esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PLAXIS Numerical Simulation
### Discussion of the Findings  
In this study, the PLAXIS 2D analysis program was used for a deep excavation parameter feedback analysis, and the results were compared with the actual monitoring data of the wall displacement. The analysis results show good agreement in terms of the trend of wall displacement and the location of maximum wall displacement, indicating the reasonability of this simulation approach. Based on the research findings, the following conclusions can be drawn:  
* Based on the feedback analysis results from actual cases, it is shown that when using a reasonable soil elastic modulus for predictive analysis, more accurate final excavation deformation quantities can be obtained. In the Xindian area, this range is between 7840 \(\mathrm{kN/m^{2}}\); and 9800 \(\mathrm{kN/m^{2}}\);. These results align with the empirical formulas derived from the prior research of [PERSON] et al. and [PERSON] et al. on the gravel layer in Baguashan. The elastic modulus in Baguashan was found to range from 88,200 \(\mathrm{kN/m^{2}}\); to 833,000 \(\mathrm{kN/m^{2}}\); [48; 49].
* In this study, a parameter feedback analysis was conducted based on actual cases in the Xindian area of Taiwan, suggesting that this research method can be applied to regions with similar geological conditions. Extending this approach to various regions can yield a broader range of research results, which could be valuable as references in engineering design.  
Figure 4: Comparison diagram of lateral displacement in the final excavation stage of **(a)** Case 1 and **(b)** Case 2.
# Spatial Variations in Microfiber Transport in a Transnational River Basin
## 4 Discussion
### Influencing Factors of Microplastic Contamination
#### 4.2.4 The Role of Tributaries in Suspended Sediment and Microplastic Transport  
The role of tributaries in MP pollution of the main river was shown by the 2022 measurement, when the main tributaries were sampled too. Most tributaries transported higher SSC than the main river itself, reflecting that they play an important role in the sediment budget of the river system, despite of the low stages and drought conditions during the survey. In 2022, some tributaries were highly polluted (e.g., Hernad: 24 items/m\({}^{3}\), SajoE: 23 items/m\({}^{3}\), Zagyva: 62 items/m\({}^{3}\), and Maros: 25 items/m\({}^{3}\)); thus, they increase the MP transport of the main river, rather than reducing it by dilution.  
The influence of a tributary can be detected along several tens of kilometers. For example, 17.4 km downstream of the Szamos confluence at site \"i\" high (42 and 25 items/m\({}^{3}\)) MP transport was detected in both years. Here, in 2021 the amount of MP pollution was 7-times higher than at the site upstream of the confluence, and in 2022 it was still 1.5-times higher. Similar differences in MP transport were observed between the upstream and downstreamsites of the confluence of the Sajo (1.5 times in 2021), and the Koros and Maros rivers (2.5 and 2.6 times, respectively). This excess MP loading was also revealed in sediment samples of upstream and downstream sites at confluences [35; 38]; besides, similar pollution pattern caused by tributaries was reported by [PERSON] et al. [40], [PERSON] et al. [41], and [PERSON] et al. [79].  
The MP conveyance function of the tributaries is supported by the fact that the tributaries transported not only colored fibers which clearly indicate wastewater origin, but also spheres, which probably originated from health care products. It was also interesting that spheres were found especially in those tributaries (i.e., Sajo, Hernad and Zagyva) which have a catchment in Slovakia and Hungary with relatively high GDP.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### RF-Based Detection
#### 2.1.2 RF-Based Detection Using Deep Learning
This approach focuses on designing deep learning architectures that can effectively capture the relevant features from the RF signal data. By improving the efficiency and accuracy of feature extraction, these approaches aim to enhance the overall performance of RF-based UAV detection systems.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 3 Proposed Methods
### Different Implementation Methods  
In addition, we compare the effect of different implementation methods of PPL loss on the final system performance.  
* Version 1: We use the multi-task learning method to jointly train the network. On the one hand, we use the deep cluster method to generate pseudo-phoneme labels for frame-level features and then calculate the PPL loss. On the other hand, we use the speaker labels to calculate the classification loss. See more details in Section 3.1.
* Version 2: We directly calculate the PPL loss in the learnable dictionary encoding (LDE) [41; 42] pooling layer, train the dictionary components and assign weights through pseudo-phoneme labels, so that the network can better map the frame-level features to the dictionary components, and obtain the phoneme-based pooling results.
* Version 3: The PPL loss is calculated at the frame4 layer. In the process of calculating the PPL loss, a method similar to attentive statistics pooling (ASP) [20] is used to calculate the weight of each frame feature through the attention mechanism, and then the obtained weight matrix is multiplied by frame5 to obtain the features based on attention and PPL loss.
## 3 Bandgap Algorithm of the PMPIC by Extended Plane Wave Expansion Method
### Effects of the Interface Circuit (L)  
Furthermore, the unit cell with configuration B as Figure 7 is considered for the second numerical example, and \(M\) is randomly selected as 12. Then, it is divided into 12 \(\times\) 12 small elements.  
Firstly, the inductor is chosen as \(L\)= 1 H, and the first four disperse curves are calculated as Figure 8a. It can be seen that (i) besides local bandgaps, there is a full bandgap, which is called the first bandgap. The bandgap width is equal to 7.99; (ii) compared with configuration A, it may be easier to generate full bandgaps by using a nonrectangular configuration of piezoelectric material. Indeed, it provides the basis of topology optimization in this paper.  
Figure 5: The unit cell of configuration A: (**a**) configuration A and (**b**) its FEM.  
Figure 6: The dispersion curves under configuration A.  
Then, in order to investigate the effects of the interface circuit, different values of the inductor (\(L\)\(=\)\(1\) H, \(5\) H, \(10\) H) are chosen. The first-order bandgaps are calculated and shown in Figure 8b, respectively. It can be seen that (i)the first bandgap will shift to the low-frequency region with the increase of \(L\); therefore, large \(L\) will benefit low-frequency vibration energy harvesting; (ii) with the increase of \(L\), the width of the first bandgap firstly increases and then decreases. It means that there may be an optimal \(L\) to obtain the maximum width of the first bandgap, which also will indicate the feasibility of topology optimization.
# A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering
## 4 k-Recursive Reliability-Based Imputation (k-RRI)
### Threshold Reset Process  
Unlike EMDP and AutAI, k-RRI uses a recursive algorithm with stepwise iteration, whereby the final imputation matrix can vary depending on the recursive structure. The imputation error can be defined as an error incurred by imputing an empty dataset with an arbitrary value. In k-RRI, missing data are imputed in a given step based on the actual rating histories and the virtual data imputed in the preceding step. Therefore, not only does the imputation error affect the step concerned, but it is also carried forward to the next step, as expressed by Equation (10), where \(k\) is the number of recursive steps, \(\varepsilon_{r}\) is the imputation error incurred in the \(r\)-th step, and \(\alpha_{i}\) and \(\beta_{i}\) are constants.  
\[\varepsilon_{2}=\alpha_{1}\varepsilon_{1}+\beta_{1}\varepsilon_{3}=\alpha_{2} \varepsilon_{2}+\beta_{2}=\alpha_{2}(\alpha_{1}\varepsilon_{1}+\beta_{1})+ \beta_{2}=\alpha_{1}\alpha_{2}\varepsilon_{1}+\beta_{1}\beta_{2}\vdots \varepsilon_{k}=\alpha_{1}\alpha_{2}\cdots\varepsilon_{1}+\beta_{1}\beta_{2} \cdots\beta_{k} \tag{10}\]  
Owing to the recursive nature of the proposed algorithm, the propagation of imputation errors is inevitable; however, minimization of initial imputation errors can prevent  
Figure 4: An example of missing data imputation of k-RRI (step 2).  
Figure 5: An example of missing data imputation of k-RRI (step 3).  
much of the error propagation. In each step, care should be taken to select and impute only high-reliability data. Equations (11) and (12) represent the user and item similarity thresholds, respectively, of the reliability-based threshold cutoff values in the \(r\)-th step, where \(\eta\) and \(\theta\) denote the final user and item similarity thresholds, respectively. As the algorithm progresses, \(\eta_{r}\) and \(\theta_{r}\) decrease, while the value of \(r\) increases. For example, let \(\eta=0.7\) and \(k=3\); then, the threshold will have values as follows: \(\eta_{1}=0.93\), \(\eta_{2}=0.85\), and \(\eta_{3}=0.70\), whereby the deviation increases from \(-0.08\) between \(\eta_{1}\) and \(\eta_{2}\) to \(-0.15\) between \(\eta_{2}\) and \(\eta_{3}\). Therefore, by setting strict cutoff criteria in earlier steps, reliable data are left as imputation candidate data in later steps. Further into the algorithmic process, the cutoff criterion becomes increasingly relaxed. This threshold cutoff reset method contributes to minimizing the propagation imputation errors that occur when imputing data from low-reliability candidate data.  
\[\eta_{r} =1-\frac{(1-\eta)\times 2^{r}}{2^{k}} \tag{11}\] \[\theta_{r} =1-\frac{(1-\theta)\times 2^{r}}{2^{k}} \tag{12}\]
# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 2 Literature Review
Some damage detection methods using Cropping Segmentation, which means that cropped small size images are used to train the Deep Learning models instead of squashed full images [21; 26]. This method is only used for cracks on the concrete because there little interference in the background which may reduce accuracy, while there are few related studies for damage detection from real world images. Besides, one of methods for improving accuracy is changing the structure of the data set, such as enlarging the size of data set and making balance between images of each category [25; 29]. For methods using squashed images, the only mean is adding more images for the category with lower images. However, because not all cropped small pieces of images contain damage pixels in the data set of Cropping Segmentation, the method to delete such images are worth studying. Compared with traditional method adding images, this method can make the data balance more effectively and the concentration of pixel of each category is easier to detect. Thus, the method can make the balance of each category in pixel-level, rather than image-label. In this study, Squashing Segmentation and Cropping Segmentation based on VGG-Unet [38] (a kind of FCN) are adopted and compared with each other to respectively predict corrovisions from steel bridges and cracks from rubber bearings in the high-resolution images, in pixel-level. These images are taken in real world under unspecified light and angle. The two results are compared to find the the influence of the concentration of damage pixels in the data set on the damage detection capability of the model. After that, to improve detection accuracy, Background Data Drop Rate (BDDR) is defined to make a balance between all categories of pixels by shrinking the size of data set.
## 4 Comparison with Event of 10 to 11 October 2024
### Ionosonde Observations  
Figure 13 shows the equivalent observations as seen in Figure 6 but for the storm of 11 October 2024. The climatological conditions in October are different from those during the May event. The _foF\({}_{1}\)_ peak is not as pronounced in October as it is in May. On the other hand, the _foF\({}_{2}\)_ is slightly higher during the October event than during the May storm.  
Note that there are again some gaps in the time series, in particular during the night of the storm at the highest latitude observatory (JR055). These are primarily due to the presence of particle-induced sporadic layers precluding observing the F region of the ionosphere. Blanketing of the observations by regular Es layers is not important for this  
Figure 13: Peak electron densities for F\({}_{2}\) (red) and F\({}_{1}\) (blue) for the JR055 (**top**), DB049 (**middle**), and EB040 (**bottom**) ionosondes from 10 to 13 October 2024 0:00 UTC. Note that the maximum density (vertical axis) here is \(2.5\times 10^{6}\) (instead of \(1.5\times 10^{6}\) as in Figures 6 and 7).  
event as such layers are more rarely present at these observatories in October as compared to May.  
At the onset of the storm, a steep drop in the F\({}_{2}\) layer density can again be observed, indicating the sudden depletion of the ionospheric plasma. The main phase and recovery, on the other hand, look different for this storm. The cases of G-condition being observed are limited, in particular in the southernmost observatory, and the background conditions were restored already in the morning of 12 October.  
Figure 14 shows the critical frequencies observed by the American and Korean ionosondes. Once again, it should be noted that the F\({}_{1}\) peak is not as clearly observed as during the May event, and that the _foF\({}_{2}\)_ values are slightly higher. At these observatories, no G-condition was observed. This is due to the local time of the onset of the storm. As can be seen from Figure 13, the period of the G-condition observed by the European ionosondes was relatively short, compared to what was seen during the May event, from around 06:00 to around 12:00 UTC at JR055. This coincides with a period when no F\({}_{1}\) layer is detected at MHJ45 and IC437, so there can be no G-condition.  
Some depletion of the F\({}_{2}\) layer during the first day after the storm can still be seen at both observatories though. In both cases, the _foF\({}_{2}\)_ at the end of the period has not yet recovered entirely to the values seen before the storm. In addition, the presence of large-scale travelling ionospheric disturbances can be seen. Especially during 11 October at IC437, there are periodic oscillations evident in _foF\({}_{2}\)_.  
Figure 14: Time series of _foF\({}_{1}\)_ (blue) and _foF\({}_{2}\)_ (red) observed by the MHJ45 and IC437 ionosondes during the 11 October storm. Notice that the vertical axes are again extended to \(2.5\times 10^{6}\).
# Crystal Plasticity Finite Element Modeling on High Temperature Low Cycle Fatigue of Ti2 AlNb Alloy
## 5 Conclusions  
In this work, a CPFE model based on EBSD and EDS images was conducted to study the grain-level stress and strain distribution under cycle loading of Ti2 AlNb alloy. The following conclusions were obtained:  
1. The simulation performance of CPFEM is the same as that of Chaboche model, but the deformation mechanism Ti2 AlNb alloy at high temperature is complex, so with the larger strain range, simulation performance deteriorates;  
2. The stress concentration at the tension and compression stage is caused by the deformability of the O and \(\beta\) phases. The O phase is harder than the \(\beta\) phase, and the grain rotates at a smaller angle and has less strain. The inhomogeneous deformation at the phase boundary led to stress concentration.  
Conceptualization, H.L.; Data curation, X.L.; Formal analysis, Y.W., Z.Z. and Y.Y.; Investigation, Y.W. and X.W.; Methodology, H.L.; Project administration, H.L.; Resources, Y.W.; Software, Y.W. and Z.Z.; Validation, Z.Z.; Visualization, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Major Science and Technology Project (J2009-VI-0003-0116). The authors also gratefully acknowledge the support of the National Natural Science Foundation of China with (51835011 and 51775441).  
Not applicable.  
Due to the lack of unanimous consent from all the authors. Data is not shared.  
We thank [PERSON], [PERSON] and [PERSON] for their help in the experiments and discussion.  
The authors declare that the work is original research that has not been published previously, and is not under consideration for publication elsewhere. No conflict of interest exist in the submission of this paper, and the paper is approved by all authors for publication.  
Figure 10: 0.7% strain: (**a**) the grain rotation angle distribution; and (**b**) the stress distribution.
## 2 General Notations and Model Definition  
### Frames and Transformations  
To determine the position, orientation, and velocity of the vehicle, the paper uses two reference frames: a world-fixed reference frame \(\langle w\rangle\) and a body fixed reference frame \(\langle b\rangle\) as shown in Figure 1. The \(\langle w\rangle\) frame has its \(x\) axis pointing to the North, the \(y\) axis pointing to the East, and the \(z\) axis pointing to the center of the Earth. The unit vectors representing each directions in \(\langle w\rangle\) are \(\mathbf{i}_{w}\), \(\mathbf{j}_{w}\) and \(\mathbf{k}_{w}\), respectively. The \(\langle b\rangle\) frame relates to the vehicle with its \(x\) axis pointing to the forward direction, the \(y\) axis to the right direction, and the \(z\) axis points vertically down. The unit vectors representing each directions in \(\langle b\rangle\) are \(\mathbf{i}_{b}\), \(\mathbf{j}_{b}\) and \(\mathbf{k}_{b}\), respectively. Both these frames are related by the Euler angles yaw(\(\psi\)), pitch(\(\theta\)),and roll(\(\phi\)). Let \(\mathbf{\eta}_{1}=[x,y,z]^{\top}\) represent the position vector of origin of frame \(\langle b\rangle\) w.r.t. \(\langle w\rangle\) and \({}^{w}\mathbf{R}_{b}\) or simply \(\mathbf{R}\) represents the orientation matrix of \(\langle b\rangle\) frame w.r.t. \(\langle w\rangle\) frame as  
\[{}^{w}\mathbf{R}_{b}=\begin{bmatrix}c\psi c\phi&-s\psi c\phi+c\psi\theta s\phi&s \psi s\theta+c\psi c\phi s\theta\\ s\psi c\phi&c\psi c\phi+s\theta s\theta s\psi&-c\psi s\phi+s\theta s\psi c\phi \\ -s\theta&c\theta s\phi&c\theta c\phi\end{bmatrix}, \tag{1}\]  
where \(c\) and \(s\) denote the cosine and sine values of the respective Euler angles, respectively. The transformation matrix of \(\langle b\rangle\) frame w.r.t. \(\langle w\rangle\) frame is thus given by \({}^{w}\mathbf{T}_{b}=\begin{bmatrix}{}^{w}\mathbf{R}_{b}&\eta_{1}\\ 0&1\end{bmatrix}\).
## 4 EMOR Protocol Design for WSN in NAN
### Optimal Multi-Disjoint Path Selection for Real-Time and Non-Real-Time Traffic
Calculate \(L_{V_{s}Vi}^{RT}\) for real-time data
14. Determine \(PC_{V_{s}Vi}^{RT}\) for real-time data based on \(L_{V_{s}Vi}^{RT}\)
15. _elseif_ (delay tolerant traffic is needed), _then_
16. Calculate \(L_{V_{s}Vi}^{NT}\) for non-real-time data
17. Determine \(PC_{V_{s}Vi}^{NT}\) for non-real-time data based on \(L_{V_{s}Vi}^{NT}\)
18. _end if_
19. _end for_
20. Select the optimal path using Algorithm 2 and 3
21. _elseif_ (\(V_{s}\) has no neighbors on the spanning tree), _then_
22. Stop forwarding the data
23. _end if_
24. _end if_
25. _elseif_ (the successor node \(V_{s}\) of node \(V_{r}\) is sleeping), _then_
26. Search for other neighbors or wait until \(V_{r}\) wakes up
27. _end if_
```  
**Algorithm 4**Routing Strategy for Delay Sensitive and Delay Tolerant Traffic in EMOR  
Based on the routing strategy algorithm for delay-sensitive and delay-tolerant traffic in EMOR, the detail process of a source node forwarding its sensed data to the mobile sink can be depicted from Figure 6. When a random node \(V_{s}\) receives the data from one of its neighboring nodes, it follows the optimal multi-disjoint path selection for delay sensitive and delay tolerant traffic. Node \(V_{1}\) receives the delay sensitive traffic as it has the maximum link connectivity with \(V_{s}\) and node \(V_{2}\) receives the delay tolerant traffic as it has the second maximum link connectivity value with \(\text{V}_{s}\). During the transmission of delay-sensitive and delay-tolerant traffic in the network, we have to ensure that the nodes can be part of the optimal path towards mobile sink if they offer higher link connectivity. Although the nodes appearing as green in Figure 6 are working, they do not offer max or second max link connectivity, so they are not part of the delay-sensitive or delay-tolerant traffic paths. Moreover, the nodes appearing as pink in Figure 6 are sleeping nodes, so they could not be involved in the processes of OCRG, spanning tree, and optimal paths towards mobile sink. After the path connectivity for delay-sensitive and delay-tolerant traffic is established, we have to update the path connectivity value by comparing the path connectivity value in previous rounds with the path connectivity value in the current round of communication and select the maximum of the two values. In this way, we will be able to acquire the optimal path from any working sensor node to the mobile sink.
# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 3 Methodology
### AND Bit-by-Bit  
In the previous steps, the IDs related to the vehicle body behavior were filtered, and the bits related to the body in the corresponding messages were marked. Afterward, using fuzzified test messages and packet analysis, a large number of candidate messages that could trigger the body behavior and describe the vehicle behavior was obtained. In this phase, the bit-by-bit AND operation are used to quickly locate the bits in the candidate telegrams that are related to the control or state of a specific body behavior.  
Based on the analysis of a large number of DBC files, the control bits in CAN messages with a value of 1 are usually valid, i.e., there is a body action response when the control bit is 1. Therefore, in the control and description messages corresponding to the collected vehicle behaviors, the valid bits controlling the behavior must all have a value of 1, while the bits not related to the behaviors usually do not have a value of 1. Based on this, in this method, a column AND bit-by-bit process are performed for the candidate messages while reversing the valuable bits. After this operation, the value of the AND is also 1 because all valid bits have a value of 1. For non-valid bits, the value obtained is 0 because these bits do not all have a value of 1. Therefore, AND is suitable for quickly locating the bits of the data field with a value of \"1\" from the candidate information that can trigger the body behavior and describe the vehicle behaviors. This method is faster and more accurate than manual screening and other logical operations. Based on this theory, the method described in this paper can quickly locate the valid bits that control specified body behaviors. This phase involves the following steps:  
* First, all the bits in the candidate message that are not marked B are set to 0 to prevent other vehicle behaviors from interfering with the inverse result;
* After that, the values of the bits marked B in the data field of the candidate message undergo the column AND bit-by-bit process, and each data bit marked B is traversed. The control bits associated with the body operation have a value of 1, as shown for bits 41, 42, and 43 of each message in Figure 7. Unrelated bits have a value of 0; for example, bits 44 and 45 are shown in Figure 7;
* After this operation, this study obtained the valid bits that control or indicate the body behavior in the candidate messages.