esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis Results of Mechanical Properties
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ecotype** & **Cultivar** & **M \(\pm\) SD** \\ \hline \multirow{6}{*}{CSG} & \multirow{6}{*}{Uiseong} & Plastic & 0.30 \(\pm\) 0.03 \\  & & Aluminum & 0.72 \(\pm\) 0.04 \\  & & Steel & 0.68 \(\pm\) 0.07 \\  & & Stainless steel & 0.42 \(\pm\) 0.02 \\ \cline{2-3}  & \multirow{6}{*}{Danyang} & Plastic & 0.28 \(\pm\) 0.03 \\  & & Aluminum & 0.77 \(\pm\) 0.02 \\  & & Steel & 0.54 \(\pm\) 0.01 \\  & & Stainless steel & 0.47 \(\pm\) 0.02 \\ \hline \multirow{6}{*}{WSG} & \multirow{6}{*}{Namdo} & Plastic & 0.30 \(\pm\) 0.02 \\  & & Aluminum & 0.53 \(\pm\) 0.09 \\ \cline{1-1}  & & Steel & 0.34 \(\pm\) 0.05 \\ \cline{1-1}  & & Stainless steel & 0.40 \(\pm\) 0.03 \\ \cline{1-1} \cline{2-3}  & \multirow{6}{*}{Daeseo} & Plastic & 0.33 \(\pm\) 0.03 \\ \cline{1-1}  & & Aluminum & 0.65 \(\pm\) 0.04 \\ \cline{1-1}  & & Steel & 0.41 \(\pm\) 0.04 \\ \cline{1-1}  & & Stainless steel & 0.32 \(\pm\) 0.03 \\ \hline \hline \end{tabular}  
* M—mean; SD—standard deviation.  
\end{table} 
Table 4: Results of garlic friction coefficient measurement with cultivar with different friction materials (\(n=30\)). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Division**} & \multirow{2}{*}{
\begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} } & \multirow{2}{*}{**Modulus of Elasticity**} & \multicolumn{2}{c}{**Allowable Compressive**} & \multirow{2}{*}{\(p\)} \\  & & & **M \(\pm\) SD** & & \\ \hline \multirow{6}{*}{Cultivar} & \multirow{2}{*}{Uiseong} & - & 15.6 \(\pm\) 5.6 a\({}^{\text{x}}\) & 204.3 \(\pm\) 34.9 a & \\ \cline{2-5}  & & Danyang & - & 18.5 \(\pm\) 4.3 a & 376.4 \(\pm\) 54.6 d & \\ \cline{2-5}  & & Namdo & - & 15.5 \(\pm\) 6.7 a & 215.5 \(\pm\) 39.8 a & \\ \cline{2-5}  & & Daeseo & - & 25.5 \(\pm\) 6.1 b & 271.1 \(\pm\) 41.2 b & \\ \hline \multirow{6}{*}{Cultivar} & \multirow{2}{*}{Uiseong} & 0.14 \(\pm\) 0.1 a\({}^{\text{y}}\) & - & - & \\ \cline{2-5}  & & Danyang & 0.16 \(\pm\) 0.0 a & - & - & \\ \cline{1-1} \cline{2-5}  & & Namdo & 0.15 \(\pm\) 0.0 a & - & - & \\ \cline{1-1} \cline{2-5}  & & Daeseo & 0.16 \(\pm\) 0.1 a & - & - & \\ \hline \hline \end{tabular}  
* M—mean; SD—standard deviation.  
\end{table} 
Table 3: Cultivar-specific mechanical properties: measurement results and ANOVA statistical analysis (\(n=30\)). In order to minimize the deviation ratio and uncollected ratio of garlic due to the slipping that occurs under gravity during operation, the angle between the collection part and ground surface should be less than 15, 27, 18, and 17\({}^{\circ}\) when plastic, aluminum, iron, and stainless steel are used, respectively.
# Novel Methodology for Condition Monitoring of
## 3 Methodology
### Data Acquisition  
A thermographic infrared camera (FLIR A310 [9 Hz], FLIR Systems Inc., Wilsonville, Oregon, USA) is used as the primary sensor for data acquisition; therefore, the continuous monitoring of the thermal behavior of the gearbox transmission system under evaluation is performed by capturing images. In this regard, as a result of the acquisition and capture of images, two different images are obtained: first, a pseudo-color infrared image related to the digitized intensity values, and second, a gray-scale infrared image, which is associated with the temperature values. Thus, for the proposed condition monitoring strategy, gray-scale infrared images are used to estimate the corresponding thermal matrix.  
Figure 1: Methodology proposed to identify gear tooth wear in a gearbox transmission system.
## 1 Introduction
Our approach formulates traffic signal optimization as a Markov Decision Process (MDP) and introduces a novel reward function that jointly optimizes average vehicle waiting time, network throughput, and fairness. The framework leverages Graph Attention Networks (GATs) to model spatial dependencies among intersections, while Long Short-Term Memory (LSTM) networks are used to capture temporal traffic dynamics. Furthermore, the proposed method adopts a hierarchical agent structure with both local and global coordination levels, enabling effective multi-agent cooperation in large-scale urban networks.
# Electrocatalytic Nanomaterials Improve Microbial Extracellular Electron Transfer: A Review
## 3 Electrocatalytic Nanomaterials for Promoting EET
### Metal Sulfides
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Nanomaterials** & **Applications** & **Performances** & **Reference** \\ \hline WC & MFC anode & A maximum power density of 3.26 W m\({}^{-2}\), chemical oxygen demand removal rate of 95.5\%, coulombic efficiency of 83.2\%, 2.14-fold, 1.22-fold, and 1.71-fold of that obtained by naked carbon cloth & [128] \\ Ti\({}_{3}\)C\({}_{2}\) MXene & MFC anode & A maximum power density of 3.74 W m\({}^{-2}\), 82.4\% higher than that of carbon cloth & [129] \\ Transition metal nitrides (TMNs) & & A 523\% increase in power density (548 mW m\({}^{-2}\)), an 83\% decrease in chemical oxygen demand, and a 161\% increase in electron transfer efficiency compared to those of plain carbon cloth & [130] \\ Fe\({}_{3}\)N-Fe\({}_{3}\)C & MFC anode & A maximum power density increased from 28.57 mW m\({}^{-2}\) to 58.86 mW m\({}^{-2}\) & [131] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._palustris_[121], CdS@_Clostridium autoethanogenum_[122], CdS@_Cupiravidus reactor_[123], CdS@_Sporomusa ovata_[124], and CdS@_Methanosarcina barkeri_[125] for the production of various chemicals. These works demonstrate a promising future for artificial photosynthetic systems to combat global warming and resource depletion.
# Virtual Career Fairs: Perspectives from Norwegian Recruiters and Exhibitors
## 2 Background of the Research
### Research Rationale and Questions  
The current article makes two contributions to the literature. First, the exhibitors' perspective has already been studied in relation to virtual trade shows and virtual marketing [14], but not to date with regard to virtual fairs. Second, most information on virtual fairs is available via press reports, university websites and various blogs rather than academic journals [24; 25]. In practice, the knowledge gap is still pronounced, particularly in terms of the role of recruitment and employer branding as related to virtual fairs. Therefore, the current article hopes to explore this gap in the literature by considering one example in Norway (Global Forums, introduced in next section).  
The goals of the current work were to explore the pros and cons of virtual fairs and their potential value to employers, specifically the recruiters and exhibitors as demonstrated in a case study of virtual fairs. In addition, we wanted to provide first insights into a hitherto relatively unknown area of research as several questions currently remain unanswered. We list several here that we aim to explore in our work.  
RQ1: What are the benefits of virtual career fairs?  
RQ2: What are the perceived challenges of virtual career fairs?  
RQ3: What do recruiters and exhibitors recommend as means to improve the branding and recruitment benefits of virtual fairs?
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 4 Algorithm Description
### Clustering
#### 4.1.2 Determination of Cluster Heads  
In a multi-hop sensor network, cluster heads near the sink find it necessary to forward data from other more distant ones and thus will consume energy faster. Meanwhile, cluster heads with higher densities will, in turn, consume energy faster than those with relatively lower densities. As a result, it is necessary to close the distance between cluster heads and the sink and reduce the size of denser cluster heads to balance the energy consumption of all cluster heads in the network. In addition, nodes that are very near each other should be stopped from becoming cluster heads. Thus, the competitive radius \(R_{m}\) of candidate cluster heads is designed as follows:  
\[R_{m}=(1-a_{2}\frac{D_{max}-D_{m0}}{D_{max}-D_{min}}-b_{2}\frac{\mathrm{Y}_{m}} {Y_{max}})R_{max}, \tag{13}\]  
where \(D_{min}\) and \(D_{max}\) denote the minimum and maximum distances from nodes to the sink in the network, respectively; \(D_{m0}\) represents the distance between the candidate cluster head \(h_{m}\) and the sink; \(R_{max}\) stands for the maximum communication radius of nodes; \(a_{2}\), \(b_{2}\) are adjustment factors, and \(a_{2}+b_{2}=1\).  
The candidate cluster head broadcasts a message that it has become a candidate cluster head to the nodes in the contention radius. If the candidate cluster head receives no messages from other ones, it means that no other candidate cluster heads are in this contention radius. If the candidate cluster head also receives messages from other nodes to become a candidate cluster head, it means that they are in the same contention radius. The set of final cluster heads can be selected through Equation (7). For the convenience of illustrating the process of the final cluster head campaign, its pseudo-code representation is given below (see Algorithm 1).
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 4 The Experiments
### Qualitative Analysis  
To qualitatively illustrate the SSTrack's superiority, we visualized tracking results on selected video sequences from LaSOT. These results were compared with those of mainstream tracking algorithms to intuitively assess the tracking performance of each algorithm. The study chose video sequences in complex environments, such as common scenarios involving Fast Motion (Tiger), Illumination Variation (Person), and Scale Variation (Truck), aiming to recreate realistic tracking scenes. Experimental results, as depicted in Figure 8, demonstrate that SSTrack can still achieve accurate target state estimation, yielding high-quality tracking results even in complex tracking environments.  
Figure 8: Visualization of tracking results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Feature Extraction** & & **Feature Fusion** & \\ \hline \multirow{2}{*}{**ResNet**} & **Swin** & **Spatial Scale** & **Exemplar** & **Suc (\%)**\(\uparrow\) \\  & **Transformer** & **Attention** & **Transformer** & \\ \hline \multirow{5}{*}{\(\surd\)} & \(\surd\) & \(\surd\) & 71.5 \\  & \(\surd\) & & 71.1 \\ \cline{1-1}  & \(\surd\) & & 70.4 \\ \cline{1-1} \cline{2-5}  & \(\surd\) & & 67.6 \\ \hline \hline \end{tabular} Note: “\(\uparrow\)” means that the larger the Suc, the better the tracking effect, “\(\surd\)” represents the use of this method.  
\end{table}
Table 3: Ablation experiments.  
1. Impact of Fast Motion: In the Tiger sequences, the tiger experiences significant position changes starting from frame 220 due to its rapid movement. The traditional KCF algorithm exhibits poor feature extraction capabilities, resulting in tracking drift. Siamese tracking algorithms like SiamFC focus on the local features of the target, often including background information within the tracking box. However, Transformer tracking algorithms, represented by the approach in this paper, can accurately track the target. The tracking box in this method closely adheres to the target, achieving superior tracking performance.
2. Impact of Scale Variation: In the Truck sequences, the truck undergoes scale changes from frame 1230 to 1322 due to a turn, introducing challenges for tracking. The proposed algorithm, incorporating spatial scale features, possesses richer semantic information during the tracking process, leading to more precise object localization than other algorithms.
3. Impact of Illumination Variation: In the Person sequences, the parachutist is continuously affected by background light from frame 91 to 314. This interference makes algorithms like TransT unable to locate the target's outline accurately. However, SSTrack consistently achieves accurate object tracking, demonstrating that the feature extraction and feature fusion sub-networks proposed in this paper enhance the expressive power of features.
## 3 Results and Discussion
### DSC Assessment
[49] who reported that fatty acid content may differ when different extraction methods are applied; however, the proportion of fatty acids remains stable or changes slightly, and it is a characteristic feature of oil originating from the same source. Taking into account fatty acid distribution in TAG structure, the tendency remains similar to percentage share of fatty acids. The proportions are specific for particular oil, without significant impact of the extraction method applied. The type of TAG molecules determines its nutritional properties. The sn-2 position of TAGs in plant oils is usually occupied by unsaturated fatty acids [50], which was also confirmed for the studied oils (Figure 5). However, chokeberry seed oil was surprisingly characterized by high contribution of palmitic acid (C16:0) in the sn-2 position of TAG. The fatty acid position in TAG may affect the oxidative stability of oils, and according to [PERSON] et al. [51], unsaturated fatty acids located in the sn-2 position contribute to improved oxidative stability of oil, which stands in agreement with our findings, as chokeberry seed oil was characterized by lower OIT than other oils. Figure 4: The percentage share of fatty acids in tested oils obtained in conventional extraction procedure (BB_C, CH_C, RB_C) and under optimized UAE conditions (BB_US, CH_US, RB_US), C16:0—palmitic acid, C18:0—stearic acid, C18:1—oleic acid, C18:2—linoleic fatty acid, C18:3—\(\alpha\)-linolenic acid, C20:0—arachidic acid.
## 4 Results and Discussion
### Influence of the Relative Position between Pipeline and Tunnel
#### 4.2.2 Pipeline Horizontal Distance  
Figure 9a-c successively show the cloud diagram of vertical deformation, horizontal deformation, stress and axial force of the pipeline when the pipeline buried depth was set to h = 1.5 m, h = 2.0 m and h = 2.5 m. In the three conditions, the maximum vertical deformation of the pipeline was 16.0, 16.5, and 17.3 mm, the maximum horizontal deformation of the pipeline was 4.73, 4.28, and 4.14 mm, the maximum stress of the pipeline was 1.09, 1.18, and 1.11 MPa, and the maximum axial force of the pipeline was 52.4, 54.0, and 54.8 kN. Both vertical maximum deformation and axial force of pipeline increased with the increase of buried depth. With the increase of the pipeline buried depth, the maximum horizontal deformation of the pipeline decreased slightly, and the change of the pipeline stress was not obvious.  
Figure 9: _Cont_.  
To clearly analyze the influence of the buried depth of the pipeline on the settlement of the pipeline, the settlement value of the top of pipeline 1 above the central axis of the tunnel was drawn, as shown in Figure 10. It can be seen from the vertical deformation of the pipeline under different buried depths that the settlement of the pipeline at the portal section was greater than that at the middle of the tunnel, and greater than that at the tunnel tail. Under the same conditions of other factors, the pipeline settlement caused by tunnel excavation increased with the increasing buried depth. The vertical deformation of the pipeline with the buried depth of 2.5 m increased by 1.45 mm at most compared with the pipeline with the buried depth of 1.5 m. The buried pipelines of the project were within 2.5 m below the ground. Therefore, special attention should be paid to the deformation of pipelines with large buried depth, that was, pipelines close to the tunnel.  
Figure 10: Vertical deformation of pipelines with different buried depths.  
Figure 9: Vertical deformation, horizontal deformation, stress and axial force of pipeline with different buried depths: (**a**) h = 1.5 m; (**b**) h = 2.0 m; (**c**) h = 2.5 m.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 5 Algorithmic Design
### Pre-Processing  
Individual pre-processing steps are performed for each method, to prepare the data in two different formats.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Application Challenges
#### 5.1.5 Deployment Cost  
Water distribution networks are large-scale and complex infrastructure. As such, using a CPS for autonomous monitoring of such systems requires the deployment of a wide range of sensors to provide measurements in real time. For this application, the associated cost of deployment of the sensing device is an issue of concern. The cost of deployment concerns the cost of installing, configuring, and maintaining the sensing devices and actuators, as well as the communication protocol used by a CPS. Assuming the cost of deploying a sensing device \(A\) is denoted by \(C_{A}\), then in a water distribution system where several of these components are required, the deployment cost (\(Cost_{D}\)) may be expressed as  
\[Cost_{D}=\sum_{k=1}^{N}C_{A_{k}} \tag{1}\]  
where \(N\) is used to represent the number of sensing devices needed for the specific application. Depending on the requirement of each application, Equation (1) shows that as the number of sensing devices required to provide continuous measurement increases, the associated cost of deployment also increases. This increases the cost of deploying the cyber-physical systems for water supply system applications. Thus, as the application requirements improve requiring some other sensing devices to be developed to meet the application requirements, the overall cost of deploying such a system increases.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## Appendix A Architecture and Design Strategies of the Main Blocks  
The custom deep learning-based classifier utilized in our study consists of two main blocks: a residual block [22] and an inception block [23]. The architecture of these blocks is shown in Figure A1.  
The design strategy of the residual block is to handle the degradation problem as the network goes deeper [22]. The residual block contains skip connections between adjacent convolutional layers and helps mitigate the vanishing gradient problem. The goal of the residual network is to allow flexible training of the features as the network depth increases.  
The design strategy of the inception block involves calculating features with different filter sizes in the same layer [23]. The inception block contains parallel convolutional layers with different filter sizes. The results for each layer are concatenated in the filter axis and pass through the next layer. These parallel connections can extract features with multiple receptive field sizes, which are useful when the features vary in location and size.  
The spectrogram contains the physical measurements of the SF signals. It represents the power densities of the SF signals along the time-frequency axes. To train these two-dimensional density behaviors of the SF signals, we aimed to filter the spectrogram on multiple filter scales in the temporal and spatial domains by applying inception blocks.
## 3 Profile Fitting and Geometric Modeling
### Coordinate System Transformation and Curve Fitting
#### 3.2.2 Least Squares Curve Fitting  
Least squares is a common mathematical optimization technique that finds the best functional match of the data by minimizing the sum of squares of the errors, i.e., by requiring that the sum of squares \(e\) of the deviations \(\delta_{i}\) of the curve \(y_{i}\) and the approximate curve \(\varphi(x_{i})\) be minimized, which can be expressed as  
\[e=\min\sum\limits_{i=0}^{m}\delta_{i}^{2}=\sum\limits_{i=0}^{m}(y_{i}-\varphi( x_{i}))\ ^{2}.\]  
The studies in the literature, [9; 11], proved that polynomial fitting is the best fitting scheme for weld shapes. Polynomial fit is one of the most commonly used types of curves for the least squares method. When using a polynomial least squares fit, find the polynomial \(\varphi(x)=\sum\limits_{k=0}^{n}a_{k}x^{k}\), so that  
\[e=\sum\limits_{i=0}^{m}(y_{i}-\varphi(x_{i}))\ ^{2}=\sum\limits_{i=0}^{m}(y_{i}- \sum\limits_{k=0}^{n}a_{k}x^{k})\ ^{2}=e_{min}.\]  
The \(\varphi(x)\) that satisfies the equation is called the least squares fitting polynomial, and the corresponding curve is the fitting curve. When the number of polynomials is too high, the phenomenon of overfitting will occur to facilitate the geometric relationship through the curve function to find the coordinates of the measurement point here so that the highest number of polynomials is 2, using MATLAB programming for the least squares polynomial fitting of the characteristic points on the weld profile; the fitting results are shown in Table 3.  
R-square is the coefficient of determination of the curve fit, which characterizes the quality of the quadratic polynomial fit, and the closer the value is to 1, the better the fit is. R-square is the curve fitting coefficient, which characterizes the quality of the quadratic polynomial fit, and the closer the value is to 1, the better the fit is. The fitting coefficients of the above four curves are all greater than 94%, which is a good fit. In addition, 20 groups of key point data were individually selected, their coefficients of determination in the least squares fitting were calculated, and R-square curves were plotted, as shown in Figure 8. The average R-square values of the fitted curves were all greater than 94%, indicating that the method can be used to fit the weld profile.  
Figure 7: Diagram of coordinate system transformation: (**a**) image coordinate system; (**b**) weld coordinate system.
## 3 Results and Discussion
### Implications for Design  
The various aspects that are presented here are now related to process design in rather general terms. In order for the SID technology to work, the concentration profile needs to be established fast and within reasonable distance after entering the system. For all of the conditions investigated here (10-50% particles, channel heights 50 and 100 \(\upmu\)m), this requirement is met. The transmissions that are achieved in our systems were for monodisperse particles. We expect that if applied for  
Figure 11: Relative development length at the center of the channel (95%) between the recover section and the entrance section versus the volume fraction extracted from the inlet for a bulk particle volume fraction of 10% (**a**) and 30% (**b**). Each graph shows two different lines for the different channel heights: 50 \(\upmu\)m (black) and 100 \(\upmu\)m (grey).  
Figure 12: Relative particle volume fraction versus the relative channel height at a fully developed profile before the porous region for two different bulk particle volume fractions: 10% (black) and 30% (grey) and two different channel heights: 50 \(\upmu\)m (solid line) and 100 \(\upmu\)m (dashed line). Vertical lines indicate the amount of volume extracted, from left to right: 2.5%, 5%, 7.5%, and 10%.  
fractionation of bi- or polydisperse dispersions the system will become even more interesting. Last but not least, the concentration profile re-established fast after the pore, and this indicates that in a full process, multiple pores can be placed within realistic distances from each other. Therewith, the overall productivity of the process can be enhanced considerably, as we hope to establish in future work that is directed toward the simulation of multiple pores leading to detailed design of porous regions.