esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Process Tool Condition Forecasting of Drilling CFRP/Ti Stacks Based on ResNet and LSTM Network
## 4 Experimental Results and Discussion  
### Model Validation  
The signals for drilling every eight holes are modified using the proposed preprocessing method and used as the inputs of TCM model. In addition, the ResNet-based TCM model with 20 residual blocks is implemented, which has acquired outstanding performance. The predicted tool-wear values are described in Figure 10. It can be seen that the monitored tool-wear values fit nicely with the measured tool-wear curve, and the MAE is 21.35 \(\upmu\)m. However, compared to the measured tool wear, there are stronger fluctuations in predicted values. Moreover, the monitored tool-wear values are not non-decreasing. As shown in Figure 10, the errors of predicted values are large for drilling holes No. 104 and No. 208 because the random impact still existed. The drill enters the sharp wear stage nearly after drilling hole No. 100, where the tool-wear rate is fast and the chipping occurs occasionally. The complex changes of cutting edge condition have variable influence on the cutting signals and increase the difficulty of monitoring, which causes the larger TCM errors. The MAE will be changed to 14.30 \(\upmu\)m by removing these two values. The random wave will reduce the model accuracy; therefore, data correction is needed.  
For the LSTM network, the inputs are several time series tool-wear values predicted by ResNet-based TCM model, which are denoted by \(y_{k-n+1}\), \(y_{k-n+2}\),..., \(y_{k}\) in sequence. The outputs are also time series tool-wear values, denoted by \(y_{k+1}\), \(y_{k+2}\),..., \(y_{k+m}\), in sequence. The LSTM network with \(n=3\) and \(m=2\) is implemented in order to acquire outstanding performance. The proposed data correction method in Section 2.3 is used to obtain the final outputs for forecasting tool-wear values using the first and second forecasting values, and the TCF results are depicted in Figure 11. The results of the LSTM network integrating the ResNet-based in-process TCM model fit nicely with the measured tool-wear curve. The MAE of the first and second forecasting values is 22.92 \(\upmu\)m and 29.22 \(\upmu\)m, respectively, which are larger than the MAE of the TCM results. The small errors accumulate to greater errors for tool-wear forecasting. The proposed data correction method reduces the MAE to 20.43 \(\upmu\)m. Using the method integrating the ResNet model with the LSTM network, tool-wear values of the next drilling holes could be forecasted during the machining process.
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 4 Metabolic Regulation of Secondary Metabolites
### Metabolic Regulation by Abiotic Stresses
#### 4.1.5 Metabolic Regulation by Heavy Metal Ions  
The role of heavy metal ions as antifungal agents may consists in part in inducing defense-response genes and in part in inhibiting the pathogens. Among the metal ions, copper ions (Cu\({}^{2+}\)) were the most effective to induce defense-related genes involved in phytoalexin biosynthesis [183]. Heavy metal ions are abiotic elicitors. Some metal ions can affect the production of plant secondary metabolites including phytoalexins [184]. Typical examples included tanshinone accumulation stimulated by metal ions Co\({}^{2+}\), Ag\({}^{+}\), and Cd\({}^{2+}\) in _Salvia miltiorrhiza_ cell cultures [185], andrographide production elicitated by Cd\({}^{2+}\), Ag\({}^{+}\), Cu\({}^{2+}\), and Hg\({}^{2+}\) in _Andrographs paniculata_ cell cultures [186], resveratrol production enhanced by Co\({}^{2+}\), Ag\({}^{+}\) and Cd\({}^{2+}\) in _Vitis vinifera_ cell cultures [187], and phaseollin production enhanced in Colombian bean (_Phaseolus vulgaris_) seedlings treated with CuCl\({}_{2}\)[188].  
The induction of phytoalexins by heavy metal ions in rice leaves was studied by punching detached leaves with a glass capillary tube and applying droplets of a heavy metal salt solution into the holes. Application of 1 mM copper chloride (CuCl\({}_{2}\)) induced the accumulation of oryzaeins A (**163**), B (**164**), C (**165**), and D (**166**) and of momilactones A (**148**) and B (**149**) in the tissues around the holes and in the droplets. Among the momilactones, momilactone A (**148**) showed a marked induction. Among the oryzaeins, oryzaein B (**164**) was induced to the greatest extent. The accumulation of momilactone A (**148**) was first noted 12 hr after the application of copper ions, reaching a maximum after 72 h. As these phytoalexins accumulated, brown spots appeared in areas surrounding the punctured holes. Iron and mercury ions made up approximately 37% and 20% of the elicitor activity of copper ion (Cu\({}^{2+}\)), respectively. Manganese (Mn\({}^{2+}\)) and cobalt (Co\({}^{2+}\)) ions hardly showed any elicitor activity [189]. Further study showed that the endogenous level of JA increased rapidly in CuCl\({}_{2}\)-treated rice leaves. If rice leaves were treated with JA biosynthesis inhibitors, the production of phytoalexin elicited by CuCl\({}_{2}\) decreased. JA is thus suggested to play an important role as a signaling molecule in CuCl\({}_{2}\)-elicited rice phytoalexin biosynthesis [126].
## 4 Discussion
### Influence of the Borehole Number on Fracturing Effect  
From the fracture traces exposed in Figure 5, it can be observed that compared to scenarios H1 and H2, which have similar temperatures, dual-wellbore hydraulic fracturing has produced slightly more cracks. This is similar to scenario H3 under high-temperature conditions. Table 3 also supports this point from a quantitative perspective. According to the analysis in Section 4.1, hydraulic fracturing is greatly influenced by the stress difference arising from external loads. At the scale of the samples in this study, the fact that the dual wells are located closer to the boundaries of the sample will further complicate the stress field within the rock sample, leading to the generation of a few cracks and localized damage zones in the X\(-\)Y+ region. Because of the serial connection of the two wellbores, the pressure reduction in the left wellbore after fracture initiation prevents the formation of cracks in other regions of the sample.  
Figure 12: Fluid pressure within the wellbore changes with time when the tensile strength of the fracture plane is \(1\times 10^{3}\) Pa.  
From Figure 10, it can be initially observed that in the case of deflagration fracturing, the increase in wellbores does not significantly enhance the fracturing effect. With the aid of Table 3, it can be further noted that compared to the two single-wellbore configurations (H1 and H2), the crack areas produced under the two dual-wellbore conditions (H3 and H4) are actually slightly smaller. The presence of dual wellbores makes the XZ plane passing through the center of the sample the dominant failure plane, which is more conducive to achieving directional fracturing. The rapid penetration and severe damage of this failure plane also allow the high-energy gas within the wellbore to escape swiftly, which may explain why only a single fracture is observed in sample D3. For sample D3, a larger powder charge makes the wellbore pressure rapidly increase to its peak value, followed by a swift decline in pressure upon the formation of the main fracture. Although the peak pressure during this process is high, its duration is relatively short [45]. In contrast, a smaller powder charge in the sample D4 maintains a relatively lower wellbore pressure for a longer duration, while still remaining significantly above the rock's fracture initiation stress, thereby resulting in the generation of more cracks.  
In general, for hydraulic fracturing, dual wellbores can generate more cracks; meanwhile, for deflagration fracturing, the configuration of dual wellbores primarily aids in achieving directional fracturing. Merely increasing the number of wellbores does not necessarily lead to better fracturing effects. Instead, it is crucial to consider the wellbore locations and other fracturing parameters in combination to generate more cracks in the rocks.
## 4 Application of Fe-MOF in Advanced Oxidation Processes  
AOPs can be divided according to the type of reaction involved or to the process underlying as shown in Figure 6[150; 151]. The catalytic processes are based on the Fenton reaction, in which the hydroxyl radicals are generated through the use of iron and hydrogen peroxide (Equation (1)) [152]. Similar to the conventional process, the Fenton-like process  
Figure 5: Schematic of the procedure found in papers using this synthesis technique.  
can generate powerful oxidants, replacing iron by transition metals [153] or generate sulphate radicals following the reactions shown in Equations (2) and (3) [154].  
\[Fe^{2+}+H_{2}O_{2}\to Fe^{3+}+OH^{\bullet}+OH^{-} \tag{1}\]  
\[Fe^{3+}+S_{2}O_{8}^{2-}\to Fe^{2+}+S_{2}O_{8}^{-\bullet} \tag{2}\]  
\[Fe^{2+}+S_{2}O_{8}^{2-}\to Fe^{3+}+SO_{4}^{-\bullet}+SO_{4}^{2-} \tag{3}\]  
However, there are some drawbacks to using these catalysts, particularly the acid pH regulation, sludge generation and the loss of catalyst in the effluent. Several heterogeneous AOPs, with a wide variety of materials as catalysts or supports, have been reported to overcome these drawbacks. Despite the homogeneous catalyst disadvantages, the reaction rate is higher than in heterogeneous systems, which require an increase in the catalyst surface area [155; 156]. Thus, it is critical and challenging to develop new catalysts with low cost, high activity, and good chemical stability. Among them, MOFs have stood out in the last ten years.  
The same trend was observed when reviewing the Scopus database for a period of five years (2018 to 2022) using the keywords: \"metal-organic frameworks\", \"advanced oxidation processes\" and \"wastewater\". An analysis of the co-occurrences of keywords using bibliographic network maps from VOS Viewer@is shown in Figure 7. The figure below illustrates the co-occurrence network of the top 29 most frequently occurring keywords. Each node represents a keyword, and each line represents the co-occurrence relation between them. Moreover, the results were normalized using fractionalization, which is commonly employed to normalize the strength of the links between items.  
As shown in Figure 7, the color of each node (keyword) corresponds to its average year of appearance. Before 2020, the number of MOF studies in this field was insignificant. Therefore, there are no nodes with the color purple, which represents the papers reported in 2018, and the only keyword in 2019, which is in dark blue, is synthesis. The number of keywords used after 2020 increased very rapidly, and it is possible to see the evolution of different AOPs over time.  
As shown in the previous sections, the synthesis of these Fe-MOFs is crucial for obtaining some of the properties of these catalysts that are of particular interest for the development of different AOPs. Based on the current interest, this article will focus on the application of MOFs in several AOPs such as Fenton, electro-Fenton, photocatalysis and mainly those focused on the use of Fe-MOF as a catalyst.  
Figure 6: Classification of AOP by the fundamentals of the process itself.
## 4 Results of the Optimizations
### Distributed Generation Solution
The HPs result more suitable in the environmental optimal solution. In all, optimal solutions, the thermal and cooling wasted energy is negligible. If two (or more) objectives are considered, the choice of the proper configuration has to be based on further technical evaluation of the designer and on an economic evaluation of the stakeholders. If the only aim is to achieve the lowest total annual cost, the economic optimal solution will be adopted, but in a different context, where the sensitivity to environmental problems is more important, the 90% environmental solution could be chosen. It allows the total annual emissions to be significantly reduced, controlling, at the same time, the Total annual cost. In this specific case study, the 60% environmental optimal solution has been identified as the proper compromise. Figure 9 shows the optimal configuration and the lay out of the network in this case. This solution provides a subdivision of the DHN into two different subsystems: the first one is made up of sites 1-6, the second one is made of the sites 7-9. In the first subsystem the site 2 can be identified as a central node, where the greatest part of the thermal energy is produced and sent to the other sites through the DHN. In the second subsystem the sites are integrated with each other and a main site cannot be identified. Distributed generation solutions allow a slight improvement of the economic objective function (less than 1%) to be achieved, jointly with an improvement of the environmental one of about 2%, compared with the isolated solutions. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Environmental** & **90\% Env.** & **60\% Env.** & **30\% Env.** & **Economic** \\  & **Optimization** & **Optimization** & **Optimization** & **Optimization** & **Optimization** \\ \hline ICE electricity & 3131 & 5190 & 8014 & 10,341 & 12,933 \\ MGT electricity & 0 & 0 & 0 & 0 & 0 \\ PV panels electricity & 0 & 0 & 141 & 239 & 239 \\ Bought electricity & 9431 & 6060 & 2625 & 767 & 123 \\ Electric user demand & 6968 & 6968 & 6968 & 6968 & 6968 \\ CC electricity & 217 & 230 & 266 & 180 & 231 \\ HP electricity & 4617 & 3232 & 2266 & 1560 & 532 \\ Sold electricity & 760 & 820 & 1281 & 2729 & 5565 \\ ICE thermal energy & 4619 & 7468 & 11,510 & 15,022 & 18,742 \\ MGT thermal energy & 0 & 0 & 0 & 0 & 0 \\ BOH thermal energy & 0 & 0 & 0 & 0 & 3 \\ HP thermal energy & 11,581 & 8663 & 5406 & 3450 & 834 \\ ST panels thermal energy & 1387 & 1387 & 566 & 0 & 0 \\ Thermal user demand & 17,319 & 17,319 & 17,319 & 17,319 & 17,319 \\ ABS thermal energy & 0 & 0 & 0 & 809 & 1704 \\ Wasted thermal energy & 0 & 0 & 0 & 0 & 9 \\ CC cooling energy & 652 & 689 & 797 & 540 & 692 \\ ABS cooling energy & 0 & 0 & 0 & 523 & 1140 \\ HP cooling energy & 2096 & 2059 & 1951 & 1686 & 917 \\ Cooling user demand & 2748 & 2748 & 2748 & 2748 & 2748 \\ Wasted cooling energy & 0 & 0 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Total optimal annual energy magnitudes (MWh)—distributed generation solutions.
## 3 Results
### Bimodal Sea State  
In cases where the sea state is bimodal (two peaks), comprising both wind sea and swell, the wave spectrum becomes a combination of these two individual sea spectra. It is of interest whether the present ML method can also be applied to this kind of bimodal wave spectra. In this regard, we evaluated the performance of our artificial neural network (ANN) model in predicting the bimodal spectrum with respect to the original-frequency basis using a dataset of both unimodal and bimodal sea conditions. For this model, we generated the dataset comprising 192 unimodal cases and 192 bimodal cases, as outlined in Table 5, which details the wind-sea and swell components. Table 6 provides the parameters used to train the ANN model, which were optimized formerly.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**JONSWAP Wave Parameter** & **Values** \\ \hline \(H\)s, wind sea (m) & [2.5, 3.5] \\ \(H\)s, swell (m) & [0 *, 3, 4] \\ \(Tp\), wind sea (s) & [5; 7] \\ \(Tp\), swell (s) & [10; 11] \\ \(\gamma\), wind sea & [1; 2] \\ \(\gamma\), swell & [4; 5] \\ \(\beta\) (deg) & [5; 10] \\ \(V\) (knots) & [10; 40] \\ \hline \hline \end{tabular}  
* \(H\)s = 0 for unimodal seas.  
\end{table}
Table 5: JONSWAP wave parameters for Model 4 dataset.  
Figure 13: Encounter wave frequency spectrum (theoretical vs. estimated) and ANN-predicted original wave spectrum (\(H\)s = 5 m, \(Tp\) = 9 m, peak enhancement factor \(\gamma\) = 2, wave headings \(\beta\) = 0 deg, and forward speed \(V\) = 7 knots).
# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 2 Proposed Method  
As shown in Figure 2, the method includes two stages, which can be divided into five modules. The initial stage is used to generate the initial pose, including the extraction of significant features, viewpoint sampling and projection, feature matching and viewpoint searching, virtual image rendering, and initialization of the evolution stage. The significant feature refers to the geometric feature (e.g., points, lines, and curves) that are well visible and distinguishable from different viewpoints. Virtual viewpoints are generated following the spherical or ellipsoidal rule [38]. The rough pose is provided by the sampled virtual viewpoint with the best similarity to the significant feature of the real image. The feature matching and similarity evaluation are performed by a weighted point pair matching algorithm [39]. Some CNN-based coarse pose estimators with a small number of samples are also suitable choices for obtaining the initial pose.  
The evolution stage is redesigned as population initialization and migration, offspring generation, viewpoint quality assessment and selection, pose feasible region construction and update, and a learnable evolution function optimization inspired by importance sampling in reinforcement learning [40].
# AE-CGAN Model based High Performance Network Intrusion Detection System
## 2 Related Works  
### A Studies on Feature Extraction  
Studies have been conducted on various approaches to feature selection and dimension reduction to address the problem of low detection rates and poor generalization capabilities when NIDS is used in large network environments  
As a study on feature extraction, [PERSON] [15] used the KDD99 dataset and, based on PCA (principal component analysis) technology, selected the characteristics of the dataset and compared the performance using the dataset's full characteristics. Comparisons show that using the full features increases accuracy but also increases delays in terms of time and memory. The PCA is not much higher than the autoencoder algorithm proposed in this paper because PCA projects the data in the dimension that maximizes the variance by using only linear properties without learning process.  
[PERSON] et al. [16] used NSL-KDD datasets to suggest learning methods based on SELL (self =taught learning) framework by combining SAE (sparse autoencoder) and SVM (support vector machine) to detect network intrusion. This approach dramatically reduces learning and testing time and effectively improves SVM's predictive accuracy. The method of dimension reduction usingautoencoder is the same as presented in this paper. However, the SVM classifier increases the learning time when using a large volume dataset. Therefore, in this study, the classifier uses random forest to reduce the learning time and the test time to process large network data.  
In another impressive study using autoencoder, [PERSON] et al. [17] suggested a deep learning self-adaptive use network input detection system. In this study, we propose an IDS (intrusion detection system) that can adapt itself to changes continuously in the network environment by making the best use of a sparse autoencoder (SAE) for unsupervised learning. AE can be used as a feature extraction method and can be employed to directly detect intrusions.  
In recent NIDS, the deep learning approach has used models of deep neural network, self-learning liaring (STL), and current neural network (RNN). [PERSON] et al. [18] used autoencoder as models of STL and were able to classify attack types accurately. However, DNN or RNN relies heavily on the learning of models, so when there is an imbalance in the data, they are significantly less accurate, and the performance is inconsistent according to the hyper-parameters used for learning. In addition, in real-world network environments based on large amounts of data, there is a significant slowdown in detection rates when NIDS is used with deep learning.  
NIDS is primarily aimed at improving the detection accuracy of attack data. Therefore, this study aims to extract features based on autoencoder, a deep learning model commonly used in NIDS, and to improve the detection accuracy of attack data by applying the latest deep learning model, the GAN model.
###### Abstract  
Massive multiple-input multiple-output (MIMO) technology, which is characterized by the use of a large number of antennas, is a key enabler for the next-generation wireless communication and beyond. Despite its potential for high performance, implementing a massive MIMO system presents numerous technical challenges, including the high hardware complexity, cost, and power consumption that result from the large number of antennas and the associated front-end circuits. One solution to these challenges is the use of hybrid beamforming, which divides the transceiving process into both analog and digital domains. To perform hybrid beamforming efficiently, it is necessary to optimize the analog beamformer, referred to as the compressive measurement matrix (CMM) here, that allows the projection of high-dimensional signals into a low-dimensional manifold. Classical approaches to optimizing the CMM, however, are computationally intensive and time consuming, limiting their usefulness for real-time processing. In this paper, we propose a deep learning based approach to optimizing the CMM using long short-term memory (LSTM) networks. This approach offers high accuracy with low complexity, making it a promising solution for the real-time implementation of massive MIMO systems.  
massive MIMO; hybrid beamforming; compressive measurement matrix; long short-term memory network +
Footnote †: journal: 2023, 16, 261. [[https://doi.org/10.3390/a16060261](https://doi.org/10.3390/a16060261)]([https://doi.org/10.3390/a16060261](https://doi.org/10.3390/a16060261))