esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Detection of _Spodoptera frugiperda_ by Feeding Symptoms Directly from UAV RGB Imagery
## 2 Materials and Methods
### Image Preprocessing  
The data processing included two main steps: Cropping and Classing.  
Cropping: Due to the size of the images, we tailored them from the sizes in Table 2 to 200 \(\times\) 200 to speed up training and to reduce the pressure on the graphics memory. We used the OpenCV-Python tool in Python language to read and crop images in batches. For visual effect, Figure 3 shows the conversion process of an image from 1 \(\times\) 3000 \(\times\) 4000 to 25 \(\times\) 200 \(\times\) 200.  
Classing: After cropping, the main body of the image is basically composed of corn leaves, and part of the image is land and weeds. By combining the edge detection tool in Opencv2-Python library and RGB channel calculation, the image containing only land and weeds is removed. According to the habits of _Spodoptera frugiperda_ and specific representation on the image, we divided the rest of the images into 3 categories by visual interpretation, as shown in Figure 4 below.  
(Condition 1): Healthy leaves: Green healthy leaves, complete without damage.  
(Condition 2): Translucent silver window (TSW), the first instar and part of the second instar of _Spodoptera frugiperda_ only feed on one side of corn leaves, creating a translucent silver windowpane.  
(Condition 3): Irregular wormhole (IW), the rest of the instars of _Spodoptera frugiperda_ cause significant irregular pores, and some of the leaves infected in the undeveloped period show symmetry in the holes.  
(Condition 4): Other objects, the picture contains only land and weeds (drop).  
For pest control, the earlier the intervention, the fewer losses and pesticides used, so the first translucent silver windowpane is the most important object. However, in the image, most of the leaves were presented as healthy. In order to balance the number of positive and negative samples, we selected the number of healthy leaves in condition 1 according to the number of infected leaves in conditions 2 and 3, and we removed the leaves in condition 4 at the same time.  
After the above processing, we finally obtained more than 5000 maize images in the joint stage, including 2043 healthy leaves, 1866 condition 1 images, and 1430 condition 2 images. The quantities are shown in Table 3. The above data are used for training and validation of the models. During the training, the ratio of training to verification was 9:1.  
To test the robustness of the model detection ability, 1545 images in the heading stage were used, including 532 in condition 1 and 417 in condition 2. This part of the image does not participate in training and verification at all, and it was used as an independent test set for testing the model after training.
## 5 Discussion
### Accuracy-Influencing Factors Analysis  
#### 5.1.1 The Size of Dataset  
Generally, in the field of object detection, with the increase of the training dataset, the effect of detection will be improved. This is because the neural network will constantly adjust the weights in the hidden layer during the training process. The larger the dataset is, the smaller the error of the result will be as well as the risk of overfitting, and the closer the detection result will be to the truth. In this paper, the detection effect of structural components using YOLO is tested under the original dataset and the augmented dataset. Since each image in each different dataset contains a different number of structural components, for a better understanding, the weighted averages of several metrics (like Pre., Rec., MR, FS, OA and AA) are calculated to measure the detection performance of YOLO in a different dataset.  
According to Figure 6, it is obviously observed that the YOLO-based method trained by the augmented dataset receives much better results with Pre. of 85.30%, Rec. of 97.21% and FS of 90.86%, and OA and AA are improved by 5.79% and 5.32%, respectively. Meanwhile, MR decreases from 5.89% to 2.79%. Therefore, the thorough improvements using the augmented dataset rather than the original dataset demonstrate that the size of the dataset does have a marked impact on the performance of object detection. When using more data for training YOLO, the better the result of structural object detection will be obtained.  
Figure 6: Comparison of the performance in different sizes of datasets via YOLO.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **TD** & **TA** & **OA** & **AA** & **Speed** \\ \hline Grid & 3459 & 3125 & 85.78\% & 82.67\% & - \\ Column & 1008 & 925 & 82.64\% & 80.18\% & - \\ Beam & 675 & 583 & 76.54\% & 77.14\% & - \\ Beam\_v & 642 & 583 & 77.92\% & 78.57\% & - \\ Beam\_s & 150 & 142 & 83.33\% & 85.00\% & - \\ Average & 2338 & 2113 & 83.31\% & 81.25\% & 0.71 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: TD, TA, OA, AA, and speed for different objects in test set.
# Optimal Waveform of the Partial-Respond Signal Based on Minimum Out-of-Band Radiation Criterion
## 2 Optimized Mathematical Modeling of the Optimized Waveform
### Signal Solving Process Using Fourier Series
Moreover, the bandwidths of \(\Delta F_{-40\text{dB}}\) and \(\Delta F_{-60\text{dB}}\) have been decreased to \(43.6\%\) and \(15.13\%\) of that of the MSK signal, respectively. This verifies that the signal's out-of-band radiation is prevented. Applying the optimized efficient spectrum signal as the baseband modulation signal, the modulated signal after the quadrature modulation presents the envelope characteristics. Moreover, the cross-correlation function \(K\) of the optimized baseband signal is too small, which reduces the inter-symbol interference and increases the reliability of the receiving.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
###### Abstract  
From the analysis of chirp spread spectrum (CSS) signals in radar and communication applications, we can distinguish the cases of estimating only data or time-delay propagation or Doppler frequency when all the other parameters are known or set to zero. The case of estimating all three parameters at the same time has not been treated in the literature of radar or communications. We consider in this paper the use of a CSS signal for a LEO-PNT signal, which perfectly fits with the case of estimating the three parameters mentioned above. Specifically, the main goal of this paper is to explain the design of a CSS signal for PNT. This will include the design of the ranging component and the data component. The performance evaluation of both components will also be analyzed and linked. It is important to note that the focus of this paper will be the data component, and we link it with the pilot component through sensitivity performance. The results given in this paper show the capabilities of CSS for PNT. Previous works have shown accuracies on the level of meters with achievable data rates shown in this paper in the order of tens of kbps.  
chirp spread spectrum; LEO-PNT; low complexity +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Landscape Ecology Analysis of Traditional Villages: A Case Study of Ganjiang River Basin
## 3 Results
### Analysis of Driving Forces behind Traditional Village Distribution  
#### 3.2.1 Divergence and Factor Detection  
The spatial distribution of traditional villages varies under different natural geographical conditions, but the varying impact strength of different influencing elements needs further research and judgment. This part of the research content corresponds to Experiment 1. This paper utilizes geographical detectors for divergence and factor detection to analyze the driving forces behind traditional village distribution. In the experiment, the average elevation, average slope, average terrain undulation, proportion of cultivated land area surrounding the village, proportion of forest land area, proportion of construction land area, proportion of water area, and river distance were selected as assessment factors \(X\), and choose the core density value of traditional villages as the dependent variable \(Y\), to evaluate the explanatory power of natural environmental factors on the continuation of traditional villages. The extracted influencing factors of traditional villages are imported into R for calculation, and after discretizing the continuous data, the impact of each factor on the distribution of traditional villages is computed.  
In differentiation and factor detection, the Q value represents the degree to which factor \(X\) explains the dependent variable \(Y\). Four groups of data--average elevation, average terrain undulation, average slope, and cultivated land area--show credibility with Q values of 0.4154, 0.3092, 0.2842, and 0.0718, respectively. However, the four groups of data consisting of forest land area, construction land area, water area, and river distance did not pass the credibility test, with Q values of 0.0041, 0.0034, 0.0012, and \(<\)0.0001, respectively, indicating a lack of credibility. Average elevation is the most significant factor affecting the distribution of traditional villages, followed by average terrain undulation, average slope, and cultivated land area (Figure 6).  
Figure 5: Histogram of land use type area proportion distribution around traditional villages: (**a**) forest land; (**b**) cultivated land; (**c**) water bodies; (**d**) construction land.
# A Low Complexity Channel Estimation and Detection for Massive MIMO Using SC-FDE
## 4 Performance Results
### Channel Estimation with Superimposed Pilots
It is worth noting that, beyond 4 iterations, the performance improvement is residual (not shown). Therefore, the proposed receivers and channel estimators should employ 4 iterations to achieve good performance. Figure 9 considers the performance results with superimposed pilots, assuming a pilots power of 0 dB versus \(-3\) dB, using a block length of 5 data symbols. It is worth noting that 0 dB corresponds to using the same power for the pilots and for the data, whereas \(-3\) dB corresponds to using half of the power for the pilots, as compared to the data power. Regardless the receiver employed, the best overall performance is always achieved with a pilots power of 0 dB. It is worth noting that simulations with a pilots power of 3 dB were implemented (not shown in this article), but the best performance was also 0 dB. By using \(-3\) dB of pilots power, small degradation of performance can be observed, being valid for both MRC, ZF and EGC. Figure 10 shows the performance results for the \(4\times 32\) MIMO with Superimposed Pilots, with a block length of five versus 10 data symbols. The performances obtained with 10 data symbols are always slightly better than those obtained with five data symbols. This is valid for both MRC, ZF and EGC. Nevertheless, this is obtained at the cost of much higher processing power, as 10 data blocks requires processing the double of the data than five data blocks. It is worth noting that degradation of performance is experienced when less than five data blocks are employed (simulations carried out, but not shown in this article). Therefore, considering the differences of performance, one can conclude that five data blocks is a good tradeoff between performance and processing complexity. As before, the best overall performance is obtained with the MRC, being followed by the ZF and, finally, by the EGC. Figure 8: BER results for \(4\times 32\) MIMO with superimposed pilots, with two versus four iterations of the iterative channel estimator.
## 4 Separation Mechanisms of Networks
### Proximity in Sequential Grouping  
Results of sequential organization experiments for alternating tones A and B are shown in Figure 6. In general, no matter which structure and parameter are used, the proximity in time and frequency for sequential grouping is learned by all networks. When \(\Delta\)F is large, and TRT is short (top left corner of each subgraph), sequences A and B are more likely to be separated. The temporal coherence boundary proposed by [PERSON] [21] through psychoacoustical experiments is shown by the white dashed curve in Figure 6. For the human auditory system, when the tone interval is higher than the temporal coherence boundary, they will be perceived as two sources. Therefore, for proximity in the sequential grouping, the networks also show a high degree of consistency with humans.  
Although the proximity principle is embodied in each subgraph, there are some differences in different structures and parameters. ConvTasNet with a 32-sample window fits best with the temporal coherence boundary. As the window size increases, the separation range on TRT and \(\Delta\)F decreases. DPRNN and DPTNet are more sensitive to frequency intervals rather than time intervals. DPTNet with a 4-sample window will separate components larger than a certain frequency interval (about five semitones) regardless of the time interval. In general, the performances of DPRNN and DPTNet are better than ConvTasNet for this sequential grouping experiment.  
In our previous attempt [18] to unravel underlying separation principles learned by networks, results showed that the proximity principle could be learned by ConvTasNet with different encoders. Furthermore, in this study, the proximity principle is still effective for different network structures and parameters. It indicates that in the face of such tasks, the separation based on proximity principles may be one optimal and inevitable choice. No matter what kind of network, even the human ear, the same separation mechanisms will eventually be chosen to solve this problem.
# Enhancing biogas Plant Efficiency for the Production of Electrical and Thermal Energy
## 1 Introduction
In this context, the development and optimization of biogas plants are crucial for maximizing their environmental and economic benefits [34; 35]. This study aims to provide a detailed analysis of the biogas production process, including feedstock selection, fermentation management, and energy utilization. The findings of this research will offer valuable insights for the design and operation of efficient biogas plants, contributing to the advancement of renewable energy technologies. Overall, the integration of biogas technology into energy and waste management systems represents a promising approach to addressing some of the most pressing environmental and economic challenges of our time. By harnessing the power of anaerobic digestion, biogas plants can transform waste into a valuable resource, supporting sustainable development and enhancing the resilience of energy systems [36; 37; 38]. Numerous studies have investigated various aspects of biogas production and optimization, contributing valuable insights to the field. [PERSON] et al. examined the potential of biogas production from aquatic biomass, providing insights into the scalability and efficiency of using algae and seaweed as substrates [39]. In the paper [40], the authors investigated the impact of pretreatment methods on biogas yield from lignocellulosic biomass. They demonstrated that certain pretreatment techniques can significantly enhance biogas production by improving substrate digestibility. In the paper [41], [PERSON] and [PERSON] focused on the life cycle assessment of biogas production from dairy manure, evaluating the environmental impacts and potential benefits of using dairy manure as a feedstock. [PERSON] et al. examined the biogas production from co-digestion of dairy manure and crop residues, evaluating performance and economic feasibility [42]. In the paper [43], the authors analyzed the anaerobic co-digestion of food waste and yard waste for enhanced biogas production, using experimental and kinetic modeling. Our study stands out in the field of biogas production through its comprehensive approach to integrating theoretical knowledge with practical applications in biogas plant design. By focusing on substrate selection, fermenter heating techniques, and process optimization, we offer a detailed methodology that encompasses the entire process--from substrate selection to energy utilization and environmental impact assessment. This approach bridges the gap between technological aspects and the economic and environmental benefits, providing valuable operational and environmental insights for efficient and sustainable biogas production. The study's novelty lies in its holistic and integrative approach, combining theoretical insights with practical applications to optimize biogas production. This includes innovative techniques for digester heating and a detailed analysis of substrate combinations that have not been extensively studied together.
## 4 The Third-Order Beam Theory
### Beam Stiffness Coefficients for FGM Beams  
For the FGM beams, the integrals in Equations (85a)-(85c) can be evaluated as:  
\[\begin{split} A_{xx}&=E_{2}bh\frac{M+n}{1+n}\,,\,\,\,B _{xx}=E_{2}\frac{bl^{2}}{2}\frac{n(M-1)}{(1+n)(2+n)}\,,\\ D_{xx}&=E_{2}\frac{bl^{3}}{12}\bigg{[}\frac{(6+3n+3n^{2}) M+(8n+3n^{2}+n^{3})}{(1+n)(2+n)(3+n)}\bigg{]},\\ E_{xx}&=E_{2}\frac{bh^{4}}{8}(M-1)\bigg{[}\frac{n(8+3n+n^{2})}{(1 +n)(2+n)(3+n)(4+n)}\bigg{]},\\ F_{xx}&=E_{2}\frac{bl^{5}}{80}\,f(n),\,\,\,H_{xx}=E_{2} \frac{bl^{7}}{448}\,g(n),\\ A_{xy}&=E_{2}\ell^{2}\frac{bh}{2(1+\
u)}\frac{M+n}{1+n} \,,\,\,\,F_{xy}=\frac{E_{2}\ell^{2}bh^{5}}{120(1+\
u)}\,f(n),\\ D_{xy}&=E_{2}\ell^{2}\frac{bh^{3}}{24(1+\
u)}\bigg{[} \frac{(6+3n+3n^{2})M+(8n+3n^{2}+n^{3})}{(1+n)(2+n)(3+n)}\bigg{]},\end{split}\] (86a) where \[\begin{split} f(n)&=\frac{f_{1}\,M+nf_{2}}{f_{3}},\,\,\,g(n)=\frac{g_{1}\,M+g_{2}}{g_{3}},\,\,\,M=\frac{E_{1}}{E_{2}}\,,\\ f_{1}&=(24+18n+23n^{2}+6n^{3}+n^{4}),\\ f_{2}&=(184+110n+55n^{2}+10n^{3}+n^{4}),\\ f_{3}&=(1+n)(2+n)(3+n)(4+n)(5+n),\\ g_{1}&=(720+660n+964n^{2}+405n^{3}+115n^{4}+15n^{5}+n^{6}),\\ g_{2}&=(720+1764n+1624n^{2}+735n^{3}+175n^{4}+21n^{5}+n^{6}),\\ g_{3}&=(1+n)(2+n)(3+n)(4+n)(5+n)(6+n)(7+n).\end{split} \tag{86b}\]  
If the higher-order terms are neglected in the governing equations of motion but not in the constitutive relations, we obtain the third-order theory developed by [PERSON] [38].  
If one neglects the higher-order terms selectively in the constitutive relations, one obtains the so-called simplified Reddy-Bickford beam theory. These ideas will be discussed shortly.
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 2 Materials and Methods
### IPSO-AR Method
#### Illustration of the IPSO-AR Algorithm  
In accordance with the ambiguity float resolution (\(\hat{a}\)) and the corresponding VC matrices (\(\hat{Q}_{\hat{a}\hat{a}}\)), we can determine the ambiguity search range by using the three times error of estimated \(\hat{a}\) or by directly entering one fixed constant into the restricted ambiguity search range.  
\[\left|SearchRange_{d}\right|<3\hat{Q}_{\hat{a}\hat{a}}^{dd}\quad\text{or} \quad\left|SearchRange\right|<ar\_range\ \, \tag{14}\]  
where \(1\leq d\leq N\); \(SearchRange\) denotes the ambiguity search range; \(\hat{Q}_{\hat{a}\hat{a}}^{dd}\) denotes the estimated error of the \(d\)-th dimension ambiguity; and \(ar\_range\) denotes one given constant.  
On the basis of the algorithm described above, we illustrate the specific process of the IPSO-AR algorithm showed in Figure 1.
## 2 IGBT Gate Driver Hardware Solutions  
The driver is shown in Figure 1a. The input power supply is generated by a DC/DC converter with a wide input voltage range of 9 to 36 V DC. It has galvanic insulation of 6000 V and provides power to all the circuits of the driver PCB. The driver has two optical communication channels for exchanging information with the upper-level control system. The first channel is the input, used to receive the transistor control signal, which is converted into a voltage of +15 V/-9 V that is supplied to the IGBT gate. The second optical channel is the output and serves as feedback. The feedback signal generates pulses of varying duration to inform the control system about the correct operation of the transistor or about a fault. The board has three power pins for connecting the collector, gate, and emitter of the IGBT. For analyzing transient processes in the IGBT, the internal analog-to-digital converter (ADC) of the microcontroller is used. Operational amplifiers (OAs) are used to implement the necessary gains in each channel. Thus, the TMS320F28002x microcontroller measures the values of gate current, gate voltage, and collector voltage and monitors voltage supply. Moreover, two collector voltage measurement circuits are implemented. Circuit #2 (see Figure 1b) makes it possible to control the entire collector voltage in the entire range of 0 to 1300 V, and circuit #3 (see Figure 1b) operates in a truncated range of 0 V to 15 V for a more accurate measurement of the desaturation of the transistor in its conducting state. A functional diagram of the driver is shown in Figure 1b.  
Figure 1: \(Cont.\)
# LDM-Ex-FDM: A Novel Multi-Service Transmission Scheme for the ATSC 3.0 System
## 2 Advanced Television System Committee (ATSC) 3.0 System Overview
### Bit-Interleaved Coded Modulation (BICM) Capacity Analysis
Figure 9: Capacities of mobile service and fixed service in Advanced Television System Committee (ATSC) 3.0 system with and without LDM-Ex-FDM scheme over the additive white Gaussian noise (AWGN) channel, where the parameter of ATSC 3.0 system was set to \(\Delta=10\) dB, the _CL_ constellation was set to QPSK, the _EL_ constellation was set to NU\(-\)64 QAM, and the puncturing rate of the LDM-Ex-FDM scheme was set to 1/12.