esa-sceva/satcom-chunk-collection huggingface dataset:
# Least-Squares Estimation of Tangent Space for a Triangular Mesh
## 5 Conclusions  
In this paper, we have developed an intuitive yet rigorous method for computing per-vertex tangent spaces in a triangular mesh with texture coordinates. Our method utilizes least-squares estimation of rigid transformation to align the 1-ring neighbor vertices of a vertex in texture space with the corresponding vertices in world space. One notable advantage of our method is its inherent generation of orthonormal tangent spaces, eliminating the need for the intricate operations, such as averaging and orthonormalization, required in the previous methods [2; 3; 5]. In addition, we have introduced a novel approach for aligning texture space triangles to address seams inherent in texture mapping to a closed surface mesh. This approach also employs least-squares estimation of rigid transformations in texture space to align both sides of every seam as closely as possible. To our knowledge, texture space alignment for seams has been introduced for the first time in this paper. Furthermore, it can be used to average tangent spaces in the previous methods. Currently, our method deals with triangular meshes only, but it can potentially be extended to polygonal meshes by modifying the weights for the least-squares estimation process.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (NRF-2021R1F1A1062796) and the Research Grant of Kwangwoon University in 2023.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
Figure 10: Differences of normal vectors obtained by our method and the linear method. (**a**) Differences and histogram in a sphere model; (**b**) Differences and histogram in a cow model.  
**Acknowledgments:** The research was conducted during the sabbatical year of Kwangwoon University in 2023.  
**Conflicts of Interest:** The author declares no conflicts of interest.
# Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
## 2 Related Work
A single station enables the generation of a large amount of homogenous data. Therefore, earthquake detection from multiple station data is a more challenging task than single station data for deep learning models [17]. CNN-based models are often used for single-station datasets [26; 27] while GCNN models are good to process multiple-station datasets [5; 18]. A problem with deep learning models is the fine-tuning of their parameters. Batch normalization is a technique used for faster and stable training of a deep learning model [13; 14]. In training, the objective of batch normalization is to normalize the layer output using the statistics of each mini-batch size. Recently, many studies used batch normalization in their proposed deep learning models to improve the model performance such as brain tumor detection [6], gas-liquid interface construction [28], and fault diagnosis of a machine [29]. Unfortunately, automatic earthquake detection from seismic data generated from multiple stations is deprived of the batch normalization technique. A set of studies have been summarized in Table 1. It is seen that CNN-based studies only use single-station seismic data for earthquake detection and do not use geographical information of the station for prediction. The studies which use graph-based neural networks use the dataset where events have been collected from multiple seismic stations. Moreover, these studies include station geographical information that significantly increase model prediction. It is because GNNs are good to handle spatial information [30]. A few studies are based on other models such as transformers or RNN models such as LSTM or BiLSTM.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### SPDT Integration with X-Band OFF and K/Ka-Band ON
#### 7.3.2 Noise Performance  
Figure 26 shows the K/Ka-band noise figure, which varies from 2.3 dB to 2.6 dB. By turning the X-band path off, the circuit ensures minimal interference or additional noise contribution in the K/Ka-band.  
Figure 26: Noise performance of the switchable wideband receiver front-end with the X-band arm OFF and K/Ka-band ON.  
Figure 25: Gain performance of the switchable wideband receiver front-end with the X-band arm OFF and K/Ka-band ON.
## 3 Results
### Cross Validation and Spot Sampling Evaluation Results  
The predicted and observed values were compared to the NN-PSO+EBK method using the correctness measures to test the robustness of the predicted models. The results shown in Table 6 exhibit a robust and accurate result based on the R values close to 1. The cross-validation results suggested that all values provided more accurate spatial distribution for the study area. The cross-validation results are presented in Table 6.  
Figure 11: Heavy metal concentration map of Calapan City (**a**) Ba, (**b**) Cu, (**c**) Fe, (**d**) Mn, and (**e**) Zn.  
A spot sampling analysis was performed using the data from the households in different barangays of Calapan City. A total of 21,559 households were utilized in the spot sampling analysis which is presented in Figure 12. The distribution of the households included in the spot sampling analysis per barangay is presented in Figure 13.  
The spot sampling results was compared to the spatial concentration maps created in Figure 11. Table 7 exhibits the spot sampling comparison results for all heavy metals detected in the GW resources in Calapan City. The results showed that the created maps provided good results based on its MSE which is approaching zero when contrasted to the spot sampling values.  
Considering each barangay, the MSEs for each element were also obtained as presented in Table A2 of Appendix C. Figure 14 presents the summary of the spot sampling comparison results in each barangay for all heavy metals considered in the study.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Criteria** & **Temp** & **pH** & **EC** & **TDS** & **Ba** & **Cu** & **Fe** & **Mn** & **Zn** \\ \hline R & 0.989 & 0.990 & 0.934 & 0.977 & 0.994 & 0.976 & 0.985 & 0.975 & 0.984 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cross Validation Results for NN-PSO+EBK simulation.  
Figure 12: Concentration of Zn in GW samples.  
Figure 13: Distribution of the Number of Households included in the Spot Sampling Analysis.  
Figure 14: Cont.
## 5 Performance Evaluations
### Path length  
The path length is the length of the distance traveled by the MA, following the proposed model as it travels through the network. Path length does not affect localization error, but it helps identify parameters such as time and energy spent for the entire localization process. Mathematical equations of path length designs are calculated according to two variables: network size, S, and the distance between both points, dx. The following equations show the mathematical expressions of each static path planning model for the obstacle-free scenario. Equations (13)-(18) show the path length of HILBERT, LMAT, Z-curve, M-curves, H-curve, and NHExCurves path planning models, respectively.  
For example, if dx = 12.5 m, S = 64 (dx)\({}^{2}\) is obtained. However, as can be shown from Figures 4 and 5, dx is not always 12.5 m in all models.  
\[\mathrm{L_{Hilbert}}=\frac{\mathrm{S}}{\mathrm{dx}} \tag{13}\]  
\[\mathrm{L_{LMAT}}=\frac{\mathrm{S}}{\mathrm{dx}}+20\mathrm{dx} \tag{14}\]  
\[\mathrm{L_{Z}}=\left[\left(\frac{5}{8}\times 4^{3}\right)-1\right]\mathrm{dx} +\left[\left(\frac{3}{8}\times 4^{3}\right)\right]\times\sqrt{2}\mathrm{dx} \tag{15}\]  
\[\mathrm{L_{M}}=\frac{109}{2}\mathrm{dx}+16\sqrt{2}\mathrm{dx} \tag{16}\]  
\[\mathrm{L_{H}}=\frac{\mathrm{S}}{\mathrm{dx}}+18\mathrm{dx} \tag{17}\]  
\[\mathrm{L_{NHex}}=\frac{\mathrm{S}}{\mathrm{dx}\sqrt{2}}+38\mathrm{dx} \tag{18}\]  
Figure 19a shows the path lengths of the path planning models for the obstacle-free scenario. The HILBERT model is the model that travels the shortest distance with 800 m. The Z-Curves takes a little longer, with a travel distance of about 912 m. LMAT, H-curve, NHExCurves travel longer distances than other models. However, according to most evaluation criteria, these models give better results than other models. Figure 19b shows the path lengths of the path planning models in the case of obstacle-presence. When the two figures are compared, path length differences can be seen for path planning models between the obstacle-free and obstacle-presence scenarios. These results show that when the path length and the other performance criteria are evaluated together, H-curve and NHExCurves are optimum models that can be used in the obstacle-presence scenarios.  
Figure 19: The path length for the different mobility models (**a**) obstacle-free (**b**) obstacle presence.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 3 Methodology
### Multitask Encoder for Style Disentanglement
#### 3.2.3 Rationale for Patch Size (32 \(\times\) 32) and Overlap (\(5\times 5\))  
We determined these values empirically for a balance between capturing local details (serifs, stroke width) and maintaining reasonable computational costs. Preliminary experiments showed that smaller patches (e.g., 16 \(\times\) 16) could miss broader stroke patterns, while larger patches (64 \(\times\) 64) often overlooked finer details. The 5 \(\times\) 5 overlap also ensures coverage of all critical stroke regions.
# PD Detection and Analysis Triggered by Metal Protrusion in GIS Through Various Methods
## 2 Experimental Setup  
### Experimental Platform and Detection System  
The experiment was conducted on a 330 kV GIS platform, which used a gas-insulated transformer, directly connected with the GIS, as the power source. The test transformer capacity was 560 kVA, and the maximum output voltage was 280 kVrms. A photograph of the experimental system is shown in Figure 1.  
Figure 2 shows the detection system. During the process of the experiment, pulse current, ultrasonic and UHF methods were used to detect the PD signals at the same time. As shown in Figure 2, the pulse current method instrument measured the signal of the detection impedance in series with the coupling capacitor, which was inside the gas-insulated transformer. Prior to testing, the system was calibrated to convert the measured impedance  
Figure 1: Photograph of the experimental system.  
results into picocoulombs (pC). The signal of the ultrasonic detector was obtained using a piezoelectric ultrasonic sensor placed on the GIS shell. The UHF detector utilized a built-in planar antenna sensor (operating at 300-1500 MHz with an average effective height of 11.86 mm) to obtain the GIS signal.  
The detection frequency bands of the pulse current, ultrasonic and UHF methods were 10\(\sim\)300 kHz, 20-80 kHz and 500-1500 MHz, respectively.
## 4 Experiments
### Experimental Setup  
To generate MSFI embedding, we set the hyperparameters of 3D-ResNet as follows. The batch size was 2, and the number of training epochs was set to 100. We used the Radam optimizer [48] to alleviate the local minima convergence problem that may occur when an adaptive learning rate is used. Initial learning rate was \(10^{-3}\), and warmup-proportion was 0.1. We used 512 as the dimension of MSFI embedding throughout the experiments.  
The hyperparameters of existing pCR classifiers were set as follows. For tree-based classifiers, such as xgboost, lightgbm, and random forest, the number of decision trees was set to 1000 to obtain stable pCR prediction results. For a logistic regression classifier, we selected features with L2 regularization. For an MLP-based classifier, a two-layer MLP was used with ReLU as an activation function. We trained it using the Adam optimizer [49]. An ensemble classifier performs soft voting by averaging the pCR probabilities predicted by five classifiers: logistic regression, xgboost, lightgbm, random forest, and MLP.  
We examine three different dimension reduction methods in {No dimension reduction, PCA, and Kernel PCA} and compare the AUC of an ensemble classifier.
# AI Applications in Supply Chain Management: A Survey
## 3 Artificial Intelligence
### Demand Forecasting  
The integration of AI into demand projection presents many benefits, such as improved production planning, strategic inventory allocation, risk mitigation and new-product development [19]. Machine Learning techniques, like support vector machines, Neural Networks and decision trees, utilize data-informed insights to produce more precise forecasts and give companies tools to develop dynamic inventory policies to satisfy ever-changing demand [64]. In another research work [65], the AI techniques used for demand forecasting are the auto-regressive integrated moving average, long short-term memory networks, gradient boosting machines, support vector machines and Deep Neural Networks.  
Supply chain demand prediction and administration is a primary focus for SCM, where Artificial Neural Networks, data mining and fuzzy models are employed to foresee consumer consumption [25]. Demand forecasting is solely based on historical data for existing products and services. In cases where there is a new product or an innovative service, the absence of any chronological records remains deterring for any estimated demand. Precisely for these instances, AI is a viable alternative for sales projection and planning [24]. AI-based analysis of social media, like sentiment analysis [72], can help gain deeper insights into customer behaviors and preferences, allowing predictive models to identify potential markets and profit margins [73].  
The application of AI in demand forecasting for irregular demands [63] was investigated, and the most effective approaches were identified. The Neural Networks that were employed improved demand forecast accuracy, especially in cases of intermittent demands, lowering overall financial burdens, such as higher stock levels. Large Language Models have shown noteworthy progress in enhancing demand prediction accuracy by processing and analyzing large volumes of data, in both text and numeric forms [68].
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 4 Video and Audio Distortions
### Audio Degradations
#### 4.2.4 Chop  
A choppy speech effect was generated by discarding samples from an audio signal. Four test conditions (Table 5) were obtained by varying three parameters: (a) length of discarded samples, (b) sample discard frequency and (c) discarded samples treatment.  
The audio and video degradations and their corresponding test conditions described in this section were used as the basis to build three audio-visual sub-sets, which compounds the UnB-AV dataset [47]. Each sub-set was used in a different experiment which will be described in the following sections.
## 4 Discussion
In this study, the analysis was performed using color information in the visible light region, which can be obtained from digital images taken with common commercial cameras, to ensure that the study's results can be widely applicable. The three bands of red, green, and blue were converted to HSV and grayscale color space and used to extract color, texture, and shape features. However, using multispectral or hyperspectral cameras can provide additional information beyond visible light wavelengths. If it becomes possible to acquire this information using standard image sensors in the future, we predict that more features can be extracted to enhance the model's accuracy. Overall, the DNN regression model, based on features extracted from in situ soil surface images, has proven to be a powerful tool for predicting WC and BD accurately. The study's findings have great potential to impact the fields of agriculture and construction, paving the way for more effective soil management practices.
## 3 Materials and Methods
### CBAM Attention  
Although Resnet50 extracts multiscale features to ensure the diversity of features, it cannot focus on the importance of features. Adding the SE module after the Resnet50  
Figure 4: The bottleneck structure of ResNet 50.  
module can allow a better focus on the effective characteristics of sampling. CBAM is an attentional mechanism module combining space and channel. Compared with SENet [26], CBAM achieves better performance in image feature extraction. In deep learning, due to the difference in weight value between each channel, the product of each weight value and the value of the original channel is the characteristic value really required by deep learning. Based on SENet, the CBAM module considers the importance of pixels in different positions of each channel, obtains the importance of feature maps in channel and space by learning, and changes the weight value of different features to extract a more effective feature map. Suppose the input feature maps of the module in the middle of the network are \(F\in R^{C\times H\times W}\), \(C\), \(H\), and \(W\) are the number, height and width of input feature graph channels, respectively, and the output of feature graph \(F^{\prime}\) is obtained through the operation of the one-dimensional channel attention mechanism \(M_{C}\)= \(R^{C\times 1\times 1}\), as shown in Equation (7). After the two-dimensional space attention mechanism \(M_{s}\)= \(R^{1\times H\times W}\), the feature graph output \(F^{\prime\prime}\) is obtained, as shown in Equation (8), where \(\otimes\) is the feature graph multiplied element by element.  
\[F^{\prime}=M_{C}(F)\ \otimes F \tag{7}\]  
\[F^{\prime\prime}=M_{s}\big{(}F^{\prime}\big{)}\ \otimes F^{\prime} \tag{8}\]  
In Formula (7), the channel attention pays attention to the meaningful learning features in the feature graph. By using maximum pooling and average pooling, the feature graph is compressed in the spatial dimension to obtain two different spatial background description factors, \(F^{C}_{max}\) and \(F^{c}_{avg}\). The shared network of multi-layer sensing units is used to calculate the background description factors to obtain the channel attention feature graph, as shown in Formulas (9) and (10), with \(W_{0}\in R^{C/r\times c}\), b, Relu as the activation function after \(W_{0}\).  
\[M_{c}(F)=\sigma\big{(}\text{MLP}\big{(}\text{AvgPool}(F)\big{)}+\text{MLP} \big{(}\text{MaxPool}(F)\big{)} \tag{9}\]  
\[M_{c}(F)=\sigma\big{(}W_{1}\Big{(}W_{0}\Big{(}F^{c}_{avg}\Big{)}\Big{)}+W_{1} \Big{(}W_{0}\Big{(}F^{C}_{max}\Big{)}\Big{)} \tag{10}\]  
In Formulas (10) and (11), the spatial attention pays more attention to the location information in the feature graph. Maximum pooling and average pooling are adopted to obtain two different feature description factors, \(F^{c}_{max}\in R_{1\times H\times W}\) and \(F^{c}_{avg}\in R_{1\times H\times W}\), in the channel dimension. The calculation process is as shown in Formulas (12) and (13). Finally, the two features are combined and the convolution operation is used to obtain the spatial attention feature figure \(M_{s}(F)\in R_{H\times W}\).
## 4 Simulation and Evaluation
### Parameters  
The experiments were performed with the ns-2 Network Simulator ([[http://www.isi.edu/nsnam/ns/](http://www.isi.edu/nsnam/ns/)]([http://www.isi.edu/nsnam/ns/](http://www.isi.edu/nsnam/ns/))). The simulation parameters were based on the Mica2 Sensor Node ([[http://www.xbow.com/](http://www.xbow.com/)]([http://www.xbow.com/](http://www.xbow.com/))): transmission power of 45.0 mW, reception power of 24.0 mW, bandwidth of 19200 bits/s, and a communication radius of 40 m. As this platform uses a CSMA/CA like MAC layer protocol, we used the IEEE 802.11 implementation available in ns-2 in ad hoc mode with the frame structure necessary for the routing protocols, i.e., we only used the RTS/CTS channel access scheme of the implementation with collision treatment. In fact, we cannot ignore the power consumption relative to the channel listening (corresponding to idle power), which is close to the reception power in the Mica2 nodes. But this consumption is much reduced in solutions like B-MAC [24] due to periodical wake-ups. Also, as it is the same for all the evaluated algorithms, which results in a constant amount of energy added to all algorithms (verified through simulations), we do not considered it for the comparative analysis.  
In all simulations, we considered a 50-node network randomly distributed in an area of \(100\times 100\) m\({}^{2}\) and only one sink. Both data and control messages have 20 bytes and are transmitted every 10 s and 100 s, respectively. The main metric evaluated was the energy consumption. All experiments were executed 33 times with a confidence interval of 95% (vertical bars in graphics).  
Figure 6: Multi-MAF’s adaptive rule.
## 3 Experimental Campaigns  
The sample used for the tests was an RC wall of 1 m \(\times\) 1 m dimensions and a 6.5 cm thickness, cast with a water/cement/sand/coarse aggregate mixing ratio of 1:1.6:4.45:4.7 (by weight). The reinforcements inside the RC wall were steel bars of 12 mm in diameter, regularly placed at 10 cm. As the reinforcements had one layer, there were two values of concrete cover thickness: 3.8 cm and 1.5 cm. The sample was subjected to testing at the age of 3 months. The experimental setup of the tests is shown in Figure 4.  
A mid-wavelength infrared camera of FLIR, sensitive to medium waves in the range of 3-5 \(\mathrm{\SIUnitSymbolMicro m}\), with a 320 \(\times\) 256 matrix detector in InSb (indium antimonite), was placed at 2.6 m from the wall in the normal direction so as to detect the whole area of the wall to be heated by the microwaves. The microwaves used for the tests had a frequency of 2.45 GHz, and were guided onto the tested sample by a horn antenna with an opening of 40\({}^{\circ}\), a gain of 21.67 dB in its main direction, and aperture dimensions of 59 cm \(\times\) 56 cm. The sample was placed at 40 cm from the antenna's aperture.  
Figure 4: Experimental setup of the detection of RC wall by microwave thermography with transmission approach.  
Five tests were carried out for five different antennas' directions corresponding to the angles of the incident waves (\(\theta_{\mathrm{i}}\)): 0\({}^{\circ}\) (the normal direction of the microwave antenna), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\). The thermograms were recorded at regular intervals (one image per second). The sample was heated with an average power of 600 W for 5 min.  
It is essential to note that the concrete cover deduced from the methodology (with the transmission approach) was behind the reinforcement layer, not exposed to the microwave antenna. The use of the normal direction of the infrared camera is an advantage for the transmission approach, compared to the reflection approach, which can give real dimensions to the thermograms.