esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Obstacle Avoidance Scenario  
The primary motivation for obstacle avoidance, specifically for sprayer UAVs, comes from a fast-growing number of commercial sprayer UAVs and their full autonomy. Unlike the other types of UAVs, the avoidance scenarios of sprayer UAVs are different, for three reasons: (a) the UAV's liquid load transfer, which is comparatively heavy; (b) the UAV must maximize its spray coverage; and (c) the UAV must use its full battery usage so that it can spray more. Various techniques have been proposed for obstacle avoidance. The basic idea behind the obstacle avoidance scenarios for sprayer UAV is to detect the obstacle precisely and create a suitable avoidance trajectory concerning spraying and safety. This section describes the most important obstacle avoidance scenarios.  
Several technologies have been developed for obstacle detection, and numerous works have been performed using sonar, radar, laser, visionary, and fusion methods. For mission follower UAVs, three control architectures have been developed, namely, the reactive control architecture, deliberative planning architecture, and hybrid control architecture. Thus, combining different obstacle detection methods and control architectures, several techniques have been developed for obstacle avoidance. We categorize the detection methods and describe some obstacle avoidance approaches remembering the terms of sprayer UAVs. Finally, we summarize the scenario using existing works about the control, detection sensors, detection methods, and avoidance approaches, as shown in Figure 7.
## 5 Model Evaluation
### Ensemble Models
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Hyperparameters** & **Search Space** & **Optimal Value** \\ \hline \multirow{4}{*}{RF} & n\_estimators & \([10,50,100,200]\) & 100 \\  & max\_depth & None, \(5,10,20,30]\) & 30 \\  & min\_samples\_split & \([2,10,20]\) & 2 \\  & min\_samples\_leaf & \([1,2,4]\) & 1 \\ \hline \multirow{4}{*}{Bagging} & n\_estimators & \([10,50,100,200]\) & 10 \\  & max\_samples & \([0.5,0.7,1.0]\) & 0.7 \\  & max\_features & \([0.5,0.7,1.0]\) & 1 \\ \hline \multirow{4}{*}{AdaBoost} & n\_estimators & \([10,50,100,200]\) & 100 \\  & learning\_rate & \([0.001,0.01,0.1,1.1,10]\) & 0.1 \\ \hline \multirow{4}{*}{Gradient Boosting} & n\_estimators & \([50,100,150,200]\) & 50 \\  & learning\_rate & \([0.01,0.05,0.1,0.2]\) & 0.2 \\ \cline{1-1}  & max\_depth & \(3,5,7,9\) & 3 \\ \cline{1-1}  & subsample & \([0.8,0.9,1.0]\) & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyperparameter optimization for ensemble machine learning models. and 0.651 on the training and test sets, proving that the model's prediction results are highly unstable. Comparatively speaking, the Gradient Boosting model shows relatively strong performance, with an RMSE of 66.2 and 266.5 on the training and test set, proving that the model is more reliable on unknown data. The model also has a relatively high R\({}^{2}\) of approximately 0.94 on the test set, showing good predictive ability. Its MAE and MSE are relatively moderate, and the model variability is relatively small, with a CoV of 0.175 and 0.203, respectively, showing that the model is more stable. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**CoV**} \\ \cline{2-10}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline RF & 71.1 & 285.4 & 4894.1 & 81,460.7 & 34.3 & 120.9 & 0.99 & 0.93 & 0.085 & 0.179 \\ Bagging & 83.1 & 288.2 & 4591.3 & 83,016 & 36.8 & 130.8 & 0.99 & 0.93 & 0.099 & 0.206 \\ AdaBoost & 241.4 & 382.1 & 73,017.7 & 145,930.2 & 237.5 & 264.6 & 0.91 & 0.88 & 0.541 & 0.651 \\ GradientBoosting & 66.2 & 266.5 & 4600.7 & 71,000.4 & 46.1 & 110.3 & 0.99 & 0.94 & 0.175 & 0.203 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Predictive performance metrics of the ensemble models. Figure 6: Comparison between the anticipated and experimental values of each RCSW specimen obtained from the ensemble ML models. In summary, the RF and Gradient Boosting demonstrate proficiency on the training set, but exhibit some overfitting on the test set. The Gradient Boosting model performs relatively better on the test set, while the Bagging model is relatively moderate. The Adaptive Enhancement model, on the other hand, needs further optimization to improve its prediction stability and generalization ability.
## 4 Verification
### Comparison with the Results from the Commercial Numerical Software  
#### 4.1.1 Establishment of the Numerical Model  
The commercial numerical simulation software FLAC\({}^{\text{3D}}\)[34] was employed here to verify the accuracy of the proposed model. As shown in Figure 7, only a quarter of the circular tunnel was considered due to the symmetry of the problem. To simulate a deep tunnel under the hydrostatic and plane-strain condition, the outer boundary was \(15R\) from the tunnel center, and its thickness was \(1\) m. Roller boundaries were set at the left and bottom boundaries, and the displacements in the tunnel axes directions were fixed. The hydrostatic stress field \(p_{0}\) was applied to the outer boundary. The passive bolts were installed uniformly around the tunnel, which was simulated using the cable element in FLAC\({}^{\text{3D}}\).
# A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra
## 2 Materials and Methods
### Pesticide Residue Detection System  
Pesticide residue detection system developed by the lab was used to collect Raman spectral information in the study. The pesticide residue detection system included a laser device, Raman spectrometer, photoelectric charge coupled device, camera, optical fiber, probe, computer and other hardware. The effective detection range of the system was \(-\)186.45\(-\)2325.72 cm\({}^{-1}\). During the study, the excitation light power of the system was 500 mW with a wave length of 785 nm. The pesticide residue detection system was shown in Figure 1.  
Figure 1: Pesticide residue detection system.
# FarpScun: Fully Anonymous Routing Protocol with Self-Healing Capability in Unstable Sensor Networks
## 3 Fully Anonymous Routing Protocol with Self-Healing Capability
### Architecture  
In the wireless sensor networks, sensor nodes need to send the data to the base station. In this situation, these sensor nodes act as source nodes and the base stations act as destination nodes. However, in the wireless sensor networks, the source node \(S\) cannot directly communicate with the destination node \(G\), and intermediate nodes are needed in order to form a multi-hop route to relay the information.  
This paper presents a routing protocol based on on-demand route discovery. When the source node \(S\) advocates a transmitting route to the destination node, it sends the routing request message RREQ to all of its neighbors. When an intermediate node receives RREQ, its address is attached to the message, and the information is shared with its neighbors, and so on. When the destination receives the request RREQ, it generates a route reply message RREP. The reply is transmitted to the source node \(S\) along the reverse route where all of the intermediate nodes have been constructed. The source node \(S\) thus obtains a multi-hop route to the destination node \(G\). Then, the node \(S\) saves the established route to the routing table. However, as the number of the sessions increases, the routing table is quite large. Node \(S\) can first use the timeliness of the routing table entry to record only the most recent route. Its implementation can learn from the \(LRU\) algorithm (the Least Recently Used Page Replacement Algorithm). The basic idea is to select the record with the longest distance from the current access time and eliminate it. Node \(S\) set up a record stack, when a record is accessed, immediately push it into the record stack, and check whether or not the same records are just pushed into the top of the stack, if there are, then pull out the original records from the record stack, in order to ensure that the same records are not stacked. When a record is eliminated by the system, a record elimination is always taken from the bottom of the record stack, which is, the eliminated record is the longest unused.  
In Figure 1, the route \(S-A-B-G\) is a multi-hop route to the destination node G established by the on-demand routing strategy.
# Intelligent Reflective Surfaces for Wireless Networks:
## 4 IRS Applications
The use of IRSs is also interesting for unmanned aerial vehicle (UAV) networks, where the IRS is used to enhance the quality of the communication between the UAV and the ground users, thus being instrumental for the optimization of the UAV trajectory and the system performance (cf. [3]). The last application of the IRS technology illustrated in Figure 5 is related to the power transfer systems, where the IRS phase-shift matrix is designed to enhance the received signal strength at the energy receivers in the charging zone with the aim of ensuring the energy harvesting requirements [83].
## 1 Introduction
Firstly, the physical and chemical properties of biomass alternative fuels differ significantly from those of conventional fossil fuels. These fuels exhibit low calorific value and high volatility, leading to rapid combustion reactions and challenges in controlling the heat transfer rate. Most biomass fuels are characterized by high moisture content and low density, which significantly differ from conventional fossil fuels. These inherent instabilities can adversely impact combustion efficiency and disrupt the heat balance of cement kilns, ultimately increasing energy consumption in the cement production process. Consequently, investigating the combustion characteristics of alternative fuels is essential to understanding their potential applications and impacts. Furthermore, biomass alternative fuels exhibit complex compositions with high alkali content (e.g., potassium and sodium) in their ash, which can negatively affect cement clinker quality. Currently, most studies primarily focus on the feasibility of biomass as an alternative fuel, with limited attention given to the specific effects of excessive potassium and sodium on clinker mineral phases [32; 33; 34; 35]. To address this gap, this study uniquely incorporates potassium carbonate and sodium carbonate to investigate the evolution trends of clinker mineral phases under excessive alkali content. This study selected four types of biomass substitute fuels with representative ash compositions: rice straw (rich in silicon), corn cob (abundant in alkali and chlorine), pine sawdust (high in calcium), and sycamore leaves (high silicon and calcium). The combustion characteristics of biomass substitute fuels mixed with coal, along with their impacts on cement quality, were systematically analyzed. The experimental findings indicate that biomass substitute fuels markedly enhance the combustion performance of coal and exhibit minimal impact on cement clinker quality at low substitution levels, primarily evidencedby a slight reduction in the three-day flexural strength. However, as the substitution ratio increases, the introduction of additional alkali leads to progressively evident negative effects. At higher substitution ratios, the quality of cement clinker deteriorates significantly, particularly in terms of calcium oxide content and mineral composition.
# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
###### Abstract  
The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal advantage over aircraft and unmanned aerial vehicles (UAVs) in oil spill detection due to their wide-area monitoring capabilities. While oil spill detection has traditionally relied on synthetic aperture radar (SAR) images, the combined use of optical satellite sensors alongside SAR can significantly enhance monitoring capabilities, providing improved spatial and temporal coverage. The advent of deep learning methodologies, particularly convolutional neural networks (CNNs) and Transformer models, has generated considerable interest in their potential for oil spill detection. In this study, we conducted a comprehensive and objective comparison to evaluate the suitability of CNN and Transformer models for marine oil spill detection. High-resolution optical satellite images were used to optimize DeepLabV3+, a widely utilized CNN model; Swin-UPerNet, a representative Transformer model; and Mask2 Former, which employs a Transformer-based architecture for both encoding and decoding. The results of cross-validation demonstrate a mean Intersection over Union (mIoU) of 0.740, 0.840 and 0.804 for all the models, respectively, indicating their potential for detecting oil spills in the ocean. Additionally, we performed a histogram analysis on the predicted oil spill pixels, which allowed us to classify the types of oil. These findings highlight the considerable promise of the Swin Transformer models for oil spill detection in the context of future marine disaster monitoring.  
oil spill detection; optical satellite image; deep learning CNN; transformer +
Footnote †: journal: Marine Science and Engineering  
0
Footnote 0: Copyright © 2024**, 12, 2005. [[https://doi.org/10.3390/jmse12112095](https://doi.org/10.3390/jmse12112095)]([https://doi.org/10.3390/jmse12112095](https://doi.org/10.3390/jmse12112095))
## 3 Results and Discussions
### Cation Concentration of Pore Solution  
In the soil-water-electrolyte system, it is difficult to accurately measure the cation concentration of soil bulk solution. According to the Boltzmann distribution equation, the cation concentration of soil pore solution can reflect the change of that in soil bulk solution. Therefore, in this section, the cation concentration of pore solution in expansive soil at different depths was measured to analyze the migration regulation of cations.  
Figure 4: The relationship of the measured and calculated swelling deformation of expansive soil at different depths.  
3.2.1. In the Saturated Process  
To research whether the overlying soil layer can induce the cations to migrate downward and aggregate, and then form the electrical environment, on the second day after injecting de-ionized water into the model, the pore solution was extracted to measure its ion concentration.  
In so far as cations in expansive soils in China are mainly Ca\({}^{2+}\), Mg\({}^{2+}\) and Na\({}^{+}\), while K\({}^{+}\) accounts for only about 2-3% of the total cations [26]. Moreover, the expansive soil selected in this study mostly contained Ca\({}^{2+}\) and Mg\({}^{2+}\) (Table 1). Therefore, only Ca\({}^{2+}\) and Mg\({}^{2+}\) concentrations were analyzed and the results are presented in Figure 5.  
It is seen in Figure 5 that the cation concentration of pore solution in the surface expansive soil layer is the minimal while that of middle and bottom ones is the maximal. Moreover, the concentration in the surface expansive soil layer decreases whereas those of middle and bottom ones increase when the soil gradually reaches the saturated state, which indicates the Ca\({}^{2+}\) and Mg\({}^{2+}\) will migrate downward along with water molecules and aggregate around clay particles to form the electrical environment.
# A Location Routing Problem with Time Windows Consideration: A Metaheuristics Approach
## 4 Discussion  
Considering that the instances are spawned based on the best solution of LRP, the best-known solution of this problem will be the same as the LRP problem. In this problem, the effectiveness of VNSPR can be evaluated based on the following notations: \begin{tabular}{c c c c c c} _Cost_ & Solution cost achieved by VNSPR algorithm; & & & \\ _Gap_ \% & & Percentage difference between the cost of the solution found by the VNSPR algorithm and & that of the BKS (the best solution is based on CNRP’s solution); & \\ _NV_ & Number of vehicles used. & & & & \\ \end{tabular}   
From a total of 85 instances to solve, VNSPR can find the 21 best solutions. The average gap after adding the time window constraint increases slightly. In addition, total vehicles used also increase in some instances due to a strict time window constraint. Consequently, there is an additional cost. In [PERSON]'s instance, an increase in vehicles used results in an impact to total travel distance while in two other instances, it results in impact to vehicle cost and the total travel distance. The detailed results are shown in Tables 6-8. \begin{table}
\begin{tabular}{c c c c c c} _Cost_ & Solution cost achieved by VNSPR algorithm; & & & & \\ _Gap_ \% & & Percentage difference between the cost of the solution found by the VNSPR algorithm and & that of the BKS (the best solution is based on CNRP’s solution); & & \\ _MV_ & Number of vehicles used. & & & & \\ \end{tabular}  
\end{table} 
Table 5: Computational results CNRP for all instances compared with other algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Prob. ID** & **n** & **m** & **Q** & **BKS** & **NV** & **VNSPR** & **NV** & **Gap (\%)** \\ \hline B1 & 21 & 5 & 6000 & 424.9 & 4 & 424.9 & 4 & **0.00\%** \\ B2 & 22 & 5 & 4500 & 585.1 & 3 & 585.1 & 3 & **0.00\%** \\ B3 & 29 & 5 & 4500 & 512.1 & 4 & 512.1 & 4 & **0.00\%** \\ B4 & 32 & 5 & 8000 & 562.2 & 3 & 562.2 & 3 & **0.00\%** \\ B5 & 32 & 5 & 11,000 & 504.3 & 3 & 504.3 & 3 & **0.00\%** \\ B6 & 36 & 5 & 250 & 460.4 & 4 & 460.4 & 4 & **0.00\%** \\ B7 & 50 & 5 & 160 & 565.6 & 5 & 565.6 & 5 & **0.00\%** \\ B8 & 75 & 10 & 140 & 844.4 & 11 & 861.171 & 11 & 1.99\% \\ B9 & 100 & 10 & 200 & 833.4 & 8 & 857.837 & 8 & 2.93\% \\ B10 & 12 & 2 & 140 & 204 & 2 & 204 & 2 & **0.00\%** \\ B11 & 55 & 15 & 120 & 1112.10 & 10 & 1113.30 & 10 & 0.11\% \\ B12 & 85 & 7 & 160 & 1622.50 & 11 & 1628.01 & 11 & 0.34\% \\ B13 & 318 & 4 & 25,000 & 557,275 & 9 & 564,409.88 & 9 & 1.28\% \\ B14 & 318 & 4 & 8000 & 670,119 & 27 & 678,689 & 27 & 1.28\% \\ B15 & 27 & 5 & 2500 & 3062 & 4 & 3062 & 4 & **0.00\%** \\ B16 & 134 & 8 & 850 & 5709 & 11 & 5913.36 & 10 & 3.58\% \\ B17 & 88 & 8 & 9,000,000 & 355.8 & 7 & 368.231 & 6 & 3.49\% \\ B18 & 150 & 10 & 8,000,000 & 43,919.90 & 12 & 44,855 & 13 & 2.13\% \\ B19 & 117 & 14 & 150 & 12,290.30 & 6 & 12,583.10 & 7 & 2.38\% \\ \hline \multicolumn{8}{c}{Average} & \multicolumn{7}{c}{7.57895} & \multicolumn{7}{c}{7.57895} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computational results for LRP with time windows for Barreto’s instances.
## 2 Demonstration Sites  
The demonstration sites are scattered at three typical locations over the area of the lower northern Mississippi from the Mississippi Delta to the Black Prairie:  
1. Stoneville, Mississippi  
This location is in the area of the Mississippi Delta, one of the largest contiguous agricultural areas in the U.S. and close the Mississippi river. At this location, the United States Department of Agriculture (USDA), Agricultural Research Unit (ARS) has land of about 1.2 km\({}^{2}\) (33\({}^{\circ}\)26\({}^{\prime}\)42\({}^{\prime\prime}\) N, 90\({}^{\circ}\)52\({}^{\prime}\)50\({}^{\prime\prime}\) W and 37 m elevation), with numerous fields specialized for various crop research and experiments of precision agriculture, weed science, and irrigation water management.  
2. Brooksville, Mississippi  
This location is close to the Black Prairie wildlife area in Mississippi where Mississippi State University professors are conducting irrigation research experiments over an area of 2.7 km\({}^{2}\) (33\({}^{\circ}\)15\({}^{\prime}\)31\({}^{\prime\prime}\) N, 88\({}^{\circ}\)32\({}^{\prime}\)60\({}^{\prime\prime}\) W and 79 m elevation) in eastern Mississippi. Within the area, a field of 9700 m\({}^{2}\) (33\({}^{\circ}\)15\({}^{\prime}\)40\({}^{\prime\prime}\) N, 88\({}^{\circ}\)32\({}^{\prime}\)22\({}^{\prime\prime}\) W and 73 m elevation) was used for an irrigation study in a soybean field by USDA ARS scientists, with two treatments, rainfed and irrigated, replicated four times.  
3. Pontotoc, Mississippi  
This location is in the middle of the lower northern Mississippi, and at this location, USDA ARS scientists have conducted crop science experiments in numerous fields in the farm of Mississippi State University over an area of 2.6 km\({}^{2}\) (34\({}^{\circ}\)08\({}^{\prime}\)17\({}^{\prime\prime}\) N, 89\({}^{\circ}\)00\({}^{\prime}\)15\({}^{\prime\prime}\) W and 141 m elevation) in northeastern Mississippi.  
Figure 1 shows the maps of the study areas generated by Google Maps and Google Earth Pro (Google, Mountain View, CA, USA).
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 4 Proposed Mitigation Mechanism for Jamming Attacks  
We propose a two-phase mechanism for mitigation. The first phase concerns the detection of those nodes with communications that are being directly exposed to jamming. The second phase consists of a reconfiguration of the routes to avoid the affected nodes.  
The rationale behind the proposed scheme is that if we only exclude the jammed nodes, then the overall performance of the network may be reduced. However, the performance can be improved by reconfiguring the routes with the unaffected nodes. In this way, we avoid the propagation of the effects of the jamming and the waste of bandwidth and energy due to the packet re-transmissions and overhead of the routing protocol.  
Since a WSN has very limited energy resources, and jamming may cause waste of energy, therefore, we will also analyze the energy consumption of the nodes and it is expected that the proposed mitigation scheme reduces the waste of the energy associated due to the jamming.  
The detection algorithm is designed for applications where the sensor networks is periodically time-sampling different part of a large process, where each sensor can be configured to sample in the range of 1 to 10 s. At the beginning, the sensor network is calibrated for a specific process and once calibrated it will remain like that; therefore, the traffic conditions generated for the sampled information is very stable. However, since the environment where the sensor network is going to work is diverse, we train the metrics of the detection algorithm to adapt to this environment (attack-free scenario). For example, if the network is in a harsh environment, then this will be the normal operating conditions, and the metrics would be markedly different to the metrics in a cleaner environment. Then, the training step reflects the normal conditions and it will be used to detect anomalies in the network. In addition, the proposed detection scheme in its testing phase has an epsilon constant, which could be adjusted to reduce errors and can be selected depending on the environment and error in the performance metrics. Besides, by using three parameters of evaluation we reduce false positives which can reinforce the good method operation under non-ideal conditions.