esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Effective Wavelengths Selection
\(h\) (10 \(\leq h\leq\) 40) cycles are set, the whole spectral region is divided into h subintervals of equal width, the local PLS regression models of N, P, and K in the sample to be measured at each subinterval are, respectively, established, and the \(V_{\text{RMSEC}}\) values of N, P, and K of \(h\) local PLS regression models are obtained. 3. The \(V_{\text{RMSEC}}\) values of the full spectrum model and each local model are compared, respectively, the \(V_{\text{RMSEC}}\) which is greater than the global \(V_{\text{RMSEC}}\)-N, \(V_{\text{RMSEC}}\)-P, and \(V_{\text{RMSEC}}\)-K is removed, and the remaining \(q\) subintervals with smaller \(V_{\text{RMSEC}}\) are selected. 4. \(l\) (1 \(\leq l\leq q\)) intervals in q subintervals are combined for PLS regression modeling, and the \(V_{\text{RMSEC}}\) value of PLS regression model established by all possible combination intervals is calculated according to Formula (5). 5. Similarly, the \(V_{\text{RMSEC}}\) value is taken as the accuracy measurement standard of the model established for each combination interval. The interval combination of the joint regression model corresponding to the minimum \(V_{\text{RMSEC}}\) is treated as the last selected joint interval of the characteristic wavelength. According to the above steps, the number of spectral information variables involving n was reduced from 2000 to 171. The final three subintervals, 12, 24, and 34, are preferable, while the effective wavelength ranges are 1033-1089, 1717-1773, and 2287-2343 nm. The number of spectral information variables involving P was reduced from 2000 to 182. The final preferable subranges are 5, 19, and 22, while the effective wavelength ranges are 645-705, 1499-1559, and 1681-1740 nm. The number of spectral information variables related to K was reduced from 2000 to 182. Finally, the three subintervals 4, 19, and 28 are preferred, while the effective wavelength ranges are 584-644, 1499-1559, and 2041-2100 nm. The schematic diagram of characteristic wavelength selection is shown in Figure 5, in which the width of the rectangular box is equivalent to the range of the wavelength.
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 3 Applications of the Laser Feedback Interferometer
### Gear Measurement  
The possibility of the gear measurement using the laser feedback interferometer is discussed here. The gear is placed on a rotation stage with the measuring light incident perpendicularly at a certain eccentricity distance as shown in Figure 16. The unidirectional linear displacement curve is obtained in the experiment when the gear rotates in one direction. According to the Doppler theory, the integral of the velocity along the axis of light is the measuring displacement, which can be deduced as \(d=H\times\theta\). Thus, the displacement measurement is related to the eccentricity distance and the rotation angles. The result is in agreement with the derived formula with the parameters of \(\theta=25^{\circ}\) and \(H=20\) mm. Therefore, the system can be utilized for the measurement of gear speed or the alignment error of the central gear shaft. In further research, the gear profile measurement is considered and hopefully to be realized in on-line monitoring.
## 5 Simulation Experiment
### Loading Force Tracking  
#### 5.2.1 Typical Loading Force Case  
The command loading forces are applied to verify the tracking performance of the designed H\(\infty\) feedback controller, because the strong disturbance force has already been effectively restrained by the compensators.  
Two typical types of loading force spectrum are acted on the system. They are all in the form of dynamic load superimposed on a large static load, and they are different in amount and frequency of dynamic load. One is \(10+5\)sin\(80\pi t\) (kN), and the other is \(4+\)sin\(160\pi t\) (kN). During the experiment, the motion disturbance is always exerted on the loading system. At the same time, the influence on force tracking by the perturbation parameters is also taken into account. For different command loading forces, the curves of \(F_{\mathrm{L}}\) and \(x_{\mathrm{L}}\) to \(x_{\mathrm{M}}\) are not regular because their frequencies are not the same. Therefore, we analyze the loading performance in the time domain. The results of two kinds of loading forces are shown in Figures 20 and 21 respectively.  
From the experimental results, there are some relatively consistent results. The perturbation of \(m_{\mathrm{L}}\) has almost no effect on the load output. The perturbation of \(p_{\mathrm{s}}\) has a small effect on the load output, and the best tracking performance is achieved at the maximum supply pressure. The perturbation of \(\beta_{\mathrm{e}}\) has an obvious effect on the load output, and when \(\beta_{\mathrm{e}}\) equals 700 MPa the maximum magnitude error to the loading force command reaches 5.2% in Figure 20a and 3% in Figure 21a, respectively. This is because larger \(p_{\mathrm{s}}\) and \(\beta_{\mathrm{e}}\) increases the open loop bandwidth of the system, and thus the error between the physical plant and the nominal model will be reduced, and that means the H\(\infty\) controller can use more gain in tracking. It can be verified that if the \(\beta_{\mathrm{e}}\) is set to 1100 MPa, the output error will be less than 2% for all the above experiments, and this can be easily implemented in engineering. So, the tracking performance of the designed H\(\infty\) controller can be guaranteed in the range of allowable perturbations.  
Figure 19: The restraining effects on disturbance, (**a**) with perturbation of \(p_{\mathrm{s}}\); (**b**) with perturbation of \(m_{\mathrm{L}}\); (**c**) with perturbation of \(\beta_{\mathrm{e}}\).
## 1 Introduction
[27] proposed a model capable of describing the transport of moisture and gases within landfill covers under isothermal conditions; however, it neglected the effects of CH\({}_{4}\) oxidation. The models developed by [PERSON] et al. [28] and [PERSON] et al. [25] advanced this approach by incorporating the impact of CH\({}_{4}\) oxidation on liquid-gas transport within the covers. Nevertheless, these models did not consider the influence of water vapor under non-isothermal conditions. Most of the existing models primarily focus on the interactions of water and gas between the atmospheric environment and the cover while lacking a consideration of the effects of the underlying landfill waste [29; 30; 31]. However, the biochemical degradation of landfill waste generates significant amounts of heat, with waste temperatures potentially reaching up to 90 \({}^{\circ}\)C [32]. [PERSON] et al. [9] observed that a significant temperature difference of up to 30 \({}^{\circ}\)C was recorded within the 0.9 m thick soil cover, which markedly influenced the transport of LFG. The existing models [33; 34] frequently neglect the effects of vapor transport and the temperature gradient induced by the waste temperature, thus failing to adequately account for the impact of landfill waste. The high temperature generated by the biochemical degradation of landfill waste induces a significant temperature gradient within the cover, and its impact on the performance of the cover system remains poorly understood. This study aims to explore the influence of waste temperature on the transport of LFG within soil-based covers, with a particular emphasis on the effects of the temperature gradient on liquid-gas migration and MAMO within the cover system. A fully coupled model was employed to account for moisture-gas-heat reactive transport and MAMO, incorporating the effects of vapor flow and temperature gradients [26]. A series of parametric studies were conducted to reveal the effects of waste temperature on the migration of moisture and LFG within a capillary barrier cover. The results help to improve the understanding of the thermal effects on LFG transport, which is essential for accurately predicting LFG emissions and ensuring the effectiveness of landfill cover systems in mitigating environmental impacts.
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
###### Abstract  
With the development of artificial intelligence technology, automatic guided vehicle (AGV) path planning is widely used in many fields. Aiming at the problems of low convergence efficiency and easy to fall into local optimization of the traditional ant colony algorithm, this paper proposes an AGV path-planning method based on the artificial potential field-ant colony algorithm. The performance of the algorithm is improved by incorporating the artificial potential field attraction to construct the potential field heuristic function, dynamically adjusting the pheromone volatility coefficient, introducing multiple parameters to dynamically adjust the pheromone increment, and optimizing the path by using the pruning method and other improvement measures. The simulation experiments in 20 \(\times\) 20 and 30 \(\times\) 30 grid environments show that the improved algorithm already has significant advantages over the traditional algorithm and other improved ACO algorithms in terms of path length, convergence speed and the number of path inflection points, verifying its high efficiency and stability, and providing a better solution for AGV path planning.  
PP; ACA; APF; pheromone update; pruning method Article
###### Abstract
Ion production in the lower atmosphere (near the Earth's surface and in the planetary boundary layer) is mainly due to cosmic rays and radioactive radiation [33; 34]. Since the intensity of cosmic rays varies little with time at specific locations around the globe, this variation in small ions in different environments suggests that radioactive elements such as Rn are the main factors in the ionization of the lower atmosphere [35; 36]. Any change to the radioactive elements significantly affects atmospheric ionization and thus atmospheric conductivity, which in turn produces changes in the AEF [37]. Radioactivity in the soil and air accounts for 80% of the total ionization near the surface. However, while they are limited in intensity, they can be transported over great distances. At an altitude of approximately 1 km from the surface, the radioactivity caused by the soil is only 0.1% of its ground value. The ionization caused by radioactivity varies considerably with location and time, depending on the amount of radioactive minerals present. [PERSON] [38] and [PERSON] et al. [39] studied the relationship between radon radiation at ground level and the potential gradient. There are also considerable observational AEF data under different meteorological conditions [26; 32; 40; 41]. Several AEF observational studies have already been performed in Antarctica as well. [PERSON] et al. [42] used AEF data over Maitri (70.76\({}^{\circ}\) S, 11.74\({}^{\circ}\) E, 117 m above mean sea level) in 2005-2014 to explore the mechanisms and anomalous diurnal variation of the atmospheric potential gradient. They concluded that the fair-weather electric phenomena over Maitri could be attributed to global charged convection on the one hand and to convective regional phenomena caused by warm wind substitution on the other hand, which would cause the space charge of the polar plateau to interfere with the AEF monitored off the coast of Antarctica. Similarly, [PERSON] and [PERSON] [43] analyzed the mean Carnegie curves for 20 fair-weather days at the same Antarctic stations using electric field data from 10 January to 24 February 1997. The results show that the curve is a single-period curve with a maximum at 1300 UT, another sub-maximum at 1900 UT, and a minimum at 0100 UT. The focus of the present paper is on the fair-weather AEF at the Antarctic Chinese Zhongshan Station from 1 March-1 August 2022 (5 months). For this study, 40 fair-weather days were selected, and the average daily variation in the AEF was determined and compared with the diurnal variation in Beijing. The measurement location, environment, laboratory experiments, measurement results, comparison results, and some discussions are described in this paper. Understanding atmospheric electrostatics at different latitudes will be meaningful and helpful for researchers who study atmospheric electricity.
## 4 Discussion
[41], who reported that small-seeded tested plants were usually more susceptible to phytochemicals, because the concentration of the phytochemicals necessary to produce suppression is influenced by seed size. Taking into consideration the 1000 seed weight, the weight of timothy is around 5-times lower than cress, which is why timothy is more sensitive to the allelochemicals [42,43]. The data from our experiment also revealed that methyl phloretate has much more inhibitory activity than vanillic acid against tested plant species. The allelochemicals affect plant growth through different chemical reactions. The toxicity of phytochemicals is regulated by the various functional groups in the structure of compound, which act on different positions of the enzymes, and affect their activity [40,44]. Vanillic acid is a mono hydroxy benzoic acid with one methoxy group in its aromatic ring. [PERSON] et al. [45] reported that the methoxy groups on benzoic acid ring increase the phytotoxicity, whereas the hydroxy groups decrease the phytotoxicity of benzoic acid derivatives in cucumber seed germination and early growth. [PERSON] et al. [45] also showed that the hydroxy group in the C4 position scavenges the inhibitory action of the methoxy group in vanillic acid. [PERSON] et al. [46] showed that 4-hydroxy-3-methoxy benzoic acid is much more phytotoxic against wheat growth compared with 4-hydroxy benzoic acid. We therefore conclude that the methoxy group in the C-3 position contributes to the phytotoxicity of vanillic acid. On the other hand, methyl phloretate is a monohydroxy derivative of cinnamic acids. With cinnamic acid derivatives, the more hydroxyl groups in its benzene ring the less the phytotoxicity [47]. Therefore, a single hydroxyl group in benzene ring of methyl phloretate may influence its phytotoxic potential. In addition, some researchers proposed that the hydrophobicity of the compound corresponds with higher toxicity in case of cinnamic acid derivatives [48,49]. Hydrophobic compounds bearing lipophilic properties make them capable of passing through the cell membrane more readily [50]. Some researchers reported that the methyl ester of cinnamic acid derivatives has higher growth-inhibitory activity corresponding to their free acids because esterification of the carboxyl group results in more hydrophobic compounds having more phytotoxic potential [51,52]. Accordingly, esterification of phloretic acid (corresponding free phenolic acid of methyl phloretate) which is already documented to have growth-inhibitory activity, might lead to producing more phytotoxic methyl phloretate. [PERSON] et al. [52] showed that the allelopathic activity of methyl p-coumarate, which has a similar structure to methyl phloretate, was more inhibitory than its corresponding free phenolic acid, p-coumaric acid. In general, cinnamic acid derivatives are more phytotoxic than benzoic acid derivatives due to their higher hydrophobicity [47; 53]. Our results corroborate this finding because the more phytotoxic methyl phloretate is a derivative of cinnamic acids, while the less phytotoxic vanillic acid is a derivative of benzoic acid.
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## 3 ESRA Routing Algorithm
### Generation of Minimum Routing Graph  
Using the link cost in Section 3.1 as a weight, all shortest paths from any node \(i\) to sink can be found to form the minimum routing graph. Specifically, as it has been shown in Algorithm 1, the Dijkstra algorithm can be used to obtain an initial shortest path from node \(i\) to the sink, which is recorded as \(mp_{0}^{is}\) and added to the shortest path set \(MP^{is}\) of node \(i\). At the same time, it can be known from [28] that those nodes on the other shortest paths must be distributed around the initial shortest path \(mp_{0}^{is}\). Therefore, the adjacent nodes in \(NBS(i)\) and the nodes in the shortest path \(mp_{0}^{is}\) can be chosen to form \(n\) candidate paths \(path_{t}^{is}(1\leq t\leq n)\) from node \(i\) to the sink, and these paths are recorded as \(p^{is}\). It should be noted that when \(t=1\), \(path_{t}^{is}\) is set as the initial shortest path \(mp_{0}^{is}\).  
Figure 1: Spatial positional relationship between nodes.  
Here, the total link cost of any path \(path_{t}^{is}\) is defined as follows:  
\[w_{t}^{is}=\sum_{j=1}^{j=|path_{t}^{is}|-1}u_{j,j+1} \tag{6}\]  
Thus, the total link cost \(w_{t}^{is}\) of all candidate paths \(path_{t}^{is}\) in the set \(P^{is}\) can be sequentially calculated according to Equation (6), and when \(w_{t}^{is}\) is equal to the total link cost of the initial shortest path \(w_{1}^{is}\), this candidate shortest path will be added to \(MP^{is}\).  
```
Input: the link cost matrix \(U\), the network size \(N\) Output: the shortest path set \(MP^{is}\)
1.for\(I=1\):\(N\)
2. Calculate the initial shortest path \(mp_{0}^{is}\) of node \(i\) by Dijkstra algorithm.
3. update\(MP^{is}\gets MP^{is}+mp_{0}^{is}\)
4. Find the set of adjacent nodes \(NBS(i)\) of the shortest path \(mp_{0}^{is}\).
5.\(k=1\)// Start finding adjacent nodes from the first node of \(mp_{0}^{is}\).
6.while (\(k<\ \left|mp_{0}^{is}\right|\))
7. Insert adjacent nodes from the \(k\)th node of \(mp_{0}^{is}\), and get the candidate path \(path_{t}^{is}\) under the current \(k\).
8. update\(P^{is}\gets P^{is}+path_{t}^{is}\)
9.\(k=k+1\)
10. end if // Get the candidate path set \(P^{is}\).
11.\(g=2\)
12.while (\(g\leq n\))
13. Calculate the link cost \(w_{g}^{is}\) by Equation (6).
14.if\(w_{g}^{is}=w_{1}^{is}\)
15. update\(MP^{is}\gets MP^{is}+path_{g}^{is}\)
16. end if
17.\(g=g+1\)
18. end if
19.end
20.return the shortest path set \(MP^{is}\)
```  
**Algorithm 1** The Generation of Minimum Routing Graph
# Microorganism Detection in Activated Sludge Microscopic Images Using Improved YOLO
###### Abstract  
Wastewater has detrimental effects on the natural environment. The activated sludge method, a widely adopted approach for wastewater treatment, has proven highly effective. Within this process, microorganisms play a pivotal role, necessitating continuous monitoring of their quantity and diversity. Conventional methods, such as microscopic observation, are time-consuming. With the widespread integration of computer vision technologies into object detection, deep learning-based object detection algorithms, notably the You Only Look Once (YOLO) model, have garnered substantial interest for their speed and precision in detection tasks. In this research, we applied the YOLO model to detect microorganisms in microscopic images of activated sludge. Furthermore, addressing the irregular shapes of microorganisms, we developed an improved YOLO model by incorporating deformable convolutional networks and an attention mechanism to enhance its detection capabilities. We conducted training and testing using a custom dataset comprising five distinct objects. The performance evaluations used in this study utilized metrics such as the mean average precision at intersections over a union threshold of 0.5 ([EMAIL_ADDRESS]), with the improved YOLO model achieving a [EMAIL_ADDRESS] value of 93.7%, signifying a 4.3% improvement over the YOLOv5 model. Comparative analysis of the improved YOLO model and other object detection algorithms on the same dataset revealed a higher accuracy for the improved YOLO model. These results demonstrate the superior performance of the improved YOLO model in the task of detecting microorganisms in activated sludge, providing an effective auxiliary method for wastewater treatment monitoring.  
wastewater treatment; YOLO model; microorganisms detection; attention mechanism; deformable convolutional networks +
Footnote †: journal: _Applied Sciences_  
10.3390/app132212406  
13.12406. [[https://doi.org/10.3390/app132212406](https://doi.org/10.3390/app132212406)]([https://doi.org/10.3390/app132212406](https://doi.org/10.3390/app132212406))
# Sustainable Pavement Management: Harnessing Advanced Machine Learning for Enhanced Road Maintenance
## 4 Results and Analysis  
In this study, we assessed the effectiveness of three different convolutional neural network (CNN) models, ResNet50, ResNet18, and EfficientNetB3, in classifying images depicting pavement distress. The table provided outlines the training and testing performance for each model in terms of both accuracy and loss.  
The experimental results from Table 1 highlight the performance metrics of different models for pavement distress classification. Three models were evaluated: ResNet50, ResNet18, and EffNetB3 + SwinGAN, each assessed over three runs for training accuracy  
Figure 6: Project’s pseudo-code.  
ResNet50: Across its three runs, ResNet50 demonstrated consistent performance with training accuracies ranging from 72.9% to 74.2% and testing accuracies from 69.0% to 71.0%. However, it exhibited moderate to high computational demands throughout all runs, with an F1 score peaking at 0.81, indicating good overall performance but with noticeable variability in efficiency.  
ResNet18: This model consistently showed improvements over ResNet50 in both training and testing accuracies, achieving ranges from 75.0% to 77.1% and 72.6% to 74.3%, respectively. ResNet18 also maintained high F1 scores ranging from 0.82 to 0.85. It exhibited moderate computational demands, making it a more efficient alternative to ResNet50, especially in scenarios where computational resources are constrained.  
EffNetB3 + SwinGAN: Combining EfficientNetB3 with SwinGAN resulted in the highest accuracies among the models evaluated, with training accuracies ranging from 78.3% to 79.8% and testing accuracies from 76.7% to 78.2%. This model consistently achieved the highest F1 scores, ranging from 0.87 to 0.91, indicating robust performance in classifying pavement distress. Despite its very high efficiency in terms of F1 score and moderate computational demand, it demonstrated low computational requirements, making it suitable for resource-constrained environments.  
While ResNet50 and ResNet18 showed competitive performance, EffNetB3 + SwinGAN emerged as the most effective model for pavement distress classification, balancing high accuracy with efficient computational demands. The results underscore the importance of model selection based on both performance metrics and computational feasibility in real-world applications of infrastructure management.  
To further explain the model training loss in more detail, Figure 6 depicts the variation in training loss over epochs for the different convolutional neural network models used in this study, showcasing their convergence patterns and relative performance.  
Standing out among the three, EfficientNetB3 delivered the best results with the highest accuracies (78% training and 76.5% testing) and the lowest losses (1.47 in training and 1.60 in testing). This model leverages a compound scaling method that optimally adjusts its depth, width, and resolution, providing superior performance with fewer parameters compared to the ResNet models.