esa-sceva/satcom-chunk-collection huggingface dataset:
# A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes
## 4 Experimental Analysis
### Ablation Experiments
#### 4.6.2 Effect of SE Module  
This section discusses the influence of the SE module on feature extraction and trains feature extraction networks with SE block and without SE block, respectively. The results are shown in Table 2. Compared with the network without SE module, the network with SE module has better performance, which proves that adding SE module in the network has a positive effect on feature extraction of partial fingerprint images. The weight value of each channel can be calculated through the SE module, which makes the network pay more attention to the information in the important channel. This structure enables the network to accurately obtain the important feature information while ignoring the unimportant fingerprint features during the feature extraction of a low-quality partial fingerprint.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Loss Function**} & **Complete** & **Images with Effective** & **Images with Effective** & **Images with Effective** \\  & **Images** & **Areas of 75\%** & **Areas of 50\%** & **Areas of 25\%** \\ \hline Arc-Loss & 98.6 & 98 & 92.5 & 88.6 \\ Triplet Loss & **99.1** & 97.4 & 89.8 & 76.1 \\ Contrast Loss & 97.8 & 97 & 88.4 & 77.2 \\ Improved Arc-Loss & 98.9 & **98.6** & **94.9** & **88.9** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The identification rate of the network trained with different loss functions (Rank-20). The best results are marked in bold.
## 4 Conclusions  
In this experiment, in order to develop the composition ceramics for the application of a thickness vibration mode ultrasonic transducer device with a high k\({}_{t}\), high Q\({}_{mt}\), and high D.R for alleviating temporomandibular joint disorder pain, Pb\({}_{0.88}\)(La\({}_{0.6}\) Sm\({}_{0.4}\))\({}_{0.08}\)(Mn\({}_{1/3}\) Sb\({}_{2/3}\))\({}_{0.02}\) TiO\({}_{1.98}\) O\({}_{3}\) ceramics were manufactured using CuO as sintering aids, and their piezoelectric and resonant properties were investigated.  
1. The specimens sintered at 1200 \({}^{\circ}\)C and 1230 \({}^{\circ}\)C exhibited pure perovskite phase, and few secondary phases were observed.
2. For the specimens sintered at 1200 \({}^{\circ}\)C, the dielectric constant (\(\varepsilon_{t}\)) of 202, piezoelectric constant (d\({}_{33}\)) of 56 pC/N, first and third overtone electromechanical coupling factors k\({}_{t\,1}\) of 0.548 and k\({}_{t\,3}\) of 0.219, and first and third overtone mechanical quality factors Q\({}_{mt1}\) of 345 and Q\({}_{mt3}\) of 292 were suitable for the device application as an ultrasonic transducer, respectively.
3. When the side length of the piezoelectric device was 7.7 mm, the first and third overtone electromechanical coupling factors k\({}_{t}\) of 0.555 and k\({}_{t3}\) of 0.196, first and third overtone mechanical quality factors Q\({}_{m}\) of 381 and Q\({}_{m\,3}\) of 393, and first and third overtone dynamic ratios (D.Rs) of 63.7 and 37.7 dB were suitable for the device applications as an ultrasonic transducer for temporomandibular joint disorder pain relief and facial skin massage, respectively.
4. Finally, it is considered that the first vibration mode near 3 MHz was very suitable for device application as an ultrasonic transducer for alleviating temporomandibular joint disorder. In particular, the third vibration mode at 9.8 MHz can be utilized for device application as an ultrasonic transducer for an ultrasonic facial massager. Accordingly, the fabricated piezoelectric device can be simultaneously utilized as 3 MHz and 9.8 MHz ultrasonic generating devices.  
Conceptualization, J.Y. and S.A.W.; Data curation, J.Y. and S.A.W.; Writing--original draft, J.Y.; Writing--review & editing, S.A.W.; Funding acquisition, S.A.W. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the Semyung University Research Grant of 2023.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
# Fault Diagnosis of Wind Turbine Gearbox Based on Mel Spectrogram and Improved ResNeXt50 Model
## 3 Model
### Attention Mechanism
#### 3.2.1 Channel Attention Mechanism  
Channel attention mechanisms evaluate the significance of individual feature channels, adaptively amplifying useful channels while suppressing less relevant ones. The process begins with an input feature map \(F\) of dimensions \(H\times W\times C\). To condense spatial information, global max pooling and global average pooling are applied across the spatial dimensions (\(H\times W\)), yielding two compact \(1\times 1\times C\) feature descriptors. This spatial compression facilitates efficient subsequent learning of channel characteristics. Both pooled results are then fed into a shared MLP (Multi-Layer Perceptron). The MLP architecture employs a bottleneck design: its first layer contains \(C/r\) neurons with ReLU activation, followed by a second layer restoring the dimensionality to C neurons. The outputs from the MLP (corresponding to the two pooling paths) are element-wise summed. This combined signal is then passed through a sigmoid activation function, producing the final channel attention weight matrix \(Mc\). Among them, \(M_{C}\in R^{C/r\times 1\times 1}\), \(M_{C}\) represents the channel attention weight matrix, and \(r\) is the dimensionality reduction coefficient used in the MLP.  
\[\begin{array}{ll}M_{c}(F)&=\sigma(MLP(AvgPool(F)))+MLP(MaxPool(F))\\ &=\sigma(W_{1}(W_{0}(F_{avg}^{c})+W_{1}(W_{0}(F_{max}^{c}))\end{array} \tag{11}\]  
In Equation (11), \(\sigma\) represents the sigmoid activation function; \(F_{avg}^{c}\) and \(F_{max}^{c}\) represent the global average pooling and max pooling features, respectively; \(W_{0}\in R^{C/r\times C}\) and \(W_{1}\in R^{C\times C/r}\).
## 1 Introduction
In the presence of harmonics, it is not appropriate to consider a unique parameter value, such as in the cases of stator reactance, magnetizing branch reactance, rotor reactance, and rotor resistance. These parameters vary considerably with frequency, and it becomes necessary to analyze them for each harmonic circuit [20; 21]. In [22], the authors present a study about supply harmonic effects on the operation of an IM, including the electrical, vibration, and torque effects analysis. Paper [23] examines how the noise and vibration level of machines is affected by the level of the current distortion. This paper follows a systematic analysis of the effects of the voltage waveform distortion on the IM, both electrically and mechanically. For this, the supply voltages are deformed, overlapping on the fundamental of a single harmonic. The harmonics introduced at a time in the supply voltage are of negative-sequence harmonics k \(\in\) [5,11,17], respectively of positive-sequence harmonics k \(\in\) [7,13,19], each having the following percentage levels \(\gamma_{\text{Vk}}\)\(\in\) [5%, 10%, 15%, 20%, 25%, 30%] of the fundamental. Under these conditions, the variations of root mean square (RMS) values of the stator and rotor currents, as well as the level of the stator current harmonics corresponding to the injected voltage harmonics, have been followed. In addition, the influence of the voltage harmonics on the electromagnetic torque, power factor (PF), and efficiency of the IM have been followed. We use the MATLAB/Simulink programming environment to analyze the IM behavior in harmonics. Modeling and simulation were chosen as study methods; these have allowed the physical models of the motor to be adapted to analyze the harmonics consequences both on the IM itself and on the power grid. In addition, we could highlight possible operating situations that are difficult to put into practice. With the proposed methodology, the estimation of the consequences of each harmonic voltage in part follows the basis for a generally applicable method to assess, for any application, the overall effects of the harmonics.
# Precise Point Positioning Using Triple GNSS Constellations in Various Modes
## 3 BSSD Post-Processing PPP Models
The elements along the diagonal line represent the variances of the code and carrier phase measurements. In our solution, we consider the ratio between the standard deviation of the code and carrier-phase measurements to be 100. When forming BSSD, however, the differenced observations become mathematically correlated. This leads to a fully populated covariance matrix at any particular epoch.
## 2 Materials and Methods
### Printing Strategies  
In this study, 34 samples, namely dog bones, short pentagon towers (height 80 mm), and tall pentagon towers (height 156 mm) were 3D-printed at the point-of-care according to their different printing settings. Computer-aided design (CAD) software SolidWorks (Dassault Systems SOLIDWORKS Corp., Waltham, MA, United States) was used to design and generate the standard tessellation language (STL) files of the test samples. They were exported into slicing software (Simplify 3D version 4.1.2, Cincinnati, OH, USA) and, along with printing parameters, printing files were built and uploaded into the 3D printer for the printing process [33; 36]. Samples were printed in different lateral and top/bottom positions on the build plate and different printing orientations (XY, XZ, ZX) with similar nozzle size (0.4 mm diameter) and same build chamber temperature of 200 \({}^{\circ}\)C and build plate temperature of 250 \({}^{\circ}\)C. All parts were printed as filled structures, with a theoretic infill percentage of 100% and rectilinear internal and external infill patterns. Laminar airflow guaranteed homogeneous temperature distribution and therefore no temperature gradient within the building part [37]. As shown in Figure 1, samples of group A were printed in different orientations of XY (group 1), XZ (group 2), and ZX (group 3). Samples of group 1 were directly printed in dog bone shape, planar with their surface in Z-direction. The longitudinal axis was oriented along the \(x\)-axis of the build platform. The samples of group 2 were printed in rectangular bars with their surface tilted by 90\({}^{\circ}\). On the other hand, group 3 samples were printed in pentagon towers [38; 39]. Dog bone samples of group 2 and group 3 were then milled out of rectangular bars and from each side of short pentagon towers, respectively. This strategy was implemented to have a consistent reporting and evaluation criteria in all the end-product specimens, i.e., dog-bone samples. Furthermore, group 3 samples were printed in excess to assess the reproducibility of printing in ZX orientation. Group B was considered to assess the influence of different top/bottom and lateral positions of the part in the build room on its performance and repeatability in terms of mechanics. It is essential to mention that the printing process parameters for these groups were identical, and the only difference was the position of the part on the printer build plate. The printing orientation in group B samples was ZX; therefore, pentagon tower structures were considered [38; 39]. Two similar tall towers, at one per build, were printed in each lateral position on the build plate. Each side of the tall pentagon towers was milled into two dog bones--one on top and one on the bottom (Figure 2). Figure 1: Samples of group A were printed in different XY, XZ, and ZX orientations. An overview of all groups, including their number of samples, their subgroups, and differences in production strategy, is given in Table 1. PEEK 3D-printed parts were manufactured at one per build.
## 2 System Model and Assumptions
### Transmitter  
Due to its ease of implementation and theoretical analysis, on-off keying (OOK) modulation has found widespread application in the domain of optical communication. In our system, we employ OOK modulation to process the raw information and obtain the initial signal for the system. Let \(S_{k}\) denote the modulated data that the transmitter needs to send to \(\text{User}_{k}\), whereas the corresponding \(\boldsymbol{S}=[S_{1},\cdots,S_{K}]^{T}\) denotes the data vector that the transmitter needs to send to all users. We assume that OOK modulation is equiprobable, with \(S_{k}\in\{-1,1\}\) and \(\Pr(S_{k}=1)=\Pr(S_{k}=-1)=\frac{1}{2}\). The analysis and algorithms developed in this paper can be easily extended to other modulation methods, such as \(M\)-PAM, but require more complex analysis and higher computational complexity.  
The symbol \(\boldsymbol{S}\) undergoes a linear transformation through a precoding matrix \(\boldsymbol{W}\in\mathcal{R}^{N\times K}\). The aim is to facilitate collaboration among multiple users, utilizing the  
Figure 1: Schematic diagram of the proposed downlink MU-PhC-MIMO VLC system.  
same temporal resources for communication tasks and mitigating interference among users to a certain extent. We introduce the constraint \(\|\mathbf{W}\|_{\infty}\leq 1\) for matrix \(\mathbf{W}\) to ensure that the designed precoding matrix is normalized.  
Subsequently, the data are modulated into a current quantity, \(\mathbf{I}_{\rm S}=[I_{\rm S,1},\cdots,I_{\rm S,N}]\in\mathcal{R}^{N\times 1}\), without DC bias. \(\mathbf{I}_{\rm S}\) can be calculated from \(\mathbf{S}\), \(\mathbf{W}\), and \(\Lambda\), i.e., \(\mathbf{I}_{\rm S}=\Lambda\mathbf{W}\mathbf{S}=\Lambda\sum_{k=1}^{K}\mathbf{w}_{k}S_{k}\), where \(\Lambda\in\mathcal{R}_{+}\) represents the parameter for current modulation [24], and \(\mathbf{w}_{k}\) represents the \(k\)-th column of matrix \(\mathbf{W}\). To ensure that all data within the IM remain non-negative, we need to add DC biases, denoted as \(\mathbf{I}_{\rm d}=[I_{\rm d,1},\cdots,I_{\rm d,N}]^{T}\in\mathcal{R}^{N\times 1}\), after \(\mathbf{I}_{\rm S}\). Finally, the emitted current signal \(\mathbf{I}_{\rm Tx}\in\mathcal{R}^{N\times 1}\) can be expressed as:  
\[\mathbf{I}_{\rm Tx}=\mathbf{I}_{\rm S}+\mathbf{I}_{\rm d}=\Lambda\sum_{k=1}^{K}\mathbf{w}_{k}S _{k}+\mathbf{I}_{\rm d}. \tag{1}\]  
Ultimately, through optical modulation, we can obtain the transmitted optical signal vector \(\mathbf{P}\in\mathcal{R}^{N\times 1}\), defined as follows:  
\[\mathbf{P}=U\Lambda\sum_{k=1}^{K}\mathbf{w}_{k}S_{k}+U\mathbf{I}_{\rm d}=P_{\rm s}\sum_{k =1}^{K}\mathbf{w}_{k}S_{k}+U\mathbf{I}_{\rm d}, \tag{2}\]  
where \(U\) represents the voltage parameter, and we define \(P_{\rm s}=U\Lambda\) as the peak transmit power per symbol.
# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
## 4 Experimental Results and Discussion
### Temperature Interpolation for Macau Stations Based on CNN Modeling  
From the evaluation metrics (Table 1), the model prediction shows significant advantages in the immediate prediction (0 h) scenario. Compared with the traditional interpolation methods Nearest, Linear, and Cubic, the \(RMSE\) of the model prediction is only 0.924, which is reduced by 21.4%, 30.7%, and 30.7% compared to Nearest, Linear, and Cubic, respectively; the \(MAE\) is 0.685, which is reduced by 27.4%, 37.0%, and 37.7%; the \(MRE\) is as low as 0.530%, while the \(MRE\) of the traditional method is between 4.401 and 5.308%; the \(MSE\) is 0.854, which is much lower than that of the traditional method, which is between 1.381 and 1.779; the correlation coefficient is as high as 0.986, which indicates that the model is able to accurately capture more than 99% of data fluctuation pattern, far more than the 0.975-0.983 of the traditional method.  
The comparative time-series plot of temperature forecasts constructed in this study (Figure 4) clearly presents the dynamic matching relationship between the model predictions and the actual observations. The horizontal axis of the time series plot covers the complete observation period from 1 January 2021 to 1 January 2023 (with a time resolution of 1 h), and the vertical axis shows the temperature range from 5 \({}^{\circ}\)C to 35 \({}^{\circ}\)C. The blue solid line represents the actual temperature profile, and the orange dashed line represents the model prediction. The solid blue line represents the actual observed temperature profile and the dashed orange line represents the model prediction, and the two trends are highly  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **RMSE** & **MSE** & **MAE** & **MRE (\%)** & **Correlation** \\ \hline Nearest & 1.175 & 1.381 & 0.944 & 4.401 & 0.983 \\ Linear & 1.315 & 1.730 & 1.073 & 5.250 & 0.977 \\ Cubic & 1.334 & 1.779 & 1.087 & 5.308 & 0.975 \\ Ours & 0.924 & 0.854 & 0.685 & 0.530 & 0.986 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of nearest-neighbor interpolation, bilinear interpolation, cubic spline interpolation, and deep learning methods.  
coincident (Correlation \(r=0.986\)), indicating that the model is able to effectively capture the core pattern of diurnal temperature fluctuations and sudden weather events.
## 3 Results
### SIF Data Accuracy Improvement Analysis
#### 3.3.1 Analysis of Canopy-Based Accuracy Improvement  
From Figure 7, it can be seen that the R\({}^{2}\) fitting values after improvement (based on the canopy improvement method) at each site are sorted by underlying surface conditions as follows: cultivated and farmland area (Figure 7a) = pasture grassland area (Figure 7b), with values equal to 0.90, maximizing close to the optimal value. The overall significance is greater than 0.5 with a confidence interval of 95%. Compared to before improvement, the R\({}^{2}\) values after improvement of each underlying surface increased by 0.06 (pasture grassland area) and 0.05 (cultivated and farmland area), respectively. Additionally, the overall improvement ranges of the MB, RMSE, and SD at each site were between 0.0009-0.0013, 0.0012-0.0065, and 0.0348-0.0864, respectively. The overall fitting parameter errors were improved to varying degrees, with the cultivated and farmland area showing the largest overall improvement and the pasture grassland area having the smallest improvement. This indicates that the canopy method has good applicability in areas with high vegetation coverage in arid regions.  
However, owing to the short growth cycle and small leaf area of short-lived vegetation on the underlying surface of the Kelameili Station (desert vegetation area), the LAI in some areas during the corresponding time period cannot be accurately measured, resulting in the inability of the canopy method to accurately achieve the SIF data accuracy improvement for this type of underlying surface. This indicates that the canopy method is highly applicable in areas with dense vegetation in arid regions, whereas its applicability is weaker in areas with sparse vegetation, indicating certain limitations overall.
## 3 Results and Discussion
### Relative Standard Deviation  
In all analysis cases, we noticed that the relative standard deviation had an inverse correlation to the peak area (Figure 1) and therefore also to the peak area %. The relative standard deviation is larger in compounds that are present in lower amounts.  
The maximal relative standard deviations for the different methods are presented in Table 2. It was observed in our study that adding sodium chloride lowers the relative standard deviation of the peak area in extracted samples. The relative standard deviation of the peak area was lowest in the 1:1 extracted samples using split 100, where the maximal RSD was 8%. When split 10 was used, the maximal RSD was 20% with no sodium chloride used and 14% with sodium chloride. In the 10:1 extracted samples, the maximal RSD was 41% with no sodium chloride used and 33% with sodium chloride.  
The standard deviation was the worst in direct hydrosol analysis using split 10, where the maximal RSD was 46%. When split 100 was used, the maximal RSD was 10%. In general, water samples have higher RSDs, and using split 10 gives higher RSDs comparedto split 100; however, in extracted samples, this is only true for compounds that are present in low concentrations (Figure 1). One of the possible reasons of higher RSDs for water samples compared to the hexane extracted samples is the difference in expansion of the sample when gasified. Water has higher density and lower molar mass compared to hexane; therefore, it expands more when evaporated. Consequently, it behaves differently in the GC system.  
In conclusion, 1:1 extraction and using split 10 in GC-MS program gives the best results in terms of relative standard deviation. Relative standard deviation is comparable with a split 100 method in compounds that are detected with both methods, which is under 8% (Figure 1).
## 3 False Data Injection Attacks on RL-Based Charging Coordination
### False Data Injection Attacks
#### 3.1.2 Attack 2: Time-Varying Reduction Factor  
This attack is an advanced version of Attack 1. To make Attack 2 more stealthy than Attack 1, instead of using a fixed reduction factor like in Attack 1, the malicious consumer varies the reduction factor (\(\alpha_{t}\)) at each time step, making the attack more difficult to detect. The random variation of the reduction factor across different time periods disrupts the RL  
Figure 1: Actual and reported SoC values after applying Attack 1 with \(C_{max}=6\) and 10.  
agent's ability to identify a consistent attack pattern, as the behavior appears more dynamic. Equation (10) is used to calculate the false SoC values based on the actual values.  
\[f_{2}(SoC_{i}^{(t)})=\alpha_{t}\times SoC_{i}^{(t)},\quad\alpha_{t}\in(0.1,0.7) \tag{10}\]  
where:  
* \(\alpha_{t}\) is the reduction factor that changes over time, making the attack appear less predictable and more difficult to detect.
# Upper Limb Orthoses: Integrating Topology Optimization and 3D Printing for Custom Fit and Function
## 3 Generating the Design Domain and the Associated FE Model
### Developing the FE Model
#### 3.4.1 Mesh Generation  
Mesh generation was conducted using HyperMesh, resulting in the creation of three distinct models. Each model is constructed from 4-node tetrahedral elements. The first two models were designed for the first and second load cases, respectively, with the primary difference being their mesh densities. The third model, intended for the third load case, is similar to the second in terms of mesh density. The first model features a coarse mesh, with element sizes of 1.5 mm, comprising 211,422 elements and 56,168 nodes. In contrast, the second and third models employ a finer mesh, with element sizes of 1 mm. This higher node density results in each model containing 668,088 elements and 157,395 nodes (Figure 6).  
Using HyperMesh allows for precise control over mesh density, which is crucial for accurately simulating different load cases. The coarse mesh of the first model provides a broad approximation, suitable for initial analyses where computational efficiency is a priority. However, the fine meshes of the second and third models offer greater detail and accuracy, essential for more refined and precise simulations. This differentiation ensures that the models can adequately capture the structural responses under varying conditions, enhancing the reliability and validity of the simulation results.  
Another approach regarding the discretization of the section could be the use of shell elements (2.5D shell elements), choosing either the inner or the outer surface of the splint and considering a constant thickness of 3 mm, i.e., equal to the thickness of the structure. This methodology would lead to a smaller number of FEs and would decrease the computational time to solve the TO problem. This method was followed to solve a similar problem in [PERSON] et al. [10].