esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Evaluation of the Model and Clustering of RF Signals  
Evaluating the network based on wideband radio spectrogram requires generating subspectrograms containing detected radio signals, which will be processed by the neural network. Similar to the training process, the annotation files of the rfspec-db [22] database were used, enabling the extraction of signals from the wideband spectrum without the need for energy detectors such as ED [12; 13; 14] or RFROI-CNN [15]. The annotation files also provide information about the modulation used, which was used for validating the proposed simple hierarchical clustering method.  
The generated subspectrograms formed a batch in the form of a tensor, which was processed by the trained neural network. The output of the network was a tensor containing feature vectors of length 128 for each signal. With this set of features, we could compare signals to each other. Firstly, we needed to calculate the Euclidean distances between each pair of vectors using Formula (4).  
\[d(A,B)=\sqrt{\sum_{i=1}^{n}(x_{iA}-x_{iB})^{2}} \tag{4}\]  
After calculating the distance matrix, we had sufficient data to find signals that are similar in terms of modulation. We could search for the \(n\) most similar spectrograms, similar to searching for similar images, or we could define an empirically chosen threshold of Euclidean distance below which signals would be considered similar, and above which signals would be considered different from each other.
## 3 Topology Optimization and Parametric Analysis
### Topology Optimization Process and Reconstruction of the Layout of Reinforced Ribs  
After 21 iterations, the maximum displacement variation within the designed region was as illustrated in Figure 15. The absolute values of displacements for all nodes within the optimized design region were consistently below 0.82 mm:  
The topology optimization results are illustrated in Figure 16. The overall principle of the reinforced rib design is to preserve regions with high unit density during the design  
Figure 15: Optimization process curve.  
process, while discarding regions with densities close to or equal to zero. The specific design process for the reinforced ribs is as follows:  
1 Feature extraction for high-density regions: High-density regions are identified, primarily concentrated in the central and lower regions of the intermediate reflector surface. Based on the trends in high-density regions, slant and vertical reinforced ribs are extracted.  
2 Design of moderate-density regions: For regions with moderate density, designing is conducted considering the deformation characteristics of the reflector surface without reinforced ribs. For the medium density areas of 1-6 on the left and right reflector surfaces, the deformation in areas 1 and 4 was significant, and cross reinforcement ribs were used. Regions 2 and 5 were compared to regions 1 and 4, and the deformation was smaller, so horizontal and vertical ribs were adopted. Due to the position close to fixed constraints and a deformation approaching zero, only transverse ribs were designed in regions 3 and 6.  
The resulting layout for the reinforced ribs is depicted in Figure 17.
This dynamic adjustment ensures that the control model accurately reflects the changing conditions of the system, maintaining consistency and improving control performance over time [25]. These controllers show significant potential for application in industrial processes. However, gaps remain in the literature regarding the energy efficiency of CFOH systems at the pilot-plant level and the feasibility of implementing advanced process control in such settings. This paper aims to address these gaps by analyzing the performance of the traditional Proportional-Integral-Derivative (PID) control, Model Predictive Control (MPC), and Adaptive Model Predictive Control (AMPC) in the processing of tomato basil sauce using a CFOH pilot plant.
# Treatment Methods for Antibiotic Mycelial Residues: A Review
## 3 Treatment Methods for AMR
### Multi-Technology Coupling
#### 3.4.2. Physicochemical Technologies for Enhanced AD
Post-HT, the increased solubility of organic matter renders residual components more accessible for microbial degradation, thereby optimizing subsequent AD efficiency [56; 135]. Furthermore, HT effectively removes antibiotics from fermentation residues and markedly reduces the abundance of ARGs, thereby mitigating antibiotic-induced inhibition. Notably, the removal efficiencies of antibiotics and ARGs exhibit a positive correlation with HT temperature [41; 55; 136]. [PERSON] et al. [137] experimentally demonstrated that hydrothermal pretreatment can converts 40.2% of solid organic matter in AMR into dissolved forms, with 75% of organic compounds in the filtrate being converted into biogas during AD, achieving 8-11 times higher methane production compared to untreated residue. HT exhibits broad applicability, showing efficacy across 10 AMR types spanning 5 antibiotic classes, including \(\beta\)-lactams, aminoglycosodes, and macrolides. Researchers have further proposed a systematic solution involving an integrated hydrothermal-pyrolysis-AD system [50]. HT at 180 \({}^{\circ}\)C enhances biodegradability and dewaterability of antibiotic fermentation residues, reducing filter cake moisture content to 39% and decreasing ARGs' absolute abundance by 2.4-5.2 logs. Subsequent pyrolysis and upflow anaerobic sludge blanket processes facilitate resource recovery from filter cakes and filtrate, achieving a total mass reduction rate of 97.7% and organic utilization efficiency of 80.2%. Energy balance analysis confirms that byproduct biogas and pyrolysis gas meet system energy demands, enabling energy self-sufficiency. However, excessive treatment may elevate ammonia levels and trigger Maillard reaction-driven organic repolymerization, counteracting digestion efficiency [55; 138]. Innovatively, [PERSON] et al. [139] employed tea polyphenols to pretreat kanamycin residue. Quinones generated through polyphenol oxidation polymerize with amino groups present in kanamycin, achieving high-efficiency antibiotic removal while enhancing the compost humification index and nitrogen retention rates. This breakthrough provides novel pathways for producing high-activity biostimulants.
## 4 Results
### Spatial Distribution and Evolution of Sedimentary Facies
#### 4.2.2 Lateral Evolution of Sedimentary Facies  
Building upon single-well microfacies characterization and vertical depositional system evolution, this study employs stratigraphic correlation methodologies to construct regional depositional profiles, elucidating both lateral facies distribution patterns and vertical stacking architecture within the Triassic succession. A representative cross-section oriented perpendicular to the principal sediment transport direction (Figure 9) was established across the central Wushi Depression toward the Tianshan orogenic belt, revealing systematic facies transitions from proximal to distal settings.  
During Middle Triassic Kelamayi Formation deposition, lacustrine expansion facilitated distinct spatial differentiation: proximal wells (WS2, YL2, WC1) adjacent to source areas developed thick braided delta plain-floodplain deposits dominated by reddish-brown mudstones with intermittent channel sandstones. The transitional ST1 well records  
Figure 9: Sedimentary facies correlation profile across wells WS2-YL2-WC1-SM4 and TLK outcrop (profile location corresponds to line B–B\({}^{\prime}\) in Figure 1a), showing a transgressive succession from the Ehuobulake Formation to the Huangshanjie Formation, with maximum lacustrine extent achieved by the terminal Huangshanjie interval. Depth (m); GR log (API); RD (\(\Omega\)-m).  
a downstream facies shift, with lower intervals containing delta plain-floodplain facies that grade upward into delta front deposits characterized by distributary channel sandstones, interchannel silts, and thin littoral-shallow lacustrine mudstones.  
The distal TLK outcrop exhibits an analogous vertical succession to ST1, though with notable scale differences: while lower Kelamayi Formation intervals contain comparable delta plain-floodplain deposits, upper sections display expanded littoral-shallow lacustrine mudstone packages exceeding 50 m thickness. This thickness disparity indicates TLK's paleogeographic position closer to the lacustrine depocenter during late Kelamayi deposition, contrasting with ST1's intermediate position within the delta front to lacustrine transitional zone.  
The cross-section demonstrates two key evolutionary trends: (1) laterally, progressive fining and increased lacustrine influence from NE (proximal) to SW (distal) directions, evidenced by decreasing sandstone/mudstone ratios and enhanced organic matter preservation; (2) vertically, episodic delta progradation punctuated by lacustrine transgressive events, recorded through cyclic alternations of channelized conglomerates and laminated lacustrine shales. These patterns collectively document the dynamic interplay between orogenic uplift-driven sediment supply and base-level fluctuations in controlling basin-fill architecture.
## 4 Implementing Models with the Geographic Automata Add-In: Two Case Studies
### Case Study 1: Multi-Class CA Modeling of a Forest Insect Infestation
#### Cell States  
Three cell states are represented in the hypothetical models: (0) not infested, (1) light-moderate infestation, and (2) severe infestation (30% or more trees in the location recently killed) based on intensity classes from the aerial overview survey used to derive the initial data layer [91].  
Scenario Setup and Transition Rules  
The scenarios demonstrate increasing complexity of MPB infestation behavior encoded using one or several transition rule types. In this case study, each scenario expands on the  
Figure 8: Raster GIS datasets prepared for case study 1, including (**a**) the MPB infestation extent for the year 2004 (T\({}_{0}\)), (**b**) the infestation susceptibility values, and (**c**) the constraint map with water bodies.  
Figure 9: Overview of implementing the MPB CA model using the _Geographic Automata_ add-in tools to parameterize Scenario 5.  
rule set implemented in its predecessor. For example, the behavior portrayed in Scenario 1 is augmented in Scenario 2, and so forth. The transition rules and scenario settings are outlined in Table 3.  
Model Execution  
The _Advanced CA_ tool is used to execute the infestation model with the MPB infestation data for 2004 (Figure 8a) provided as the initial raster layer. The number of iterations is set to five, where each iteration of the model represents one year of MPB dispersal.
## 3 Case Study: Recorder
### Results and Discussion
We can see this value is significantly lower for all the PM-OPT deviation from Recording cases than the corresponding PM deviation from Recording cases. The average value for all the nine notes prior to the optimization framework integration (PM deviation from Recording) is 41 cents, whereas, after the integration (PM-OPT deviation from Recording) diminishes to only 2 cents. For eight out of nine notes, the PM-OPT and the recording have almost identical spectrum contents (partials average absolute deviation \(\leq\)2 cents). This improvement is a byproduct of the precise tuning of the fundamental frequency. PM produces partials that resemble a harmonic series, which can be found in the recordings as well. Therefore, tuning the fundamental frequency tunes, correspondingly, the overtones. Figure 4: The average absolute deviation of the fundamental frequency of the synthesized and the recorded signals in cents in various notes for PM (blue) and PM with optimal correction factors (orange). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Note**} & \multicolumn{6}{c}{**Synthesized Signals Frequency (Hz)**} \\ \cline{2-7}  & **Fundamental** & **1 st Overtone** & **2 nd Overtone** & **3 rd Overtone** & **4 th Overtone** \\ \hline
1 & 522 & 1046 & 1568 & 2090 & 2614 \\
2 & 591 & 1183 & 1774 & 2365 & 2957 \\
3 & 661 & 1322 & 1982 & 2644 & 3305 \\
4 & 721 & 1439 & 2160 & 2883 & 3604 \\
5 & 781 & 1560 & 2347 & 3131 & 3901 \\
6 & 883 & 1767 & 2659 & 3536 & 4414 \\
7 & 1003 & 2005 & 3011 & 4013 & 5017 \\
8 & 1117 & 2334 & 3352 & 4469 & 5586 \\
9 & 1213 & 2428 & 3642 & 4855 & 6070 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The first five partials of the recorded signals per 9 notes, in Hertz. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Note**} & \multicolumn{6}{c}{**Synthesized Signals Frequency (Hz)**} \\ \cline{2-10}  & **Fundamental** & **1 st Overtone** & **2 nd Overtone** & **3 rd Overtone** & **4 th Overtone** \\ \hline
1 & 511 & 523 & 1022 & 1045 & 1533 & 1568 & 2044 & 2091 & 2555 & 2613 \\
2 & 594 & 591 & 1188 & 1183 & 1782 & 1774 & 2376 & 2365 &
## 6 Results and Discussion
### Multi-Switch Boost Converter  
The main purpose of this topology is to divide the thermal stress created on one single pair of switch/diode into several parallel pairs. Therefore, although the number of used components will increase, the average thermal stress will be lowered. The components' temperature variations are illustrated in Figure 14.  
As it is clear from Figure 14, this topology has been able to greatly reduce the temperature of the switches so that the junction temperature drops from 110 \({}^{\circ}\)C for the switch utilized in the conventional boost converter to 70.21 \({}^{\circ}\)C by the equalization of the thermal stress on two parallel switches. Because the duty cycle is divided equally between the two switches, their temperature behavior would be the same during the operation. Additionally, this topology is capable of reducing all diodes' temperature--the anti-parallel diode from 66.60 to 49.93 \({}^{\circ}\)C and D3 from 112.11 to 97.83 \({}^{\circ}\)C.
## 1 Introduction
[21] developed a Lagrangian approach for the coupled simulation of aquaculture net panels in a Eulerian fluid model based on the immersed boundary methods. Instead of using a porous media model, the net was represented by Lagrangian points, and the RANS equations were solved in a Eulerian fluid domain. Their model has been validated for rigid [18] and flexible nets [22]. Still, the flow characteristics of the individual net twines and their interactions have not been settled. These drawbacks of the porous media model and the net deformation in the FSI framework should be resolved to significantly advance the numerical modeling of flexible nets and cages. Depending on whether the governing fluid-structure equations are solved together or separately, the numerical schemes of the FSI problems are classified into two approaches: monolithic and partitioned schemes. In a monolithic scheme [23; 24; 25; 26; 27], the coupled nonlinear governing equations are solved together using an iterative technique such as the Newton-Raphson scheme. This approach requires coupling matrices and much more computational resources. In the partitioned/staggered scheme [28; 29; 30], the coupled equations are solved separately in the fluid and solid subdomains to obtain the fluid-structure solutions. This approach allows variables to be transferred between individual solvers via an interface [31; 32; 33; 34]. It seems that the partitioned approaches are virtually favored for accuracy and efficiency as they support staggered coupling among fluid, structural and mesh movement in either a loose or tight manner. To facilitate a better understanding of the flow characteristics of the flexible net panel and mutual interactions of the net twines, this paper proposes a coupled fluid-structure partitioned scheme, to conduct accurate and computationally efficient simulations for the FSI problems regarding net deformation under steady flow. This partitioned coupling scheme has second-order accuracy in time. By constructing the subdomains for the fluid and the structure, and imposing Dirichlet boundary conditions on the fluid and Neumann boundary conditions on the structure, the governing fluid-structure equations can be solved separately. Then the variables, namely the forces, are transferred along the interface via a predictor-corrector scheme. An arbitrary Lagrangian-Eulerian formulation is employed to accommodate the independent mesh motion at each time step. In what follows, we first introduce the governing fluid-structure equations in Section 2. This is followed by the numerical solutions of fluid-structure equations in Section 3. Section 4 presents the partitioned coupling formulation. Section 5 elaborates on the numerical setup and verification. With this, we then provide the velocity and vorticity distribution of flexible net panels under steady flow in Section 6. Section 7 gives a summary of the method and the conclusions of this paper.
## 3 The ML Strategy within Network Slicing
### The Role of Deep Learning  
Deep learning, a subset of ML, employs artificial neural networks to tackle tasks ranging from classification to decision-making. The standard artificial neural networks are Convolutional Neural Networks and Recurrent Neural Networks. Deep learning mimics how the human brain works, allowing machines to recognize patterns, classify information, and make decisions [85]. Unlike traditional ML methods, deep learning algorithms learn intricate patterns and features directly from raw data without requiring extensive human intervention to handle undesirable outputs, fine-tune algorithms, and manual feature extraction, deep learning operates differently. Its nested layers process data hierarchically, independently learning intricate features and patterns [86].  
Deep learning algorithms can process vast amounts of data, providing detailed insights and precise predictions. In contrast to traditional analytics methods, which struggle with the scale and complexity of these networks, deep learning shines in recognizing complicated data patterns [87].  
Also, DL techniques can effectively predict new attacks, often mutations of previous ones, by learning from current instances, showcasing strong performance in identifying cyber threats [86; 88].  
Thus, deep learning is a powerful tool for analysing large datasets and detecting complex patterns, especially within network slicing and 5G, where networks' dynamic and heterogeneous nature demands robust security enforcement solutions.  
However, despite its immense potential, deep learning poses challenges such as computational intensity, the need for larger datasets, and the interpretability of the decision-making process [79].
## 5 Results and Discussions
The performance of the container is much closer to bare metal with only a difference of 2%, and it can be considered as near real-time with enabling HT, isolation, and pinning techniques. Figure 4: HEPSPEC06 benchmark results of test-3. Figure 5: HEPSPEC06 benchmark results of test-4. Finally, the average performances of virtualization and containerization are tested and comparative analysis of each test case is calculated. As illustrated in Figure 6, test-3, using pinning and isolation with all configurations of second test case (i.e., test-2), can achieve improved efficiency in the cloud environment compared to the other test cases. The average performance of virtual machines increases up to 20% while containers up to 25% compared with the other test cases. This test case can also be used for the scenario with the mixed size of virtual machines or containers required for scientific jobs. In a cloud computing environment, pinning and isolation help to enhance overall throughput. The proper selection mechanism of vCPU cores along with pinning and isolation can improve the throughput and performance of containers as well as VMs in cloud environments for scientific jobs.
## 3 Results and Discussion
### Volatile Compounds in Foxtail millet Determined by HS-GC-IMS  
To analyze the volatile compounds in foxtail millet and discriminate the differences among the 20 varieties, HS-GC-IMS was employed to obtain the global IMS information and identify volatile compounds based on the retention time retention time and ion migration time of gas chromatography [30]. A total of 34 volatile compounds were identified in foxtail millet (Table 2), including 13 aldehydes, 7 alcohols, 6 ketones, 5 esters, 2 furans, and 1 ether. The major volatile compounds in foxtail millet were hexanol, hexanal, 1-pentanol, ethyl acetate and acetone (Figure 2A). Volatile compounds in foxtail millet were presented in 2D and 3D topographic photographs (Figure 2A,B). Each point on the 2D topographic photographs (Figure 2A) represents a volatile compound, and the color of the point is related to the content of a volatile compound: red for a higher content and white for a lower content [31]. In the 3D topographic photographs (Figure 2B), the transverse, longitudinal and vertical axes represent ion migration time, gas chromatography retention time, and signal peak intensity, respectively. The color and the signal intensity in the vertical axis of the plot are related to the contents of the volatile compounds. As shown in Figure 2A,B, there were many signals dispersed in a region with a GC retention time of 100-200 s and a relative drift time of 1.0-1.5 ms. Based on the fact that non-polar compounds have a longer retention time on non-polar columns than polar compounds [32], it was suggested that volatile compounds in foxtail millet were rich in polar compounds. Although the composition of the volatile compounds resembled those in the various foxtail millet, the content of volatile compounds varied due to variety differentiation (Figure 2A,B). For instance, the ranges of 2-heptanone and hexanol (Area A in Figure 2A,B) in Samples 2 (Jingu 21) and 16 (Jingu 59) were relatively lower than other varieties. The lower contents of hexanal and 1-pentanol (Area B in Figure 2A,B) were observed in Samples 2 (Jingu 21), 6 (Jingu 34(2)), 7 (Jingu 34(3)), and 16 (Jingu 59). Also, differences in the components and concentration of volatile compounds were visually found in Area C. To compare the differences in the different foxtail millet more clearly, Sample 1 (77-322) was chosen as the reference, and other varieties were subtracted from the reference on the original basis (Figure 2C). After subtraction, the background color changed to white. A blue plot denotes that the volatile compound's content is lower than the reference, while a red plot indicates that it is higher than the reference. Differences in the composition and content of volatile compounds were directly observed. These results demonstrate that the volatile compositions of the 20 varieties of foxtail millet were similar, but the volatile contents were significantly different. Figure 1: Principal component analysis (PCA) of 20 foxtail millet varieties based on E-Nose.