two most common ones that obtained the highest SNR and spatial resolution in the previous work are explained in more detail:  
The polarity thresholding (PT) that is expressed as:  
\[\rm{y_{PT}[m]}=\left\{\begin{array}{cc}\rm{x[m]}&\rm{if\ all\ x_{i}[m]>\ 0,\ \ \ i=1,\ldots,\ n}\\ \rm{x[m]}&\rm{if\ all\ x_{i}[m]<\ 0,\ \ \ i=1,\ldots,\ n}\\ 0&\rm{Otherwise}\end{array}\right. \tag{2}\]  
where \(\rm{y_{PT}}\) is the output result of the signal that is obtained after processing at m, n is the number of filter band signals, x[m] is the unprocessed signal, and x[m] care the sub-band signals. This method  
Figure 3: Filter bank parameters of SSP.  
looks at the signal sub-bands at each sample time, and if the samples are all negative or all positive, then the output is the unchanged input signal. Otherwise, the output is zero. This has the effect of only passing the time samples where the polarity is not affected by the frequency. Thus, those sections of the signal that are highly frequency-dependent must be removed. However, the amplitude of the signal of interest needs to be larger than the amplitude of the coherent noise response, whereas if the noise signal has greater amplitude, then it will change the signal's sign.  
Polarity thresholding with minimisation (PTM), which is defined as:  
\[\mathrm{y_{PTM}}[\mathrm{m}]=\left\{\begin{array}{ll}\mathrm{min}(\mathrm{ x}[\mathrm{m}])&\mathrm{if\ all\ x}_{\mathrm{i}}[\mathrm{m}]>\ 0,&\mathrm{i=1,\ldots,\ n}\\ \mathrm{min}(\mathrm{x}[\mathrm{m}])&\mathrm{if\ all\ x}_{\mathrm{i}}[ \mathrm{m}]<\ 0,&\mathrm{i=1,\ldots,\ n}\\ 0&\mathrm{Otherwise}\end{array}\right. \tag{3}\]  
PTM is the combination of the minimisation and the PT algorithm where the output is the minimum amplitude of PT when there is no change in polarity. This method takes the minimum points that the PT algorithm has passed in order to suppress the noise even further than is obtained by the PT algorithm. Since the variance of the points containing noise is generally larger than those that are containing the signal of interest, the use of the minimisation algorithm reduces those that are the result of noise. However, this method is less effective when the noise level is larger than the actual signal's amplitude; thus, reducing the noise level will significantly reduce the signal amplitude in certain sub-bands, and gives minimum values for the PTM's output in that region.
## 7 Conclusions and Future Work  
This paper discussed multi-hop LoRaWAN networks as solutions for pipeline networks' monitoring systems. The focus was to design a linear multi-hop sensor network based on LoRa technology. Also, the paper presented existing multi-hop LoRaWAN sys  
Figure 11: Daily energy consumption per device for the pressure sensors—different multi-hop scenarios and 50 devices.  
Figure 10: Daily energy consumption per device for the temperature sensors—different multi-hop scenarios and 50 devices.  
tems and defined the limitations in terms of network throughput depending on the volume of data traffic that is generated by the sensors and energy consumption.  
We defined the network capacity in terms of the number of sensors of different types and the total throughput. We evaluated the multi-hop LoRaWAN network performance using an analytical model that considered different multi-hop communication scenarios.  
The analytical model evaluation for the multi-hop LoRaWAN monitoring system included network capacity, energy, and throughput. The results showed that the multi-hop solution increased the throughput by 38% compared to single-hop LoRaWAN. In terms of energy consumption, we observed that the second multi-hop scenario improved energy by 44% and the third scenario by 59%. The tradeoff was that enhancing energy consumption decreased the network throughput. When we have a large-scale network, such as an oil pipeline with medium traffic load requirements, multi-hop will be an efficient solution with low deployment costs, and the best multi-hop LoRaWAN network configurations require small SF, payload size, and low transmission power. Reducing the transmission range via selecting small SF and TP results in decreases in the total energy costs and packet collisions due to interference.  
In future work, we will consider different configurations for relay sensors, which will allow us to accommodate more devices and decrease collisions, in combination with the use of various intermediate GWs, and we will also study synchronization protocols to achieve better throughput by minimizing packet collisions.  
Conceptualization, H.A. and N.D.; methodology, H.A. and N.D.; validation, N.D., M.F. and H.B.; investigation, H.A.; writing--original draft preparation, H.A.; writing--review and editing, N.D. and M.F.; visualization, H.A.; supervision, M.F. and H.B.; project administration, G.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
The authors declare no conflicts of interest.
# AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications
## 6 Generalized Analytical Method for Directed Acyclic Graphs
### Algorithm  
Algorithm 1 shows a complete procedure for finding optimal runnable periods for a given DAG by our analytical method. As inputs, the algorithm accepts (i) a list of worst-case execution times for \(n\) runnables, (ii) a list of \(m\) paths in the DAG, (iii) \(\alpha\) and \(\beta\) of a given control cost function, and (iv) a utilization bound \(U_{B}\). The algorithm just needs a list of paths instead of the entire structure of the DAG. Thus, the algorithm itself does not consider generating paths from a DAG. As an output, the algorithm returns a list of optimal runnable periods. Note that the algorithm's computational complexity is just \(\mathcal{O}(n\times m)\), which is caused when finding the heaviest path in line 3. This polynomial time complexity makes our analytical method practical for use with large systems.  
```
Input:\(\mathbb{E}=<e_{1},e_{2},\cdots,e_{n}>:\) list of runnable execution times Input:\(\mathbb{P}=<P_{1},P_{2},\cdots,P_{m}>:\) list of paths Input:\((\alpha,\beta)\): coefficients of a control cost function Input:\(U_{B}\): utilization bound Output:\(<p_{1},p_{2},\cdots,p_{n}>:\) optimal runnable periods FunctionFindOptimalRunnablePeriods(\(\mathbb{E}\), \(\mathbb{P}\), \(\alpha\), \(\beta\), \(U_{B}\)):
1\(n\leftarrow\left|\mathbb{E}\right|/*\) n: number of runnables */ \(m\leftarrow\left|\mathbb{P}\right|/*\) m: number of paths */ \(e_{c}\leftarrow\max\limits_{P\in\mathbb{P}}\left(\sum\limits_{i\in\mathbb{P}}e_ {i}\right)-(e_{1}+e_{n})\) \(p_{1}\leftarrow\dfrac{e_{1}+\sqrt{(n-2)e_{1}e_{c}}+\sqrt{\dfrac{( \alpha+\beta)e_{1}e_{n}}{\beta}}}{U_{B}}\) \(p_{c}\gets p_{1}\sqrt{\dfrac{(n-2)e_{c}}{e_{1}}}\) \(p_{n}\gets p_{1}\sqrt{\dfrac{\beta e_{n}}{(\alpha+\beta)e_{1}}}\) for\(i\gets 2\)to\(n-1\)do
2\(p_{i}\leftarrow\dfrac{e_{i}}{e_{c}}p_{c}\) return\(<p_{1},p_{2},\cdots,p_{n}>\)
```  
**Algorithm 1**Find optimal runnable periods for a DAG
## 3 Distributed Positioning
### Comparison between Triangular and Squared Shapes and Parameters  
An a priori analysis of the anchor constellation shapes suitable for a proper coverage of the positioning domain can be effectively obtained by the results of the information theory. Extracting the inverse of the measurement covariance matrix, \(\mathbf{F}\), as the weight matrix, the variance leads to the Cramer-Rao lower Bound (CRB) for an unbiased estimator, E. We can write that [26],  
\[var[E]\geq\mathrm{CRB}(x,y)=\mathrm{CRB}_{x}(x,y)+\mathrm{CRB}_{y}(x,y), \tag{6}\]where  
\[\text{CRB}_{x}(x,y)=\sigma^{2}\frac{\sum_{n=1}^{\text{NM}}\left[\frac{\partial_{G_{n }}}{\partial x}\right]^{2}}{det(\text{F})};\ \text{CRB}_{y}(x,y)=\sigma^{2}\frac{\sum_{n=1}^{\text{NM}}\left[\frac{\partial_{ G_{n}}}{\partial y}\right]^{2}}{det(\text{F})} \tag{7}\]  
In Equation (7) \(G_{n}\) represents the projection on the (\(x\),\(y\)) domain of the \(n\)-th antenna element radiation pattern of the SBA and \(\sigma^{2}\) the variance of the observed data. The CRB represents the accuracy limit of the positioning estimator with respect to its several parameters, which in this work case are the shape of the constellation (i.e., triangular versus rectangular), their size, and the additive white Gaussian noise characterized by its variance \(\sigma^{2}\). The Figure 6 illustrates the CRB calculated in a \(10\ \text{m}\times 10\ \text{m}\) square domain, corresponding to a constellation of three anchors arranged in an equilateral triangular shape, with various triangle edge size, and \(\sigma=1\). The domain corresponds to the empty space. From the picture we can see that the minimum error is expected in correspondence of the anchors and reducing the edge size some kind of fusion of the minima occurs, leading to a unique circular region of minimum around the center of the domain. From the graphs we see that the triangular mesh can return an average accuracy across the entire domain which is almost flat up to \(7\ \text{m}\), while within the shape the average CRB increase with the square of the shape size. In Figure 7 we report a similar analysis considering a square shape constellation. In this case we can see a behavior qualitatively similar to the previous one but with a more uniform distribution within the entire domain. Figure 8 reports the accuracy in terms of CRB for both cases. From this we can observe that the rectangular mesh leads to better average values for the accuracy for the ideal mobile tag position estimation, being always better than the corresponding triangular mesh area. Given this, the article will focus on the rectangular shape, although it requires an additional anchor with respect to the triangular case.  
Figure 6: Cramer–Rao lower Bound (CRB) evaluation for triangular mesh at different edge sizes: (a) 0.85 m; (b) 3.45 m; (c) 6 m.  
Figure 7: CRB evaluation for rectangular mesh at different edge sizes: (a) 1 m; (b) 4 m; (c) 7 m.
## 4 Data Enhancement Experimental Methodology
### Data Enhancement for Rare Data in NSL-KDD  
In this study, we applied three data enhancement methods, namely Gaussian noise, _WGAN-GP_, and SOMTE, to enhance the number of rare data types in the NSL-KDD dataset. As shown in Table 3, the original training dataset in NSL-KDD contained a total of 125,973 data items, including 67,343 normal traffic data items, 11,656 Probe attack data items, 45,927 DoS attack data items, 52 U2R attack data items, and 995 R2L attack data items. In our data augmentation experiments, data augmentation was performed for each of the three types of rare data attacks, namely Probe, U2R, and R2L, respectively, adding an additional 5000 data items for each type. The percentage distribution of the Probe-enhanced training data only is illustrated in Table 4, which clearly shows that the percentage of Probe rare data increased from 9.25% before the enhancement to 12.72% after the enhancement. When only U2R was enhanced for the training data, the obtained percentage distribution of the category for the enhanced U2R is illustrated in Table 5, which clearly shows that the percentage of U2R rare data increased from 0.04% before enhancement to 3.59% after enhancement. Similarly, if the training data were enhanced for R2L only, the category distribution is illustrated in Table 6, which clearly shows that the percentage of R2L rare data increased from 0.79% before enhancement to 4.58% after enhancement.  
Figure 4: Second stage of two-stage fine tuning: parameter fine-tuning procedure.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Label** & **Class** & **Quantity** & **Percentage** \\ \hline
0 & Normal & 67,343 & 53.46\% \\
1 & Probe & 11,656 & 9.25\% \\
2 & DoS & 45,927 & 36.46\% \\
3 & U2R & 52 & 0.04\% \\
4 & R2L & 995 & 0.79\% \\ \hline  & All & 125,973 & 100.00\% \\ \end{tabular}
\end{table}
Table 3: Class distribution of the original NSL-KDD training dataset.  
In the first stage of the two-stage fine-tuning algorithm, we used three data augmentation methods to enhance the rare data classes according to the data quantities of classes in Tables 4-6, respectively. Then, we utilized four machine learning algorithms and two neural network models for training and classifying these augmented datasets. The obtained experimental results were used to analyze the performance of three data augmentation methods for recognizing these rare data types.
## 6 Performance Analysis
### Time on Air (ToA)  
In this study, to calculate the ToA, we set certain parameters, like payload = 25 bytes, preamble = 8 symbols, CF = 433 MHz, and Tx power = 14 dBm. The tool for calculating the ToA is considered from this [43]. Here, we considered three different BW, namely 125 kHz, 250 kHz, and 500 kHz, SF from 7 to 12, and CR from 1 to 4. The calculated results are recorded in the below tabular form. ToA is calculated at the distinct CR, namely 1, 2, 3, 4, and the ToA is illustrated in Tables 8-11. The table delivers that the ToA increases when the SF moves from 7 to 12 and ToA is reduced by half when the bandwidth increases to 125 kHz to 500 kHz. Figure 10 illustrates the ToA for the payload size of 25 bytes. The graph of the ToA at distinct CR reveals that the ToA starts increasing after the SF 9. Of all three BWs, the ToA is high for the 125 kHz BW and low for the 500 kHz BW. It is observed that the CR is directly proportional to the increase in the ToA; for example, at CR 1, the ToA of BW 1 is 56.58 ms, and, at the same BW 1, the ToA at CR 4 is 78.08 ms.
## 4 Aeroelastic Stability Analysis
### Linear Stability Analysis and Critical Wind Velocity  
The dynamic of the bridge, close to the rest position, is ruled by the linear part of Equation (25), which admits the solution \(a\left(t\right)=a\left(0\right)e^{\lambda t}\), where \(\lambda:=d_{0}+ud_{1}\). The position is asymptotically stable if \(\lambda<0\) and unstable if \(\lambda>0\). The condition of incipient instability occurs for \(\lambda=0\), that is for the wind velocity getting its critical (galloping velocity) value:\[u_{c}:=-\frac{d_{0}}{d_{1}} \tag{28}\]  
at which a Hopf bifurcation occurs. Since, from Equation (26), \(d_{0}<0\) (dissipative damping), bifurcation occurs only if \(d_{1}>0\) (self-exciting aerodynamics, entailing \(A_{1}<0\)). Once the wind direction has been fixed, this last condition occurs only for some cross-section shape (just said, aerodynamically unstable).  
The critical wind velocity depends on the mode at which the bridge is oscillating, each velocity being associated to each mode. The lowest critical velocity is associated with the lowest mode (at which the internal damping is minimum), which can be either symmetric (Equation (13)) or anti-symmetric (Equation (11)). For this latter, an explicit formula can be derived, namely:  
\[u_{c}=-\frac{c_{e}+16\pi^{4}\eta_{b}\rho^{2}+4\pi^{2}\eta_{c}}{b_{1}} \tag{29}\]  
The beneficial influence of the flexural stiffness should be noticed, since the critical wind velocity increases with \(\rho^{2}\). This is due to the fact that the internal damping is proportional to the stiffness, according to the Kelvin-Voigt model adopted.
## 3 Experimental Results
### Parameter Settings
DICE coefficient of 83% is reported in [57] by evaluation on thoracic and lumbar vertebrae, and the method utilizes the statistical multiobject shape and pose for verteb segmentation. However, this model lacks automatic initialization and possible segmentation overlap at the boundaries for the closely spaced thoracic vertebra. Meanwhile, an average DICE score of 81% is obtained for lumbar vertebrae CT image segmentation using level set formulation in the presence of different levels of salt and pepper noises [15]. A mean DICE coefficient of 87% is achieved by deep learning method for spine segmentation, and the method is evaluated on thoracic and lumbar CT vertebrae images [58]. However, this model fails in the structural consistency of the vertebrae parts segmentation or sometimes entire vertebra from starting and ending slices. Another deep learning method [41] is proposed for cervical vertebrae segmentation from X-ray images and achieved DICE similarity coefficient of 84%; however, the patch-based center localization method has the limitation of not knowing which center belongs to which vertebra. Our method is implemented in MATLAB and thus consumes time to segment the vertebrae. A possible solution to improve the efficiency of our method is to implement it in Keras with TensorFlow backend in Python or in Caffe. In this way, the computational time will be reduced.
# Characteristic of _Pseudomonas syringae_ pv. _atrofaciens_ Isolated from Weeds of Wheat Field
## 1 Introduction  
Nowadays, around the world, researchers have noted the increasingly threatening spread and aggressiveness of phytopathogenic bacteria [1, 2]. This trend is also observed in Ukraine [3, 4]. This also applies to wheat, one of the most valuable and important grain crops and ranks first in the world's sown area [5]. Bacterial diseases spread data are often not realistic, as they are based as a rule on observing symptoms without isolation of the pathogen and its identification [2]. _Pseudomonas syringae_ pv. _atrofaciens_ has been the main causative agent of bacterial diseases of wheat, particularly basal glume rot of wheat, for many years [6, 7, 8, 9]. The study of this pathogen's biological properties, the patterns of population formation and the circulation of pathogens, and techniques that will allowinfluencing these processes are of fundamental and practical importance and will not lose their relevance.  
The causative agent of the basal glume rot of wheat belongs to the species of _Pseudomonas syringae_, which, according to much research, is the most important bacterial plant pathogen in terms of economics and scientific studies [10]. _Pseudomonas syringae_ pv. _atrofaciens_ causes basal glume rot of wheat in Russia, Bulgaria, Italy, Germany, New Zealand, and Iran [8,11]. The scientific literature data confirm the high harmfulness of this pathogen and its ability to cause epiphytotics [12]. This pathogen's danger is its ability to colonize both the host plant and other plant species of the phyllosphere effectively. Forming epiphyto populations, which is the primary inoculum for plant infection, _P. syringae_ pv. _atrofaciens_ can cause mass plant damage when favorable weather conditions occur [13, 14, 15].  
It is known that for a long time, the causative agent of the basal glume rot of wheat had been found as an epiphyto on non-crops plant species and also on annual and perennial weeds [14]. Weeds' ability to serve as an ecological niche for agents of bacterial diseases that can affect crops has been discussed [16], and it was proved experimentally that weeds could serve as sources of dangerous epiphyto populations of phytopathogenic bacteria [17]. At the same time, the presence of the pathogens common both for cultivated plants and weeds in crops and the problem of the specialization of the pathogens that affect weeds remain unclear. Therefore, our study aimed to identify phytopathogenic bacteria on affected weeds in wheat crops and to establish their relationship with the causative agent of the basal glume rot of wheat.