esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors
## 2 The Wavelet Transform  
WT is a modern mathematical development for the treatment of signals both the time and frequency domains. Therefore it is specially useful in observing the changes in patterns over time. The Fourier Transform, and most techniques derived from it, are inappropriate to treat non-stationary signals due to the absence of any temporary information. A technique that is suitable for treating non-stationary signals is STFT. However, the major disadvantage for this technique is the resolution of the frequency obtained, which remains constant for the whole signal as the same window is applied. On the other hand, WT allows a high frequency resolution at low frequencies, and high time resolution at high frequencies, as desired.  
The first concept of wavelets was set up by [PERSON] in 1984. Later, [PERSON], [PERSON], and [PERSON] contributed with important developments regarding orthogonal wavelet functions. However, the use of WT became popular in engineering applications since the end of the 1980s, when [PERSON] [26] found that there was a relationship between Quadrature Mirror Filters and orthonormal wavelet functions. From this, new techniques allowed for the application of WT to a signal using recursive-filtering banks. Recently, WT has become the most widely applied tool in signal processing in many different fields such as voice recognition [27; 28], noise reduction [29; 30], electrocardiographs [31], and radio-frequency interference mitigation [32], amongst others.  
Specifically in the field of fault detection, WT has been widely applied with the purpose of detect faults in shafts [33], bearings [34; 35; 36], gears [37; 38; 39] and beams [2; 40].
## 3 Result and Discussion
### Rain Gauge Network Using Weighted Entropy  
We also considered weighted entropy, as precipitation varies seasonally, and entropy performance varies depending on the amount of precipitation. On heavy precipitation days, entropy was better to select the optimal rain gauge network. We applied monthly average precipitation, regional precipitation, 1-regional precipitation, and regional monthly precipitation as weights.  
Table A4 shows the result of calculating the entropy using the monthly average precipitation as a weight. Methods A and F showed one + (better rank), method C showed two + and one - (worse rank). Method D showed one + and one -, and method E showed three - only. Method B did not show any change. In the calculation of entropy using observation data, we can see that a better rain gauge network was selected. However, it can be seen that the entropy calculation using the prediction error was slightly worse.  
Figure 3: Maximum information of empirical probability distribution: (**a**) observation data; (**b**) prediction error.  
and Gyeongbuk regions showed excellent prediction performance. However, if one selected more than two rain gauge stations, you can see that stations located outside Daegu and Gyeongbuk were mainly selected. In other words, the station when selecting one rain gauge station was not often selected in more than one combination. Rain gauge stations located in Pohang (3) were frequently selected from a combination of two stations, and rain gauge stations in Bonghwa (5) were also frequently selected. From the combination of three rain gauge stations, Yeongju (6) was added and selected, and from the combination of five rain gauge stations, Uijin (1) can be confirmed to be included in all combinations. Pohang, Bonghwa, Yeongju, and Uijin are rain gauge stations located along the north and the coast in Daegu and Gyeongbuk region. In several combinations of rain gauge stations, Uiseong (9), Gumi (10), and Yeongcheon (11) were added, and the prediction performance of the rain gauge network was improved.  
In the case of a rain gauge network using conditional entropy, Mungyeong (7) and Daegu (4) appear to have been selected first, and the performance seems to have deteriorated. In the case of method B, which was judged to be the best method for selecting the rain gauge network using entropy, the performance was improved with the addition of Bonghwa and Yeongju in the north. In other  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Num.** & **Rank** & **Combination** & **RMSE** & **Num.** & **Rank** & **Combination** & **RMSE** \\ \hline  & 1 & 2 & 11.62 & 1 & 1,3,4,5,9 & 6.94 \\  & 2 & 9 & 11.77 & 2 & 1,3,4,5,7,9 & 7.00 \\
1 & 3 & 10 & 12.23 & 6 &  
words, it is considered that the rain gauge stations in the north are important locations for rain gauge networks in Daegu and Gyeongbuk.
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 2 Dataset  
To investigate the impact of rainfall on C-band SAR backscattering and develop an automated rain area detection for SAR images, we need both SAR and rain rate measurements. To this aim, we systematically collocate Sentinel-1 data and weather radar data from the National Oceanic Atmospheric Administration (NOAA) and the Japanese Meteorological Agency (JMA). This section describes these data and the collocation method.
## 3 Component Selection and Criteria
### Estimation of Thrust and Specifications  
In this work, the thrust-to-weight ratio was assumed to be two for aggressive gust-load conditions. The complete relationship is given in Equation (8) as follows:  
\[\text{Thrust to Weight Ratio}=\frac{\text{Thrust+Number of Rotor}}{\text{ Weight of the UAV}} \tag{8}\] \[\text{Thrust requirement of the }\sin\text{gle propeller}=\frac{2\times 4426.8}{8}\Rightarrow 1106.7\text{ g}.\]  
From Equation (3), the calculations are as follows:  
\[10.856727=0.5\times 1.2256\times\pi\times\text{r}^{2}\Big{[}(\text{V}_{\text{ UAV}})^{2}-(4)^{2}\Big{]}.\]where \"r\" is the radius of the propeller (0.0719655 m), and V\({}_{\rm UAV}\) is the forward velocity of the UAV in m/s, which is also equal to the maximum induced velocity provided by the propellers. The corresponding calculations are as follows:  
\[10.856727=0.009965454\left[\left({\rm V_{UAV}}\right)^{2}-\left(4\right)^{2} \right];\]  
\[0.0099655\left[\left({\rm V_{UAV}}\right)^{2}\right]=10.857+0.15945\  
The particles entered the inlet via an HEPA filter fitted with a collecting bag (a tiny bag or box). This filter minimized the particles that could disturb the motor. A HEPA filter can remove 99.97% (ASME standard) of particles and debris greater than or equal to 0.3 \(\upmu\)m. When the sucked air particles flowed behind the motor with a divergent passage to the HEPA filter mounted with the collecting bag, the depressurized and particle-free air entered the atmosphere.
_Appl. Sci._**2015**, \(5\), 1440-1456; doi:10.3390/app5041440  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**2D Saturable Absorbers for Fibre Lasers**  
**[PERSON] * and [PERSON]**  
Femtosecond Optics Group, Department of Physics, Imperial College London, London SW7 2 BW, UK; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +44-20-7594-7639.  
Academic Editor: [PERSON]  
_Received: 2 November 2015 / Accepted: 23 November 2015 / Published: 30 November 2015_  
**Abstract:** Two-dimensional (2D) nanomaterials are an emergent and promising platform for future photonic and optoelectronic applications. Here, we review recent progress demonstrating the application of 2D nanomaterials as versatile, wideband saturable absorbers for Q-switching and mode-locking fibre lasers. We focus specifically on the family of few-layer transition metal dichalcogenides, including MoS\({}_{2}\), MoSe\({}_{2}\) and WS\({}_{2}\).  
**Keywords:** saturable absorbers; pulsed lasers; mode-locking; Q-switching; nanomaterials; nonlinear optical materials; graphene; MoS\({}_{2}\); MoSe\({}_{2}\); WS\({}_{2}\)
## 3 Methodology
### Sentiment Lexicon-Enhanced Dynamic Modulation Layer
#### 3.3.1 Sentiment Lexicon  
SenticNet-7 [41] is chosen as the source of word-sentiment pairs in this study. It contains over 300,000 lexical items, each associated with a unique sentiment score ranging from \(-1\) to \(1\). The construction of SenticNet-7 employs advanced sentiment computingtechniques and a large-scale common-sense knowledge base, ensuring the precision of sentiment tendency measurement and extensive vocabulary coverage.
# Hyperpolarised \({}^{1}\)H-\({}^{13}\)C Benchtop NMR Spectroscopy
## 3 Results
### SABRE-Enhanced 2D \({}^{13}\)C-\({}^{1}\)H Benchtop NMR Spectroscopy  
In addition to the single-shot 1D \({}^{13}\)C NMR experiments presented above, it is also of interest to consider the feasibility of acquiring SABRE-enhanced \({}^{13}\)C-\({}^{1}\)H 2D NMR spectra of samples at natural isotopic abundance. SABRE-enhanced 2D \({}^{1}\)H-\({}^{1}\)H benchtop NMR experiments have been reported previously [49]. Here we extend this approach to heteronuclear experiments, exemplified by a \({}^{13}\)C-\({}^{1}\)H HETCOR spectrum acquired using the pulse sequence in Figure 2f. Figure 5 presents a comparison between a 2D HETCOR benchtop NMR spectrum acquired using polarisation at thermal equilibrium for neat 4-methylpyridine (10.3 M, 16 scans, 307 min total experiment time, Figure 5a) and one acquired using SABRE hyperpolarisation of 260 mM 4-methylpyridine (1 scan, 69 min total experiment time, Figure 5c). The SABRE spectrum was achieved by re-hyperpolarising the solution outside of the spectrometer between the acquisition of each transient, as described previously [49].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \({}^{13}\)C Resonance & **Refocused PA** & **Refocused INEPT** \\ \hline _para_ & 9.9 \(\pm\) 0.5 & 31 \(\pm\) 2 \\ _ortho_ & 18.9 \(\pm\) 0.9 & 23 \(\pm\) 2 \\ _meta_ & 43 \(\pm\) 2 & 91 \(\pm\) 7 \\ _methyl_ & 6.4 \(\pm\) 0.6 & 73 \(\pm\) 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Signal-to-noise ratios (SNR) for SABRE-enhanced \({}^{13}\)C[\({}^{1}\)H] NMR spectra of 4-MP acquired with the refocused PA and refocused INEPT pulse sequences. Values are the average over 7 (PA) and 3 (INEPT) repeat experiments.  
The 2D spectrum in Figure 5c demonstrates the ability of SABRE hyperpolarisation to enable high-sensitivity 2D \({}^{13}\)C-\({}^{1}\)H benchtop NMR on relatively low concentration samples at natural isotopic abundance and in reasonable experiment times. In order to obtain a comparable SNR, a neat sample (10.3 M) of 4-MP and 16 scans were used for the reference spectrum in Figure 5a. The use of hyperpolarisation allowed for a reduction in concentration by two orders of magnitude as well as a reduction in experiment time by a factor of \(\sim\)4.5. We note that in order to achieve a comparable result to the SABRE spectrum at the lower concentration, signal averaging for over 300 days would have been required. As in the 1D INEPT case, efficient hyperpolarisation transfer from \({}^{1}\)H to \({}^{13}\)C was observed using a relatively long-range coupling constant of \(J_{CH}=10\) Hz due to the inefficiency of SABRE hyperpolarisation of protons directly bonded to \({}^{13}\)C.
# Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation
## 2 Preliminaries
### Energy Efficiency in Different IR-HARQ Protocols
#### 2.2.1 IR-HARQ with Equal Power Transmission  
In the IR-HARQ-EP transmission scheme, we have \(P_{1}=P_{2}=\ldots=P_{L}=P\). \(N_{\text{s}}\) symbols are transmitted with power \(P\) in each HARQ round. The receiver decodes the original information bits with all the received symbols. We assume the IR-HARQ protocol approaches the capacity of the channel, so the accumulated mutual information \(I_{k}^{tp}\) after \(k\) rounds is:  
\[I_{k}^{ep}=\sum_{i=1}^{k}\log_{2}\biggl{(}1+\frac{{P_{h}}^{2}\gamma_{i}}{ \sigma_{n}^{2}}\biggr{)}. \tag{12}\]  
We define \(\Re_{k}\triangleq\left\{I_{k}^{ep}\geq R\right\}\) and \(\Re_{k}^{c}\triangleq\left\{I_{k}^{ep}<R\right\}\) as the decoding success event and the decoding failure event after \(k\) rounds respectively. For the IR-HARQ protocol, we have \(\Re_{0}\subseteq\Re_{1}\subseteq\ldots\subseteq\Re_{k}\) and \(\Re_{0}=\varnothing\). Suppose \(\mathcal{T}^{ep}\) is the total number of transmission rounds at the end of a packet transmission; then we have:  
\[\Pr\{\mathcal{T}^{ep}=k\}=\Pr\{\Re_{1}^{c}\cap\Re_{2}^{c}\ldots\cap\Re_{k}\}, \tag{13}\]  
\[\Pr\{\mathcal{T}^{ep}=L\}=\Pr\{\Re_{1}^{c}\cap\Re_{2}^{c}\ldots\cap\Re_{L-1}^ {c}\}. \tag{14}\]  
The average total energy used for the IR-HARQ-EP scheme at the end of a packet transmission is:  
\[\mathcal{E}^{ep}=N_{\text{s}}\sum_{i=1}^{L}(i\cdot P\cdot\Pr\{\mathcal{T}^{ep} =i\}). \tag{15}\]  
Let \(e_{k}^{ep}\) denote the probability of a decoding failure after \(k\) transmission rounds; then we have:  
\[\begin{array}{ll}e_{k}^{ep}&=\Pr\{\Re_{1}^{c}\cap\Re_{2}^{c}\ldots\cap\Re_{ k}^{c}\}\\ &\qquad=\Pr\{\Re_{k}^{c}\}.\end{array} \tag{16}\]  
Figure 3: The transmission model of IR-HARQ.  
According to the expression of \(\Re_{k}^{c}\), we have:  
\[\begin{array}{ll}\epsilon_{k}^{ep}&=\Pr\Bigl{\{}I_{k}^{ep}<R\Bigr{\}}\\ &=\Pr\Bigl{\{}\sum\limits_{i=1}^{k}\log_{2}\Bigl{(}1+\frac{P_{i}h^{2}\gamma_{i} }{\sigma_{n}^{2}}\Bigr{)}<R\Bigr{\}},\end{array} \tag{17}\]  
by combining (13), (14), and (17), we get:  
\[\Pr\{\mathcal{T}^{ep}=k\}=\epsilon_{k}^{ep}-\epsilon_{k-1}^{ep}, \tag{18}\]  
\[\Pr\{\mathcal{T}^{ep}=L\}=\epsilon_{L-1}^{ep}. \tag{19}\]  
In the IR-HARQ-EP scheme with the maximum transmission rounds \(L\), \(\mathcal{K}\) can be expressed as:  
\[\mathcal{K}^{ep}=N_{b}(1-\epsilon_{L}^{ep}). \tag{20}\]  
The optimization problem of the IR-HARQ-EP transmission scheme can be expressed as follows:  
\[\begin{array}{ll}\underset{P}{\text{argmax}}&\eta_{EE}^{ep}=\frac{R(1- \epsilon_{L}^{ep})}{\sum\limits_{i=1}^{L-1}iP(\epsilon_{i-1}^{ep}-\epsilon_{ i}^{ep})+\epsilon_{L-1}^{ep}L^{p}}\\ &s.t.\,\left\{\begin{array}{c}\epsilon_{L}^{ep}\leq\epsilon_{0}\\ 0<P\leq P_{\max},1\leq i\leq L,\end{array}\right.\end{array} \tag{21}\]  
where \(\epsilon_{0}\) is the maximum outage probability allowed by the communication system and \(P_{max}\) is the maximum transmission power of the transmitter. We use \(\eta_{EE}^{ep}\) to measure the energy efficiency of the IR-HARQ-EP transmission scheme.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 4 Proposed Multi-Class Neural Network-Based Decoder  
The proposed multi-class NN-based decoding architecture is depicted in Figure 8. The NN takes the received sequence \(\mathbf{y}\) to estimate occurrence probabilities of all possible message \(\mathbf{m}_{i}\), called \(\delta_{i}\), where \(i=0,1,\ \ldots,2^{k}-1\) and \(k\) is the message length. Then, the sorting operation is performed based on the occurrence probability \(\delta_{i}\). Let the ordered set of the occurrence probability be \(\Delta\equiv\left\{v_{0},v_{1},\ \ldots,v_{2^{k}-1}\right\}\), where \(\delta_{v_{1}}\geq\delta_{v_{\mathbf{x}}}\), for \(l\geq\kappa\). \(\mathbf{m}_{(i)}\) is denoted as the message with the occurrence probability \(\delta_{v_{i}}\). The messages of \(l\) highest occurrence probabilities, called \(\mathbf{m}_{(1)},\mathbf{m}_{(2)},\ \ldots,\mathbf{m}_{(l)}\), are thus selected. These messages are encoded as the codewords \(\widetilde{\mathbf{c}}_{1},\widetilde{\mathbf{c}}_{2},\ \ldots,\widetilde{ \mathbf{c}}_{l}\) by the generator matrix \(\mathbf{G}\). We evaluate \(D_{1}\left(\widetilde{\mathbf{c}}_{j}\right)=\left\{i\middle|r_{i}=\widetilde {\mathbf{c}}_{j,i},0\leq i\leq n-1\right\}\) and compute the maximum likelihood metric \(\sum_{i\in D_{1}\left(\widetilde{\mathbf{c}}_{j}\right)}\left|y_{i}\right|\) of the codeword \(\widetilde{\mathbf{c}}_{j}\). Finally, the best codeword is selected as \(\mathbf{dc}\) with the minimum value of \(\sum_{i\in D_{1}\left(\widetilde{\mathbf{c}}_{j}\right)}\left|y_{i}\right|\),\(j=1,2,\ \ldots,l\).  
Figure 8: Multi-class NN-based Decoder.  
Figure 6: Decoding Architecture based on DNN-based Denoiser.  
Figure 7: DNN Architecture for Noise Removal.  
The proposed NN is depicted in Figure 9, which includes only one fully connected layer. The related parameters of NN are listed in Table 1. As compared to the framework in [13], the input of the proposed NN architecture includes the received signal sequence only. No syndrome values serve as the input. As the message length \(k\) increases, the total numbers of nodes in the fully connected and output layers will exponentially increase with respect to \(k\). The sorting for \(2^{k}\) elements becomes time-consuming. The proposed NN-based decoder is suitable for the short linear block codes because of message length \(k\).
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 8 Experimental Evaluation
### Lossy Data Reduction Algorithms
#### 8.1.2 Quantization  
To evaluate the efficacy of the Quantization algorithm in data reduction, our methodology involved truncating the decimal cases in numeric datasets to two decimal places, thereby reducing the number of bits in the final dataset. It is worth noting, however, that the decimal case Quantization algorithm was not applied across all datasets due to the variable number of decimals. Thus, we opted for a consistent reduction to two decimal places. As a result, we omitted \"The Daily Minimum Temperatures in Melbourne\", \"The Monthly Beer Production in Austria\", and \"The IoT Temperatures\" from Table 7 because the numeric columns of these datasets already have two decimal places. Applying our Quantization methodology to these datasets would not change the original data, and reducing the truncation to one decimal place might oversimplify or lose critical details, potentially distorting the nuanced characteristics of the data. Because the Quantization algorithm requires processing all datasets to reduce numeric fields to two decimal places, it may not be the most efficient option for processing performance for all datasets, as our results illustrate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Type** & **Name** & \begin{tabular}{c} **Original** \\ **Size** \\ **(MB)** \\ \end{tabular} & \begin{tabular}{c} **Final** \\ **Size** \\ **(MB)** \\ \end{tabular} & \begin{tabular}{c} **Percentage** \\ **Variance** \\ \end{tabular} & \begin{tabular}{c} **Execution** \\ **Time** \\ **(ms)** \\ \end{tabular} & \begin{tabular}{c} **Processing** \\ **Time per** \\ **MB** \\ **(ms/MB) 2** \\ \end{tabular} \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Temporal, \\ Numeric \\ \end{tabular} } & Daily Minimum Temperatures in Melbourne & 0.0558 & 0.0600 & +7.53\% & 0.002 & _0.0358_ \\  & Electric & 0.0730 & 0.0732 & +0.27\% & 0.001 & _0.0137_ \\  & Production & Monthly Beer & & & & \\  & Production in Austria & 0.0690 & 0.0800 & +15.94\% & 0.001 & _0.0145_ \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Numeric \\ Text \\ \end{tabular} } & Accelerometer & 3.7000 & 2.9501 & \(\mathbf{-20.27\%}\)1  & 0.007 & 0.0019 \\  & Smoke Detection & 5.8000 & 4.8203 & \(-16.89\%\) & 0.007 & **0.0012** \\ \hline \hline Temporal, & IoT & 6.7000 & 6.7202 & +0.30\% & 0.002 & _0.0003_ \\  & Temperatures & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Random Projection results.  
Table 7 shows that the Quantization algorithm achieved the best result with the Electric Production dataset from the temporal and numeric data types, reducing \(-17.81\%\) of the original dataset size in 0.0006 ms, meaning it processes 1 MB each 0.0082 ms.
## 1 Introduction
In particular, pickup coils, which have higher degrees of design freedom than conventional toroidal Rogowski coils, can be affected by various magnetic fluxes according to the corresponding design configuration and can have various mutual inductance and self-inductance values. However, due to the characteristics of the pickup coil, which is composed of a built-in PCB, external conductors significantly influence the PCB [33]. This study analyzes the influence of external wires on the sensor in the PCB and the influence of coil parameters for a pickup coil configuration that measures the switch current in a half-bridge structure system, employing a non-modular WBG power semiconductor. The process of analyzing the mutual inductance is performed by comparing the magnetic flux and bandwidth between the five pickup coil models according to the configurable coil and conductor shape. The remainder of this paper is organized as follows. Section 2 covers the basics of PCB-embedded pickup coils, and Section 3 deals with the influence of each coil parameter through the magnetic flux and mathematical analysis. Section 4 addresses the experimental verification of this study, and Section 5 provides the conclusion.
## 4 Experimental Results
### Inner Race  
In this section, the bearing inner race fault signal measured from the test rig is analyzed and the corresponding results are shown in Figure 6.  
The time domain waveforms of the noisy inner race fault signal measured are shown in Figure 6a. The length of this signal was 76,800. The envelope spectrum analysis of the noisy bearing fault signal is shown in Figure 6b. The fault feature frequency also could not be obtained directly. The result of the estimated transient components in the time domain is shown in Figure 6c. The envelope spectrum analysis of the estimated transient components is shown in Figure 6d. From Figure 6d, it could be obtained that the fault feature frequency of the inner race was \(f_{2}=206\) Hz, which is nearly equal to the theoretical value (206.2 Hz). Since there are always unavoidable errors in the measurement and calculation process, 206 Hz should be the correct required fault feature frequency.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 1 Introduction
### Related Work
#### 1.1.1 IEEE802.15.4  
IEEE 802.15.4 [4] is a standard specifying the physical layer and data link layer for Low-Rate Wireless Personal Area Networks (LR-WPANs). Supporting three un-licensed frequency bands (868 MHz, Europe; 928 MHz, North America; 2.4 GHz, worldwide), IEEE 802.15.4 can offer data rates up to 250 kbit/s at a transmission range largely dependent on the environment; while for a clear line-of-sight, up to 1000 m is possible; alas in most cases, the transmission range is measured in tenths of meters. Built on top of the IEEE 802.15.4 physical and data link layers, ZigBee [5] offers application-facing communications profiles and a network layer.
## 3 Results
### Departmental Decision-Making Module
#### 3.1.1 Steel Mill  
The module in the steel mill department was tested on decisions such as OK (when the product is valid to move on to the next phase), BLOCK (when the product is retained in the current phase for a more thorough inspection to make another decision), ALARM (there has been some alarm in the inspection systems of this stage during production, so it is suspected that the product may have some serious fault and has to be examined to make another decision), and SCRAP (the product has been classified as faulty, so it is going to be turned into scrap). The results are presented in the confusion matrix of the test in Table 1 and the resulting metrics in Table 2.  
The results demonstrate that the system achieved a score of about 80%, which is good enough to take this expert as a reliable source of information. Similar performance  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **OK** & **BLOCK** & **ALARM** & **SCRAP** \\ \hline \multirow{4}{*}{True labels} & OK & 42,331 & 3133 & 587 & 97 \\  & BLOCK & 1923 & 7901 & 137 & 15 \\  & ALARM & 488 & 191 & 2201 & 42 \\  & SCRAP & 96 & 26 & 7 & 403 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix of the AI expert for steel mill.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sensitivity** & **Precision** & **F1-Score** \\ \hline OK & 91.73\% & 94.41\% & 93.05\% \\ BLOCK & 79.20\% & 70.22\% & 74.44\% \\ ALARM & 75.33\% & 75.07\% & 75.20\% \\ SCRAP & 75.75\% & 72.35\% & 74.01\% \\ Mean & 80.50\% & 78.01\% & 79.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metric scores of the AI expert for steel mill.  
levels were observed in the \"bad\" labels, indicating strong consistency in replicating expert decisions.