esa-sceva/satcom-chunk-collection huggingface dataset:
# Calibration of Model Parameters for Soda Saline Soil-Subsoiling Component Interaction Based on DEM
## 1 Introduction
The equal diameter soil particles cannot fully reflect the influence of the particle size distribution of the actual soil on the interaction between the soil particles and the working parts [26]. It is imperative to recognize that spherical particles alone cannot adequately characterize the accurate shape of soil particles. [PERSON] et al. calibrated and optimized the bonding parameters between soil particles by taking the non-equal-diameter soil particles in a mulberry field as the research object. [27]. [PERSON] et al. established four soil particle models with different sizes and shapes for simulation and verified the accuracy of the simulation model [28]. At present, there are few studies on the particles and particle aggregates of soda saline-alkali soil in Northeast China. When DEM is used for a simulation, the contact parameters between the soil particles are worth studying; the meticulous verification of the accuracy of the model also demands thorough analysis and consideration. In this study, we used DEM to calibrate soil particle contact parameters in soda saline soils of the Songnen Plain, Northeast China, using the angle of repose as the response value. To verify the accuracy of the calibration parameters, we conducted the DS test and added the bond between soil particles based on the JKR model to accurately simulate the soil bonding characteristics. Finally, the contact model and contact parameter combination of soil particles were determined, which provided a theoretical basis for the subsequent development of subsoiling machinery in soda saline-alkali land in Northeast China.
# The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement
## 3 Results
### Comparison of the Accuracy of Digital Terrain Models Using Raster Passability Analysis
Evaluation of digital terrain models: * The 1 m \(\times\) 1 m models (DTM 5): * The accurate model was suitable for detailed CCM analysis. * The 5 m \(\times\) 5 m models (DTM 4): * The deviation of the area of the passable terrain was small; \(\bigcirc\) DTM 5 was more suitable for fragmented terrain in detailed CCM analyses. * The 10 m \(\times\) 10 m models (DTM 3): * The impassable area was not usually displayed in flat territories in these models (steeper slopes with shorter lengths); \(\bigcirc\) The area of the impassable territory was close to DTM 5 in mountainous areas with long slopes; \(\bigcirc\) The model could be used for less detailed CCM analyses. * The 30 m \(\times\) 30 m models (DTED 2): * The impassable area was not usually displayed in flat areas in these models; \(\bigcirc\) The impassable area was close to DTM 5 in mountainous areas with long slopes; \(\bigcirc\) Reliable results of CCM analysis could not be achieved with DTED 2.
## 4 Experiment Result
### Answer to RQ3--Ablation Studies
#### 4.3.2 Adjustment of the Fitness Function  
The experimental results are shown in Figures 3 and 4. We can see that, in addition to the CNNFL method in the adjustment of the three fitness functions, the positioning effect is close. Among other fault localization methods, the three genetic algorithm optimization functions only consider fit_i in most cases. The effect is better.  
We have analyzed the results. For the CNNFL method, the scale of training data can affect its fault localization effect. However, the fitness function type does not cause changes in test case size. Therefore, we can see that the effect of the fault localization method for deep learning is not affected by the adjustment of the fitness function.  
However, spectrum-based fault localization methods depend on the quality of the test cases. The fitness function adjustments of fit_l and fit_b both generate test cases that cover a wider range. However, the fitness function adjustment of fit_i can generate more test cases covering suspected fault lines, which is not sufficient for the statements that may generate faults.  
Figure 4: Adjustment of the fitness function with NDCG.  
Figure 3: Adjustment of the fitness function with EXAM.  
Therefore, we can conclude that the improvement of the fitness function in the genetic algorithm in this paper improves the software fault localization effect.
## 4 Visual Odometry
### Direct Image Alignment
When the reference frame was set to non-keyframe, the transformation \(\mathbf{\xi}_{nk}\) between the reference and last keyframe was optimized, as well as the keypoint \(\mathbf{p}_{n}\) in the reference frame was associated with the corresponding keypoint \(\mathbf{p}_{k}\) in the last keyframe, so its coordinate \(\mathbf{P}_{n}\) in the reference frame could be calculated by transformation: \(\mathbf{P}_{n}=\exp(\mathbf{\xi}_{nk}^{*})\pi_{ck}^{-1}(\mathbf{p}_{k},d_{\mathbf{ p}_{k}})\). Although the first stage would be sufficient to estimate the camera pose, errors accumulated very easily, especially in the process of back-end optimization. The pose of the last keyframe may be updated, so it was very important to update the current frame camera pose in a timely manner according to the last keyframe. Two step sparse direct image alignment could improve the accuracy of the current frame pose, which was the main reason that the current frame was aligned with the last keyframe on the basis of the alignment with the previous frame. The frames were downsampled to construct an image pyramid of eight levels and improve the efficiency of image alignment. Images were aligned at each pyramid level, beginning at the top of the pyramid and ending at the bottom. The results of each layer's alignment were initial values for the next level of optimization, which dramatically reduced the number of iterations in the optimization process. To save optimization processing time, in the optimization process from top to bottom of the pyramid images, if the results converged, then several layers of images between them would be skipped, and the bottom layer images would align.
## 4 Results and Analysis
### Vegetation Classification and Accuracy Verification
Moreover, the confusion matrix of the classification results was calculated to verify the accuracy of the vegetation classification, as shown in Table 6. From Table 6, it can be seen that the classification accuracy of the random forest-based classification algorithm is significantly higher than that of the support vector machine classification algorithm and the maximum likelihood estimation. The overall classification accuracy and Kappa coefficient of the random forest classification algorithm are 85.27% and 0.82, and 86.59% and 0.84, respectively. Compared with the verification results of the single classification accuracy, the classification effect of _Sparting alterniflora_ is notably superior, with mapping accuracy exceeding 85%. On the contrary, the classification effect of _Suueda glauc_ is less impressive, and the mapping accuracy is only 78.89%. Meanwhile, the classification effect of _Tumarisk_, _Typha orientalis Presl_, and _Phragmites_ is moderate, and the mapping accuracy is between 80% and 85%.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACCA & Anticipated Cooperative Collision Avoidance \\ ASN.1 & Abstract Syntax Notation One \\ BTP & Basic Transport Protocol \\ CAM & Cooperative Awareness Message \\ CAN & Control Area Network \\ CCAM & Cooperative, Connected and Automated Mobility \\ C-ITS & Cooperative Intelligent Transport System \\ C-V2X & Cellular Vehicle-to-Everything \\ DENM & Decentralized Environmental Notification Message \\ DSRC & Dedicated Short-Range Communications \\ ETSI & European Telecommunications Standards Institute \\ HMI & Human-Machine Interface \\ ITS & Intelligent Transport System \\ MQTT & Message Queuing Telemetry Transport protocol \\ MEC & Mobile Edge Computing \\ MNO & Mobile Network Operator \\ OBU & On-Board Unit \\ OEM & Original Equipment Manufacturer \\ RAN & Radio Access Network \\ ROI & Region of Interest \\ RSU & Roadside Unit \\ TMS & Traffic Management System \\ V2I & Vehicle-to-Infrastructure \\ V2N & Vehicle-to-Network \\ V2P & Vehicle-to-Pedestrian \\ V2V & Vehicle-to-Vehicle \\ V2X & Vehicle-to-Everything \\ \end{tabular}
# Robust Airport Surface Object Detection Based on Graph Neural Network
###### Abstract
On the contrary, artificial perturbation is implemented by malicious hackers who create imperceptible perturbation on an image. The wrong prediction in airport surveillance caused by artificial perturbation may lead to a serious safety problem. So, it is necessary to improve model robustness against adversarial attacks in our scenario. Gradient-based adversarial attack methods, e.g., FGSM [25], PGD [26], are well studied these years as well as black box attack methods [27]. Meanwhile, researchers proposed several methods to tackle the problem of model robustness. Adversarial training methods, such as AdvProp [28] for classification and Det-AdvProp [29] for detection, create adversarial samples and add them to the training process to improve the model's robustness. RobustDet [30] modifies the model framework to defend against attack. In this paper, we propose a Vision GNN-Edge (ViGE) block that is integrated into a one-stage detector's (i.e., YOLOv5s) backbone for airport surface surveillance. We explore different receptive field definitions and implement adversarial training. Our method aims to improve the detection accuracy and robustness of the GNN-based backbone, which can be applied in various backbones and other areas. Our contributions are mainly summarized in three aspects:  
* We propose a novel GNN-based block named ViGE and plug it into a one-stage detector's (i.e., YOLOv5s) backbone to improve the performance of object detection. * We explore several receptive field (neighbor) definition approaches and find that defining the receptive field in pixel space is better than that in representation space empirically. * We apply an adversarial training framework to improve model robustness against adversarial attacks for airport surface object detection.
# Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aldar
## 4 Results and Analysis  
### Fiber-Based Signal Transmission Experiment  
Before conducting the ISAL imaging experiment, we performed preliminary tests on signal transmission within the optical fiber. As shown in Figure 7, we modified the target channel by transmitting the EDFA output signal through a long polarization-maintaining optical fiber, which was then directly returned to the optical fiber rather than using collimators for transmission and reception. After coherent mixing with the LO signal, BPD1 receives the signal. For example, we investigated an experiment with a target channel fiber length of 650 m. We present the results of one-dimensional matched filtering and two-dimensional compressed imaging to demonstrate the effectiveness of the phase noise compensation method based on dual reference channels.
# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
###### Abstract  
Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption-generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy.  
short-term forecasting; multistep forecasting; artificial neural networks; renewable energy sources; smart grids; microgrids +
Footnote †: journal: Applied Sciences
# Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
## 3 IATN for Bearing Fault Diagnosis under Variable Working Conditions
### Feature Extractor  
As shown in Figure 3, differently from previous studies, the input of IATN is a 2D time-frequency diagram rather than a 1D vibration signal. Therefore, the feature extractor consists of a 2D CNN. Since this paper performs STFT on the raw signal, the feature extractor can use a lighter network. The structure of the feature extractor of the model is shown in Table 1.  
Moreover, the hyperparameters of IATN for all tasks are shown in Table 2.  
In addition, since convolution only operates in a local space, obtaining enough information to extract the relationship between channels takes much work. Therefore, this paper introduces the channel attention mechanism to determine the importance of each feature channel, thereby enhancing the weight of essential features and reducing the weight of irrelevant information [60].  
The channel attention mechanism includes three steps: squeezing, excitation and scaling. As shown in Figure 3, the number of channels of the feature \(X_{2}\) of the input channel attention layer is \(C_{2}\), and the data are expressed as \(H_{2}\times W_{2}\). The squeeze operation applies global average pooling to convert the data into \(Z\), with a shape of \(1\times 1\times C_{2}\). This process expands the receptive field and encodes the entire spatial feature on a channel into a global feature, as shown in (4).  
\[Z_{c}=F_{sq}(u_{c})=\frac{1}{H\times W}{\sum_{i=1}^{H}{\sum_{j=1}^{W}{u_{c}(i,j)}}} \tag{4}\]  
where \(z_{c}\) denotes the \(c\)-th element of matrix \(Z\), and \(u_{c}\) denotes the \(c\)-th channel of \(C_{2}\).  
Subsequently, excitation obtains the feature weight of each channel through two fully connected layers, as shown in (5):  
\[S=F_{ex}(Z,W)=\sigma(g(Z,W))=\sigma(W_{2}\delta(W_{1}Z)) \tag{5}\]  
where \(\sigma(x)\) denotes sigmoid function, \(\delta(x)\) denotes the Relu function.  
Finally, scaling multiplies the obtained feature weights \(S\) with the input \(X_{2}\) channel by channel, as shown in (6):  
\[X_{3}^{c}=F_{\text{scale}}\ (u_{c},s_{c})=s_{c}u_{c} \tag{6}\]  
where \(X_{3}^{c}\) denotes the \(c\)-th channel of \(X_{3}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Kernel** & **Channel** \\ \hline Conv2D & \(3\times 3\) & 16 \\ BatchNorm & / & 16 \\ MaxPool & / & 16 \\ Relu & / & 16 \\ Conv2D & \(3\times 3\) & 32 \\ BatchNorm & / & 32 \\ MaxPool & / & 32 \\ Relu & / & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The structure of feature extractor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **First Appeared** & **Value** \\ \hline \(d\) & (4) & 32 \\ \(w\) & (4) & 64 \\ \(\beta\) & (14) & 0.04 \\ \(\gamma\) & (13) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters used in IATN.  
After that, in the training, both the samples from source datasets and the target dataset are input into the feature extractor, and then the feature extractor outputs source features \(F_{S}\) and \(F_{T}\).
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 2 Related Work  
Peer-to-peer-based overlay networks, i.e., structured overlay networks, provide key-based routing, as summarized in \"Towards a Common API for Structured Peer-to-Peer Overlays\" [1]. It is a common optimization goal to provide an efficient lookup interface through the overlay structure using a low hop count, few messages (i.e., low overhead), and aiming for a 100% lookup ratio. The network structure has demonstrated its scalability in various disruptive entries in den media industry [8], such as those shown by Skype, Spotify, Zattoo, and file sharing networks. Researchers in the literature discuss variants, how one can shape the overlay topology, how one can structure the routing table, and how one can deal with churn; these topics have been thoroughly investigated in peer-to-peer research. While research on peer-to-peer overlays has been popular in the last decade, the issue of maintaining such overlays in the presence of network partitioning has been discussed in only a few works.  
Merging algorithms are used to stabilize overlays during network partitioning events by detecting and uniting participants that have become separated from the overlay. As fist step, a merging algorithm should be able to detect reachable nodes which have been separated. Nodes which belong to the same region especially should find each other quickly. Secondly, different groups which are connected in the underlying network should merge again. Once the connectivity of the underlying network is fully established again, the various overlay parts from different regions should detect their presence and merge to one global ring again. This niche of research question has not faced sufficient attention up to now. We summarize the available merging algorithms from the literature as we compare them to our Ring Reunion Algorithm in Section 4.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 2 Materials and Methods
### Optimization Problem Formulation
#### 2.1.1 Design Variables  
The design variables are sensor positions and orientations. In 3D structures, for each sensor \(j=1,\;2,\ldots,m\), the sensor positions are expressed as follows:  
\[\mathbf{x_{\_}{j}}=\left(x_{\_}{1j},\;x_{\_}{2j},\;x_{\_}{3j}\right)\in \mathbf{\Omega_{s}} \tag{1}\]  
The orientations of the sensors are defined by azimuth \(\mathbf{a_{\_}{j}}=\left(\theta_{\_}{j},\;\phi_{\_}{j}\right)\) (see Figure 2).  
Figure 2: Sensor orientation angles, azimuth angle (\(\theta\)), and elevation angle (\(\phi\)).
## 1 Introduction
The occlusion area is used as the rainfall detection area of the radar image to improve the detection accuracy, and the detection threshold is determined by statistical analysis of a large number of radar image data. Additionally, the wave area of the radar image is also used as rainfall detection area to verify the detection performance of the proposed method. The rainfall intensity fitting function is obtained by the optimal fitting of a large number of radar image data, which is used to retrieve the radar image detected as rainfall to determine the rainfall intensity level. This article is the first article to select the occlusion area for rainfall detection, and also the first article to retrieve the rainfall intensity level from X-band marine radar images. The method of this article not only retains the advantages of the ZPP method, but also solves the problem of threshold selection. The remainder of this article is organized as follows. In Section 2, the data used in this study and the preprocessing of these data are introduced. In Section 3, the concepts of RZE parameter and occlusion area are proposed. In Section 4, the RZE method for rainfall detection and rainfall intensity level retrieval is presented. In Section 5, the effectiveness of the proposed method is verified by the collected data. Conclusions are summarized in Section 6.
## Appendix A  
_Appendix A.1. Energy Consumption Calculations for FOFSA, ATAT, and OLB_  
Appendix A.1.1. FOFSA  
The energy consumption of FOFSA [15] translates to approximately 4 KWH for 50 VMs, 10 KWH for 100 VMs, 18 KWH for 150 VMs, 20 KWH for 200 VMs, 23 KWH for 250 VMs, and 28 KWH for 300 VMs.  
Our analysis of the execution time graph for VMs handling different numbers of tasks, as presented in [15], indicates that a single VM can execute a maximum of 10,000 tasks. Therefore, a VM can handle up to 10,000 tasks.  
Consequently, the total number of tasks allotted equates to 500,000 for 50 VMs, 1,000,000 for 100 VMs, 1,500,000 for 150 VMs, 2,000,000 for 200 VMs, and 2,500,000 for 250 VMs.  
Based on this analysis, we deduce the following energy consumption figures: 4 KWH for 500,000 tasks, 10 KWH for 1,000,000 tasks, 18 KWH for 1,500,000 tasks, 20 KWH for 2,000,000 tasks, and 23 KWH for 2,500,000 tasks.  
Since the number of tasks has been considered equivalent to the number of packets, the energy consumption for each task aligns with the corresponding packet count. Thus, for 500,000 packets, the energy consumption amounts to 4 KWH, which translates to (4 \(\times\) 20)/500,000 KWH, or (4 \(\times\) 3600 \(\times\) 20)/500,000 KJ, equalling 0.56 KJ.  
Similarly, for 1,000,000 packets, the energy consumption corresponds to 10 KWH, translating to (10 \(\times\) 40)/1,000,000 KWH, or (10 \(\times\) 40 \(\times\) 3600)/1,000,000 KJ, equalling 1.44 KJ.  
Continuing this trend, for 1,500,000 packets, the energy consumption amounts to 18 KWH, which translates to (18 \(\times\) 60)/1,500,000 KWH, or (18 \(\times\) 60 \(\times\) 3600)/1,500,000 KJ, resulting in 2.592 KJ.  
For 2,000,000 packets, the energy consumption corresponds to 20 KWH, translating to (20 \(\times\) 80)/2,000,000 KWH, or (20 \(\times\) 80 \(\times\) 3600)/2,000,000 KJ, resulting in 2.88 KJ.  
Lastly, for 2,500,000 packets, the energy consumption amounts to 23 KWH, which translates to (23 \(\times\) 100)/2,500,000 KWH, or (23 \(\times\) 100 \(\times\) 3600)/2,500,000 KJ, resulting in 3.312 KJ.