esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
## 4 Methods
### Baselines
#### 4.1.1 Convolutional  
The baseline convolutional neural network model, or **CNN**, consists of a 1D temporal convolutional neural layer. This convolutional layer consists of different kernels, which are filters with a specific pattern that are tested against all sequences of the input data with the same length. This is followed by a (max-)pooling layer, to reduce the data size by only remembering the maximum value over a certain range. More formally, for an input \(x\) and a given 1D kernel \(f\  
BiGRU  
The **GRU**, or Gated Recurrent Unit, is a gating mechanism in recurrent neural networks. It is the mechanism of recurrent neural networks allowing the model to \"learn to forget\" less important fragments of the data and \"learn to remember\" the more important fragments with respect to the learning task. More formally, consider an input vector \(x_{t}\), update gate vector \(z_{t}\), reset gate vector \(r_{t}\), and output vector \(h_{t}\) at time \(t\). The respective values can be calculated as follows.  
\[z_{t} =\sigma(W_{z}\cdot[h_{t-1},x_{t}]+b_{z}) \tag{3}\] \[r_{t} =\sigma(W_{r}\cdot[h_{t-1},x_{t}]+b_{r})\] (4) \[h_{t}^{*} =tanh(W_{h}\cdot[t_{t}\times h_{t-1},x_{t}]+b_{h})\] (5) \[h_{t} =(1-z_{t})\times h_{t-1}+z_{t}\times h_{t}^{*} \tag{6}\]  
This leads to weight matrices \(W_{z}\), \(W_{r}\), and \(W_{h}\) to train as well as biases \(b_{z}\), \(b_{r}\), and \(b_{h}\), \(\sigma\) stands for the sigmoid activation function. **BiGRU** is the bidirectional variant of such a model that processes the input data front-to-back and back-to-front in parallel.  
BiLSTM  
An **LSTM**, or Long Short-Term Memory neural network model, is very similar to a GRU but replaces the update gate with a forget gate and an additional output gate. This way it usually has more computational power than a regular GRU, but at the expense of more trainable parameters and more chance of overfitting when the amount of training data is limited [40; 41; 42]. Formally, consider again an input vector \(x_{t}\) and a hidden state vector \(h_{t}\) at time \(t\). Activation vectors for the update gate, forget gate, and output gate are, respectively, represented by \(z_{t}\), \(f_{t}\), and \(o_{t}\). These states relate to each other like follows.  
\[z_{t} =\sigma(W_{z}\cdot[h_{t-1},x_{t}]+b_{z}) \tag{7}\] \[f_{t} =\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f})\] (8) \[o_{t} =\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\] (9) \[c_{t}^{*} =tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c})\] (10) \[c_{t} =(f_{t})\times c_{t-1}+z_{t}\times c_{t}^{*}\] (11) \[h_{t} =o_{t}\times tanh(c_{t}) \tag{12}\]  
This again leads to weight matrices \(W_{z}\), \(W_{f}\), \(W_{o}\), and \(W_{c}\) to train as well as biases \(b_{z}\), \(b_{f}\), \(b_{o}\), and \(b_{c}\) with \(\sigma\) being the sigmoid activation function. **BiLSTM** is the bidirectional variant of a regular LSTM, analogous to BiGRU, which is the bidirectional variant of GRU.
## 3 Problem Statement and Results
### Kalman Filter for Trajectory Smoothing
#### 3.5.1 State Variability  
The Kalman Filter model assumes the true state at time \(k\) is evolved from the state at \(\left(k-1\right)\) as expressed by:  
\[\mathbf{x}\left(k\right)=\mathbf{F}\cdot\mathbf{x}\left(k-1\right)+\mathbf{B} \cdot\mathbf{u}\left(k\right)+\mathbf{w}\left(k\right) \tag{9}\]  
where \(\mathbf{F}\) defines the state transition model applied to the previous state \(\mathbf{x}\left(k-1\right)\); \(\mathbf{B}\) denotes the control-input applied to the controls \(\mathbf{u}\left(k\right)\); \(\mathbf{w}\left(k\right)\) is the process noise, which is assumed to be drawn from a zero mean multivariate normal distribution with covariance \(\mathbf{Q}\left(k\right)\):  
\[\mathbf{w}\left(k\right)\sim N\left(0,\mathbf{Q}\left(k\right)\right) \tag{10}\]
## 2 Impact of Process Variations (PV) on Different Operation Modes
### Impact of PV on Physical Unclonable Function (PUF) Quality  
During the power-up process, all transistors in an SRAM cell operate in the sub-threshold region. The currents that drive the two output nodes, BIT and BITB, in Figure 1b are determined by the strength of the two inverters in the cell. The currents flowing through M1-M2 and M4-M5 will simultaneously pull up the voltage at BIT and BITB, respectively. Owing to the transistor mismatches between the two inverters, one of the two nodes may reach the \"trip-point\" (\(V_{trip}\)) of the inverter faster than the other. If the voltage at BITB increases to \(V_{trip}\) first, the output node BIT of INV-1 will be pulled down to ground. As the voltage at BIT descends, M5 will be fully turned on eventually and pull the voltage at BITB to the supply voltage \(V_{DD}\). This process is shown in Figure 3a. Figure 3b shows the state evolution of the SRAM in the phase plane [8]. During the initial phase of power-up, BIT and BITB are approaching the meta-stable point (\(V_{trip}\)) together. Due to mismatches of the two inverters, the SRAM cell will stabilize to one of the two stable states (\(V_{BIT}=\) \"1\" or \"0\").  
Since each response bit is independently generated from an SRAM cell and dominated by independent intra-die variations such as random dopant fluctuation and line edge roughness, the randomness among bits generated is guaranteed. While the response bits produced from individual SRAM cells are generally uncorrelated, their reliability may be je  
Figure 3: **(a)** BIT and BITB node voltages of SRAM cell during power-up process. The two curves set apart when one of them reaches the trip point of the inverter; **(b)** state evolution at power-up.  
Figure 2: Design conflict of a SRAM cell for dual-mode applications. Increasing the variation of \(V_{th}\) improves the quality of PUF at the expense of higher failure rates in regular memory read/write operations.  
effects overwhelm the mismatches between the two inverters in an SRAM cell. In addition, temporal post-manufacturing variations such as unexpected change of environmental conditions in which the devices operate will also impact the reliability.
## 4 Discussion and Conclusions
Another relevant area concerns the environmental management of electronic waste and heavy metals. Here, AI is used to predict contaminant accumulation in soils and water bodies, identifying environmental risk hotspots [48; 49; 50]. Random forest techniques and advanced spatial analysis have proven particularly effective in locating critical areas, contributing to the prevention and management of environmental damage [56; 57]. From a practical standpoint, these insights point to specific policy and operational implications. Municipalities and industries should prioritize the integration of interoperable data infrastructures capable of supporting predictive maintenance, environmental surveillance, and materials tracking. For example, Reuter's 2016 [43] framework for metallurgical process optimization via DT and m-IoT represents a prototype for sectoral transformation that can be generalized to other industrial chains. The use of deep learning technologies for environmental monitoring also shows great potential. [PERSON] et al., 2023 [44] and [PERSON] et al., 2020 [54] demonstrate how the application of Convolutional Neural Networks (CNNs) and complex neural networks can improve pollutant detection, allowing for faster and more localized responses to environmental issues. These applications fit into an increasingly integrated vision linking environmental data, public health, and sustainability. Finally, the integration of AI, industrial systems, and DT is confirmed as an emerging trend in production processes aimed at waste valorization. [PERSON] et al., 2020 [51] show how data-driven approaches, supported by methodological frameworks such as CRISP-DM, foster the adoption of intelligent technologies in industrial contexts, facilitating waste reduction and optimization of by-product management. Theoretically, this convergence of digital intelligence and environmental planning supports the emergence of a new paradigm--what can be called \"anticipatory circularity\"--where waste is not only managed but preemptively optimized through real-time simulations and learning algorithms. This concept deserves further theoretical development and empirical validation in future research. By integrating bibliometric mapping with thematic analysis, the study not only revealed zones of concentrated scientific activity but also uncovered emerging links between predictive methodologies, environmental monitoring, and circular economy models. In particular, the growing role of AI and DT stands out as key to the development of intelligent waste management systems capable of dynamically adapting to changes in material flows, environmental conditions, and territorial needs.
## 2 Materials and Methods  
### Data for Training and Validation  
#### 2.1.1 ECG MIT-BIH  
This study utilized the widely recognized MIT-BIH dataset [18], which comprises ECG recordings provided by the Massachusetts Institute of Technology and meticulously annotated by medical experts in accordance with international standards. The MIT-BIH database has been extensively utilized within the academic community for research aimed at the recognition and classification of arrhythmic heartbeats. The MIT-BIH dataset comprises 48 ECG recordings, each with a duration of 30 min. The recordings comprised two leads per record and were sampled at a frequency of 360 Hz, thereby providing comprehensive data for the investigation. Expert annotations were assigned to the ECG recordings to ensure the accuracy and reliability of the dataset. The MIT-BIH database employs optimization methodologies and actively solicits contributions from the research community to facilitate future advancements. This enables the dataset to benefit from ongoing advancements and self-optimization methodologies aimed at enhancing its quality and usability.
## 2 Laboratory Study
### Mix Design
In order to evaluate the effect of an increased air content on the different properties of the hardened concrete, particularly the salt-frost scaling resistance of concrete with GGBS, an additional mix with 50% of slag replacement and targeted air content of 6.0% \(\pm\) 0.5% was produced (Mix 5). Other additional concrete mix (Mix 6) with 50% GGBS was defined, by using a k-factor of 1, i.e., in this mix, the mass of slag replaces the exact same reduced mass of Portland cement. The proportion of each concrete mix was chosen so that its consistency would correspond to an S3 class of slump (slump between 100 and 150 mm). The desired slump was achieved by using a superplasticizer, namely Sikament 56/50, a third generation Polycarboxylate Ether (PCE) based superplasticizer produced by Sika Sverige AB. Summarizing, a total of six different mixes were produced:  
* Mix 1: 0% GGBS, 4.5% \(\pm\) 0.5% Air (reference concrete);
* Mix 2: 25% GGBS, 4.5% \(\pm\) 0.5% Air, k = 0.6;
* Mix 3: 50% GGBS, 4.5% \(\pm\) 0.5% Air, k = 0.6;
* Mix 4: 100% GGBS, 4.5% \(\pm\) 0.5% Air, k = 0.6;
* Mix 5: 50% GGBS, 6.0% \(\pm\) 0.5% Air, k = 0.6;
* Mix 6: 50% GGBS, 4.5% \(\pm\) 0.5% Air, k = 1.0. The proportions of all the components of the final mixes are presented in the Table 1 below:For each concrete quality, two mixes of about 30 litres were produced using a Zyklos rotating pan mixer. The surface of the mixer was moist, and all dry materials (aggregates and binders) were added, followed by the water. After mixing these components for 30 s, the air entraining agent and the superplasticizer were added, and the concrete was mixed for another 120 s. Directly after mixing, the following tests on the freshly mixed concrete were performed:  
* Slump test, according to SS-EN 12350-2 (Figure 1);
* Air content, according to SS-EN 12350-7 (Figure 2);
* Air void analysis  
After mixing, concrete was cast in cylindrical and cubic moulds that comply with specifications in SS-EN 12390-1 for testing hardened concrete. After 24 h, the specimens were removed from the moulds and curing (in accordance with the specifications of different test methods) began. After curing was completed, the hardened concrete specimens were subjected to the following tests:  
* Compressive strength, according to SS-EN 12390-3;
* Rapid chloride migration test, according to NT Build 492;
* Salt-frost scaling, according to SS 13 72 44 [9]. This standard corresponds to CEN/TS 12390-9:2016.
## 5 Conclusions  
The thermal behavior of the tunnel segment joints exposed to fire and the strengthening of the fire-damaged joints with CFSTs were investigated experimentally. The effect of joint gap on the temperature distribution and the residual performance of the fire-damaged segment joint were studied. A method for strengthening fire-damaged segment joints was demonstrated. The experimental observations support the following conclusions.  
(1) When the initial joint opening is smaller than 10 mm, the temperature at most positions in the joint gap remains lower than in the neighboring concrete with over 60 min of exposure to fire. After 120 min, however, the temperature in the joint gap is higher than in the surrounding concrete.  
(2) The temperature of the bolt ends is much higher than that of a bolt's mid-point throughout the heating process. To prevent the temperature of the joint bolts from exceeding the allowable value in a real tunnel fire, they should be insulated.  
(3) The bearing capacity and flexural stiffness of the segment joints after a fire can be significantly improved by strengthening with CFSTs. They may even surpass those of untreated joints.  
(4) After exposure to fire for 60 or 120 min, specimen strengthened with the proposed CFSTs joint opening increment considerably less than untreated joints under equal loads.  
(5) Strengthening near a joint play a crucial role during initial loading, but its effectiveness gradually decreases, and strengthening far from the joint becomes more important as the loading increases.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data and wrote the paper.  
The authors would like to gratefully acknowledge research grants from the National Basic Research Program of China (Project NO: 2011 CB013800).  
The authors declare that there is no conflict of interests regarding the publication of this paper.
## 5 Conclusions  
To fully utilize vibration data to achieve accurate and robust diagnosis of the mechanical faults of HVCBs, the AKNN-DMGCN model is presented in this paper. A new AKNN graph construction method is proposed that can construct a more informative and accurate graph structure. According to the experimental results, the accuracy of AKNN-DMGCN on dataset1, dataset2, dataset3, and dataset4 is 2.58%, 4.34%, 2.75%, and 5% higher than that of KNN-DMGCN, respectively. In addition, the accuracy of AKNN-GCN on the four datasets is 3.35%, 2.54%, 4.21%, and 1.65% higher than that of KNN-GCN, respectively. It can be seen that, compared with KNN graph construction, AKNN graph construction extracts the internal information of the data more comprehensively. At the same time, the DMGCN algorithm is proposed to judge the type of fault. DMGCN not only introduces a multi-attention mechanism but also achieves the transformation from static attention to dynamic attention, which solves the problem of the GCN not paying sufficient attention to key nodes and paying too much attention to noise. Therefore, DMGCN can differentiate the weights of neighbor nodes of the central node and take full advantage of the topological information, endowing the model with more anti-interference and robustness. For dataset1, dataset2, dataset3, and dataset4, the accuracy of AKNN-DMGCN is 4%, 7.01%, 5.76%, and 7.05% higher than that of KNN-GCN, respectively. This demonstrates that AKNN-DMGCN can achieve the best performance on both balanced and imbalanced datasets, providing a new and effective solution for HVCB mechanical fault diagnosis.  
Conceptualization, G.S. and J.Y.; methodology, G.S.; software, G.S.; validation, G.S., Y.W., Z.X. and M.Q.; formal analysis, G.S.; investigation, G.S., Y.W. Z.X. and M.Q.; resources, Z.Z.; data curation, G.S.; writing--original draft preparation, G.S.; writing--review and editing, J.Y.; visualization, G.S.; supervision, J.Y.; project administration, J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under Grant 2022 YFB2403700.  
The data are not publicly available due to privacy or ethical restrictions.  
The authors declare no conflicts of interest.
## Materials and Methods  
### Materials, Microorganisms, and Chemicals  
#### 2.1.1 Chemicals  
Microbial media were purchased from BIOMAXIMA (Lublin, Poland): Endo Agar, TBX, TSA, TSB, BHI Agar, BHI Broth, NA, NB, Sabouraud with Chloramphenicol, Mueller-Hinton (MH) broth; and Sigma-Aldrich (Steinheim, Germany): MRS broth, MRS agar. Folin-Ciocalteu reagent (Sigma-Aldrich, Steinheim, Germany) and sodium carbonate (POCh, Gliwice, Poland) were used to determine total phenolic content, with gallic acid (Sigma-Aldrich, Steinheim, Germany) as a reference. For the Trolox equivalent antioxidant capacity (TEAC) method, the ABTS* + radical cation was prepared using 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulfonic acid (ABTS)) diammonium salt from Roche (Mannheim, Germany) and potassium persulfate from Fluka (Buchs, Switzerland). Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) was purchased from Sigma-Aldrich and used as a standard. Methanol (98%) and ethanol (98%) (POCh, Gliwice, Poland) were used as solvents in antioxidant assays and in the extraction process. For acidification of the ethanol during extraction, trifluoroacetic acid (Merck, Darmstadt, Germany) was used. All chemicals used for the studies were of analytical grade.
## 5 Conclusions  
The results of the present study indicate that WWTPs play a significant role in the acquisition and spread of antibiotic resistance genes among bacterial populations. The presence of ARGs in live bacterial cells that are capable of multiplying constitutes a considerably greater potential threat of transmission of these genes from WWTPs to the environment. Therefore, the present study may be helpful in identifying the risks to human public health and the well-being of animals exploiting water bodies that receive treated wastewater. The analysed genes of resistance to beta-lactam antibiotics (\(\mathit{bla}_{\text{OXA}}\), \(\mathit{bla}_{\text{TEM}}\), \(\mathit{bla}_{\text{SHV}}\)) and to tetracyclines (\(\mathit{tetA}\), \(\mathit{tetM}\)) were detected in the DNA of bacterial populations isolated from wastewater from most wastewater treatment plants under study. In the DNA of _E. coli_ population, the occurrence of _tet_M and _blda_OXA genes was not detected in any of the tested WWTPs. It should be noted that not all gene concentrations were reduced following the treatment processes. For certain genes (_bla_TEM, _tet_A), their average concentrations increased by as much as one order of magnitude following the treatment process. The results of this study indicate the presence of significant concentrations of genes that are responsible for antibiotic resistance, even in the treated wastewater from WWTPs. Generally, in wastewater treatment plants with a mechanical-biological system, an increased number of ARGs copies after treatment processes were observed. This indicates the need for continuous monitoring of wastewater treatment processes, as the discharge of wastewater to aquatic environments following treatment processes may pose a serious hazard to environmental safety.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/3/387/s1](http://www.mdpi.com/2076-3417/9/3/387/s1)]([http://www.mdpi.com/2076-3417/9/3/387/s1](http://www.mdpi.com/2076-3417/9/3/387/s1)), Table S1: Technical description of wastewater treatment plants (WWTPs) and physicochemical parameters of wastewater, Table S2: Quantitative PCR (qPCR) primers and conditions used in this study, Table S3: Correlations between concentration of genes, total number of bacteria, WWTP parameters and basic physico-chemical parameters, Table S4: Correlations between concentration of genes, number of _E. coli_, WWTP parameters and basic physico-chemical parameters.  
Data curation, E.K. and A.O.; Investigation, E.K., M.H. and A.O.; Methodology, E.K. and M.H. and A.O.; Writing--original draft, A.O. and E.K.; Writing--review & editing, A.O., E.K., M.H. and S.N.  
This study was supported by grants No. 2017/27/B/NZ9/00267 and No. 2016/23/BNZ9/03669 from the National Science Center (Poland).  
We would like to thank the staff of the WWTPs for the possibility of collecting samples.  
The authors declare no conflict of interest.
## 3 Basic Theory
### Frequency Domain Analysis
#### 3.3.2 Fast Fourier Transform (FFT)  
Fast Fourier transform is an efficient method for calculating the discrete Fourier transform of time series data [26; 27]. The discrete Fourier transform is a useful mathematical tool as it  
condition of a machine changes. A commonly used statistical feature for condition monitoring is the frequency center (_FC_). The FC indicates position changes of the characteristic frequencies as in Equation (4).  
\[HI_{\text{FC}}=FC=\frac{\sum_{k=2}^{N}\overline{X}_{k}^{\prime}\overline{X}_{k}}{2 \pi\sum_{k=1}^{N}\overline{X}_{k}^{2}} \tag{4}\]
# Interaction Strength Analysis to Model Retweet Cascade Graphs
## 7 Evaluation of IS Weights
### Experimental Results on the Entire Cascade Sample
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Class** & **Set** & **Edge Count** & **CCC** & **GCS (\%)** & **RFR (\%)** & **GRC (\%)** & **CAS (\%)** & **Depth** & **SNI (\%)** \\ \hline Guideline & 0.0 & 5.88 & 1.08 & 6.25 & 93.81 & 6.19 & 87.06 \({}^{\dagger,\circ}\) & 0.53 & 1.98 \\ Guideline & 0.1 & 5.88 & 1.08 & 6.29 & 93.75 & 6.18 & 85.39 \({}^{*,\circ,\bullet}\) & 0.53 & 2.05 \\ Guideline & 0.2 & 5.89 & 1.08 & 6.27 & 93.68 & 6.15 & 84.33 \({}^{\dagger}\) & 0.53 & 2.13 \\ Guideline & 0.3 & 5.89 & 1.08 & 6.29 & 93.68 & 6.15 & 84.32 \({}^{\dagger,\circ}\) & 0.53 & 2.11 \\ \hline \hline \multicolumn{10}{l}{\({}^{*,\dagger,\circ,\bullet}\)  
Statistically significant (\(p\)-value \(<\) 0.05) under a pairwise comparison when compared with the guideline sets: 0.0 (\({}^{\star}\)), 0.1 (\({}^{\dagger}\)), 0.2 (\({}^{\circ}\)), 0.3 (\({}^{\bullet}\)).} \\ \end{tabular}
\end{table} 
Table 5: Average metrics for setting up guidelines. Table 7 reports the results of the analysis focused on deep cascades (cascades with more than six levels) in order to compare the impacts of quotes and replies in maximizing the CAS. It emerges that weight sets with higher quotes incidence are able to achieve higher CAS values (e.g., set 0.3 for group \(a\) or set 4 in group _c_), but, at the same time, when the relative difference between quote and reply weights is high, it also leads to a higher standard deviation. For example, for groups \(a\) and \(c\), the difference between quotes and replies weights is high, generating higher average CAS for sets 0.3 and 4, but involving major variability in the results. It follows that, from Table 7, to limit the CAS decrease and ensure the absence of drops when the cascades are characterized by high numbers of levels, a solution is to overweight the quote interaction, limiting the relative difference with the reply weights. Figure 4: Cascade average strength (CAS) and cascade depth for guideline sets. Figure 5: CAS and cascade depth for experimental sets. interaction. In fact, Table 7 shows that the set able to minimize the standard deviation is the number 10, which also ensures a limited retweet weight (0.2) with respect to the quote and reply weights (0.5 and 0.3 respectively).