esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 3 Preliminaries
### Fuzzy String Matching
#### 3.1.4 Longest Common Subsequence  
The Longest Common Subsequence (LCS) algorithm identifies the longest subsequence shared between two sequences [46]. A subsequence refers to a sequence that can be derived from another sequence by omitting some or no elements, while preserving the order of the remaining elements. Specifically, the algorithm generates a matrix \(L\) based on the given strings \(T\) and \(S\), and calculates the elements of \(L\) using Equation (6) [45].  
\[L[i,j]=\left\{\begin{array}{cc}0,&i=0\ or\ j=0\\ L[i-1,j-1]+1,&i,j>0,Si=Tj\\ \max(L[i,j-1],L[i-1,j]),&i,j>0,Si=Tj\end{array}\right. \tag{6}\]  
The value of \(L\)\([i,j]\) represents the minimum number of edits needed to transform the substring of the first string up to index \(i\) into the substring of the second string up to index \(j\). The characters at indices \(i\) and \(j\) of the first and second strings are denoted as \(S_{i}\) and \(T_{j}\), respectively. If either \(i\) or \(j\) is 0, it means that one of the substrings is empty, and the minimum number of edits required to transform one substring into another is simply the length of the other substring. If \(S_{i}\) is equal to \(T_{j}\), no edit is necessary to transform the substrings up to indices \(i\) and \(j\). Hence, the minimum number of edits required is equal to the minimum number of edits required to transform the substrings up to indices \(i-1\) and \(j-1\), plus 1. If \(S_{i}\) is not equal to \(T_{j}\), it implies that an edit operation (insertion, deletion, or substitution) is necessary to transform the substrings up to indices \(i\) and \(j\). In this scenario, one takes the minimum number of edits required to transform the substrings up to indices \(i\) and \(j-1\) (by inserting \(T_{j}\)) or \(i-1\) and \(j\) (by deleting \(S_{i}\)), and take the maximum of these two values. The LCS algorithm fills the matrix \(L\) row by row, and the final value at \(L\)\([n,m]\) gives the minimum number of edits required to transform the first string into the second string, where \(n\) and \(m\) represent the lengths of the first and second strings, respectively.
## 3 Results and Discussion
### Styrene-Acrylic Emulsion  
Looking at the ATR-FTIR results (Figure 7e), hydrolytic degradation appears to have taken place, and this reaction can be detected by the increase in the intensity of the main binder bands (2931, 2872, and 1728 cm\({}^{-1}\)) and the region between 3600 and 2600 cm\({}^{-1}\) (Supplementary Table S12) [24]. Indeed, the absorption of water and the formation of carboxylic acids following the rupture of the polymeric chains lead to an increase in the intensity of almost all the spectra signals [69]. As for the other samples, bands at 2346, 1150, and 853 cm\({}^{-1}\) could be an indication that sulfonation reactions took place [69; 84]. In polyester-based polymers, sulfuric acid can promote the formation of a crosslinked material through the insertion of sulfonic groups between the aromatic rings [84; 85], and this would explain the presence of these bands. Oxidation reactions seem to have occurred, confirmed by the presence of a shoulder at 1640 cm\({}^{-1}\), probably related to the C=O _stretching_ of ketones formed as oxidation products [76]. SO\({}_{2}\) is likely to oxidase the side groups of the polymeric chain, leading to the formation of carboxylic acids, aldehydes, and ketones [22; 24; 86].  
In Supplementary Table S14, the results obtained with Py-GC/MS before and after accelerated aging are compared. A decrease in peak area % of the dimeric and trimeric fractions of styrene, \(n\)BA, and the combination of styrene and \(n\)BA is visible. For example, the area of the styrene-styrene-\(n\)BA trimer peak decreases so much that it is below the detection limit. Consequently, there is a strong increase in the area% of the styrene peak, from 40.78% to 87.26%. As already supposed by IR spectroscopy, these changes may have been caused by oxidation and hydrolysis reactions that occur during aging [24]. These reactions could have resulted in the breaking of the polymer network, leading to an increase in the area of the main low-molecular-weight peaks [24].
## 4 Simulation Analysis
### Effect of Bit Error on Speed Estimation  
Bit errors are usually caused by signal interference, noise, attenuation, quality of the transmission medium, or equipment failure. In digital communications, electromagnetic interference (EMI), temperature variations, and unstable signal strength can all cause bit errors. In addition, improper coding and modulation may also increase the bit error rate. This section mainly studies the relationship between the number of bit errors and the speed estimation accuracy.  
It is evident from the simulation comparison of Figures 11 and 12 that the accuracy of speed estimation is significantly impacted by the bit error produced by demodulation. The number of symbols of demodulation bit errors is inversely related to the precision of the real speed measurement. The accuracy of speed estimation increases with decreasing bit errors.  
The experiment in this paper simulates and analyzes the impact of different signal-to-noise ratios, different numbers of symbols, different symbol modulation periods, and the number of bit errors generated during demodulation on the speed estimation accuracy.  
Figure 11: Simulation diagram of 100 m/s when there is one demodulation error within 10 symbols.  
Figure 12: Simulation diagram of 100 m/s when there is one demodulation error in 100 symbols.  
Figure 10: SNR \(-\)10 dB speed 100 m/s simulation diagram.
# Tourism Demand Forecasting Based on an LSTM Network and Its Variants
## 2 Methods  
### LSTM Network  
In RNN, the output can be given back to the network as input, thereby creating a loop structure. RNNs are trained through backpropagation. In the process of backpropagation, RNN will encounter the problem of vanishing gradient. We use the gradient to update the weight of the neural network. The problem of vanishing gradient is when the gradient shrinks as it propagates backwards in time. Therefore, the layers that obtain small gradients will not learn, butwill instead cause the network to have short-term memory.  
The LSTM network was introduced by [PERSON] and [PERSON] [27] to alleviate the problem of vanishing gradients. LSTMs can use a mechanism called gates to learn long-term dependencies. These gates can learn which information in the sequence is important to keep or discard. LSTMs have three gates: input, forget and output. Figure 1a shows the architecture of LSTM cell [22; 28; 29]. The horizontal line between \(C_{t-1}\) and \(C_{t}\) is called the cell state. This is the core of the LSTM model, where pointwise addition and multiplication are performed to add or delete information from the memory. These operations are performed using the input and forget gate of the LSTM block, which also contains the output \"tanh\" activation function. The computations inside the LSTM neurons are shown as follows [27]:  
Forget gate:  
\[f_{t}=\sigma\Big{(}\mathbf{W}_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]  
Input gate:  
\[i_{t}=\sigma(\mathbf{W}_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
Output gate:  
\[o_{t}=\sigma(\mathbf{W}_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{3}\]  
Process input:  
\[\widetilde{C}_{t}=\tanh(\mathbf{W}_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{4}\]  
Cell update:  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{C}_{t} \tag{5}\]  
Output:  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{6}\]  
where \(\sigma\) refers to sigmoid function, \(h_{t-1}\) represents the output of pervious cell state, \(x_{t}\) represents the input of current cell state, \(\mathbf{W}_{f},\mathbf{W}_{i},\mathbf{W}_{o}\) and \(b_{f},b_{i},b_{o}\) are the weight matrices and bias of the forget, input and output gates, respectively. \(\mathbf{W}_{C}\) and \(b_{C}\) are the weights and bias of the cell state, and \"\(\cdot\)\" means point-wise multiplication. \(o_{t}\) is used to evaluate which part of the cell state to be exported, and \(h_{t}\) calculates the final outputs.
# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
###### Abstract  
Industrial automation and control systems have gained increasing attention in the literature recently. Their integration with various systems has triggered considerable developments in critical infrastructure systems. With different network structures, these systems need to communicate with each other, work in an integrated manner, be controlled, and intervene effectively when necessary. Supervision Control and Data Acquisition (SCADA) systems are mostly utilized to achieve these aims. SCADA systems, which control and monitor the connected systems, have been the target of cyber attackers. These systems are subject to cyberattacks due to the openness to external networks, remote controllability, and SCADA-architecture-specific cyber vulnerabilities. Protecting SCADA systems on critical infrastructure systems against cyberattacks is an important issue that concerns governments in many aspects such as economics, politics, transport, communication, health, security, and reliability. In this study, we physically demonstrated a scaled-down version of a real water plant via a Testbed environment created including a SCADA system. In order to disrupt the functioning of the SCADA system in this environment, five attack scenarios were designed by performing various DDoS attacks, i.e., TCP, UDP, SYN, spoofing IP, and ICMP Flooding. Additionally, we evaluated a scenario with the baseline behavior of the SCADA system that contains no attack. During the implementation of the scenarios, the SCADA system network was monitored, and network data flow was collected and recorded. CNN models, LSTM models, hybrid deep learning models that amalgamate CNN and LSTM, and traditional machine learning models were applied to the obtained data. The test results of various DDoS attacks demonstrated that the hybrid model and the decision tree model are the most suitable for such environments, reaching the highest test accuracy of 95% and 99%, respectively. Moreover, we tested the hybrid model on a dataset that is used commonly in the literature which resulted in 98% accuracy. Thus, it is suggested that the security of the SCADA system can be effectively improved, and we demonstrated that the proposed models have a potential to work in harmony on real field systems.  
critical infrastructure; SCADA; cybersecurity; DDoS; deep learning; testbed +
Footnote †: journal: Applied Sciences
# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 2 Materials and Methods
### Electrograms  
The numerical solution of the AF electrophysiological model provides the transmembrane potentials at specific grid points within the atrial tissue, which are then used to compute EGM signals. Unipolar ECMs are modeled as the extracellular potential measured by a positive polarity electrode whose reference (zero potential) is at infinity. The extracellular potential (\(\varphi_{e}\)) was calculated using the large-volume conductor approximation [40], as described by the following equation:  
\[\varphi_{e}(\vec{r})=-K\iiint\vec{\
abla}^{\prime}V_{m}(\vec{r}^{\prime}) \cdot\vec{\
abla}^{\prime}\bigg{[}\frac{1}{|\vec{r}^{\prime}-\vec{r}|}\bigg{]} dv, \tag{1}\]  
where \(K\) is a constant representing the ratio of intracellular to extracellular conductivity. The extracellular potential \(\varphi_{e}\) is computed at the measurement point \(r\). This potential depends on the configuration of the transmembrane potential \(V_{m}\) across the tissue. The source points \(r^{\prime}\) contribute to the measured potential at \(r\). The nabla operator \(\
abla^{\prime}\) is the gradient that is being taken with respect to the source coordinates \(r^{\prime}\). This is necessary because the potential \(V_{m}\) is being integrated over the source points \(r^{\prime}\), while \(r\) represents the position of the measurement point. \(|r^{\prime}-r|\) is the distance from the source (\(x\), \(y\), \(z\)) to the measurement point (\(x^{\prime}\), \(y^{\prime}\), \(z^{\prime}\)), and \(dv\) is the volume differential. A total of 22,500 virtual electrodes (150 \(\times\) 150), spaced by 0.4 mm, were used to calculate the EGM signals (one for each node of the model).  
In this study, CFAEs were defined as irregular signals exhibiting multiple (\(>\)2) positive or negative deflections with varying amplitudes [41]. Non-CFAEs were defined as signals with single and regular deflections. The power spectral density (PSD) of the signals was estimated using the fast Fourier transform. Figure 3a shows a stable simulated rotor. Figure 3b shows a non-CFAE signal from a region far from the rotor tip and a CFAE signal from the rotor tip area, while Figure 3c presents their corresponding PSDs.
## 3 Results
### Relationship between Burial and Catchment Area  
Stream burial-catchment area relationships exhibit a generally consistent pattern across all counties, regardless of development stage (Figures 9 and 10). Predicted burial rates are elevated for the very smallest streams, decreasing sharply to a local minimum (corresponding with maximum slope values, discussed below) around catchment areas of 0.1-0.2 log\({}_{10}\) ha, after which they increase linearly to a maximum burial rate in the mid-range of catchment area (~0.8-2.5 log\({}_{10}\) ha, depending on development stage). After this local maximum, burial rates generally decrease with increasing catchment area, until approximately 3.5 log\({}_{10}\) ha, where predicted burial rates tend to level off and remain very low (~zero) as catchment area continues to increase. Figure 9: Stream burial (%) and slope (degrees) in relation to catchment area (log\({}_{10}\) ha) for Tier 1 and Tier 2 counties. Dashed vertical lines represent local burial maxima (**left**) and minima (**right**) in % stream burial. Data for the independent cities of Virginia were analyzed as a single entity, and the results plotted together. The magnitude of the maximum predicted burial rate, and thereby the slope of the distribution of burial data on either side of the local maximum, becomes steeper (more positive to the left, and more negative to the right of the maximum) with increasing urban development (Table 4, Figures 9 and 10). In the cities and counties with the highest levels of development (e.g., Tier 1, Figure 9), rather than decreasing sharply as catchment area increases past the initial maximum rate, predicted burial remains high across a wide range, before decreasing sharply at catchment areas approaching 3.5 log\({}_{10}\) ha. The maximum predicted burial generally occurs at approximately 1.0 log\({}_{10}\) ha (range 0.8-1.4 log\({}_{10}\) ha, with the exception of two counties--Washington, MD and Clarke, VA--where the maximum is shifted far to the right (\(\sim\)2.5 log\({}_{10}\) ha), and Clarke County, where maximum predicted burial occurs at 2.1 log\({}_{10}\) ha catchment area. The relationship between predicted burial rates and catchment area was highly significant for the majority of counties, and across both ascending and descending limbs of the burial curve (Table 5). In some cases, e.g., the Independent Cities of Virginia and for Frederick County, MD, catchment area was not significantly correlated with predicted burial rates for the descending limb of the burial distribution. Catchment area did not explain predicted burial rates at any point in the distribution for Jefferson County, West Virginia. Figure 10: Stream burial (%) and slope (degrees) in relation to catchment area (log\({}_{10}\) ha) for Tier 3 and Tier 4 counties. Dashed vertical lines represent local burial maxima (**left**) and minima (**right**) in % stream burial.
## 6 Conclusions  
Addressing the problem of grid SSO affecting the stable operation of DFIG systems, and considering the influence of the oscillation frequency change on the SSO suppression strategy, this paper proposed a back-step SSO suppression strategy based on an adaptive quasi-resonant controller. The following conclusions can be drawn from the simulation and experimental research:  
(1) The application of the RSC current controller designed based on the back-stepping method could simplify the RSC control strategy and reduce the number of transmission paths in the grid SSO from three to two, and its control effect was better than that of the traditional PI controller. In the case of unknown perturbations in the input signal, the back-stepping controller could quickly achieve the control goal of the RSC and complete the adjustment of the DFIG output power within 0.1 s;  
(2) The adaptive quasi-resonant controller could suppress the sub-synchronous current from the input DFIG and reduce the sub-synchronous current of the stator side to less than 5%. When the grid SSO frequency was abruptly changed from 30 Hz to 10 Hz, the adaptive quasi-resonant controller could still respond quickly, maintaining the sinusoidal output of the stator current. The experimental results showed that the adaptive quasi-resonant controller could suppress the sub-synchronous components of different frequencies in the DFIG system.  
In summary, this study verified the effectiveness of the proposed SSO suppression strategy through a simulation and experiments. Adopting this strategy can improve the anti-interference ability of DFIG systems subject to the SSO of the power grid and provide a guarantee for their stable operation, which has a certain engineering value.  
Conceptualization, writing--review and editing, supervision, project administration, funding acquisition, D.S.; validation, formal analysis, software visualization, F.M.; methodology investigation, data curation, writing--original draft preparation, W.S.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
[PERSON], [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 3 Degradation and Fate of PFCs in the Environment
### Microbial Degradation Pathway of PFOA and PFOS
[49] reported that during microbial degradation of PFCs, C-F bond cleavage occurs at the \(sp^{2}\) and \(sp^{3}\) C-F bonds. However, [PERSON] and co-workers reported that, despite the tertiary \(sp^{3}\) C-F bond having the lowest bond dissociation energy for the model PFCs (i.e., perfluoro (4-methylpent-2-enoic acid) and 4,5,5,5-tetrafluoro-4-(trifluoromethylomethyl)-2-pentenoic acid), the \(sp^{2}\) C-F bond was more microbially active. The production of fluoride ions [40] and intermediate products [43; 44; 45] during the biodegradation of PFOA and PFOS is an indication that the C-F bond was cleaved, despite having a bond dissociation energy of up to 130 kcal/mol [47]. There is no available literature reporting on the specific pathways for PFOA and PFOS biodegradation. It is worth noting that the extensively reported biodegradation of fluorotelomer alcohols and other PFCs, apart from PFOA and PFOS, is limited to the removal of non-fluorinated moieties rather than C-F bond cleavage. Factors Affecting PFOA and PFOS Biodegradation  
It is apparent that the microbial biodegradation of PFOA and PFOS can be affected by certain factors, such as the type of microbial species, initial concentration of model PFCs, and co-substrates. To date, _Acidimicrobium_ sp. strain A6, _Pseudomonas parafulva_ strain YAB1, _Pseudomonas aeruginosa_ strain HJ4, and _Pseudomonas picoglossicida_ 2.4-D are the only reported microbial species in the literature that are capable of PFOA and PFOS biodegradation. This suggests that the type of microbial species plays a significant role in the biodegradation process of PFCs, which are characterized as xenobiotic during their bio-remediation process. Moreover, the available literature on the biodegradation of PFOS and PFOA have demonstrated that higher PFC concentrations, ranging between 100 mg/L and 1000 mg/L, can inhibit microbial growth rate depending on the microbe strain, thus compromising PFC degradation. Moreover, selected microbes use PFOA and PFOS as carbon sources and simultaneously perform bio-defluorination [40, 47]. The presence of co-substrates can enhance microbial growth rate due to microbial preference [40, 43, 45].
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 3 RLQR Approach
### 1-Neighbor-Selection Algorithms
#### 3.2.1 EGNS: Epsilon-Greedy Based 1-NS Algorithm  
The epsilon-Greedy is a MAB strategy that balances between exploiting the best action with a probability (\(1-\epsilon\)) and exploring randomly the available actions with a probability \(\epsilon\) (\(\epsilon\) is a specified threshold) [25]. We rely on this strategy to define our 1-NS algorithm EGNS. In fact, as illustrated in Algorithm 1, EGNS works by oscillating between (1) exploiting, with a probability (\(1-\epsilon\)), the neighbor with the highest estimated reward based on previous queries (see line 5 Algorithm 1) and (2) exploring at random, with a probability \(\epsilon\), among all the neighbors (see line 9 Algorithm 1). For example, if we set \(\epsilon\) to 0.05, EGNS exploits 95% of the time the best neighbor with the highest average reward and selects at random any neighbor about 5% of the time. Worth noting that the average reward \(r_{n_{i}}\) of the  
Figure 1: The global architecture of the reinforcement learning-based query routing approach (RLQR) approach.  
selected neighbor \(n_{i}(q_{t})\) will be updated after receiving the reward \(r_{n_{i}}(q_{t})\) for the query \(q_{t}\) (see line 13 Algorithm 1).  
```
1:\(q_{t}\): search query at trial \(t\). \(N=\{n_{1},\ldots,n_{m}\}\): set of all neighbors \(R=(r_{n_{1}},\ldots,r_{n_{m}})\): average rewards of the \(m\) neighbors \(\varepsilon\): specified value of epsilon \(n_{i}(q_{t})\): the neighbor to be selected for \(q_{t}\)
2:\(value=random()\) # choose a random value
3:if\((value>\varepsilon)\)then
4:\(n_{i}(q_{t})=\operatorname*{argmax}_{n_{i}\in N}\{r_{n_{i}}\in R\}\)
5:else
6:\(n_{i}(q_{t})=random(n_{i}\in N)\)
7:endif
8:update(\(r_{n_{i}},r_{n_{i}}(q_{t})\))
```  
**Algorithm 1** EGNS: Epsilon-greedy-based 1-NS algorithm