esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Works
### Approximate Computing (AC)
As mentioned earlier, AC techniques are worthy only in applications that have the flexibility to tolerate inaccuracy in the results. Applications classified as RMS (recognition, mining and synthesis) have been characterized by tolerating inaccurate results. These applications include several fields, such as: Digital signal processing, machine learning, and computer vision, among others. More recently, the scope of the AC paradigm has also been extended to the design of fault-tolerant systems [9; 29; 30].
## 3 Post-Processing Methodologies
### Time-Domain Analysis
#### 3.1.1 Single-Bin Evaluation: Dynamic Thermal Tomography (DTT)  
Dynamic thermal tomography (DTT) is an often-used time-domain analysis technique, which operates on the thermal contrast \(\Delta T\)[21; 22; 35]. The thermal contrast is calculated by subtracting the thermal response of a manually chosen reference (sound) pixel's thermal response from all other pixel responses (see Figure 4a). The resulting thermal contrast \(\Delta T\) is close to zero for a sound pixel and has a typical shape for a defected pixel (see Figure 4b). In this approach, the maximum thermal contrast value \(\Delta T_{max}\) and the time at which this occurs \(t_{\Delta T,max}\) are determined for each pixel individually. This is schematically illustrated in Figure 4b. Next, surface maps of the maximum thermal contrast (called _'maxigram'_) and the time of maximum thermal contrast (called _'timegram'_) could be generated. Typically, the shallower the defect, the higher the maximum thermal contrast and the lower the time of maximum thermal contrast, and vice versa. Thus, DTT's timegram can be used to perform defect depth inversion, which requires calibration curves [22]. As such, DTT can be classified as a single-bin evaluation technique in the time domain.  
Figure 3: Thermogram at an arbitrary time instance before and after standardization for the (**a,b**) CFRP\({}_{\text{FBH}}\) coupon; (**c,d**) impacted side of the CFRP\({}_{\text{BVID}}\) coupon; (**e,f**) back side of the CFRP\({}_{\text{BVID}}\) coupon; and (**g,h**) CFRP\({}_{\text{PROD}}\) aircraft panel.
## 3 Biosynthesis and Function of Glandular Trichome-Produced Compounds
### Terpenes
The newly formed terpenoids are often subject to (multistep) secondary transformations, catalyzed by various enzymes in different organelles [62, 77], leading to a wide range of structurally related terpenoids, which can be non-volatile like pigments and phytohormones, or volatile like the herimetepenes (C5; derived from DMAPP), monoterpenes (C10), sesquiterpenes (C15), diterpenes (C20), triterpenes (C30), _etc._, and norterpenes (e.g., C11 and C16) [61, 63, 78]. Most terpene synthases are able to generate multiple products from a single substrate, which, together with the large size of TPS gene families, explains the diversity of terpenoids found in plants [62, 77]. Terpenoids are major components of herbivore-induced volatile blends and they play an important role in the attraction of predators and parasitoids to herbivore-infested plants, a phenomenon known as indirect plant defense [79, 80]. Indirect defenses mediated by plant volatiles have been reported from plant species with glandular trichomes, including model plants like cultivated tobacco [81], corn (_Zea mays_) [80], cotton [81] and cultivated tomato [49], but also from species without glandular trichomes, for example Arabidopsis (_Arabidopsis thaliana_) [82] and lima bean (_Phaseolus lunatus_) [83]. Terpenes may also play a role in direct defenses against pests as they can have a deterrent or repellent effect and at higher concentrations they are often toxic. For instance, in the wild potato (_Solanum berthaulli_), the release of the sesquiterpene (E)-\(\beta\)-farnesene from its glandular trichomes was shown to repel aphids (_Myzus persicae_) [84], while the parasitoids of this aphid, like the hypmenopteran _Diaeretiella rapae_, were attracted to (E)-\(\beta\)-farnesene [85]. The sesquiterpenes 7-epizingiberene and _R_-curcumene, produced by glandular type VI trichomes of some _Solanum_ species [30], were shown to have a repellent effect on silverleaf whiteflies (_Bemisia tabaci_) [86, 87]. Other herbivorous arthropods are affected as well by sesquiterpenes like zingiberene. For example, [PERSON] _et al._[88] showed that zingiberene is toxic to Colorado potato beetle (_Leptinotarsa decemlineata_) larvae and removal of sesquiterpenes by wiping _S. habrochaites_ foliage with methanol increased the survival of beet armyworm larvae (_Spodoptera exigua_) from 0% to 65% [89]. In the South American tomato pinworm (_Tuta absoluta_), the presence of zingiberene was associated with a reduction in oviposition and feeding damage [90]. Finally, increased zingiberene levels were shown to correlate with increased repellency of the tobacco spider mite (_Tetranychus evansi_) [91].
## 2 Background and Related Work
To verify the research directions, their progress, and their relevance in the ongoing scientific discourse, elements of a systematic literature review were used. For this purpose, the Scopus bibliographic database was used, which is considered one of the most suitable for this type of research [11]. In order not to miss relevant articles, all documents were retrieved according to the following query, which intentionally broadly defines the scope of the domain in question:  
TITLE-ABS-KEY ((k8s OR kubernetes) AND autoscaling AND (hpa OR autoscaler))  
The query (as of 14 December 2023) retrieved 44 documents, of which, after verification, 15 were on topics consistent with those covered in this article (see Table 1). The oldest document was published in 2020, while 11 out of 15 articles were published between 2022 and 2023, which undoubtedly indicates that the issues discussed here are very topical. It is noteworthy that all identified scientific works related to the addressed issues are centered around proposals for implementing autoscaler algorithms. These implementations predominantly leverage prediction methods based on complex neural networks such as GRU, RL, and LSTM. Alternatively, they focus on monitoring a set of parameters describing the current cluster operation and incorporating them into custom algorithms for calculating the desired number of pods in a given situation. The application of these methods is rather intricate and necessitates custom scaler implementations, raising concerns about their seamless integration into production environments. This would require extensive testing across various real-world production environments. In contrast, the proposed method in this article was built upon an existing and well-tested standard implementation of horizontal pod autoscaler (HPA), relying solely on the adjustment of one of its parameters. From this perspective, the approach is evidently innovative and, crucially, can be safely applied in production systems. The demand for scaling systems to streamline the management of cloud computing resources has intensified, particularly for systems with broad applications and a large user base. It is not surprising that research on optimizing the scaling process of Kubernetes clusters is often conducted based on platforms supporting the healthcare sector [25; 26; 27]. In this domain, complex performance and security requirements pose unique challenges for system engineers [28].
# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 3 Design of AMP-RTR Model
### Summary of AMP-RTR Model  
The Anomaly Monitoring of Process based on Recurrent Timeliness Rules (AMP-RTR) is oriented to the manufacturing process. The AMP-RTR iteratively evaluates the timeliness of data according to the time series generated periodically in intelligent manufacturing. At the beginning of the first data update cycle, the timeliness score of the data should be one. In each data update cycle, the timeliness evaluation value of data declines exponentially (regardless of constant product attributes, such as product number). After each data update, the initial timeliness score of the current cycle is set according to the difference between the time when the data are updated and the set cycle. Whether the following two exceptions occur in the update iteration of timeliness score is monitored: the first abnormal state is that the timeliness evaluation value is lower than the lower limit after updating; the second abnormal state is that the number of times the timeliness evaluation value is higher than the upper limit reaches the set threshold after being updated.
## 5 Proposed Algorithm
### Group Comparison  
Referring to Section 4.3, the temporary nondominated solutions play a paramount role in most multi-objective algorithms. Up to now, the most popular method to achieve the above goal is pairwise comparison, which takes O(N\({}^{2}\)) [13; 17; 19] for each solution in each generation, where N is the number of solutions from which we determine temporary nondominated solutions. Hence, the corresponding related time complexity of the tSSO and NSGA-II are both O(4N\({}_{\text{sol}}\)\({}^{2}\)). Therefore, the most time-consuming aspect of solving multi-objective problems using machine learning algorithms is the search for all temporary nondominated solutions from all offspring.  
In the proposed tSSO, the parents are selected from temporary nondominated solutions found in the previous generation and offspring generated in the current generation. To reduce the computation burden, a new method called group comparison is proposed in tSSO. All temporary nondominated solutions in offspring are found first using the pairwise comparison, which takes O(N\({}_{\text{sol}}\)\({}^{2}\)) as the number of offspring in N\({}_{\text{sol}}\). The temporary nondominated solutions obtained in the previous generations are then compared with the new temporary nondominated solutions found in the current generation. The number of both sets of temporary nondominated solutions are at most N\({}_{\text{sol}}\), i.e., the time complexity is O(N\({}_{\text{sol}}\) log(N\({}_{\text{sol}}\))) based on the merge sort.  
Hence, the time complexity is O(N\({}_{\text{sol}}\) log(N\({}_{\text{sol}}\))) + O(N\({}_{\text{sol}}\)\({}^{2}\)) = O(N\({}_{\text{sol}}\)\({}^{2}\)), which is only one quarter of the pairwise comparison.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metabolite**} & \multicolumn{2}{c}{**Nonparametric ANOVA**} & \multicolumn{3}{c}{**Mann–Whitney U-test,**} \\  & \multicolumn{2}{c}{**(Kruskal–Wallis)**} & \multicolumn{2}{c}{**Only Significant Changes (\(p\) < 0.05) are Listed,**} \\  & & & & **Arrows** & **Indicate the Direction of Change** \\ \hline  & **chi.** & & & **Group A** & **Group B** & **Group A** \\  & **Squared** & \(p\)**-Value** & **Adjusted** & **Against** & **Against** & **Against** \\  & & & & **Controls** & **Controls** & **GroupB** \\ \hline glucose & 80 & \(3.9\times 10^{-15}\) & \(1.4\times 10^{-14}\) & Day1\(\uparrow\), Day3\(\uparrow\) & Day7\(\uparrow\) & \\
3-OH-butyrate & 130 & \(1.1\times 10^{-24}\) & \(1.2\times 10^{-23}\) & Day1\(\uparrow\), Day3\(\uparrow\), Day7\(\uparrow\) & Day7, A \(<\) B \\ citrate & 77 & \(1.2\times 10^{-14}\) & \(3.2\times 10^{-14}\) & Day1\(\downarrow\), Day3\(\downarrow\), Day7\(\downarrow\) & Day7\(\downarrow\) \\ leucine & 39 & \(7.6\times 10^{-7}\) & \(1.5\times 10^{-6}\) & Day1\(\uparrow\), Day3\(\uparrow\), Day3\(\uparrow\), Day7\(\uparrow\) & \\  & & & & Day7\(\uparrow\) & Day7\(\uparrow\) \\ isoleucine & 31 & \(2.8\times 10^{-5}\) & \(4.9\times 10^{-5}\) & Day1\(\uparrow\), Day3\(\uparrow\), Day3\(\uparrow\), Day7\(\uparrow\) & \\  & & & & Day7\(\uparrow\) & Day7\(\uparrow\) \\ valine & 13 & \(0.040\) & \(0.047\) & Day1\(\uparrow\), Day3\(\uparrow\), Day1\(\uparrow\), Day3\(\uparrow\), Day7\(\uparrow\) & \\  & & & & Day7\(\uparrow\) & Day7\(\uparrow\) \\ ketoleucine & 25 & \(3.7\times 10^{-4}\) & \(5.5\times 10^{-4}\) & Day1\(\uparrow\), Day3\(\downarrow\), & Day1, A \(>\) B \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results from statistical tests; \(p\)-value derived from nonparametric ANOVA and Mann–Whitney U-test. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**System**} & **OOB Error** & \multirow{2}{*}{**AUC**} & **Number of** & **Metabolites in** \\  & **(5 Variables)** & & **Variables** & **Importance Order** \\ \hline All Patients Day & & & & 3-hydroxybutyrate, \\
1/Controls & 3/108 & 0.984 & 2 & phenylalanine, \\  & & & 0.995 & 5 & Phe/Tyr ratio, acetate, \\  & & & & & glucose \\ Group A Day & 1/89 & 0.977 & 2 & phenylalanine, \\
1/Controls & & 0.996 & 5 & glucose, Phe/Tyr ratio, \\  & & & & & acetate, \\  & & & & phenylalanine, \\ Group B Day & 1/74 & 0.972 & 2 & 3-hydroxybutyrate, \\
1/Controls & & 0.991 & 5 & Phe/Tyr ratio, acetate, \\  & & & & & glutamine or glucose \\ Group A/Group & - & 0.568 & 2 & AUC value too low \\ B Day1 & - & 0.674 & 5 & AUC value too low \\ Group A/Group & - & 0.754 & 2 & alanine, lysine, \\  & & 0.783 & 5 & glutamine, Phe/Tyr \\  & & & & ratio, phenylalanine \\ Group A/Group & - & 0.487 & 2 & AUC value too low \\ B Day 7 & - & 0.503 & 5 & AUC value too low \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Outputs from random forest discriminatory analyses for selected systems.
## 3 Application of the Proposed Methodology
### Design of Rough Dimensions in CAD  
This is the next step of the proposed methodology (see D on Figure 2). The rough dimensions of the multifunctional portal centre had to be defined for further simulations, see Figure 6b.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Tool** & **Workpiece/Tool Diameter** & **Power** & **Cutting Torque** & **Cutting Force** \\ \hline Cutting unit C10-PSRNL-58110-25 & 190 mm & 34.9 kW & 7690 Nm & 40 kN \\ \hline Square shoulder milling cutter A490-254R63-14M & 254 mm & 96.3 kW & 6270 Nm & 24.6 kN \\ \hline Square shoulder milling cutter R390-160Q40-18L & 160 mm & 48 kW & 1360 Nm & 8.5 kN \\ \hline Face milling cutter 345-100Q32-13H & 100 mm & 30.3 kW & 431 Nm & 4.3 kN \\ \hline Square shoulder milling cutter 490-050C5-08M & 50 mm & 16.6 kW & 110 Nm & 2.2 kN \\ \hline Solid carbide end mill 1K223-2000-050-NH H10F & 20 mm & 31.8 kW & 14.9 Nm & 0.75 kN \\ \hline Solid carbide end mill 1K212-200-XA 1730 & 2 mm & 0.09 kW & 0.0381 Nm & 0.02 kN \\ \hline Face disc milling cutter R331.32-315Q60 RM23.50 & 315 mm & 23.2 kW & 1438 Nm & 8 kN \\ \hline \end{tabular}
\end{table}
Table 2: Load spectrum with derived cutting parameters.  
Figure 6: Three-dimensional model of the rough dimensions of the multifunctional portal machine (**a**), and scheme with rough dimensions of the machine (**b**).  
These dimensions were chosen based on the dimensions of gantry machines of selected manufacturers. The dimensions were also chosen with regard to the basic parameters of the machine and also based on the dimensions of representative workpieces. Based on these dimensions, an initial model was created to be used as input to the following advanced simulations. Primarily this involved topology optimisation and subsequent stiffness, stress, and modal analyses.
## 4 Results and Discussion
### Ablation Experiment
Some of it is also distributed in the window area in the upper left corner of the image, which means that when the left edge of the conveyor belt is in different backgrounds, especially when the top and bottom scenes of the left edge region of the conveyor belt in the image undergo obvious changes, the belt deviation monitoring network is very likely to fail, thus not succeeding in identifying the left edge. Comparatively speaking, environmental changes have little impact on the identification effect of the right edge. Further, by observing the feature distribution after adding the attention mechanism, it can be seen that, in the improved network proposed in this paper, for the detection of the left belt edge, the SENet and ECA attention mechanisms do not produce obvious effects. The feature distribution of the attention paid by the network is not shifted significantly and is still concentrated at the top and bottom of the annotation box. Moreover, when the attention mechanism is added to some positions, the feature distribution is partially transferred to the material flow, as in the case of \"YYY\" and \"NYY\" in SENet and \"YNY\" and \"NYY\" in ECA. At this time, the change in the material flow distribution may immediately affect the conveyor belt edge detection result. The CBAM attention mechanism transfers more network attention to the interior of the annotation box, which is conducive to improving the robustness of the network. Similarly, for the detection of the right belt edge, the effect of the SENet and ECA attention mechanisms is not ideal, and some of them even deviate from the annotation box, such as \"NNY\" in SENet and \"YNN, NYN, NNY\" in ECA. Comparatively, CBAM shifts the center of gravity of the feature distribution from the upper right corner center of the annotation box to the inside of the annotation box, which will also improve the anti-interference ability of the network. Based on the above visualization of the feature distribution, it can be seen that for the scene where the test image is located in Figure 8, during the actual installation and arrangement of the system, large changes should be avoided in the top and bottom areas within the left visual field so as not to affect the influence of the network on the left edge.
## 3 Experimental Results
### Impact of SNR  
Figure 11 shows the PCC and RMSE of air traffic control speech with varying SNRs. Figure 11 demonstrates that the proposed ResNet-BiLSTM model maintains good performance when evaluating air traffic control speech with different SNRs.
## 4 Conclusions  
We have investigated the optical performance of a three-turn 3D plasmonic Archimedes spiral lens under circularly polarized light illumination by numerical simulations without conducting experiments. This type of miniature plasmonic structure can either generate a focal point at the center of the structure under excitation by a left-hand circular polarization plane wave or a plasmonic vortex under excitation by a right-hand circular polarization plane wave. Therefore, this structure may have many more new potential applications than the traditional 2D planar plasmonic Archimedes spiral lens. By adjusting the parameters of the structure, such as the rise height \(h_{0}\) of the AS, or by switching the circular polarization, the 3D PASL can be reconfigured flexibly for use in practical applications. We have analyzed the important factors for control of the SPP field in detail, and found that the focus and the vortex length both increase with increments in \(h_{0}\). Finally, we investigated the stability of the device for trapping and rotation of microparticles suspended in PBS buffer solution. Additionally, the optical torque (T) of a cylindrical micro-rod when tilted at different rotation angles is discussed for rotation applications; the results provide a theoretical basis for potential applications of this structure in biomedicine and microbiology in the future. This 3D PASL structure may find further applications in plasmonic-assisted optical trapping and enhanced spectroscopy techniques.  
Conceptualization, L.C. and X.L.; Methodology, L.C. and X.L.; Formal Analysis, L.C., X.L. and Z.W.; Writing-Original Draft Preparation, X.L.; Writing-Review & Editing, L.C., X.L., Z.W., P.C., X.H. and T.N.  
This work was founded by the Natural Science Foundation of Gansu Province, grant number 1606 RJZA068; the National Natural Science Foundation of China, grant number 61701207, and the Fundamental Research Funds for the Central Universities, grant number 2016-143.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Description of the Case Study
#### 2.5.2 Design Fire Scenario and Design Fire  
The set of possible scenarios, characterized by their probability of occurrence and expected consequences, is understood as design fire scenarios. The process of selecting fire scenarios is referred to as qualitative analysis from a fire engineering perspective. From the overall set of scenarios, the scenarios to be further evaluated are selected. As a rule, these are the scenarios with the highest probability of occurrence or the highest consequences.  
A design fire is a quantitative description of the selected design fire scenarios. It is usually an expression of the heat release rate and its components, the density of the heat release rate, the temperatures, the amount of smoke generated, the pressure ratios, etc. [3,4].  
The stability of the platforms was evaluated on the basis of the expected fire development in the production plant, which can be described by the so-called characteristic fire types [3,5,6].  
The fire types were determined for different \"groups of production and operations\", which are characteristic in the design of buildings of production facilities in the Czech Republic. Operations in production buildings are divided into seven groups (1 to 7), with the eighth group consisting of auxiliary non-production operations that also occur in production buildings (e.g., dressing rooms, sanitary facilities, offices) [26,27]. In general, as the value of the production operation increases, the amount of combustible substances increases and thus the dynamics of fire development also increases.  
For the case study, the groups 4 to 7 of production and operation according to CSN 73 0804) were chosen [17]. The fire outbreak was located in the middle of the platform at floor level. The heat release rate for each production and plant group were then determined in accordance with EN 1991-1-2 [7].  
The sprinkler heads were located at a clear height of 5.4 m and their reaction temperature was 68 \({}^{\circ}\)C. The distance between the sprinkler heads was 3 m.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning
### Application of Convolutional Neural Network in Modulation Recognition  
#### 3.1.1 Convolutional Neural Networks  
The convolutional neural network (CNN) is one of the most popular and successful deep learning architectures, which consists of multiple convolutional layers, pooling layers, and fully connected layers, and its structure diagram is shown in Figure 6. Among them, the convolutional layer can extract different features of the input data, the pooling layer can downscale the high-dimensional features after convolution to improve the computation speed, and the fully connected layer can combine the previously extracted local features into global features and finally complete the classification according to the features.  
Figure 5: Deep learning-based modulation recognition model.  
Figure 6: Convolutional neural network structure.  
CNN is ideal for image processing because it can accurately extract feature information from images using convolution. Therefore, when identifying the type of the modulated signal, scholars usually process the signal into two-dimensional images such as constellation diagrams and time-frequency diagrams, then use convolutional layers to extract the features of the signal from the image, and the classification is done by the fully connected layer. On the other hand, CNN is also widely used in text and signals, so many scholars use CNN to directly extract features from signals.