esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Industrial Control Device Anomaly Detection Model Based on Fine-Tuned Llama3 Model
The model architecture has been deeply optimized to improve computational efficiency. Through more rational hierarchical design and parameter adjustments, under identical conditions, this model's performance significantly surpasses previous versions, especially excelling in processing large-scale data. During experiments, it was found that the number of training epochs significantly affects the detection accuracy of anomalous devices. If the number of training epochs is too small, the model cannot accurately grasp the characteristics of anomalous devices, leading to misidentification. Therefore, a sufficiently large number of training epochs is needed to ensure that the model learns effective features. However, increasing the number of training epochs can easily lead to overfitting due to the unique characteristics of industrial control device anomaly detection samples, still resulting in reduced detection accuracy. To address this issue, this study proposes using an annealing algorithm instead of the traditional linear algorithm. With a linear algorithm, if the model is trained for too many epochs, it has enough time to learn all details in the training data, including noise and outliers, leading to overfitting. In contrast, the annealing algorithm, with its probabilistic acceptance mechanism and temperature control strategy, helps the model to avoid local optima and reduces the risk of overfitting. This approach allows for selecting a larger number of training epochs for the model to fully learn the data features while avoiding overfitting, thus improving the accuracy of anomaly detection. The selection of training epochs in the model fine-tuning process depends on multiple factors such as dataset size and model complexity. In practice, a small number of epochs is usually set initially and gradually increased based on performance on the validation set until the requirements are met, requiring numerous experiments to find the optimal number of training epochs, which is time-consuming and labor-intensive. Through experiments, this study observed that the relationship between the sample size and the optimal number of training epochs resembles a Sigmoid function trend. By uncovering this relationship, it would be possible to provide a rough estimate of the number of fine-tuning epochs for different sample sizes, offering a basis for setting training epochs and saving experimental time. Based on the above observation, this study designed the following formula for predicting the number of training epochs for model fine-tuning:  
\[f(x)=\left[\alpha+\frac{\beta}{1+\mathrm{e}^{4-\frac{1\pi^{2}}{3}}}\right] \tag{8}\]where \(\alpha\) represents the adjustment constant of the fine-tuning epoch function, \(\beta\) represents the linear coefficient of the fine-tuning epoch function, and \(x\) represents the number of training set samples.
###### Abstract  
Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.  
short-term traffic flow prediction; decomposition; TVF-EMD; LSSVM; forecasting accuracy
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 4 Experimental Results
### CityPersons Dataset
#### 4.3.1 Training Configuration  
During training, we also use a multi-scale training strategy. Considering the image size, batch size, and GPU memory, the images were resized, on the short side, into 11 scales (960, 992, 1024, 1056, 1088, 1120, 1152, 1184, 1216, 1248, and 1280). For the CityPersons dataset, the learning rate was decayed at 15,000 and 20,000 iterations with a total of 25,000 iterations. We used a batch size of 1 for training and a batch size of 2 for evaluation. It took about 6 h for training.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Miss Rate**} & \multirow{2}{*}{**Hardware**} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Run Time (s/img)**} \\ \cline{2-2} \cline{5-6}  & **R** & & & & \\ \hline MS-CNN [5] & 9.54 & 48.60 & 55.77 & Titan GPU & \(\times 1\) & 0.067 \\ RPN+BF [4] & 7.28 & 54.60 & 59.88 & Tesla K40 GPU & \(\times 1.5\) & 0.5 \\ SDS-RCNN [2] & 6.43 & 38.70 & 56.77 & Titan \(\times\) GPU & \(\times 1.5\) & 0.21 \\ ALF [6] & 6.07 & 50.98 & 59.06 & GTX1080 Ti GPU & \(\times 1\) & 0.05 \\ CSP [1] & 4.54 & 45.81 & 56.94 & GTX1080 Ti GPU & \(\times 1\) & 0.058 \\ Pedestron [42] & 1.48 & 22.11 & 25.48 & Nvidia tesla V100 & \(\times 1\) & - \\ \hline DAGN (ours) & 6.03 & 33.22 & 46.83 & Titan \(\times\) GPU & \(\times 1\) & 0.11 \\ \hline DAGN++ (ours) & 1.84 & 9.67 & 17.68 & Titan \(\times\) GPU & \(\times 1\) & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons with the state-of-the-art methods of the Caltech test set. R, reasonable; HO, heavy occlusion; and A, all. Scale \(\times 1\) presents original image scale was used for testing. Red color values denote the best results.
# Characterization of Volatile Substances in Pu-erh Tea (Raw Tea) at Different Storage Times
## 3 Results and Analysis
### PCA Combined with HCA Was Used to Group Samples of Different Years  
A total of 290 aroma components from 14 Pu-erh tea (raw tea) samples were analyzed by principal component analysis (PCA) to preliminarily assess the magnitude of differences between the samples. Samples that are close together on the score plot may indicate similarity in metabolic characteristics. The PCA plot (Figure 4A) shows that the samples from 2002 and 2005 (P14 and P11) are distributed in the same region. The samples from 2003, 2004, 2009, and 2010 (P13, P12, P8, and P7) are clustered in another region. The samples from 2011, 2013, 2015, and 2016 (P6, P4, P2, and P1) are grouped in a separate area, and the samples from 2006, 2007, 2012, and 2014 (P10, P9, P5, and P3) are distributed in a distinct region. Samples located in the same region suggest similarity in their volatile components and minimal differences between the samples, allowing them to be grouped into a single category. In addition, the further the component is from the Origin on the PCA loading plot, the greater the contribution to sample classification is considered. As can be seen from Figure 4B, these five substances: trans-(-)-5-methyl-3-(1-methylehenyl)-Cyclohexene, L-\(\alpha\)-Terpineol, Limonene, 2-[(2R,5S)-5-ethenyl-5-methyltetrahydrofuran-2-yl]propan-2-ol, o-Cymene may be the key substance affecting the grouping.  
Figure 3: Classification and percentage of volatile compounds.  
To further refine the grouping and similarity of the samples, hierarchical clustering analysis (HCA) was subsequently applied to the 14 Pu-erh tea (raw tea) samples (Figure 4B). The aim of HCA is to classify the samples based on the similarity of their aroma components, thereby uncovering the underlying structure and distinct aroma types among the samples. The HCA dendrogram shows that the 14 samples are divided into 4 groups, and this grouping result is consistent with the PCA results. Combined with the results of sensory evaluation, 14 samples of Pu-erh tea (raw tea) could be divided into 4 groups: the first group A1 (P1, P2, P4, P6), the second group A2 (P3, P5, P9, P10), the third group A3 (P7, P8, P12, P13), and the fourth group A4 (P11, P14).
## 2 Materials and Methods
### Lesion Segmentation  
Skin lesion segmentation becomes a challenging task due to the presence of hair, bubbles, different illumination conditions, blurry boundaries, blood vessels, scars or different skin colors, thus, the segmentation step turns into a very delicate and complex process.  
Over the years, many techniques that successfully overcome the segmentation challenges have been developed. Unsupervised segmentation methods, such as thresholding, edge-based, region-based or energy minimization-based ones, and supervised methods, such as support vector machines (SVM), Bayes-based, or deep learning-based segmentation methods (DLBSM) have been successfully tested over any kind of images [23; 24].  
Lately, regarding dermoscopic images, many works have been focused on deep learning-based methodologies [25]. This kind of segmentation technique combines low-level feature information with high-level semantic information [26] and takes the advantage of its learning capacities, focusing on its learning properties to identify structures that allow us to segment the image. DLBSM allow us to segment images with low contrast, different intensity distribution or images with artifacts [27].  
In this paper, we compare unsupervised with supervised segmentation techniques. In fact, we compare the performance of one unsupervised method based on energy minimization, and two segmentation methods based on deep learning. More specifically, the two supervised methods consist of the following: (1) A CNN as feature extractor combined with a classic segmentation method (thresholding), and (2) Semantic neural network (SegNet). These three methods were tested over ISIC-2017 database [28].
## 2 Materials and Methods
### Batch Adsorption Studies  
The granulocyte of both ACK and CC used in the adsorption assays was 50-70 mesh. DCF standard solution with a concentration of 1000 mg L\({}^{-1}\) was prepared by dissolving diclofenac sodium salt (Alfa Aesar, Haverhill, MA, USA, 98%) in deionized water, and the solutions used in the adsorption assays were obtained from a dilution of the standard. PARX aqueous standard solution with a concentration of 100 mg L\({}^{-1}\) was prepared from paroxetine hydrochloride extracted from a commercial drug, and subsequent working solutions were prepared from a dilution of the standard. The molecular structures of the adsorbates are presented in Figure S1 (Supplementary Materials).  
The adsorption of the pharmaceutical compounds onto ACK and CC in batch mode was investigated through kinetics and equilibrium studies by stirring 10 mg of adsorbent with 25 mL of adsorbate solution at 200 rpm in an orbital shaker. The effect of the solution pH on the uptake of DCF and PARX was also assessed by adjusting the initial pH of the solutions in the range of 6-10 for DCF and from 5-9 for PARX. The studied pH ranges were based on the stability and solubility of the molecules and on carbon's surface charge [29]. The solutions used in the pH study presented a concentration of 100 mg L\({}^{-1}\) and pH values of 6.0 and 5.5 for DCF and PARX, respectively. A contact time of 24 h and an initial adsorbate concentration of 100 mg L\({}^{-1}\) were used in these assays. Kinetic tests were carried out from 5 min until 1440 min (24 h). Equilibrium experiments (adsorption isotherms) were performed by using solutions with concentrations from 20 to 200 mg L\({}^{-1}\). The temperature effect on DCF and PARX adsorption was evaluated between 298 and 323 K (25-50 \({}^{\circ}\)C) by using an initial concentration of 100 mg L\({}^{-1}\) of each adsorbate. Blank tests and duplicates were performed.  
The concentrations of each adsorbate after each adsorption assay (and after filtration) were determined by UV-Vis spectrophotometry (GBC UV/VIS, model 916, Hampshire, MA, USA) by using a wavelength of 274 and 293 nm for DCF and PARX, respectively. The calibration curves for each compound were determined.  
The adsorption capacity of DCF and PARX, \(q_{t}\) (mg g\({}^{-1}\)), and their removal efficiency, \(\eta\) (%), were determined according to Equations (1) and (2):  
\[q_{t}=\frac{\left(C_{0}-C_{f}\right)\times V}{m} \tag{1}\]\[\eta=\frac{\left(C_{0}-C_{f}\right)\times 100}{C_{0}} \tag{2}\]  
\(C_{0}\), initial concentration (mg L\({}^{-1}\)); \(C_{f}\) final concentration (mg L\({}^{-1}\)); \(V\), volume of the solution (L); \(m\), mass of adsorbent (g).
## 3 Results and Discussion
### Attenuated Total Reflection Spectroscopy  
Figure 3 shows the infrared spectra obtained for the powder, polymers and composites. The zirconia's peak at 3355 cm\({}^{-1}\) corresponds to the hydroxyl groups (-OH) bound to the powder surface [7; 51; 52; 53]. Both unfilled polymers (HDPE00 and AAHDPE00) showed the characteristic polyethylene CH peaks at 1470, 2847 and 2916 cm\({}^{-1}\)[54; 53]. For AAHDPE00 additional peaks were observed at 1167, 1246 and 1700 cm\({}^{-1}\). The strong peak at 1700 cm\({}^{-1}\) corresponds to the stretching vibration of the carboxyl (C=O) in the acrylic acid group (-COOH) [48; 55].  
The reduction in the peaks' intensity for the composites (Figure 3) is produced by the incorporation of the zirconia as a second component. No new peaks appeared due to the chemisorption of the acrylic acid onto the zirconia surface [55] or to the formation of ester links between the carboxyl (C=O) in the acid and the -OH groups in the zirconia [24; 56; 57]. However, the C=O peak of the AAHDPE has  
Figure 3: (**a**) Infrared absorption spectrum of the HDPE00, HDPE30, AAHDPE00 and AAHDPE30 with the main peaks of the pure components in different colors; (**b**) Magnification of the infrared absorption spectrum for the AAHDPE00 and AAHDPE30 in the 2000–1500 cm\({}^{-1}\) range with the carboxyl peak for the polymer and the composite.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Interfacial Tension (mN/m)**} \\ \hline
**Material** & **ZrO2** & **Tetragonal Y2O3 Stabilized ZrO2** & **3\%Y2O3-ZrO2** \\ \hline
**AAHDPE00** & 3.74 & 3.46 & 5.53 \\
**HDPE00** & 4.43 & 4.12 & 6.37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculated interfacial tension between the two types of polymers and different zirconia types.  
a slight shift of approximately 2 cm\({}^{-1}\) after incorporating the powder (Figure 3). The other peaks remained exactly at the same frequencies. The shift of the carboxyl group could be produced by the formation of hydrogen bonds with the hydroxyl groups in the powder oxide surface [58; 59] and even with the zirconium dioxide [59] itself. The hydrogen bonding with the oxide would be in agreement with the orientation of the acrylic acid towards the oxygen in the zirconia surface and the high adhesion for the grafted polymers observed in the MD simulations (see Figure 3b,c).
## 2 Related Work
### Resource Scheduling Optimization Methods  
As satellite networks increasingly play a key role in providing global coverage and efficient communication, optimizing resource scheduling has become a central research issue. To address the varying needs of different users and tasks, researchers have proposed a range of resource scheduling optimization methods. These approaches mainly focus on enhancing resource allocation efficiency, reducing delays, and improving the adaptability of networks. Various studies have introduced optimization algorithms that leverage machine learning and deep learning techniques, which can predict network states and optimize resource allocation, thereby improving resource utilization and minimizing delays. For example, [PERSON] et al. [14] proposes a mixed-integer programming model for the EDSSP problem and a Genetic Algorithm based on reinforcement learning (RL-GA).  
Other researchers have also explored data-driven scheduling methods, particularly in satellite network environments, where these methods predict future network states and optimize resource scheduling through historical data analysis [15; 16; 17; 18]. For instance, [PERSON] et al. [15] introduced a data-driven parallel scheduling approach to predict the probabilities that a task will be fulfilled by different satellites. Similarly, [PERSON] et al. [19] proposed a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields.  
Despite the significant progress made by these optimization algorithms and machine learning methods, challenges still exist when dealing with complex dynamic network environments. While these approaches improve resource utilization efficiency, they lack effective strategies to address unexpected changes, such as network topology variations and transmission conditions in high-dynamic environments.
# WGAN-Based Realization Process of Gravel Soil for Hydraulic Property Simulation
## 4 Generation of Gravel Soil Models and Evaluation Criteria
### Pore-Scale Fluid-Flow Simulation  
A pore-scale seepage simulation was implemented to determine the absolute permeability of each gravel soil specimen based on the Avizo (2020.1) software. The controlled equation includes the Stokes flow equation for conservation of momentum and the continuity equation for conservation of mass regardless of the inertia term:  
\[\left\{\begin{aligned} \
abla\cdot\mathbf{u}=0\\ \mu\
abla^{2}\mathbf{u}-\
abla P=\mathbf{0}\end{aligned}\right. \tag{6}\]  
where \(\
abla\cdot\) is the divergence operator; \(\
abla\) is the gradient operator; \(\mathbf{u}\) is the velocity of the fluid in the fluid phase of the material; \(\mu\) is the dynamic viscosity of the flowing fluid; \(\
abla^{2}\) is the Laplacian operator; and \(P\) is the pressure of the fluid in the fluid phase of the material.  
Equation (6) is derived from the simplified Navier-Stokes equations, specifically for an incompressible Newtonian fluid under steady laminar flow. To solve these equations, a finite volume method is applied, as described by [PERSON] (1999) (17) and [PERSON] (1965) (18). The model uses a staggered grid discretization, which improves accuracy in handling the no-slip boundary condition. In this setup, the solid phase (which adheres to the no-slip condition) is separated from the fluid phase by additional grid faces that are not perpendicular to the main flow direction. Three main conditions--input pressure, output pressure, and flow rate--are specified in the model. Typically, two of these conditions are chosen, with the third is calculated from the selected two. This approach allows a comprehensive analysis of fluid dynamics, which is particularly useful in complex geometries and boundary conditions. Figure 5 illustrates the encapsulated assembly of a direct pore-scale CFD simulation for permeability estimation. It is a constraint container with slip and solid boundaries. More specifically, the Stokes problem is addressed by applying pressure boundary conditions at the inlet and outlet set at 20 kPa and 0 kPa, respectively.  
According to the simulated flow rate, the permeability can be calculated using [PERSON]'s law.  
\[\kappa=\frac{Q}{S}\mu\frac{L}{\Delta p}\quad\text{or}\;\;k_{h}=\kappa\frac{gg }{\mu} \tag{7}\]  
where \(Q\) is the global flow rate passing through the porous medium (m\({}^{3}\).s\({}^{-1}\)); \(S\) is the cross-section of the specimen that the fluid travels through (m\({}^{2}\)); \(\kappa\) is the permeability (\(\mu\)m\({}^{2}\)); \(\Delta P\) is the pressure difference applied around the sample (Pa); and \(L\) is the length of the specimen in the flow direction (m).  
The parameters of the pore-scale fluid-flow simulation used are shown in Table 2.  
Figure 5: Assembly of a numerical model for the direct pore-scale fluid-flow simulation in our research.
###### Abstract  
The emergence of 5G-IoT opens up unprecedented connectivity possibilities for new service use cases and players. Multi-access edge computing (MEC) is a crucial technology and enabler for Beyond 5G, supporting next-generation communications with service guarantees (e.g., ultra-low latency, high security) from an end-to-end (E2E) perspective. On the other hand, one notable advance is the platform that supports virtualization from RAN to applications. Deploying Radio Access Networks (RAN) and MEC, including third-party applications on virtualization platforms, and renting other equipment from legacy telecom operators will make it easier for new telecom operators, called Private/Local Telecom Operators, to join the ecosystem. Our preliminary studies have discussed the ecosystem for private and local telecom operators regarding business potential and revenue and provided numerical results. What remains is how Private/Local Telecom Operators can manage and deploy their MEC applications. In this paper, we designed the architecture for fully virtualized MEC 5G cellular networks with local use cases (e.g., stadiums, campuses). We propose an MEC/Cloud Orchestrator implementation for intelligent deployment selection. In addition, we provide implementation schemes in several cases held by either existing cloud owners or private and local operators. In order to verify the proposal's feasibility, we designed the system level in E2E and constructed a Beyond 5G testbed at the Ookayama Campus of the Tokyo Institute of Technology. Through proof-of-concept in the outdoor field, the proposed system's feasibility is verified by E2E performance evaluation. The verification results prove that the proposed approach can reduce latency and provide a more stable throughput than conventional cloud services.  
Beyond 5G; virtualization; multi-access edge computing; cloud; private/local telecom operator; MEC B5G orchestrator; MEC proof-of-concept +
Footnote †: journal: _Sensors_