esa-sceva/satcom-chunk-collection huggingface dataset:
# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 2 Theoretical Background  
### Big Data and Smart Factories  
The growing complexity of manufacturing processes and the continuous expansion of data volumes present significant opportunities for enhancing decision-making. When dealing with vast, heterogeneous, and high-velocity data, we refer to the concept of Big Data. Extracting meaningful insights from Big Data requires a combination of models, algorithms, software, hardware, and technologies designed to improve the accuracy and reliability of analytical results [35]. However, working with Big Data comes with challenges at multiple levels, including data capture, storage, retrieval, sharing, analysis, management, and visualisation. Data scientists face significant hurdles in efficiently handling Big Data to extract reliable insights while optimising costs. Traditional data management typically follows five key steps: cleaning, aggregation, encoding, storage, and access. However, the unique characteristics of Big Data, defined by the five Vs (Volume, Velocity, Variety, Veracity, and Value) [36], complicate these processes, requiring distributed environments and advanced analytics solutions. One of the main challenges is determining how to clean and validate vast datasets to ensure that only relevant and reliable information is retained [37].  
In the context of Industry 4.0, Big Data plays a pivotal role in improving operational efficiency, enhancing product quality, and driving overall productivity [38]. However, as noted by [39], many organisations struggle to translate their Big Data investments into tangible business outcomes. This often stems from systemic weaknesses in data management frameworks and the absence of structured methodologies for capturing and leveraging Big Data Value effectively. To fully exploit the potential of Big Data in Industry 4.0, companies must adopt not only advanced analytical techniques but also robust data governance and integration strategies [39]. As highlighted [40], many manufacturing companies face challenges in assessing their Big Data maturity, which hinders their ability to fully exploit data-driven strategies for operational improvement. To overcome these limitations, it is essential to develop robust frameworks that enable real-time monitoring and predictive capabilities.  
EWSs fit naturally into the Big Data paradigm as they rely on large volumes of diverse data and require real-time information processing to be effective. These systems continuously analyse data streams from multiple sources, detecting patterns and anomalies that signal potential disruptions. Their ability to integrate and process heterogeneous, high-frequency data makes them a crucial component in modern industrial environments, where timely decision-making is essential.
## 3 Rotor-Side External Retrofit Techniques
### Comparative Results Analysis of Rotor-Side External Retrofit Techniques  
Rotor-side external retrofit techniques have a desirable control effect in restraining rotor overcurrent, controlling DC-link voltage, and improving terminal voltage dynamics. To compare the control effect of various rotor-side external retrofit techniques, a single-machine infinite bus (SMIB) system was established, as depicted in Figure 11. The rated active power of the wind farm was 9 MW with six 1.5 MW DFIG-WTs, and the detailed models of DFIG-WTs were provided by Matlab/Simulink R2016a. The wind speed in the wind farm was maintained as a constant of 15 m/s. A three-phase to ground fault with grounding resistance of 0.5 \(\Omega\) occurs at the end of 25 kV feeder at 0 s, and the fault is cleared at 0.1 s. According to the GCR for LVRT, the maximum limit of rotor current was set as 2 p.u. Typical models of an active crowbar [20], SDR [30], ESS [36], and DC chopper [25] were implemented in MATLAB/Simulink for simulation. The durations of the active crowbar circuit and SDR were set as 0.02 s, while the ESS and DC-chopper remained activated during grid fault. The comparative results are shown in Figure 12.  
Figure 12a shows the control effect in restricting rotor current. Since the ESS and a conventional DC chopper may result in rotor overcurrent, they are not included. As shown in Figure 12a, the active crowbar circuit shows better performance in restricting rotor overcurrent compared with the SDR, because it creates a low-resistance path to allow high-current flow through during transient periods. The desirable control effect in restricting rotor current endows the active crowbar circuit with the ability to avoid overcharging in DC capacitors, such that the active crowbar circuit can restrain DC-link overvoltage better than the SDR, as shown in Figure 12b. The DC-chopper can maintain DC-link voltage in an acceptable range by quickly discharging the excess active power in the DC-link. Figure 12c shows the comparative results of transient reactive power support. Since the SDR maximizes the operation time of the RSC, more reactive power is generated when the SDR is applied compared with the active crowbar circuit. The ESS offers more transient reactive power support during transient periods, which results in better terminal voltage dynamics, as shown in Figure 12d. Since the reactive power generation mainly depends on RSC control, the active crowbar circuit and SDR provide a similar control effect for supporting terminal voltage. The DC-chopper has no control effect on reactive power support and voltage dynamics improvement; thus, it is not presented in Figure 12c,d.  
Figure 11: The structure of single-machine infinite bus (SMIB) system.
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 4 Background
### Autoencoders  
Autoencoders use a dimensionality reduction process to detect anomalies in the data. They assume that data are correlated and can be compressed into a lower-dimensional subspace where regular and anomalous data appear significantly different. An autoencoder tries to find the encoder that reduces the input dimension \(d\) to \(p\), \(A:\mathbb{R}^{d}\longrightarrow\mathbb{R}^{p}\). Then, a decoder maps the latent representation of the encoder \(p\) to the original input space \(d\), \(B:\mathbb{R}^{p}\longrightarrow\mathbb{R}^{d}\), which satisfies Equation (3).  
\[arg\min_{A,B}\mathrm{E}[\Delta(x,B\circ A(x)], \tag{3}\]  
where \(\mathrm{E}\) represents the expected distribution of the input data \(x\), and \(\Delta\) is the reconstruction loss function to measure the distance between the input and the output of the decoder [55]. In our case, the loss function \(\Delta\) is the Mean Squared Error (MSE) function, in line with other relevant scientific research such as [19; 55; 56], and defined as per Equation (4) on distributions \(x\) and \(y\).  
\[MSE(x,y)=\frac{1}{d}\cdot||x-y||_{2}^{2},\forall\;x,y\in\mathbb{R}^{d}, \tag{4}\]  
Different variants of autoencoders are available, including regularized autoencoders, variational autoencoders, and sparse autoencoders [57]. Even when the bottleneck consists of a single node, overfitting remains a concern if the encoder and decoder capacities are sufficiently large to encode each sample into an index. Autoencoders involve a crucial trade-off between minimizing the reconstruction error for effective input reconstruction and ensuring that the low-dimensional representation generalizes meaningfully.  
Figure 4: Typical CNN architecture.  
Historically, autoencoders have primarily found application in image generation, specifically in generating image sets that resemble the input data. However, security researchers take advantage of autoencoders, especially for anomaly detection, i.e., to detect the occurrence of abnormal events beyond the usual ones. For example, assume that an autoencoder \(ae\) is trained with a probability distribution \(P\) of clear samples, and \(Q\) is the distribution of malicious samples not used during the training process. Then, any unseen test sample with the same statistical distribution of \(P\) exhibits a smaller reconstruction error (calculated using Equation (4)). This reconstruction error is used to set a threshold to classify the testing dataset as either an anomaly or regular.
## 3 Results and Discussions
### Principal Component Analysis  
Principle component analysis was conducted to investigate the effects of LAB on the pattern of silage fermentation characteristics. Figure 1 shows the biplots for maize and rice straw silages based on fermentation products (lactic acid, acetic acid, and pH). The biplots divided the observed data into two clusters. The left cluster is mainly comprised of rice straw silage, and the right cluster maize silage. This indicates that the fermentation patterns of rice straw and maize were different. The differences in nutrient content between maize and rice straw silage are probably the reason for these apparent differences. Maize is likely to have a higher starch content compared to rice straw, which is rich in lignin and silica [45; 52]. Consequently, maize produced higher amounts of fermentation products compared to rice straw during ensiling, as described in Figure 1.  
Figure 1: Principle component analysis (PCA) of fermentation products (lactic acid, acetic acid, L: A ratio, and pH) in 40 days ensiled maize and rice straw silage. M: maize silage; RS: rice straw, NC: negative control; PC: positive control; LPA: _Lactobacillus paracasei_, LPL: _Lactobacillus plantarumum_; LA: _L. acidophilus_.  
Such contrast results (see Figure 1) seem to be associated with the nutritive values of maize and rice straw, which will lead to tremendous differences in animal performance. Nevertheless, maize silage is often lacking crude protein, so that either expensive plant protein such as soybean meal or a legume forage in the form of fresh or silage must be supplemented to improve animal performance [52; 53]. On the other hand, rice straw as an agricultural byproduct with limited nutritive values plays an important role when forage sources are limited in a country like South Korea [18]. Therefore, improving the quality of either maize silage or rice straw silage, or both is imperative in the future for the ruminant industry.  
Another point to highlight was the environmental and genetic variation of forages used. This was well-documented in the study by [PERSON] and [PERSON] [54], who reported that the genetic and environmental variation of maize affected the cell wall composition that would be related to the cell wall degradability. Similarly, [PERSON] et al. [55] reported differences in chemical composition and the degradability of rice straw according to the varieties and environmental conditions. Others [56; 57] acknowledged genetic and/or environmental variation of such forages on digestibility in vitro. In this study, only a single cultivar from maize or rice straw was used. However, evidence concerning genetic and environmental factors provides more reason to continue this type of study, examining novel inoculants and their application to diverse varieties even within the same forage species.
# EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism
## 5 Conclusions  
The mold protection problem is bothering engineers because of the high maintenance price and time-consuming maintenance process. Vision-based methods need an additional vision system, and customized algorithms. Without a vision system, existing methods always take a lot of time for field engineers to adjust mold protection parameters. Therefore, we proposed an EKF-based self-adaptive method in this paper. We built a mathematical model for a toggle mechanism driven by servo systems. Using the rough values from the mathematical model and toggle mechanism system, we applied the EKF to obtain an accurate electric current. Then a self-adaptive method was proposed to obtain the safety current range. At last, the proposed method was implemented in a 140-ton \(EIMM\), and the results showed that the detected distance of foreign bodies was decreased by 22% compared with the general method.  
In future, a combination of the vision and proposed methods will be studied to further improve the robustness and sensitivity of the \(MP\) for higher speed applications.  
Conceptualization, B.C. and H.W.; methodology, D.S.; software, B.C.; validation, H.Z. and D.S.; formal analysis, B.C. and D.S.; investigation, B.C.; resources, H.Z.; data curation, B.C.; writing--original draft preparation, B.C. and D.S.; writing--review and editing, H.W. and H.Z.; visualization, D.S.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (U1609211).  
The authors declare no conflict of interest.  
Figure 11: Detection precision comparison of the \(EKF\) based self-adaptive \(MP\) method and general one for 100 rounds. The thickness of each foreign body was arbitrary. Through 100 experiments, the average detected distance of the proposed method was 0.94 mm, while that of the general one was 1.21 mm. Namely, the detected distance of the proposed method was decreased by 22%.
# Two-Way Linear Probing Revisited
## 5 Upper Bounds
### Two-Way Locally Linear Probing: LocallyLinear Algorithm  
Recall that algorithm \(\textsc{LocallyLinear}(n,m)\) inserts keys into a hash table with disjoint blocks of size \(\beta_{1}(n)\). We show next that \(\beta_{1}\) can be defined such that none of the blocks are completely full, w.h.p. This means that, whenever we search for any key, most of the time, we only need to search linearly and cyclically the two blocks that the key chooses initially.  
**Theorem 8**.: _Let \(n\in\mathbb{N}\), and \(m=\lfloor\,\alpha n\,\rfloor\), where \(\alpha\in(0,1)\) is a constant. Let \(C\) be the constant defined in Theorem 2, and define_  
\[\beta_{1}(n):=\bigg{\lfloor}\,\frac{\log_{2}\log n+C}{1-\alpha}+1\,\bigg{\rfloor}.\]  
_Then, w.h.p., the maximum unsuccessful search time of \(\textsc{LocallyLinear}(n,m)\) with blocks of size \(\beta_{1}\) is at most \(2\beta_{1}\), and the maximum insertion time is at most \(\beta_{1}-1\)._  
Proof.: Notice the equivalence between algorithm \(\textsc{LocallyLinear}(n,m)\) and the allocation process \(\textsc{GreedyMC}(n/\beta_{1},m,2)\) where \(m\) balls (keys) are inserted into \(n/\beta_{1}\) bins (blocks) by placing each ball into the least loaded bin among two bins chosen independently and uniformly at random, with replacement. It suffices, therefore, to study the maximum bin load of \(\textsc{GreedyMC}(n/\beta_{1},m,2)\), which we denote by \(L_{n}\). However, Theorem 2 says that, w.h.p.,  
\[L_{n}\leq\log_{2}\log n+C+\alpha\beta_{1}<(1-\alpha)\beta_{1}+\alpha\beta_{1} =\beta_{1}.\]  
and similarly,  
\[L_{n}\geq\log_{2}\log n+\alpha\beta_{1}-C>\frac{\log_{2}\log n+C}{1-\alpha}-2C \geq\beta_{1}-2C-1.\]
## 3 Secrecy Rate Maximization
As a result, Problem P2 is written as follows:  
\[ \begin{split}(\mathbf{P2})\max_{\beta_{1},\beta_{2},z}R_{\text{ sec}}\\ \text{s.t.}\ \ \text{C1, C2, C3, C4, C5, C7, C8, C9, C10, C11}\end{split}  \tag{28}\]  
The proposed iterative algorithm for the system model in this work is given in Table 1. This algorithm reaches the maximum value of \(R_{\text{sec}}\) by finding optimal \(\beta_{1}\), \(\beta_{2}\) and \(z\) values together. \begin{table}
\begin{tabular}{l l} \hline
**Steps** & **Iterative Algorithm** \\ \hline
1: & Initialize \(z^{0}\), \(\beta_{1}^{0}\), and \(\beta_{2}^{0}\). Let \(t=0\) \\
2: & **repeat** \\
3: & For given \(\beta_{1}^{t}\) and \(\beta_{2}^{t}\), obtain \(z^{t+1}\) that gives the maximum value of \(R_{\text{sec}}(\beta_{1}^{t},\beta_{2}^{t},z^{t+1})\) \\  & under constraints. \\
4: & For given \(z^{t+1}\) and \(\beta_{2}^{t}\), obtain \(\beta_{1}^{t+1}\) that gives the maximum value of \(R_{\text{sec}}(\beta_{1}^{t+1},\beta_{2}^{t},z^{t+1})\) \\  & under constraints. \\
5: & For given \(z^{t+1}\) and \(\beta_{1}^{t+1}\), obtain \(\beta_{2}^{t+1}\) that gives the maximum value of \(R_{\text{sec}}(\beta_{1}^{t+1},\beta_{2}^{t+1},z^{t+1})\) \\  & under constraints. \\
6: & Update iteration index \(t=t+1\). \\
7: & **until** \\
8: & The global maximum \(R_{\text{sec}}(\beta_{1},\beta_{2},z)\) under constraints is achieved \\ \hline \end{tabular}
\end{table} 
Table 1: Joint optimization of dynamic time allocation and power reflection coefficient-based iterative algorithm for average secrecy rate maximization. Figure 5: Function \(R_{\text{sec}}(z)\).
## 3 Results and Discussion  
The full results are presented in Appendix B. Of the NACA 0012 simulations using the physics-controlled meshes, only the physics-controlled \"extra coarse\" and \"coarser\" meshes converged. The lift coefficients are depicted in Figure 13 alongside the reference user-controlled mesh and experimental data. The reference mesh matches the experimental data closely, showing good agreement overall. The physics-controlled meshes show a similar trend of an increasing lift coefficient with an increasing angle of attack; however, they significantly underestimate the amount of lift. Considering the mesh specifications reported in Tables 3 and 4, the poor results of the automated meshes can be attributed to the low amount of elements and boundary layers. Consequently, the automated mesh has a low element density throughout, including close to the airfoil itself. In turn, it is unable to accurately capture the boundary layer flow around the airfoil. As a result, the computed lift coefficient is significantly lower than the experimental one. Decreasing the automated element size setting to increase the mesh density does not lead to a solution, as those meshes led to models that were not able to converge.  
Overall, these results show how the automated meshes introduce significant deviation from the experimental results. The automated meshes are unsuitable and a user-controlled mesh is required to yield usable results. In turn, user-controlled meshes are used for the investigations into the addition of a slotted flap, the non-isothermal model, and the extension of the stock NACA 0012 airfoil to 3D.  
Figure 11: Mesh of the 3D model with a wingtip.  
Figure 12: \(C_{L}/C_{D}\) ratio versus increasing mesh boundary layer count.
### Machine Learning and Deep Learning in SPECT Nuclear Cardiology Imaging
The same team of authors presented another interesting application of DL in the prediction of CAD. A three-fold feature extraction convolutional layer joined with three fully connected layers was deployed to analyze SPECT myocardial perfusion clinical data and polar maps from 1638 patients [51]. These scientific works have investigated the integration of clinical and imaging data and show how to formulate new autonomous systems for the automatic interpretation of SPECT and PET images. The authors in [52] proposed a graph-based convolutional neural network (GCNN) which used Chebyshev polynomials, achieving the highest accuracy (91%) compared with other neural-network-based methods. Recently, authors in [53] were the first to study CAD diagnosis using solely SPECT MPI scans in deep learning. They developed two different classification models. The first one is based on deep learning (DL), while the second is based on knowledge to classify MPI scans into two types automatically, ischemia or healthy, exclusively employing the SPECT MPI scans at the input level. Performing exploitation of the well-known DL methods in medical image analysis (such as AlexNet, GoogleNet, ResNet, DenseNet, VGG16, VGG19), the best DL model was determined to be VGG16 with support vector machine (SVM) deep features shallow, concerning classification accuracy. The first model to be developed exploits different pre-trained deep neural networks (DNNs) along with the traditional classifier SVM with the deep and shallow features extracted from various pre-trained DNNs for the classification task. The knowledge-based model, in its turn, is focused on converting the knowledge extracted from experts in the domain into proper image processing methods such as color thresholding, segmentation, feature extraction and some heuristics to classify SPECT images. First, the images were divided into six segments (A, B, C, D, E, F), and the features were extracted from each segment to measure the shapes. Next, a classification rule set assigned by experts was applied. The parameters were empirically identified and fine-tuned on the training and validation images. The produced overall 2-class classification accuracy was 93% for both methods. As has emerged from the related literature regarding SPECT MPI, PET and PET-CT [54], a system based on deep learning provides similar performance to a nuclear physician in standalone mode. However, the overall performance is notably improved when it is used as a supportive tool for the physician [33; 55; 56; 57; 58]. Although SPECT MPIscans are of high importance for diagnosing CAD in nuclear cardiology, only one study was reported in the literature to apply CNNs in these types of MPI images. Thus, there is plenty of space for further research over the investigation on the advantageous and outstanding capabilities of CNNs in the field of nuclear cardiology.
## 2 Data Sources and Methods
### HSCNN Training
#### 2.3.3 Model Training  
Images from two different periods were selected as the training data. We selected images from different periods for increasing the anti-interference abilities of the HSCNN and mitigating the complications such as the change in the seasons, and thus enhancing applicability. The training stage proceeded through the following steps:  
1. Image-label pairs are input into the HSCNN as training samples. Network parameters are initialized.
2. Forward propagation is performed on the sample images.
3. The [loss]_inner is calculated and back propagated to the inner-CNN, whereas the [loss]_edge is calculated and back propagated to the edge-CNN.
4. The network parameters are updated using the stochastic gradient descent (SGD) [41; 48] with momentum.
5. Steps (2)-(4) are iterated until both [loss]_inner and [loss]_edge are less than the predetermined threshold values.  
The training yields two sub-networks, an inner-CNN and edge-CNN. The former can accurately extract the inner pixels of the winter wheat plantation from the sweet GF-2 remote sensing images, whereas the latter allows the best possible distinction between the edge pixels of the winter wheat planting region and other pixels.  
In our training, the SGD method with momentum was used for parameter updates, which is illustrated in the following expression:  
\[\mathrm{W}^{(\mathrm{n}+1)}=\mathrm{W}^{(\mathrm{n})}-\Delta\mathrm{W}^{( \mathrm{n}+1)} \tag{6}\]  
where \(\mathrm{W}^{(\mathrm{n})}\) denote the old parameters, \(\mathrm{W}^{(\mathrm{n}+1)}\) denote the new parameters, and \(\Delta\mathrm{W}^{(\mathrm{n}+1)}\) is the increment in the current iteration, which is a combination of the old parameters, gradient, and historical increment, i.e.,  
\[\Delta\mathrm{W}^{(\mathrm{n}+1)}=\vartheta\bigg{(}\mathrm{d}_{\mathrm{w}} \cdot\mathrm{W}^{(\mathrm{n})}+\frac{\vartheta\mathrm{J}(\mathrm{W})}{ \vartheta\mathrm{W}^{(\mathrm{n})}}\bigg{)}+\mathrm{m}\cdot\Delta\mathrm{W}^{( \mathrm{n})} \tag{7}\]  
where \(\mathrm{J}(\mathrm{W})\) is the loss function, \(\vartheta\) is the learning rate for step length control, \(\mathrm{d}_{\mathrm{w}}\) denotes the weight decay, and \(\mathrm{m}\) denotes the momentum.