esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing and Analysis  
To ensure the reliability of the experimental results, the test was repeated three times and the results were expressed as the mean \(\pm\) standard deviation (SD). Differences between treatments were analyzed via one-way analysis of variance (ANOVA) at the 0.05 level of significance using IBM SPSS Statistics 27 software. Compound identification was performed using HMDB ([[https://www.hmdb.ca/](https://www.hmdb.ca/)]([https://www.hmdb.ca/](https://www.hmdb.ca/)), accessed on 10 December 2024, MassBank ([[https://massbank.eu/MassBank/](https://massbank.eu/MassBank/)]([https://massbank.eu/MassBank/](https://massbank.eu/MassBank/)), accessed on 10 December 2024), McCloud ([[https://www.mzcloud.org/](https://www.mzcloud.org/)]([https://www.mzcloud.org/](https://www.mzcloud.org/)), accessed on 10 December 2024), and self-constructed substance libraries. The NIST2020 database was used to annotate the raw data with flavor compounds using Chroma TOF library search software (version 5.55.35). The preprocessingsteps included normalization using the LOESS signal correction method based on pooled QC samples to eliminate systematic errors, log2 transformation to reduce the influence of high-intensity peaks and improve data distribution, and autoscaling (mean-centered and divided by the standard deviation) to ensure comparability across variables. PCA, PLS-DA, and OPLS-DA were performed using R ([[https://www.r-project.org](https://www.r-project.org)]([https://www.r-project.org](https://www.r-project.org)), accessed on 10 December 2024) and SIMCA-P (v13.0, Umetrics, Umea, Sweden), respectively. Based on the Kyoto Encyclopedia of Genes and Genomes (KEGG, [[https://www.genome.jp/kegg/](https://www.genome.jp/kegg/)]([https://www.genome.jp/kegg/](https://www.genome.jp/kegg/)), accessed on 10 December 2024), metabolic pathway enrichment analysis was performed. Histograms, clustered heatmaps, and volcano maps were drawn using R software (plot package and heatmap package, version 1.22.0).
## 3 PIV Experimental Apparatus
### Verification for Grid Independence  
After the qualitative verification of the EE-KTGF model, the simulation results for the axial, radial and tangential velocities at \(z=0.2H\) obtained using computational grids with 700, 1300 and 1800 k cells were compared with the experimental data from [PERSON] et al. (2015) to ensure grid independency and reliable results in high solid loading systems. The impeller speed was 405 rpm and the solid volume fraction was 5.2%. Figures 6 and 7 display three velocity components of both the liquid and solids with different mesh cells. The velocity components were normalized by the impeller tip speed, \(u_{\text{tip}}\). It is evident that the results with different cells have the same variation trend, but the 700 k cells had a large deviation compared with experimental data. A further-refined mesh was required to acquire accurate results. The normalized velocity components obtained using numerical simulation with 1300 k cells and 1800 k cells were in good agreement with the experimental results. The average relative error between the CFD and the experimental results was 9.7%, indicating that the simulation method can reasonably predict complex flow in the stirred tank. The relative error for the normalized velocity component between 1300 k cells and 1800 k cells was less than 5%. Therefore, the mesh with 1300 k cells was used for the subsequent simulations to achieve a compromise between accuracy and computational cost.  
Figure 5: Liquid averaged velocity field. (**a**) PIV experiment, (**b**) CFD simulation.  
Figure 6: Comparison of radial profiles of liquid velocity with experimental results (\(z=0.2H\)). (**a**) Axial velocity component \(u_{z}\), (**b**) radial velocity component \(u_{\
u}\), (**c**) tangential velocity component \(u_{\theta}\).
# Virtual Source Array-Based Multiple Time-Reversal Focusing
## 5 Robust VSA-Based Multiple TR Focusing
In addition, Figure 9 shows how the SVD-based method, unlike the single focusing, can achieve multiple focusing. Figure 9b shows the snapshot result of the propagated field as the weight vector calculated through Equation (9) is back propagated. In this case, the weight vector, \(\mathbf{w}\), that is backpropagated is a vector that best approximates the synchronized transfer functions corresponding to the two locations, \(\overline{r}_{s1}\) = (2.2 km, 20 m) and \(\overline{r}_{s2}\) = (2.3 km, 40 m), so that one dominant field propagates unlike the MCM-based method. That is, although the SVD-based TR focusing is not a simultaneous focusing method, it is a robust multiple focusing method of forming a field that passes through multiple focal locations even in sound speed mismatch environment. In this case, two fields separated in the vertical direction are propagated to satisfy multiple focusing. The field corresponding to the number 2 is an unnecessary field at the focal location, \(\overline{r}_{s1}\) = (2.2 km, 20 m), but is a field for focusing at the location, \(\overline{r}_{s2}\) = (2.3 km, 40 m) (i.e., the number 2\({}^{\prime}\)).
## 4 Evaluation of Quality and Efficiency
### Definition of the Material Law in the FE Software  
In order to determine the influence of the number of support points \(\rm n_{M}\) for the material law on the load-notch-strain curve, load-notch-strain curves are generated on a flat specimen notched on both sides while varying the number of support points in the material law. The flat specimen with a stress concentration factor \(\rm K_{t}=3\) and a tensile strength \(\rm R_{m}=800\) MPa is loaded in tension; see Section 4.2. The reference load-notch-strain curve is determined with the same boundary conditions and \(\rm n_{M}=200\) support points for the material law.  
The service lives for constant amplitude loading \(\rm N_{const,n_{M}}\) and variable amplitude loading \(\rm N_{var,n_{M}}\) serve as quality criteria, and the computing \(\rm CP-Time_{n_{M}}\) serves as the efficiency criterion. Figure 15 shows the comparative values related to the value at \(\rm n_{M}=200\) support points for a low load \(\rm L_{max,1}\) and a high load \(\rm L_{max,3}\).  
Figure 15: Influence of the number of support points \(\rm n_{M}\) of the material law on the load–notch-strain curve.  
Both the service life at constant and variable amplitude loading are underestimated for only few support points \(n_{M}\), regardless of the load level, resulting in a conservative service life estimation in the applied case. At low loads, the service life converges more quickly to a constant value than it does at high loads, where no influence of the number of support points on the service life can be seen from \(n_{M}=50\) support points on. The greater the number of support points is, the longer the simulation time is. In the case considered here, this results in time savings of about 10% when using \(n_{M}=50\) support points compared to \(n_{M}=200\) support points.  
In Figure 16, the influence of the number of support points \(n_{M}\) for the material law on the service life with variable amplitude loading is examined for all flat specimens of the database under tensile loading at high load \(L_{max,3}\); see Section 4.2. Here, too, it can be seen that the material law is described precisely enough for 50 and more support points \(n_{M}\) for all examined specimens. It may be concluded that from 50 support points onwards, no significant influence on the service life and thus on the load-notch-strain curve results. With less than 50 support points \(n_{M}\), especially the slightly notched specimens react strongly to the poorly described material law.  
As a recommendation for the user, \(n_{M}=50\) support points are therefore suggested to describe the material law. In order to exclude the influence of inaccuracies in the material law in the following sections, the value \(n_{M}=100\) is used below.
# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 5 Methodology
### Convolutional Neural Networks (CNNs)  
Currently, CNN is considered the best machine-learning (ML) algorithm for analyzing medical images [27; 28; 29]. The reason behind this is that after filtering the input images, CNN preserves the spatial relationships. These relationships are extremely significant in the field of radiology and other medical tasks. CNN has several types of layers such as convolution, pooling, rectified linear unit (ReLU), and fully connected layers [23]. Generally, its structure consists of a convolutional layer followed by a ReLU layer, a pooling layer, one or more convolutional layers, and one or more fully connected layers, respectively. The key feature that characterizes CNN apart from a normal neural network is its image structure during processing. The CNN's main layers are described below.
# A Low-Profile Dual-Polarized Transmitarray with Enhanced Gain and Beam Steering at Ku Band
## 3 Equivalent Circuit Analysis of the UC  
The ECM of the UC was developed using the same principle followed in the literature [45] for a better understanding of its operation. Then, it was simulated with Advanced Design System (ADS) software (2023 version). The simulated S parameter values of ADS software (2023 version) were compared with those obtained by the CST simulation. Figure 6 shows two adjacent UCs and the ECM. Starting from the UC geometry, the inductive and the capacitive behavior of the different parts of the metallic pattern are represented by means of discrete component as capacitors or inductors. In particular, the inductances of the inner circular metal portion and the outer ring are modeled by inductors L\({}_{\rm c}\) and L\({}_{\rm r}\), respectively. The gap between the metal circle and the outer ring is represented by a capacitor C\({}_{\rm g}\). The capacitance formed between the metallic circles of two adjacent cells is represented by C\({}_{1}\). Similarly, the capacitance formed between the outer metal rings of two adjacent cells is represented by C\({}_{0}\). The substrate is modelled using transmission line theory. It is modeled as a transmission line having an impedance of \(Z_{\rm g}=Z_{0}/\sqrt{\epsilon_{\rm r}}=255\ \Omega\), where \(Z_{0}=377\ \Omega\) is the characteristic impedance of free space and \(\epsilon_{\rm r}=2.2\) is the relative permittivity of the substrate.  
\(Z_{\rm s}\) is the substrate impedance and \(Z_{0}\) represents the characteristic impedance of free space. The value of the capacitances and inductances are the same for all three metal layers.  
Figure 5: Transmission magnitude \(\mid\)S\({}_{2,1}\mid\) as a function of frequency f for cross-polarized components.  
Figure 6: (**a**) Top layer of two adjacent UCs and (**b**) ECM of a single UC.  
UC\({}_{6}\) is chosen for computing the circuit component values of ECM. For UC\({}_{6}\), providing \(\angle\)S\({}_{2,1}\) = 270\({}^{\circ}\) transmission, the outer radius of the metal ring, the inner radius of the metal ring, and the radius of the inner circle, are R\({}_{1}\) = 4.325 mm, R\({}_{2}\) = 4.20 mm, and R\({}_{3}\) = 3.07 mm respectively. The circuit component values of the ECM for UC\({}_{6}\) are listed in Table 3. These values are computed using gradient-based optimization method by ADS software. The target of this optimization is to obtain the same magnitude 1S\({}_{2,1}\), and phase \(\angle\)S\({}_{2,1}\) values of the scattering parameters at the corresponding frequencies f found in CST simulation by tuning the values of the circuit components. The transmission phases of the UC are mainly dependent on the values of the capacitors and the inductors. Figure 7 shows the magnitude and phase values of scattering parameter S\({}_{2,1}\) of UC\({}_{6}\) for both CST simulation and ECM calculation. A good agreement is reached.
# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## 2 Background
### Data Compression  
Data compression is usually used to save storage and to reduce data transmission, thereby speeding up transmission of data from one endpoint to another. We can compress not only text but also images, audio, and video files. Several techniques of data compression, such as Huffman Code, Shannon Fano Code, and Lempel Ziv are designed to ensure that the size of the compressed file is smaller than the original [13; 14].  
As in many contemporary applications, we will make use of the Huffman Codes [15] because they can easily recover from errors and faults.  
The Huffman Codes were invented in 1952 by a student who studied with Prof. [PERSON] from MIT. At the time, the Shannon-Fano Codes were the best available method for data compression. While [PERSON] was thinking about doing research, however, he came up with an algorithm called \"Symbol Coding Method\", \"Prefix Coding\" or \"Huffman Coding\". This compression technique offers encoding symbols such as text characters without data loss [16].  
The Huffman Codes are an algorithm that belongs to a group called prefix codes. This algorithm provides good data compression and stores the items in a minimum number of bits when their frequency is high--according to the probabilities at which each item appears. This method is based on assigning a variable-length code to each item according to its frequency, so that a frequent item will be represented by a small number of bits whereas an infrequent item will be represented by a longer code.  
[PERSON]'s coding has a legendary and important status in the field of computer science and engineering, for its simplicity and applicability make it an idyllic example in algorithm courses. Moreover, it is one of the most common techniques used for data compression [17].
## 4 Results and Discussions
### UAVIC Investigation on Bridge  
The proposed method is validated in the laboratory conditions and investigated in the field conditions under controlled climate. Image analysis of different vehicle-loading conditions is performed in the DIC Ncorr software. The investigated displacement records are presented in Table 2.  
Figure 17a depicts displacement at failure load in the longitudinal direction (U-Displacement) is 2.82 mm, which indicates compression in the deformed image. The median value was found to be \(-0.0519\) mm. Figure 17b depicts that displacement at failure load in the lateral direction (V-Displacement) is \(-4.1928\) mm, which indicates tension. The median value was found to be \(-3.2359\) mm.
# Computationally Efficient PM Power Loss Mapping for
## 5 Modification in the Full Operational Envelope  
The above analysis of PM loss is based on the assumption that the operating temperature of PMs is constant. However, when PM loss leads to a temperature increase in PMs, the PM material demagnetizes and the residual flux decreases. As a result, the amplitude of space harmonic and time harmonic changes in the PM, which affects the generation of PM loss. At the same time, the conductivity of the PM changes with the temperature. The conductivity of the PM decreases with an increase in temperature. There is a direct linear relationship between the PM loss and the conductivity of PM. The lower the conductivity, the lower the PM loss. However, the PMSMs for vehicle applications need to work in a variety of complex and variable conditions, and the temperature of the PM dramatically changes. Therefore, it is necessary to embed the temperature factor into the fitting formula method of PM loss to eliminate the calculation error of PM loss caused by temperature changes.  
Figure 4: Comparison of the additional PM loss under different carrier ratios calculated by finite element analysis (FEA) and the fitting formula in Equation (8) at two speeds under constant-power conditions.
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 2 Materials and Methods
### Spatial-Temporal Attention Layer  
Based on the multi-head self-attention mechanism, we developed a spatial-temporal attention layer to capture both dynamic and long-range spatial-temporal correlations. The attention layer is comprised of two key modules. The first, a spatial self-attention module, incorporates an adaptive similarity matrix that dynamically identifies and emphasizes the interactions between a node and others that exhibit similar traffic patterns, enhancing the model's capacity to capture spatial dynamics. Concurrently, the temporal self-attention module is engineered to discover long-range and dynamic temporal dependencies, ensuring that important temporal information is preserved and integrated throughout the predictive process. By maintaining a continuous flow of essential spatial and temporal features, these modules collectively improve the accuracy and reliability of traffic flow predictions.  
To precisely describe the operations of multi-head self-attention, we employ the following slicing symbol. In the context of an embedding, \(X\in\mathbb{R}^{T\times N\times d}\), the slicing operation along the T-axis yields the matrix \(X_{t:}\in\mathbb{R}^{N\times d}\), and slicing along the N-axis produces the matrix \(X_{m:}\in\mathbb{R}^{T\times d}\).
_applied sciences_  
_Article_  
Calibrated Convolution with Gaussian of Difference  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 2,**  
Footnote 11: footnot  
+++ ==WARNING: Truncated because of repetitions==
emark:  
++  
SK has a stronger ability to represent features than the single-scale method, SE). However, while such methods are effective in carrying out a multi-scale feature fusion, they seldom touch the distinctive importance of invariance in the scale space. Still, scale-invariant features can be of crucial importance in a wide variety of visual tasks.  
In view of the above issues, we have developed a novel convolution operator called Calibrated Convolution with Gaussian of Difference that unifies scale-invariance and attention mechanisms. The focus of the method is upon collecting scale-invariant information for the execution of self-calibration. This generates more discriminative feature representations by explicitly incorporating more accurate information. As is shown in Figure 1, CCGD is better to extract comprehensive salient regions than traditional convolutions or other attention mechanisms, no matter what shape they are. Figure 2 shows a structural diagram of CCGD. It is composed of two paths. One is a scale-invariant attention module that can transform a feature map into a pixel-level attention map. This attention map is used as a reference to guide the feature transformation process by means of its own invariant feature information. The other path is an original scale path. Here, feature maps are only executed as convolution operations to retain the original scale features and reduce the number of parameters. These two paths form a heterogeneously grouped structure that can aggregate contextual information at different levels. The work presented here offers the following contributions:  
* A novel convolution operator, called Calibrated Convolution with Gaussian of Difference (CCGD), which introduces scale-invariant information into the attention mechanism and has a heterogeneously grouped structure. CCGD is especially well-suited to feature transformation because of its remarkable capacity for self-calibration and multi-scale representation.
* The capacity to replace traditional convolution and plug-and-play methods in modern CNNs to form CCGDNet without introducing any extra parameters. This enables them to have a more robust representation learning capability.  
Comprehensive experiments were undertaken that demonstrate that our approach outperforms most methods on both the Cifar100 [15] and ImageNet [14] datasets when adopting the same standard training strategies.  
Figure 1: Heatmaps for different learning models using Grad-CAM [12]. All the selected models were based on ResNet50 [13] and trained on ImageNet [14]. It can be seen that CCGD offers more comprehensive region identification.
###### Abstract
In addition, the CMLOA is mainly derived from the integration of the tent chaotic mapping concept into the traditional LOA, which tends to improve the convergence speed and precision of node localization. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. In short, the key contributions of the paper are given as follows. * Design a new CMLOA-NLA technique to effectually determine the location of the nodes based on ANs, which enhances the localization by leveraging chaotic mapping concepts, leading to faster convergence and more precise results. * CMLOA-NLA demonstrates robustness against localization errors and variations in transmission range, making it suitable for real-world WSN deployments where environmental factors can introduce uncertainty. The rest of the paper is organized as follows. Section 2 provides the related works and Section 3 offers the proposed model. Then, Section 4 gives the result analysis and Section 5 concludes the paper.
# A Review on 5G Sub-6 GHz Base Station Antenna Design Challenges
## 3 Challenges in BSA Design
### Port Measurements: Impedance Bandwidth
#### 3.1.2 Modifications to Radiator  
Some of the commonly used techniques include various shapes of dipole [37], multi-dipole [13], and loop-shaped dipoles [10; 14]; however, some of these techniques result in larger aperture size and limit the design freedom. In [15], a 55% wide impedance bandwidth is achieved from 1.65 to 2.9 GHz by using a fan-shaped etching slot and chamfering quadrants along the diagonal dipole arms. The resulting prototype increased the radiation area and extended the current path to help improve the bandwidth. A bowtie antenna modeled by a Bezier spline was proposed to have a bandwidth of 68% from 1.427-2.9 GHz [38]. The multi-dipole antenna proposed in [13] achieved wider impedance bandwidth of 60% from 1.55 to 2.87 GHz.  
A multimode antenna with an embedded double loop configuration proposed in [14] has an impedance bandwidth of 51% from 1.68 to 2.83 GHz. In this design, a small loop inside an outer loop is added to generate a new resonant mode and hence widen the impedance bandwidth. In [39], a comparison of different patches with and without slots,  
Figure 7: (a) Circuit representation of the matching circuit for the feed. (b) Microstrip implementation of the matching circuit (c) Side view of the radiating element.  
including shorting strip, is performed to identify the structure that provides the widest impedance match. This comparison shows that the position of the coaxial feed cables, chamfer dimensions, and shape of the slotted patch contributed to 21.7% (0.82-1.02 GHz) and 49.5% (1.64-2.72 GHz) impedance bandwidth.