esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Rapid Fabrication of Conformal Liquid-Metal Patterns
## 4 Discussion  
This work improves upon previous efforts in atomizing LM. Although spraying LM is a quick procedure that consumes small amounts of LM to create conductive patterns, the use of micromachined shadow masks [14] to pattern the LM increases the cost and time of fabrication. The techniques demonstrated here address these issues using polyimide tape and PUA masks. For both methods described in this paper, the cost of materials is less than $1 to implement a sub-millimeter mask, and the fabrication time is less than 5 min. A commercially available craft cutter (Silhouette, approximately $110) was used to cut the polyimide tape to create masks, whilst a commercially available UV light (approximately $30) was used to cure the PUA for the UV-curable masks.  
A problem that has been associated with the spraying of LM was the smearing of LM below the edges of the stencil masks [14; 15; 16]. This issue has been resolved by using masks made of polyimide tape and PUA membranes, which conformally adhere to the surface of the substrate. The minimum feature sizes reported in previous efforts were 500 um[14], 200 um[15], and 234 um[16], produced using a micromachined mask, screen mesh, and a vinyl mask, respectively. This paper demonstrated a feature size of 150 um using the PUA mask, and a line width below 50 um using the polyimide mask. The reported minimum spacing between patterns in a previous study [16] was 75 um, compared to 34 um in this paper, obtained by scraping the sprayed LM with a needle. Introducing gaps at this resolution can be useful for terahertz circuits, although this is beyond the scope of this work due to limitations on the available equipment. Moreover, the scraping technique can be automated by mounting the needle to the printhead of a 3D printer, as illustrated in Figure 11. In addition to the transmission line and antenna demonstrated here, this method of spraying LM was used to create LM contacts for a graphene transistor [27].
# Animal Sound Classification Using Dissimilarity Spaces
## 4 Clustering
### K-Medoids  
K-medoids is a clustering technique that follows the same general logic behind k-means but differs in the specific way it partitions data points into clusters: K-medoids minimizes the sum of distances between a given pattern and the center of that pattern's cluster. In short, the center of a cluster in K-Means ends up being the centroid of the cluster, but the center in K-Medoids is a member (medoid) of the cluster. In other words, a medoid is that member in a cluster whose sum of distances from all other members is minimal.  
The standard K-medoids algorithm involves three steps:  
1. Step one is a build-step where each \(k\) cluster is associated with a potential medoid. There are many ways to select the first medoid; the standard MATLAB's implementation does this employing the k-means++ heuristic.
2. Step two is a swap-step where each point in a cluster is tested as a potential medoid by checking whether the sum of the within-cluster distances is smaller when using that point as the medoid. Every point is then assigned to the cluster with the closest medoid.
3. The last step repeats previous steps until convergence.
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 2 Preliminary Understanding of VANETs
### Security Requirements in Vehicular Networks  
Security requirement specifies the attributes that should exist in any security solution. After observing various security aspects of VANET, services which are required by VANETs are discussed below [37; 38].  
_Confidentiality_: It is the process of making information visible only to those persons for whom this information is generated, and for other people it remains hidden. Sensitive information always requires confidentiality.  
Integrity:It means receiving the same information that was sent by the sender. Information sent by the sender may be hacked in between, and may be re-transmitted after the alteration.  
_Availability:_Requested services and resources should be always available to legitimate users whenever they require it.  
_Non-repudiation:_ It provides a kind of surety that data received by the receiver is sent by a genuine user and initiated from a protected source. It also makes sure that the integrity of data is also maintained.  
_Authentication:_ It ensures only authentic users are accessing that information or service, and only that section that belongs to them or for which they are authorized.  
_Authorization:_ It defines the kind and extent of information accessible by a particular user. In this, it is decided how much information may be accessed by the user, for what duration, and what other services the user can access.  
_Accounting:_ It means observing user activities and maintaining the record of it, like active time for which the user has used resources and services, and other relevant statistics. It is implemented using log files.  
_ID traceability:_ In this process, real identities of vehicles are identified, which locates the real source of the message [39]. In normal circumstances, these IDs are used to refer to the original sender and receiver.  
_Revocability:_ When any vehicle misbehaves in the network, central authority may revoke its certification and de-register it from the network. In this process, malicious nodes are removed from the network [40].  
_Liability identification:_ It is based on the non-repudiation service, which makes drivers liable for the mistakes they have made (if any).  
_Real-time Constraints:_ Vehicular information should always be delivered in real-time for better performance. Any kind of delay may lead to serious consequences [41].
# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## 4 Validation Activities at Reference Sites  
As shown in previously reviewed studies, a key component of a validation framework has been substantial short-term and large-scale intensive field campaigns. In addition, in our VS, to experimentally evaluate the validity of Component 1 in Figure 1 requires considerable validation efforts, with various measurements jointly collected through multi-source and multi-platform venues. In this section, we focus on site-based validation activities (presented as Component 2 in Figure 1) in a twofold manner: validation efforts in HiWATER and validation experiments over other places.
## 3 Nonlinear Mechanical Properties for the PRC with Interfacial Debonding
### Intrinsic Microstructure Parameters
#### 3.2.2 Particle Shape Influences on the Mechanical Properties of the PRC  
In order to investigate particle shape influences on mechanical properties of the PRC, the ellipsoid particle is introduced. The volume fraction of SiC particles is equal to 15%. For the ellipsoid particles, two different semi-major axis ratios denoted by \(a/b\) are 1.5 and 1.2, respectively. While the semi-minor ratios denoted by \(c/b\) are equal to 1.0. Here, the interfacial debonding influences on mechanical responses along with semi-major axis are investigated. Figure 3 shows that the mechanical properties  
Figure 2: Stress-strain responses of composites with different particle volume fractions.  
of the PRC are also dependent on interfacial properties when the particle shapes are considered. For comparison, the numerical results of the spherical PRC (the ratio of _a:b:c_ = 1:1:1) as shown in Figure 2 are also plotted in Figure 3. It is difficult for researchers to discern the difference in the linear elastic stage. However, with the strain increase to the inelastic region, different effects of semi-major axis ratios on plastic deformation can be easily discerned. In detail, the higher semi-major axis ratios tend to stiffen the constitutive equation of composites without interfacial debonding. It is indicated that the higher semi-major axis ratio can improve the ability to resist the plastic deformation. In contrast, an opposite conclusion can be acquired for the PRC with interfacial debonding. This means that the stiffness of the PRC with semi-major axis ratio 1.5 is lower than the semi-major axis ratio 1.2 when the true strain is greater than 3.2%. This is due to the increase of the semi-major axis ratio, similar to the continuous fiber-reinforced composites [38; 39]. Once the interfacial debonding occurs between particles and the matrix, less matrix region will undertake the overall stress. Compared with the PRC with the semi-major axis ratios of 1.5 and 1.2, the spherical PRC provides nearly a 7.9% and 6.7% increase when the true strain is equal to 0.04. It can be seen that the interfacial cohesive properties play an important role in studying mechanical behaviors when the interfacial stress reaches the cohesive strength.  
This is due to the stress concentration between particles and matrix. The lower strain rate provides enough time to relax stress concentration, which leads to the higher inelastic strain.
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
## 5 Results
### ICI Unaware Iterative Receiver  
#### 5.1.1 Simulation Results  
The simulation is based on a 2 \(\times\) 2 MIMO-OFDM system and Bellhop is used to generate the impulse responses of the four-channels. The simulation parameters used are given in Table 1.  
The proposed iterative receiver is simulated and compared with the non-iterative receiver as well as with the basic turbo iterative receiver with soft information feedback, where the channel  
\begin{table}
\begin{tabular}{c c c} \hline
**Serial \#** & **Parameter** & **Value** \\ \hline
01 & Sampling frequency & 48 kHz \\ \hline
02 & Communication bandwidth & 6 kHz-12 kHz \\ \hline
03 & Total number of subcarriers & 1024 \\ \hline
04 & Number of data carriers per transmitter & 726 \\ \hline
05 & Number of pilots at each transmitter & 250 \\ \hline
06 & Number of Null carriers per transmitter & 48 \\ \hline
07 & OFDM symbol period & 170.67 ms \\ \hline
08 & Cyclic prefix length & 40 ms \\ \hline
09 & Spectrum utilization & 1.15 b/s/Hz \\ \hline
10 & Communication rate & 6.89 kb/s \\ \hline \end{tabular}
\end{table}
Table 1: MIMO-OFDM simulation parameters.  
estimation is kept outside the iteration loop. Channel estimation is performed after the preprocessing of the data and then the information is iterated between the equalizer and decoder while the channel estimation does not participate in the iterative process. Furthermore, the proposed receiver is also compared with the standard Alamouti MIMO receiver as a reference, using the same data rate and same channel. Figure 4 shows the comparison of BER curves of the standard Alamouti scheme, non-iterative, basic turbo iterative, and the proposed iterative receivers. Figure 4a shows that even the non-iterative MIMO system performance is better than the Alamouti MIMO system performance using the same channel and data rate. The reason is that in the Alamouti design, the transmitted signals from both the transducers are not entirely different, rather one is the complex conjugate of the other, while in the MIMO system, the transmitted signals from both the transmitters are entirely different and independent. Figure 4a further shows that the proposed turbo iterative receiver with channel estimation included in the loop performs better than the basic turbo iterative receiver without the channel estimation in the loop and there is an obvious difference in the performance of the non-iterative receiver. Figure 4b shows that the performance of the proposed iterative receiver improves with each iteration and it gets stable after four iterations.
## 1 Introduction
It holds broad application potential in fields such as intelligent traffic surveillance, military reconnaissance, and remote sensing monitoring. By mitigating fog's adverse impact on machine vision systems, the method significantly enhances image contrast under hazy conditions. The remainder of this paper is structured as follows: Section 2 details fundamental theories, including the atmospheric scattering model, polarization optics, and transmittance estimation algorithms. Section 3 presents the experimental workflow, encompassing data preprocessing through to final dehazing results. Section 4 provides both qualitative and quantitative analysis of the proposed dehazing algorithm. Section 5 concludes with a discussion of the method's advantages and limitations.
###### Abstract  
There has been an increase in the use of antibiotics by the aquaculture industry in marine aquaculture for the prevention of diseases in fish. Antibiotics in the water discharged into the sea without treatment can cause disturbances to the marine ecosystem. Therefore, there is a need for research on how the removal of antibiotics used in aquaculture can be achieved. In this study, the removal of two types of antibiotics (florfenicol, FF, and oxolinic acid, OA) used in the aquaculture industry, by ozonation, was evaluated. Currently, there is a lack of research studies on FF and OA removal from seawater by ozonation. Seawater ozonation shows a significantly different oxidation mechanism as compared to that of freshwater. The high amount of Br\({}^{-}\) in seawater (60 mg/L) allows for a rapid reaction with ozone to produce bromine (HOBr/OBr\({}^{-}\)) at a rate of 160 M\({}^{-1}\)s\({}^{-1}\). To predict the removal efficiency of antibiotics by ozone and bromine, the species-specific rate constants for the reaction of FF and OA with ozone and bromine were determined. The predicted removal efficiencies of FF and OA using measured rate constants were verified by the ozonation process in water containing bromide ions in similar concentrations as in seawater. The result for FF indicated less than 10% removal during 20 min, with the rate constants of FF with ozone and bromine being 3.2 M\({}^{-1}\)s\({}^{-1}\) and 3.5 M\({}^{-1}\)s\({}^{-1}\), respectively. However, the removal of OA using ozonation was approximately 99% or higher within 90 s. In the presence of bromide ions, approximately 60% of OA was removed by trace ozone within 15 s, and approximately 30% of OA was removed by the generated bromine after 15 s. Comparing the removability of FF and OA used in aquaculture by ozone, it was observed that FF was more difficult to remove because of its low reaction rate constant. Meanwhile, the reaction rates of OA with ozone and bromine were \(2.4\times 10^{3}\) M\({}^{-1}\)s\({}^{-1}\) and \(4.0\times 10^{2}\) M\({}^{-1}\)s\({}^{-1}\), respectively. At the beginning of the reaction, OA was removed by the trace ozone. Subsequently, OA was removed by the generated bromine after the ozone was decomposed.  
ozone; bromine; seawater; florfenicol; oxolinic acid; species-specific rate constant Article Highlights  
1. It is necessary to prevent antibiotics used in marine aquaculture from entering the oceans and harming the aquatic environment.  
2. In seawater ozonation, ozone reacts rapidly with bromide ion to produce bromine. The primary oxidants of seawater ozonation are trace ozone and stable bromine.  
3. Ozone and bromine were recommended for FF and OA removal in seawater.
## 2 Materials and Methods
### Combined Ceemdan-Svd-Te  
The vibration signal of a hydropower house is a kind of nonlinear and non-stationary signal, including high-frequency white noise and electromagnetic noise. The CEEMDAN algorithm can effectively extract the required feature signal according to frequency, but because of the interference of strong background noise, a lot of high-frequency white noise cannot be separated accurately. To ensure the accuracy of feature-information extraction, the vibration signal needs to be further processed. As a classical denoising method, the SVD algorithm has a strong ability to filter the high-frequency random noise in the signal and extract the signal-feature information. The transfer-entropy algorithm can effectively analyze the correlation between signals, identify the source of information transmission and clarify the direction of vibration-signal transmission.  
Based on the working characteristics of the hydropower house and the advantages of each method, the CEEMDAN-SVD-TE method is introduced in this paper. The flow chart of the CEEMDAN-SVD-TE is presented in Figure 1, and the principal procedures can be summarized as follows:  
1. The collected vibration signal is decomposed to IMFs from high frequency to low frequency by CEEMDAN processing, and the IMF component can represent the vibration characteristics of the structure.
2. The optimal singular value order \(r\) is selected according to the singular entropy increment theory, and the high-frequency noise in the IMF signal is filtered out utilizing the singular value decomposition algorithm.  
Figure 1: Flow chart of the CEEMDAN-SVD-TE method.  
3. The IMF component is reconstructed by CEEMDAN-SVD joint filtering, and the target feature signal is obtained.
4. The reconstructed feature signals are analyzed for transfer effects with the help of the transfer-entropy algorithm to identify the direction of information transmission between signals.
## 4 Results and Discussion
### Feature Importance Analysis  
Through the proposed IRI prediction model based on XGBoost, the ranking of key factors is shown in Figure 8.  
As can be seen in Figure 8, there are differences in the importance of each independent variable to pavement roughness. The feature importance values of the equivalent number of actions of a standard axle load, number of freeze-thaw cycles, annual average temperature, freezing index, annual precipitation and road section maintenance times were 0.24494, 0.18916, 0.18911, 0.16422, 0.11301 and 0.09957, respectively. The results showed that the equivalent number of actions of a standard axle load, number of freeze-thaw cycles and annual average temperature have a significant effect on pavement roughness. The freezing index and annual average temperature are next in importance, and road section maintenance times have the least impact. And the differences in Table 7 and Figure 8 are expected due to the contrasting purposes of the two analysis methods. Feature selection aims to reduce dimensionality and identify the key influencing factors, whereas feature importance in the XGBoost model reflects both direct contributions and interactions within the fitted model structure.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**ML Models**} & \multicolumn{4}{c}{**Training**} & \multicolumn{4}{c}{**Test**} \\ \cline{2-9}  & **MAE** & **MAPE** & **RMSE** & **\(\mathbf{R^{2}}\)** & **MAE** & **MAPE** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline RF & 0.085 & 0.074 & 0.122 & 0.932 & 0.194 & 0.163 & 0.264 & 0.736 \\ GBDT & 0.020 & 0.018 & 0.029 & 0.956 & 0.187 & 0.160 & 0.254 & 0.756 \\ XGBoost & 0.030 & 0.033 & 0.042 & 0.987 & 0.186 & 0.159 & 0.243 & 0.778 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance indices for training and testing data.  
Figure 7: Predicted versus observed IRI: RF, GBDT and XGBoost.
## 3 Methods
### Performance Evaluation of Reasoning
#### 3.5.2 Accuracy Evaluation  
The accuracy evaluation of the risk performance reasoning is expressed using the root-mean-square error (RMSE) and modified Nash-Sutcliffe model efficiency coefficient (MNSE) [36], defined as follows:  
\[\text{RMSE = \sqrt{\frac{1}{T}\sum_{t\ =\ 1}^{T}\left(O(t)-F(t)\right)^{2}}, \tag{9}\]  
\[\text{MNSE = 1-\frac{\sum_{t\ =\ 1}^{T}(O(t)-F(t))^{2}}{\sum_{t\ =\ 1}^{T} \left(O(t)-\overline{O}\right)^{2}}, \tag{10}\]  
where \(O(t)\) and \(\overline{O}\) are the observations and the mean values of the observations, respectively; \(F(t)\) stands for the reasoned values, while \(T\) is the length of series to be reasoned.  
RMSE is mainly used to represent the standard deviation of the differences between observations and forecasts. The range of the RMSE lies between 0 and infinity. A value of 0 for the RMSE indicates that the forecasts are as accurate as the mean of the observations, while bigger values show that the reasoning model is worse than the observed expectation.  
MNSE is used to measure the evaluation accuracy and is defined as one minus the sum of the absolute squared differences between the observed and reasoned values divided by the sum of the absolute squared differences between the observations and observed expectation. The range of the MNSE lies between negative infinity and 1.0 (perfect match). A value of 0 for MNSE denotes that the value reasoned is the same as the observed expectation, while negative values for MNSE show that the reasoning model is worse fitted to the observation or explains relationships poorly compared to the model defined using the mean value of observations.
# Prefetching Method for Low-Latency Web AR in the WMN Edge Server
## 2 Background Theory
### Prefetching  
Caching and prefetching methods have been proposed for the prediction of data usage patterns and to fetch data from a location close to the user in advance. The caching method sends cached data through a proxy server; this saves frequently requested data closer to the user, and it aims to reduce bandwidth consumption, network congestion, and traffic [15]. This caching method causes a bottleneck in the origin server with an increase in the number of users. This method becomes less efficient because of the limited system resources of the cache server. Prefetching was proposed to solve this caching problem [35]. The prefetching method solves bottlenecks and traffic jams, and it allows the faster transmission of data. A proxy can effectively handle more user requests than caching, and it can help reduce the load on the origin server. However, one disadvantage of the prefetching method is that prefetched data may not be requested by the user. To solve this problem, a high-accuracy prediction model needs to be used [36]. [PERSON] et al. [35] conducted a study on indices-related prediction, resource usage, and latency evaluation related to prefetching.
## 2 Simulation Methodology  
### Basic Theory of the Smoothed Particle Hydrodynamics Method  
#### 2.1.1 Control Equations  
Smoothed particle hydrodynamics (SPH) is a mesh-free particle method which discretizes a computational domain into a number of particles that can interact with each other. The particles include mass, density, energy, and other physical quantities [34]. For solid media, the control equations of the SPH method include the conservation of mass equation, conservation of momentum equation, conservation of energy equation, and equation of motion as follows:  
\[\frac{d\rho}{dt}=-\rho\frac{\partial v^{\alpha}}{\partial s^{\alpha}} \tag{1}\]  
\[\frac{dv^{\alpha}}{dt}=\frac{1}{\rho}\frac{\partial\sigma^{\alpha\beta}}{ \partial s^{\beta}}+g^{\alpha} \tag{2}\]  
\[\frac{dU}{dt}=\frac{\sigma^{\alpha\beta}}{\rho}\frac{\partial\sigma^{\alpha}} {\partial s^{\beta}} \tag{3}\]  
\[\frac{ds^{\alpha}}{dt}=v^{\alpha} \tag{4}\]where \(\alpha\) and \(\beta\) are the Cartesian components in the directions \(x\) and \(y\), respectively; \(\rho\), \(v\), and \(s\) stand for the density, velocity, and spatial coordinates of particles, respectively; \(\sigma^{\alpha\beta}\) is the total stress tensor component; \(g\) is the gravitational acceleration; \(U\) is the internal energy per unit of mass; and \(t\) represents the time. The total stress tensor component \(\sigma^{\alpha\beta}\) can also be the sum of the hydrostatic pressure \(T\) and the stress deviation \(S^{\alpha\beta}\):  
\[\sigma^{\alpha\beta}=-T\delta^{\alpha\beta}+S^{\alpha\beta} \tag{5}\]  
where \(\delta^{\alpha\beta}\) is the Dirichlet function, satisfying  
\[\delta^{\alpha\beta}=\begin{cases}0&\alpha\
eq\beta\\ 1&\alpha=\beta\end{cases} \tag{6}\]