esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
## 4 Results  
The field gap map relies on two processes managed by two distinct files. The first file contains the YOLOv5 model, which is responsible for identifying all field gaps. Within this file are the neural network weights used for field gap identification. The training parameters included an input size of 416 \(\times\) 416 pixels2, a batch size of 16, and a planned duration of 300 epochs. However, training was stopped early at 210 epochs, after approximately 2 h, due to a lack of improvement in the validation metrics (early stopping). The final training loss achieved was 0.728, with a mean average precision (mAP) of 0.691.  
The second file is the ImageJ script, which selects field gaps based on the minimum and maximum values specified during the Analyze Particles procedure. Additionally, it generates a table containing information such as the area and location of each selected field gap. After the selection process, the script assigns a unique number to each field gap according to the information provided in the table, enabling visualization of each field gap on a map and access to its corresponding data in the table.  
Figure 17 shows the results of applying the best model to a larger image. The next step involves using an ImageJ script for selection. First, planting failures larger than 4200 pixels2, which represent gaps greater than 100 cm, will be selected and mapped. Subsequently, failures larger than 2100 pixels2, representing gaps larger than 50 cm, will be identified and mapped.  
Figure 17: Gaps identified using YOLOv5.  
Figure 18 illustrates the output of the ImageJ script for failures exceeding 4200 pixels2, identifying 53 planting failures with 3 instances of displacement errors, as shown in Figure 19. Based on the findings, the accuracy can be calculated by dividing these three misidentification issues by the total number of planting failures, resulting in an accuracy of 94.34%. The report detailing planting failures larger than 4200 pixels2 is available in Table A1 of Appendix A.  
Figure 19: Three wrong identifications: (**a**) misidentification joined with another gap (**b**) misplaced gap (**c**) misplaced gap joined with another gap.  
Figure 18: Gaps greater than 4200 pixels2 selected using the ImageJ script.  
Figure 20 shows the detection of failures larger than 2100 pixels2, resulting in the identification of 154 planting failures. In this second analysis, in addition to the displacement errors, three false negatives were identified (Figure 21). These false negatives, which are smaller than 4200 pixels2, were only considered in the second run. The accuracy in this second analysis is the 6 errors (three misidentifications and three false negatives) divided by 154, resulting in a 96.1% accuracy. The report mapping failures larger than 2100 pixels2 can be found in Table A2 of Appendix B.
## 6 Conclusions  
In this study, Sentinel-1 images and the StaMPS SBAS method were utilized to extract the time series displacement of the Muyubao landslide. During this monitoring period, the changes in the reservoir water level were the main triggering factor of the Muyubao landslide, and its deformation mainly occurred during the fluctuation period and high-water level period of the reservoir. This landslide also showed a significant spatial difference, which was the strongest on the eastern upper portion of the landslide, followed by the middle part, and the western front portion showed the slightest deformation.  
The Muyubao landslide experienced a translational mode during its evolution process. The natural geological characteristics (formation mechanism, slope structure, topography and geomorphology) are beneficial to the overall stability of the landslide under natural conditions. However, when the external environment changed, the combined effects of reservoir water and rainfall contributed to the deformation of the Muyubao landslide.  
The monitoring and characterization of the Muyubao landslide was carried out by applying Sentinel-1 images and the time series InSAR technique. Through field investigation and verification with GPS, we found that this method can effectively monitor slow-moving landslides and presents the advantage of high-density coverage of monitoring points. The InSAR monitoring results can provide us more information for comprehensive understanding of the target landslides. In short, the application of Sentinel-1 images and InSAR techniques in carrying out landslide monitoring is an effective and economical method.  
[PERSON] processed the dataset, performed the analysis and wrote the original draft of this paper. [PERSON] contributed to processing the dataset and performed part of the analysis. [PERSON] and [PERSON] supervised the interpretation of the results from the geomorphological point of view. [PERSON] supervised the analysis. [PERSON], [PERSON] and [PERSON] edited the manuscript. All authors contributed to paper writing and revision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 41907253 and 41702330.  
We thank the assistance of the Research Center of Geohazard Monitoring and Warning in the Three Gorges Reservoir, China. We thank the reviewers for their suggestions that improved the quality of this paper. We thank [PERSON] for helping us in data collection.  
The authors declare no conflict of interest.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 2 Materials and Methods
### Model Performance Evaluation Metrics
#### 2.4.3 Mean Intersection over Union (_MloU_)  
_MloU_ focuses on the accuracy of the prediction of the target region and is a standard accuracy metric. Firstly, _IoU_ is calculated for each class separately, _IoU_ is the ratio of the intersection of observed labels and predicted labels, i.e., the number of correctly predicted pixels, to the union of observed labels and predicted labels, i.e., the number of pixels in the class that were incorrectly predicted, and then the _IoU_ is averaged across all the classes to obtain _MloU_, which is computed by using Equation (5).  
\[MloU=\frac{1}{c}\times\sum_{i=1}^{c}\frac{n_{ii}}{\sum_{j=1}^{c}n_{ij}+\sum_{j= 1}^{c}n_{ji}-n_{ii}} \tag{5}\]  
where \(c\), \(n_{ii}\), \(n_{ij}\), and \(n_{ji}\) are defined as above.
## 7 Limitations and Future Work  
Only 30 participants were able to participate in the second part of our study, which included a total of 121 participants. In comparison to the large number of Tanzanian teenagers who use the internet and mobile devices, this is a small number. We advocate increasing the sample size in the future to represent the wider population and variability of the participants for more accurate comparisons. We also encourage employing smart gadgets such as JINS MEME eyewear to monitor participants' mental focus, discover characteristics that cause people to miss dangerous content, and improve their efforts in making the right decision. In future research, we recommend that the time spent playing the game be extended to allow participants to become accustomed to it and retain the knowledge they have gained for a longer period. Furthermore, rather than utilizing a survey questionnaire to assess participants' knowledge, we recommend that future research should investigate employing an experimental setup to examine participants' degree of comprehension in a real-world setting. This may have been accomplished, for example, by creating a testing environment in which participants were exposed to phishing scenarios from the real world by receiving a mix of malicious content. As a result, researchers would receive immediate feedback based on their activities, and participants' ability to detect phishing information in real time would be assessed. We also recommend using customized mobile games to teach cybersecurity concepts such as phishing, which is the most common attack nowadays in public places such as schools and workplaces, because mobile phonesand tablets are inexpensive, and teenagers are the most frequent users of the internet and mobile devices.
# Image-Caption Model Based on Fusion Feature
## 2 Related Work
### Grid-Level Feature  
CNN, as an encoder, extracts grid-level features of images. To improve the image-caption model, research focuses on the attention mechanism. [PERSON] et al. [7] first applied the attention mechanism in the image-caption field. They proposed an image-caption model based on the attention mechanism, which includes \"soft attention\" and \"hard attention\". The soft attention assigns weights to all grids for each decoding, ranging from 0 to 1, and uses backward propagation for training. The hard attention mechanism focuses on only one graph grid for each decoding. It uses one-hot coding, which takes less time but is not differentiable, and it generally uses Monte Carlo sampling to estimate the gradient first and then backward propagation for training. The attention mechanism commonly used in image captioning is mainly a \"soft\" attention mechanism. Attention-mechanism models optimize the CNN extracted image features from global to grid features. Before generating words, the model calculates the correlation between the words generated and each grid in the image by the attention mechanism to select the grid features with high correlation. It passes them to the decoder and guides the decoder to generate the image captions. The introduction of the attention mechanism gives the image caption model the ability to focus on the key grid. However, this attention mechanism will force words to correspond to the going grid, and dummy words such as \"of\" and \"the\" will also force them to correspond to the images' grids, resulting in wasted arithmetic power. Therefore, [PERSON] et al. [8] proposed an adaptive attention mechanism. This attention mechanism introduces the \"Visual Sentinel\" vector, which indicates the relevance of the generated words to the visual information between 0 and 1. When the words are directly related to the image, the value is 1, and the model will focus on the image grid and generate the words. When words, such as \"of\" and \"the\", are generated, they are inferred directly by the language model. In recent years, various attention mechanisms have been applied to grid-level features to improve model performance continuously.
## 3 DFIG-Based Load Flow Analysis
### PQ Bus Model  
The reactive power requirements of the system depend on the wind turbine involved. Wind turbines (WTs) based on the DFIG, for instance, require reactive compensation. The control scheme design defines the relationship between the following variables: active and reactive power, power delivered, and nodal voltage. The capacitor banks or reactive power sources are individually added to wind turbines for supplying the reactive power deficit locally without importing from other parts of the electrical grid. The fluctuations of the active and reactive power caused by the wind speed are necessary since the active power depends on the speed [9]. Therefore, the three variables required as input for the PQ bus model are: wind speed \(V_{W}\), active \(P\) and reactive Q power. Thus, the Equations (17)-(23) are solved, with the unknown values given in Equation (26). This process provides the machine's operating point in a steady-state.
# STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
## 4 Hierarchical STN-Homography
### Training, Results and Comparison with Other Approaches  
As shown in Figure 4, when the hierarchical modules are trained, the training data of the current stage model depends on the predictions of the previous stage model, i.e., for each quadruplet training sample (patch_a_i, patch_b, \(\overline{H}_{ba\_i}\), patch_a_t_i) of the stage i model, the patch_a_i, \(\overline{H}_{ba\_i}\) and patch_a_t_i all depend on the prediction results of the previous stage i-1 model. Therefore, we adopted a step-by-step training strategy. Using the three-stage hierarchical STN-Homography model as an example, first, we trained the stage 1 model and then used the trained stage 1 model to prepare the training dataset for the stage 2 model. Secondly, we trained the stage 2 model and then used the trained stage 1 and stage 2 models to prepare the training datasets for the stage 3 model. Finally, we trained the last stage 3 model. For the training of all stage models, we used the same training parameters as those used previously for the training of the STN-Homography model and we used the same loss weight of 1.0 for all losses of the model for simplicity. The only difference is that we used a total training steps of 130,000 for the stage 1 model and 90,000 steps for the stage i model (i = 2, 3).  
As shown in Figure 3, the mean corner error of our three-stage hierarchical STN-Homography model is only 1.57 pixels. Compared with the outcomes of the study of [PERSON] et al. [22] which reported an error of 9.2 pixels, we decreased the mean corner error by 82.9%. Compared with the four-stage hierarchical model of [24], which has the mean corner error of 3.91 pixels, we also decreased the corner error by 59.8%.
# Cladding-Pumped Erbium/Ytterbium Co-Doped Fiber Amplifier for C-Band Operation in Optical Networks
## 1 Introduction
The fiber's core is rare-earth-doped phosphosilicate glass, the inner cladding is pure silica, and the outer cladding is fluorine-doped silica glass. The EYDFA performance is evaluated in terms of gain (G), noise figure (NF), and the output signal bit error ratio (BER). We analyze the wavelength dependence of these characteristics by varying the number of DWDM channels and their optical power levels and scaling the fiber's emission and absorption cross-sections. Furthermore, we quantify the power penalty due to the amplification of 40 \(\times\) 10 Gbps non-return-to-zero on-off keying (NRZ-COK)-modulated wavelength channels. Note that a single-core configuration of the cladding-pumped EYDFA is used throughout the research to exclude distortions related to inter-core crosstalk (IC-XT) and cross-gain modulation and build a reference cladding-pumped EYDFA model for further studies. The rest of the article is organized as follows. The simulation setup, alongside the description of the measurements and estimations of the gain medium parameters, is described in Section II. Section III analyzes the amplifier's performance under different operating conditions (number of DWDM channels, input optical power, absorption and emission characteristics of the Er\({}^{3+}\)/Yb\({}^{3+}\) doping) and quantifies the induced power penalty. Finally, Section IV summarizes the research findings.
# Research on Pure Hydrogen Production Using a Fuel-Processing System Combined with a PSA System
## 3 Methane Steam-Reforming Reaction/WGS Reaction  
The steam-reforming reaction is an endothermic reaction in which natural gas reacts with steam to produce hydrogen and carbon monoxide, while the water-gas shift reaction is an exothermic reaction in which carbon monoxide reacts with steam to produce additional hydrogen and carbon dioxide. There are more than 11 chemical reactions in the reformer internal reaction, including the steam-reforming reaction, the water-gas shift reaction, the carbon dioxide reforming reaction, the methanation reaction, and the methane decomposition reaction, which are summarized in Table 1[34; 35].  
Among those reactions, two main reactions were considered to calculate the concentration of each gas at the exit stream of the steam reformer, assuming that these two reactions are dominant reactions [36; 37]. In the water-gas shift reactor, only the water-gas shift reaction is considered [36; 37]. In the water-gas shift reactor, CO concentration is reduced to less than 0.05% [23; 37; 38].  
To calculate the outlet composition for each reactor, equilibrium constants in Equations (1) and (2) are applied, respectively.  
\[K_{SR}=\frac{p_{CO}\ p_{H_{2}}^{3}}{p_{CH_{4}}p\ p_{H_{2}}\ atm^{2}} \tag{1}\]  
\[K_{WGS}=\frac{p_{H_{2}}\ p_{CO_{2}}}{p_{H_{2}O}\ p_{CO}} \tag{2}\]  
Equation (1) represents the formula for the steam-reforming reaction, and Equation (2) represents the formula for the water-gas shift reaction. In Equations (1) and (2), \(K_{SR}\) represents the equilibrium constant for the steam-reforming reaction, and \(K_{WGS}\) represents the equilibrium constant for the water-gas shift reaction, which are temperature-dependent variables. These equilibrium constants have been compiled and are listed in Table 2[39]. Also, \(p_{x}\) denotes the partial pressure of component \(x\). Based on this information, it is possible to determine the composition at an equilibrium state using equilibrium constants and partial pressure. For reforming reactors, Ni-based catalysts are used to prevent carbon decomposition in the temperature range of 500 \({}^{\circ}\)C to 800 \({}^{\circ}\)C. However, above 600 \({}^{\circ}\)C and up to 1100 \({}^{\circ}\)C, Ru-based catalysts are used [23; 40; 41]. For water-gas shift reactors, Cr-based and Cu-based catalysts are predominantly used and are operated within the temperature range of 150 \({}^{\circ}\)C to 350 \({}^{\circ}\)C [41].
## 4 Discussion
There were several limitations in this FEA study while investigating the CM with two types of pads in comparison with that without a pad. Firstly, the soft tissues and insole material were simplified as linear elastic material properties to reduce the computational load acquired by the FEA. Therefore, the material properties in the current models were assigned as linear elastic, as our focus was to forecast the pattern of plantar pressure distribution while changing the pad designs and materials for a diabetic foot with neuropathy. Moreover, to identify the exact values it would be recommended to investigate more CMI designs wherein hyperelastic material properties could be assigned to the soft tissues and insoles during a gait cycle. Secondly, the current FEA study assumed a balanced standing with a ground reaction force, representing half of the body weight. The application of force following the center of pressure might represent a more realistic condition during gait. Thirdly, the bones were fused together as one entity to represent the bones to reduce the complexity of the foot model, as the region of interest was the plantar surface of the foot, rather than the bones in the foot. Lastly, the results are based on a single model of foot. Therefore, future studies are necessary to focus on types of foot and the effectiveness of CMIs. For further FE foot model development, the load bearing condition during different phases of the gait cycle requires muscle loading input. In addition, the motion within foot joints during walking might give more insight into the diabetic foot with the use of CMIs with different interventions.
## 5 Our Contribution  
This work introduces machine learning approaches to efficient path loss prediction to address the issues related to empirical and deterministic models. Considering a multi-transmitter situation, we developed and validated models for estimating path losses using MLP. The inputs to the machine learning algorithms change from one dataset to another. The parameters we used in the outdoor environment were: longitude and latitude extracted from payload, received signal strength indicator, signal-to-noise ratio, transmission frequency, spreading factor, packet sequence number, transmission bandwidth, distance between the gateway and the end-device, and power received at the gateway. Every parameter has an impact on signal propagation, e.g., the terrain causes attenuation in the signal. We used longitude and latitude to define the nature of the environment where the model can be applied. The essential indoor parameters were the number of floors and walls between the gateway and the end device. These parameters enable us to test the model in a building similar to the studied building. The output layer is the path loss in both environments.
###### Abstract  
This study assessed the applicability of Fe-impregnated biochar derived from cattle manure (Fe-CMB) as an adsorbent for removing Sb(V) from aqueous solutions and investigated the Sb(V) adsorption mechanism. Fe-CMB was mainly composed of C, O, Cl, Fe, Ca, and P, and the adsorption of Sb(V) onto Fe-CMB was identified using an energy dispersive spectrometer and Fourier transform infrared spectroscopy. Sb(V) adsorption reached equilibrium within 6 h, and the Sb(V) adsorption data as a function of time were well described by the pseudo-second-order model. The Langmuir isotherm model fit the equilibrium data better than the Freundlich model. The maximum adsorption capacity of Fe-CMB for Sb(V) obtained from the Langmuir model was 58.3 mg/g. Thermodynamic analysis of Sb(V) adsorption by Fe-CMB indicated that the adsorption process was exothermic and spontaneous. The Sb(V) removal percentage increased with the Fe-CMB dose, which achieved a removal of 98.5% at 10.0 g/L Fe-CMB. Increasing the solution pH from 3 to 11 slightly reduced Sb(V) adsorption by 6.5%. The inhibitory effect of anions on Sb(V) adsorption followed the order: Cl\({}^{-}\approx\) NO\({}_{3}\)\({}^{-}<\) SO\({}_{4}\)\({}^{2-}<\) HCO\({}_{3}\)\({}^{-}<\) PO\({}_{4}\)\({}^{3-}\).  
antimony; biochar; cattle manure; removal; adsorption; Fe impregnation +
Footnote †: journal: Applied Sciences