esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Multi-Objective Mixed Loss \(L_{mul}\)  
The unsupervised weight map generation network presented in this paper differs from supervised LLIE methods as it operates independently of reference images, relying solely on a set of pre-enhanced images. It should be noted that each pre-enhanced image has already closely approximated the reference image in the image space. The network's objective is to explore the domain defined by these pre-enhanced images and identify the highest quality images suitable for fusion results. This task also involves fine-tuning the network parameters to generate the most effective weight maps and determine optimal parameter values. In this study, we introduce a multi-objective mixed loss function, denoted as \(L_{mul}\), which consists of several \(L_{MSE_{i}}\) subterms. We leverage the multiple complementary pre-enhanced images obtained during the preprocessing phase to enhance its guidance capability. Specifically, the network learns to preserve common parts among the initial enhanced images, akin to a voting mechanism. The shared details are essential for preserving in the output fused image, while avoiding undesired artifacts or changes. Therefore, the overall loss function \(Loss_{mul}\) is defined as:  
\[L_{mul}=\sum_{l}L_{MSE_{i}}=L_{MSE_{1}}+L_{MSE_{2}}+\cdots+L_{MSE_{n}}\] \[=\sum(\hat{x}_{l}-x_{1})^{2}+\sum(\hat{x}_{l}-x_{2})^{2}+\cdots+ \sum(\hat{x}_{l}-x_{N})^{2}, \tag{8}\]  
where \(x_{1}\), \(x_{2}\),..., and \(x_{N}\) are pre-enhanced images obtained with those mainstream low-light enhancement methods, which serve as the target images for the fusion stage, as shown in Figure 4.
# Deep Convolutional Neural Network-Based Approaches for Face Recognition
## 4 Methodology and Experiments
### Comparison with the State-of-the-Art Models  
This section presents statistical analysis in the comparison of the performance with the state-of-the-art models in terms of the datasets (FEI face, YouTube face, LFW, and ORL). The statistical analysis was performed on the basis of the accuracy, mean, and variance. Table 6 shows the performance of our model and the state-of-the-art models, including BQA [39], Hybrid ConvNet-RBM [40], DeepFace [42], CNN-2+ Raw image [43], Deep CNN [49], CNN + SVM [51], DeepFace system [13], DeepID [53], DeepID2 [54], DeepID2+ [55], global expansion ACNN and global + local expansion ACNN [58], and sparse representation face recognition [59]. First, with the FEI faces dataset, the three models achieved a good accuracy--AlexNet with SVM (97.5%), ResNet-50 with SVM (98.5%), and transfer learning on AlexNet (98.7%). The results were higher than sparse representation face recognition [59], and the accuracy was 61.31%. Second, with the ORL dataset, all three models--AlexNet with SVM, ResNet-50 with SVM, and transfer learning on AlexNet--achieved a higher accuracy than the state-of-the-art models [58] with 91.67% and 93.30%, whereas [51] was 97.5%, and [43] was 95%. We obtained 100% with (ResNet-50 + SVM), 99.17% with (AlexNet + SVM), and transfer learning on AlexNet. Third, the results of the YouTube face dataset were 100% for all three models--AlexNet with SVM, ResNet-50 with SVM, and transfer learning on AlexNet. The result was higher than [13; 39; 42; 49; 55]. We compared our model with DeepID2+ in [55] rather than the previous DeepID model, as DeepID2+ is the latest model of DeepID1 and DeepID2, and gives a final enhancement of the models and high accuracy. Fourth, with the LFW dataset we conducted our experiments without applying any pre-processing, we simply cropped the face image region to remove the complex background from the images. We obtained 95.63% when using transfer learning from AlexNet and achieved 94% with both AlexNet with SVM and ResNet-50 with SVM. Our results were higher than that of [PERSON] et al. [40]. Also,
## 4 Discussion
Similar to the current study, [PERSON] et al. [16] reported a similar trend in their investigation. According to them, this fact could be explained by the relationship between the athlete's movement rhythm and the vibration frequency of the SVB. At slower rhythms, the athlete experiences more vibration cycles than at faster rhythms. Each vibration cycle generates a vertical force that tends to separate the body from the SVB, resulting in a noncontact phasethat reduces grip and heightens the challenge of maintaining control [12]. Hence, it is not surprising that athletes face greater demands for rebalancing at the slower rhythm. WBV exercise has been used as an adjunct to physical training for athletes and untrained individuals alike who seek to improve strength and power or facilitate recovery [32; 47; 48]. At the metabolic and cardiac level, previous studies that have combined WBV simultaneously with exercise have reported increases in HR, VE, and VO\({}_{\text{2 max}}\)[31; 49; 50; 51]. Previous research focuses on exercises such as dynamic squat, isometric squat, push-ups, or lunges. In addition, some of this research has employed WBV frequencies and amplitudes that exceed those used in the present study, reaching up to 50 Hz. At that frequency, the only significant differences were observed in VO\({}_{\text{2 max}}\)[49]. On the other hand, because lateral gliding is considered a unilateral action, the muscle demand could be lower compared to a bilateral exercise, which could generate higher energy demands and thus an increase in VO\({}_{\text{2 max}}\)[31]. In the present study, although it was not statistically significant, the performance of the test in non-WBV conditions showed a tendency towards a higher VO\({}_{\text{2 max}}\) (see Figure 2D). This could be due to different reasons. On the one hand, it should be taken into consideration that the combination of the crouched position, the relatively long gliding phase, and high intramuscular forces can lead to reduced blood flow to the working muscles [52]. In addition, WBV combined with physical exercise could further decrease muscle oxygenation. A decrease in tissue oxygenation has been observed during squatting exercises that was further aggravated in the presence of WBV [53]. Consequently, exercise combined with WBV could induce muscle deoxygenation and, therefore, a decrease in VO\({}_{\text{2 max}}\). On the other hand, in WBV platforms, there is no firm grip and the only downward force acting on the body is gravity [12]. The application of vibration to individual muscles causes an illusion of movement in the opposite direction to reflex contraction [54]. Consequently, the rigid body will lose contact and become airborne. This effect together with the sliding action could decrease the resistance and, therefore, the metabolic muscle demand. The present study is not without limitations, including (i) a small sample size (n = 10), (ii) an absence of female players, and (iii) an absence of sample-size analysis to determine whether the sample was representative.
# Simulation Study on Nanofluid Heat Transfer in Immersion Liquid-Cooled Server
## 2 Model Description
### Nanofluid Physical Parameters  
In submerged liquid cooling, the coolant occupies an important position, and the core aim of submerged liquid cooling is to submerge the electronic components in the coolant to achieve efficient heat dissipation. After reviewing the relevant literature, FC-40 was selected as the coolant base fluid for this paper's numerical simulation study, and its thermal and physical parameters are shown in Table 1.  
Among them, thermal conductivity \(\lambda\) is the key parameter for heat transfer in fluid flow. The thermal conductivity of the nanofluids is shown in Table 2. Nanofluids have greater thermal conductivity compared with conventional fluids, and the thermal conductivity of Cu-FC40 is slightly higher compared with the other four types of nanofluid under the same working conditions.
## 3 Results
### Differences in Physical Performance Variables Based on Maturity Status and Gender
#### 3.4.1 Interaction Effects of Chronological Age and Maturity Factors
\begin{table}
\begin{tabular}{l l l l l l l} \hline  & & \multicolumn{2}{c}{**MANOVA Age \(\times\) Maturity**} & \multicolumn{2}{c}{**MANOVA Age**} & \multicolumn{2}{c}{**MANOVA Maturity**} \\ \cline{3-8}  & & **[F (\(p\))]** & \(\mathbf{n^{2}}\) & **[F (\(p\))]** & \(\mathbf{n^{2}}\) & **[F (\(p\))]** & \(\mathbf{n^{2}}\) \\ \hline \hline \multirow{2}{*}{Body mass (kg)} & Boys & 1.694 (0.124) & 0.044 & 2.506 (0.005) \({}^{*}\) & 0.112 & 20.458 (0.001) \({}^{*}\) & 0.157 \\  & Girls & 2.445 (0.035) & 0.055 & 4.121 (0.001) \({}^{*}\) & 0.177 & 5.727 (0.004) \({}^{*}\) & 0.051 \\ \multirow{2}{*}{\(\mathrm{Standing}\) height (cm)} & Boys & 1.874 (0.086) & 0.049 & 2.993 (0.001) \({}^{*}\) & 0.131 & 21.008 (0.001) \({}^{*}\) & 0.161 \\  & Girls & 3.146 (0.009) \({}^{*}\) & 0.069 & 3.636 (0.001) \({}^{*}\) & 0.159 & 3.904 (0.022) \({}^{*}\) & 0.036 \\ \multirow{2}{*}{\(\mathrm{Sitting\,height}\) (cm)} & Boys & 3.528 (0.002) \({}^{*}\) & 0.088 & 3.071 (0.001) \({}^{*}\) & 0.134 & 84.000 (0.001) \({}^{*}\) & 0.434 \\  & Girls & 2.650 (0.024) \({}^{*}\) & 0.059 & 3.785 (0.001) \({}^{*}\) & 0.165 & 35.415 (0.001) \({}^{*}\) & 0.251 \\ \multirow{2}{*}{\(\mathrm{Leg\,length}\) (cm)} & Boys & 4.269 (0.001) \({}^{*}\) & 0.105 & 5.236 (0.001) \({}^{*}\) & 0.208 & 0.062 (0.940) & 0.001 \\  & Girls & 3.173 (0.009) \({}^{*}\) & 0.070 & 6.349 (0.001) \({}^{*}\) & 0.249 & 15.942 (0.001) \({}^{*}\) & 0.131 \\ \multirow{2}{*}{\(\mathrm{Body\,index}\) index (kg\(\cdot\)m\({}^{-2}\))} & Boys & 0.871 (0.517) & 0.023 & 1.776 (0.060) \({}^{*}\)
# Design of an Energy Efficient Future Base Station with Large-Scale Antenna System
## 4 Numerical Results
To analyze the TP and EE, we use the RF power consumption model shown in Figure 1. As the RF power consumption increases, the TP also increases logarithmically for both ZF and MF. If we look at the EE, there are is a maximum EE point that can be achieved. Based on the analysis to now, we finally show how much of a performance improvement is possible using the proposed BS. The PA power reduction factor versus TP/EE with variations in the RF power consumption and BB algorithm (ZF/MF) is presented in Figure 12. Each graph is the result of adjusting the BB/RF/PA power consumption. Figure 12 effectively shows how the performance changes by using the proposed BS structure since it shows the results of adjusting all three parameters in the proposed BS. As shown in the figure, based on the proposed BS structure, we can adjust various parameters and choose the best TP/EE depending on the situation. The expected improvement in performance for the EE relative to the reference point (MF, PA power consumption factor = 1, \(R_{RF}=1\) W) is around 83.05% (2.36 \(\rightarrow\) 4.32 bps/W), as summarized in Figure 7.0  
Figure 7.1 **(a)** total power consumption; and **(b)** power consumption of each component based on RF power consumption, when \(N_{t}=400\) and \(K=40\), \(\varrho=50\) Gflop/W, and \(P_{PA}=160\) W; **(c)** TP (bps); and **(d)** EE (bps/W) based on RF power consumption, when \(N_{t}=400\) and \(K=40\), \(\varrho=50\) Gflop/W and \(P_{PA}=160\) W.
## 3 Classification of Optical Waveguide Sensors
### Cascaded Structured Optical Waveguide Refractive Index Sensors
#### 3.3.3 Double MZI Structure  
[PERSON] et al. [121] prepared a refractive index photopolymer microresonator sensor by direct laser writing. The sensor includes two tapered waveguides that can be directly coupled and two MZIs (\"Y\"-shaped waveguide of the MZI) and microylinders transiently coupled to the MZI arms. The sensor had a sensitivity of 154.84 nm/RIU and was I confirm.used to measure the refractive index of liquids. At approximately 1550 nm, the \(FSR\) of the device was approximately 16.69 and 16.48 nm in the air and water, respectively. The experiment obtained the highest \(Q\) factor of 6400.8 and 3902.5 in the air and water, respectively. The total loss of the sensor was approximately 15 dB. Using NaCl solution with a concentration of 0%-5% to measure the performance of the refractive index sensor, a sensitivity of 154.84 nm/RIU was obtained.  
[PERSON] et al. [122] proved an ultra-sensitive temperature sensor based on the SOI dual-MZI cascade, as shown in Figure 21. Sensor sensitivity is related to the \(FSR\) of the two-stage MZI, which is related to temperature sensitivity. When the \(FSR\) of the two-stage MZI is the same and the temperature sensitivity is different, the sensitivity of the entire sensor is significantly improved. This can be achieved by adjusting the structural parameters of the two-stage sensor. The sensitivity of the proposed sensor reaches 1753.7 pm/\({}^{\circ}\)C, which is higher than that of any temperature sensor based on the previously reported SOI platform, and 21.9 times higher than that of the conventional all-silicon temperature sensor.
# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 5 Results and Analysis  
We tested our proposed methodology using a three-dimensional simulator platform. This simulation was useful to determine the performance of the proposed method before being applied directly in the real world. We used 40 genomes for one generation, and we selected the ten best agents and four worst agents. The term genome represents the agent or USV unit that is generated to produce a solution. We conducted the experiment in two different environments: static and dynamic. For the experiments in each environment, we varied the number of sensor inputs and the mutation rate value. We used three different types of sensors input according to the configuration in Figure 4. Variations in the mutation rate values started from 0.025, 0.055, 0.105, 0.155, and 0.205 to 0.225. We conducted experiments with these variations using a baseline method (the neural network-based genetic algorithm or NNGA) and compared it to our proposed methodology (the modified neuronal GA).
###### Abstract
[PERSON] [23] introduced a GAN-based method for reconstructing vehicle-induced strain, considering spatial-temporal relationships with other strain sensors. [PERSON] [24] applied a Self-Attention GAN (SAGAN) to learn correlations and reconstruct structural responses from precise monitoring data. Moreover, GANs have been used in bridge Weight-in-Motion (WIM) systems [25] to reconstruct bridge responses, highlighting traffic flow variations across different months. Nevertheless, time-series forecasting techniques require substantial data to construct predictive models that map inputs to outputs. These models must account for temporal variables, including seasonal and daily changes in air temperature and traffic volume, as noted in [26; 27; 28]. For long-term monitoring, significant shifts in these conditions are expected. Thus, these methods demand comprehensive data over an extended period, which can be difficult to obtain if the monitoring data are limited or incomplete. This study aims to reconstruct the high-frequency responses induced by vehicles in short- and medium-span bridges and explores the feasibility of using short-term monitoring data for long-term data reconstruction. A theoretical analysis was conducted to understand the physical behavior of high-frequency bridge responses within a simplified vehicle-bridge interaction (VBI) system [29]. A VBI system, as outlined in [30], includes a beam, a vehicle, and tire-bridge road surface interaction. We derived theoretical dynamic responses from dynamic equations of the VBI system, particularly focusing on the transfer function of high-frequency responses across different bridge sections. The findings indicated that the relationship remained stable over time, unaffected by traffic and temperature changes, and was determined solely by the bridge mode shape. Numerical simulations were proposed to test this hypothesis. Based on these characteristics, response reconstruction is treated as a regression problem. The backpropagation artificial neural network (BP-ANN), known for its effectiveness in regression tasks as cited in [31; 32; 33; 34], was utilized for reconstructing high-frequency responses. Unlike other time-series prediction methods, the BP-ANN does not consider a temporal component, which is consistent with the time-invariant nature of the target relationship. The trained neural network enables the reconstruction of responsesat a specific section based on data from other sections. Field experiment monitoring data were used to validate the BP-ANN's performance and to confirm the potential of using short-term monitoring data to develop a model for long-term monitoring reconstruction. Section 2 examines the physical characteristics of a simple VBI system, specifically the time-invariant relationship between high-frequency responses at different cross-sections. Additionally, it outlines the application strategy of the BP-ANN. Section 3 further examines the target relationship through a series of finite element simulations. Section 4 tests the proposed method with the field monitoring data.
## 4 Discussion
### Timber Harvesting Effect  
In their investigation of disturbance effects on stream metabolism, [PERSON] _et al._[8] found that as catchment disturbance level increases (in %), _GPP_ and _CR_ decrease. The streams studied in Fort Benning, Georgia, are highly similar to Turkey Creek in climate (humid subtropical), topography (low gradient), and stream substrate (highly organic). In their study, intensive erosion from US Army training areas and unpaved roads contributed the most to water quality degradation, burying benthic organic matter and creating low organic matter-containing, unstable bottom sediments. In observing specific road construction regulations and leaving an SMZ of 11.4 m\({}^{2}\) ha\({}^{-1}\) (with a minimum width of 15.2 m from the stream) in accordance with Louisiana's current BMPs, the 2007 Turkey Creek timber harvest acted to prevent similarly high levels of sediment runoff [53] like those seen at the Fort Benning sites. The results from this study indicate that logging using current BMPs had modest, if any, impact on the metabolism of Turkey Creek.  
[PERSON] _et al._[54] proposed a three-level impairment scale for streams and river: (1) in \"good health\" when _GPP_ is in the range of 0.8 to 4.0 g O\({}_{2}\) m\({}^{-2}\) day\({}^{-1}\) and _CR_ is in the range of 1.5 to 5.5 g O\({}_{2}\) m\({}^{-2}\) day\({}^{-1}\); (2) in \"satisfactory health\" when _GPP_ is \(<\)0.8 or 4.0 to 8.0 and _CR_ 0.7 to 1.5 or 5.5 to 10.0; and (3) in \"poor health\" when _GPP_\(>\)8.0 and _CR_\(<\)0.7 or \(>\)10.0 g O\({}_{2}\) m\({}^{-2}\) day\({}^{-1}\)[43]. According to metabolic averages at both N1 and N2, and based on this impairment scale, the Turkey Creek system in our study can be considered to have been in good health, both before and after the timber harvest. However, without the ability to incorporate reaeration, our ability to make assumptions regarding overall stream health is limited. In addition, _DO_ concentrations below 5 mg L\({}^{-1}\) for more than 70% of the time should be taken into account when considering impairment.
## 3 Aip Array Platform Applications
### Investigating the Effectiveness of Calibration Methods on Large Phased Array
The errors are mainly introduced by two factors: one is the limited control accuracy of the element attenuator and phase shifter and the second is that during the measurement, there is some measurement noise. In particular, when the element complex filed amplitude is small, the measurement noise has a large impact on the 'on-off' calibration accuracy. As shown in Table 1, the calibration error of the 'on-off' method for the amplitude of the fourth element reaches 1.5 dB. In addition, the phased array tends to work with all elements turned on at the same time. When the phased array is calibrated by the 'least squares' method, which is an 'all-on' method, the circuit state, the platform temperature and the coupling between the element antennas of the array are largely consistent with the working conditions. Therefore, the calibration results of the 'least squares' method are closer to the working state of the phased array. If the calibration results of 'least squares' is used as a reference, it can be concluded from the similarity of the calibration results of 'on-off' and 'least squares' that the coupling between the element antennas is small when the AiP array is working. Hence, if the coupling between the element antennas of the phased array is not large, the 'on-off' method can also effectively calibrate the phased array.
## 4 Numerical Example  
### Calculation Model  
The primary focus of this study is on the Daikai subway station, severely damaged during the 1995 Kobe earthquake in Japan. Due to the significant impact of the earthquake damage and the comprehensive post-earthquake investigation, there is a substantial amount of actual damage data available, making it conducive to the corresponding research. As a result, this paper will adopt this case for its subsequent research efforts. The station's structural dimensions and reinforcement are depicted in Figure 3. Specifically, the structure has a cross-section measuring 17 m in width and 7.17 m in height. The center columns are longitudinally spaced at 3.5 m intervals, with a clear height of 3.82 m and cross-sectional dimensions of 0.4 m \(\times\) 1.0 m. Figure 3b details the center column's reinforcement. The station is buried approximately 4.8 m underground, and the soil layer parameters for the site are provided in Table 1.  
Due to its efficient nonlinear analysis capabilities, as well as a rich set of material and contact effect models, ABAQUS software (Abaqus, 2022) is well suited for simulating the  
Figure 2: Comprehensive analysis of intensity and structural damage metrics. mechanical characteristics of underground structures. Consequently, a two-dimensional finite element for both the soil and structures is established using ABAQUS software, as illustrated in Figure 4. This model extends 1000 m in length and rises 40 m in height. Beam element B21 is employed for structural simulation, while the plane strain element CPE4R is used for soil modeling. The concrete's elastic-plastic behavior is modeled using the plastic damage model proposed by [PERSON] et al. (2018) and [PERSON] et al. (2018). The concrete has a density of 25 \(\mathrm{kN/m^{3}}\) and an elastic modulus of 24 GPa. Reinforcement is implemented at the structural element level using the rebar command. Steel bars have a yield strength of 312 MPa and an elastic modulus of 200 GPa. The Mohr-Coulomb model is employed to account for the soil's elastic-plastic behavior. A frictional contact interface with a coefficient of friction (\(\upmu\)) of 0.4 is established between the soil and the structure. The top of the soil is free, while its bottom is fixed with both horizontal and vertical constraints. The ground motion is input horizontally at the bedrock. To achieve more accurate calculations, the main structure is arranged in a dense, uniform 1 m \(\times\) 1 m grid. The soil near the structure is densely arranged in a 1 m \(\times\) 1 m grid to achieve precise dynamic responses. The remaining soil farther from the structure is discretized based on its distance, as illustrated in Figure 4.  
Boundary conditions for the soil utilize infinite element CINPE4, and the analysis is executed in two stages. Initially, the structure is subjected to static calculations with  
Figure 4: Numerical model of the structure and the surrounding soil. Figure 3: Cross-section of the subway station and reinforcement details of the central column (mm).