esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experimental Results and Analysis
#### 4.3.3 Data Enhanced Ablation Experiments  
To verify the effectiveness of data augmentation in enhancing the model's generalization capabilities and performance, we compared the impact of various data augmentation strategies on the performance of object detection models. The results are shown in Table 3.  
Based on the experimental results, we observed that applying data augmentation strategies on top of the baseline model led to improved model performance. Among these strategies, the flipping approach contributed most significantly to the performance enhancement, increasing accuracy by 2.4 percentage points. Contrast enhancement, brightness adjustment, and rotation strategies also had a positive impact on model performance, raising it by 2.5, 3.2, and 2.1 percentage points, respectively. The noise addition strategy had a relatively minor effect on performance improvement, with an increase of only 1 percentage point.  
Experimental results indicate that employing data augmentation strategies can effectively enhance the generalization capabilities and performance of object detection models. In practical applications, appropriate data augmentation strategies can be selected according to specific scenarios and requirements.
# Novel Microfluidics Device for Rapid Antibiotics Susceptibility Screening
## 3 Flow Modeling  
As already discussed, in order to choose the optimal length of the narrow channel, connecting the chambers with the main flow of the cultivation channels, we carried out finite-volume-modeling of the experimental flow conditions, utilizing Ansys Fluent 2021R1. Since the fabrication of the channels was constrained due to the resolution of the utilized printer (described in the section below), the width of the narrow channel was fixed at 80 \(\upmu\)m. The main question therefore remained: how long should the narrow channel be in order to obtain optimal flow conditions, and how long would it take for the fluid from the main channels to reach the inside of the traps?  
Since the flow going inside the chambers is mostly driven by diffusion phenomena, we utilized the Species Transport Model [27], which predicts the local mass fraction of each species through the solution of a convection-diffusion equation. The molecular  
Figure 1: (**a**) Schematic of the utilized microfluidic device; (**b**) zoomed view on the cell chambers with their dimensions.  
concentration differences of the utilized working fluids (Lysogeny broth (LB) with or without Chloramphenicol, described in the sections below) are negligibly small compared to water; hence, pure water was used for the simulations. The data for the self-diffusion coefficient of water were taken from [28] at D = 2.29 \(\upmu\)m\({}^{2}\)/ms. The results for the four narrow channel lengths are shown in Figure 2.  
From Figure 2, it is clear that for all four of the analyzed geometries, the fluid penetrating the chambers from the cultivation channels manages to infuse the whole trap, leaving no zones without the supply of fresh medium. However, due to the constraints of fabrication process precision, we were forced to utilize the second smallest chamber, which was expected to provide capacity for relatively fast bacterial growth. Last but not least, the simulation results clearly demonstrate that the time required for equilibration between the concentrations of small molecules, such as Chloramphenicol (CA), in our preferred chip geometry and the supply channel (16 min) is much shorter than the waiting period before the beginning of the measured observations (4 h).
## 3 Factors Affecting the Properties of Mechanically Alloyed Nanocomposites
### Milling Time  
Time duration of milling is a vital parameter in the ball milling process in the phase formation, microstructural distribution, and homogenous distribution of components or ingredients. It also influences the physicochemical-mechanical properties of the composites. The size of the nanomaterial (reinforcing material) reduces with an increase in milling time. The morphological and particle size transformation of nanocomposite material as a function of time of milling is depicted in Table 1. Plastic deformation occurred for Mg\({}_{20}\)SiC with a prolonged grinding process, resulting in crystalline defects and dislocation defects [73]. The average size of the crystallites for the Mg material decreased and the powder became finer and equiaxed with the increased milling time. Furthermore, no phase change occurred during milling [71].  
Figure 5: (**a**) Microhardness vs. milling time and (**b**) crystallite size vs. milling time for Cu-10 wt.% Zn alloy cryomilled for 4 h at 77 K followed by 6 h room temperature milling. “Reprinted/adapted with permission from Ref. [67]. 2011, Springer Nature Publications”. More details on “Copyright and Licensing are available via the following link: [[http://Creativecommons.org/licenses/by/4.0/](http://Creativecommons.org/licenses/by/4.0/)]([http://Creativecommons.org/licenses/by/4.0/](http://Creativecommons.org/licenses/by/4.0/)) (accessed on 29 August 2022).  
With the progression of milling time, graphene transforms from platelet to particulate shape. The reinforced material becomes protected since the particle is embedded on the interface of the matrix and, hence, there is no further deformation introduced onto the nanomaterial. However, this significantly aided the uniform distribution of nanomaterials in the matrix phase. As the milling time increases, there is an enhancement in the mechanical strength and flexural strength. With shorter milling time, there is a chance of agglomeration as the time increases the chance of agglomeration weakens, and, therefore, improved mechanical property could be observed [74].  
Studies suggested that the magnetic properties also increase with the progression of milling time. As shown in Figure 6, Co/Al2O3 nanocomposite initially showed feeble ferromagnetism, while after increasing the milling time to 12 h, it revealed a strong saturation magnetization of 118 emu/g [75].
## 4 Method to Extract Foundation Settlement by Satellite Monitoring
This tool is based on artificial neural networks (ANNs) and makes use of the precise leveling data. For the bridge tower in the process of construction, the tower mass, GNSS signals, precise leveling data, and observation time are regarded as the input layer by BP neural network to establish the non-linear mapping relationship between the model parameters a' and b' obtained through GNSS-based parameter fitting and the parameters a and b obtained through the precise leveling measurement, as shown in Figure 5. For the bridge tower in the operation process, there is generally no need to implement regular precise leveling measurement. As the above consequence, the measured data can be mapped to the revised foundation settlement data according to the GNSS signal and the non-linear mapping relationship obtained through training in the process of construction. Then, the final settlement can be predicted according to the selected settlement model. The flow chart representing the method in this paper is shown in Figure 6.  
The major steps of the proposed method are outlined as follows:  
1. The poor values can be eliminated according to the median absolute difference of three times, and the data loss induced by signal masking can be compensated by linear interpolation, to obtain the pre-processed signal. 2. The GNSS signal denoised by SWT and EMD. 3. The hyperbolic model is applied to fitting the parameters of GNSS signal and the settlement data obtained by precise leveling, respectively. In order to correct the foundation settlement of bridge tower, two three-layer back-propagation neural network structures were used for mapping from the \(a\)'and \(b\)'fitting by GNSS signal to the a and b fitting by the precise leveling.
## 4 Use Cases and Application Scenarios of Internet of Emergency Services
### Industrial Accident
This scenario demonstrates how the IoES can facilitate an effective emergency response to an industrial accident, providing emergency responders with real-time data, analytical tools, and communication capabilities to coordinate their efforts and mitigate the impact of the disaster. The Industrial Accident scenario in the IoES highlights the importance of an effective emergency response in industrial and workplace environments. By leveraging the latest technologies and tools, the IoES can enable rapid communication, coordination, and response, helping to mitigate the impact of incidents on both personnel and the environment.
# Buffer with N Policy and Active Management
## 5 Numeric Examples
### Combined Impact of Active Management during Both Phases  
In this section, we test the most powerful option for controlling system performance: active management in both phases, with different parameterizations for each phase. Specifically, we use the dropping functions (50) and (49) in the energy-saving and transmission phases, respectively. This allows us to manipulate parameters \(a\) and \(b\) in (50) and (49), adjusting the aggressiveness of each function.  
Figure 7 depicts the system's energy efficiency versus parameters \(a\) and \(b\). The achievable range of energy efficiency is now significantly wider, from \(E=0.50\), achieved with \((a,b)=(\infty,\infty)\), up to \(E=0.71\), achieved with \((a,b)=(0,0)\). For example, to set \(E=0.55\), there are now many possible combinations of \(a\) and \(b\) that satisfy this requirement. The red line in Figure 7 illustrates such combinations of \(a\) and \(b\).  
In Figure 8, the average queue length versus \(a\) and \(b\) is depicted. Because of the complex dependence on \(b\), the minimum value is not reached on the boundary. The shortest average queue size is obtained for \((a,b)=(\infty,0.39)\), while the longest is obtained for \((a,b)=(0,\infty)\).  
Figure 6: Energy efficiency vs. parameter \(a\) of active management during the energy-saving phase. The color changes with \(a\) as in Figure 4.  
Figure 7: Energy efficiency vs. parameters \(a\) and \(b\) of active management during both phases of operation. The red line corresponds to points \((a,b)\) which provide \(E=0.55\).  
Now, by manipulating \(a\) and \(b\), we can seek to achieve a tradeoff between several performance characteristics. For instance, we may aim to minimize the average queue size while maximizing the throughput simultaneously. To design a cost function for this operation, we need to decide which of the two goals is more important and by how much. If, for example, we value minimizing the queue size three times more than maximizing the throughput, we can consider using the following cost function:  
\[c_{1}=\frac{\overline{X}^{3}}{\gamma}. \tag{51}\]  
In Figure 9, the cost (51) versus \(a\) and \(b\) is depicted. The optimal setting (lowest cost) is therefore \((a,b)=(\infty,2.18)\), which corresponds to passive management in the energy-saving phase and dropping function \(d_{2}(n,2.18)\) in the transmission phase.  
Figure 8: Average queue size vs. parameters \(a\) and \(b\) of active management during both phases of operation.  
Figure 9: Cost \(c_{1}\) vs. parameters \(a\) and \(b\) of active management during both phases of operation.  
Alternatively, if we value maximizing the throughput three times more than minimizing the queue size, we can perhaps use the following cost function:  
\[c_{2}=\frac{\overline{X}}{\gamma^{3}}. \tag{52}\]  
In Figure 10, the cost (52) versus \(a\) and \(b\) is shown. The optimal setting is, therefore, \((a,b)=(\infty,\infty)\), i.e., \(d_{1}(n)=d_{2}(n)=d^{*}(n)\).
## 3 Results and Discussion
### Transient Performance under High Heat Flux Millisecond Pulses
#### 3.2.3 The Effects of the Heat Pluses' Peak Heat Flux and Duty Cycle
With the same filling area and thickness (0.05 mm) of the metallic PCM, the transient response under heat pluses of thermal silicon grease (thermal conductivity is 3 K/(m W)) is shown in Figure 15. The peak heat flux is 600 W/cm\({}^{2}\), and the duty cycle is 0.25. At the very beginning of the heating period, the temperature of both the thermal silicon grease and the metallic PCM rise rapidly until reaching the melting point of the PCM, the temperature of the metallic PCM increases slowly and is controlled around the melting point due to the phase change, while the temperature of the thermal silicon grease continues to increase at a high rate and reaches 119 \({}^{\circ}\)C, 57.5 \({}^{\circ}\)C higher than the metallic PCM. After the heating period, the temperature of the thermal silicon grease decreases rapidly, while the temperature of the metallic PCM shows a more moderate decreasing trend both during and after the solidification process due to its better thermal properties. Compared to the thermal silicon grease, metallic PCM can achieve a lower temperature and a smaller amplitude of fluctuations, showing a better thermal contact performance.
## 6 Conclusions  
This study proposes a modified ECM to calculate the characteristics of the 2 DoFDDIM with SSR from the perspective of a single-degree-of-freedom motor. The correction coefficient is used to consider the influence of the arc-shaped stator structure. The characteristics of the rotary and linear parts (i.e., stator phase current, output torque/force, efficiency, and power factor) under different slips are computed and compared with those found by FEM. The comparison shows that the analytical values are in good agreement with the simulation values. The errors, which are \(<\)8.2%, are acceptable. So, we can draw the conclusion that the modified ECM presented in this paper is reasonable ant it provides reference for calculating the parameters of the induction motor with SSR.  
Data curation, W.W. and L.D.; Formal analysis, W.W.; Funding acquisition, J.S.; Methodology, H.F. and Z.C.; Project administration, L.D.; Software, H.F.; Writing--original draft, H.F., J.S. and W.W.; Writing--review & editing, H.F.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Slip** & \multicolumn{3}{c}{**Efficiency**} & \multicolumn{3}{c}{**Power Factor**} \\ \hline s & FEM & Math & Error & FEM & Math & Error \\ \hline
0.2 & 0.219 & 0.232 & 5.94\% & 0.383 & 0.395 & 2.53\% \\
0.3 & 0.229 & 0.230 & 0.44\% & 0.442 & 0.441 & 0.23\% \\
0.4 & 0.192 & 0.200 & 4.17\% & 0.474 & 0.475 & 0.21\% \\
0.5 & 0.156 & 0.164 & 5.13\% & 0.480 & 0.499 & 3.96\% \\
0.6 & 0.122 & 0.126 & 3.28\% & 0.503 & 0.517 & 2.78\% \\
0.7 & 0.085 & 0.090 & 5.88\% & 0.511 & 0.529 & 3.52\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Efficiency and power factor.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Slip** & \multicolumn{3}{c}{**Stator Phase Current (A)**} & \multicolumn{3}{c}{**Output Toque (N)**} \\ \hline s & FEM & Math & Error & FEM & Math & Error \\ \hline
0.2 & 11.2 & 10.6 & 5.35\% & 136 & 125.7 & 7.57\% \\
0.3 & 11.9 & 11.2 & 5.88\% & 180 & 168.3 & 6.50\% \\
0.4 & 12.7 & 11.8 & 7.09\% & 207 & 197.1 & 4.78\% \\
0.5 & 13.5 & 12.4 & 8.15\% & 220 & 215.6 & 2.00\% \\
0.6 & 14.2 & 13.1 & 7.75\% & 224 & 226.1 & 0.94\% \\
0.7 & 14.9 & 13.8 & 7.38\% & 221 & 230.3 & 4.21\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Stator phase current and force.  
**Funding:** This work is supported by National Natural Science Foundation of China under grant 51777060 and 51277054.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 PFAC Calibration Results
### Sentinel-3B Cal/Val
#### 4.2.1 Sentinel-3B Transponder Cal/Val Results  
The range bias of Sentinel-3B has regularly been determined at the CDN1 Cal/Val using its descending Pass No. 335, and continuously after Cycle No. 21 (5-February-2019). Figure 20 presents the results of this transponder calibration, covering its nominal satellite orbit.  
The range bias of Sentinel-3B is calculated to be B(S3B) = -3.46 mm (\(\pm\)3.50 mm, \(\pm\)35 mm FRM uncertainty). This value displays an opposite sign with respect to the previous Cal/Val results of Sentinel-3A (Section 4.1.1, B(S3A) = +4.90 mm). In both satellites, the absolute value of their range bias appears to be about \(\pm\)5 mm: This means that Sentinel-3A and Sentinel-3B seem to measure altimetric distances of about 814 km as accurately as \(\pm\)5 mm based on observations of this transponder.
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 2 Methods
### Dehazing Techniques for Weld Fume Removal
#### 2.2.1 DW-GAN Architecture  
In general, there are two major components in generative adversarial networks (GANs), namely, the generator and discriminator [22]. The generator is used for generating new examples, while the discriminator is used for classifying images as real or fake.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Width (mm) & 100 \\ Height (mm) & 100 \\ Weld gap (mm) & 2 \\ Flip & Horizontal, Vertical \\ Rotate 90\({}^{\circ}\) & Clockwise \\ Rotation & \(-15^{\circ}\) to +15\({}^{\circ}\) \\ Grayscale & 11\% \\ Saturation & \(-30\%\) to +30\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weld plate and data augmentation specifications for data acquisition.  
Figure 2: Actual weld seam datasets with weld fumes, flip, and rotate conditions for training YOLO Algorithms.  
The major applications where GANs have achieved great performance include synthesizing realistic images [23; 24], image denoising and producing high-resolution images [25], single image dehazing [26], and image deraining [27]. DW-GAN architecture has two sub modules, namely, discrete wavelet and knowledge adaption. The discrete wavelet branch is used to learn direct mapping between ground truth and weld fume images, whereas the knowledge adaption branch uses prior knowledge from image classification for the current dehazing task. The architecture of DW-GAN is shown in Figure 3.  
From the above diagram, the inputs were weld plates with fume images of size 1600 \(\times\) 1200 \(\times\) 3, which were resized to 572 \(\times\) 572 \(\times\) 3 for sending to the DW-GAN block, and the output was the fume-free image similar to the ground truth.
## 4 Discussion
### Strengths, Implications, and Limitations  
[PERSON] et al. (2017) suggested that application-integrated diagnostic tools may have significant accuracy in detecting problematic smartphone use and identifying problematic smartphone use based on data recorded in psychiatric patient interviews and apps [24]. However, our work is distinct in that we measured problematic smartphone use, anxiety,  
Figure 4: Results of linear mixed effects models of the post hoc analysis (S-scale, GAD, PHQ).  
and depression and how these symptoms can be reduced through manual and automatic intervention by continuously providing programs to alleviate depression or anxiety rather than simply reducing usage time.  
Prior research has shown that using applications for mental health have been found to reduce anxiety and depression and interventions can be effective on problematic smartphone use [20; 24] when associated with anxiety and depression [11]. The results of this study showed that integrated interventions using these application aspects are effective. Although no prior relationship was noted, it was predicted that a relationship existed connecting problematic smartphone use, depression, and anxiety, but this is a topic for future study.  
The unique findings in our research support our research goal in that both continuous intervention and self-management are important to mitigate problematic smartphone use. While conducting the study, the proportion of problematic smartphone users was higher than that of normal people, although there were no artificial constraints to recruit problematic smartphone users when conducting the study enrollment.  
The limitations of this study are that the findings cannot be generalized to the entire population and are limited to adults, due to the difficulty in gaining access to IRB and consent from non-adults. According to recent research by [PERSON] et al. (2020), the use of mobile devices increases utility and efficiency, which increases the learning effect compared to other devices for young children [36]. Since existing studies show that adolescents are relatively more vulnerable to smartphone use [37; 38], future research on adolescents using a similar research protocol based on the results of this study would be valuable. Furthermore, despite numerous studies from various perspectives, \"problematic smartphone use\" has not yet been classified as an \"addiction.\" Therefore, interventions that match the therapeutic effects of problematic smartphone use and the factors that cause them are difficult to establish.
## 3 System Architecture and Test Environment
### The Counting of Vehicles at Intersections  
The principle used for the counting of vehicles in the different lanes [20] of the intersection involves drawing virtual custom lines that cut across the six lanes of the roundabout. Figure 7 shows an illustrative diagram of the method used. Each line drawn must reach both sides of each lane in a single direction. For the detecting and tracking of vehicles in different images, the system calculates the intersection between the defined line and the segment composed of two points: the centroid of the current box of the vehicle and the centroid of the old box of the same detected vehicle (Figure 8). When the segment crosses the imaginary line, the system calculates the number of vehicles in increments and detects the vehicles in the relevant category.  
Figure 6: The architecture of the Deep SORT algorithm.  
Figure 7: Illustrative diagram of the virtual lines used for counting the vehicles.  
To calculate the number of vehicles passing from one lane to another, we recorded the IDs of the vehicles that crossed each lane. If a certain vehicle with the same ID crossed two lanes simultaneously, we calculated the number of vehicles, in increments, that were making this maneuver. For example, if the vehicle identified by the number 1 passes through lane 3 at first, and then passes through lane 4, we calculated the number, in increments, of vehicles that passed from lane 3 to lane 4.  
In Figure 9, we give an example of the possible trajectories for a vehicle coming from lane 2. As illustrated in the figure, we can conclude that a vehicle coming from lane 2 can pass through lane 5, lane 3, or lane 1, thus making the following routes V2 \(\to\) V5, V2 \(\to\) V3, and V2 \(\to\) V1. Additionally, the same principle applies to all the other lanes of the intersection.  
Figure 8: The mechanism used to check if a bounding box was intersected by the count line.  
Figure 9: Diagram that illustrates the possible trajectory of vehicles coming from Lane 2.