esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Recognition of Road Internal Void Using Ground-Penetrating Radar
## 4 Discussion  
This study utilized 3D GPR to detect internal road voids across multiple administrative regions in Shenzhen, establishing a dataset of 1700 void images. In existing related research, scholars have employed two approaches to build void image datasets: numerical simulation and on-site GPR collection. While numerical simulation is cost-effective, simulated images show characteristic differences from real images [50]. On-site collection, though costly, results in smaller datasets typically not exceeding 1000 images [51; 52; 53; 54; 55]. Therefore, compared to existing research, our dataset provides a larger-scale data foundation for internal road void image enhancement and intelligent recognition.  
A supervised learning-oriented void image processing method was proposed based on Fourier transform low-frequency filtering and Gaussian filtering. This method increases pixel differences between degraded and enhanced images by decreasing and increasing grayscale values in void regions, thereby expanding the model's learning space. In road-related research, image enhancement technology primarily focuses on image quality improvement under complex conditions, dynamic scene correction, and target detection enhancement [56; 57; 58]. Image enhancement includes traditional and intelligent methods. Traditional methods comprise histogram processing, spatial domain processing, and morphological processing. Compared to traditional methods, intelligent enhancement methods offer advantages such as scene-adaptive parameter adjustment, stronger robustness and generalization ability, end-to-end processing without manual feature design and rules, and efficient processing after training through learning optimal enhancement strategies from large datasets. Among intelligent enhancement algorithms, the Unet model and its improved structures are widely applied, demonstrating advantages in medical imaging, remote sensing imaging, and natural scene imaging, including good image structure preservation, strong detail recovery capability, and fast processing speed [59; 60; 61]. However, research on internal road void image enhancement technology is still in its early stages. Our study combines the advantages of MHSA and MHCA in global modeling and cross-domain feature fusion [62; 63], and based on the Unet model structure, introduces MHSA and MHCA to construct an improved Unet model (MHUnet) for enhancing internal road void images. Compared to original images, this model significantly improves enhanced image quality in both visual effects and quantitative indicators. The model improves the brightness, clarity, and contrast of internal road void images, enhancing visual quality and image perception consistency. The enhanced internal road void images can provide more effective features for intelligent recognition models, effectively enhancing their learning capability and improving model accuracy and sensitivity.
## 1 Introduction
Reduce the charging load fluctuation by configuring the energy storage system, considering the size of the ESS and the profit by using time-of-use tariffs, and the cycle lifetime of BESS is rarely considered. A few studies have been performed over the electric bus and fast-charging, while the optimization objective is usually one route with several buses. The configuration of the station seriously affects the operation of the bus fleet and charging optimization. The BESS is usually used to directly regulate the charging power without charging optimization. There have been few studies on the issue of distribution capacity suppression at fast-charging stations in megawatts level. This work aims at reducing the peak charging power of the PEB fast-charging station in Beijing. The characteristics of configuration and operation of the PEB fast-charging station are presented through the analysis of actual data. A two-step strategy is presented; first, decreasing the peak charging power by the charging mode optimization in the operating hours and at night, and second, further reducing the power by the configuration of BESS. In the charging mode optimization, the charging sequence optimization model in the operating hours is established, therefore, the particle swarm optimization (PSO) algorithm was applied to find the optimal solutions. Thus, a new charging topology concept was recommended, where it was easy for the high power and voltage fast-charging station to change the number of charging piles. In order to further decrease the peak charging power, the BESS was used to shift the charging power according to the TOU tariffs, and to reduce the total cycle lifetime cost of the battery. A linear programing algorithm is used to accomplish the optimization. The results of the charging optimization case showed that the power requirement could be reduced by 43.06%. The cases of BESS configuration with the original and optimized charging load were developed, and the results indicated that the two-step strategy could significantly restrain the power demand by up to 56.67%, which could make maximum profits at the same time. The rest of this paper is organized as follows. The configuration and operation of the typical DC PEB fast-charging station is presented in Section 2. In Section 3, the new charging topology is shown and the proposed charging optimization model is developed. The BESS configuration method considered the battery cycle lifetime and the simulation results are detailed in Section 4. The case study and result analysis are carried out in Section 5. Finally, the conclusions are given in Section 6.
###### Abstract  
Metasurfaces offer great opportunities to control electromagnetic (EM) waves, attracting intensive attention in science and engineering communities. Recently, many efforts were devoted to multifunctional metasurfaces integrating different functionalities into single flat devices. In this article, we present a concise review on the development of multifunctional metasurfaces, focusing on the design strategies proposed and functional devices realized. We first briefly review the early efforts on designing such systems, which simply combine multiple meta-structures with distinct functionalities to form multifunctional devices. To overcome the low-efficiency and functionality cross-talking issues, a new strategy was proposed, in which the meta-atoms are carefully designed single structures exhibiting polarization-controlled transmission/reflection amplitude/phase responses. Based on this new scheme, various types of multifunctional devices were realized in different frequency domains, which exhibit diversified functionalities (e.g., focusing, deflection, surface wave conversion, multi-beam emissions, etc.), for both pure-reflection and pure-transmission geometries or even in the full EM space. We conclude this review by presenting our perspectives on this fast-developing new sub-field, hoping to stimulate new research outputs that are useful in future applications.  
metasurface; multifunctional device; metamaterial; meta-atom +
Footnote †: journal: Applied Sciences
## 3 Architecture of the Platform and Related Software and Hardware Aspects
### Core Facilities for Nodal Interactions and Ad Hoc Networking
#### 3.1.2 The Beacon-Based Signaling  
As already mentioned in Section 2 and at the beginning of Section 3, each mobile node advertises its current position and velocity through beacon messages broadcast periodically (again, with a frequency that can be tailored) to the node's neighborhood. The structure of a beacon message is depicted in Figure 3. As it can be seen, the beacon contains information about the current position of the node (expressed in Cartesian coordinates, as obtained by the last activation of the localization module), the previous position (obtained by the immediately preceding activation of the localization module and used for determining the direction of motion) and the current speed (again obtained by the localization module), along with fields for marking the time the beacon was issued and for identifying the sender.  
On the receiving side, each node listening to a beacon detects that the sender is a neighbor node. To implement this feature, each node maintains a \"beacon table\", including one entry per neighbor node. Once a beacon is received, the receiver checks its beacon table to determine if an entry for the sender of the beacon already exists (by checking the beacon sender's ID against the IDs of the entries). If the entry exists, the relevant location and motion data are updated in accordance with the data in the beacon message. Otherwise, an entry is created for the newly encountered neighbor. Moreover, apart from the beacon-triggered updates just mentioned, the table is inspected periodically to eliminate obsolete entries, i.e., entries for nodes that were neighbors in the past, but no updated beacon message has been heard from them for a time interval greater than a parameter-specified threshold.  
With the aforementioned management of the beacon table, all nodes within communication range are treated as neighbor nodes. However, the platform provides additional facilities for emulating a precisely controlled smaller communication range through software. Specifically, when a node receives a beacon, it calculates its own distance from the sender of the beacon (by using its own location and the location data in the beacon) and considers the creation or update of a beacon table entry only if the calculated distance does not exceed the emulated communication range (specified as a parameter). This software mechanism enables experiments that can address a broader range of network densities, especially when the experiments are conducted in smaller terrains and is handier (and perhaps more precise) than the hardware-based alternative of employing radio signal attenuators (as in other testbeds [39; 40]) to reduce the signal range by decreasing the radio transmit power (i.e., the signal strength).
## 4 Efficiency and Fairness Signal Optimization Model
### Optimization Model Construction
#### 4.2.1 Objective Function  
To simultaneously consider the efficiency and fairness of signalized intersections, it is necessary to organically combine the Webster intersection total delay model with the phase-average delay fairness evaluation function to construct a dual objective comprehensive function. Based on the conclusion of the feasibility analysis of the comprehensive model in the previous section, a variable \(n\) is introduced into the comprehensive function, where \(n\) takes any positive integer, to adjust the weight ratio of total delay and fairness at the  
Figure 5: Four-saturation delay sacrifice conversion rates.  
intersection in the comprehensive function. The final decision on the comprehensive examination model is as shown in Equation (20):  
\[\max\frac{H(k)}{\sqrt[n]{D}} \tag{20}\]
# Collaborative Use of a Shared System Interface:
## 2 Theoretical Development
### Synchronous Dual Gaze Recording  
As the ubiquity of eye-tracking technology has increased over the past decade, so too has its affordability [7]. Today, there is far more potential to employ eye-tracking in studies regarding collaborative IT use. Correspondingly, eye-tracking experiments with multiple participants have become more common [7; 25]. However, a major limitation of prior multiuser eye-tracking studies is that the recordings have essentially been done separately for each participant and have commonly not been synchronized, making eye-tracking data analysis tedious and imprecise. Indeed, not synchronizing the recording computers means that their clocks may not be linked, permitting an artificial temporal mismatch between concurrent actions between the participants. Although realignment of the data can be achieved by using timestamps, the procedure is prone to inaccuracies or errors [7], and it is time-consuming and complicated. However, synchronous recording of eye gaze of multiple participantshas started gaining some attention, notwithstanding the use of low-cost, low-accuracy eye-tracking devices [25].  
To address the above concerns, the present study implemented synchronous dual gaze recording through a high-accuracy eye-tracking setup. All data recording computers for user dyads were synchronized during simultaneous collaborative use of a shared system interface. We used a dual eye-tracking method involving participants sitting each in front of a separate display. Compared to setups in which one display is shared for all participants on the same device, our method provides a higher accuracy of the eye-tracking data, since it is as precise as the eye-tracking method with one participant [25]. An important advantage of synchronized dual eye-tracking is that it brings order to the gaze data files and eases later data analysis, allowing to track exact time and order in which the gaze data were collected [7]. For details, please see the Methodology section.
## 4 Simulation and Physical Experiments  
The distribution of platform hinge points is shown in Figure 4, the origin \(o^{\prime}\) of the moving coordinate system is located at the center of the upper surface of the top platform, and the origin \(o\) of the global coordinate system is located at the center of the circle where the center point of the lower hinge is. Due to the symmetry of the SGP structure, the following parameters are enough to define the platform: radius \(r\) and \(R\) of top and base circumscribed circle of hinge point, the angle between adjacent hinge points \(\theta_{1}\) and \(\theta_{2}\) and distance \(h_{1}\), \(h_{2}\) from upper and lower hinge points to the top platform surface. The SGP can be uniquely determined by the above six parameters. The parameters of the top and base platform for simulation are set as follows:  
\[r=0.2\;\text{m},\;R=0.3\;\text{m},\;h_{1}=0.05\;\text{m},h_{2}=0.3\;\text{m}, \theta_{1}=\theta_{2}=\frac{\pi}{6}\;rad\]  
Table 1 shows the coordinates of the hinge points, while the top coordinates are based on the moving coordinate system, and the base coordinates are described in the global coordinate system.  
With the points in Table 1, the initial outrigger length \(\mathbf{l_{0}}\) is calculate by IK function.  
\[\mathbf{l_{0}}=29.7619\times 10^{-2}\;\text{m}\]
# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
###### Abstract  
Multiple access channel (MAC) networks use a broadcasting algorithm called the Binary Exponential Backoff (BEB) to mediate access to the shared communication channel by competing nodes and resolve their collisions. While the BEB achieves fair throughput and average packet latency in jamming-free environments and relatively small networks, its performance noticeably degrades when the network is exposed to jamming or its size increases. This paper presents an alternative broadcasting algorithm called the K-tuple Full Withholding (KTFW), which significantly increases MAC networks' resilience to jamming attacks and network growth. Through simulation, we compare the KTFW with both the BEB and the Queue Backoff (QB), an efficient and high-throughput broadcasting algorithm. We compare the three approaches against two different traffic injection models, each approximating a different environment type. Our results show that the KTFW achieves higher throughput and lower average packet latency against jamming attacks than both the BEB and the QB algorithms. The results also show that the KTFW outperforms the BEB for larger networks with or without jamming.  
jamming; wireless network; broadcast algorithm; multiple access channels; broadcast protocol +
Footnote †: journal: Applied  
1
Footnote 1: email: {[[https://doi.org/10.3390/app11031156](https://doi.org/10.3390/app11031156)]([https://doi.org/10.3390/app11031156](https://doi.org/10.3390/app11031156))}
## 3 Results
### NDVI Difference Calculation  
According to the cycle pattern of characteristics of the crop phenology in the study area (Figure 2), the GF-6 NDVI in March and November 2021 and February, May, July, and November 2022, and the fused GF-6 resolution-like NDVI (resolution of 2 m) in April and September 2021 and October 2022 from STARFM were selected to construct the NDVI time series dataset in 2021 and 2022. This ensured that a high-resolution NDVI covered the growing and non-growing stages and growth changes of the main crops in the study area. The selected dates for the GF-6 NDVI and fused NDVI are shown in Table 4.  
The difference in the NDVI for each of the two adjacent stages was calculated separately, and the absolute value was taken to measure the degree of drastic changes in the NDVI between the two stages. Then, the maximum value of each NDVI difference was composited for 2021 and 2022 as the degree of drastic change in the NDVI for 2021 (Figure 8a) and 2022 (Figure 8b), respectively.
# Deflection Modeling and Curvature Manipulation of a Variable-Stiffness Flexible Needle
## 2 Materials and Methods
### Simulation of the Effect of Stylet Diameter on Deflection
(**b**) Simulation results for deflection (directional deformation along the Z-axis). The legend is in meters. Figure 5: Comparison of deflection for different stylet diameters under a stylet length of 20 mm. (**a**) Deflection of Structure 1. (**b**) Deflection of Structure 2. (**c**) Deflection of Structure 3. (**d**) Deflection fitting for three structures. under the concentrated load \(F\), as their moments of inertia are identical. The tip deflections at different styled diameters are recorded and curve-fitted, as plotted in Figure 5d. In Equations (1) and (15), there is \(w\propto 1/R_{s}^{4}\). Thus, a power series fit in MATLAB R2021b (the number of terms is set to 2 and the equation is \(a*x^{b}+c\)) is used. The fitted curves are \(y_{1}=0.2124x^{-4.532}+7.096,R^{2}=0.9996\) (Structure 1), \(y_{2}=0.1369x^{-3.786}+7.223\), \(R^{2}=0.9786\) (Structure 2), and \(y_{3}=0.1236x^{-3.918}+7.269,R^{2}=0.98179\) (Structure 3). Structure 1 has a circular styled cross section, as illustrated in Figure 3d. However, its exponential coefficient is less than the theoretical value of \(-4\). This discrepancy may be due to the selection of an inappropriate length for the needle tip (which includes a portion of the styled and the tube). The effective range of the flexible needle tip is beyond the scope of this article, although we will take these factors into account in future work. Structure 2 and Structure 3 have a non-complete circular cross section, and the styled thickness is denoted as \(d_{s}\), as depicted by the solid line region in Figure 3e or Figure 3f. Taking a microarea \(\mathrm{d}A=2\sqrt{R_{t}^{2}-y^{2}}\mathrm{d}y\) in the styled cross section and integrating over the cross-section range \(y\in[-R_{t},-R_{t}+d_{s}]\):  
\[I_{s^{\prime}}=\int_{-R_{t}}^{-R_{t}+d_{s}}2y^{2}\sqrt{R_{t}^{2}-y^{2}}\mathrm{ d}y \tag{16}\]  
Let \(y=R_{t}\sin\theta,\theta\in[-\pi/2,\arcsin[(-R_{t}+d_{s})/R_{t}]]\), then \(I_{s^{\prime}}\) can be rewritten as  
\[I_{s^{\prime}}=\frac{R_{t}^{4}}{4}[\theta_{s}-\frac{\sin 4\theta_{s}}{4}+ \frac{\pi}{2}] \tag{17}\]  
where \(\theta_{s}=\arcsin[(-R_{t}+d_{s})/R_{t}],d_{s}\in(0,2\cdot R_{t}]\). Equations (1) and (17) can be calculated in MATLAB R2021b, noting that \(d_{s}=2R_{s}\), and the moments of inertia are obtained. The circular cross section (Structure 1) has a larger moment of inertia than the non-complete circular cross section (Structure 2 and Structure 3), and thus the exponential terms of the fitted curves for Structure 2 and Structure 3 in Figure 5d are smaller than those for Structure 1.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 4 Performance Evaluation
### Evaluation against SkySensor  
In this section, EDDS is evaluated against SkySensor in terms of data loss, data uniformity, success rate of the skyline query and resilience to node failure. All subsequent experiments were conducted with a network size of 150 \(\times\) 150 m\({}^{2}\) with 500 nodes deployed uniformly. The dimension of attribute varied from 2 to 7, data and query generation rate varied from 0.02 packets/node/second to 0.1 packets/node/second. The default value of data and query rate was 0.02 packets/node/second unless otherwise stated.
## 3 Grant-Free Scheduling
### Collisions with Other UEs
 \tag{12}\]  
where \(n_{4}^{act}\), \(|R_{4}|\), \(n_{4}^{max}\) and \(n_{4}^{min}\) are defined as:  
\[n_{4}^{act}=\sum\
olimits_{j=1}^{4}n_{j} \tag{13}\]  
\[|R_{4}|=N-1-\sum\
olimits_{j=1}^{3}n_{j} \tag{14}\]  
\[n_{4}^{max}=|R_{4}| \tag{15}\]  
\[n_{4}^{min}=\left\{ \begin{array}{ll}1&\text{if}\ |R_{4}|=N-1\\ 0&\text{otherwise}\end{array} \right. \tag{16}\]  
The process followed to account for possible collisions of the replicas transmitted in \(s_{5}\) and \(s_{6}\) is similar to that considered for \(s_{4}\). \(P_{c}\) can then be expressed as follows when \(K=4\):  
\[ \begin{array}{l}P_{c}=\sum\limits_{n_{0}=n_{0}^{min}}^{n_{0}^{max}}\left\{P _{tx}(n_{0},R_{0})\cdot\sum\limits_{n_{1}=n_{1}^{min}}^{n_{1}^{max}}\left\langle P _{tx}(n_{1},R_{1})\cdot\sum\limits_{n_{2}=n_{2}^{min}}^{n_{max}^{max}}\left[P_{tx }(n_{2},R_{2})\cdot\right.\right.\\ \left.\left.\sum\limits_{n_{3}=n_{3}^{min}}^{n_{max}^{max}}\left\{\left\langle P _{tx}(n_{3},R_{3})\cdot(1-P_{nrc}(n_{3}^{act},U))\right\}\cdot\sum\limits_{n_ {4}=n_{4}^{min}}^{n_{max}^{max}}\left[\left\{P_{tx}(n_{4},R_{4})\cdot(1-P_{nrc }(n_{4}^{act},U))\right\}\cdot\right.\right.\\ \left.\left.\left.\sum\limits_{n_{3}=n_{3}^{min}}^{n_{max}^{max}}\left\{\left\langle P _{tx}(n_{5},R_{5})\cdot(1-P_{nrc}(n_{5}^{act},U))\right\}\cdot\sum\limits_{n_ {6}=n_{6}^{min}}^{n_{max}^{max}}\left\{P_{tx}(n_{6},R_{6})\cdot(1-P_{nrc}(n_{ 6}^{act},U))\right\}\right\rangle\right\}\right\}\right\}\end{array}  \tag{17}\]where \(n_{i}^{act}\), \(|R_{i}|\), \(n_{i}^{max}\) and \(n_{i}^{min}\)\(\forall i\in[0,\,2\cdot K-1]\) are defined as:  
\[n_{i}^{act}=\sum\
olimits_{j=\max\{i-(K-1),0\}}^{i}n_{j} \tag{18}\]  
\[|R_{i}|=N-1-\sum\
olimits_{j=\max\{i-(K-1),0\}}^{i-1}n_{j} \tag{19}\]  
\[n_{i}^{max}=|R_{i}| \tag{20}\]  
\[n_{i}^{min}=\left\{ \begin{array}{ll}1&\text{if }i\geq K-1\text{ \&}|R_{i}|=N-1\\ 0&\text{otherwise}\end{array} \right. \tag{21}\]  
The process illustrated for \(K=4\) can be followed to compute \(P_{c}\) for any value of \(K\). As shown in (22), \(P_{c}\) can be computed using the auxiliary function \(h_{i}(K,N,U)\) defined in (23) with \(i\) equal to zero. To simplify the notation, \(h_{i}(K,N,U)\) is also represented as \(h_{i}\) in (22) and (23). As it can be observed in (23), \(h_{0}\) depends on \(h_{1}\), and in general, \(h_{i}\) depends on \(h_{i+1}\), until \(h_{2K-1}\). \[P_{c}(K,N,U)=h_{0}(K,N,U)=h_{0} \tag{22}\]  
\[h_{i}=\left\{ \begin{array}{ll}\sum\limits_{n_{i}=n_{i}^{min}}^{n_{max}^{max}} \left[P_{tx}(n_{i},R_{i})\cdot h_{i+1}\right]&\text{if }i\in[0,\,K)\\ \sum\limits_{n_{i}=n_{i}^{min}}^{n_{max}^{max}}\left[P_{tx}(n_{i},R_{i})\cdot(1 -P_{nrc}(n_{5}^{act},U))\cdot h_{i+1}\right]&\text{if }i\in[K,\,2\cdot K-1)\\ \sum\limits_{n_{i}=n_{i}^{min}}^{n_{max}^{max}}\left[P_{tx}(n_{i},R_{i})\cdot(1 -P_{nrc}(n_{5}^{act},U))\right]&\text{if }i=2\cdot K-1\end{array} \right. \tag{23}\]  
The parameters \(n_{i}^{act}\), \(|R_{i}|\), \(n_{i}^{max}\) and \(n_{i}^{min}\) in (23) correspond to those expressed in (18)-(21).