esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis and Results of CGAN-IFCM
### Performance Evaluation of CGAN-IFCM  
As shown in Table 1, there are four cases of formulations: (i) the binary detection model using the SVD database; (ii) the binary detection model using the VOICED database; (iii) the multi-class detection model using the SVD database; (iv) the multi-class detection model using the VOICED database. Section 3.1 will present and analyze the results of CGAN-IFCM in all these cases.
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 2 Materials and Methods
### Feature Pyramid Network  
Feature pyramids involve the integration of high-level feature maps spanning various scales, collaborating harmoniously with backbone networks. This synergy yields enhanced and equitable performance across multiple scales, augmenting object detection capabilities [69; 70; 71], and semantic segmentation [71; 72; 73]. The primary objective of object detection, a fundamental facet of computer vision, entails identifying the positions and categorizing objects within an image. This pursuit lays the foundation for other related tasks, including human pose estimation, expanding the scope of its utility [74]. FPN [70], Feature Pyramid Network (FPN) is a groundbreaking method in object detection using convolutional neural networks. It uses multi-scale feature data to enhance accuracy, particularly in detecting small-scale objects. The FPN's core concept is a feature pyramid that scales features across different tiers, allowing for a more detailed representation of lower-level information. This approach surpasses the granularity of higher-level features, allowing for more accurate detection of smaller objects. FPN's versatility makes it ideal for identifying and discerning small target objects, demonstrating its remarkable efficacy. The FPN algorithm is a trilateral process consisting of an initial traversal through a bottom-up convolutional neural network, an iterative descent from a top-down perspective, and a pivotal lateral connection that links features with their counterparts. The bottom-up phase in convolutional neural networks involves the gradual evolution of feature map dimensions as data moves forward. This dynamic fluctuation leads to the stratification of layers into discrete stages, where layers that maintain the feature map's size coalesce into a stage. This categorization helps assign unique attributes to each extraction task, which crystallizes as the final output from each step. This process creates a composite feature pyramid, with diverse characteristics from each stage contributing to a hierarchical ensemble. The top-down progression entails an intricate movement accomplished through Upsampling, a technique consistently reliant on the interpolation principle. In this context, Upsampling orchestrates the magnification of the original image, artfully infusing new elements between pixels. This augmentation is executed via a judicious interpolation algorithm woven around the original image's pixel configuration bedrock. It is paramount to underscore that the feature map ensuing from this Upsampling operation retains an identical dimension as the feature map is immediately contiguous in the layered hierarchy. This study compares various encoder configurations using EfficientNet, U-Net, and SE-ResNets lineages, examining their performance and result.
## 2 Results
### Analysis of DEGs  
To comprehensively assess the effects of light and darkness treatments on gene expression, we analyzed gene expression levels based on Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values. Among the 37,791 assembled transcripts, 3771 had FPKM values of 0, indicating that approximately 10% of genes were not expressed under darkness treatment. The largest group (12,941 genes; 33% of the total) exhibited FPKM values below 1, suggesting that one-third of the genes were expressed at extremely low levels under darkness treatment and that these genes may inhibit the synthesis of terpenoids. In total, 5085 (13%) and 6428 (17%) transcripts had FPKM values of 1-3 and 3-10, respectively, while 1857 transcripts displayed FPKM values greater than 60, accounting for approximately 4% of the total number of genes.  
A total of 13,074 of the genes were found to be differentially expressed between light and darkness treatments (L1-L2-L3-L4 vs. D1-D2-D3-D4), including 6227 that were upregulated and 6847 that were downregulated. Hierarchical clustering analysis was performed based on gene expression patterns (Figure 5A). Under light treatment, most genes in \(L\). _cubea_ showed high expression levels, while under darkness treatment, some genes showed low expression levels (Figure 5B). These results demonstrated that light is essential for terpenoid synthesis in \(L\). _cubea_.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Treatment** & **Raw\_Bases** & **Clean\_Reads** & **Q20** & **Q30** & **GC\_pct** \\ \hline L1 (Light-0 h) & 7.18 G & 44,639,390 & 98.85 & 96.9 & 46.9 \\ L2 (Light-6 h) & 7.57 G & 47,260,316 & 98.79 & 96.82 & 46.48 \\ L3 (Light-12 h) & 6.99 G & 42,747,128 & 98.91 & 97.03 & 45.56 \\ L4 (Light-18 h) & 7.45 G & 46,877,574 & 98.78 & 96.7 & 46.6 \\ D1 (Dark-0 h) & 7.36 G & 45,436,082 & 98.85 & 96.91 & 45.46 \\ D2 (Dark-6 h) & 7.62 G & 48,571,858 & 98.87 & 96.94 & 46.1 \\ D3 (Dark-12 h) & 6.66 G & 42,364,516 & 98.8 & 96.75 & 46.25 \\ D4 (Dark-18 h) & 8.97 G & 55,470,416 & 98.84 & 96.83 & 45.95 \\ \hline \end{tabular} Note: Treatment is the name of the sample; raw_bases is the total base number of original data; clean_reads are the number of remaining reads after filtration; Q20 rate is the proportion of bases with a mass value greater than 20 (error rate less than 1%) in the total sequence after filtration; Q30 rate is the proportion of bases with a mass value greater than 30 (error rate less than 0.1%) in the total sequence after filtration; GC_pct indicates the GC content of the filtered data.  
\end{table}
Table 2: Statistics of transcriptome data under light and darkness treatments.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 3 Error Bound Analysis
### Metric of Double Measurements
#### 3.2.2 Metric of RSS/TOF  
When the metrics of RSS and TOF are selected to conduct the indoor localization, the metric of RSS/TOF follows the bivariate normal distribution. The PDF of the metric of RSS/TOF with respect to \(\theta_{i}\) is:  
\[f_{\theta_{i}}\left(\text{R/T}\right)=\prod\limits_{k=1}^{m}\frac{1}{2\pi\sigma _{1}\sigma_{2}\sqrt{1-\rho_{12}{}^{2}}}\exp\left(-\frac{\sigma_{12}}{2(1-\rho_ {12}{}^{2})}\right) \tag{25}\]  
where \(\rho_{12}\) is the correlation coefficient with respect to \(\mathbf{P}_{ik}\) and \(\mathbf{d}_{ik}\), \(\sigma_{12}=\frac{\xi_{31}{}^{2}}{\sigma_{1}{}^{2}}+\frac{\xi_{32}{}^{2}}{ \sigma_{2}{}^{2}}-2\rho_{12}\frac{\xi_{31}}{\sigma_{1}}\frac{\xi_{32}}{\sigma_{ 2}}\). From (5), we can obtain:  
\[\left\{\begin{array}{l}J_{xx}\left(\text{T/A}\right)=\frac{I_{xx}\left(\text{ TOF}\right)}{1-\rho_{23}{}^{2}}+\frac{I_{xx}\left(\text{AOA}\right)}{1-\rho_{23}{}^{2}}- \frac{2\rho_{23}\sqrt{\rho_{2}\rho_{3}}}{1-\rho_{23}{}^{2}}\sum\limits_{k=1}^{ m}\frac{\sin\alpha_{ik}\cos\alpha_{ik}}{d_{ik}{}^{2}}\\ J_{xy}\left(\text{T/A}\right)=\frac{I_{yy}\left(\text{TOF}\right)}{1-\rho_{23}{}^{ 2}}+\frac{I_{yy}\left(\text{AOA}\right)}{1-\rho_{23}{}^{2}}-\frac{\rho_{23} \sqrt{\rho_{2}\rho_{3}}}{1-\rho_{23}{}^{2}}\sum\limits_{k=1}^{m}\frac{\sin^{2} \alpha_{ik}-\cos^{2}\alpha_{ik}}{d_{ik}}\\ J_{yx}\left(\text{T/A}\right)=\frac{I_{yy}\left(\text{TOF}\right)}{1-\rho_{23}{}^{ 2}}+\frac{I_{yx}\left(\text{AOA}\right)}{1-\rho_{23}{}^{2}}+\frac{\rho_{23} \sqrt{\rho_{2}\rho_{3}}}{1-\rho_{23}{}^{2}}\sum\limits_{k=1}^{m}\frac{\sin^{2} \alpha_{ik}-\cos^{2}\alpha_{ik}}{d_{ik}}\\ J_{yy}\left(\text{T/A}\right)=\frac{I_{yy}\left(\text{TOF}\right)}{1-\rho_{23}{}^{ 2}}+\frac{I_{yy}\left(\text{AOA}\right)}{1-\rho_{23}{}^{2}}+\frac{2\rho_{23} \sqrt{\rho_{2}\rho_{3}}}{1-\rho_{23}{}^{2}}\sum\limits_{k=1}^{m}\frac{\sin \alpha_{ik}\cos\alpha_{ik}}{d_{ik}}\end{array}\right. \tag{26}\]  
Therefore, based on (8), the error bound by using the metric of RSS/TOF with respect to \(\theta_{i}\) is:  
\[V_{m}\left(\text{R/T}\right)_{i}=\frac{J_{xx}\left(\text{R/T}\right)+J_{yy} \left(\text{R/T}\right)}{J_{xx}\left(\text{R/T}\right)\cdot J_{yy}\left(\text{R/ T}\right)-J_{yy}\left(\text{R/T}\right)^{2}} \tag{27}\]
###### Abstract  
Although widely used in various fields due to its powerful capability of signal processing, empirical mode decomposition has to decompose signals separately, which limits its application for multivariate data such as the structural monitoring data recorded by multiple sensors. In order to avoid this shortcoming, a multivariate extension of empirical mode decomposition is proposed to deal with the multidimensional signals synchronously by employing a real-valued projection on hyperspheres. This study presents a hybrid modal identification method combining the multivariate empirical mode decomposition with stochastic subspace identification and fast Bayesian FFT methods to more conveniently and accurately identify structural dynamic parameters from multi-sensor vibration measurements. Deployed as a preprocessing tool, the multivariate signals are decomposed into several aligned intrinsic mode functions, which contain only a dominant component in the frequency domain. Then, the modal parameters can be identified by advanced fast Bayesian FFT and stochastic subspace identification directly. The combined method is first validated by a numerical illustration of a frame structure and then is applied in a shaking table test and a full-scale measurement under nonstationary earthquake excitation. Compared with the finite element method, the peak-pick, the half-power bandwidth methods, and Hilbert-Huang transform method, the results show that this hybrid method is more robust and reliable in the modal parameters identification. The main contribution of this paper is to develop a more effective integrated approach for accurate modal identification with the output-only multi-dimensional nonstationary signal.  
multivariate empirical mode decomposition; parameter identification; stochastic subspace identification; fast Bayesian FFT; posterior uncertainty +
Footnote †: journal: Applied Sciences
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
## 3 Sensitivity Analysis
### Communication Range for Lossless Transmission Mediums  
An essential property for communication systems is the maximal transmission distance. In this section, the influence of different crucial parameters on the communication range is theoretically analyzed for coil-to-coil and coil-to-AMR communication. The system parameters used in the numerical results throughout this paper are listed in Table 2. The transmission medium is assumed to be lossless.
## 2 System Model
### Communication Model  
The received communication signal of the downlink UEs can be represented as  
\[\mathbf{Y}=\mathbf{H}\mathbf{X}+\mathbf{W} \tag{2}\]  
where \(\mathbf{W}=[\mathbf{w}_{1},\mathbf{w}_{2},\ldots,\mathbf{w}_{L}]\in\mathbb{C }^{U\times L}\) denotes the zero-mean Gaussian white noise matrix with variance \(N_{0}\), i.e., \(\mathbf{w}_{l}~{}\sim\mathcal{CN}(\mathbf{0},N_{0}\mathbf{I}_{U})\). In this communication system, to estimate the transmitted constellation points, users could calculate the proportionally received signal \(\mathbf{\hat{S}}=\rho\mathbf{Y}\) by using a precoding factor \(\rho\), thus directly obtaining some estimated transmitting symbols. Building on these concepts, we aim to minimize the average error power experienced by users by designing a dual-function precoding matrix \(\mathbf{X}\). In our  
Figure 1: Overview of a dual-function massive MIMO downlink system with low-resolution DACs.  
approach, the combined average power from downlink MUI and quantization errors is quantified as described in [8]  
\[\begin{split}\mathbb{J}_{c}&=\mathbb{E}_{\mathbf{S}} \Big{\{}\left\|\mathbf{S}-\mathbf{\tilde{S}}\right\|_{F}^{2}\Big{\}}\\ &=\mathbb{E}_{\mathbf{S}}\Big{\{}\left\|\mathbf{S}-\rho\mathbf{H} \mathcal{T}(\mathcal{P}(\mathbf{S},\mathbf{H}))-\rho\mathbf{W}\right\|_{F}^{2 }\Big{\}}\\ &=\mathbb{E}_{\mathbf{S}}\Big{\{}\left\|\mathbf{S}-\rho\mathbf{H} \mathcal{T}(\mathcal{P}(\mathbf{S},\mathbf{H}))\right\|_{F}^{2}\Big{\}}+\rho^{ 2}ULN_{0}\\ &=\mathbb{E}_{\mathbf{S}}\Big{\{}\left\|\mathbf{S}-\rho\mathbf{H} \mathbf{X}\right\|_{F}^{2}\Big{\}}+\rho^{2}ULN_{0}\\ &=\|\mathbf{S}-\rho\mathbf{H}\mathbf{X}\|_{F}^{2}+\rho^{2}ULN_{ 0}\end{split} \tag{3}\]  
where \(\mathbb{E}_{\mathbf{S}}\{\cdot\}\) denotes the mathematical expectation over \(\mathbf{S}\)[8], \(\left\|\mathbf{S}-\rho\mathbf{H}\mathbf{X}\right\|_{F}^{2}\) quantifies the interference among multiple users due to concurrent transmissions from several antennas, and \(\rho^{2}ULN_{0}\) denotes the interference caused by scenario noise. The expectation operator can be removed in the last equality of Equation (3) due to the deterministic symbol matrix \(\mathbf{S}\). To optimize communication, we address DAC precoding, as follows  
\[\begin{split}\min_{\mathbf{X},\rho}\left\|\mathbf{S}-\rho \mathbf{H}\mathbf{X}\right\|_{F}^{2}+\rho^{2}ULN_{0}\\ \text{s.t.}\ x_{b,l}\in\mathcal{X},\ \forall b,l.\end{split} \tag{4}\]
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 3 Proposed Methodology  
This study aims to use machine learning techniques to predict demand in the supply chain. The first step involves gathering input data related to the supply chain, which are then cleaned to remove any inconsistencies, errors, or irrelevant information. The cleaned data are then used to extract features, with a particular focus on retail features. Probabilistic and Apriori approaches are used to extract retail-specific features, which are then combined into a single retail feature using a weighted sum. Along with retail features, statistical features like mean, variance, standard deviation, and skewness over time are also extracted. Time series feature extraction employs methods such as simple average, log-log linear regression, weighted average, and two-level linear model to identify temporal patterns in the data. Log-log linear regression uses logarithmic transformations to analyze and measure the rate of change over time, giving a more nuanced understanding of trends [45]. The weighted average assigns varying weights to data points based on their importance, resulting in a more precise representation of evolving patterns. Two-level linear model methods use linear modeling at two levels to analyze complex relationships within the data. These techniques collectively contribute to accurately forecasting future demand in the dynamic context of the supply chain by identifying and understanding time-dependent trends. Feature extraction for demand indicators involves considering features related to sales frequency and inverse sales frequency. These extracted features then serve as inputs to a recurrent BiLSTM network and NARX. The recurrent BiLSTM network is utilized in the initial stage of demand forecasting to capture temporal dependencies and patterns in the data. The NARX model with autoregression considers historical data and external factors to anticipate future demand in demand forecasting. To enhance the accuracy and reliability of the forecast results, a combination of forecasts from the recurrent BiLSTM network and NARX model is used. This fusion approach is presented in the proposed framework. The architecture of the developed demand forecasting model is presented in Figure 1.
## 7 Discussion  
The results of this study indicate that the model combining Linear Discriminant Analysis with the Support Vector Machine (LDA-SVM) exhibits superior accuracy in classifying EMG signals for prosthetic control, achieving 90.69% accuracy with a comprehensive dataset spanning three sessions [25]. This performance surpasses that of the K-Nearest Neighbor (KNN) and Naive Bayes (NB) models, which is consistent with findings in the existing literature where the SVM often outperforms other classifiers in high-dimensionaland complex data scenarios [26; 27]. The effectiveness of LDA in dimensionality reduction and enhancing class separability aligns with prior studies highlighting its utility in EMG signal processing [28]. The use of a detailed dataset like GRABMyo, encompassing diverse gestures and participants, underscores the importance of comprehensive data in developing robust machine learning models [29]. These findings have significant implications for prosthetic control systems and rehabilitation technologies, suggesting that advanced machine learning models can improve the intuitiveness and responsiveness of robotic arm movements, ultimately enhancing user experience and quality of life. Future research should explore integrating deep learning approaches, such as convolutional neural networks (CNNs), which have shown promise in EMG signal classification [7]. Additionally, addressing limitations related to inter-subject variability and real-time implementation could further advance the practical applications of these methodologies in clinical settings. Expanding the dataset to include more varied and complex gestures, as well as real-world usage scenarios, could also help in developing more generalized and adaptable prosthetic control systems.
## 6 Conclusions  
This paper combines theoretical analysis with CFD numerical simulations to investigate the issue of asymmetric flow force characteristics in slide valves under inlet and outlet throttling modes. Based on the found steady flow force characteristics, a compensation method is proposed by introducing a guiding shoulder and matching it with an appropriately sized inner annular chamber. The main conclusions are as follows:  
1. The primary cause of the asymmetry in steady flow force curves under bidirectional throttling modes is the difference in fluid flow conditions at the non-throttling valve port. For slide valves with large openings in inlet throttling mode, the fluid momentum at the non-throttling valve port significantly influences the steady flow force and can even reverse its direction;  
Figure 28: Variation curve of steady flow force with \(\lambda_{1}\) and valve opening \(x_{0}\).  
Figure 29: Comparison of steady flow force for original and optimized structures.  
2. Vortices caused by turbulent flow are an intrinsic reason for the nonlinearity of flow forces in inlet throttling slide valves. Suppressing vortex formation helps reduce the asymmetry in steady flow forces under bidirectional throttling conditions;
3. Increasing the fluid momentum at the non-throttling valve port is an effective means to reduce the steady flow force in slide valves. Experimental results show that the proposed compensation structure reduces steady flow forces by 34.45% and 76.47% for the inlet and outlet throttling modes, respectively. Notably, the standard \(k\)-\(\varepsilon\) model combined with enhanced wall treatment is a common approach for simulating spool flow forces. However, this method has limitations in handling anisotropy and complex turbulence. Future research could explore large eddy simulation, detached eddy simulation, or the development of more accurate wall treatment methods for improved analysis.  
Conceptualization, Q.M. and Q.Y.; methodology, Q.M. and X.J.; software, X.J.; validation, Z.L. and G.L.; formal analysis, Q.M.; investigation, X.J.; resources, Y.C.; data curation, Y.C.; writing--original draft preparation, X.J.; writing--review and editing. Q.M.; visualization, G.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Defense Science and Technology Fund (2022-JCJCJ-1065).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company China Electronics Technology Group Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Materials and Methods
### Methods
#### 3.2.7 Modeling with Petri Nets

Table 1: Structure of the stores Table. The initial state of the system is represented by the initial marking vector, which specifies the initial distribution of tokens across the places. The initial state is shown in vector (2). \[\mathbf{M}_{0}=[1,0,0,0,0,1,0,0,0,0,0]. \tag{2}\]  
The flow of tokens within the net ensures event synchronization and prevents collisions in the communication between the mobile device and the API. The distribution and transition of tokens effectively model the system's concurrent behavior. Algorithm 2 represents the operational process of the Petri net for the proximity-based promotion delivery system. It describes the key steps of the system, from obtaining the user's location to delivering relevant promotions based on proximity. \begin{table}
\begin{tabular}{c c} \hline
**Places** & **Transitions** \\ \hline P1: Location query & T1: Send location to API \\ \hline P2: API calculates distance & T2: Proximity check (600 m or less) \\ \hline P3: Proximity confirmed & T3: API sends offers to mobile device \\ \hline P4: Read send & T4: Send message \\ \hline P5: Buffer out & T5: Receive message \\ \hline P6: Ready receive & T6: Send acknowledgment \\ \hline P7: Message received & T7: Receive acknowledgment \\ \hline P8: Acknowledgment sent & T8: Process 1 \\ \hline P9: Buffer in & T9: Process 2 \\ \hline P10: Wait for acknowledgment \\ \hline P11: Acknowledgment received \\ \hline \end{tabular}
\end{table} 
Table 3: Places and transitions of the proposed Petri net. Figure 3: Petri net representation of Table 3.  
The input matrix (I) describes the number of tokens consumed in each place when a transition is triggered. \[\textbf{I}= \begin{bmatrix}1&0&0&0&0&0&0&0&0\\ 0&1&0&0&0&0&0&0&0\\ 0&0&1&0&0&0&0&0&0\\ 0&0&0&1&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0\\ 0&0&0&0&0&1&0&0&0\\ 0&0&0&0&0&0&0&1\\ 0&0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&1&0&0\\ \end{bmatrix} \]  
The output matrix (O) describes the number of tokens placed in each place when a transition is triggered. \[\textbf{O}= \begin{bmatrix}0&0&0&0&0&0&0&1&0\\ 1&0&0&0&0&0&0&0&0\\ 0&1&0&0&0&0&0&0\\ 0&0&1&0&0&0&0&0\\ 0&0&0&1&0&0&0&0\\ 0&0&0&0&0&1&0&0\\ 0&0&0&1&0&0&0&0\\ 0&0&0&1&0&0&0&0\\ 0&0&0&0&0&0&1&0&0\\ \end{bmatrix} \]  
The incidence matrix (C) is obtained by subtracting the input matrix from the output, solving Equation (3) :  
\[\textbf{C}=\textbf{O}-\textbf{I}. \tag{3}\]