esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-Scale Feature Fusion Siamese Network Guided by Edge Contour Model Design  
### Overall Model Design  
Figure 3 illustrates the overall design of the multi-scale feature fusion guided by edge contour model (MFS-GC). The local regions of conjugable bone stick images exhibit a symmetrical structure in terms of color, texture, contours, and other information, thereby possessing similar core features. The approach presented in this paper utilizes a similarity measurement to assess the matching degree of bone sticks. A Siamese network framework is used for feature extraction, which is followed by a subsequent measurement of the extracted features' similarity.  
In the image preprocessing stage, we normalize the dimensions of the input original bone stick images, \(x_{1}\) and \(x_{2}\), to 224 \(\times\) 224 pixels, resulting in \(x_{1\_1}\) and \(x_{2\_1}\). Subsequently, the images undergo grayscale conversion, binarization, and edge detection algorithms, yielding the corresponding edge contour images, \(x_{1\_2}\) and \(x_{2\_2}\). As the size of the input contour images differs from the feature maps, \(x\), at each stage, it becomes necessary to resize the contour images to match the size of the feature maps. This process generates multi-scale contour images.  
Figure 2: Residual block: (**a**) identity block; (**b**) conv block.  
During the training phase, preprocessed bone stick images are input into a feature fusion residual network with two shared parameters for training. The contrastive loss function for the two bone stick images is computed, and network parameters are iteratively updated through backpropagation. The objective is to minimize the L2 distance between feature vectors of positively pairable samples and maximize the L2 distance between feature vectors of non-pairable bone stick samples, thereby achieving discrimination between the two.  
During the inference phase, the bone stick image to be appended is input, along with each bone stick image from the dataset, into the trained network to extract feature vectors, and L2 distances are calculated. Similarity scores for each pair are sorted, and the top-T ranked bone stick fragment images are selected to form the candidate set for conjugable bone sticks.
# Microseismic Electronic Fencing for Monitoring of Transboundary Mining in Mines
## 2 Factors Affecting Localization Accuracy
### Monitoring Depth  
When the microseismic method is used to locate the seismic source, the aperture size of the observation system in the plane is directly related to the depth of the target body that can be located. To study the relationship between aperture size and target depth, seismic source events are set up at different depths in the forward model to simulate mining activities at different levels. In this context, the distance between the outer and inner circles of the surface part of the nested observation system is d = 600 m. The localization accuracy with the addition of 15 in-well sensors is also discussed during the experiments. The localization errors shown in Figure 4b are an average value after multiple localizations per  
Figure 3: Comparison of localization results of different observing systems: (**a**) comparison of localization errors of 5 arrangements (**b**) comparison of horizontal and depth localization errors.  
event, and the results indicate that both observation systems achieve the highest localization accuracy for sources at depths around H = 1/2d. Moreover, the depth localization errors are significantly reduced with the inclusion of in-well sensors, which aligns with the conclusions established in Section 2.2.
## 4 Simulation Results and Complexity Analysis
### Computational Complexity Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Detection Scheme** & **Complex Multiplications and Additions (CMAs)** \\ \hline SOR & \(\frac{1}{2}(5N_{T}^{2}+N_{T})+i(2N_{T}^{2}+N_{T})\) \\ modified SOR & \(\frac{1}{2}(5N_{T}^{2}-3N_{T})+i(2N_{T}^{2}+N_{T})\) \\ SSOR & \(5N_{T}^{2}-N_{T}+2i(2N_{T}^{2}+N_{T})\) \\ AOR & \(3N_{T}^{2}+3 iN_{T}^{2}\) \\ CAOR & \(3N_{T}^{2}+3i(N_{T}^{2}+N_{T})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computational complexity of different detection schemes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Detection** & **CMAs** & **CMAs** & **CMAs** & **CMAs** \\
**Scheme** & \(i=2\) & \(i=3\) & \(i=4\) & \(i=5\) \\ \hline SOR & \(1704\) & \(2232\) & \(2760\) & \(3288\) \\ modified SOR & \(1672\) & \(2200\) & \(2728\) & \(3256\) \\ SSOR & \(3376\) & \(4432\) & \(5488\) & \(6544\) \\ AOR & \(2304\) & \(3072\) & \(3840\) & \(4608\) \\ CAOR & \(2400\) & \(3216\) & \(4032\) & \(4848\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The numerical comparison of the complexity for different detection schemes with \(N_{T}=16\). Clearly, as shown in Figure 10, the BER performance of the proposed scheme only requires two iterations due to the sufficient number of antennas, which can achieve better BER performance than SOR, modified SOR, SSOR, and AOR. Meanwhile, as shown in Table 7, the complexity of CAOR at \(i=2\) and AOR at \(i=3\); they require 2400 CMAs and 3072 CMAs, respectively. The BER performance of CAOR and AOR is equal to \(9.26\times 10^{-5}\) and \(8.24\times 10^{-5}\), respectively; they are very close to each other. In light of the above discussion, we can conclude that as the number of antennas increases, the number of iterations will decrease gradually, and thus it is conducive to reducing the CMAs. Therefore, as a whole, the computational complexity of CAOR is actually lower than that of conventional AOR. To show the computational complexity of different detection schemes more clearly, we utilize a bar graph to depict them in Figure 11.
## 4 Performance Evaluation
### Outage Probability
#### 4.1.3 Outage Analysis of Relay-Destination-Select Relaying  
In case 3, the relay \(i=I_{3}\) is selected according to the SNR of the second hop, as given in Equation (9); the maximum instantaneous SNR between AWACS-to-UAV links is  
\[\gamma_{SR_{I_{3}}}=\max\Bigl{\{}\gamma_{SR_{I_{3}}}^{FSO},\gamma_{SR_{I_{3}}}^{ RF}\Bigr{\}} \tag{40}\]  
The SNR in the UAV-to-GS link is  
\[\gamma_{R_{I_{3}}D}=\max_{i=1,2,\cdots N}\Bigl{\{}\gamma_{R_{i}D}^{FSO},\gamma_ {R_{i}D}^{RF}\Bigr{\}} \tag{41}\]  
The maximum end-to-end instantaneous SNR of case 3 obtained is  
\[\gamma_{SD3}=\min\Bigl{\{}\gamma_{SR_{I_{3}}},\gamma_{R_{I_{3}}D}\Bigr{\}} \tag{42}\]  
As a result, the outage probability of case 3 can be given as  
\[P_{out3}=1-\text{Pr}\Bigl{(}\gamma_{SR_{I_{3}}}\geq\gamma_{th}\Bigr{)}\text{Pr }\Bigl{(}\gamma_{R_{I_{3}}D}\geq\gamma_{th}\Bigr{)}=1-(1-F_{\gamma_{SR_{I_{3}}} ^{FSO}}(\gamma)F_{\gamma_{SR_{I_{3}}}^{RF}}(\gamma))(1-\prod_{i=1}^{N}\Bigl{(} F_{\gamma_{R_{i}D}^{FSO}}(\gamma)F_{\gamma_{R_{i}D}^{RF}}(\gamma)\Bigr{)}) \tag{43}\]  
After substituting (28) and (31) in (44), the outage probability can be obtained.
## 4 Experiment
### Objective Evaluation

Table 4: Objective evaluation results of the six indicators PSNR, SSIM, PI, RMSE, NIQE, and RISQUE for the LOL [1] dataset. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PI\(\downarrow\)** & **RMSE\(\downarrow\)** & **NIQE\(\downarrow\)** & **BRISQUE\(\downarrow\)** \\ \hline RetinexNet (2018) & 15.6477 & 0.7897 & 3.6348 & 36.7010 & 6.1259 & 32.9049 \\ \hline KinD (2019) & 18.6718 & 0.8046 & 2.8930 & 40.1974 & 4.0720 & 28.5728 \\ \hline EnGAN (2020) & 11.2798 & 0.6019 & 4.4450 & 67.2756 & 7.0337 & 28.6887 \\ \hline DLN (2020) & 17.0217 & 0.5277 & 2.7925 & 40.5834 & 4.2376 & 23.3972 \\ \hline Zero-DCE (2020) & 17.6574 & 0.8098 & 3.0649 & 30.4134 & 4.8660 & 25.1409 \\ \hline SDD (2020) & 18.6515 & 0.8095 & 3.0368 & 33.4495 & 4.6589 & 29.1570 \\ \hline DRBN (2020) & 17.3580 & 0.8480 & 3.7196 & 30.1505 & 4.5263 & 23.9609 \\ \hline Zero-DCE++ (2021) & 16.4965 & 0.5538 & 4.0115 & 35.7106 & 4.1429 & 19.1223 \\ \hline RetinexDIP (2021) & 17.2111 & 0.7544 & 3.9552 & 29.4634 & 4.5509 & 25.3002 \\ \hline Ours & 23.8017 & 0.8746 & 2.7460 & 24.7726 & 4.1447 & 18.7094 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average performance of the different enhancement methods in Figure 7.  
Table 5 shows the objective metric values of different methods for the whole dataset of MIT [32]. We used PSNR, SSIM, PI, RMSE, BRISQUE, and NIQE to evaluate the performance metrics of various enhancement methods. In terms of PSNR and SSIM, our method achieved a PSNR of 25.6867 and SSIM of 0.9197. In terms of PSNR and RMSE metrics, EFF achieved the second best result, EFF fused two images with different exposure levels, and the goodness of the enhancement results was easily affected by the fusion method. Compared with our method, the PSNR index of EFF is lower than that of our index. Our index is 0.9197 for SSIM, and better results are obtained for EnGAN and DRBN without reference index of PI and NIQE. Our method achieves the best results on all the metrics. This also shows the effectiveness of our method. For the five datasets without true labels, MEF, DICM, NPE, VV, and LIME were objectively compared with the complete dataset using the BRISQUE and NIQE quality evaluation metrics, respectively, and the results are shown in Table 6. EnGAN outperforms the other compared methods on the MEF, DICM, VV, and LIME datasets. EnGAN uses a double discriminator to guide the learning of global and local information without the supervision of real labels, which is prone to detail loss. Despite the reference-free dataset used, the statistics in Table 6 show that our method outperforms 14 other low-light image enhancement methods in terms of both spatial information of the image and naturalness of the image, despite the reference-free dataset used.
# The Stationary Thermal Field in a Multilayer Elliptic Cylinder
## 1 Introduction
The above and wider literature reviews show that multilayer systems of an elliptical structure have not been calculated analytically. This gap was partially filled by the present study. Its purpose was to perform the analysis of the stationary two-dimensional thermal field in a layered elliptical cylinder with boundary conditions of the third kind on the outer surface. The eigenfunctions of the heat conduction equation were determined using the method of separation of variables. In turn, the coefficients of the eigenfunctions resulted from the system of algebraic equations obtained from the Hankel condition.
# Heat Transfer Enhancement of Nanofluids with Non-Spherical Nanoparticles: A Review
## 3 Convective Heat Transfer of NSN Nanofluids
### Experimental Data Evaluation  
The Nusselt number is used to evaluate the ratio of CHT across the boundary. A more significant Nusselt number corresponds to more active convection. The dimensionless local Nusselt number in the channel is defined as:  
\[\mathrm{Nu}=\frac{h_{nf}D}{k_{nf}} \tag{3}\]  
where the CHT coefficient is:  
\[h_{nf}=\frac{q}{T_{w}-T_{b}} \tag{4}\]  
where \(T_{w}\) is the wall temperature, \(T_{b}\) is the bulk temperature, and \(q\) is the heat flux. Flow loop experiments measure the parameters in Equation (4). A test section is set in a fully developed region with a straight or curved pipe/microchannel and a square or circular cross-section. In addition, it includes boundary layer flow, cavity flow [86; 87], impinging jets, peristaltic flow, and flow in heat exchangers [12]. It can be divided into forced convection heat transfer or natural convection heat transfer. Based on the Reynolds number (Re = \(u_{0}\)\(L/v_{nf}\)), the flow state is laminar, turbulent, or transitional flow [88]. [PERSON] et al. [89] analyzed the studies on the forced convection of nanofluids in circular and non-circular channels in terms of geometry, particles, methodology, and regime. Researchers evaluated the CHT features of nanofluids affected by different types of parameters and flow conditions. Table 3 presents the experimental and numerical studies for CHT of NSN nanofluids. The nanofluid type, particle shape and size, particle loading, and flow state are listed with CHT characteristics. Notably, all of the variations of the CHT coefficient or Nusselt number are typically nonlinear.
###### Abstract  
In this paper, the vertical vibration behaviour of an experimental scale demonstrator of a railway vehicle carbody fitted with an anti-bending bar system of innovative design is studied to highlight its ability to raise the bending natural frequency of the railway vehicle carbody and to reduce the intensity of the vibration. The anti-bending bar system has been previously proposed by the second author as a new passive method to improve comfort in passenger coaches with long carbodies that have a natural bending frequency located in the range of maximum sensitivity of human beings to vertical vibration. The experimental scale demonstrator consists of an aluminium plate supported on four rubber supports. The aluminium beam reproduces to a scale of 1:10 the length of a passenger coach carbody, and its thickness is set so that the first bending frequency of the plate is close to the usual value of real carbodies. The anti-bending bar system consists of two steel bars arranged longitudinally in the middle of the aluminium plate, near its side edges. Each anti-bending bar is fixed at the ends to the aluminium plate by joints consisting of a clamping arm welded to a fixing flange. The two anti-bending bars oppose the bending movement of the aluminium plate, thus increasing its bending stiffness. This results in increasing the bending natural frequency of the aluminium plate and reduction its vibration. Testing the effectiveness of the anti-bending bar system consists of experimentally determining the frequency response of the aluminium plate acceleration without/with the anti-bending bar system by the impact hammer method. The experimental results show the first bending natural frequency increases from 9.01 Hz to 13.4 Hz and the acceleration amplitude decreases by more than 50% when the anti-bending bar system is used. To confirm these results, a theoretical model of the experimental scale demonstrator based on the finite element method was developed. The results obtained with this model are in line with those obtained experimentally.  
carbody; experimental scale demonstrator; anti-bending bar system; bending frequency; impact hammer method; finite element method; modal analysis; harmonic analysis +
Footnote †: journal: Applied Sciences
# A Four-Stage Method for Active Control with Online Feedback Path Modelling Using Control Signal
## 1 Introduction
Unlike the existing methods, the proposed method has three advantages. First, it avoids the need for an extra auxiliary noise generator; second, it uses a single filter for feedback path modelling and neutralization to reduce the overall computational complexity, and third, it decouples the feedback path modelling and control process to improve the performances. Furthermore, a stability detector is set in the ANC system to ensure the system stability in the event of howling or a feedback path change. In the first stage of the proposed method, if howling is detected, a nominal estimate of the feedback path is used in order to stabilize the system. Then feedback path is estimated followed by the control operation. If a feedback path change is detected depending upon the preset thresholds in the middle of control operation, the control filter update is ceased and feedback path is remodeled. The proposed method, therefore, helps in decoupling the control operation and feedback path modelling.
###### Abstract  
We present a tunable terahertz (THz) metamaterial using an electric split-ring resonator (eSRR), which exhibits polarization-sensitive characteristics. The proposed eSRR is composed of double symmetrical semicircles and two central metal bars. By changing the lengths of two metal bars, the electromagnetic responses can be tuned and switched between dual-band and triple-band resonances in transverse magnetic (TM) mode. Furthermore, by moving the bottom metal bar to change the gap between the two metal bars, the first resonance is stable at 0.39 THz, and the second resonance is gradually blue-shifted from 0.83 to 1.33 THz. The tuning range is 0.50 THz. This means that the free spectrum ranges (FSR) could be broadened by 0.50 THz. This proposed device exhibits a dual-/triple-band switch, tunable filter, tunable FSR and polarization-dependent characteristics. It provides an effective approach to perform tunable polarizer, sensor, switch, filter and other optoelectronics in THz-wave applications.  
metamaterial; terahertz optics; optical switch; optical filter; tunable resonator +
Footnote †: journal: Applied Physics Letters
# Evaluating the Energy-Saving Potential of Solar Dedicated Ventilation Systems Based on ADRC
## 2 Photovoltaic and Solar Thermal System Combined with Dedicated Ventilation System
### Photovoltaic and solar thermal system combined with dedicated ventilation system  
A schematic diagram of the proposed solar dedicated ventilation system based on ADRC is shown in Figure 1. For the air treatment processes of the proposed system, the system can be used as a heater and all the electrical power can be supplied by the photovoltaic system. The direct expansion (DX) and its cooling and dehumidification system can be applied to pretreat the fresh air (state 0). Fresh air can be cooled and dehumidified to state 2 by the DX. When the reheating process is finished by the solar thermal system, the treated fresh air (state 3) will be combined with the return air (state 4), which was processed by the fan coil unit at the mixing box. The combined air (state 5) will be provided to the indoor space (state 6) under the setting circumstances to meet the sensible load and latent load of the indoor space.  
Figure 1: Schematic diagram of the proposed solar dedicated ventilation system based on active disturbance rejection controller (ADRC).  
4 ADRC  
The ADRC can be designed and applied to the entire control system for the proposed solar dedicated ventilation system. This ADRC involves analogy input (AI), analogy output (AO), digital input (DI), and digital output (DO) ports, a power supply port, and an Ethernet communication port. The Ethernet communication port can be used for signal transmission between the ADRC and sensors, the DI and DO ports collect the digital signals that control the on/off running of the proposed solar dedicated ventilation system, and the AI and AO ports obtain the analog signals that adjust the opening of valves to achieve the accurate control goal. The details of ADRC will be introduced in Section 3.
# Accurate Characterization of the Properties of the Rare-Earth-Doped Crystal for Laser Cooling
## 4 Results and Discussions
(**c**) The measured \(\eta_{c}(\lambda)^{exp}\) as shown by blue dots and model fitting of Equation (1) with the parameters of \(\eta_{ext}=99.4(\pm 0.1)\%\) and \(\alpha_{b}=1.5(\pm 0.1)\times 10^{-4}\) cm\({}^{-1}\) as shown by the red solid curve. (**d**) The cooling window of 5% Yb\({}^{3+}\):LuLiF\({}_{4}\) crystal. The blue region and the red region correspond to cooling and heating, respectively. Czochralski-grown bulk 5% Yb\({}^{3+}\)-doped LuLiF\({}_{4}\) under 1025 nm pump laser of 220 mW with the corresponding parameters \(\eta_{ext}=99.0\) (\(\pm 0.1\)), \(\alpha_{b}=1.3\) (\(\pm 0.2)\times 10^{-3}\) cm\({}^{-1}\)[57]. Later, a Czochralski-grown bulk 5% Yb\({}^{3+}\)-doped LuLiF\({}_{4}\) of better purity was cooled to 117.3 K under 1020 nm pump laser of 33 W with estimated cooling parameters being \(\eta_{ext}=99.4\%\) and \(\alpha_{b}=4.5\times 10^{-4}\) cm\({}^{-1}\)[29] Recently, a Bridgman-grown 5% Yb\({}^{3+}\)-doped LuLiF\({}_{4}\) was cooled to \(\sim\)195 K under 1020 nm pump laser of 45 W with \(\eta_{ext}=98.9\) (\(\pm 0.1\))% and \(\alpha_{b}=3.3\) (\(\pm 0.2\)) \(\times 10^{-4}\) cm\({}^{-1}\)[58]. Based on the cooling window from the LITMoS test, the crystal sample under current study can be potentially cooled to \(\sim\)110 K under 1020 nm pump laser with its measured parameters \(\eta_{ext}=99.4\) (\(\pm 0.1\))% and \(\alpha_{b}=1.5\) (\(\pm 0.1\)) \(\times 10^{-4}\) cm\({}^{-1}\). In a recent report working with 5% Yb, Tm co-doped LiYF\({}_{4}\) crystal, [PERSON] et al. found that when the sample temperature decreased from 300 K to 100 K, the corresponding background absorption \(\alpha_{b}\) dropped more than an order of magnitude [41]. This study indicated that the background absorption \(\alpha_{b}\) does not remain constant but changes dramatically during the cooling process. As an isomorph of the LiYF\({}_{4}\) crystal, the LuLiF\({}_{4}\) crystal displays similar optical behavior [57; 58; 59; 60]. Considering the reduction in \(\alpha_{b}\) with decreasing temperature [41], one can expect that the 5% Yb\({}^{3+}\)-LuLiF\({}_{4}\) crystal under our study can be potentially cooled to even lower temperatures than 110 K.
## 3 Results and Discussion
### Wind Tunnel Test Preparation  
The calibration phase, which involved determining the flow parameters inside the test chamber, was a crucial step to complete prior to experimental testing. The primary parameters used to describe the flow in a wind tunnel test included the total, static and dynamic pressures; the temperature variation during the test; the controlled flow speeds; and the Reynolds number. Temperature and pressure values are essential for calculating air density and flow rate. In the wind tunnel, temperature was measured using a Type K thermocouple sensor with an accuracy of \(\pm\)0.5 \({}^{\circ}\)C. The physical forces were digitized using a National Instruments USB-6210 multifunction data acquisition system, enabling the software to process the data.  
After calibrating the wind tunnel, the prototyped morphing wing was mounted in the test section using appropriate fixations. The test section had dimensions of 0.62 m in height, 0.91 m in width and 1.83 m in length, with a maximum speed capacity of 0.1 Mach (35 m/s). The air speed used in the previous aerodynamic study, and to be used in the wind tunnel for validation, was 28.3 m/s, well below the maximum limit of 50 m/s. Figure 20 shows the UAS-S45 morphing wing section installed in the wind tunnel.  
Figure 19: Price–Paidoussis subsonic wind tunnel: (**a**) actual model, (**b**) schematic of sections with coordinates.  
When models are tested in wind tunnels for performance evaluations, the airflow they encounter differs from the conditions they would experience in an open-air environment [41]. Therefore, wind tunnel experiments have several limitations, primarily related to the thickness of the boundary layer, the scale and size of the models, obstruction effect adjustments and levels of turbulence flow intensities, even for the same flow conditions. The two primary sources of error in subsonic flow experimentation are (1) alterations in the flow field caused by flow confinement around the body; and (2) the flow interaction with the wind tunnel boundary (closed walls), which is represented by the obstruction, and wall interference.  
The corrections of these flow effects in wind tunnel data can be addressed through various methods. To reduce uncertainties in the results, model scaling is often applied whenever it is feasible to achieve a blockage area ratio of smaller than 10%. Despite the benefits of reduced-scale models for mitigating interference and blockage effects, their application can give inaccurate results. In this study, the wing was positioned at the center of the test section, as shown in Figure 20. The prototype model was scaled by a factor of 0.466 to accommodate the test section dimensions and the resulting blockage ratio was calculated to be 5.2%, which was below the acceptable blockage limit. Table 5 shows the air conditions inside the test section.