esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Attention U-Net for Elastic Wavefield Decomposition in Anisotropic Media
## 5 Conclusions  
This study presents a cross-attention enhanced U-Net architecture for elastic wavefield vector decomposition in anisotropic media. The method combines deep learning with a dual-branch encoder structure to process displacement components separately while maintaining their relationships through cross-attention mechanisms. The results show that the cross-attention mechanisms improve the decomposition quality, achieving better accuracy and fewer artifacts compared to conventional U-Net architectures. The model's performance on larger domains and different geological settings, demonstrated through experiments with the BP and Hess VTI models, indicates its potential for practical seismic applications. The cross-attention U-Net effectively preserves physical relationships between displacement components while reducing the computational complexity compared to traditional methods that rely on solving the Christoffel equation.  
This research was supported by a National Research Foundation of Korea (NRF) grant, funded by the Korea government (MSIT) (No. 2023-00212423) and was also supported by a research grant from the Gyeongsang National University in 2022.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
The anonymous reviewers and the associated Editor are appreciated for their constructive comments that helped improve the quality of the manuscript. The 2007 BP anisotropic velocity benchmark dataset, created by [PERSON] and provided courtesy of BP Exploration Operation Company Limited, was used for training purposes. The dataset is publicly available at [[https://wiki.seg.org/wiki/2007_BP_Anisotropic_Velocity_Benchmark](https://wiki.seg.org/wiki/2007_BP_Anisotropic_Velocity_Benchmark)]([https://wiki.seg.org/wiki/2007_BP_Anisotropic_Velocity_Benchmark](https://wiki.seg.org/wiki/2007_BP_Anisotropic_Velocity_Benchmark)), accessed on 1 April 2025. The Hess VTI migration benchmark dataset, generated using finite-difference modeling software developed by Hess Corporation, was used for model evaluation. The dataset is publicly accessible at [[https://wiki.seg.org/wiki/Hess_VTI_migration_benchmark](https://wiki.seg.org/wiki/Hess_VTI_migration_benchmark)]([https://wiki.seg.org/wiki/Hess_VTI_migration_benchmark](https://wiki.seg.org/wiki/Hess_VTI_migration_benchmark)), accessed on 1 April 2025.  
The author declares no conflicts of interest.
## 4 Innovation through SDN-Based Network Applications
### SDN Network Management and Traffic Engineering  
The most well-known deployed SDN network in industry is Google's OpenFlow-based WAN, which is used as a production network. As presented at the Open Networking Summit 2012 and in their SDN experience paper [38] at SIGCOMM 2013, Google describes their internal global network that interconnects their data centers worldwide. They discuss the actual implementation of their network and show how traffic engineering can be supported by routing decisions. Their traffic engineering server schedules operations depending on the available bandwidth in the network. They show significant effects in resource utilization that can be achieved with SDN and OpenFlow in the context of data center synchronization and communication. This is possible in the data center environment of Google, but is not generally applicable for other use cases.  
SDN eases the introduction of new protocols in the network. In [39], IP multicast is implemented in the control plane using OpenFlow by handling multicast subscribe requests within the control software. The control software installs the forwarding entries in the switches according to the multicast application. This is achieved without modification of the forwarding devices, because their OpenFlow switches supported the required forwarding operations. However, the OpenFlow data plane has to be upgraded if a new protocol requires other operations than those offered by the OpenFlow specification in use. For SDN solutions that support a programmable data plane, e.g., FLARE [40] or programmable networks, the control and data plane can evolve more independently from each other.  
Some researchers consider network updates and the consequences of mismanaged updates. Inconsistent routing, e.g., routing loops, can occur when flow tables are updated in the wrong order. In [41], the authors consider consistent network updates by determining the correct switch update order.  
First, they introduce powerful abstractions for network updates and define various properties, such as \"no security wholes\", \"connection affinity\" and a \"loop-free property\". Then, the authors verify that their abstractions are valid and lead to the expected network-wide behavior according to the properties. They also discuss the update process on a \"per-packet\" and \"per-flow\" basis, as well as the additional state requirements of their proposal. Their work allows network operators to focus on the state before and after a configuration change without worrying about the transition in between.  
The creation of user-friendly interfaces for SDNs is discussed in [42]. The authors present a remote network management interface to OpenFlow networks, called OMNI. It eases the management of OpenFlow-based networks, which can be difficult, due to the number of monitoring variables and multiple network configuration options. OMNI monitors and configures the dynamic creation of flows. To that end, it collects data statistics of the network devices and provides an easy to understand network-wide visualization of the current network state.
## 5 The Query Management Scheme
### The Query Processing Phase (QPP)
#### 5.3.2 The Message Payload  
With the query result tuple, the node creates a response message. Figure 7 is the message payload format for the node's application level. The data fields in the payload are explained below.  
1. Node ID: The node ID is the ID of sensor node that sent the response message.
2. Query Set ID: The query set ID is the set ID of the most recently arrived query set. This field is used to identify the query set start time for different query sets.  
Figure 6: Timeline of queries sent to a node. The numbers indicate the time of the readings from the node clock. Each colored box indicates query execution.  
Figure 7: Response message payload format at the application level.  
3. Time-Stamp: This field indicates when the response was made. Using the time-stamp, the Node Manager can generate a query timeline to determine which queries are executed at each moment. The Query Set time-stamp is added to adjust to message transmission failures, as explained in the Section 5.3.1. 1. Node-Time Stamp: The node time-stamp is the time indicated by the node clock. The zero time point for the node clock is the query set start time of the first query set. 2. Query Set Time-Stamp: The query set time-stamp represents the number of unit time after the start time of the most recently arrived query set. For example, if the latest query set arrived at Node TS = 14 and the current time is Node TS = 16, then Query Set TS = 2.
4. Disambiguation Code (DC): This is a binary code included in case there is an ambiguity caused by multiple queries requesting the same data attributes under different query conditions. Each of the ambiguous queries is sequentially represented by each bit of the code. If the query condition is satisfied, the digit is set to 1; else the digit is clear. For example, assume that the node has to execute the following three queries: \[q_{2}=\Pi_{temp}(\sigma_{pressure>20}(S))\] \[q_{3}=\Pi_{temp,DO}(\sigma_{pressure>20}(S))\] \[q_{4}=\Pi_{DO}(\sigma_{temp<30}(S))\] If the sensor data readings are: \(pressure=30\), \(temp=20\), the node sends both the temperature and the DO values in response to \(q_{2}\) and \(q_{4}\). However, with only the two field data, the Node Manager cannot recognize whether the pairs are responses to query \(q_{3}\) (which also demands both the temperature and the DO values) or to queries \(q_{2}\) and \(q_{4}\). Therefore, a three-digit disambiguation code \"101\" is added to indicate which query conditions (\(q_{1}\) and \(q_{3}\) in our case) are satisfied.  
Figure 8 is an example of two response messages at different time frames for a sensor node whose ID is 5. Notice that no disambiguation code is required for the first response message because there is only one query, which indicates no possibility of ambiguity.  
Figure 8: Examples of response messages at two different points in time. The first response message has no disambiguation code because there is no possible ambiguity.
## 3 Methodology
### Feature Extraction Module
#### 3.3.1 HOG Feature Descriptor  
For image data, there is a whole branch of feature engineering that deals with the extraction of features from images. HOG computes pixel-wise gradients and orientations and plots them on a histogram. The main motive is to simplify the representation of the image by extracting the most useful information from the image and removing the redundant parts. In this way, it helps in minimizing the noise. This technique detects objects by calculating feature descriptors using horizontal and vertical gradients. The image splits into 8 \(\times\) 8 cells, with each cell pixel having direction and magnitude that helps develop a compact representation that can be quickly learned by computer [24]. This method computes the gradient of each pixel in x (horizontal) and y (vertical) directions since the image is a discrete function. It is a vector having direction and magnitude given in Equations (1) and (2).  
\[\theta=arctan\bigg{(}\frac{f_{x}}{f_{y}}\bigg{)} \tag{1}\]  
\[mag=\sqrt{\bigg{(}f_{x}^{2}+f_{y}^{2}\bigg{)}} \tag{2}\]  
A 9-bin histogram that ranges from 0-180 degrees quantizes the orientation of the gradient with values in the range of (0-255). All histograms are merged to produce a feature vector H. The final HOG feature vector is given by Equation (3), which provides shape information of regions or points within an image [24].  
\[F_{HOG}=\{f_{h1},\,f_{h2},f_{h3},.....f_{hm}\} \tag{3}\]  
We applied a Laplacian filter to sharpen the edges of the hand gestures and reduce noise. The intensity values of the images are rescaled to improve the image's contrast. This model extracted 8100 features using the HOG method from each of the input images of the dataset. Figure 4 depicts the input image and the extracted HOG image.
# A Method for Borehole Image Reverse Positioning and Restoration Based on Grayscale Characteristics
## 4 Borehole Image Error Analysis and Restoration  
### Color Space Conversion  
The model we established above utilizes image luminance for probe reverse positioning. Repairing luminance errors in images requires separate operations on image luminance. Conventionally, borehole images are captured, stored, and represented in the RGB color space. The RGB color model is the most basic, commonly used, and hardware-oriented model in image processing. However, in this color space, image luminance is correlated with all three channel values. To facilitate probe reverse positioning and subsequent image restoration operations, it is necessary to reveal the luminance channel of the image.  
The HSV (hue--saturation--value) color space is a user-oriented color model designed based on the intuitive properties of human perception of color. Compared with other color models, HSV is closer to how humans perceive color [33]. In the HSV color space, \(H\) represents hue, \(S\) represents saturation, and \(V\) represents brightness. The conversion of images from the RGB color space to the HSV color space is well-established and widely used in digital imaging, as is shown below.  
\[\begin{split}& V=\max(R,G,B)\\ & S=\left\{\begin{array}{ll}\frac{V-\min(R,G,B)}{V}&,V\
eq 0\\ 0&,V=0\end{array}\right.\\ & H=\left\{\begin{array}{ll}60(G-B)/(V-\min(R,G,B))&,V=R\\ 120+60(B-R)/(V-\min(R,G,B))&,V=G\\ 240+60(R-G)/(V-\min(R,G,B))&,V=B\end{array}\right.\end{split} \tag{14}\]  
To facilitate subsequent analysis, borehole digital images are transferred from the RGB color space to the HSV color space, as shown in Figure 11. For the analysis of the integrity of in situ geological formations in borehole images, analyzing the V channel can more efficiently compress the data volume and eliminate invalid information.
## 4 Results
### User Results
#### 4.2.1 Multi-Frequency
In Figure 6b, the temporal variation of the mean and the 0.95-quantile horizontal and vertical difference from the reference position are shown. After 1 min, the mean horizontal and vertical offsets are 3.9 cm and 11.0 cm, respectively, and after 2 min, they are 2.0 cm and 6.1 cm. The horizontal 0.95-quantile converges to an offset of around 2.5 cm after 3 min, while it takes around 6.5 min for the 0.95-quantile of the vertical offset to converge to around 7.5 cm. At last, we take a closer look at the single epoch positioning performance and how it is affected by the fixing of the reparameterized wide-lane ambiguities. In Figure 7, the coordinate residuals correspond to the reference position at the first epoch of each hourly solution. The residuals are shown for the case that no wide-lane ambiguities are fixed to integers and for the case that wide-lane ambiguities are attempted to be fixed to integers. It can be seen that the reparameterization of the ambiguities and subsequent ambiguity resolution of the wide-lane ambiguities drastically improve the instantaneous positioning accuracy. The RMS of the north coordinate residuals improves from 29 cm to 10 cm, the RMS of the east residuals from 26 cm to 8 cm, and the RMS of the vertical residuals from  
Figure 5: One-hour static coordinate solution north, east, and up differences to the reference and mean solutions, further comparing the differences between constant phase biases and a phase reference clock. 127 cm to 59 cm. The mean residuals improve slightly or remain comparable. The mean north residual is 1 cm in both cases, the mean east residual improves from \(-2\) cm to \(-1\) cm, and the mean vertical residual improves from 8 cm to 5 cm. Figure 6: Visualizing the temporal characteristics of the user solution. Kinematic processing refers to the epoch-wise estimation of user coordinates, using static coordinates as constant parameters. Processing is done with the constant phase bias assumption. Note that the temporal resolution of the observations used is 30 s. (**a**) Epochs to first fix (ETTF) for kinematic and static processing and the number of satellites observed and fixing rate (ratio of fixed to observed satellites) at the last epoch. (**b**) Horizontal and vertical distances of estimated receiver coordinates (kinematic) to the reference position. Figure 7: Single epoch positioning performance for multi-GNSS observations. Reparameterized ambiguities can be fixed to integers in just one epoch, improving the performance significantly.
## 2 Proposed PLL Architecture
### Programmable VCO Circuit  
In the proposed PLL architecture, the VCO is tunable, meaning that its output frequency can be optimized based on the change in the input control voltage.  
As we know for the VCO output,  
\[\omega_{\text{out}}=\omega_{\text{o}}+\text{Kvco.Vctr} \tag{1}\]  
Here, the proposed VCO contains a voltage-to-current (V-I) converter with a programmable iDAC and a four-stage differential ring oscillator as the delay cell. The V-I converter provides a linear relationship between the output current and the control voltage. As Vctr increases, there is a corresponding increment in the output current, and bias Vbp1 is observed with a constant resistor value. The programmable iDAC is used to optimize VCO delay cells by increasing or decreasing the tail current for a change in bias Vbp1<0:3> and Vbp2<0:3> values. A VCO with sub-blocks is shown in Figure 6.  
Figure 4: Lock-in range for Vctr with the minimum and maximum Vref levels.  
Figure 3: Two major reference voltage levels vrefm_sel and vrefp_sel.  
Figure 5: Simulation results of (**a**) automatic band selection circuits (ringing) and (**b**) automatic band selection circuits (final convergence).  
Figure 6: VCO circuit sub-blocks.
## 2 Motor Topology and Simulation Analysis
### Motor Topology
Since the proposed model is an asymmetric rotor, in this particular arrangement of the permanent magnets, the permanent magnet flux linkage \(\psi_{pm}\) shifts and forms an angle of \(\alpha\) with the _d-axis_ as shown in Figure 3b. Based on the constant coordinate transformation of the magnetic potential, the magnetic flux linkages in the proposed model with regard to the _d-axis_ and _q-axis_ can be expressed by Equation (3) [27]. \[\left\{ \begin{array}{ll}\psi_{d}=\psi_{pmd}+L_{d}i_{d}=\psi_{pm}cos\alpha-L_ {d}i_{s}sin\beta\\ \psi_{q}=\psi_{pmq}+L_{q}i_{q}=\psi_{pm}sin\alpha+L_{d}i_{s}cos\beta\end{array} \right. \tag{3}\]  
where \(\psi_{pmd}\) and \(\psi_{pmq}\) are the _d-axis_ and _q-axis_ permanent magnet flux linkages, respectively. According to Equation (3), the torque formula of the proposed model can be obtained as shown in Equation (4). \[ \begin{array}{ll}T_{E2}&=\frac{3}{2}p(\psi_{d}i_{q}-\psi_{q}i_{d})\\ &=\frac{3}{2}p(\psi_{pmd}i_{q}-\psi_{pmq}i_{d}+(L_{d}-L_{q})i_{d}i_{q})\\ &=\frac{3}{2}p\psi_{pmi}cos(\beta-\alpha)+\frac{3}{4}p(L_{q}-L_{d})i_{s}^{2} sin(2\beta)\\ &=T_{PM2}+T_{RE2}\end{array}  \tag{4}\]  
where \(T_{E2}\), \(T_{PM2}\), and \(T_{RE2}\) are the total torque, permanent magnet torque, and reluctance torque of the proposed model, respectively. By analyzing Equation (4), it is found that the permanent magnet torque and the reluctance torque of the proposed model can reach their peak values at the same current phase angle when the angles \(\beta\) and \(\alpha\) satisfy the following equations. The relationship between angle \(\beta\) and \(\alpha\) is shown in Equation (5). \[\alpha=\beta=\frac{\pi}{4} \tag{5}\]  
Substituting Equation (5) into Equation (3) yields the theoretical peak torque formula for the proposed model as shown in Equation (6). \[ \begin{array}{ll}T_{E2 MAX}&=\frac{3}{2}p\psi_{pm}i_{s}+\frac{3}{4}p(L_{q}- L_{d})i_{s}^{2}\\ &=T_{PM2 MAX}+T_{RE2 MAX}\end{array}  \tag{6}\]  
where \(T_{E2 MAX}\), \(T_{PM2 MAX}\), and \(T_{RE2 MAX}\) are the theoretical maximum total torque, permanent magnet torque, and reluctance torque of the proposed model, respectively. By comparing Equations (2) and (5), it is found that the permanent magnet torque and the reluctance torque of the conventional model can never both reach their maximum values at the same current phase angle. The proposed model with the MAS effect can theoretically maximize the permanent magnet torque and reluctance torque at the same or a similar current phase angle, resulting in a higher total superimposed torque.
# Electromagnetic Induction Imaging with Atomic Magnetometers: Progress and Perspectives
## 4 Electromagnetic Induction Imaging with Atomic Magnetometers
### Development of EMI with AMs and the Quest for Ultimate Sensitivity
Figure 4 illustrates this development from the early demonstrations to the state-of-the-art sensitivities measurements of [37] by reporting the milestones in the quest for extreme sensitivity to small volumes of low-conductive materials. Among the various technical developments, two certainly played an essential role in reaching extreme sensitivity: the use of near-resonant imaging [35], and the introduction of the dual-coil configuration for the rf-field [36; 37; 38]. Near-resonant imaging consists in choosing the frequency of the rf field slightly different from exact atomic resonance. This may seem counterintuitive, as maximum sensitivity requires exact resonance. However, it was found that by slightly detuning the rf field an optimum compromise can be reached between absolute sensitivity of the magnetometer and robustness against ambient magnetic noise, which would otherwise degrade the image. This allows for the operation of the EMI-AM system with high sensitivity and stability over the duration of the measurement. The second key development, the dual-coil configuration, results from the observation that a larger primary field produces a corresponding increase of the amplitude secondary field produced by the object of interest, thus leading to an improvement in sensitivity. However, in a standard single coil configuration, the primary field also drives the atomic spin precession in the magnetometer. Thus an excessive increase results in the saturation of the atomic resonance, and a resulting decrease in sensitivity. A solution to this issue, already put forward in the context of MIT with inductive coils [1] and also implemented in the context of nuclear quadrupole resonance spectroscopy with RF-AMs [39], is the use of a dual-coil configuration, so to have a large field on the target object and a significantly reduced field driving the atomic spins. Figure 4: Development, in terms of sensitivity, of EMI-AM systems from the first proof-of-concept in 2014 to the latest results in 2020. The horizontal axis reports the materials used for the different demonstrations. Filled symbols represent results in unshielded environment, while open symbols indicate measurements in shielded environment. Filled diamonds data are from Ref. [26], filled square from [29], open diamonds from [30], filled triangle up from [22], filled triangle down from [40], filled triangle left from [41], filled triangle right [35], open squares from [36], closed disks from [37].