esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning in Chronic Pain Research: A Scoping Review
## 4 Discussion  
### Important Findings  
The considerable number of papers that appeared in the literature search indicates that the use of ML methods in chronic pain research has drawn significant attention and is considered potentially useful. A wide variety of ML methods were employed in the considered studies. Most of these methods involve supervised classification or regression algorithms. Classical ML algorithms, such as SVM and DT, were commonly used in the selected studies. Deep learning was applied to a limited extent, indicating that the uptake of these algorithms may be more problematic for the research community. The limited interpretability and explainability of deep learning-based models may be important reasons why these methods, the use of which has become the de facto standard in modern image and video analysis, are less visible in the scientific publications in the medical field.  
A limited number of the studies focused on the treatment of chronic pain. The majority of the studies contributed to treatment indirectly through various types of classification. The task of finding the best treatment for a specific patient is still performed by the clinician, without the assistance of clinical decision support tools. Hence, there is a need for tools that help clinicians make decisions concerning safe, effective, and efficient treatment plans based on either contemporary chronic pain models or directly on the data. Useful tools for these tasks could include DTs and reinforcement learning (RL).  
Various types of data have been utilised in chronic pain studies. However, methods that integrate multiple data sources (e.g., both EHR data and patient-generated data) are underdeveloped. Consequently, existing work typically depends on a limited number of data types. The issue of data quality in clinical data and how it affects the results of studies have still not been fully explored. For genomics data, translation of findings from animal to humans is still limited and remains a challenging topic [26]. Furthermore, the sample size used in studies is typically small [63]. Moreover, long-term monitoring with large datasets is required in future work to test whether these techniques can be applied to assess rehabilitation or treatment outcomes for patients [36].  
As shown in Table 3, previous research has concentrated on the early stages of the chronic pain care pathway. However, few studies have focused on the rehabilitation and self-management phases. CLBP is the pain condition that has attracted the most research interest, as it was the focus of 20 of the included studies. This is also the only condition in which research focused on all phases of the care pathway. The significant amount of research that has focused on CLBP reflects the high prevalence of the condition. FM was the second most frequently investigated condition, with 11 studies covering the diagnosis and clinical decision support phases. While the majority of the research focused on CLBP and FM, several chronic pain conditions were addressed by only one or two studies.
# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 4 TTR Using Reinforcement Learning  
### Improved Q-Learning Algorithm  
The Q-learning algorithm is a reinforcement learning algorithm utilized for decision-making in a dynamic environment [49]. It involves iteratively interacting with the environment and adjusting decisions based on trial and error. The objective is to discover the  
Figure 5: Schematic diagram of the construction of station dwelling time.  
optimal strategy that maximizes cumulative rewards for a given state and action. Traditional Q-learning methods have certain drawbacks, such as dealing with a large state space, difficulties in defining states precisely, and a lack of clear operational steps. Existing research often involves complex state constructions, which can result in inefficient problem-solving. Moreover, these methods may not adequately consider all the factors that contribute to train delays, thereby affecting the real-time responsiveness and prediction accuracy. For real-time train schedule adjustments, we employ an improved Q-learning algorithm, offering the following advantages.  
1. The Q-learning algorithm offers a concise description and representation of actual states by utilizing a limited set of variables. The usage of limited variables makes the algorithm more practical, reducing the complexity of the state space.
2. Pre-training the Q-value state table for various scenarios facilitates quick retrieval during delays or emergencies, enabling a real-time and dynamic selection of strategies such as detailing specific trains or modifying running time. This efficient table lookup and reusability enables our algorithm to make accurate and rapid adjustments in the dynamically changing train operation environment, ensuring both system efficiency and safety.
3. The incorporation of a simulated annealing dynamic factor enhances the algorithm's convergence stability and computational speed. This enables the algorithm to achieve the optimal strategy within a relatively small number of training iterations [50].
## 3 Proposed Anonymization Method, Iterative _k_-Anonymous Clustering Method (I\(k\)-ACM)
### Complexity Analysis of I\(k\)-ACM  
I\(k\)-ACM bases on the \(k\)-Anonymous Clustering Method given in [9]. In terms of complexity analysis, they do not differ with each other. In this subsection, complexity analysis is described as in [9].  
Suppose that I\(k\)-ACM works on an input consisting of \(n\) event records and each record has \(m\) attributes. All of the \(m\) has distinct \(V\) different attribute value. Initialization phase mainly calculates the initial distance matrix and the running time of this part is \(O(n^{2}.m.V)\). Initially there are \(n\) clusters and at the end of \(k_{n}\)-anonymization phase, the minimum number of clusters is \(n/k_{n}\). Therefore, \(n-n/k_{n}\) cluster combination operation occurs. Cluster combination consists of finding the minimum distance in the distance matrix and matrix reorganizing so that the distance values of new cluster are added and distance values of previous clusters are removed. If binary heap structure is used for finding minimum distance, formation of initial min heap structure with \(n^{2}\) elements is \(O(n^{2})\). In a heap, finding the minimum operation is \(O(1)\). However, removing distances of merged clusters from heap and adding the distances of new cluster to the heap need \(2n\) deletion and \(n\) addition operations which cost \(O(n\log(n))\). Reorganization of distance matrix can be done in \(O(n.m.V)\) time sequentially with maintaining the heap. As a result, cost of each cluster combination operation is \(O(nogn+nmV)\)  
Figure 6: A sample case for cluster combination with encryption operations.  
Recall that maximum number of cluster combination operations is \(n-n/k_{n}\), the algorithm reaches to the end of \(k_{n}\)-anonymization phase in \(O(n^{2}logn+n^{2}mV)\). Forming the output of I\(k\)-ACM takes \(O(n/k_{n}.m)\). Formation of output does not change the overall running time. Totally, I\(k\)-ACM takes \(O(n^{2}logn+n^{2}.2 mV)\). \(m\) and \(V\) generally have lower values so they can be assumed as a constant factor. The running time can be fine-tuned to \(O(n^{2}logn)\).
## 4 Mechanism of Landslide Reactivation  
According to borehole data and formation information analysis, there are several aquifers in the landslide area [32], including the upper overburden and the middle strongly weathered shale. Groundwater in the slope is distributed in the form of phreatic water [33] and bedrock fissure water. A field pumping test shows that the water level drops rapidly after pumping and groundwater in the borehole is exhausted. It takes approximately 24 h for the groundwater level to recover, indicating that the permeability of the landslide is poor. The water body infiltrates into the sandy mudstone of the slope foot formation due to external factors, such as rainfall, and groundwater enrichment occurs in the fractured mudstone zone due to poor permeability of the upper overburden layer and lower strongly weathered shale of mudstone [34]. Hydrostatic pressure will be formed on the clay layer at the bottom of the avalanche deposit and strong weathered shale, which will cause the phenomenon of \"boating\" in the upper overburden layer and result in the occurrence of a landslide in Zone I. Groundwater flows slowly in the slope body and the existing retaining wall drainage tunnel is seriously blocked. The impermeability of moderately differentiated shale leads to long-term saturation of strongly weathered shale, which increases the self-weight of the slope body and reduces the sliding resistance at the same time. Therefore, an excessive groundwater level is the main cause of landslide deformation in Zone III.  
According to the field investigation and displacement monitoring data, the status quo of Zone II is stable, without any slip marks. Excavation and support work have been carried out for the accumulation of the rear edge of the landslide, which has reduced the sliding force of the shallow landslide. Landfilling and construction of drainage ditches for  
Figure 8: Monitoring data of borehole BK24.  
cracks at the rear edge reduced the increase of the groundwater table caused by rainfall infiltration. Construction of a water guide pipe at the front edge shear outlet accelerated the diversion of groundwater and reduced the groundwater level (Figure 9). The displacement rate of deformation before treatment in Area I reached 1.301 mm/day. After the water level was reduced, the displacement rate in Area I was 0.091 mm/day, and the displacement rate of the landslide in Area III decreased from 0.674 mm/day to 0.341 mm/day. This shows that the deformation of landslides can be effectively reduced by lowering the groundwater level [35], and the landslide in Zone III should be treated by setting drainage facilities to a lower water level.
# Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry
## 2 Materials and Methods
### Multichannel Singular Spectrum Analysis (MSSA)
#### 2.2.1 Embedding  
The type of trajectory matrix method depends on the specific research issue, the nature of the data, and the objectives of the analysis. It is important to consider the relationships between variables, temporal dynamics, and desired level of detail to determine the most appropriate way to combine trajectory matrices when using MSSA.  
The embedding consists of composing \(L\)- and \(K\)-embedded vectors that form a trajectory matrix. The trajectory matrices have the structure of stacked Hankel ones, i.e., such matrices that have equal elements on all diagonals perpendicular to the main one. Let us denote by \(L\) a positive integer called the window width. In addition, \(1<L<\min\left(N_{p}\right)/2,\ p=1\ldots s\), where \(s\)--the number of time series.  
At a horizontal trajectory matrix compilation for each time series, \(X^{(p)}\) forms \(K_{p}=N_{p}-L+1\ L\)-embedded vectors like \(X^{(p)}_{j}=\left(x^{(p)}_{j},\ldots,x^{(p)}_{j+L-1}\right)^{T}\), \(1\leq j\leq K_{p}\). So, the trajectory matrix of a multivariate time series under horizontal integration is a matrix of size \(L\times K\), where \(K=\sum_{p=1}^{s}K_{p}\), which is a sequentially arranged trajectory matrix of one-dimensional rows obtained at the same window width \(L\). Therefore, the size of the trajectory matrix will be \(L\times s*K\).  
In vertical trajectory matrix compilation, the embedding procedure generates \(K=N-L+1\ L\)-embedded vectors. So, the trajectory matrix of a multivariate time series under vertical integration is a matrix of \(s*L\times K\).  
In this way, the trajectory matrix will take Equation (1) when compiled horizontally and Equation (2) when compiled vertically.  
\[X=\left(x_{ij}\right)_{i,j=1}^{L,L^{\mathrm{S}}_{p=1}K_{p}}=\begin{pmatrix}f^{( p)}_{0}&f^{(p)}_{1}&f^{(p)}_{2}&\ldots&f^{(p)}_{K-1}&f^{(p)}_{0}&f^{(s)}_{1}&f^{(s)} _{2}&\ldots&f^{  
\(S\) corresponding to singular numbers \(\lambda_{1},\ \ldots,\ \lambda_{L}\). Then, the singular expansion of the matrix \(X\) can be written as follows (Equation (3)):  
\[X=X_{1}+\ldots+X_{L},\ \text{where}\ X_{i}=\sqrt{\lambda_{i}}U_{i}V_{i}^{T},\ \ V_{i}=X^{T}U_{i}/\sqrt{\lambda_{i}} \tag{3}\]  
The sets \(\sqrt{\lambda_{i}}U_{i}V_{i}^{T}\) are called the \(i\)-th singular triple of the decomposition, which are the result of the decomposition of the trajectory matrix \(X\).
# Chaotic Orthogonal Composite Sequence for 5G NR Time Service Signal Capture Algorithm
## 2 5G NR Time Service Signal Model
### Generation of 5G Signal  
The system principle diagram for the generation of the 5G baseband signal is shown below:  
Figure 2 illustrates a simplified process for generating a 5G signal in practice. The serial data bit stream undergoes QPSK modulation (for simplicity, we assume that all subcarriers use QPSK modulation in this diagram, although in actual 5G signals, each subcarrier can use different modulation schemes). The modulated signal then undergoes serial-to-parallel conversion to allow for parallel processing (this can significantly reduce the time required to generate the 5G signal in practice, while it demands higher hardware capabilities). Each \(X_{k}(t)\) signal is modulated by the corresponding \(e^{ik\omega_{0}t}\) (where \(k=1,2\cdots N\)). The modulated signals are then summed, with their real parts modulated by a high-frequency cosine signal of frequency \(\omega_{c}\) and their imaginary parts modulated by a high-frequency sine signal of frequency \(\omega_{c}\). Finally, the modulated signals are summed to obtain the desired 5G signal \(S_{c}(t)\).  
Figure 1: Principle diagram for the generation of 5G NR time service signal.  
Figure 2: Principle diagram for the generation of 5G baseband signal.  
This can be expressed by the following formula:  
\[S_{c1}(t)=Re\Bigg{\{}\sum_{k=1}^{k=N}X_{k}e^{i\omega_{k}t}\Bigg{\}}cos(\omega_{c}t) \tag{4}\]  
\[S_{c2}(t)=Im\Bigg{\{}\sum_{k=1}^{k=N}X_{k}e^{i\omega_{k}t}\Bigg{\}}sin(\omega_{c }t) \tag{5}\]  
\[S_{c}(t)=S_{c1}(t)+S_{c2}(t) \tag{6}\]  
where \(N\) represents the number of subcarriers, \(X_{k}\) represents the complex data signal of the \(k\)-th subcarrier, \(\omega_{k}\) represents the subcarrier frequency, \(Re\{\ \}\) denotes the real part, \(Im\{\ \}\) denotes the imaginary part, \(\omega_{c}\) represents the high-frequency carrier
# Mode-Selective Photonic Lanterns for Orbital Angular Momentum Mode Division Multiplexing
## 3 Simulation Results  
As a result of the mode evolution analysis in MSPLs described above, we design the OAM-MSPL with a taper length of 2.0 cm, similar to practical PLs. When injecting light into the Fiber 1 core, the mode coupling efficiencies of OAM\({}_{0}\), OAM\({}_{-1}\), and OAM\({}_{+1}\) are 99.372%, 0.306%, and 0.306%, respectively, which were obtained by calculating the correlation coefficients between the target modes of the PL and the matching FMF as the waist [18]. As for Fiber 2, they are 0.318%, 99.665%, and 0.001%, respectively. As for Fiber 3, they are 0.318%, 0.001%, and 99.665%, respectively. Simulation results show good characteristics of multiplexing when the few-mode ends of one pair of the designed PLs are connected, as shown in Figure 5a.  
When injecting light into the target fiber core of the MUX-PL (where light is injected into the SMF ends and is emitted from the FMF end), we obtain the mode crosstalk, which is defined as the ratio of power in the corresponding fiber core of the DEMUX-PL (where light is injected into the FMF end and is emitted from the SMF ends) over power in the other cores. The matrix elements shown in Figure 5b are displayed in units of decibels (dB), with the mode crosstalk of one pair of the designed PLs below \(-21\) dB, based on the symmetric MUX/DEMUX simulation structure. The individual simulation shows the mode crosstalk of the single PL is below \(-24\) dB, which matches the result of the MUX/DEMUX simulation. The diagonal elements represent the simulated insertion loss (IL) of one pair of the designed PLs, which is below \(-0.06\) dB, as the IL of a single PL is below \(-0.03\) dB on average.  
We calculate correlation coefficients between the target OAM modes and FMF via the overlap integral method by scanning the linear taper lengths and the light wavelengths, as shown in Figure 6a,d. With the mode crosstalk shown in Figure 6b,c, it is indicated that the OAM-MSPL has a taper length error tolerance longer than \(1\) mm, which can be controlled by common PL-tapering processors. The mode crosstalk of a single PL can be controlled below \(-21\) dB, with the IL below \(-0.06\) dB. Furthermore, as simulations performed, the designed PL works stably in the whole C-band and L-band with low loss and crosstalk.  
Figure 5: (**a**) The schematic of the multiplexer/de-multiplexer (MUX/DEMUX) simulation. (**b**) Calculated mode crosstalk matrix of one pair of the OAM-MSPLs during selective excitation (bottom axis) and measurement (left axis). The diagonal matrix elements represent the simulated insertion loss of one pair of the OAM-MSPLs. OAM-MSPLs: orbital angular momentum mode-selective photonic lanterns.
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 4 Results
### Scenario Exhibition  
In the FANET consisting of the ACP, 12 relay UAVs, and two monitoring UAVs, the TA&CRFO algorithm is used to simultaneously carry out continuous reconnaissance of mobile targets. Figure 4 shows the simulation results at \(t_{i}\). The circle with the relay UAV as the center in Figure 4 represents the maximum communication range of the RUs. During the reconnaissance, the monitoring UAVs move closely with the movement of monitored targets. Since the trajectory of targets is unpredictable for the ACP, the MUs' movements are completely controlled by themselves. The RUs adjust their positions according to the displacement of the MUs and the variation in threat PDF to avoid hazards and ensure the communication quality between the ACP and the MUs.
## 3 Proposed Approach
### Explainability-Guided Vote (EGV) Approach
#### 3.2.1 Extracting IG Attribution Scores  
The first component involves generating attribution scores using IGs. These scores help identify the tokens within the input text that have the most positive and negative influence on the model's prediction. By focusing on the tokens that significantly impact predictions, we can analyze the model's decision-making process more effectively. As shown in Figure 1, the IG attribution scores are extracted to determine both highly positively and negatively impacted tokens towards the target prediction. These tokens are then passed to the nextstage. Algorithm 1 includes the computation of IG scores and how tokens are ranked based on their influence.  
```
Input: x = {\(w_{1}\), \(w_{2}\),..., \(w_{n}\)}: Input text, \(f\): Target classifier, \(p\): Substitution rate, \(k\): Number of votes Output: Adversarial example or not, Restored label \(X\leftarrow\{\}\) Initialize transformed text set \(E\leftarrow\)IntegratedGradients(f(x)) \(half\_count\leftarrow\)length(x)/2*\(p\)\(M\_Pos\leftarrow\)half_count of words and indexes with high positive influence excluding stopwords from \(E\)\(M\_Neg\leftarrow\)half_count of words and indexes with high negative influence excluding stopwords from \(E\)for\(i=0\)to\(k\)do \(x_{i}\leftarrow\) initialize a new text for each\(w_{p}\in\)M_Posdo  Select random synonym \(\hat{w}_{p}^{j}\in S(w_{p})\) Substitute \(w_{p}\in x_{i}\) with \(\hat{w}_{p}^{j}\) for each\(w_{n}\in\)M_Negdo  Select random synonym \(\hat{w}_{n}^{j}\in S(w_{n})\) Substitute \(w_{n}\in x_{i}\) with \(\hat{w}_{n}^{j}\) \(X\gets X\cup x_{i}\) \(\hat{g}\gets f(x)\) prediction label of original example \(\hat{y}_{v}=\arg\max\sum_{i=1}^{k}f(x_{i})\) calculate the voted label if\(\hat{y}_{v}\
eq\hat{g}\)then return\(True,\hat{y}_{v}\) return\(False,\hat{y}\)
```  
**Algorithm 1**Explainability-Guided Vote
## 3 Results and Discussion
### Enzymatic Study  
Compounds **1-3**, **6-7**, and streptonigrin (ST) were tested as a substrate for the NQO1 according to the literature methods [13; 22; 23]. Results are presented in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Compound** & **logS** & **logP** & **MW (g/mol)** & **HA** & **HD** & **LC\({}_{50}\) (mM)** & **Hepatotoxicity** \\ \hline
**1** & \(-3.08\) & \(2.14\) & \(228\) & \(3\) & \(0\) & \(8.710\) & No \\
**2** & \(-3.39\) & \(2.45\) & \(242\) & \(3\) & \(0\) & \(6.531\) & No \\
**3** & \(-3.01\) & \(1.96\) & \(256\) & \(4\) & \(0\) & \(8.810\) &The derivatives **1-3** and **6-7** show a higher enzymatic conversion rate of the NQO1 protein than reference substance (**ST**), indicating that they are good substrates for NQO1. 6,7-Dichloro-5,8-quinolinedione **1** and 6,7-dichloro-2-methylo-5,8-quinolinedione **2** exhibit comparable conversion rates, which are equal to 872 umol NADPH/umol NQO1/min and 890 umol NADPH/umol NQO1/min, respectively. The oxidation of methyl to the formyl group (compound **3**) increases the conversion rate. A similar effect is observed for compounds with a hydroxyl group (derivatives **6**) and chloride atom (derivatives **7**) at the C2 position. These results show that the introduction of various groups at the C2 position increases the enzymatic conversion rate of the NQO1 enzyme, and it depends on the type of substituent.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 1 Introduction
### Outline of the Paper  
Section 2 describes the related work to our proposed scheme. Section 3 discusses the research questions for existing SCADA security standards based on current attacks and possible quantum threats. We also discuss the research questions for the post-quantum scheme, namely, SPHINCS-256. Section 4 describes the proposed framework. Sections 5 and 6 present the formal analysis and statistical analysis of the framework, respectively. Section 7 provides conclusion and scope for future work. Table 1 provides a list of abbreviations.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviations/Notations** & **Definitions** \\ \hline SCADA & Supervisory control and data acquisition \\ \hline AGA-12 & American Gas Association-12 \\ \hline RSA & Rivest-Shamir–Adleman \\ \hline AES & Advanced Encryption Standard \\ \hline ECDSA & Elliptic Curve Digital Signature \\ \hline SHA & Secure Hash Algorithm \\ \hline SPHINCS & Stateless Hash-based Signatures \\ \hline B92 & Bennett 1992 protocol \\ \hline BB84 & Bennett and Brassard 1984 protocol \\ \hline QKD & Quantum Key Distribution \\ \hline HORS & Hash to Obtain Random Subset \\ \hline HORST & Hash to Obtain Random Subset signature with Tree \\ \hline OTS & One-time signature \\ \hline FTS & Few-Time signature \\ \hline WOTS+ & Winternitz One-time Signature \\ \hline PRISM & Probabilistic Model Checking for Performance and Reliability Analysis \\ \hline NIST & National Institute of Standards and Technology \\ \hline QRNG & Quantum Random Number Generator \\ \hline PRNG & Pseudo-Random Number Generator \\ \hline LOTSS & Lamport One-Time Signature Scheme \\ \hline MSS & Merkle-Signature Scheme \\ \hline RTU & Remote Terminal Unit \\ \hline MTU & Master Terminal Unit \\ \hline QBER & Quantum bit error rate \\ \hline ECC & Error Correction Code \\ \hline R-S & Reed-Solomon code \\ \hline IoT & Internet of Things \\ \hline \(w\) & Parameter used to generate Winternitz One-time Signature \\ \hline \(M\) & Number of messages \\ \hline \(X_{i}\) & Public key of Merkle-Signature Scheme \\ \hline \(Y_{i}\) & Private key of Merkle-Signature Scheme \\ \hline \(i\) & Leaf index \\ \hline \(K\) & Number of published HORST secret key elements \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Abbreviations and Notations.