esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Direction Parameter in Performance of Modified AODV in VANET
## 6 Conclusions and Future Work  
In this research paper, we simulated the performance analysis of VANET using OMNeT++ and SUMO by collecting the map from OpenStreetMap. We modified the AODV routing protocols to reduce the number of RREQ and RREP messages by adding direction parameters and two-step filtering. The two-step filtering process reduced the number of RREQ and RREP packets, reduced the packet overhead, and helped selection of the stable route. We also compared the performance metrics in terms of E2E delay, packet delivery ratio, and throughput with other state-of-the-art techniques.  
The inclusion of direction parameters along with two-step filtering in route discovery and route selection procedures reduced the average end-to-end delays and packet loss by \(11\%\) and \(1.3\%\), respectively, as compared to other state-of-the-art methods. We also found an improvement in throughput that was due to the combined effect of direction parameters and filtering in the same direction to find the stable route, which reduced the packet loss during communication. Therefore, the proposed method increases the QoS in VANET, providing more safety on the road.  
In our future research, we will study and propose new protocols that might help to improve the performance of protocols such as dynamic source routing with a grid or geographic location service protocol for VANETs concerning road safety in real-time applications.  
Conceptualization, methodology, software, and preparation of original draft by A.A.; supervision, review, recommendation for changes, and editing by H.V. All authors have read and agreed to the published version of the manuscript.  
The APC is funded by the Department of Electrical and Computer Engineering, University of Nebraska-Lincoln.  
The authors declare no conflict of interest.
# Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
## 5 Conclusions and Future Work  
In this study, we proposed an attention-based bidirectional long short-term memory fully convolutional network to predict multistep network-wide traffic speed. The contributions of this study are as follows:  
* We proposed a deep-stacked model studying both forward and backward dependencies of the traffic data. Firstly, it integrates both attention-based BDLSTMs and FCN as an introductory module to capture the spatiotemporal correlation in the network-wide traffic data. We examined the attention layer behavior in our model and concluded that using attention mechanisms can enhance the model performance accordingly.
* The proposed model exploited the benefits of deep-learning-based architectures, e.g., bidirectional long short-term memory and fully convolutional neural networks, to improve prediction performance in this study.
* Our study demonstrated that AttBDLSTM-FCN achieved better performance in network-wide traffic speed prediction when predicting traffic for 15, 30, and 60 min, respectively. Moreover, the proposed model is also competent for multistep network-wide traffic speed prediction in the future.  
Nevertheless, as the results indicate, we know that deep-learning-based models for predicting network-wide traffic speed still need further study. In the future we will extend this work to improve the models by combining evolutionary computation solutions to automatically optimize deep neural network hyperparameters. The other possible future direction is to extend this work of speed prediction in between different levels, which is still missing.  
Conceptualization, A.R.; Data curation, A.R., M.A.-A. and M.N.; Formal analysis, A.R. and M.A.A.; Investigation, M.N.; Methodology, A.R. and H.R.; Software, A.R.; Validation, A.R. and M.H.A.-A.; Visualization, A.R. and A.Y.; Writing--original draft, A.R.; Writing--review & editing, H.R., A.Y., M.H.A.-A., E.T.E. and N.A.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable for studies not involving humans or animals. Informed Consent Statement: Not applicable. The code is available at the following link: [[https://github.com/[PERSON]/AttBDLSTM_FCN](https://github.com/[PERSON]/AttBDLSTM_FCN)]([https://github.com/[PERSON]/AttBDLSTM_FCN](https://github.com/[PERSON]/AttBDLSTM_FCN)) (accessed on 31 June 2022). The authors declare no conflict of interest.
###### Abstract
Distributed dynamic coherent radar can be regarded as a special sparse array radar, and hence, spatial undersampling will result in an angle ambiguity problem for the synthetic transmit pattern. However, dense grating lobes inevitably occur in the pattern due to their distributed configurations, and thus the dynamic coherent radar detection performance deteriorated. In the literature [25], the spatial ambiguity resolution method is presented based on a multi-scale combination technique, but its disadvantage lies in the requirement of multiple antennas in each spaceborne radar. Staggered carrier frequency [26] and spacing [27] strategies can be applied to solve angle ambiguity resolution problems at the expense of consuming higher system resources. From the perspective of configuration optimization, a particle swarm optimization algorithm was employed in [28] to design a coherent and unambiguous transmit pattern, but this method only applies to radar node spacing optimization. In this manuscript, a novel transmit beampattern optimization method for dynamic coherent radar based on a distributed satellite constellation is proposed. On one hand, the impacts of synchronization error, beam pointing error, and high-speed motion characteristics on coherent integration are quantitatively evaluated based on coherent detection constraints. On the other hand, due to the insufficient satellite number, robust low-sidelobe beamforming could be realized by a multi-dimensional optimization technique. In this way, the unambiguous spatial field can be extended for distributed coherent radar arrays. The remaining parts are outlined as follows. The transmit beampattern optimization method for dynamic coherent radar based on a distributed satellite constellation is given in Section 2, where the distributed coherent radar detection range, the coherent integration efficiency analysis, and the multi-dimensional low sidelobe beamforming technique are discussed comprehensively. In Section 3, numerical results are demonstrated to verify the effectiveness of the proposed method. The performance of our research scheme is discussed in detail in Section 4. We draw a brief conclusion in Section 5.
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 4 Parameter Study and Discussion
### Shear Strength  
Mechanical properties of the b/g face directly affect the axial performance of rockbolts. Among them, the b/g face strength is an important input parameter. The b/g face strength is closely related to the geometry of rockbolts. When threaded rockbolts were used, the b/g face strength could be improved to 5 MPa.  
To analyse the impact of the b/g face strength on the axial performance of rockbolts, this article selected five b/g face strengths. The minimum strength was 3 MPa, and the maximum strength was 5 MPa. The axial performance of rockbolts is shown in Figure 12. The b/g face strength had an apparent impact on the axial performance of rockbolts. When  
Figure 11: Deformation at the peak force when the rockbolt modulus of elasticity varied.  
the b/g face strength was 3 MPa, the peak force of rockbolts was 213 kN. When the b/g face strength increased to 5 MPa, the peak force of rockbolts increased to 243 kN. Therefore, increasing the strength of the b/g face benefited improving the tensile performance of rockbolts.  
Moreover, a linear relationship existed between the peak force of rockbolts and the b/g face strength. The corresponding calculation equation is shown below.  
\[F_{bp}=15\tau_{1}+167.8 \tag{12}\]  
When the b/g face strength increased, the softening force also increased. This led to the result that when the b/g face strength increased, the full force-deformation curve became higher.
###### Abstract
Among the water mapping algorithms, the thresholding-based methods have been most widely used since they are computationally less time-consuming and could produce accurate results compared to other approaches of higher complexity [34; 35; 36; 37; 38]. In addition, automated and rapid flood inundation modeling using a digital elevation model (DEM) can supplement the necessary flood information when real-time satellite images are unavailable, as shown in the latest studies [18; 39; 40]. The applications above using multiple data sources with automated flood mapping are highly beneficial and desirable for a time-sensitive disaster situation in flood response practices. However, rapid-deployed and reliable flood mapping based on satellite or DEM data during extreme events is still very challenging due to uncertainties and limitations of the high-quality remote sensing data, the methods, and the high demand for computational resources. Advances in cloud-based data providers and computational platforms such as Google Earth Engine (GEE) [41] provide a way to address computational difficulties and allow state-of-the-art tools to operate seamlessly in any region of interest. Many Earth observation datasets are made easily accessible and analyzable free of charge through GEE. These include the latest Landsat-9 dataset (launched in September 2021 and made available in October 2021), which is designed to replace Landsat-7. The combination of Landsat-8 and Landsat-9 data collection can provide global observation every 8 days. With the proliferation of the computational power of GEE and the latest free but high-quality satellite data now and forthcoming, there is still an urgent need to understand the new satellite data and water mapping algorithms regarding their strengths and limitations, especially with quick deployment and automation. Therefore, this research aims to (1) investigate the pros and cons of different satellite imagery for water surface mapping with a focus on the SAR and optical images and (2) compare and evaluate the different automated methods for surface water extraction from satellite images and DEM. We achieved these goals by conducting a flood inundation study for a flood event in Madagascar (January 2022). This study allows us to gain insight into the advances in leveragingremote sensing image analysis and cloud computing for developing countries' flood hazard monitoring and management.
## 3 Mathematical Model for the Proposed Framework
### Mathematical Model for the Upper Layer
Therefore, the total electrical loads of a household user under time slot \(t\) in a day can be expressed as follows:  
\[L_{H,t}=L_{H,t}^{f}+L_{H,t}^{c}+L_{H,t}^{s} \tag{8}\]  
where \(L_{H,t}^{f}\), \(L_{H,t}^{c}\), and \(L_{H,t}^{s}\) are the fixed, curtailable, and shiftable loads of a household user at time slot \(t\), respectively. The energy cost function of a household user in a day is as follows:  
\[E_{Hh}=\sum_{t=1}^{T}\left(E_{Hh,t}^{Ag}+E_{Hh,t}^{EST}+E_{s,t}-E_{Hh}^{DR}\right) \tag{9}\]  
where \(E_{Hh,t}^{Ag}\) and \(E_{Hh,t}^{EST}\) are the cost of electricity purchased by household users from the VPP aggregator and the cost of electricity purchased from EST at time slot \(t\); \(E_{s,t}\) is the satisfaction cost arising from load adjustment for household users. \[E_{s,t}=\frac{y_{k}}{2P_{Hh,t}^{r}}(L_{Hh,t}^{r})^{2}-y_{k}L_{Hh,t}^{r}+\frac{ y_{k}}{2}P_{Hh,t}^{r} \tag{10}\]  
where \(L_{Hh,t}^{r}\) is the power of class \(r\) load after user DR at time slot \(t\); \(y_{k}\) is the comfort factor, which is set by the user according to their preference; \(P_{Hh,t}^{r}\) is the initial value of class \(r\) load before the user DR at time slot \(t\). (4) Laddered DR mechanism  
Household users' electricity behavior is uncertain, so the predicted DR scheme and the actual load-side demand situation may have deviations, that is, the effect of load-side adjustment cannot meet the baseline load. To reduce the impact of this phenomenon, a laddered DR mechanism is proposed as follows:  
\[E_{K,t}^{DR,d}= \begin{cases}\begin{array}{cc}\begin{array}{c}E_{K,b}^{DR,d }\\ E_{K,b}^{DR,d}-(P_{K,t}^{DR}-Ba_{t})(1+z)\\ E_{K,b}^{DR,d}-(P_{K,t}^{DR}-Ba_{t})(1+2z)\\ E_{K,b}^{DR,d}-(P_{K,t}^{DR}-Ba_{t})(1+3z)\\ 0\end{array}&\begin{array}{c}B_{t}^{DR}\leq Bq_{t}\\ B_{t}<P_{K,t}^{DR}\leq Bq_{t}+j\\ B_{t}+j<P_{K,t}^{DR}\end{array}\end{cases}  \tag{11}\]\[E_{K,t}^{DR,c}= \begin{cases}E_{K,b}^{DR,c}&Bat_{t}\leq P_{K,t}^{DR}\\ E_{K,b}^{DR,c}-(Ba_{t}-P_{K,t}^{DR})(1+z)&Ba_{t}-j\leq P_{K,t}^{DR}<Ba_{t}\\ E_{K,b}^{DR,c}-(Ba_{t}-P_{K,t}^{DR})(1+2z)&Ba_{t}-2j\leq P_{K,t}^{DR}<Ba-j\\ E_{K,b}^{DR,c}-(Ba_{t}-P_{K,t}^{DR})(1+3z)&Ba_{t}-3j\leq P_{K,t}^{DR}<Ba-2j\\ 0&P_{K,t}^{DR}<Ba-3j\end{cases}  \tag{12}\]  
where \(E_{K,t}^{DR,d}\) and \(E_{K,t}^{DR,c}\) are the incentive values for the DR program corresponding to the load spike and load trough moments, respectively; \(K\) is the user type, which contains VPP aggregator, EST, and household users; \(E_{K,b}^{DR,d}\) is the value of the reward for meeting the DR standard; \(P_{K,t}^{DR}\) and \(Ba_{t}\) are the adjusted and baseline loads of subject \(K\) at time slot \(t\), respectively; \(z\) and \(j\) are the penalty factor and load regulation margin, respectively.
## 3 Experiment and Result Analysis
### Training Process  
The experimental environment is listed as follows: Linux system 4.18.0, Pytorch framework 1.8.0, and NVIDIA GeForce RTX 3080 Ti 12 GB GPU, which is manufactured by NVIDIA in Santa Clara, CA, USA. Hyperparameter tuning is the process of selecting the optimal hyperparameters for the model. Hyperparameters are tuned to generate better results in different datasets. During the model training, hyperparameters are set as follows: the size of all input samples is 256 \(\times\) 256, the batch size is 1, and the number of Epochs is 100. The Adam optimization algorithm is used to update the weights. The constructed landscape painting dataset and ChipPhi dataset are selected to train models, respectively.  
Figure 4: Construction process of landscape painting samples.  
Figure 5: Samples of the constructed dataset ((**a**) is the samples of Dwelling in the Fuchun Mountains, (**b**) is the samples of A Thousand Li of Rivers and Mountains, and (**c**) is the landscape photo samples).  
The initial learning rate is set to 0.0001, 0.00015, and 0.0001 for the samples of Dwelling in the Fuchun Mountains, the samples of A Thousand Li of Rivers and Mountains, and the ChipPhi dataset, respectively. However, generative models are difficult to train and challenges, such as model collapse and vanishing gradient, may be encountered during training. In order to demonstrate the dynamic changes in the model training, the loss curves with Epochs of the generator and discriminator on both datasets are shown in Figure 6. The model has converged when the curve has stabilized. Taking the training process of samples (i.e., Dwelling in the Fuchun Mountains) shown in Figure 6a as an example, we can observe the loss curve during training. When the Epoch is small, the generator loss (G loss) function oscillates violently and decreases rapidly. As the Epoch continues to increase, the amplitude of the generator loss function gradually decreases and eventually stabilizes. When the Epoch is small, the discriminator loss (D loss) function also decreases rapidly. As the Epoch continues to increase, the discriminator loss function still tends to stabilize. The loss curve of the ChipPhi dataset shown in Figure 6b also follows a similar trend. In conclusion, with the increase in training Epochs, the proposed Paint-CUT can rapidly tend to a stable state.
## 3 Methodology
### Monitoring Method of Glacier Flow Velocity in Wide-Area POT
#### 3.1.1 Data Preprocessing
Registration at the sub-pixel level: On the basis of (2), the coherence coefficient interpolation method is used to interpolate the coherence coefficient matrix obtained from the auxiliary image, and the smaller-sized search window is selected for iterative operation. The pixel offset of the peak position of the new correlation coefficient matrix obtained by iteration is used to refine the registration of the primary and secondary images. The accuracy of the offset estimation is correlated with the interpolation interval. In this case, a number of accurately registered control points will be obtained, and the offsets of the control points will be used to establish the least square fitting polynomial to obtain the offsets of the primary and secondary images to any position: \[ \begin{cases}u=a_{0}+a_{1}x+a_{2}y+a_{3}x^{2}+a_{4}y^{2}+a_{5}xy\\ v=b_{0}+b_{1}x+b_{2}y+b_{3}x^{2}+b_{4}y^{2}+b_{5}xy\end{cases} \] (1) where \(a_{0}\sim a_{5}\) and \(b_{0}\sim b_{5}\) is the conversion parameter, \((x,y)\) are the coordinates of the control points in the primary image, and \((u,v)\) is the relative coordinate offset between the fitted secondary image and the points to be registered in the primary image. In the calculation, the control points with large gross errors are eliminated by the registration evaluation index to obtain a more robust estimation. After the coordinate transformation between primary and secondary images is determined, the offset of each pixel can be calculated and the secondary image can be resampled.
## 2 Traditional A* Algorithm with Improvements
### Traditional A* Algorithm  
The fundamental concept of the A* algorithm revolves around devising a path of minimal cost from the initial point to the destination point on the map. This cost is encapsulated within a cost function \(f(n)\), which is mathematically represented by Equation (1).  
\[f(n)=g(n)+h(n) \tag{1}\]  
In the above formula, \(n\) represents the current position node, \(g(n)\) represents the actual cost from the starting point to the current point, and \(h(n)\) represents the estimated cost from the current point to the target point.  
In Equation (1), if \(g(n)\) is much greater than \(h(n)\) and \(f(n)\) is about equal to \(g(n)\), then the A* algorithm is similar to Dijkstra algorithm, which will lead to an increase in node traversal and a decrease in search efficiency. If \(g(n)\) is far less than \(h(n)\) and \(f(n)\) is about equal to \(h(n)\), the A* algorithm is similar to the best priority search algorithm, although as the speed of path planning becomes faster, it is easy to fall into the local optimal result. Therefore, the quality of the A* algorithm is determined by the heuristic function \(h(n)\). Usually, there are three methods for estimating the heuristic function \(h(n)\): Manhattan distance, Chebyshev distance, and Euclidean distance.  
Let the coordinates of the current node be \((x_{n},y_{n})\) and the coordinates of the target point be \((x_{m},y_{m})\). Euclidean distance heuristic function is expressed as Equation (2).  
\[h(n)=\sqrt{\left(x_{m}-x_{n}\right)^{2}+\left(y_{m}-y_{n}\right)^{2}} \tag{2}\]  
The shortcomings of the traditional A* algorithm are as follows:  
1. When the difference between the estimated generation value \(h(n)\) of the heuristic function and the actual value is large, the search space becomes larger.
2. The size of AGV is not considered but is simplified to a point, which may lead to the collision of AGV with obstacles.
3. There are too many inflection points in the path, which increases the difficulty of AGV operation.
## 4 Experimental Validation
### Case 1  
4.1.1. Case Western Reserve University Dataset Description  
The Case Western Reserve University (CWRU) dataset is chosen as Case 1. The test rig is shown in Figure 6. Vibration acceleration sensors collect the vibration acceleration data from the test rig. Vibration acceleration sensors were positioned at the 12 o'clock positions  
Figure 5: Principles of domain adaptation algorithms incorporating MK-MMD and CDAN+E.  
on both the drive end and fan end of the motor housing. The experiment employed a 16-channel DAT recorder to capture the vibration signals. The selected data include normal baseline data and 12k drive end bearing vibration fault data [27]. The drive end bearing used is a 6205-2 RS JEM SKF model, which is a deep groove ball bearing. The specific information about the bearing is shown in Table 2.  
The drive end has four health conditions: healthy, roller fault, inner race fault, and outer race fault. Each fault type includes three fault depths, created using electric discharge machining: 0.007, 0.014, and 0.021 inches (1 mil = 0.001 inches). Therefore, the bearings have 10 different health states, as categorized in Table 3. The time series waveforms of the bearing under different health conditions are shown in Figure 7.  
Based on different loads and speeds, the dataset is divided into four operating conditions, with specific information detailed in Table 4. In the load information, 0 HP represents a 0 hp motor. The sampling points for the CWRU dataset are calculated using Equation (11), with the final selected sampling point set to 800. During training, the training and test sets are divided in a ratio of 75:25. The number of epochs for the CWRU dataset is set to 80.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Description** \\ \hline Bearing model & SKF 6205-2 RS JEM \\ Outer diameter (mm) & 52 \\ Inner diameter (mm) & 25 \\ Width (mm) & 15 \\ Number of rolling elements & 8 \\ Diameter of rolling elements (mm) & 7.94 \\ Cage material & Synthetic resin cage \\ Sealing type & Double seal (2 RS, indicating both sides of the bearing have rubber seals) \\ Operating conditions & Performance of the bearing under various fault simulations and operating conditions on a test rig \\ \hline \end{tabular}
\end{table}
Table 2: Detailed description of bearing models in the CWRU dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Class Label** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Fault size (mils) & 0 & 7 & 7 & 7 & 14 & 14 & 14 & 21 & 21 & 21 \\ Fault location & Normal & Inner race & Roller & Outer race & Inner race & Roller & Outer race & Inner race & Roller & Outer race \\ Abbreviation & Normal & IR07 & R07 & OR07 & IR14 & R14 & OR14 & IR21 & R21 & OR21 \\ \hline \end{tabular}
\end{table}
Table 3: CWRU dataset fault condition information.  
Figure 6: CWRU dataset experimental platform.