esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Technological and Policy Solutions
### Policymaking Arenas
#### 2.2.1 Imo
There will be reviews/revisions in 2023 and 2028. In the context of the present article, a key question is whether black carbon will be included in the specifics of the strategy as it progresses through IMO processes. As for black carbon at the IMO to date, its Maritime Environment Protection Committee (MEPC) took up black carbon issues in 2011, when it instructed what has since become the subcommittee concerning Pollution Prevention and Response (PPR) to develop a definition, identify appropriate measurement methods and investigate control measures. [71] These tasks have been the focal topics of a series of expert workshops organized by the International Council on Clean Transportation (ICCT) beginning in 2014 [72]. The workshop participants included government officials, maritime industry representatives, NGO staff experts, and academic specialists. The expert group agreed on a definition, as reproduced above at the end of this paper's Annex, which was accepted by the IMO's MEPC. A workshop in 2015 approved a measurement-reporting protocol, which was accepted by the IMO's PPR in 2016 [73]. The expert workshops in 2016 and 2017 agreed that there were three appropriate measurement methods: filter smoke number (FSN), photo-acoustic spectroscopy (PAS) and laser-induced incandescence (LII), and those were accepted by the IMO PPR in 2018 [74]. At the expert workshop in 2018 [3], there was agreement on a list of 13 appropriate control technologies, and the IMO PPR agreed with that list (see Table 8). These are thus an agreed subset of the possibilities noted above in Table 7.  
In 2018, the IMO MEPC decided that it would make a policy decision in 2021--on the basis of the work over several years by the workshops and PPR meetings. (As of this writing in late 2020, many IMO meetings were being delayed or canceled during the coronavirus pandemic.)
## 3 Methodology
### Satellite Image Preprocessing  
So far, the presented algorithm is able to estimate the UAV location, but the prediction error accumulates over time. To address this issue, we introduce a novel algorithm based on satellite images (geo-referenced) and the flight plan.  
Using satellite imaging for localization has been explored before [58; 59; 60]. In [58], the authors proposed a method to index ortho-map databases by image features, facilitating the rapid search of geo-referenced images. Conversely, ref. [59] introduces an adaptation of the Cross-View Matching Network (CVM-Net) tailored for aerial imaging, which includes a Markov localization framework to ensure temporal consistency. Finally, ref. [60] presents a localization approach for multirotor drones that employs an autoencoder to map images into an embedding space, enabling image comparison using an inner product kernel.  
Although the above-mentioned projects successfully achieve localization for their intended tasks, they are not suitable for a tactical fixed-wing UAV that can navigate up to 200 km. In the presented case, we have the following limitations:  
1. Power consumption of the hardware: We are limited to using small, low-power hardware such as the Raspberry Pi 4 [61] or the Orange Pi 5 [62] to avoid overloading the system. This constraint means we cannot use many advanced techniques that rely on deep learning.
2. Onboard processing: In areas without GPS coverage, communication can be unreliable or nonexistent. As a result, the UAV must be capable of performing GPS estimation on board.
3. Altitude estimation: While low-altitude multirotors can achieve high precision with laser or ultrasonic sensors, in our case, we would require expensive LiDAR equipment that exceeds the payload capacity of the vehicle. In our situation, we estimate the altitude using a barometer, which can introduce errors of up to several hundred meters.
4. High-range missions: Tactical UAVs can operate over distances of up to 200 km. Therefore, the algorithms used must be able to handle long-duration flights.  
To reduce the cumulative error in an efficient way, we present the following preprocessing scheme (Figure 8); we detail this scheme in the following subsections.  
Figure 7: Comparison of GPS coordinate estimation against the flight path (ground truth), represented with the blue markers, using different values (\(\delta=0.0005 px^{-1}\), \(\delta=0.001 px^{-1}\), \(\delta=0.0015 px^{-1}\)) and its error accumulation.
## 2 Research and Significance  
From the comprehensive analysis of a large number of domestic and foreign studies, it can be seen that current research on slopes and retaining walls mainly focuses on soil pressure and seepage under certain assumptions. In this paper, based on the traditional research on soil pressure and the anti-slip and overturning stability of retaining walls, a new method for force and displacement analysis of slopes and retaining walls is proposed, which can obtain the solutions of stress distribution at each point inside the slopes and retaining walls under the corresponding boundary conditions. On this basis, [PERSON]'s principal structure models are adopted for the slope and retaining wall,respectively, to obtain the strain and displacement solutions for the slope and retaining wall. The new theoretical solutions proposed in this paper can be used not only to study the damage forms of retaining wall overturning and foundation sliding processes but also to study the damage forms of retaining wall tensile bulging. It can provide a theoretical basis for the design of slope control engineering, and according to the different forms of stress, different forms, and materials of retaining walls can be adopted, and more economical, reasonable, and effective control methods can be derived.
## 3 The Proposed Model
### Model Evaluation  
The suggested model's performance was evaluated using four metrics, namely, recall, accuracy, precision, and F1-score.  
The class of each data object is established using specific classification methods, providing each sample with a predicted label (positive or negative) [31]. As a result, after the detection phase, each sample falls into one of four categories:  
* True positives are actual positives, including the number of cases that are correctly identified as anomalous by the model (TP).
* False negatives are positives, including the number of cases that are misclassified as non-anomalous by the model (FN).
* True negatives are actual negatives, including the number of cases that are correctly classified as non-anomalous by the model (TN).
* False positives are actual negatives, including the number of cases that are misclassified as anomalous by the model (FP).  
Four parameters, comprising recognition precision, recall, F1-score, and accuracy, are utilized to evaluate the effectiveness of the suggested model, utilizing the four classes listed above.  
The accuracy value is a statistical measure of how efficiently a model can predict an outcome [32]. Equation (8) illustrates how the accuracy measure is calculated.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{8}\]  
Recall and precision are frequently utilized to evaluate a result's correctness [33]; these are correctly expressed in Equations (9) and (10).  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{9}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{10}\]  
The F1-score is the weighted sum of recall and precision and is utilized when the data are uneven [34]; Equation (11) illustrates how the F1-score is calculated.  
\[\text{F1}-\text{score}=2\cdot\frac{\text{precision}\cdot\text{recall}}{ \text{precision}+\text{recall}} \tag{11}\]
# Forward and Inverse Studies on Scattering
## 3 Inverse Problem by Modified BEM
### Numerical Examples
#### 3.2.3 Reconstruction Results Using Different Frequency Range  
It is necessary to discuss how the frequency range affects the inverse reconstruction results, which helps us understand the proper choice of incident wavelength in experiments. We have recalculated the inverse reconstruction for the flaws with \(d_{\mathrm{max}}/r\) equaling 0.2 and 0.5, using frequency range \(\Omega=0\sim 5.0\), \(0\sim 2.0\), and \(0\sim 1.0\), respectively.  
The reconstruction results are plotted in Figure 10. For the case of \(d_{\mathrm{max}}/r=0.2\) (see Figure 10a), the frequency range of \(\Omega=0\sim 1.0\) (see the dash-dot curve) only gives a vague image, yet it still obtains the correct location. When the frequency range increases to \(\Omega=0\sim 2.0\), the flaw shape seems to agree well with the reality, however, the image underestimates the flaw depth and overestimates the width. As the full data of reflection coefficients with \(\Omega=0\sim 5.0\) are used in the inversion process, the flaw image becomes clearer, and the width and depth are more accurate, although the image for part of the bottom is deviated from reality.  
For the case of \(d_{\mathrm{max}}/r=0.5\) (see Figure 10b), if only the low frequency parts of reflection data (e.g., \(\Omega=0\sim 1.0\) and \(\Omega=0\sim 2.0\)) are taken into the inversion process, the results imply the existence of the flaw but the images are very blurred. It is only when higher frequency portions are taken into consideration that the flaw image becomes clearer and width and depth are much more accurate.  
From these two examples, it can be concluded that the low-frequency or long-wavelength components of the reflection signals (e.g., \(\Omega=0\sim 1.0\)) control the canyon's basic information, such as existence and location; while the usage of higher frequency components (e.g., \(\Omega=1.0\sim 5.0\)) increases the clarity of image and gives more detailed geometric information, such as depth and  
Figure 10: Inverse reconstruction results of surface canyon-shaped flaws with different frequency ranges (**a**) \(d_{\mathrm{max}}/r=0.2\); (**b**) \(d_{\mathrm{max}}/r=0.5\).  
width. It should be noted that the effect of high frequency components of enhancing image resolution is limited by Born approximation. As the Rayleigh wave becomes shorter in wavelength, it will penetrate less deeply into the elastic half-space, thus, the canyon becomes a large scatterer, and Born approximation loses validity. Actually, in the previous cases, if the high frequency components for \(\Omega>5.0\) are taken into consideration, the reconstructed images change little from the ones obtained from data with \(\Omega=1.0\)\(\sim\) 5.0.
## 3 System Modeling  
### Overview of Prototype  
The prototype we built is simple. A 2D lidar Hokuyo UST-10 LX [30] is used to scan the environment, its angle resolution is 0.25\({}^{\circ}\). A 42-stepper motor with its driver and controller are used to rotate the 2D lidar. A photoelectric switch is used for the definition of the initial position of the motor shaft. In addition to the above-mentioned electronic devices, there are also some mechanical parts. A connector is used for it is necessary to fix the 2D lidar on the motor shaft, and sufficient installation accuracy should be ensured. A shading sheet which rotates with the motor shaft is required to block the light beam of the photoelectric switch so as to trigger it. A triangular plate is needed to correct the attitude of the 3D point cloud. The photo of our prototype is shown in Figure 3.  
Figure 2: Improved action sequence of the 2D lidar and stepper motor.  
In some prototypes mentioned by several literatures (such as [8]), a slip ring is used to connect the rotating end (the 2D lidar) and the stationary end (the stationary part of the rotating unit) of a rotating 2D lidar in power supply and communication. Under the action of the slip ring, the rotating cables of the 2D lidar is transformed into stationary cables. By using a slip ring, the motor can rotate the 2D laser continuously without twisting or pulling the cable. However, the use of a slip ring can also lead to an increase of weight, size and cost. Therefore, we have not used a slip ring in our prototype, as a result of which, we need to pay attention to the layout of the cable to ensure that the cable cannot be pulled when the motor shaft rotates in a 3D scan. After each 3D scan, it is necessary to return the motor shaft to the original position, because due to the limitation of the cable, the motor can only rotate in a certain range and cannot rotate continuously.  
According to the different angular resolution in motor shaft direction, our prototype has 7 scanning modes. In mode 1, the angular resolution is lowest, and it takes only 1 s to finish a 3D scan, which is suitable for applications with high real-time requirement. In mode 7, the angular resolution is highest, and it takes 16 s to finish a 3D scan. In this mode, dense point cloud with meticulous details of the environment can be collected. These 7 scanning modes make our prototype suitable for different applications. The time \(T\) and the angular resolution \(\gamma\) of each mode are shown in Table 1. The relationship between \(T\) and \(\gamma\) is can be found in Equations (1) and (2).
## 4 Performance Evaluation
### Packet Delay Ratio  
The PDR calculation demonstrates the ratio of delivered packets to total transmitted packets as shown in Equation (6) [63]:  
\[PDR=\frac{\sum All\ \text{Delivered\ Packets}}{\sum All\ \text{Sent\ Packets}} \tag{6}\]  
The PDR is calculated by varying the size of the cluster and speed of UGVs in various scenarios to analyze the performance of the proposed protocol. The PDR remains above 86%, even at a maximum vehicle speed of 60 m/s, because of a combination of an adaptable  
Figure 4: (**a**) UGAVs-MDVR overhead versus mobility; (**b**) UGAVs-MDVR overhead versus the number of CMs.  
Figure 5: (**a**) UGAVs-MDVR throughput versus mobility; (**b**) UGAVs-MDVR throughput versus the number of CMs.  
LBCHE scheme and a multicast communication approach, as depicted in Figure 6a. Alternatively, the PDR of MAODV and DSR decrease gradually because of instability caused by dynamic vehicular mobility, as shown in Figure 6a. The performance of the proposed protocol remains unaffected by varying the number of CMs from 6 to 30, as shown in Figure 6b.  
The simulation results show that the proposed protocol performs efficiently compared to existing schemes even by increasing the mobility of the vehicles.  
The packet delivery ratio is declining in MAODV and DSR by increasing the mobility of the vehicles. The MAODV and DSR do not maintain the inter-vehicular links stable upon varying the speed of vehicles and this results in weak inter-vehicular links, which cause network partitioning. The comparison of the proposed protocol illustrates that the important need is to maintain the network links as stable. Stable links are only possible by keeping the network organized and stable irrespective of the network's dynamic changes and mobility variations. The UGAVs-MDVR protocol maintains stable inter-vehicular links by keeping the clusters maintained and organized, irrespective of any dynamic changes in the network. The rapid updating process of the cluster record plays a major role in keeping the cluster organized in the proposed protocol.
###### Abstract  
Elliptical superconducting cavities are widely used in particle accelerators because they can provide stronger acceleration fields than regular cavities. Pre-tuning is required to improve their performance, but the process is time-consuming. This research introduces an automated pre-tuning system for multi-cell elliptical superconducting cavities. This platform integrates three main components: real-time electric field measurements, data analysis based on tuning algorithms, and automated mechanical adjustment systems. During copper cavity tests, the system achieved an electric field flatness of 96.5%. Results show the system can accurately tune cavity frequencies while keeping electric fields uniform. This solves the problem of low efficiency in manual tuning. The new system improves cavity reliability and offers useful solutions for precise tuning needs. It also offers a foundational framework for analogous system development in future projects.  
particle accelerators; elliptical superconducting cavities; pre-tuning; equivalent circuits; electric field flatness +
Footnote †: journal: Physics Letters B
## 3 Fault Diagnosis Method Based on 1 DCNN and INPSO-SVM  
### Overall Framework of the Proposed Method  
To improve the fault diagnosis accuracy of rolling bearings, a novel hybrid fault diagnosis scheme which combines the 1 DCNN and INPSO-SVM is proposed in this paper. Figure 1 shows the flowchart of the proposed method, which can be divided into three major steps (i.e., data collection, feature extraction and fault classification). The raw fault data from multiple sensors is directly input into the improved 1 DCNN model for the training of a CNN model. The outputs of the fully connected layer are taken as the feature vectors which are input into the SVM classifier for fault classification. Since the classification performance of an SVM classifier is susceptible to the values of parameters \(c\) and \(g\), the proposed INPSO is adopted to tune the parameters of the SVM.
## 5 Results and Discussion
### Average Nusselt Number at Hot Source  
Figure 7 presents the average \(\widetilde{\mathrm{Nu}}\) number along the heat sources for all cases. It can be noted that the average \(\widetilde{\mathrm{Nu}}\) number decreases as the \(\widetilde{\mathrm{Ri}}\) number increases. This is because the temperature near the heat sources increases with an increase in \(\widetilde{\mathrm{Ri}}\), resulting in a decrease in \(\widetilde{\mathrm{Nu}}\) (as given in Equation (9)). At lower values of \(\widetilde{\mathrm{Ri}}\) number, a higher \(\widetilde{\mathrm{Nu}}\) number is observed. The average \(\widetilde{\mathrm{Nu}}\) number also decreases as \(\widetilde{\mathrm{Ha}}\) increases. This is due to the rise in heat dissipation in the transverse direction, leading to an increase in the average temperature above the heat sources. It is clear that introducing the nanoparticles into the porous medium leads to an increase in the average Nusselt number. The rate of increment depends on the value of \(\widetilde{\mathrm{Ri}}\). At \(\widetilde{\mathrm{Ri}}=1\), the effect of increasing \(\widetilde{\Phi}\) on the average \(\widetilde{\mathrm{Nu}}\) number reduces as \(\widetilde{\mathrm{Ha}}\) increases due to low fluid movement (low convection). However, for high values of \(\widetilde{\mathrm{Ri}}=10\) and \(100\), the effect of increasing \(\widetilde{\Phi}\) on the average \(\widetilde{\mathrm{Nu}}\) number is roughly the same for all values of \(\widetilde{\mathrm{Ha}}\). The percentage increase in the average \(\widetilde{\mathrm{Nu}}\) number due to added \(\widetilde{\Phi}\) depends on \(\widetilde{\mathrm{Ri}}\) and \(\widetilde{\mathrm{Ha}}\). For example, at \(\widetilde{\mathrm{Ri}}=1\), \(\widetilde{\mathrm{Ha}}=0\), and \(\widetilde{\Phi}=0.15\), the average \(\widetilde{\mathrm{Nu}}\) number increases by approximately 18.45% for case-
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
## 4 Results and Discussion
### Evaluation Parameters  
Two periodical (duty cycle scheduling) and two aperiodical (adaptive sample rate) sampling strategies were considered for the experimental evaluation:  
* \(\gamma_{0}\), a time-triggered approach with a sampling period of 10 min.
* \(\gamma_{1}\), a self-triggered approach based on error measurement.
* \(\gamma_{2}\), a self-triggered approach based on Lyapunov dynamics.
* \(\gamma_{3}\), a time-triggered approach with a sampling period of 80 min.  
The periodical sampling of 10 min represents the minimum practical time interval typically used in agriculture, while the 80 min periodical strategy has an average of a similar number of sampling events compared with the self-triggered approaches. The experimental evaluation was conducted simultaneously in the four irrigation areas, depicted in Figure 1, for four weeks. The evaluated strategies were compared in terms of:  
* \(J_{1}\), the number of communication messages initiated by the sensor node during the evaluation period.
* \(J_{2}\), the accumulated water consumption during evaluation in m\({}^{3}\).
* \(J_{3}\), the mean squared error or irrigation error obtained from \[J_{3}=\frac{1}{n}\ \sum_{i=1}^{n}{(\theta_{i}-MAD)^{2}},\] (15) where \(\theta_{i}\) is the current soil moisture measurement, and the maximum allowable depletion \(MAD\) is the soil moisture reference value for the irrigation process. This parameter represents how precise the irrigation is conducted, lower values means higher precision.
## 6 Conclusions  
Numerous studies have been conducted to solve interoperability in the AEC domain. Various ontology applications and conceptual frameworks have been proposed. However, most developed frameworks or applications have not been fully applied or validated. Since most research has focused on building ontology-based applications or frameworks without building a BFO, proper validation cannot be performed without developing a BFO or domain ontology. In addition, if domain experts do the actual ontology modeling along with the developed conceptual frame, the domain experts may encounter unexpected difficulties, and it will take a lot of time and effort to solve this. Therefore, in order to build a better knowledge-sharing system, it is most important to establish a BFO knowledge base in the AEC field. In this study, the basic format ontology was developed in the BIM domain. This is also the first study to attempt to model ontology by reusing an existing BFO. Therefore, when conducting an ontology-related study in a BIM domain, domain experts can use the proposed basic formal domain ontology to save effort and time and obtain information about reuse. This is especially useful when extracting classes from domains and establishing the relationships between classes. It can also help to develop a complete domain ontology model, as it can be modified and supplemented for each project if it is partially available as needed. The BFO model proposed in this study can be reused as the basis for future BIM-related ontology research and future construction automation research and development. Future research should continue to study more precise relationships between term extraction and terminology. In addition, research should be continued to extract knowledge from other guides. Reusing existing domain ontology is strongly recommended to reduce the time and effort of developing an ontology for domain experts.  
H.P.: methodology, formal analysis, writing--original draft. S.S.: methodology, conceptualization, writing--review. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea, grant number \"NRF-2022R1F1A1073308.\"  
Not applicable.  
The data presented in this study are available on request from the first and corresponding authors. The data is not publicly available due to the policy of the data provider.  
The authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.