esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on 2D Laser Automatic Navigation Control for Standardized Orchard
[PERSON] [29] considered the wheel slip and used the cheap 2D laser to realize the straight walking and ground turning of the tractor in the apple orchard. Environmental awareness is an important part of the navigation system. In laser positioning system, positioning accuracy and quality depend on many factors such as the laser incidence angle [30], scanning object geometry [31] and object surface characteristics [32; 33]. In order to improve the positioning accuracy and quality, different algorithm models were used to complete the noise reduction processing with different recognition of the surface features of objects. For the sphere, [PERSON] [34] provided two methods of parameter acquisition and verified the accuracy and effectiveness of the algorithm. In this paper, the standardized apple orchard with good consistency of row spacing parameters were the research environment. The trunks are relatively smooth and complete which can be viewed as cylinder approximately. Considering the principle of economy, 2D laser will play a huge potential in the application of standardized orchard fruit positioning. Many scholars have done a lot of research on solving the problem of noise interference in fruit trees [35; 36; 37], and some progress has been made. However, the existing research of the acquisition and processing of laser point cloud data ignores the influence of obstacles such as weeds and irregular tree trunks. If the necessary filtering is not carried out, it will cause a large deviation in the fitted navigation baseline. Some researchers have been developed path-tracking controller with high accuracy and strong robustness by using Proportion Integration Differentiation (PID) [38], fuzzy control [39] or sliding mode control [40], but due to the lack of positioning accuracy, the navigation control accuracy will be seriously affected. This paper designed an automatic navigation system for standardized orchards with 2D laser for orchard low-speed operation wheeled vehicles. The system considers the influence of small interfering objects on the traveling route of the wheeled vehicle on the accuracy of the point cloud data acquired by the 2D laser. The distance threshold and the statistical filtering were used to complete the filtering process. Euclidean clustering algorithm and the important geometric theorems of three points collinearity was used to extract the central feature points of the trunk. Finally, the linear navigation path was identified and extracted by the least square method. Based on obtaining lateral deviation and heading deviation, this paper designed an adaptive pure pursuit path-tracking controller combined with the advantages of fuzzy control without the need to construct an accurate mathematical model. The modified agricultural electric vehicle was used as an experimental platform for simulation and field tests in order to verify the accuracy and robustness of the automatic navigation system.
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 4 Results of the Analysis of the Selected Works
### Analysis of the Application Layer
#### 4.1.1 Agriculture  
IoT supports the process of data analytics providing environmental parameters such as humidity and temperature that are important for the life of plants and animals [22]. Variations in temperature and humidity are associated with climate changes and can lead to the appearance of pests or diseases. The size and shape of the sensors used in agriculture depend on the characteristics of the plants examined. They can be expensive, e.g., those used in large farms, and the information they provide can be enhanced by complementary data such as geo-space images [23]. The data generated by sensors can be processed by cloud or edge computing [24]. Additionally, it is possible to determine adverse effects on the plantation by using mathematical algorithms as time series [25].  
The agriculture sector faces enormous challenges due to the need of producing food for the world's population, which will reach 9.6 billion people by 2050, according to the Food and Agriculture Organization (FAO). This means that the increase in food production must grow by 70% in the next years [26]. One alternative to address this challenge is improving the quality and quantity of the agricultural production. For this, the automation and continuous monitoring of pest and diseases as well as the identification of optimum weather and geographic field parameters would be very useful. IoT solutions integrated with the use of drones and 3D mapping allow the collection of information about the geography of a field and the soil composition to plan the plantation process [27]. Drones monitoring the status of crops and farms in real time facilitate the access of data coming from dangerous areas. In many cases, sensor nodes are installed in the farm, and the gateway node is carried by a drone to gather information from the sensors; then, the data are sent to servers, fog or cloud infrastructures, for processing.  
Smart irrigation is another relevant application in agriculture that allows utilizing water efficiently at the appropriate moment and in the right amount. The gateway node of an IoT architecture can send commands to irrigation nodes to activate or deactivate a solenoid valve for starting or stopping the irrigation process [28]. The use of IoT solutions to determine the optimal water temperature and salinity allows taking actions for increasing the survival rate of larvae and adult crabs. Another IoT application in agriculture regards water quality monitoring. It is based on sensors that determine water pH, temperature, and salinity and deliver such information to the farmer in an intuitive way [29]. For forestry, it is important to find methods for the automatic monitoring of parameters like temperature, humidity, noise, solar radiation, pressure, soil, and moisture. Applications in forestry present major problems concerning the Fresnel, zone due to obstacles [30].
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 3 A Fog-Based Management and Orchestration Framework for 5G Smart Cities
### Challenges of a Fog-Based MANO Architecture
#### 3.1.7 Local Autonomous Operations  
By enabling decisions and autonomous operations locally, it is possible to reduce the amount of data that needs to be sent to the cloud layer, decreasing the latency in the communication and improving the response time in case network failures are detected.
## 4 Data Analysis in Maritime IoT Applications
### Benefits and Limitations
#### 4.3.5 Regulatory Compliance  
In an industry that is heavily regulated, ensuring compliance with environmental, safety and other regulations is crucial. Data analysis plays a key role in this, helping organizations monitor and analyze data related to emissions, waste disposal, safety protocols,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Benefits of Data Analysis** & **Limitations of Data Analysis** \\ \hline Increased Efficiency & Data Quality \\ \hline Improved Safety & Complexity \\ \hline Enhanced Decision-Making & Security and Privacy \\ \hline Predictive Capabilities & Cost \\ \hline Regulatory Compliance & Interoperability \\ \hline Innovation and Continuous Improvement & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Benefits and Limitations of Data Analysis in Maritime IoT.  
and more. This enables them to ensure they are compliant, preserving their reputation and avoiding hefty penalties [89].
###### Abstract  
A combination of theoretical analysis, numerical simulation and physical model experiments is used to explore the mechanism of pressure relief and roof blasting effects along the gob-side roadway. The stress and displacement along the gob-side roadway before and after blasting were investigated using discrete unit code (UDEC) software. The results demonstrated that blasting can effectively decrease the peak stress of the coal seam along the gob-side roadway and transfer it to the depth. The maximum displacement of the roof of the gob-side roadway, the coal pillar and the solid coal was reduced from 9.5, 10.8 and 4 cm to 6.5, 2 and 3 cm, respectively, after roof blasting. The experimental results showed that the movement of the overburden strata showed obvious regional characteristics after blasting which included the height of the caving zone on the broken side being 3.3 times higher than that observed on the unbroken side, while the height of the fractured zone was 0.52 times higher. The field application of roof blasting was controlled by a drilling method, micro-seismic monitoring and stress monitoring. The results showed good application effects. This research provides valuable insights for managing the stability of gob-side entries.  
gob-side entry; numerical simulation; physical model experiments; surrounding rock control; deep-hole blasting Article
## 4 Results and Discussion
### Experimental Results  
The ambient light sensor and our proposed system were proven to be stable and reliable with multiple tests. The experimental results of \(Lux\), \(D_{n}\), \(R_{V}\), \(R_{L}\), and \(V_{B}\) were shown in Table 4. We discovered that in high ambient light intensity, \(R_{V}\) and \(R_{L}\) were smaller, and vice versa to stabilize the reference voltage (\(V_{B}\)) of the photosensitive resistor divider.  
Figure 16: Ambient light sensor vs. photometer LM-81 LX.  
Figure 15: Flowchart for encoding/decoding and cloud monitoring.
# Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping
## 4 Results and Discussion
### Occlusion Handling
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & \multicolumn{3}{c}{**Normal Occlusion**} & \multicolumn{3}{c}{**Complex Occlusion**} \\ \cline{2-7}  & **PQ\({}^{\text{Th}}\)** & **SQ\({}^{\text{Th}}\)** & **RQ\({}^{\text{Th}}\)** & **PQ\({}^{\text{Th}}\)** & **SQ\({}^{\text{Th}}\)** & **RQ\({}^{\text{Th}}\)** \\ \hline Singularized Cutting & 85.8 & 93.6 & 91.7 & 89.1 & 94.5 & 94.4 \\ Remains & 91.0 & 94.0 & 96.9 & 86.0 & 94.1 & 91.4 \\ Occluded Cutting & 76.5 & 88.5 & 86.5 & 72.0 & 83.2 & 86.5 \\ Target Cutting & 78.2 & 89.9 & 87.1 & 86.8 & 91.4 & 95.0 \\ \hline Average & 82.9 & 91.5 & 90.6 & 83.5 & 90.8 & 91.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Panoptic segmentation results with AdaptIS on the presented occluded plants dataset. Generally, the stems of the plant cuttings are thin, thus it is a challenge for the segmentation networks to segment them completely and without any discontinuities. This makes the stems of the cuttings the most difficult part of the plant cuttings for segmentation. Since the input size of the AdaptIS network is 640 pixels, the width of the stems ranges from 2 to 7 pixels, which makes their segmentation even more difficult. As the sample results in Figure 9 show, the stems of the plant cuttings are segmented by AdaptIS completely and without any discontinuities, which proves the robustness of this network in segmentation. The goal of the intended robotic grasping application is to grasp the instances of Target Cuttings and Singularized Cuttings. Therefore, the segmentation masks of plants of these classes are used for grasp detection, and the precision of these masks impacts the result of grasp detection. As Table 2 shows, the SQ, i.e., the percentage of average IoU (Intersection over Union) of the matched segments, for these two classes is around 90%, which is a high precision for the purpose of our intended robotic grasping. However, in some rare cases such as in the top left example in Figure 9, the segmentation result of a Target Cutting instance is incorrect, and as it can be seen, one leaf is inaccurately segmented, which may affect the accuracy of the grasp detection for this instance.
## 5 Illustrative Example
### Solution Improvement by Incrementing the SCV Algorithm
Vectors' significance is given in Table 2, with supplementary details in Table 1. Figure 10 displays the solution associated with particle 1 at iteration 4 of the SCV algorithm. In this case, granularity is \(g_{r}\)= 83% (\(n_{f}\) = 5, \(n_{m}\) = 6), the reconfiguration time is \(t_{r}\) = 24 min (\(t_{m}\) = 20 min, \(m_{i}\) = 1, \(m_{a}\) = 0). The utility function is \(T_{1}\) = 1 and the convergence is 0. Thus, variant 7 from Figure 9 is another reliable solution for the structure of the machine tool. Figure 10 illustrates the solution of particle 2, at iteration 4. For variant 8, the following results are revealed: granularity \(g_{r}\)= 71% (\(n_{f}\) = 5, \(n_{m}\) = 7), reconfiguration time \(t_{r}\) = 24 min (\(t_{m}\) = 20 min, \(m_{i}\) = 1, \(m_{a}\) = 0). The utility function is \(T_{2}\) = 0.99. For particle 3, at iteration 4 of the SCV algorithm, the result is shown in Figure 11. Solution in this case reveals a granularity \(g_{r}\)= 71% (\(n_{f}\) = 5, \(n_{m}\) = 7), a reconfiguration time \(t_{r}\) = 24 min (\(t_{m}\) = 20 min, \(m_{i}\) = 1, \(m_{a}\) = 0). The utility function in this case is \(T_{3}\) = 0.99. Thus, the best position at iteration 4 is given by the first particle in the swarm. Figure 10: Particle 2 at iteration 4: variant 8. Figure 9: Particle 1 at iteration 4: variant 7. In conclusion, after four iterations, three reliable solutions were identified for the machine tool (variants 4 and 5 from iteration 2 and variant 7 from iteration 4). For this example, iterations are stopped at this point. Figure 12 illustrates the implementation of the SCV algorithm in a dedicated software application written in Java. The left-side window shows the results after the second iteration, whereas the right-side window shows the results after the fourth increment. Figure 7.0 Particle 3 at iteration 4: variant 9. Figure 7.1 Screenshots showing SCV implementation in a software application. As Figure 12 shows, in all cases where the current value exceeds the target of a certain parameter, the value of the target is introduced for calculating the utility function. This is appropriate for the intended objective, and it is done in this way to follow the form imposed by the utility function in Equation (4). Another important remark is about the values given to three performance indicators (i.e., variety of available modules, number of locations for interfaces, number of possible configurations) in all the variants highlighted in this example. It is seen in Figure 12 that, for all cases, these three performance indicators reached their target values. The reason is further justified. For module availability, all modules are simple rotation or translation units; thus, they can be easily ordered. Because all modules are of pure rotation or translation, there is no difficulty in that each module is designed with two locations for interfacing with other modules. If all parts in the family of parts (see Figure 1 for the generic part) can be manufactured with any of the proposed variants, configurability reaches the target in all cases.
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 6 Focusing of Secondary Mesons  
As discussed in Section 2.1, the total neutrino flux depends on the angle of the neutrino decay with respect to the original meson direction, which typically scales as \(\sim\)1/\(\gamma\). Therefore, we can see from the bare-target kinematics of low energy neutrino experiments that the neutrino flux toward the detector is strongly suppressed if the secondaries are not focused. Focusing of secondaries is generally achieved either by magnetic horns or by quadrupole multiplets.
## 2 Background
### Edge AI Paradigm  
Edge computing has emerged due to the limitations of cloud computing, to address the issues of latency, bandwidth, and quality of experience (QoE) for users [1]. Initially, cloud computing provided significant advantages in terms of storage and processing power, but its centralization led to high latency and bandwidth constraints, which become especially problematic for mobile and IoT devices [1]. To overcome these challenges, the concept of edge computing was introduced. Instead of relying on distant mega-data centers, edge computing distributes computing resources closer to users, at the \"edge\" of the network. This involves deploying smaller, localized data centers, such as micro data centers and cloudlets, to handle tasks closer to where data are generated and consumed. This setup reduces latency, increases bandwidth, and enhances user QoE by processing data locally rather than in a remote data center.  
The evolution of edge computing further includes the integration of AI at the edge, known as Edge AI. This development includes advancements in AI and the proliferation of edge devices to perform intelligent computations closer to the user. Edge AI has been used in a wide range of applications, from autonomous driving to real-time healthcare issues [6], and is enabled by recent improvements in hardware and network technologies such as 5G and beyond [1].
# Evolutionary Statistical System Based on Novelty Search:
## 4 Experimentation and Results
### Results
As stated in Section 4.1, for simplicity and for the sake of comparison, we decided for ESS-NS to keep the same value of fitness threshold as the other methods, which had been chosen in previous works based on calibration experiments. Nevertheless, it would be interesting to test how this value affects different methods as a future line of research. It is important to note that ESS-NS achieves quality results that are similar to the ESSIM methods but without the island model component. That is, it outperforms the original ESS only by means of a different metaheuristic. One advantage of this is that the time complexity of ESS-NS is not burdened with the additional computations for handling the islands and the migration of individuals present in the ESSIM methods. Another implication is that, just as ESSIM-EA and ESSIM-DE benefited from the use of an island model and were able to improve quality results, this will likely also be true for ESS-NS if the same strategy is applied to it. However, one should also take into account that there exist a number of variants of ESSIM-DE that have shown improved quality and runtimes [13; 25]. These results have been excluded because we decided that a fair comparison would use the base methods with their statically calibrated configuration parameters but without the dynamic tuning techniques. As a final note, it is important to emphasize that this new approach improves the quality of predictions by means of a metaheuristic that employs fewer parameters, which makes it easier to adapt to a specific problem. Another advantage is the method for constructing the final set of results, which provides more control over which solutions will be kept or discarded. In this case, the algorithm keeps a number of the highest fitness solutions found during the search, and this number can be established by a parameter (for simplicity, in our experiments we have used the same number as the population size for the results set). This mechanism improves robustness in the distribution of quality results over many different runs of the prediction process. Finally, this approach is simpler to understand and implement than its predecessors. For all these reasons, we find that, overall, the new method is the best regarding quality, robustness, and efficiency.
# Highly Sensitive Non-Enzymatic Detection of Glucose at MWCNT-CuBTC Composite Electrode
## 3 Results
### Cyclic voltammetry Measurements (CV)  
Cyclic voltammogram (CV) recordings of the MWCNT-CuBTC composite electrode in 0.1 M of an NaOH supporting electrolyte and various concentrations of glucose are shown in Figure 2. In the absence of glucose, the MWCNT-CuBTC composite electrode exhibited a large capacitive current, and no clear oxidation peaks characteristic of copper oxidation were evidenced, probably due to the large background that overlaid the peaks. In addition, no reduction peaks corresponding to Cu(II) or  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Composite-Based Epoxy Type** & **Electrical Conductivity (\(\sigma\)/Scm\({}^{-1}\))** \\ \hline MWCNT & 0.373 \\ MWCNT-CuBTC & 0.605 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrical conductivity of the composite materials.  
Figure 1: Scanning electron microscopy (SEM) image of (**a**) the multiwall carbon nanotube (MWCNT)-epoxy composite electrode and (**b**) the MWCNT-copper-1,3,5-benzentricarboxylic acid (CuBTC) composite electrode. The inset is an energy dispersive X-ray (EDX) image.  
Cu(I) species were observed. However, in the presence of glucose, the shape of the voltammogram was changed. The oxidation process of the glucose on the MWCNT-CuBTC composite electrode started at the potential value of +0.2 V/SCE, which should assume the involvement of Cu(I) and Cu(II) species in accordance with literature data [31; 32]. The current corresponding to the oxidation potential value of +0.6 V/SCE increased linearly with the glucose concentration, exhibiting two slopes characteristic to two glucose concentrations, one ranging from 0.1 to 1 mM and the other from 1 to 5 mM of glucose with good correlation coefficients (inset of Figure 2). The glucose concentration increase led to the shift of the anodic current peak to a more positive potential value, and the signal was diminished, probably due to a possible electrode surface fouling at higher glucose concentrations.
###### Abstract  
The fault diagnosis of rolling bearing acoustic radiation signals holds significant importance in industrial equipment maintenance. It effectively prevents equipment failures and downtime, ensuring the smooth operation of the production process. Compared with traditional vibration signals, acoustic radiation signals have the advantage of non-contact measurement. They can diagnose faults in special conditions where sensors cannot be installed and provide more comprehensive equipment status information. Therefore, to extract the fault characteristic information of rolling bearings from complex acoustic signals, this paper proposes an advanced deep learning model combining Gramian Angular Field (GAF), ResNet1D, ResNet2D, and multi-head attention mechanism, named CRAMNet (Combined ResNet Attention Multi-Head Net), to diagnose the faults of rolling bearing acoustic radiation signals. Firstly, this method includes converting one-dimensional signals into GAF images and performing data standardization and segmentation. Then, the method utilizes ResNet1D to extract features from one-dimensional signals and ResNet2D to extract features from GAF images. Further, it combines the multi-head attention mechanism to enhance feature representation and capture dependencies between different channels. Finally, this paper compares the proposed method with several traditional models (including CNN, LSTM, DenseNet, and CNN-Transformers). Experimental results show that the proposed method performs outstandingly in terms of accuracy and robustness. The combination of residual networks and multi-head attention mechanism in the model significantly enhances its ability to accurately diagnose rolling bearing faults, proving the superiority of the algorithm.  
rolling bearings; acoustic signal; ResNet; multi-head self-attention mechanism; fault diagnosis 2024202024148431. [[https://doi.org/10.3390/app14188431](https://doi.org/10.3390/app14188431)]([https://doi.org/10.3390/app14188431](https://doi.org/10.3390/app14188431))  
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