esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 4 Methodology
### Tensor Construction
#### 4.2.2 Factor Matrix Discussion  
The tensor decomposition resulted in three factor matrices \(\textbf{{A}}\in\mathbb{R}^{400\times 10}\), \(\textbf{{B}}\in\mathbb{R}^{n\times 10}\), and \(\textbf{{C}}\in\mathbb{R}^{8\times 10}\) as presented in Figure 4. Factor matrix \(\textbf{{A}}\) consists of extracted characteristic traffic patterns on the road network. If the column \(a_{j}\in\mathbb{R}^{400\times 1}\) of the factor matrix \(\textbf{{A}}\) is reshaped into the matrix \(20\times 20\) it represents the characteristic STM (traffic patterns). The  
Figure 4: Steps that are describing the tensor construction method using the STMs: (**1**) Grid-based map segmentation, (**2**) STM extraction, (**3**) tensor construction, and (**4**) factor matrices.  
goal of anomaly detection is to find the anomalous traffic patterns and link them to the corresponding values in spatial and temporal factor matrices. The matrix \(\mathbf{B}\) represents the spatial factor matrix, and the values in the rows \(b_{i}\) represent how well each of the characteristic STM represents the traffic flow on the corresponding transition on index \(i\). The values in the columns \(b_{j}\) show how well each characteristic matrix describes each of the transitions (spatial components) in the observed road network. The matrix \(\mathbf{C}\) represents the temporal factor matrix. The values in the rows \(c_{i}\) represent how well each of the characteristic STM represents the corresponding time interval on index \(i\), and the values in the columns \(c_{j}\) show how well each characteristic matrix describes each of the time interval (temporal components). The larger values in the factor matrices \(\mathbf{B}\), and \(\mathbf{C}\) suggest the greater impact of the spatial or temporal components on the corresponding factor [39].
## 2 Theoretical Background
### Spiral Spectrum Expansion Method  
Any field distribution can be considered as the superposition of multiple spiral harmonics [47; 48; 49].  
\[E(\rho,\phi,z)=\frac{1}{\sqrt{2\pi}}\sum_{n=-\infty}^{\infty}a_{\rm{In}}(\rho, z)\exp(in\phi) \tag{10}\]  
The expansion coefficients \(a_{\rm{In}}(\rho,z)\) are  
\[a_{\rm{In}}(\rho,z)=\frac{1}{\sqrt{2\pi}}\int_{0}^{2\pi}E(\rho,\phi,z)\exp(- in\phi)d\phi \tag{11}\]  
\[C_{\rm{In}}=\int_{0}^{\infty}|a_{\rm{In}}(\rho,z)|^{2}\rho d\rho \tag{12}\]The energy content or weight of each spiral harmonic of a field can therefore be determined as the following:  
\[P_{n|l}=\frac{C_{\rm{In}}}{\sum_{-\infty}^{\infty}Cl} \tag{13}\]  
The weight, \(P_{n|l}\), corresponds to the detection probability on the measurement plane, where the orbital angular momentum is \(n\).  
Using the spiral spectrum expansion method, there are two main approaches to analyzing the spiral spectrum characteristics of the field. One approach is to ignore the radial distribution of the field followed by sampling along the circle at a certain radius, see, e.g., [28; 50; 51; 52]. An alternative approach, e.g., [27; 47; 48; 49] is to sample radial and angular distributions of the field on the whole measurement plane at the receiving end.
## 4 Qualitative Findings and Discussion
### Providing Necessary Assistance
#### 4.2.1 Potential Barriers  
Participants' main potential barrier lay in not being sure whether they were wearing it effectively. One crucial aspect is the lack of clear usage guidelines, especially during initial use.  
\"I understand that it benefits our waist. But, when I first started wearing it, I didn't know how tight it should be--it was all based-on feeling. [...] The doctor told me to buy the one with strong support, LSAD. [...] But how tight should I wear it for it to be effective? It would be helpful to have instructions on how to wear it or markings and information provided.\" (Participant 3)  
The common perspective among participants is that LSAD should include a credible scientific basis for tension data. Participants believed that providing scientifically reliable tension data can reduce their concerns and barriers during usage, emphasizing the need to translate such data into specific usage guidelines for LSAD. For example, they felt there should be guidance on the appropriate level of tension for different types of lower-back pain to better assist them in understanding the effectiveness of LSAD.  
\"After putting this on, my back feels too swollen [...], but we are not sure about the intensity. What level of tension is more appropriate? Is tighter always better? We don't know. We can't rely on comfort for judgment. Sometimes, it's uncomfortable when it's too tight, but it helps your back recover quickly. Sometimes, it's comfortable, but it might be an ineffective wear. So, we need assistance in this regard.\" (Participant 5)  
\"To be honest, this thing is either too tight, making my waist uncomfortably swollen, or too loose, rendering it ineffective. And, I've heard people say that wearing it too tightly is actually bad for the waist. I can only rely on my feelings to adjust it. [...] But then again, if someone tells me that wearing it tightly is the most effective for my waist, I'll endure the discomfort for the sake of my waist.\" (Participant 2)
## 3 Results and Discussion  
### Fatty Acid Composition, Refractive Index, and Iodine Value  
The fatty acid composition, refractive index, and iodine value of high-oleic sunflower oil obtained by cold-pressing of sunflower hybrid seeds (NS-H-6326) are presented in Table 2.  
According to Codex Alimentarius [9], the oleic acid content in high-oleic sunflower oil should range between 75% \(w/w\) and 90.7% \(w/w\), while the linoleic acid content should range between 2.1% \(w/w\) and 17% \(w/w\). In the examined high-oleic sunflower oil, the oleic acid content was determined to be 83.32 \(\pm\) 0.02% \(w/w\), while the linoleic acid content was 7.46 \(\pm\) 0.13% \(w/w\). Among other unsaturated fatty acids, palmitoleic acid (C16:1) and alpha-linolenic acid (C18:3) were found in smaller amounts, contributing to a total content of 83.46 \(\pm\) 0.06% \(w/w\) of monounsaturated and 7.81 \(\pm\) 0.14% \(w/w\) of polyunsaturated fatty acids. Among the saturated fatty acids (total 8.71 \(\pm\) 0.09% \(w/w\)), palmitic acid (C16:0) was detected with a content of 4.32 \(\pm\) 0.07% \(w/w\), stearic acid was detected at 4.04 \(\pm\) 0.04% \(w/w\), and traces of myristic and arachidonic acids were found. Similar results have been reported by [PERSON] et al. [51] and [PERSON] et al. [52].  
The iodine value and refractive index at 40 \({}^{\circ}\)C were also examined as key parameters for oil identification. Additionally, the iodine value indicates oil quality. The obtained value of 86 \(\pm\) 1 g I\({}_{2}\)/100 g suggests a good oxidative potential of the analyzed sample. Both the iodine value and the refractive index of 1.462 \(\pm\) 0.000 are consistent with previous studies [5; 51] and the prescribed values outlined in the Codex Alimentarius [9].
## 3 Experimental Results and Discussions  
### Device Characteristics  
Detailed electrical and optical properties of the packaged 100-\(\upmu\)m blue micro-LED are measured, as shown in Figures 3 and 4.  
Figure 3 shows the current density and voltage (\(J\)-\(V\)) and current density and optical power density (\(J\)-\(L\)) of the package 100-\(\upmu\)m micro-LED. \(J\)-\(V\) characteristics were measured by a DC supply instrument under different current densities (RIGOL, DP800). JL characteristics were measured using a calibrated silicon photodiode detector (Thorlabs, PM100D). As shown in Figure 3, the linear region of the micro-LED is small. However, the nonlinear part in the subsequent experiment can be used to improve the SNR and the communication performance. The optimal value of DC and modulation depth of the system can be found by traversing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Hop 1** & **Hop 2** \\ \hline Transmitter & 100 \(\upmu\)m micro-LED & 100 \(\upmu\)m micro-LED \\ Output optical power & 67 \(\upmu\)W & 63 \(\upmu\)W \\ Receiver & APD210 (1 GHz) & APD210 (1 GHz) \\ The length of PRBS & 231\(-\)1 & 231\(-\)1 \\ Modulation format & OOK & OOK \\ Bias voltage & 4.48 V & 4.49 V \\ Modulation depth (Vpp) & 5.3 V & 5.2 V \\ Amplifier & SHF & SHF iXblue \\ Distance & 2 m & 4 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters based on Hop 1 and Hop 2.  
Figure 3: Current density vs. voltage (\(J\)-\(V\)) and current density vs. optical power density (JL) curves of the package 100-\(\upmu\)m micro-LED.  
As shown in Figure 4, the optical spectrum of the micro-LED is measured at various driving current densities, which can observe that the emission peak is around 488 nm. As the current density changes from 509.28 A/cm\({}^{2}\) to 891.24 A/cm\({}^{2}\), it can be seen that there is no obvious red/blue shift. Meanwhile, the intensity peaks will all significantly rise as the current density increases.
# VLSM-Net: A Fusion Architecture for CT Image Segmentation
## 4 Results and Discussion
### Ablation Experiment
Similarly, in the LUNA dataset, various network structures are compared to assess their segmentation performance. Table 2 illustrates that the VLSM-Net model outperforms other models in terms of lung nodules segmentation, with a \(DSC\) of 82.27%, Sensitivity of 89.91%, and Precision of 86.12%. Compared to the initial LSM and the V-Net model, the VLSM-Net model provides more precise segmentation results in the LUNA dataset. The above ablation experiments in the LiTS and LUNA datasets show that the VLSM-Net model proposed in this paper can reduce the number of disconnected parts, and improve the accuracy of the segmentation. In conclusion, the VLSM-Net model can obviously achieve better performance and results than LSM and V-Net alone.
# Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks
## 4 Deep CNN-Based Detector  
The imperfect CSI, which is introduced by the noise or delay of channel estimation, will greatly deteriorate the system performance of standard MLD. To overcome the effects of the imperfect CSI in secure MIMO communications, two types of deep CNN (DCNN)-based detectors are proposed in this section, which can be used in different application scenarios. The deep CNN models are first trained with predefined loss functions and then used to generate the refined CSI \(\widetilde{\mathbf{H}}_{UM}\), which can be fed to the MLD to obtain the original message, i.e.  
\[\hat{\mathbf{x}}_{cnn}=\arg\min_{\mathbf{x}\in\Omega}\|\mathbf{y}-\widetilde{ \mathbf{H}}_{UM}\mathbf{x}\|^{2}. \tag{11}\]  
The details of the DCNN model is given as Figure 2, where there exist \(N\) one-dimension convolutional layers excluding the input layer. In the input layer, the channel fading matrix \(\hat{\mathbf{H}}_{UM}\) is reshaped as a column vector. Since only real data can be processed in the CNN model, the complex data of \(\hat{\mathbf{H}}_{UM}\) can be treated as two real channels [40]. Please note that each convolutional layer is followed by a ReLU activation function except the output layer. Moreover, in the \(n\)-th convolutional layer, there are \(\{F_{n},n\in[1,N]\}\) features maps with filter length \(\{L_{n},n\in[1,N]\}\). Specifically, in the output layer, there is only one feature map.  
As to the detailed architecture of the DCNN, we must find the trade-off between complexity and performance. It is noted that the fully connected DNN, which may hold better performance, while its computational complexity is proportional to the square of the number of nodes. On the other hand, both the training data set and the training time required by fully connected DNN the is too large to be satisfied. There also exist some powerful CNN models, such as VGG [41] and ResNet [42], which improve the detection probability by increasing the depth of the models to 19 and 34, respectively. Specifically, the number of parameters for VGG-19 is up to 144M. Thus, to decrease the computational complexity, we must simplify the classical CNN models as follows. The architecture of DCNN model includes \(N\)=4 layers, and is described by \(F_{n}\) and \(L_{n}\) as \(F_{n}=\{32,16,8,1\},L_{n}=\{36,3,3,36\}\).  
In the following sections, two different methods, denoted as DCNN type-I and type-II, are introduced to train the DCNN model.
## 4 Discussion  
Our study compared two types of tissue expanders: those with traditional metallic injection ports and those with RFID ports. We focused on how the dose of RT affected the heart in left-sided breast cancer. We also analyzed how different types of injection ports influenced RT planning and how the dosimetry affected the surrounding normal tissues. In comparison to the metallic ports, the RFID ports showed superior results in terms of RT doses to the heart and lungs as well as in the dosimetric factors related to the target volume.  
Currently, most RT planning systems have been developed for CT. However, in the presence of metal implants within or around the target volume, metal artifacts may affect the accuracy required for dose calculation. In fact, many authors have reported on the effects of metallic ports on PMRT. [PERSON] et al. [13], for one, reported their preference of using a permanent implant over TE due to the potential dosimetric effects. Their investigation of single-beam film dosimetry showed that the Magna-Site may attenuate a standard 6 MV photon beam by as much as 22% and a 15 MV beam by 16%. Considering the difference in beam attenuation despite the minimal volume of tissue affected, they recommended the use of a 15 MV photo beam rather than a 6 MV photon beam. [PERSON] et al. [14] also reported reductions of approximately 20% in the photon tangent treatment and 56% in electron boost fields immediately downstream of the implants with magnetic disks.  
When treating patients with left-sided breast cancer, measures should be taken to reduce the dose to the heart, as this may increase the risk of heart diseases [15; 16; 17; 18; 19]. In addition to this, the present study analyzed the RT doses to the surrounding critical organs. On analysis, RFID ports showed superior results for the doses to the heart and lungs, supporting the notion to keep radiation dose to the heart as low as reasonably achievable. Although metal artifacts vary depending on the size and composition of the metallic port inside the TE, RIFD ports make use of copper rings instead of a solid magnet, thereby reducing the metal volume. Moreover, the RFID ports in this study uses a plastic material, polyetheretherketone (PEEK), as the needle guide, which differs from the stainless steel in conventional TEs. As PEEK has a relatively lower HU than other metals, this potentially reduces the artifacts on CT imaging [20].  
Despite these findings, one limitation of the study is that a phantom was used for analysis, which does not accurately reflect the varying chest contours and distance between the heart and target volume in actual practice. Therefore, the comparison of RFID and metallic ports should be further applied and analyzed in different clinical situations. Another limitation was that the metal artifacts were not completely eliminated despite the use of O-MAR.  
Figure 5: Dose-volume histogram for planning target volumes and organs at risk in radiation therapy plans of both radio frequency identification port and metallic port.
## 3 Materials and Methods
### Sentinel-1 and Sentinel-2 Layers  
The first step of the methodology was the processing of the S1 and S2 data into layers that are relevant for dry forest monitoring. The data were processed on the Google Earth Engine (GEE, [26]) platform. For the processing of the initial selection of the S1 and S2 image tiles, a buffer of 100 km was added to the study area's polygon in order to include those tiles that do not have their centroid inside the initial polygon.
# Numerical Study on Scale Effect of Repetitive Plate-Loading Test
###### Abstract  
Repetitive plate-loading test is intended to identify the elastic modulus of a target structure subjected to dynamic loading; such tests are mainly applied to railway roadbeds. The repetitive plate-loading test uses the same equipment as the plate-loading test but different loading methods. The plate-loading test derives the subgrade reaction modulus (\(k_{30}\)), while the repetitive plate-loading test derives the strain modulus (\(E_{v2}\)). The former considers the scale effect of the loading-plate size, whereas the latter does not, thereby reducing the reliability of the results. Therefore, numerical analysis was conducted to propose a scale effect that can applied to field tests. First, to verify the 50-mm loading plate, a previous study comparing the results of the 300-mm loading plate in a field test was simulated by a numerical analysis, and the results were compared and analyzed. Next, the strain modulus was investigated according to the loading-plate size under subgrade conditions. An equation to estimate the scale effect applicable to loading plates with diameters of less than 762 mm was derived. The relationship between the calculated strain and elastic moduli was additionally analyzed.  
repetitive plate-loading test; railway roadbed; plate-loading test; strain modulus; loading-plate size; scale effect; numerical analysis +
Footnote †: journal: Applied Sciences
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion
### Congestion Control Under Constant Bandwidth
#### 5.1.2 Network Throughput  
We analyze both the overall throughput of the network and the data transmission rate on the consumer side. The simulation scenario remains the same as before, with a bandwidth of 10 Mbps for the congested link. We evaluate the performance of CCLF and PCON, and the results are presented in Figures 10 and 11.  
Figure 9: The flow completion time in the network with constant bandwidth.  
As shown in Figure 10, with the CCLF method, initially, 8.552 Mbps of bandwidth is allocated to the highest-priority Flow 4, and Flow 3 with the second-highest priority occupies the remaining 1.448 Mbps. After 5.2 s, Consumer 4 has received all Data packets. Then, the Data packets of Flow 3, backlogged in the CS of Router 3, occupy all bandwidth for forwarding. By 8.7 s, Consumer 3 has received all Data packets. Then, the bandwidth is equally shared by Flow 1 and Flow 2 with the same priority. Due to the smaller size of Flow 2, its transmission ends early at 13.8 s. Afterwards, Flow 1 occupies all bandwidth. The result reflects the swift forwarding of high-priority data flows and the balanced bandwidth usage among flows with the same priority.  
In contrast, the PCON method cannot meet the requirements of urgent flows. Figure 11 shows that with the PCON method, the bottleneck bandwidth is allocated among all flows, leading to a nearly equal distribution. Except for Flow 2, which is completed within 12.7 s, the FCT of other critical flows, such as Flow 3 and Flow 4, is close to 18 s.  
The experimental results indicate that although the throughput on the consumer side is influenced by various factors, the overall network throughput under the CCLF method has consistently remained at a high level.
## 3 Material and Methods
### The Recommended Method
Within these residual blocks, a soft-thresholding function is applied to suppress low-importance or noisy feature activations. This residual shrinkage mechanism adaptively filters irrelevant information, contributing to the model's noise resistance. In parallel, an attention mechanism computes channel-wise weights by aggregating global contextual information and assigning higher importance to feature channels more indicative of defects. The model distinguishes itself by incorporating a logarithmic transformation into the attention computation. Unlike conven  
Figure 3: The overview of Log-DRSN. tional attention that may suffer from numerical instability or overlook subtle differences between high- and low-activation regions, the logarithmic function stabilizes learning dynamics, dampens extreme activations, and facilitates nuanced feature discrimination. This mechanism enhances the attention module's precision, especially when multiple defect-relevant patterns compete for representational priority. After the attention-weighted features are computed, the enhanced feature maps are forwarded through additional convolutional layers, followed by global average pooling and a fully connected classification layer. Moreover, the use of the logarithmic function within the loss function computation (via its interaction with cross-entropy loss) enables more efficient and stable optimization by transforming multiplicative terms into additive ones, thus improving convergence rates and reducing overfitting. In summary, the modular structure of Log-DRSN--comprising residual learning, adaptive shrinkage via soft thresholding, and enhanced logarithmic attention--not only allows the model to prioritize meaningful defect cues under real-world noise conditions but also ensures high classification performance with fewer parameters and faster training convergence.
# Effect of Thermal via Design on Heat Dissipation of High-Lead QFN Packages Mounted on PCB
## 2 Finite Element Simulation
### Geometric Models  
In the finite element model, we have efficiently implemented the relevant parts of the QFN package with appropriate simplifications. Due to the symmetry of the QFN package, we only need to model 1/4 of the model to obtain reliable results. As visually depicted in Figure 2, the models corresponding to the conventional QFN, dual-row QFN, and high-lead QFN packages are provided. These QFN models encompass a fundamental array of elements, notably the PCB, die, molding compound, die attach, thermal pad, leads, and the solder connecting the QFN package to the PCB.
# Defining Blood Plasma and Serum Metabolome by GC-MS
## 1 Introduction  
Metabolites are substances with low molecular weight (<1500 Da), intermediates, and products of chemical reactions catalyzed by various enzymes in living systems. In other words, metabolites are small molecules reacting to intrinsic or environmental challenges. The metabolome, in turn, is a \"snapshot\" of all metabolites in the biological object at a specific time point.  
Metabolomics is an essential member of the \"omics\" family. First, the genome and the transcriptome, made up of four bases, provide possible scenarios of the functioning of biological systems. Next, assembled from 20 amino acids, the proteome shows which protein machines are available. Finally, the metabolome indicates the reaction of the biological system to disturbances happening right now [1].  
As the final stage of the spectrum of \"omics,\" metabolomics reflects the diversity of chemicals arising from the sequential and synergistic interactions of 20,000+ human genes, 60,000+ transcripts, and 1,000,000 types of proteins in the dynamically changing environment [2, 3]. The information about how an organism or a cell attempts to retain nutrients while eliminating xenobiotics is vital for predicting the phenotype of a biological system [4].  
Exploring the human metabolome is valuable for understanding pathophysiological processes and searching for new diagnostic and prognostic biomarkers of various disorders [5, 6], accelerating drug discovery [7], and assessing the impact of diet [8, 9], lifestyle [10], and other factors [11].  
In the professional community of scientists working in metabolomics, the gas chromatography-mass spectrometry tandem (GC-MS) has a reputation as one of the most reliable, robust, and used analytical platforms, accompanied by a variety of spectral libraries for processing experimental data [12].  
Metabolomics, as we imagine it today, appeared relatively recently. In this review, we tried to highlight some excellent methods of sample preparation, subsequent GC-MS analysis, and post-processing that have emerged over the past 20 years to providea high-throughput characterization of small molecules in blood plasma and serum, the substrates-of-choice for many years.